Mapping Environmental Degradation and Climate Vulnerability using GIS and Remote Sensing
Environmental degradation and climate vulnerability pose significant threats to the stability of ecosystems, agriculture, water supply, and humans’ well-being. The use of Geographic Information Systems (GIS) and remote sensing can prove beneficial in detecting spatial patterns of environmental changes, detecting signs of land degradation, and assessing regions’ vulnerability to climate threats. In this study, a geospatial framework is suggested to map environmental degradation and climate vulnerability through satellite imagery analysis, vegetation indexes, land use and land cover classification, land surface temperature, precipitation, terrain properties, and socioeconomic factors. Landsat imagery, Sentinel-2 imagery, digital elevation models, and climate gridded data will be combined in GIS to assess changes in vegetation cover, urbanization, bare lands, land surface temperature, and drought. To do that, NDVI, NDBI, LST, and precipitation anomalies will be used to derive indicators of environmental degradation. Climate Vulnerability Index (CVI) will be created using weighted overlay models with environmental exposure, sensitivity, and adaptive capacity indicators. The accuracy of classification will be measured using independent reference data sets, whereas temporal variations will be evaluated via post-classification assessment. As illustrated in the figure below, a conceptual example of the application of the framework is shown by partitioning a study site into different vulnerability zones (low, medium, high, and very high), wherein 35% of the study site has been assigned to high and very high vulnerability zones. The values are purely illustrative and are not obtained from any satellite processing. This framework can contribute to environmental monitoring, climate adaptation strategy, land management, and identification of vulnerable sites.
关注点该框架将NDVI、NDBI、LST与降水异常等指标经加权叠加构建气候脆弱性指数，但文中脆弱性分区数值仅为示意、非卫星处理结果，方法可借鉴而结论不可直接引用。
DTW-kNN and Sentinel-2 NDVI Time Series for Field-Scale Maize Identification and Multi-Crop Classification in an African Smallholder Landscape
Accurate crop mapping provides spatially explicit information for agricultural planning, monitoring and resource allocation, while supporting agribusiness decisions related to supply chains and risk management. However, mapping crops in smallholder landscapes remains difficult because fields are often fragmented, small, and irregularly shaped, with varying planting dates. This study evaluated Sentinel-2 Normalised Difference Vegetation Index (NDVI) time series and Dynamic Time Warping (DTW) for maize identification and multi-crop field-boundary classification in a smallholder farming area. A maize reference trajectory was developed from known maize fields and tested using an independent set of maize fields. The maize reference fields showed low DTW distances (0.106 - 0.281) to the reference trajectory, while independent validation fields produced comparable distances (0.074 - 0.325), indicating that maize followed a recognisable NDVI trajectory despite variable planting dates. DTW distances (0.151 to 0.565) of candidate fields suggested that some fields had maize-like phenological behaviour while others were less similar to the maize reference. Days After Planting (DAP) alignment produced a clear maize curve, with NDVI increasing after planting, peaking between 90 and 120 DAP, and later declining. The approach was then extended to multi-crop classification using maize, soybean, potato and tea reference trajectories. The multi-reference DTW classifier achieved an overall validation accuracy of 80.0%, with maize and tea classified most reliably. When applied to 128 unlabelled fields, most were predicted as maize, although DTW confidence varied. The findings show that Sentinel-2 NDVI time series and DTW provide an interpretable and data-efficient approach for maize detection and field-level crop classification in smallholder systems.
关注点该研究用Sentinel-2 NDVI时序与DTW在非洲小农户地块实现玉米识别和多作物分类，总体验证精度80%，但样本仅128个未标注地块，结论外推需谨慎。
