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Cui Y, Dong J, Zhang C, Yang J, Chen N, Guo P, Di Y, Chen M, Li A, Liu R. Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets. Sci Data 2024; 11:671. [PMID: 38909027 PMCID: PMC11193745 DOI: 10.1038/s41597-024-03508-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 06/12/2024] [Indexed: 06/24/2024] Open
Abstract
Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels. In this study, we constructed multi-sourced samples independent of the data producers, taking advantage of open-source validation datasets and sampling to rectify the accuracy of ten contemporary cropland maps in southwestern China, decoded their inconsistencies, and generated a refined cropland map (CroplandSyn) by leveraging ten state-of-the-art remotely sensed cropland maps released from 2021 onwards using the self-adaptive threshold method. Validations, conducted at both prefecture and county scales, underscored the superiority of the refined cropland map, aligning more closely with national land survey data. The refined cropland map and samples are publicly available to users. Our study offers valuable insights for improving agricultural practices and land management in under-monitored areas by providing high-quality cropland maps and validation datasets.
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Affiliation(s)
- Yifeng Cui
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jinwei Dong
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Chao Zhang
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Jilin Yang
- College of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Na Chen
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Peng Guo
- Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, 100871, China
| | - Yuanyuan Di
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, 315100, China
| | - Mengxi Chen
- Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, 100871, China
| | - Aiwen Li
- College of Resources, Sichuan Agricultural University, Chengdu, 611130, China
| | - Ronggao Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
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2
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Choukri M, Laamrani A, Chehbouni A. Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:3618. [PMID: 38894409 PMCID: PMC11175247 DOI: 10.3390/s24113618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 06/21/2024]
Abstract
Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent.
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Affiliation(s)
- Maryam Choukri
- Center for Remote Sensing Applications (CRSA), UM6P, Benguerir 43150, Morocco; (A.L.); (A.C.)
| | - Ahmed Laamrani
- Center for Remote Sensing Applications (CRSA), UM6P, Benguerir 43150, Morocco; (A.L.); (A.C.)
- College Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco
- Department of Geography, Environment & Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Abdelghani Chehbouni
- Center for Remote Sensing Applications (CRSA), UM6P, Benguerir 43150, Morocco; (A.L.); (A.C.)
- College Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco
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Simon WJ, Hijbeek R, Frehner A, Cardinaals R, Talsma EF, van Zanten HHE. Circular food system approaches can support current European protein intake levels while reducing land use and greenhouse gas emissions. NATURE FOOD 2024; 5:402-412. [PMID: 38806686 PMCID: PMC11132985 DOI: 10.1038/s43016-024-00975-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 04/08/2024] [Indexed: 05/30/2024]
Abstract
Protein transition and circular food system transition are two proposed strategies for supporting food system sustainability. Here we model animal-sourced protein to plant-sourced protein ratios within a European circular food system, finding that maintaining the current animal-plant protein share while redesigning the system with circular principles resulted in the largest relative reduction of 44% in land use and 70% in greenhouse gas (GHG) emissions compared with the current food system. Shifting from a 60:40 to a 40:60 ratio of animal-sourced proteins to plant-sourced proteins yielded a 60% reduction in land use and an 81% GHG emission reduction, while supporting nutritionally adequate diets. Differences between current and recommended total protein intake did not substantially impact minimal land use and GHG emissions. Micronutrient inadequacies occurred with less than 18 g animal protein per capita per day. Redesigning the food system varied depending on whether land use or GHG emissions were reduced-highlighting the need for a food system approach when designing policies to enhance human and planetary health.
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Affiliation(s)
- Wolfram J Simon
- Farming Systems Ecology Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands.
| | - Renske Hijbeek
- Plant Production Systems Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands
| | - Anita Frehner
- Department of Food System Sciences, Research Institute of Organic Agriculture (FiBL), Frick, Switzerland
| | - Renee Cardinaals
- Farming Systems Ecology Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands
| | - Elise F Talsma
- Division of Human Nutrition and Health, Department of Agrotechnology and Food Sciences Group, Wageningen University and Research, Wageningen, The Netherlands
| | - Hannah H E van Zanten
- Farming Systems Ecology Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands
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Du B, Ye S, Gao P, Ren S, Liu C, Song C. Analyzing spatial patterns and driving factors of cropland change in China's National Protected Areas for sustainable management. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169102. [PMID: 38056649 DOI: 10.1016/j.scitotenv.2023.169102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 12/08/2023]
Abstract
Farming in protected areas frequently challenges ecological conservation goals while supporting local livelihoods. To balance protection and agriculture, a comprehensive understanding of cropland dynamics in protected areas is of paramount importance. However, studies addressing this trade-off are relatively scarce, especially considering explicit Chinese government regulations on population relocation and cropland retirement in National Protected Areas (NPAs). Our study examined the spatial and temporal pattern of cropland in NPAs and explored the covariance between cropland density and species richness. Concurrently, the driving factors of cropland development in NPAs were analyzed using Multiple Linear Regression. The results indicate that the cropland area in NPAs continued to expand, growing from 1.93 to 2.34 million hectares in 2000-2020, with a cropland density of approximately 0.4. Cropland expansion in the northern NPAs, particularly in the resource-rich Northeast (28.12 %) and the Northwest with high marginal agricultural returns (38.26 %), have encroached upon species habitats and aggravated biodiversity loss. Moreover, cities with higher cropland densities in NPAs are usually located at borders, possibly due to decentralized management. The Multiple Linear Regression results show that high cropland density is usually associated with a high population density (β = 0.156) and lower levels of rural education (β = -0.101) and income (β = -0.122). To mitigate the issue of cropland development in NPAs, it is crucial to avoid one-size-fits-all management strategies, strengthen regional legal supervision, adjust fiscal incentives, and promote eco-friendly agriculture. In the north regions, the expansion of cropland in NPAs should be strictly controlled. For the southwest, the positive role of preserving cropland in NPAs for alleviating human-nature conflict and maintaining social stability should be emphasized. This study provides research support for China's exploration of geographically suitable strategies for controlling cropland in NPAs. Moreover, the findings could serve as a reference for the governance of NPAs in other countries.
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Affiliation(s)
- Bin Du
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Sijing Ye
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China.
| | - Peichao Gao
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
| | - Shuyi Ren
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Chenyu Liu
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Changqing Song
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
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5
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Jung M, Boucher TM, Wood SA, Folberth C, Wironen M, Thornton P, Bossio D, Obersteiner M. A global clustering of terrestrial food production systems. PLoS One 2024; 19:e0296846. [PMID: 38354163 PMCID: PMC10866528 DOI: 10.1371/journal.pone.0296846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 12/23/2023] [Indexed: 02/16/2024] Open
Abstract
Food production is at the heart of global sustainability challenges, with unsustainable practices being a major driver of biodiversity loss, emissions and land degradation. The concept of foodscapes, defined as the characteristics of food production along biophysical and socio-economic gradients, could be a way addressing those challenges. By identifying homologues foodscapes classes possible interventions and leverage points for more sustainable agriculture could be identified. Here we provide a globally consistent approximation of the world's foodscape classes. We integrate global data on biophysical and socio-economic factors to identify a minimum set of emergent clusters and evaluate their characteristics, vulnerabilities and risks with regards to global change factors. Overall, we find food production globally to be highly concentrated in a few areas. Worryingly, we find particularly intensively cultivated or irrigated foodscape classes to be under considerable climatic and degradation risks. Our work can serve as baseline for global-scale zoning and gap analyses, while also revealing homologous areas for possible agricultural interventions.
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Affiliation(s)
- Martin Jung
- International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | | | - Stephen A. Wood
- The Nature Conservancy, Arlington, Virginia, United States of America
- Yale School of the Environment, New Haven, United States of America
| | - Christian Folberth
- International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | - Michael Wironen
- The Nature Conservancy, Arlington, Virginia, United States of America
| | - Philip Thornton
- Clim-Eat, c/o Netherlands Food Partnership, Utrecht, The Netherlands
| | - Deborah Bossio
- The Nature Conservancy, Arlington, Virginia, United States of America
| | - Michael Obersteiner
- International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
- Environmental Change Institute, University of Oxford, Oxford, United Kingdom
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6
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Zhang G, Dilday S, Kuesel RW, Hopkins B. Phytochemicals, Probiotics, Recombinant Proteins: Enzymatic Remedies to Pesticide Poisonings in Bees. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:54-62. [PMID: 38127782 PMCID: PMC10785755 DOI: 10.1021/acs.est.3c07581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023]
Abstract
The ongoing global decline of bees threatens biodiversity and food safety as both wild plants and crops rely on bee pollination to produce viable progeny or high-quality products in high yields. Pesticide exposure is a major driving force for the decline, yet pesticide use remains unreconciled with bee conservation since studies demonstrate that bees continue to be heavily exposed to and threatened by pesticides in crops and natural habitats. Pharmaceutical methods, including the administration of phytochemicals, probiotics (beneficial bacteria), and recombinant proteins (enzymes) with detoxification functions, show promise as potential solutions to mitigate pesticide poisonings. We discuss how these new methods can be appropriately developed and applied in agriculture from bee biology and ecotoxicology perspectives. As countless phytochemicals, probiotics, and recombinant proteins exist, this Perspective will provide suggestive guidance to accelerate the development of new techniques by directing research and resources toward promising candidates. Furthermore, we discuss practical limitations of the new methods mentioned above in realistic field applications and propose recommendations to overcome these limitations. This Perspective builds a framework to allow researchers to use new detoxification techniques more efficiently in order to mitigate the harmful impacts of pesticides on bees.
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Affiliation(s)
- Ge Zhang
- Department of Entomology, Washington State University, Pullman, Washington 99164, United States
| | - Sam Dilday
- Department of Entomology, Washington State University, Pullman, Washington 99164, United States
| | - Ryan William Kuesel
- Department of Entomology, Washington State University, Pullman, Washington 99164, United States
| | - Brandon Hopkins
- Department of Entomology, Washington State University, Pullman, Washington 99164, United States
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7
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Park T, Gumma MK, Wang W, Panjala P, Dubey SK, Nemani RR. Greening of human-dominated ecosystems in India. COMMUNICATIONS EARTH & ENVIRONMENT 2023; 4:419. [PMID: 38665186 PMCID: PMC11041707 DOI: 10.1038/s43247-023-01078-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/31/2023] [Indexed: 04/28/2024]
Abstract
Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India's agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India's croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India's agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO2 fertilization as a primary driver of greening whereas land use and management have marginal impacts on the simulated leaf area changes. This finding urges a closer collaboration of the modeling, Earth observation, and land system science communities to improve representation of land management in the Earth system modeling.
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Affiliation(s)
- Taejin Park
- NASA Ames Research Center, Moffett Field, California USA
- Bay Area Environmental Research Institute, Moffett Field, California USA
| | - Murali K. Gumma
- International Crop Research Institute for Semi-Arid Tropics, Patancheru, Telangana India
| | - Weile Wang
- NASA Ames Research Center, Moffett Field, California USA
| | - Pranay Panjala
- International Crop Research Institute for Semi-Arid Tropics, Patancheru, Telangana India
| | | | - Ramakrishna R. Nemani
- NASA Ames Research Center, Moffett Field, California USA
- Bay Area Environmental Research Institute, Moffett Field, California USA
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8
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Boogaard H, Pratihast AK, Laso Bayas JC, Karanam S, Fritz S, Van Tricht K, Degerickx J, Gilliams S. Building a community-based open harmonised reference data repository for global crop mapping. PLoS One 2023; 18:e0287731. [PMID: 37440484 PMCID: PMC10343028 DOI: 10.1371/journal.pone.0287731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 06/12/2023] [Indexed: 07/15/2023] Open
Abstract
Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles.
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Affiliation(s)
- Hendrik Boogaard
- Wageningen Environmental Research (WENR), Wageningen University & Research, Wageningen, Netherlands
| | - Arun Kumar Pratihast
- Wageningen Environmental Research (WENR), Wageningen University & Research, Wageningen, Netherlands
| | | | - Santosh Karanam
- International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | - Steffen Fritz
- International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | | | | | - Sven Gilliams
- Vlaamse Instelling Technologisch Onderzoek (VITO), Mol, Belgium
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Becker-Reshef I, Barker B, Whitcraft A, Oliva P, Mobley K, Justice C, Sahajpal R. Crop Type Maps for Operational Global Agricultural Monitoring. Sci Data 2023; 10:172. [PMID: 36977689 PMCID: PMC10050185 DOI: 10.1038/s41597-023-02047-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 03/01/2023] [Indexed: 03/30/2023] Open
Abstract
Crop type maps identify the spatial distribution of crop types and underpin a large range of agricultural monitoring applications ranging from early warning of crop shortfalls, crop condition assessments, production forecasts, and damage assessment from extreme weather, to agricultural statistics, agricultural insurance, and climate mitigation and adaptation decisions. Despite their importance, harmonized, up-to-date global crop type maps of the main food commodities do not exist to date. To address this critical data gap of global-scale consistent, up-to-date crop type maps, we harmonized 24 national and regional datasets from 21 sources covering 66 countries to develop a set of Best Available Crop Specific masks (BACS) over the major production and export countries for wheat, maize, rice, and soybeans, in the context of the G20 Global Agriculture Monitoring Program, GEOGLAM.
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Affiliation(s)
- Inbal Becker-Reshef
- Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA.
- GEOGLAM Secretariat, Geneva, Switzerland.
- University of Strasbourg, The Engineering science, computer science and imaging laboratory (Icube), Strasbourg, France.
| | - Brian Barker
- Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - Alyssa Whitcraft
- Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA
- GEOGLAM Secretariat, Geneva, Switzerland
| | - Patricia Oliva
- Universidad de Alcalá, Environmental Remote Sensing Research Group, Departamento de Geología, Geografia y Medio Ambiente, Alcalá de Henares, Spain
- Hémera Centro de Observación de la Tierra, Escuela de Ingeniería Forestal, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Kara Mobley
- Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Christina Justice
- Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Ritvik Sahajpal
- Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA
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10
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Tesfaw A, Teferi E, Senbeta F, Alemu D. The spatial distribution and expansion of Eucalyptus in its hotspots: Implications on agricultural landscapes. Heliyon 2023; 9:e14393. [PMID: 36938386 PMCID: PMC10020106 DOI: 10.1016/j.heliyon.2023.e14393] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/09/2023] Open
Abstract
Fast coppicing plantations like Eucalyptus are becoming an ever increasingly important land use system globally, including the Eucalyptus hotspot highlands of Northwestern Ethiopia. However, comprehensive information regarding species composition is essential for proper planning and policy decisions. The current study mapped the spatial distribution of Eucalyptus globulus (hereafter referred to as Eucalyptus) and identified the key push factors for its expansion. The study used a mapping procedure that uses Landsat imagery together with ground truth data based on supervised training of a pixel-by-pixel classification algorithm within image regions to distinguish areas of Eucalyptus plantations from other classes. High-resolution multispectral and multi-temporal remote-sensing images were combined with ground truth data to produce robust features of Eucalyptus plantation distribution maps. Heckman's Two-Stage econometric model was also employed for determining the major driving factors of Eucalyptus expansion. The results of the mapping algorithm were Eucalyptus plantation distribution maps of 30 × 30 m resolution that showed temporal changes from 1999 to 2021. The findings revealed that Eucalyptus coverage increased by 55% during the period from 1999 to 2010 and the change expressively increased to 69% in 2021 with respect to the reference period. The study also found that a number of push factors influenced the size of land planted with Eucalyptus. The developed maps showing the spatial distribution and expansion of Eucalyptus will help policymakers properly manage the ecosystems and agricultural landscapes of Eucalyptus growing areas.
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Affiliation(s)
- Amare Tesfaw
- Department of Agricultural Economics, College of Agriculture and Natural Resources, Debre Markos University, Debre Markos, P. O. Box 269, Ethiopia
- Corresponding author.
| | - Ermias Teferi
- Center for Environment and Development Studies, Addis Ababa University, Addis Ababa, P. O. Box 1176, Ethiopia
| | - Feyera Senbeta
- Center for Environment and Development Studies, Addis Ababa University, Addis Ababa, P. O. Box 1176, Ethiopia
| | - Dawit Alemu
- Stichting Wageningen Research (SWR) Ethiopia, Wageningen University & Research (WUR), Addis Ababa, P. O. Box 88, Ethiopia
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Thornton P, Chang Y, Loboguerrero AM, Campbell B. Perspective: What might it cost to reconfigure food systems? GLOBAL FOOD SECURITY 2023. [DOI: 10.1016/j.gfs.2022.100669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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12
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Khan HR, Gillani Z, Jamal MH, Athar A, Chaudhry MT, Chao H, He Y, Chen M. Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery. SENSORS (BASEL, SWITZERLAND) 2023; 23:1779. [PMID: 36850377 PMCID: PMC9967001 DOI: 10.3390/s23041779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/27/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.
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Affiliation(s)
- Haseeb Rehman Khan
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
| | - Zeeshan Gillani
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Muhammad Hasan Jamal
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
| | - Atifa Athar
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
| | - Muhammad Tayyab Chaudhry
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
| | - Haoyu Chao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
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13
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Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products. Sci Data 2022; 9:407. [PMID: 35840621 PMCID: PMC9287319 DOI: 10.1038/s41597-022-01522-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/22/2022] [Indexed: 11/13/2022] Open
Abstract
As a routine agricultural practice, irrigation is fundamental to protect crops from water scarcity and ensure food security in China. However, consistent and reliable maps about the spatial distribution and extent of irrigated croplands are still unavailable, impeding water resource management and agricultural planning. Here, we produced annual 500-m irrigated cropland maps across China for 2000–2019, using a two-step strategy that integrated statistics, remote sensing, and existing irrigation products into a hybrid irrigation dataset. First, we generated intermediate irrigation maps (MIrAD-GI) by fusing the MODIS-derived greenness index and statistical data. Second, we collected all existing available irrigation maps over China and integrated them with MIrAD-GI into an improved series of annual irrigation maps, using constrained statistics and a synergy mapping method. The resultant maps had moderate overall accuracies (0.732~0.819) based on nationwide reference ground samples and outperformed existing irrigation products by inter-comparison. As the first of this kind in China, the annual maps delineated the spatiotemporal pattern of irrigated croplands and could contribute to sustainable water use and agricultural development. Measurement(s) | irrigation area and distribution | Technology Type(s) | statistics and satellite remote sensing | Factor Type(s) | agricultural irrigation | Sample Characteristic - Organism | agricultural irrigation | Sample Characteristic - Environment | agricultural field | Sample Characteristic - Location | China |
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14
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Evaluating Effects of Medium-Resolution Optical Data Availability on Phenology-Based Rice Mapping in China. REMOTE SENSING 2022. [DOI: 10.3390/rs14133134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
The phenology-based approach has proven effective for paddy rice mapping due to the unique flooding and transplanting features of rice during the early growing season. However, the method may be greatly affected if no valid observations are available during the flooding and rice transplanting phase. Here, we compare the effects of data availability of different sensors in the critical phenology phase, thereby supporting paddy rice mapping based on phenology-based approaches. Importantly, our study further analyzed the effects of the spatial pattern of the valid observations related to certain factors (i.e., sideslips, clouds, and temporal window lengths of flooding and rice transplanting), which supply the applicable area of the phenology-based approach indications. We first determined the flooding and rice transplanting phase using in situ observational data from agrometeorological stations and remote sensing data, then evaluated the effects of data availability in this phase of 2020 in China using all Landsat-7 and 8 and Sentinel-2 data. The results show that on the country level, the number of average valid observations during the flooding and rice transplanting phase was more than ten for the integration of Landsat and Sentinel images. On the sub-country level, the number of average valid observations was high in the cold temperate zone (17.4 observations), while it was relatively lower in southern China (6.4 observations), especially in Yunnan–Guizhou Plateau, which only had three valid observations on average. Based on the multicollinearity test, the three factors are significantly correlated with the absence of valid observations: (R2 = 0.481) and Std.Coef. (Std. Err.) are 0.306 (0.094), −0.453 (0.003) and −0.547 (0.019), respectively. Overall, these results highlight the substantial spatial heterogeneity of valid observations in China, confirming the reliability of the integration of Landsat-7 and 8 and Sentinel-2 imagery for paddy rice mapping based on phenology-based approaches. This can pave the way for a national-scale effort of rice mapping in China while further indicating potential omission errors in certain cloud-prone regions without sufficient optical observation data, i.e., the Sichuan Basin.
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15
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ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine. REMOTE SENSING 2022. [DOI: 10.3390/rs14133041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.
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16
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Sun Z, Behrens P, Tukker A, Bruckner M, Scherer L. Global Human Consumption Threatens Key Biodiversity Areas. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:9003-9014. [PMID: 36350780 PMCID: PMC9228074 DOI: 10.1021/acs.est.2c00506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Key biodiversity areas (KBAs) are critical regions for preserving global biodiversity. KBAs are identified by their importance to biodiversity rather than their legal status. As such, KBAs are often under pressure from human activities. KBAs can encompass many different land-use types (e.g., cropland, pastures) and land-use intensities. Here, we combine a global economic model with spatial mapping to estimate the biodiversity impacts of human land use in KBAs. We find that global human land use within KBAs causes disproportionate biodiversity losses. While land use within KBAs accounts for only 7% of total land use, it causes 16% of the potential global plant loss and 12% of the potential global vertebrate loss. The consumption of animal products accounts for more than half of biodiversity loss within KBAs, with housing the second largest at around 10%. Bovine meat is the largest single contributor to this loss, at around 31% of total biodiversity loss. In terms of land use, lightly grazed pasture contributes the most, accounting for around half of all potential species loss. This loss is concentrated mainly in middle- and low-income regions with rich biodiversity. International trade is an important driver of loss, accounting for 22-29% of total potential plant and vertebrate loss. Our comprehensive global, trade-linked analysis provides insights into maintaining the integrity of KBAs and global biodiversity.
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Affiliation(s)
- Zhongxiao Sun
- Institute
of Environmental Sciences (CML), Leiden
University, 2333 CC Leiden, the Netherlands
- College
of Land Science and Technology, China Agricultural
University, 100193 Beijing, China
| | - Paul Behrens
- Institute
of Environmental Sciences (CML), Leiden
University, 2333 CC Leiden, the Netherlands
- Leiden
University College The Hague, 2595 DG The Hague, the Netherlands
| | - Arnold Tukker
- Institute
of Environmental Sciences (CML), Leiden
University, 2333 CC Leiden, the Netherlands
- The
Netherlands Organisation for Applied Scientific Research TNO, 2595 DA The Hague, the Netherlands
| | - Martin Bruckner
- Institute
for Ecological Economics, Vienna University
of Economics and Business, 1020 Vienna, Austria
| | - Laura Scherer
- Institute
of Environmental Sciences (CML), Leiden
University, 2333 CC Leiden, the Netherlands
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17
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Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data. REMOTE SENSING 2022. [DOI: 10.3390/rs14081809] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Precise and timely information on crop spatial distribution over large areas is paramount to agricultural monitoring, food security, and policy development. Currently, automatically classifying crop types at a large scale is challenging due to the scarcity of ground data. Although previous studies have indicated that transductive transfer learning (TTL) is a promising method to address this problem, it performs poorly within regions where crop compositions and phenology differ largely. Here we transferred random forest classifiers trained in limited regions with diversified growing conditions and land covers to the rest of the study area where ground data are scarce, with more than 130,000 Sentinel-2 images processed using the Google Earth Engine (GEE) platform. We established the 10 m crop maps for four major crops (i.e., maize, rapeseed, winter, and spring Triticeae crops) across 10 European Union (EU) countries from 2018 to 2019. The final crop maps had a high accuracy with overall accuracy generally greater than 0.89, with user’s accuracy and producer’s accuracy ranging from 0.72 to 0.98. Moreover, the resulting maps were consistent with the NUTS-2 level official statistics, with R2 consistently greater than 0.9. We further analyzed the crop rotation patterns and found that the rotation intervals across these EU countries were generally at least one year. Maize was dominantly rotated with winter Triticeae crops or converted to other land covers in the following year. Rapeseed was generally grown in rotation with winter Triticeae crops, whereas the rotation patterns of winter and spring Triticeae crops were more diversified. Red Edge Position (REP) and Normalized Difference Yellow Index (NDYI) played significant roles in crop classification across the EU. This study highlights the potential of the developed TTL method for crop classification over large spatial extents where labeled data are limited and the differences in crop compositions and phenology are relatively large.
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18
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Data Fusion in Earth Observation and the Role of Citizen as a Sensor: A Scoping Review of Applications, Methods and Future Trends. REMOTE SENSING 2022. [DOI: 10.3390/rs14051263] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Recent advances in Earth Observation (EO) placed Citizen Science (CS) in the highest position, declaring their essential provision of information in every discipline that serves the SDGs, and the 2050 climate neutrality targets. However, so far, none of the published literature reviews has investigated the models and tools that assimilate these data sources. Following this gap of knowledge, we synthesised this scoping systematic literature review (SSLR) with a will to cover this limitation and highlight the benefits and the future directions that remain uncovered. Adopting the SSLR guidelines, a double and two-level screening hybrid process found 66 articles to meet the eligibility criteria, presenting methods, where data were fused and evaluated regarding their performance, scalability level and computational efficiency. Subsequent reference is given on EO-data, their corresponding conversions, the citizens’ participation digital tools, and Data Fusion (DF) models that are predominately exploited. Preliminary results showcased a preference in the multispectral satellite sensors, with the microwave sensors to be used as a supplementary data source. Approaches such as the “brute-force approach” and the super-resolution models indicate an effective way to overcome the spatio-temporal gaps and the so far reliance on commercial satellite sensors. Passive crowdsensing observations are foreseen to gain a greater audience as, described in, most cases as a low-cost and easily applicable solution even in the unprecedented COVID-19 pandemic. Immersive platforms and decentralised systems should have a vital role in citizens’ engagement and training process. Reviewing the DF models, the majority of the selected articles followed a data-driven method with the traditional algorithms to still hold significant attention. An exception is revealed in the smaller-scale studies, which showed a preference for deep learning models. Several studies enhanced their methods with the active-, and transfer-learning approaches, constructing a scalable model. In the end, we strongly support that the interaction with citizens is of paramount importance to achieve a climate-neutral Earth.
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19
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Estes LD, Ye S, Song L, Luo B, Eastman JR, Meng Z, Zhang Q, McRitchie D, Debats SR, Muhando J, Amukoa AH, Kaloo BW, Makuru J, Mbatia BK, Muasa IM, Mucha J, Mugami AM, Mugami JM, Muinde FW, Mwawaza FM, Ochieng J, Oduol CJ, Oduor P, Wanjiku T, Wanyoike JG, Avery RB, Caylor KK. High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales. Front Artif Intell 2022; 4:744863. [PMID: 35284820 PMCID: PMC8916109 DOI: 10.3389/frai.2021.744863] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 10/18/2021] [Indexed: 11/18/2022] Open
Abstract
Mapping the characteristics of Africa’s smallholder-dominated croplands, including the sizes and numbers of fields, can provide critical insights into food security and a range of other socioeconomic and environmental concerns. However, accurately mapping these systems is difficult because there is 1) a spatial and temporal mismatch between satellite sensors and smallholder fields, and 2) a lack of high-quality labels needed to train and assess machine learning classifiers. We developed an approach designed to address these two problems, and used it to map Ghana’s croplands. To overcome the spatio-temporal mismatch, we converted daily, high resolution imagery into two cloud-free composites (the primary growing season and subsequent dry season) covering the 2018 agricultural year, providing a seasonal contrast that helps to improve classification accuracy. To address the problem of label availability, we created a platform that rigorously assesses and minimizes label error, and used it to iteratively train a Random Forests classifier with active learning, which identifies the most informative training sample based on prediction uncertainty. Minimizing label errors improved model F1 scores by up to 25%. Active learning increased F1 scores by an average of 9.1% between first and last training iterations, and 2.3% more than models trained with randomly selected labels. We used the resulting 3.7 m map of cropland probabilities within a segmentation algorithm to delineate crop field boundaries. Using an independent map reference sample (n = 1,207), we found that the cropland probability and field boundary maps had respective overall accuracies of 88 and 86.7%, user’s accuracies for the cropland class of 61.2 and 78.9%, and producer’s accuracies of 67.3 and 58.2%. An unbiased area estimate calculated from the map reference sample indicates that cropland covers 17.1% (15.4–18.9%) of Ghana. Using the most accurate validation labels to correct for biases in the segmented field boundaries map, we estimated that the average size and total number of field in Ghana are 1.73 ha and 1,662,281, respectively. Our results demonstrate an adaptable and transferable approach for developing annual, country-scale maps of crop field boundaries, with several features that effectively mitigate the errors inherent in remote sensing of smallholder-dominated agriculture.
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Affiliation(s)
- Lyndon D. Estes
- Graduate School of Geography, Clark University, Worcester, MA, United States
- *Correspondence: Lyndon D. Estes,
| | - Su Ye
- Graduate School of Geography, Clark University, Worcester, MA, United States
- Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT, United States
| | - Lei Song
- Graduate School of Geography, Clark University, Worcester, MA, United States
| | - Boka Luo
- Graduate School of Geography, Clark University, Worcester, MA, United States
- Clark Labs, Clark University, Worcester, MA, United States
| | - J. Ronald Eastman
- Graduate School of Geography, Clark University, Worcester, MA, United States
- Clark Labs, Clark University, Worcester, MA, United States
| | - Zhenhua Meng
- Graduate School of Geography, Clark University, Worcester, MA, United States
| | - Qi Zhang
- Graduate School of Geography, Clark University, Worcester, MA, United States
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ryan B. Avery
- Department of Geography, University of California Santa Barbara, Santa Barbara, CA, United States
| | - Kelly K. Caylor
- Department of Geography, University of California Santa Barbara, Santa Barbara, CA, United States
- Earth Research Institute, University of California Santa Barbara, Santa Barbara, CA, United States
- Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, United States
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20
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Fritz S, Laso Bayas JC, See L, Schepaschenko D, Hofhansl F, Jung M, Dürauer M, Georgieva I, Danylo O, Lesiv M, McCallum I. A Continental Assessment of the Drivers of Tropical Deforestation With a Focus on Protected Areas. FRONTIERS IN CONSERVATION SCIENCE 2022. [DOI: 10.3389/fcosc.2022.830248] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Deforestation contributes to global greenhouse gas emissions and must be reduced if the 1.5°C limit to global warming is to be realized. Protected areas represent one intervention for decreasing forest loss and aiding conservation efforts, yet there is intense human pressure on at least one-third of protected areas globally. There have been numerous studies addressing the extent and identifying drivers of deforestation at the local, regional, and global level. Yet few have focused on drivers of deforestation in protected areas in high thematic detail. Here we use a new crowdsourced data set on drivers of tropical forest loss for the period 2008–2019, which has been collected using the Geo-Wiki crowdsourcing application for visual interpretation of very high-resolution imagery by volunteers. Extending on the published data on tree cover and forest loss from the Global Forest Change initiative, we investigate the dominant drivers of deforestation in tropical protected areas situated within 30° north and south of the equator. We find the deforestation rate in protected areas to be lower than the continental average for the Latin Americas (3.4% in protected areas compared to 5.4%) and Africa (3.3% compared to 3.9%), but it exceeds that of unprotected land in Asia (8.5% compared to 8.1%). Consistent with findings from foregoing studies, we also find that pastures and other subsistence agriculture are the dominant deforestation driver in the Latin Americas, while forest management, oil palm, shifting cultivation and other subsistence agriculture dominate in Asia, and shifting cultivation and other subsistence agriculture is the main driver in Africa. However, we find contrasting results in relation to the degree of protection, which indicate that the rate of deforestation in Latin America and Africa in strictly protected areas might even exceed that of areas with no strict protection. This crucial finding highlights the need for further studies based on a bottom up crowdsourced, data collection approach, to investigate drivers of deforestation both inside and outside protected areas.
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21
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Evaluation of Population—Food Relationship from the Perspective of Climate Productivity Potential: A Case Study of Eastern Gansu in Northwest China. ATMOSPHERE 2022. [DOI: 10.3390/atmos13020287] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Suffering from the double blow of the new crown pneumonia epidemic and floods, food security issues have once again become a source of concern. Eastern Gansu is an important dry farming area in northwestern China, and agricultural production has been greatly affected by climate change. Based on the climate data of 17 national meteorological stations in eastern Gansu from 1961 to 2020 and the data on population, grain planting area and grain production in each region from 1986 to 2019, using the Thornthwaite Memorial model, this paper analyzed the climate production potential (TSPV), population carrying capacity and population carrying capacity index in eastern Gansu, and then revealed the relationship between population and food in eastern Gansu. The findings and results revealed that: (1) over the past 60 years, the temperature in eastern Gansu has been increasing and precipitation has been decreasing; (2) TSPV has been increasing. Moreoever, the spatial distribution was significantly different, showing a trend of decreasing from the southeast to the northwest. Lintao, Huining, and Jingtai displayed a decreasing trend, while other areas exhibited an increasing trend. Precipitation was the main limiting factor for TSPV; (3) Grain production continued to increase due to changing hydrothermal conditions and improved production efficiency. Cultivated land–population carrying capacity and climate production potential–population carrying capacity (TSPV–population carrying capacity) both exhibited a significant increasing trend (p < 0.01). Cultivated land–population capacity increased from southeast to northwest, and all stations expressed an increasing trend. TSPV–population carrying capacity increased from southeast to northwest, and the whole region displayed an increasing trend. Even in extremely reduced production years, TSPV–population carrying capacity was also greater than cultivated land–population carrying capacity. This revealed that, under ideal conditions, TSPV–population carrying capacity can fully meet the needs of the current population. (4) The population carrying capacity index showed a significant downward trend (p < 0.01). It showed a trend of decreasing from south to north, and whole area underwent a decreasing trend consistently, indicating that the population–food relationship in eastern Gansu tended to be balanced. This result was conducive to correct assessment of the relationship between people and food in the study area, and provided a reference for formulating food policies.
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22
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Predicted wind and solar energy expansion has minimal overlap with multiple conservation priorities across global regions. Proc Natl Acad Sci U S A 2022; 119:2104764119. [PMID: 35101973 PMCID: PMC8832964 DOI: 10.1073/pnas.2104764119] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/29/2021] [Indexed: 01/01/2023] Open
Abstract
Protected areas and renewable energy generation are critical tools to combat biodiversity loss and climate change, respectively. Over the coming decades, expansion of the protected area network to meet conservation objectives will be occurring alongside rapid deployment of renewable energy infrastructure to meet climate targets, driving potential conflict for a finite land resource. Renewable energy infrastructure can have negative effects on wildlife, and co-occurrence may mean emissions targets are met at the expense of conservation objectives. Here, we assess current and projected overlaps of wind and solar photovoltaic installations and important conservation areas across nine global regions using spatially explicit wind and solar data and methods for predicting future renewable expansion. We show similar levels of co-occurrence as previous studies but demonstrate that once area is accounted for, previous concerns about overlaps in the Northern Hemisphere may be largely unfounded, although they are high in some biodiverse countries (e.g., Brazil). Future projections of overlap between the two land uses presented here are generally dependent on priority threshold and region and suggest the risk of future conflict can be low. We use the best available data on protected area degradation to corroborate this level of risk. Together, our findings indicate that while conflicts between renewables and protected areas inevitably do occur, renewables represent an important option for decarbonization of the energy sector that would not significantly affect area-based conservation targets if deployed with appropriate policy and regulatory controls.
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23
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Smallholder Crop Type Mapping and Rotation Monitoring in Mountainous Areas with Sentinel-1/2 Imagery. REMOTE SENSING 2022. [DOI: 10.3390/rs14030566] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Accurate and timely crop type mapping and rotation monitoring play a critical role in crop yield estimation, soil management, and food supplies. To date, to our knowledge, accurate mapping of crop types remains challenging due to the intra-class variability of crops and labyrinthine natural conditions. The challenge is further complicated for smallholder farming systems in mountainous areas where field sizes are small and crop types are very diverse. This bottleneck issue makes it difficult and sometimes impossible to use remote sensing in monitoring crop rotation, a desired and required farm management policy in parts of China. This study integrated Sentinel-1 and Sentinel-2 images for crop type mapping and rotation monitoring in Inner Mongolia, China, with an extensive field-based survey dataset. We accomplished this work on the Google Earth Engine (GEE) platform. The results indicated that most crop types were mapped fairly accurately with an F1-score around 0.9 and a clear separation of crop types from one another. Sentinel-1 polarization achieved a better performance in wheat and rapeseed classification among different feature combinations, and Sentinel-2 spectral bands exhibited superiority in soybean and corn identification. Using the accurate crop type classification results, we identified crop fields, changed or unchanged, from 2017 to 2018. These findings suggest that the combination of Sentinel-1 and Sentinel-2 proved effective in crop type mapping and crop rotation monitoring of smallholder farms in labyrinthine mountain areas, allowing practical monitoring of crop rotations.
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24
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Research Progress on Remote Sensing Classification Methods for Farmland Vegetation. AGRIENGINEERING 2021. [DOI: 10.3390/agriengineering3040061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of monitoring crop growth and estimating regional crop yields. Based on hundreds of relevant documents from the past 25 years, in this paper, we summarize research progress in relation to farmland vegetation identification and classification by remote sensing. The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing. Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. Current problems related to crop remote sensing identification are then identified and future development directions are proposed.
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25
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Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping. REMOTE SENSING 2021. [DOI: 10.3390/rs13234891] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The exploitation of the unprecedented capacity of Sentinel-1 (S1) and Sentinel-2 (S2) data offers new opportunities for crop mapping. In the framework of the SenSAgri project, this work studies the synergy of very high-resolution Sentinel time series to produce accurate early seasonal binary cropland mask and crop type map products. A crop classification processing chain is proposed to address the following: (1) high dimensionality challenges arising from the explosive growth in available satellite observations and (2) the scarcity of training data. The two-fold methodology is based on an S1-S2 classification system combining the so-called soft output predictions of two individually trained classifiers. The performances of the SenSAgri processing chain were assessed over three European test sites characterized by different agricultural systems. A large number of highly diverse and independent data sets were used for validation experiments. The agreement between independent classification algorithms of the Sentinel data was confirmed through different experiments. The presented results assess the interest of decision-level fusion strategies, such as the product of experts. Accurate crop map products were obtained over different countries in the early season with limited training data. The results highlight the benefit of fusion for early crop mapping and the interest of detecting cropland areas before the identification of crop types.
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26
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Middendorf BJ, Traoré H, Middendorf G, Jha PK, Yonli D, Palé S, Prasad PVV. Impacts of the COVID-19 pandemic on vegetable production systems and livelihoods: Smallholder farmer experiences in Burkina Faso. Food Energy Secur 2021; 11:e337. [PMID: 34900239 PMCID: PMC8646566 DOI: 10.1002/fes3.337] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/11/2021] [Indexed: 11/05/2022] Open
Abstract
At the onset of COVID-19, researchers quickly recognized the need for research on the consequences of the pandemic for agricultural and food systems, both in terms of immediate impacts on access to inputs and labor, disruptions in transportation and markets, and the longer-term implications on crop productivity, income, and livelihoods. Vegetable production and supply chains are particularly vulnerable due to the perishable nature of the products and labor-intensive production practices. The purpose of this study was to understand the impacts of COVID-19 on vegetable production in Burkina Faso in terms of both the biophysical aspects such as yields and access to inputs and socioeconomic aspects such as access to labor, markets, and social services. A survey was developed to better understand smallholder farmer experiences regarding the impacts of COVID-19 on their vegetable production systems and social well-being. The survey was administered (between August and October 2020) with smallholder farmers (n = 605) in 13 administrative regions covering all agroecological zones of Burkina Faso. The survey results clearly show impacts of COVID-19 on vegetable systems, including a reduction in access to inputs, a reduction in yields, a loss of income, reduced access to local and urban markets, reduced access to transportation, and an increase in post-harvest loss. Market access, distribution, and disruptions were a major shock to the system. Results also showed an increase in women's labor in the household, and for youth, an increase in unemployment, job loss, and concerns of poverty. Finally, food security and social supports were highlighted as major issues for resilience and livelihoods. The results from this survey should be helpful to policymakers and researchers to develop policies and strategies to minimize the negative impacts of this ongoing pandemic on the agri-food systems and support smallholder farmers to overcome stress caused by COVID-19.
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Affiliation(s)
- B Jan Middendorf
- Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification Kansas State University Manhattan Kansas USA
| | - Hamidou Traoré
- Institut de l'Environnement et de Recherches Agricoles (INERA) Ouagadougou Burkina Faso
| | - Gerad Middendorf
- Department of Sociology, Anthropology, and Social Work Kansas State University Manhattan Kansas USA
| | - Prakash K Jha
- Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification Kansas State University Manhattan Kansas USA
| | - Djibril Yonli
- Institut de l'Environnement et de Recherches Agricoles (INERA) Ouagadougou Burkina Faso
| | - Siébou Palé
- Institut de l'Environnement et de Recherches Agricoles (INERA) Ouagadougou Burkina Faso
| | - P V Vara Prasad
- Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification Kansas State University Manhattan Kansas USA.,Department of Agronomy Kansas State University Manhattan Kansas USA
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Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery. REMOTE SENSING 2021. [DOI: 10.3390/rs13173523] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops—maize and potato—and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize–cereals and potato–maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize–legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services.
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28
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Assessing food availability: A novel approach for the quantitative estimation of the contribution of small farms in regional food systems in Europe. GLOBAL FOOD SECURITY 2021. [DOI: 10.1016/j.gfs.2021.100555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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29
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Nzabarinda V, Bao A, Xu W, Uwamahoro S, Huang X, Gao Z, Umugwaneza A, Kayumba PM, Maniraho AP, Jiang Z. Impact of cropland development intensity and expansion on natural vegetation in different African countries. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101359] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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30
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Natori Y, Hino A. Global identification and mapping of socio-ecological production landscapes with the Satoyama Index. PLoS One 2021; 16:e0256327. [PMID: 34407125 PMCID: PMC8372939 DOI: 10.1371/journal.pone.0256327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 08/04/2021] [Indexed: 11/19/2022] Open
Abstract
Production landscapes play an important role in conserving biodiversity outside protected areas. Socio-ecological production landscapes (SEPL) are places where people use for primary production that conserve biodiversity. Such places can be found around the world, but a lack of geographic information on SEPL has resulted in their potential for conservation being neglected in policies and programs. We tested the global applicability of the Satoyama Index for identifying SEPL in multi-use cultural landscapes using global land use/cover data and two datasets of known SEPL. We found that the Satoyama Index, which was developed with a focus on biodiversity and tested in Japan, could be used globally to identify landscapes resulting from complex interactions between people and nature with statistical significance. This makes SEPL more relevant in the global conservation discourse. As the Satoyama Index mapping revealed that approximately 80% of SEPL occur outside recognized conservation priorities, such as protected areas and key biodiversity areas, identifying SEPL under the scheme of other area-based conservation measures (OECM) may bring more conservation attention to SEPL. Based on the issues identified in the SEPL mapping, we discuss ways that could improve the Satoyama Index mapping at global scale with the longitudinal temporal dimension and at more local scale with spatial and thematic resolution.
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Affiliation(s)
- Yoji Natori
- Conservation International Japan, Tokyo, Japan
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31
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Nagano Y, Miyashita T, Taki H, Yokoi T. Diversity of co‐flowering plants at field margins potentially sustains an abundance of insects visiting buckwheat,
Fagopyrum esculentum
, in an agricultural landscape. Ecol Res 2021. [DOI: 10.1111/1440-1703.12252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yuta Nagano
- Graduate school of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan
| | - Tadashi Miyashita
- Department of Ecosystem Studies, Graduate School of Agriculture and Life Sciences University of Tokyo Tokyo Japan
| | - Hisatomo Taki
- Department of Forest Entomology Forestry and Forest Products Research Institute Tsukuba Ibaraki Japan
| | - Tomoyuki Yokoi
- Graduate school of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan
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The Relationship Between Landscape Diversity and Crops Productivity: Landscape Scale Study. JOURNAL OF LANDSCAPE ECOLOGY 2021. [DOI: 10.2478/jlecol-2021-0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
The present study evaluates the relationship between the crops productivity and ecosystem diversity. The spatial variability in ecosystem diversity was measured using the Shannon landscape diversity index and distance from biodiversity hotspots that are nature conservation areas. Three crops were selected for the study: soybeans, sunflowers and winter rye. The initial data included the average crops yields in administrative districts within 10 regions of Ukraine. It was found that the studied crops yield dynamics from the mid-90s of the previous century to the current period could be described by a sigmoid curve (log-logistic model). The parameters of the yield model are the following indicators: the minimum level of yield (Lower Limit); maximum level of productivity (Upper limit); the slope of the model, which shows the rate of change in yields over time; ED50 - the time required to achieve half, from the maximum yield level. Our studies have shown that there is a statistically significant regression relationship between the yield parameters of all the studied crops and biodiversity, even at the landscape level. Among the studied crops, soybean shows the strongest regression relationship between yields and indicators of landscape diversity. Sunflower yield is the least dependent on landscape diversity. Most of the established dependencies are nonlinear, which indicates the existence of an optimal level of landscape diversity to achieve the maximum possible crop yields. Therefore, the obtained patterns can be the basis for land-use planning and management, especially while creating new natural protected areas.
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Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning By SAGA GIS. TRANSYLVANIAN REVIEW OF SYSTEMATICAL AND ECOLOGICAL RESEARCH 2021. [DOI: 10.2478/trser-2020-0015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
Vegetation of Cameroon includes a variety of landscape types with high biodiversity. Ecological monitoring of Yaoundé requires visualization of vegetation types in context of climate change. Vegetation Indices (VIs) derived from Sentinel-2 multispectral satellite image were analyzed in SAGA GIS to separate wetland biomes, as well as savannah and tropical rainforests. The methodology includes computing 6 VIs: NDVI, DVI, SAVI, RVI, TTVI, CTVI. The VIs shown correlation of data with vegetation distribution rising from wetlands, grassland, savanna, and shrub land towards tropical rainforests, increasing values along with canopy greenness, while also being inversely proportional to soils, urban spaces and Sanaga River. The study contributed to the environmental studies of Cameroon and demonstration of the satellite image processing.
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Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach. REMOTE SENSING 2021. [DOI: 10.3390/rs13061060] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.
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35
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A Watershed-Segmentation-Based Improved Algorithm for Extracting Cultivated Land Boundaries. REMOTE SENSING 2021. [DOI: 10.3390/rs13050939] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
To accurately extract cultivated land boundaries based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm was proposed herein based on a combination of pre- and post-improvement procedures. Image contrast enhancement was used as the pre-improvement, while the color distance of the Commission Internationale de l´Eclairage (CIE) color space, including the Lab and Luv, was used as the regional similarity measure for region merging as the post-improvement. Furthermore, the area relative error criterion (δA), the pixel quantity error criterion (δP), and the consistency criterion (Khat) were used for evaluating the image segmentation accuracy. The region merging in Red–Green–Blue (RGB) color space was selected to compare the proposed algorithm by extracting cultivated land boundaries. The validation experiments were performed using a subset of Chinese Gaofen-2 (GF-2) remote sensing image with a coverage area of 0.12 km2. The results showed the following: (1) The contrast-enhanced image exhibited an obvious gain in terms of improving the image segmentation effect and time efficiency using the improved algorithm. The time efficiency increased by 10.31%, 60.00%, and 40.28%, respectively, in the RGB, Lab, and Luv color spaces. (2) The optimal segmentation and merging scale parameters in the RGB, Lab, and Luv color spaces were C for minimum areas of 2000, 1900, and 2000, and D for a color difference of 1000, 40, and 40. (3) The algorithm improved the time efficiency of cultivated land boundary extraction in the Lab and Luv color spaces by 35.16% and 29.58%, respectively, compared to the RGB color space. The extraction accuracy was compared to the RGB color space using the δA, δP, and Khat, that were improved by 76.92%, 62.01%, and 16.83%, respectively, in the Lab color space, while they were 55.79%, 49.67%, and 13.42% in the Luv color space. (4) Through the visual comparison, time efficiency, and segmentation accuracy, the comprehensive extraction effect using the proposed algorithm was obviously better than that of RGB color-based space algorithm. The established accuracy evaluation indicators were also proven to be consistent with the visual evaluation. (5) The proposed method has a satisfying transferability by a wider test area with a coverage area of 1 km2. In addition, the proposed method, based on the image contrast enhancement, was to perform the region merging in the CIE color space according to the simulated immersion watershed segmentation results. It is a useful attempt for the watershed segmentation algorithm to extract cultivated land boundaries, which provides a reference for enhancing the watershed algorithm.
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36
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Yan J, Gao S, Xu M, Su F. Spatial-temporal changes of forests and agricultural lands in Malaysia from 1990 to 2017. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:803. [PMID: 33263164 DOI: 10.1007/s10661-020-08765-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 11/17/2020] [Indexed: 06/12/2023]
Abstract
Forests and agricultural lands are the main resources on the earth's surface and important indicators of regional ecological environments. In this paper, Landsat images from 1990 and 2017 were used to extract information on forests in Malaysia based on a remote-sensing classification method. The spatial-temporal changes of forests and agricultural lands in Malaysia between 1990 and 2017 were analyzed. The results showed that the natural forests in Malaysia decreased by 441 Mha, a reduction of 21%. The natural forests were mainly converted into plantations in Peninsular Malaysia and plantations and secondary forests in East Malaysia. The area of agricultural lands in Malaysia increased by 55.7%, in which paddy fields increased by 1.1% and plantations increased by 98.2%. Paddy fields in Peninsular Malaysia are mainly distributed in the north-central coast and the Kelantan Delta. The agricultural land in East Malaysia is dominated by plantations, which are mainly distributed in coastal areas. The predictable areas of possible expansion for paddy fields in Peninsular Malaysia's Kelantan (45.2%) and Kedah (16.8%) areas in the future are large, and in addition, the plantations in Sarawak (44.7%) and Sabah (29.6%) of East Malaysia have large areas for expansion. The contradiction between agricultural development and protecting the ecological environment is increasingly prominent. The demand for agriculture is expected to increase further and result in greater pressures on tropical forests. Governments also need to encourage farmers to carry out existing land development, land recultivation, or cooperative development to improve agricultural efficiency and reduce the damage to natural forests.
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Affiliation(s)
- Jinfeng Yan
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China.
| | - Shanshan Gao
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China
| | - Meirong Xu
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China
| | - Fenzhen Su
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
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37
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Hu Q, Xiang M, Chen D, Zhou J, Wu W, Song Q. Global cropland intensification surpassed expansion between 2000 and 2010: A spatio-temporal analysis based on GlobeLand30. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 746:141035. [PMID: 32771755 DOI: 10.1016/j.scitotenv.2020.141035] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/15/2020] [Accepted: 07/15/2020] [Indexed: 06/11/2023]
Abstract
Cropland expansion and intensification are the two main strategies for increasing food production. Here, we investigated the spatio-temporal patterns of global cropland expansion and intensification between 2000 and 2010 using the GlobeLand30 dataset. In doing so, we first analyzed the expansion and loss of global cropland at different spatial scales. Second, we quantified cropland intensification from the perspective of output and mapped its global spatial distribution. Third, nine coupled patterns of cropland expansion and intensification were identified, and the contributions of these two strategies to global crop production were finally estimated and compared. The results show that global cropland increased slightly (2.19%) during 2000-2010, with the American continent having the largest net increase (0.21 million km2) and Africa having the highest magnitude of increase (7.42%) as well as the most substantial spatial variation. Among the world's top ten countries with the largest cropland areas, China was the only country which experienced cropland decrease, while cropland in Brazil and Argentina increased the most. Moreover, we found that Brazil ranked first in cropland intensification, followed by China, India and Ukraine. More than one-third of countries' cropland had stable area and moderate intensification, suggesting that agricultural land systems did not cause significant environmental harm globally during this period. Ten countries (e.g., Brazil and Algeria) experienced significant cropland expansion as well as a high level of intensification, suggesting that they could be major contributors to global crop production as well as environmental change. Cropland expansion largely boosted crop production improvement in Asia, while cropland intensification was the dominant factor for crop production in Europe and America. Overall, cropland intensification contributed much more than expansion to improving global agricultural production during 2000 and 2010. Our results gain a comprehensive overview of spatio-temporal patterns of global cropland expansion and intensification, which can provide helpful insights for the international community and individual countries to better guide land use planning, adjust agricultural structure and coordinate food trade so as to achieve a sustainable development of agriculture.
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Affiliation(s)
- Qiong Hu
- Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
| | - Mingtao Xiang
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China.
| | - Di Chen
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jie Zhou
- Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
| | - Wenbin Wu
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Qian Song
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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38
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Sparrow BD, Edwards W, Munroe SE, Wardle GM, Guerin GR, Bastin J, Morris B, Christensen R, Phinn S, Lowe AJ. Effective ecosystem monitoring requires a multi-scaled approach. Biol Rev Camb Philos Soc 2020; 95:1706-1719. [PMID: 32648358 PMCID: PMC7689690 DOI: 10.1111/brv.12636] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 06/18/2020] [Accepted: 06/22/2020] [Indexed: 01/11/2023]
Abstract
Ecosystem monitoring is fundamental to our understanding of how ecosystem change is impacting our natural resources and is vital for developing evidence-based policy and management. However, the different types of ecosystem monitoring, along with their recommended applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecosystem monitoring, leading to poor program development, implementation and outcomes. In an effort to develop a more consistent and clear understanding of ecosystem monitoring programs, we here review the main types of monitoring and recommend the widespread adoption of three classifications of monitoring, namely, targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables questions relating to where and when ecosystem change is occurring to be addressed. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the causes of ecosystem change. The classification system is flexible and can incorporate different interests, objectives, targets and characteristics as well as different spatial scales and temporal frequencies, while also providing valuable structure and consistency across distinct ecosystem monitoring programs. To support our argument, we examine the ability of each monitoring type to inform on six key types of questions that are routinely posed for ecosystem monitoring programs, such as where and when change is occurring, what is the magnitude of change, and how can the change be managed? As we demonstrate, each type of ecosystem monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, scientists and land managers can design programs best suited to their needs. Finally, we assert that for our most serious environmental challenges, it is essential that we include information from each of these monitoring scales to inform on all facets of ecosystem change, and this is best achieved through close collaboration between the scales. With a renewed understanding of the importance of each monitoring type, along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges.
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Affiliation(s)
- Ben D. Sparrow
- Terrestrial Ecosystem Research Network, The School of Biological SciencesThe University of AdelaideAdelaideSouth Australia5005Australia
| | - Will Edwards
- Terrestrial Ecosystem Research Network, College of Science and EngineeringJames Cook UniversityPO Box 6811CairnsQueensland4870Australia
| | - Samantha E.M. Munroe
- Terrestrial Ecosystem Research Network, The School of Biological SciencesThe University of AdelaideAdelaideSouth Australia5005Australia
| | - Glenda M. Wardle
- Terrestrial Ecosystem Research Network, Desert Ecology Research Group, School of Life and Environmental SciencesUniversity of SydneySydneyNew South Wales2006Australia
| | - Greg R. Guerin
- Terrestrial Ecosystem Research Network, The School of Biological SciencesThe University of AdelaideAdelaideSouth Australia5005Australia
| | - Jean‐Francois Bastin
- Computational and Applied Vegetation Ecology Lab, Department of Applied Ecology and Environmental Biology, Faculty of Bioscience EngineeringGhent UniversityGhent9000Belgium
| | - Beryl Morris
- Terrestrial Ecosystem Research NetworkThe University of QueenslandSt LuciaQueensland4072Australia
| | - Rebekah Christensen
- Institute for Future EnvironmentsQueensland University of TechnologyGardens PointBrisbaneQueensland4000Australia
| | - Stuart Phinn
- School of Earth and Environmental SciencesThe University of QueenslandSt LuciaQueensland4072Australia
| | - Andrew J. Lowe
- School of Biological SciencesThe University of AdelaideAdelaideSouth Australia5005Australia
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Chaiban C, Da Re D, Robinson TP, Gilbert M, Vanwambeke SO. Poultry farm distribution models developed along a gradient of intensification. Prev Vet Med 2020; 186:105206. [PMID: 33261930 DOI: 10.1016/j.prevetmed.2020.105206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 11/05/2020] [Accepted: 11/06/2020] [Indexed: 11/28/2022]
Abstract
Efficient planning of measures limiting epidemic spread requires information on farm locations and sizes (number of animals per farm). However, such data are rarely available. The intensification process which is operating in most low- and middle-income countries (LMICs), comes together with a spatial clustering of farms, a characteristic epidemiological models are sensitive to. We developed farm distribution models predicting both the location and the number of animals per farm, while accounting for the spatial clustering of farms in data-poor countries, using poultry production as an example. We selected four countries, Nigeria, Thailand, Argentina and Belgium, along a gradient of intensification expressed by the per capita Gross Domestic Product (GDP). First, we investigated the distribution of chicken farms along the spectrum of intensification. Second, we built farm distribution models (FDM) based on censuses of commercial farms of each of the four countries, using point pattern and random forest models. As an external validation, we predicted farm locations and sizes in Bangladesh. The number of chicken per farm increased gradually in line with the gradient of GDP per capita in the following order: Nigeria, Thailand, Argentina and Belgium. Interestingly, we did not find such a gradient for farm clustering. Our modelling procedure could only partly reproduce the observed datasets in each of the four sample countries in internal validation. However, in the external validation, the clustering of farms could not be reproduced and the spatial predictors poorly explained the number and location of farms and farm sizes in Bangladesh. Further improvements of the methodology should explore other covariates of the intensity of farms and farm sizes, as well as improvements of the methodology. Structural transformation, economic development and environmental conditions are essential characteristics to consider for an extrapolation of our FDM procedure, as generalisation appeared challenging. We believe the FDM procedure could ultimately be used as a predictive tool in data-poor countries.
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Affiliation(s)
- Celia Chaiban
- Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium; Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Daniele Da Re
- Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium; Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Timothy P Robinson
- Livestock Information, Sector Analysis and Policy Branch (AGAL), Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 1050 Brussels, Belgium; Fonds National de la Recherche Scientifique (FNRS), 1000 Brussels, Belgium.
| | - Sophie O Vanwambeke
- Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium.
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40
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Balkovič J, Madaras M, Skalský R, Folberth C, Smatanová M, Schmid E, van der Velde M, Kraxner F, Obersteiner M. Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020; 274:111206. [PMID: 32818829 DOI: 10.1016/j.jenvman.2020.111206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 07/08/2020] [Accepted: 08/05/2020] [Indexed: 06/11/2023]
Abstract
Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1-0.5 Mg C ha-1 y-1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5-1.5 Mg C ha-1 y-1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.
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Affiliation(s)
- Juraj Balkovič
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovičova 6, 842 15, Bratislava, Slovak Republic.
| | - Mikuláš Madaras
- Crop Research Institute, Division of Crop Management Systems, Drnovská 507/73, 161 06, Praha 6 - Ruzyně, Czech Republic.
| | - Rastislav Skalský
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; National Agricultural and Food Centre, Soil Science and Conservation Research Institute, Trenčianska 55, 821 09, Bratislava, Slovak Republic.
| | - Christian Folberth
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria.
| | - Michaela Smatanová
- Central Institute for Supervising and Testing in Agriculture, Hroznová 63/2, 656 06, Brno, Czech Republic.
| | - Erwin Schmid
- Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, 1180, Vienna, Austria.
| | | | - Florian Kraxner
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria.
| | - Michael Obersteiner
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; Environmental Change Institute, University of Oxford, South Parks Road, Oxford, OX1 3QY, United Kingdom.
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Eigenbrod F, Beckmann M, Dunnett S, Graham L, Holland RA, Meyfroidt P, Seppelt R, Song XP, Spake R, Václavík T, Verburg PH. Identifying Agricultural Frontiers for Modeling Global Cropland Expansion. ONE EARTH (CAMBRIDGE, MASS.) 2020; 3:504-514. [PMID: 33163961 PMCID: PMC7608111 DOI: 10.1016/j.oneear.2020.09.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 05/06/2020] [Accepted: 09/23/2020] [Indexed: 11/05/2022]
Abstract
The increasing expansion of cropland is major driver of global carbon emissions and biodiversity loss. However, predicting plausible future global distributions of croplands remains challenging. Here, we show that, in general, existing global data aligned with classical economic theories of expansion explain the current (1992) global extent of cropland reasonably well, but not recent expansion (1992-2015). Deviations from models of cropland extent in 1992 ("frontierness") can be used to improve global models of recent expansion, most likely as these deviations are a proxy for cropland expansion under frontier conditions where classical economic theories of expansion are less applicable. Frontierness is insensitive to the land cover dataset used and is particularly effective in improving models that include mosaic land cover classes and the largely smallholder-driven frontier expansion occurring in such areas. Our findings have important implications as the frontierness approach offers a straightforward way to improve global land use change models.
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Affiliation(s)
- Felix Eigenbrod
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Michael Beckmann
- Department of Computational Landscape Ecology, UFZ—Helmholtz Centre for Environmental Research, 04318 Leipzig, Germany
| | - Sebastian Dunnett
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Laura Graham
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Robert A. Holland
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Patrick Meyfroidt
- Earth and Life Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium
- Fonds de la Recherche Scientifique (F.R.S.- FNRS), 1000 Brussels, Belgium
| | - Ralf Seppelt
- Department of Computational Landscape Ecology, UFZ—Helmholtz Centre for Environmental Research, 04318 Leipzig, Germany
- iDiv—German Centre for Integrative Biodiversity Research, 04103 Leipzig, Germany
- Institute of Geoscience & Geography, Martin-Luther-University Halle-Wittenberg, 06099 Halle (Saale), Germany
| | - Xiao-Peng Song
- Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA
| | - Rebecca Spake
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Tomáš Václavík
- Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University Olomouc, 78371 Olomouc, Czech Republic
- Global Change Research Institute of the Czech Academy of Sciences, 60300 Brno, Czech Republic
| | - Peter H. Verburg
- Institute for Environmental Studies, VU University Amsterdam, de Boelelaan 1087, 1081HV Amsterdam, the Netherlands
- Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland
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42
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The scale dependency of spatial crop species diversity and its relation to temporal diversity. Proc Natl Acad Sci U S A 2020; 117:26176-26182. [PMID: 33020278 DOI: 10.1073/pnas.2011702117] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Increasing crop species diversity can enhance agricultural sustainability, but the scale dependency of the processes that shape diversity and of the effects of diversity on agroecosystems is insufficiently understood. We used 30 m spatial resolution crop classification data for the conterminous United States to analyze spatial and temporal crop species diversity and their relationship. We found that the US average temporal (crop rotation) diversity is 2.1 effective number of species and that a crop's average temporal diversity is lowest for common crops. Spatial diversity monotonically increases with the size of the unit of observation, and it is most strongly associated with temporal diversity when measured for areas of 100 to 400 ha, which is the typical US farm size. The association between diversity in space and time weakens as data are aggregated over larger areas because of the increasing diversity among farms, but at intermediate aggregation levels (counties) it is possible to estimate temporal diversity and farm-scale spatial diversity from aggregated spatial crop diversity data if the effect of beta diversity is considered. For larger areas, the diversity among farms is usually much greater than the diversity within them, and this needs to be considered when analyzing large-area crop diversity data. US agriculture is dominated by a few major annual crops (maize, soybean, wheat) that are mostly grown on fields with a very low temporal diversity. To increase crop species diversity, currently minor crops would have to increase in area at the expense of these major crops.
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43
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Yadav K, Congalton RG. Extending Crop Type Reference Data Using a Phenology-Based Approach. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2020. [DOI: 10.3389/fsufs.2020.00099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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44
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Liu X, Yu L, Dong Q, Peng D, Wu W, Yu Q, Cheng Y, Xu Y, Huang X, Zhou Z, Wang D, Fang L, Gong P. Cropland heterogeneity changes on the Northeast China Plain in the last three decades (1980s-2010s). PeerJ 2020; 8:e9835. [PMID: 33194352 PMCID: PMC7485484 DOI: 10.7717/peerj.9835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/07/2020] [Indexed: 12/04/2022] Open
Abstract
The Northeast China Plain is one of the major grain-producing areas of China because of its fertile black soil and large fields adapted for agricultural machinery. It has experienced some land-use changes, such as urbanization, deforestation, and wetland reclamation in recent decades. A comprehensive understanding of these changes in terms of the total cropping land and its heterogeneity during this period is important for policymakers. In this study, we used a series of cropland products at the 30-m resolution for the period 1980–2015. The heterogeneity for dominant cropland decreased slowly over the three decades, especially for the large pieces of cropland, showing a general trend of increased cropland homogeneity. The spatial patterns of the averaged heterogeneity index were nearly the same, varying from 0.5 to 0.6, and the most heterogeneous areas were mainly located in some separate counties. Cropland expansion occurred across most of Northeast China, while cropland shrinking occurred only in the northern and eastern sections of Northeast China and around the capital cities, in the flat areas. Also, changes in land use away from cropland mainly occurred in areas with low elevation (50–200 m) and a gentle slope (less than 1 degree). The predominant changes in cropland were gross gain and homogeneity, occurring across most of the area except capital cities and boundary areas. Possible reasons for the total cropland heterogeneity changes were urbanization, restoration of cropland to forest, and some government land-use policies. Moreover, this study evaluates the effectiveness of cropland policies influencing in Northeast China.
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Affiliation(s)
- Xiaoxuan Liu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China.,Joint Center for Global Change Studies, Beijing, China
| | - Le Yu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China.,Joint Center for Global Change Studies, Beijing, China.,Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing, China
| | - Qinghan Dong
- Department of Remote Sensing Boeretang 200, Flemish Institute of Technology (VITO), Mol, Belgium
| | - Dailiang Peng
- Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences, Key Laboratory of Digital Earth Science, Beijing, China
| | - Wenbin Wu
- Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Key Laboratory of Agricultural Remote Sensing (AGRIRS), Beijing, China
| | - Qiangyi Yu
- Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Key Laboratory of Agricultural Remote Sensing (AGRIRS), Beijing, China
| | - Yuqi Cheng
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China.,Joint Center for Global Change Studies, Beijing, China
| | - Yidi Xu
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China.,Joint Center for Global Change Studies, Beijing, China
| | - Xiaomeng Huang
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China.,Joint Center for Global Change Studies, Beijing, China
| | - Zheng Zhou
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China
| | - Dong Wang
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China.,National Supercomputing Center in Wuxi, Wuxi, China
| | - Lei Fang
- Chinese Academy Sciences, CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Shenyang, China
| | - Peng Gong
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China.,Joint Center for Global Change Studies, Beijing, China
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45
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Waha K, Dietrich JP, Portmann FT, Siebert S, Thornton PK, Bondeau A, Herrero M. Multiple cropping systems of the world and the potential for increasing cropping intensity. GLOBAL ENVIRONMENTAL CHANGE : HUMAN AND POLICY DIMENSIONS 2020; 64:102131. [PMID: 33343102 PMCID: PMC7737095 DOI: 10.1016/j.gloenvcha.2020.102131] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 05/08/2020] [Accepted: 07/12/2020] [Indexed: 05/18/2023]
Abstract
Multiple cropping, defined as harvesting more than once a year, is a widespread land management strategy in tropical and subtropical agriculture. It is a way of intensifying agricultural production and diversifying the crop mix for economic and environmental benefits. Here we present the first global gridded data set of multiple cropping systems and quantify the physical area of more than 200 systems, the global multiple cropping area and the potential for increasing cropping intensity. We use national and sub-national data on monthly crop-specific growing areas around the year 2000 (1998-2002) for 26 crop groups, global cropland extent and crop harvested areas to identify sequential cropping systems of two or three crops with non-overlapping growing seasons. We find multiple cropping systems on 135 million hectares (12% of global cropland) with 85 million hectares in irrigated agriculture. 34%, 13% and 10% of the rice, wheat and maize area, respectively are under multiple cropping, demonstrating the importance of such cropping systems for cereal production. Harvesting currently single cropped areas a second time could increase global harvested areas by 87-395 million hectares, which is about 45% lower than previous estimates. Some scenarios of intensification indicate that it could be enough land to avoid expanding physical cropland into other land uses but attainable intensification will depend on the local context and the crop yields attainable in the second cycle and its related environmental costs.
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Affiliation(s)
- Katharina Waha
- CSIRO, Agriculture & Food, 306 Carmody Rd, St Lucia, QLD, Australia
- Corresponding author.
| | - Jan Philipp Dietrich
- Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam, Germany
| | - Felix T. Portmann
- Goethe University Frankfurt, Institute of Physical Geography, 60438 Frankfurt am Main, Germany
| | - Stefan Siebert
- University of Göttingen, Department of Crop Sciences, Von-Siebold-Strasse 8, 37075 Göttingen, Germany
- University of Göttingen, Centre of Biodiversity and Sustainable Land Use, Büsgenweg 1, 37077 Göttingen, Germany
| | - Philip K. Thornton
- CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), ILRI, PO Box 30709, Nairobi 00100, Kenya
- International Livestock Research Institute (ILRI), Nairobi 00100, Kenya
| | - Alberte Bondeau
- Institut Mediterraneen de Biodiversite et d’Ecologie Marine et Continentale (IMBE), Aix-Marseille Universite, CNRS, IRD, Avignon Universite, France
| | - Mario Herrero
- CSIRO, Agriculture & Food, 306 Carmody Rd, St Lucia, QLD, Australia
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46
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Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion. REMOTE SENSING 2020. [DOI: 10.3390/rs12172803] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of a priori data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (Ns) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing a priori information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of Ns constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.
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47
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Riggio J, Baillie JEM, Brumby S, Ellis E, Kennedy CM, Oakleaf JR, Tait A, Tepe T, Theobald DM, Venter O, Watson JEM, Jacobson AP. Global human influence maps reveal clear opportunities in conserving Earth's remaining intact terrestrial ecosystems. GLOBAL CHANGE BIOLOGY 2020; 26:4344-4356. [PMID: 32500604 PMCID: PMC7383735 DOI: 10.1111/gcb.15109] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 02/28/2020] [Indexed: 05/05/2023]
Abstract
Leading up to the Convention on Biological Diversity Conference of the Parties 15, there is momentum around setting bold conservation targets. Yet, it remains unclear how much of Earth's land area remains without significant human influence and where this land is located. We compare four recent global maps of human influences across Earth's land, Anthromes, Global Human Modification, Human Footprint and Low Impact Areas, to answer these questions. Despite using various methodologies and data, these different spatial assessments independently estimate similar percentages of the Earth's terrestrial surface as having very low (20%-34%) and low (48%-56%) human influence. Three out of four spatial assessments agree on 46% of the non-permanent ice- or snow-covered land as having low human influence. However, much of the very low and low influence portions of the planet are comprised of cold (e.g., boreal forests, montane grasslands and tundra) or arid (e.g., deserts) landscapes. Only four biomes (boreal forests, deserts, temperate coniferous forests and tundra) have a majority of datasets agreeing that at least half of their area has very low human influence. More concerning, <1% of temperate grasslands, tropical coniferous forests and tropical dry forests have very low human influence across most datasets, and tropical grasslands, mangroves and montane grasslands also have <1% of land identified as very low influence across all datasets. These findings suggest that about half of Earth's terrestrial surface has relatively low human influence and offers opportunities for proactive conservation actions to retain the last intact ecosystems on the planet. However, though the relative abundance of ecosystem areas with low human influence varies widely by biome, conserving these last intact areas should be a high priority before they are completely lost.
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Affiliation(s)
- Jason Riggio
- National Geographic SocietyWashingtonDCUSA
- Department of Wildlife, Fish and Conservation BiologyMuseum of Wildlife and Fish BiologyUniversity of California, DavisDavisCAUSA
| | | | | | - Erle Ellis
- Department of Geography and Environmental SystemsUniversity of MarylandBaltimore CountyMDUSA
| | | | | | - Alex Tait
- National Geographic SocietyWashingtonDCUSA
| | | | | | - Oscar Venter
- Natural Resource and Environmental Studies InstituteUniversity of Northern British ColumbiaPrince GeorgeBCCanada
| | - James E. M. Watson
- School of Earth and Environmental ScienceThe University of QueenslandBrisbaneQldAustralia
- Global ConservationWildlife Conservation SocietyBronxNYUSA
| | - Andrew P. Jacobson
- National Geographic SocietyWashingtonDCUSA
- Department of Environment and SustainabilityCatawba CollegeSalisburyNCUSA
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48
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Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin. REMOTE SENSING 2020. [DOI: 10.3390/rs12132096] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Having updated knowledge of cropland extent is essential for crop monitoring and food security early warning. Previous research has proposed different methods and adopted various datasets for mapping cropland areas at regional to global scales. However, most approaches did not consider the characteristics of farming systems and apply the same classification method in different agroecological zones (AEZs). Furthermore, the acquisition of in situ samples for classification training remains challenging. To address these knowledge gaps and challenges, this study applied a zone-specific classification by comparing four classifiers (random forest, the support vector machine (SVM), the classification and regression tree (CART) and minimum distance) for cropland mapping over four different AEZs in the Zambezi River basin (ZRB). Landsat-8 and Sentinel-2 data and derived indices were used and synthesized to generate thirty-five layers for classification on the Google Earth Engine platform. Training samples were derived from three existing landcover datasets to minimize the cost of sample acquisitions over the large area. The final cropland map was generated at a 10 m resolution. The performance of the four classifiers and the viability of training samples were analysed. All classifiers presented higher accuracy in cool AEZs than in warm AEZs, which may be attributed to field size and lower confusion between cropland and grassland classes. This indicates that agricultural landscape may impact classification results regardless of the classifiers. Random forest was found to be the most stable and accurate classifier across different agricultural systems, with an overall accuracy of 84% and a kappa coefficient of 0.67. Samples extracted over the full agreement areas among existing datasets reduced uncertainty and provided reliable calibration sets as a replacement of costly in situ measurements. The methodology proposed by this study can be used to generate periodical high-resolution cropland maps in ZRB, which is helpful for the analysis of cropland extension and abandonment as well as intensity changes in response to the escalating population and food insecurity.
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Sonderegger T, Pfister S, Hellweg S. Assessing Impacts on the Natural Resource Soil in Life Cycle Assessment: Methods for Compaction and Water Erosion. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:6496-6507. [PMID: 32356974 DOI: 10.1021/acs.est.0c01553] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
There are currently limited life cycle impact assessment methods existing for assessing impacts on the natural resource soil. In this paper, we develop methods for the assessment of compaction and water erosion impacts within one framework, which can be expanded with additional degradation processes in the future. Our methods assess potential long-term impacts from agricultural activities on the production capacity of soils and are able to distinguish between different management choices such as machinery selection and tillage practices. Characterization factors are provided as global raster data sets at high spatial resolution (∼1 km) and for larger geographic units including uncertainties of spatial aggregation. Uncertainties due to variability of climate and weather are provided where possible. The application of the methods is demonstrated and discussed in a simplified case study. Results show that in a highly mechanized scenario of global agriculture without any conservation measures, long-term yearly soil productivity losses due to compaction and water erosion can amount to up to double-digit percentages for major crops. This confirms the relevance of compaction and water erosion impacts for agricultural LCAs.
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Affiliation(s)
- Thomas Sonderegger
- Chair of Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, John-von-Neumann-Weg 9, 8093 Zurich, Switzerland
| | - Stephan Pfister
- Chair of Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, John-von-Neumann-Weg 9, 8093 Zurich, Switzerland
| | - Stefanie Hellweg
- Chair of Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, John-von-Neumann-Weg 9, 8093 Zurich, Switzerland
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50
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Pérez-Hoyos A, Udías A, Rembold F. Integrating multiple land cover maps through a multi-criteria analysis to improve agricultural monitoring in Africa. ACTA ACUST UNITED AC 2020; 88:102064. [PMID: 32999637 PMCID: PMC7497230 DOI: 10.1016/j.jag.2020.102064] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
A multi-criteria analysis (MCA) is developed to generate a cropland and grassland hybrid mask. Each land cover dataset is evaluated at country-level by five different criteria. The MCA approach offers a flexible and transparent methodology to combine different data. The hybrid masks are used in the JRC-ASAP early warning system and are freely accessible.
Monitoring agricultural land cover is highly relevant for global early warning systems such as ASAP (Anomaly hot Spots of Agricultural Production), because it represents the basis for detecting production deficits in food security assessment. Given the significant inconsistencies among existing land cover datasets, there is a need to obtain a more accurate representation of the spatial distribution and extent of agricultural area in Africa. In this research, we explore a fusion approach that combines the strength of individual datasets and minimises their limitations. Specifically, a semi-automatic method is developed, relying on multi-criteria analysis (MCA) complemented with manual fine-tuning using the best-rated datasets, to generate two hybrid and static agricultural masks – one for cropland and another for grassland. Following a comprehensive selection of land cover maps, each dataset is evaluated at country level according to five criteria: timeliness, spatial resolution, comparison with FAO statistics, accuracy assessment and expert evaluation. A sensitivity analysis is performed, based on an evaluation of the impact of weight settings on the resulting land cover. The proposed methodology is capable of improving agricultural characterisation in Africa. As a result, two static masks at 250 m spatial resolution for the nominal year 2016 are provided.
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