1
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Masolele RN, Marcos D, De Sy V, Abu IO, Verbesselt J, Reiche J, Herold M. Mapping the diversity of land uses following deforestation across Africa. Sci Rep 2024; 14:1681. [PMID: 38242938 PMCID: PMC10798993 DOI: 10.1038/s41598-024-52138-9] [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: 02/01/2023] [Accepted: 01/14/2024] [Indexed: 01/21/2024] Open
Abstract
African forest are increasingly in decline as a result of land-use conversion due to human activities. However, a consistent and detailed characterization and mapping of land-use change that results in forest loss is not available at the spatial-temporal resolution and thematic levels suitable for decision-making at the local and regional scales; so far they have only been provided on coarser scales and restricted to humid forests. Here we present the first high-resolution (5 m) and continental-scale mapping of land use following deforestation in Africa, which covers an estimated 13.85% of the global forest area, including humid and dry forests. We use reference data for 15 different land-use types from 30 countries and implement an active learning framework to train a deep learning model for predicting land-use following deforestation with an F1-score of [Formula: see text] for the whole of Africa. Our results show that the causes of forest loss vary by region. In general, small-scale cropland is the dominant driver of forest loss in Africa, with hotspots in Madagascar and DRC. In addition, commodity crops such as cacao, oil palm, and rubber are the dominant drivers of forest loss in the humid forests of western and central Africa, forming an "arc of commodity crops" in that region. At the same time, the hotspots for cashew are found to increasingly dominate in the dry forests of both western and south-eastern Africa, while larger hotspots for large-scale croplands were found in Nigeria and Zambia. The increased expansion of cacao, cashew, oil palm, rubber, and large-scale croplands observed in humid and dry forests of western and south-eastern Africa suggests they are vulnerable to future land-use changes by commodity crops, thus creating challenges for achieving the zero deforestation supply chains, support REDD+ initiatives, and towards sustainable development goals.
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Grants
- 20_III_108 International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety (BMUB)
- 20_III_108 International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety (BMUB)
- 20_III_108 International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety (BMUB)
- 20_III_108 International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety (BMUB)
- 20_III_108 International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety (BMUB)
- 20_III_108 International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety (BMUB)
- 101059548 European Commission Horizon Europe project "Open-Earth- Monitor"
- 101059548 European Commission Horizon Europe project "Open-Earth- Monitor"
- 101059548 European Commission Horizon Europe project "Open-Earth- Monitor"
- European Commission Horizon Europe project “Open-Earth- Monitor”
- The US Government's SilvaCarbon program
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Affiliation(s)
- Robert N Masolele
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708, Wageningen, PB, The Netherlands.
| | - Diego Marcos
- Inria, University of Montpellier, Montpellier, France
| | - Veronique De Sy
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708, Wageningen, PB, The Netherlands
| | - Itohan-Osa Abu
- Department of Remote Sensing, Julius-Maximilians-University, Oswald-külpe-Weg, 97074, Würzburg, Bayern, Germany
| | - Jan Verbesselt
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708, Wageningen, PB, The Netherlands
| | - Johannes Reiche
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708, Wageningen, PB, The Netherlands
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2
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Stanimirova R, Tarrio K, Turlej K, McAvoy K, Stonebrook S, Hu KT, Arévalo P, Bullock EL, Zhang Y, Woodcock CE, Olofsson P, Zhu Z, Barber CP, Souza CM, Chen S, Wang JA, Mensah F, Calderón-Loor M, Hadjikakou M, Bryan BA, Graesser J, Beyene DL, Mutasha B, Siame S, Siampale A, Friedl MA. A global land cover training dataset from 1984 to 2020. Sci Data 2023; 10:879. [PMID: 38062043 PMCID: PMC10703991 DOI: 10.1038/s41597-023-02798-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.
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Affiliation(s)
- Radost Stanimirova
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA.
| | - Katelyn Tarrio
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Konrad Turlej
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
- Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen, DK-1350, København K, Denmark
| | - Kristina McAvoy
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Sophia Stonebrook
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Kai-Ting Hu
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Paulo Arévalo
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Eric L Bullock
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Yingtong Zhang
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Curtis E Woodcock
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Pontus Olofsson
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
- NASA Marshall Space Flight Center, Huntsville, AL, 35808, USA
| | - Zhe Zhu
- Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT, 06269, USA
| | - Christopher P Barber
- U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, 57198, USA
| | - Carlos M Souza
- Imazon-Amazonia People and Environment Institute, Belém, Brazil
| | - Shijuan Chen
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
- Yale School of the Environment, Yale University, New Haven, CT, 06511, USA
| | - Jonathan A Wang
- School of Biological Sciences, University of Utah, Salt Lake, UT, 84112, USA
| | - Foster Mensah
- Center for Remote Sensing and Geographic Information Services, University of Ghana, Accra, Ghana
| | - Marco Calderón-Loor
- School of Life and Environmental Sciences, Deakin University, Melbourne, Australia
- Albo Climate, Ehad Ha'am, 9, Tel Aviv, Israel
- Grupo de Investigación de Biodiversidad, Medio Ambiente y Salud-BIOMAS, Universidad de las Américas (UDLA), Quito, Ecuador
| | - Michalis Hadjikakou
- School of Life and Environmental Sciences, Deakin University, Melbourne, Australia
| | - Brett A Bryan
- School of Life and Environmental Sciences, Deakin University, Melbourne, Australia
| | | | - Dereje L Beyene
- REDD+ Coordination Unit, Oromia Environmental Protection Authority, Addis Ababa, Ethiopia
| | - Brian Mutasha
- Forestry Department Headquarters, Ministry of Green Economy and Environment, Lusaka, Zambia
| | - Sylvester Siame
- Forestry Department Headquarters, Ministry of Green Economy and Environment, Lusaka, Zambia
| | - Abel Siampale
- Forestry Department Headquarters, Ministry of Green Economy and Environment, Lusaka, Zambia
| | - Mark A Friedl
- Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA, 02215, USA
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3
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Chowdhury P, Lakku NKG, Lincoln S, Seelam JK, Behera MR. Climate change and coastal morphodynamics: Interactions on regional scales. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:166432. [PMID: 37598966 DOI: 10.1016/j.scitotenv.2023.166432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/08/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023]
Abstract
Climate change and its impacts, combined with unchecked human activities, intensify pressures on coastal environments, resulting in modification of the coastal morphodynamics. Coastal zones are intricate and constantly changing areas, making the monitoring and interpretation of data a challenging task, especially in remote beaches and regions with limited historical data. Traditionally, remote sensing and numerical methods have played a vital role in analysing earth observation data and supporting the monitoring and modelling of complex coastal ecosystems. However, the emergence of artificial intelligence-based techniques has shown promising results, offering the additional advantage of filling data gaps, predicting data in data-scarce regions, and analysing multidimensional datasets collected over extended periods of time and larger spatial scales. The main objective of this study is to provide a comprehensive review of the existing literature, discussing both traditional methods and various emerging artificial intelligence-based approaches used in studying the coastal dynamics, shoreline change analysis, and coastal monitoring. Ultimately, the study proposes a climate resilience framework to enhance coastal zone management practices and policies, fostering resilience among coastal communities. The outcome of this study aligns with and supports particularly SDG 13 of the UN (Climate Action) and advances it by identifying relevant methods in coastal erosion studies and proposing integrated management plans informed by real-time data collection and analysis/modelling using physics-based models.
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Affiliation(s)
- Piyali Chowdhury
- International Marine Climate Change Centre (iMC3), The Centre for Environment, Fisheries and Aquaculture Science (Cefas), Pakefield Road, Lowestoft, Suffolk NR33 0HT, United Kingdom.
| | | | - Susana Lincoln
- International Marine Climate Change Centre (iMC3), The Centre for Environment, Fisheries and Aquaculture Science (Cefas), Pakefield Road, Lowestoft, Suffolk NR33 0HT, United Kingdom
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4
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Carrasco-Escobar G, Moreno M, Fornace K, Herrera-Varela M, Manrique E, Conn JE. The use of drones for mosquito surveillance and control. Parasit Vectors 2022; 15:473. [PMID: 36527116 PMCID: PMC9758801 DOI: 10.1186/s13071-022-05580-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 11/04/2022] [Indexed: 12/23/2022] Open
Abstract
In recent years, global health security has been threatened by the geographical expansion of vector-borne infectious diseases such as malaria, dengue, yellow fever, Zika and chikungunya. For a range of these vector-borne diseases, an increase in residual (exophagic) transmission together with ecological heterogeneity in everything from weather to local human migration and housing to mosquito species' behaviours presents many challenges to effective mosquito control. The novel use of drones (or uncrewed aerial vehicles) may play a major role in the success of mosquito surveillance and control programmes in the coming decades since the global landscape of mosquito-borne diseases and disease dynamics fluctuates frequently and there could be serious public health consequences if the issues of insecticide resistance and outdoor transmission are not adequately addressed. For controlling both aquatic and adult stages, for several years now remote sensing data have been used together with predictive modelling for risk, incidence and detection of transmission hot spots and landscape profiles in relation to mosquito-borne pathogens. The field of drone-based remote sensing is under continuous change due to new technology development, operation regulations and innovative applications. In this review we outline the opportunities and challenges for integrating drones into vector surveillance (i.e. identification of breeding sites or mapping micro-environmental composition) and control strategies (i.e. applying larval source management activities or deploying genetically modified agents) across the mosquito life-cycle. We present a five-step systematic environmental mapping strategy that we recommend be undertaken in locations where a drone is expected to be used, outline the key considerations for incorporating drone or other Earth Observation data into vector surveillance and provide two case studies of the advantages of using drones equipped with multispectral cameras. In conclusion, recent developments mean that drones can be effective for accurately conducting surveillance, assessing habitat suitability for larval and/or adult mosquitoes and implementing interventions. In addition, we briefly discuss the need to consider permissions, costs, safety/privacy perceptions and community acceptance for deploying drone activities.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- grid.11100.310000 0001 0673 9488Health Innovation Laboratory, Institute of Tropical Medicine “Alexander Von Humboldt”, Universidad Peruana Cayetano Heredia, Lima, Peru
- grid.266100.30000 0001 2107 4242School of Public Health, University of California San Diego, La Jolla, USA
| | - Marta Moreno
- grid.8991.90000 0004 0425 469XFaculty of Infectious and Tropical Diseases and Centre for Climate Change and Planetary Health, London School Hygiene and Tropical Medicine, London, UK
| | - Kimberly Fornace
- grid.8991.90000 0004 0425 469XFaculty of Infectious and Tropical Diseases and Centre for Climate Change and Planetary Health, London School Hygiene and Tropical Medicine, London, UK
- grid.8756.c0000 0001 2193 314XSchool of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
- grid.4280.e0000 0001 2180 6431 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Manuela Herrera-Varela
- grid.10689.360000 0001 0286 3748Grupo de Investigación en Entomología, Facultad de Medicina, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Edgar Manrique
- grid.11100.310000 0001 0673 9488Health Innovation Laboratory, Institute of Tropical Medicine “Alexander Von Humboldt”, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jan E. Conn
- grid.238491.50000 0004 0367 6866The Wadsworth Center, New York State Department of Health, Albany, NY USA
- grid.189747.40000 0000 9554 2494Department of Biomedical Sciences, School of Public Health, State University of New York, Albany, NY USA
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5
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Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries. REMOTE SENSING 2022. [DOI: 10.3390/rs14133046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
The mapping of field boundaries can provide important information for increasing food production and security in agricultural systems across the globe. Remote sensing can provide a viable way to map field boundaries across large geographic extents, yet few studies have used satellite imagery to map boundaries in systems where field sizes are small, heterogeneous, and irregularly shaped. Here we used very-high-resolution WorldView-3 satellite imagery (0.5 m) and a mask region-based convolutional neural network (Mask R-CNN) to delineate smallholder field boundaries in Northeast India. We found that our models had overall moderate accuracy, with average precision values greater than 0.67 and F1 Scores greater than 0.72. We also found that our model performed equally well when applied to another site in India for which no data were used in the calibration step, suggesting that Mask R-CNN may be a generalizable way to map field boundaries at scale. Our results highlight the ability of Mask R-CNN and very-high-resolution imagery to accurately map field boundaries in smallholder systems.
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6
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RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment. REMOTE SENSING 2022. [DOI: 10.3390/rs14102299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Computer vision has great potential to accelerate the global scale of photovoltaic potential analysis by extracting detailed roof information from high-resolution aerial images, but the lack of existing deep learning datasets is a major barrier. Therefore, we present the Roof Information Dataset for semantic segmentation of roof segments and roof superstructures. We assessed the label quality of initial roof superstructure annotations by conducting an annotation experiment and identified annotator agreements of 0.15–0.70 mean intersection over union, depending on the class. We discuss associated the implications on the training and evaluation of two convolutional neural networks and found that the quality of the prediction behaved similarly to the annotator agreement for most classes. The class photovoltaic module was predicted to be best with a class-specific mean intersection over union of 0.69. By providing the datasets in initial and reviewed versions, we promote a data-centric approach for the semantic segmentation of roof information. Finally, we conducted a photovoltaic potential analysis case study and demonstrated the high impact of roof superstructures as well as the viability of the computer vision approach to increase accuracy. While this paper’s primary use case was roof information extraction for photovoltaic potential analysis, its implications can be transferred to other computer vision applications in remote sensing and beyond.
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7
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An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends. REMOTE SENSING 2022. [DOI: 10.3390/rs14081863] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.
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8
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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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9
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Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery. WATER 2022. [DOI: 10.3390/w14020244] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps. This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran. To this end, multi-temporal synthetic aperture radar (SAR) and multi-spectral remote sensing data from Sentinel-1 and Sentinel-2 were processed in the Google Earth Engine (GEE) cloud computing platform. Afterward, the ANN topologies and specifications considering the number of layers and neurons, learning algorithm, type of activation function, and learning rate were examined for mangrove ecosystem mapping. The results indicated that an ANN model with four hidden layers, 36 neurons in each layer, adaptive moment estimation (Adam) learning algorithm, rectified linear unit (Relu) activation function, and the learning rate of 0.001 produced the most accurate mangrove ecosystem map (F-score = 0.97). Further analysis revealed that although ANN models were subjected to accuracy decline when a limited number of training samples were used, they still resulted in satisfactory results. Additionally, it was observed that ANN models had a high resistance when training samples included wrong labels, and only the ANN model with the Adam learning algorithm produced an accurate mangrove ecosystem map when no data standardization was performed. Moreover, further investigations showed the higher potential of multi-temporal and multi-source remote sensing data compared to single-source and mono-temporal (e.g., single season) for accurate mangrove ecosystem mapping. Overall, the high potential of the proposed method, along with utilizing open-access satellite images and big-geo data processing platforms (i.e., GEE, Google Colab, and scikit-learn), made the proposed approach efficient and applicable over other study areas for all interested users.
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10
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Burke M, Driscoll A, Lobell DB, Ermon S. Using satellite imagery to understand and promote sustainable development. Science 2021; 371:371/6535/eabe8628. [PMID: 33737462 DOI: 10.1126/science.abe8628] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field.
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Affiliation(s)
- Marshall Burke
- Department of Earth System Science, Stanford University, Stanford, CA, USA. .,Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.,National Bureau of Economic Research, Cambridge, MA, USA
| | - Anne Driscoll
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - David B Lobell
- Department of Earth System Science, Stanford University, Stanford, CA, USA.,Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Stefano Ermon
- Department of Computer Science, Stanford University, Stanford, CA, USA
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11
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Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series. REMOTE SENSING 2021. [DOI: 10.3390/rs13050974] [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
The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.
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Pyo J, Park LJ, Pachepsky Y, Baek SS, Kim K, Cho KH. Using convolutional neural network for predicting cyanobacteria concentrations in river water. WATER RESEARCH 2020; 186:116349. [PMID: 32882452 DOI: 10.1016/j.watres.2020.116349] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 07/14/2020] [Accepted: 08/26/2020] [Indexed: 06/11/2023]
Abstract
Machine learning modeling techniques have emerged as a potential means for predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river section were generated with a 3D water quality model and used to investigate the capability of a convolutional neural network (CNN) for predicting harmful cyanobacterial blooms. The CNN model displayed a reasonable capacity for short-term predictions of cyanobacteria (Microcystis) biomass. In the nowcasting of Microcystis, the CNN performance had a Nash-Sutcliffe Efficiency (NSE) of 0.87. An increase in the forecast lead time resulted in a decrease in the prediction accuracy, reducing the NSE from 0.87 to 0.58. As the spatial observation density increased from 20% to 100% of the input image grids, the CNN prediction NSE had improved from 0.70 to 0.84. Adding noise to the data resulted in accuracy deterioration, but even at the noise amplitude of 10%, the accuracy was acceptable for some applications, with NSE = 0.76. Visualization of the CNN results characterized its performance variations across the studied river reach. Overall, this study successfully demonstrated the capability of the CNN model for cyanobacterial bloom prediction using high temporal frequency images.
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Affiliation(s)
- JongCheol Pyo
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, Republic of Korea
| | - Lan Joo Park
- Water Quality Assessment Research Division, National Institute of Environmental Research, Hwangyeong-ro 42, Seogu, Incheon 22689, Republic of Korea
| | - Yakov Pachepsky
- Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD, USA
| | - Sang-Soo Baek
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, Republic of Korea
| | - Kyunghyun Kim
- Watershed and Total Load Management Research Division, National Institute of Environmental Research, Hwangyeong-ro 42, Seogu, Incheon 22689, Republic of Korea.
| | - Kyung Hwa Cho
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, Republic of Korea.
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