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Acharki S, Frison PL, Veettil BK, Pham QB, Singh SK, Amharref M, Bernoussi AS. Land cover and crop types mapping using different spatial resolution imagery in a Mediterranean irrigated area. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1309. [PMID: 37831334 DOI: 10.1007/s10661-023-11877-4] [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: 06/01/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023]
Abstract
Crop type identification is critical for agricultural sustainability policy development and environmental assessments. Therefore, it is important to obtain their spatial distribution via different approaches. Medium-, high- and very high-resolution optical satellite sensors are efficient tools for acquiring this information, particularly for challenging studies such as those conducted in heterogeneous agricultural fields. This research examined the ability of four multitemporal datasets (Sentinel-1-SAR (S1), Sentinel-2-MSI (S2), RapidEye (RE), and PlanetScope (PS)) to identify land cover and crop types (LCCT) in a Mediterranean irrigated area. To map LCCT distribution, a supervised pixel-based classification is adopted using Support Vector Machine with a radial basis function kernel (SVMRB) and Random Forest (RF). Thus, LCCT maps were generated into three levels, including six (Level I), ten (Level II), and fourteen (Level III) classes. Overall, the findings revealed high overall accuracies of >92%, >83%, and > 81% for Level I, Level II, and Level III, respectively, except for Sentinel-1. It was found that accuracy improves considerably when the number of classes decreases, especially when cropland or non-cropland classes are grouped into one. Furthermore, there was a similarity in performance between S2 alone and S1S2. PlanetScope LCCT classifications outperform other sensors. In addition, the present study demonstrated that SVM achieved better performances against RF and can thereby effectively extract LCCT information from high-resolution imagery as PlanetScope.
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Affiliation(s)
- Siham Acharki
- Department of Earth Sciences, Faculty of Sciences and Technologies of Tangier (FSTT), Abdelmalek Essaadi University, 93000, Tetouan City, Morocco.
| | - Pierre-Louis Frison
- LaSTIG/MATIS, Gustave Eiffel University, IGN, 5 Bd Descartes, Champs-sur-Marne, 77455, CEDEX 2 City, Marne-la-Vallée, France
| | - Bijeesh Kozhikkodan Veettil
- Laboratory of Ecology and Environmental Management, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City, Vietnam
- Faculty of Applied Technology, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam
| | - Quoc Bao Pham
- Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska street 60, 41-200, Sosnowiec City, Poland
| | - Sudhir Kumar Singh
- K. Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad, Prayagraj City, 211002, India
| | - Mina Amharref
- GATE Team (Géoinformation, Aménagement du Territoire et Environnement), Faculty of Sciences and Technologies of Tangier (FSTT), Abdelmalek Essaadi University, 93000, Tetouan City, Morocco
| | - Abdes Samed Bernoussi
- GATE Team (Géoinformation, Aménagement du Territoire et Environnement), Faculty of Sciences and Technologies of Tangier (FSTT), Abdelmalek Essaadi University, 93000, Tetouan City, Morocco
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Liu Y, Rao P, Zhou W, Singh B, Srivastava AK, Poonia SP, Van Berkel D, Jain M. Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems. PLoS One 2022; 17:e0277425. [PMID: 36441682 PMCID: PMC9704639 DOI: 10.1371/journal.pone.0277425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/27/2022] [Indexed: 11/29/2022] Open
Abstract
Remote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, higher-resolution satellite data from Sentinel-1, Sentinel-2, and Planet to map tillage practices in the Eastern Indo-Gangetic Plains in India. We specifically tested the classification performance of single sensor and multiple sensor random forest models, and the impact of spatial, temporal, or spectral resolution on classification accuracy. We found that when considering a single sensor, the model that used Planet imagery (3 m) had the highest classification accuracy (86.55%) while the model that used Sentinel-1 data (10 m) had the lowest classification accuracy (62.28%). When considering sensor combinations, the model that used data from all three sensors achieved the highest classification accuracy (87.71%), though this model was not statistically different from the Planet only model when considering 95% confidence intervals from bootstrap analyses. We also found that high levels of accuracy could be achieved by only using imagery from the sowing period. Considering the impact of spatial, temporal, and spectral resolution on classification accuracy, we found that improved spatial resolution from Planet contributed the most to improved classification accuracy. Overall, it is possible to use readily-available, high spatial resolution satellite data to map tillage practices of smallholder farms, even in heterogeneous systems with small field sizes.
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Affiliation(s)
- Yin Liu
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, United States of America
| | - Preeti Rao
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, United States of America
- Center for Climate Change and Sustainability, Azim Premji University, Bengaluru, India
| | - Weiqi Zhou
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, United States of America
| | - Balwinder Singh
- International Mazie and Wheat Improvement Center (CIMMYT)-India Officer, New Delhi, India
- Department of Primary Industries and Regional Development, Northam, Western Australia, Australia
| | - Amit K Srivastava
- IRRI South Asia Regional Centre (ISARC), NSRTC Campus, Varanasi, India
| | - Shishpal P Poonia
- International Mazie and Wheat Improvement Center (CIMMYT)-India Officer, New Delhi, India
| | - Derek Van Berkel
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, United States of America
| | - Meha Jain
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, United States of America
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Inter-Annual Climate Variability Impact on Oil Palm Mapping. REMOTE SENSING 2022. [DOI: 10.3390/rs14133104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The contribution of oil palm plantations to the economic growth of tropical developing countries makes it essential to monitor their expansion into the tropical forest; consequently, most studies focus on improving mapping accuracy while using satellite imagery. However, accuracy can be hampered by atmospheric phenomena that can drastically change climatic conditions in tropical regions, affecting the spectral properties of the vegetation. In this sense, we studied the accuracy of palm plantation mapping by using features from different regions of the electromagnetic spectrum and a data fusion approach, and then compared the changes in accuracy over the years 2016, 2017, and 2018 (two of them with reported climatic anomalies). Optical-based maps obtained higher accuracy than thermal- and microwave-based maps, but they were the most affected by inter-annual climate variability (error margin between 5 and 10%), while thermal-based maps were the least affected (error margin between 8 and 9%). Data fusion combinations improved accuracy and reduced dissimilarities between years (e.g., phenology-based map accuracy changed by up to 20.8%, while phenology fused with microwave features changed by up to 6.8%). We conclude that inter-annual climate variability on land-cover mapping should be considered, especially if the outputs will be used as input in future studies.
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Warren‐Thomas E, Agus F, Akbar PG, Crowson M, Hamer KC, Hariyadi B, Hodgson JA, Kartika WD, Lopes M, Lucey JM, Mustaqim D, Pettorelli N, Saad A, Sari W, Sukma G, Stringer LC, Ward C, Hill JK. No evidence for trade‐offs between bird diversity, yield and water table depth on oil palm smallholdings: Implications for tropical peatland landscape restoration. J Appl Ecol 2022. [DOI: 10.1111/1365-2664.14135] [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)
- Eleanor Warren‐Thomas
- Leverhulme Centre for Anthropocene Biodiversity, Department of Biology University of York York UK
- School of Natural Sciences Bangor University Bangor UK
- Biodiversity and Natural Resources Program International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria
| | - Fahmuddin Agus
- Indonesian Center for Agricultural Land Resources Research and Development Bogor Indonesia
| | | | - Merry Crowson
- Institute of Zoology, Zoological Society of London London UK
| | - Keith C. Hamer
- School of Biology, Faculty of Biological Sciences University of Leeds Leeds UK
| | - Bambang Hariyadi
- Biology Education Program, Faculty of Education and Teacher Training Jambi University Jambi Indonesia
| | - Jenny A. Hodgson
- Department of Evolution, Ecology and Behaviour University of Liverpool Liverpool UK
| | - Winda D. Kartika
- Biology Education Program, Faculty of Education and Teacher Training Jambi University Jambi Indonesia
| | - Mailys Lopes
- Institute of Zoology, Zoological Society of London London UK
| | | | - Dedy Mustaqim
- Biology Education Program, Faculty of Education and Teacher Training Jambi University Jambi Indonesia
| | | | - Asmadi Saad
- Faculty of Agriculture Jambi University Jambi Indonesia
| | - Widia Sari
- Biology Education Program, Faculty of Education and Teacher Training Jambi University Jambi Indonesia
| | - Gita Sukma
- Biology Education Program, Faculty of Education and Teacher Training Jambi University Jambi Indonesia
| | - Lindsay C. Stringer
- Leverhulme Centre for Anthropocene Biodiversity, Department of Biology University of York York UK
- Department of Environment and Geography University of York York UK
- School of Earth and Environment University of Leeds Leeds UK
| | - Caroline Ward
- Leverhulme Centre for Anthropocene Biodiversity, Department of Biology University of York York UK
- School of Earth and Environment University of Leeds Leeds UK
| | - Jane K. Hill
- Leverhulme Centre for Anthropocene Biodiversity, Department of Biology University of York York UK
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Rhodes MW, Bennie JJ, Spalding A, Ffrench-Constant RH, Maclean IMD. Recent advances in the remote sensing of insects. Biol Rev Camb Philos Soc 2021; 97:343-360. [PMID: 34609062 DOI: 10.1111/brv.12802] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 12/31/2022]
Abstract
Remote sensing has revolutionised many aspects of ecological research, enabling spatiotemporal data to be collected in an efficient and highly automated manner. The last two decades have seen phenomenal growth in capabilities for high-resolution remote sensing that increasingly offers opportunities to study small, but ecologically important organisms, such as insects. Here we review current applications for using remote sensing within entomological research, highlighting the emerging opportunities that now arise through advances in spatial, temporal and spectral resolution. Remote sensing can be used to map environmental variables, such as habitat, microclimate and light pollution, capturing data on topography, vegetation structure and composition, and luminosity at spatial scales appropriate to insects. Such data can also be used to detect insects indirectly from the influences that they have on the environment, such as feeding damage or nest structures, whilst opportunities for directly detecting insects are also increasingly available. Entomological radar and light detection and ranging (LiDAR), for example, are transforming our understanding of aerial insect abundance and movement ecology, whilst ultra-high spatial resolution drone imagery presents tantalising new opportunities for direct observation. Remote sensing is rapidly developing into a powerful toolkit for entomologists, that we envisage will soon become an integral part of insect science.
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Affiliation(s)
- Marcus W Rhodes
- Environment and Sustainability Institute, University of Exeter Penryn Campus, Penryn, Cornwall, TR10 9FE, U.K
| | - Jonathan J Bennie
- Centre for Geography and Environmental Science, University of Exeter Penryn Campus, Penryn, Cornwall, TR10 9FE, U.K
| | - Adrian Spalding
- Spalding Associates (Environmental) Ltd, 10 Walsingham Place, Truro, Cornwall, TR1 2RP, U.K
| | - Richard H Ffrench-Constant
- Centre for Ecology and Conservation, University of Exeter Penryn Campus, Penryn, Cornwall, TR10 9FE, U.K
| | - Ilya M D Maclean
- Environment and Sustainability Institute, University of Exeter Penryn Campus, Penryn, Cornwall, TR10 9FE, U.K
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Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1. REMOTE SENSING 2020. [DOI: 10.3390/rs12213613] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Rice (Oryza sativa L.) is a staple food crop for more than half of the world’s population. Rice production is facing a myriad of problems, including water shortage, climate, and land-use change. Accurate maps of rice growth stages are critical for monitoring rice production and assessing its impacts on national and global food security. Rice growth stages are typically monitored by coarse-resolution satellite imagery. However, it is difficult to accurately map due to the occurrence of mixed pixels in fragmented and patchy rice fields, as well as cloud cover, particularly in tropical countries. To solve these problems, we developed an automated mapping workflow to produce near real-time multi-temporal maps of rice growth stages at a 10-m spatial resolution using multisource remote sensing data (Sentinel-2, MOD13Q1, and Sentinel-1). This study was investigated between 1 June and 29 September 2018 in two (wet and dry) areas of Java Island in Indonesia. First, we built prediction models based on Sentinel-2, and fusion of MOD13Q1/Sentinel-1 using the ground truth information. Second, we applied the prediction models on all images in area and time and separation between the non-rice planting class and rice planting class over the cropping pattern. Moreover, the model’s consistency on the multitemporal map with a 5–30-day lag was investigated. The result indicates that the Sentinel-2 based model classification gives a high overall accuracy of 90.6% and the fusion model MOD13Q1/Sentinel-1 shows 78.3%. The performance of multitemporal maps was consistent between time lags with an accuracy of 83.27–90.39% for Sentinel-2 and 84.15% for the integration of Sentinel-2/MOD13Q1/Sentinel-1. The results from this study show that it is possible to integrate multisource remote sensing for regular monitoring of rice phenology, thereby generating spatial information to support local-, national-, and regional-scale food security applications.
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