Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa

Azzari, George; Jain, Shruti; Jeffries, Graham; Kilic, Talip; Murray, Siobhan

doi:10.1596/1813-9450-9609

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…Another limitation of our reference data is that it only covered three time points, which limited our ability to assess how well our methods can detect annual change events. These factors demonstrate that the lack of high-quality reference data remains one of the major hurdles to land cover change mapping [101,102]. A fuller assessment of the performance of our methods will therefore require a higher quality reference dataset, as the accuracy of the reference labels sets the upper bound on knowable map accuracy.…”

Section: The Impact Of Reference Label Uncertainty On Understanding P...mentioning

confidence: 99%

Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine

et al. 2022

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Cropland expansion is expected to increase across sub-Saharan African (SSA) countries in the next thirty years to meet growing food needs across the continent. These land transformations will have cascading social and ecological impacts that can be monitored using novel Earth observation techniques that produce datasets complementary to national cropland surveys. In this study, we present a flexible Bayesian data synthesis workflow on Google Earth Engine (GEE) that can be used to fuse optical and synthetic aperture radar data and demonstrate its ability to track agricultural change at national scales. We adapted the previously developed Bayesian Updating of Land Cover (Unsupervised) algorithm (BULC-U) by integrating a shapelet and slope thresholding algorithm to identify the locations and dates of cropland expansion and implemented a tiling scheme to allow the processing of large volumes of imagery. We apply this approach to map annual cropland change from 2000 to 2015 for Zambia (750,000 km2), a country that is experiencing rapid growth in agricultural land. We applied our cropland mapping approach to a time series of unsupervised classifications developed from Landsat 5, 7, 8, Sentinel-1, and ALOS PALSAR within 1476 tiles covering Zambia. The annual cropland changes maps reveal active cropland expansion between 2000 to 2015 in Zambia, especially in the Southern, Central, and Eastern provinces. Our accuracy assessment estimates that we have identified 27.5% to 69.6% of the total cropland expansion from 2000 to 2015 in Zambia (commission errors between 6.1% to 37.6%), depending on the slope threshold. Our results demonstrate the usefulness of Bayesian data fusion and shapelet, slope-based thresholding to synthesize optical and synthetic aperture radar for monitoring agricultural changes in situations where training data are scarce. In addition, the annual cropland maps provide one of the first spatially continuous, annually incremented accounts of cropland growth in this region. Our flexible, cloud-based workflow using GEE enables multi-sensor, national-scale agricultural change monitoring at low cost for users.

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Section: The Impact Of Reference Label Uncertainty On Understanding P...mentioning

confidence: 99%

Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine

et al. 2022

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Agricultural data collection to minimize measurement error and maximize coverage

Carletto

Dillon

Zezza

2021

Handbook of Agricultural Economics

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Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Tempa,

Ilunga,

Agarwal

et al. 2024

Applied Sciences

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Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.

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Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa

Cited by 4 publications

References 25 publications

Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine

Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine

Agricultural data collection to minimize measurement error and maximize coverage

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

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