Weighting mechanics and the spatial pattern of composite metrics of heat vulnerability in Atlanta, Georgia, USA.

This study constructs two biophysical metrics; one based on Land Surface Temperatures (LST) and an integrated spectral index. The latter is an aggregate of Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI). The goal is to determine how disparate weighting techniques, data transformation approaches, and spatial visualization pathways influence the computation of composite heat metrics. Using composite images made of aggregated images from late May to Early September within Google Earth Engine, we generated four composites by combining biophysical metrics with SoVI using equal and Eigen-based weightings informed by Principal Component Analysis (PCA). We compared equal interval classification, global and local Moran's as pathways for spatial visualization of hotspots. We utilized several data transformation techniques in a Geographic Information System (GIS), including rescaling, reclassification, zonal statistics, and spatial weighting. Mann Kendall and Sen's Slope detected and quantified monotonic trends in each spectral index. The results show that the LST biophysical metric and its composites indicate increased heat susceptibility over time, with disproportionately exposed core metro counties. The integrated spectral index and its proxies showed reduced vulnerability hence not a good proxy for LST. At the same time, the Mann Kendall and Sen's Slope found persistent increases in NDVI and NDWI and decreases in NDBI and NDBaI. However, opposite trends were evident in core city counties. The LST-based composites and spectral indices-based composites varied in the spatial-temporal distribution of hotspots. Disparate weighting mechanics, data transformation techniques, and visualization alternatives influence the magnitude and spatial-temporal distribution of heat hotspots.

Linkages between land cover change, lake shrinkage, and sublacustrine influence determined from remote sensing of select Rift Valley Lakes in Kenya.

The Great Rift Valley system is home to many volcanic and tectonic lakes including some of the world's oldest and deepest lakes. These lakes host a rich heritage of biodiversity that is endangered by recent drastic hydrologic changes due to multiple natural and anthropogenic stressors in the catchment areas of some of the lakes. This study utilized Landsat TM, ETM+, and OLI data to conduct a systematic investigation of the relationship between hydrological dynamics in the basins of four Rift Valley lakes (Nakuru, Baringo, Bogoria, and Elementaita) and recent land cover and land-use change. The Modified Normalized Difference Water Index (MNDWI) proved to be more accurate and robust for delineating water surface areas when compared to the output of Normalized Difference Vegetation Index (NDVI) and classification algorithms. NDVI was successful when delineating water surface at Lake Baringo but not in Lakes Bogoria, Nakuru, and Elementaita, whose surfaces were dominated by algae. All the lakes expanded substantially after 2010 submerging surrounding areas leading to disruption of livelihoods, property damage, and displacement of thousands of people. The recent drastic hydrologic changes have multiple causations including land cover and land use change, increase in rainfall, and possible change in geogenic water input due to tectonic activity. The rapid rise in water levels appears to have altered the biogeochemical balance of the hypersaline lakes with severe ramifications on the rich biodiversity that is supported by the lakes.