Stimulating the Impact of Hydrocarbon Micro-Seepage on Vegetation in Ugwueme, from 1996 to 2030, Based on the Leaf Area Index and Markov Chain Model

The Leaf Area Index (LAI) is an important algorithm for studying the health status of vegetation. In this study, the impact of hydrocarbon micro-seepage on vegetation in Ugwueme was investigated using the LAI image classification approach. Landsat TM 1996, ETM+ 2006, and OLI 2016 satellite images that were acquired from the United States Geological Survey (USGS) portal were used to classify various LAI maps as low, moderate, and high classes. The spatial–temporal analysis revealed that the low, moderate, and high LAI density classification changed, respectively, from 41.24 km2 (50.43%), 33.98 km2 (41.54%), and 6.56 km2 (8.02%) in 1996 to 23.70 km2 (28.98%), 29.48 km2 (36.04%), and 28.60 km2 (34.97%) in 2006, and to 38.23 km2 (46.74%), 27.54 km2 (33.68%), and 16.01 km2 (19.58%) in 2016. The stimulation analysis shows that by 2030 (the 14-year planning period), the low, moderate, and high LAI density classifications will be 8.86 km2 (10.82%), 24.28 km2 (29.70%), and 48.63 km2 (59.46%), respectively. The study shows that LAI is an important algorithm that can be effectively used to study the health status of vegetation in an ecosystem

Spatial-Temporal Mapping and Delineating of Agulu Lake Using Remote Sensing and Geographic Information Science for Sustainable Development

Water is a crucial component of ecosystems and a critical resource that cannot be replaced for social progress or human life. In this study, Agulu Lake, an inland water body located in Anambra, southeast Nigeria, was mapped, classified, and delineated with remotely sensed data so as to monitor the spatial-temporal changes that occurred in the lake’s surface water every 15 years, in 1985, 2000, and 2015, in order to achieve sustainable development by 2030. The Sustainable Development Goals (SDGs) of the United Nations emphasize the need to manage the marine environment. Some of the goals of the SDGs have some connection to open surface water, but goal 6a and indicator 6.6.1 are significant to this study. The study adopted Landsat 5 TM (1985), ETM+ (2000), Landsat 8 OLI (2015), ArcGIS 10.5 software, and the maximum likelihood classifier to create various classification maps. The Google Earth image (2015) was also used to show the general overview of Agulu Lake and its environs. The findings demonstrate that during the study period, the land surface class grew while the water surface class (Agulu Lake) shrank