Land use and land cover (LULC) mapping is very important for evaluation and management of natural resources. Classification of remote sensing data in mountainous and other heterogeneous environments are particularly problematic because of their high spectral heterogeneity. The aim of this study is to conduct accuracy analyses of LULC classification of Landsat-8 OLI composite images and multisource ancillary data using four machine learning classifiers on the Google Earth Engine (GEE) platform in the Golden Gate Highland National Park (GGHNP) and its near surrounding. Our results indicate that the inclusion of short-wave infrared bands increased accuracy of image composites by more than 10% in landcover types with wider range of spectral characteristics. Best performance was achieved by MaxEnt with overall accuracy and kappa statistics of 0.846 and 0.775, respectively. Support Vector Machine (SVM), however, produces relatively higher accuracy for handling different characteristics of multisource data. In elevated areas, aspect was found to outperform other topographic indices in reducing misclassification errors associated with confusion of grasslands with bare lands and water. The LULC produced from image composites can provide some reliable information on vegetation, land and water characteristics and reduce confusion associated with complex biophysical environments on vegetation spectral signatures in sub-tropical mountainous grasslands.

The influence of global change on vegetation cover and processes has drawn increasing attention in the past few decades. In this study, we used remotely sensed rainfall and land surface temperature to investigate the spatiotemporal pattern and trend in vegetation condition using NDVI as proxy from 2001 to 2017 in a humid and dry tropical region. We also determined the partial correlation coefficient of temperature and rainfall with NDVI and the response of NDVI to changes in landcover categories due to human activities. We found that the mean annual maximum NDVI was 0.42, decreasing at a rate of 0.06 per decade. About 27.4% of the area was found to have experienced a significant negative trend in vegetation cover, while only 0.34% exhibited significant increasing vegetation vigour. Land surface temperature increased at a mean rate of 0.75°C/decade, with higher rates in agriculture, savanna, settlements, woodlands, and riparian vegetation than in forest and mangrove vegetations. Precipitation also reduced at a mean rate of 58.69 mm/decade, with higher rates in agriculture savanna and riparian vegetation than in sahelian grasslands, mangrove, forest, and woodlands. NDVI was negatively correlated with temperature in savanna, settlements, degraded forest, and sahelian grasslands providing confirmation of ongoing land degradation. It was concluded that vegetation vigour will continue to decline under rainfall and increasing temperature conditions especially in dryer regions. The use of land surface temperature in this study is particularly valuable in highlighting areas where changes in NDVI occurred as a result of synergistic action between climate and human-induced landcover changes. Our findings underscore the importance of landuse policies that account for spatial variation in synergistic relationships between the nexus of climate and land conversion processes that influence vegetation cover change in different landcover types in tropical regions.

This study mapped out and investigated the spatial relationship between cholera incidences and environmental risk factors in the study area. The study area was stratified into eight zones. Water samples from each zone were collected and analyzed to determine the colony forming units. GIS layers including housing density, digitized roads, rivers, buildings, and cholera incidence data from hospital archives were also collected and analyzed using ArcGIS 10.1. It was observed that there was an association between the ERFs (p<0.001). Similarly, 18 out of the 44 waste dump sites, seven out of 18 markets, and two out of 36 abattoirs were found near the historical cholera cases. Similarly, 4 (21.1%) locations were traced to be predominantly close to rivers and waste dump site. All the historical cholera cases were found adjoining to roads and buildings. Highest CFU count was found in the wells and streams of areas with a cluster of all the environmental risk factors and high housing density. This study revealed that waste dump sites and market had the highest predisposing attribute while the least was abattoir. The uniqueness of the study lies in the combination of mapping and microbial analyses to identify and assess the pattern of cholera risk and also to provide clear information for development of strategies for environmental supervision.

We formalize Triangle-based Geometric Semantic Modeling (TGSM), a novel approach to time-series analysis that encodes temporal transitions as geometric primitives. Consecutive observations are mapped to right-angled triangles, linking time span and change magnitude to a unified measure of movement strength. By organizing these primitives into directional classes and aggregating them across scales, TGSM provides a transparent bridge between raw signals and semantic structure. This framework offers a new lens for interpreting dynamic behavior and establishes an audit-ready foundation for semantic compression, model transparency, and robust feature design in explainable AI. The paper highlights the formulation, key derivations, and potential applications of this approach.