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

Engineering Proceedings

article

Mfoniso Asuquo Enoh¹  Chukwubueze Onwuzuligbo²  Needam Yiinu Narinua³ 

[1] Department of Geoinformatics and Surveying, University of Nigeria;Department of Surveying and Geoinformatics, Nnamdi Azikiwe University;Department of Surveying and Geoinformatics

关键词: forest ecosystem;    Markov Chain Model;    micro-seepage;    remote sensing;    LAI;   

DOI  :  10.3390/ASEC2022-13830

来源: mdpi

【 摘 要 】

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.

【 授权许可】

Unknown