Next Article in Journal
iFogSim Simulations on IoT Computational Alternatives
Previous Article in Journal
Effective Corrosion Inhibition of Mild Steel in an Acidic Environment Using an Aqueous Extract of Macadamia Nut Green Peel Biowaste
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Engineering Proceedings
Volume 31
Issue 1
10.3390/ASEC2022-13830
engproc-logo
Submit to this Journal
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Proceeding Paper

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 †

by
Mfoniso Asuquo Enoh
1,*,
Chukwubueze Onwuzuligbo
2 and
Needam Yiinu Narinua
3
1
Department of Geoinformatics and Surveying, University of Nigeria, Enugu 400102, Nigeria
2
Department of Surveying and Geoinformatics, Nnamdi Azikiwe University, Awka 420007, Nigeria
3
Department of Surveying and Geoinformatics, Ken Polytechnic Bori, Bori 502101, Nigeria
*
Author to whom correspondence should be addressed.
†
Presented at the 3rd International Electronic Conference on Applied Sciences, 1–15 December 2022; Available online: https://asec2022.sciforum.net/.
Eng. Proc. 2023, 31(1), 47; https://doi.org/10.3390/ASEC2022-13830
Published: 9 December 2022
(This article belongs to the Proceedings of The 3rd International Electronic Conference on Applied Sciences)
Browse Figures
Versions Notes

Abstract

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.
Keywords: forest ecosystem; Markov Chain Model; micro-seepage; remote sensing; LAI forest ecosystem; Markov Chain Model; micro-seepage; remote sensing; LAI

Share and Cite

MDPI and ACS Style

Enoh, M.A.; Onwuzuligbo, C.; Narinua, N.Y. 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. Eng. Proc. 2023, 31, 47. https://doi.org/10.3390/ASEC2022-13830

AMA Style

Enoh MA, Onwuzuligbo C, Narinua NY. 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. 2023; 31(1):47. https://doi.org/10.3390/ASEC2022-13830

Chicago/Turabian Style

Enoh, Mfoniso Asuquo, Chukwubueze Onwuzuligbo, and Needam Yiinu Narinua. 2023. "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 31, no. 1: 47. https://doi.org/10.3390/ASEC2022-13830

APA Style

Enoh, M. A., Onwuzuligbo, C., & Narinua, N. Y. (2023). 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, 31(1), 47. https://doi.org/10.3390/ASEC2022-13830

Article Metrics

Back to TopTop