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Full-Text Articles in Social and Behavioral Sciences
Cropland Mapping In Tropical Smallholder Systems With Seasonally Stratified Sentinel-1 And Sentinel-2 Spectral And Textural Features, Manushi B. Trivedi, Michael Marshall, Lyndon Estes, C.A.J.M. De Bie, Ling Chang, Andrew Nelson
Cropland Mapping In Tropical Smallholder Systems With Seasonally Stratified Sentinel-1 And Sentinel-2 Spectral And Textural Features, Manushi B. Trivedi, Michael Marshall, Lyndon Estes, C.A.J.M. De Bie, Ling Chang, Andrew Nelson
Geography
Mapping arable field areas is crucial for assessing agricultural productivity but poses challenges in sub-Saharan agroecosystems because of diverse crop calendars, small and irregularly shaped fields, persistent cloud cover, and lack of high-quality model training data. This study proposes several methodological improvements to overcome these challenges. Specifically, it utilizes long-term MODIS data to stratify finer Sentinel-2 reflectance and Sentinel-1 backscatter image features on a per-pixel basis. It also incorporates texture features and employs a machine learning approach with over 300,000 samples. The eastern region of Ghana was stratified into seven seasonal strata exhibiting distinct vegetation seasonality, capturing diversity in crop …
Optimizing Automated Kriging To Improve Spatial Interpolation Of Monthly Rainfall Over Complex Terrain, Matthew P. Lucas, Ryan J. Longman, Thomas W. Giambelluca, Abby G. Frazier, Jared Mclean, Sean B. Cleveland, Yu Fen Huang, Jonghyun Lee
Optimizing Automated Kriging To Improve Spatial Interpolation Of Monthly Rainfall Over Complex Terrain, Matthew P. Lucas, Ryan J. Longman, Thomas W. Giambelluca, Abby G. Frazier, Jared Mclean, Sean B. Cleveland, Yu Fen Huang, Jonghyun Lee
Geography
Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the "autoKrige" function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using …
High Resolution, Annual Maps Of Field Boundaries For Smallholder-Dominated Croplands At National Scales, Lyndon D. Estes, Su Ye, Lei Song, Boka Luo, J. Ronald Eastman, Zhenhua Meng, Qi Zhang, Dennis Mcritchie, Stephanie R. Debats, Justus Muhando, Angeline H. Amukoa, Brian W. Kaloo, Jackson Makuru, Ben K. Mbatia, Isaac M. Muasa, Julius Mucha, Adelide M. Mugami, Judith M. Mugami, Francis W. Muinde, Fredrick M. Mwawaza, Jeff Ochieng, Charles J. Oduol, Purent Oduor, Thuo Wanjiku, Joseph G. Wanyoike, Ryan B. Avery, Kelly K. Caylor
High Resolution, Annual Maps Of Field Boundaries For Smallholder-Dominated Croplands At National Scales, Lyndon D. Estes, Su Ye, Lei Song, Boka Luo, J. Ronald Eastman, Zhenhua Meng, Qi Zhang, Dennis Mcritchie, Stephanie R. Debats, Justus Muhando, Angeline H. Amukoa, Brian W. Kaloo, Jackson Makuru, Ben K. Mbatia, Isaac M. Muasa, Julius Mucha, Adelide M. Mugami, Judith M. Mugami, Francis W. Muinde, Fredrick M. Mwawaza, Jeff Ochieng, Charles J. Oduol, Purent Oduor, Thuo Wanjiku, Joseph G. Wanyoike, Ryan B. Avery, Kelly K. Caylor
Geography
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 …
Accounting For Training Data Error In Machine Learning Applied To Earth Observations, Arthur Elmes, Hamed Alemohammad, Ryan Avery, Kelly Caylor, J. Ronald Eastman, Lewis Fishgold, Mark A. Friedl, Meha Jain, Divyani Kohli, Juan Carlos Laso Bayas, Dalton Lunga, Jessica L. Mccarty, Robert Gilmore Pontius, Andrew B. Reinmann, John Rogan, Lei Song, Hristiana Stoynova, Su Ye, Zhuang Fang Yi, Lyndon Estes
Accounting For Training Data Error In Machine Learning Applied To Earth Observations, Arthur Elmes, Hamed Alemohammad, Ryan Avery, Kelly Caylor, J. Ronald Eastman, Lewis Fishgold, Mark A. Friedl, Meha Jain, Divyani Kohli, Juan Carlos Laso Bayas, Dalton Lunga, Jessica L. Mccarty, Robert Gilmore Pontius, Andrew B. Reinmann, John Rogan, Lei Song, Hristiana Stoynova, Su Ye, Zhuang Fang Yi, Lyndon Estes
Geography
Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is …
A Generalized Computer Vision Approach To Mapping Crop Fields In Heterogeneous Agricultural Landscapes, Stephanie R. Debats, Dee Luo, Lyndon Estes, Thomas J. Fuchs, Kelly K. Caylor
A Generalized Computer Vision Approach To Mapping Crop Fields In Heterogeneous Agricultural Landscapes, Stephanie R. Debats, Dee Luo, Lyndon Estes, Thomas J. Fuchs, Kelly K. Caylor
Geography
Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize …