Assessing the Health of Akamkpa Forest Reserves in Southeastern Part of Nigeria Using Remote Sensing Techniques

Abstract

Assessment of forest health is very vital because forests form the largest terrestrial ecosystems on earth. The greenness of vegetation is one of the essential factors used in evaluating the health of forest reserves. This study is aimed at assessing the health of fifteen forest reserves in Southeastern part of Nigeria using meteorological data and MOD13A1-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Related portions of the monthly MOD13A1 data, derived for the years 2010, 2014, and 2018, were downloaded, and the monthly mean values of the vegetation indices (NDVI and EVI) were estimated for each of the forest reserves using the Spatial Analysis Module in ArcGIS software. The computed monthly mean values of NDVI range from 0.094 to 0.790 while that of EVI ranges from 0.11 to 0.624 and the rainfall data range from 0 to 780.2 mm/month within the period of study. Analyses of the correlation coefficients between monthly rainfall data and NDVI, monthly rainfall data and EVI, and that of NDVI and EVI range from −0.827 to 0.584; −0.715 to 0.914, and 0.598 to 0.980. The obtained results indicate that some of the forest reserves are moderately healthy while some areas are under great stress. We can conclude that satellite remote sensing is a veritable tool in the assessment, management, and monitoring of forest health especially where there is little or no terrestrially acquired forest inventory data.

Assessment of forest dieback on the Moroccan Central Plateau using spectral vegetation indices

“…NDVI and precipitation over cropland and mixed deciduous forest were strongly positively related, but no correlation was evident for dry evergreen forest. However, increased precipitation does not always correlate with vegetation indices; Ebinne et al (2020) reported a negative correlation between monthly precipitation and NDVI. Temperature has also been shown to have a strong negative correlation with NDVI (Wang et al 2003;Pei et al 2019); warmer temperatures cause more evapotranspiration, which limit plants growth.…”

“…With the continued development of imaging sensors, this technique has become a useful and practical tool for characterizing forest health in a variety of ways and with high accuracy. With the availability of remote sensing data from satellite sensors such as moderate resolution imaging spectroradiometer (MODIS), advanced very high-resolution radiometer (AVHRR), Landsat, Sentinel, among others, the technology is even more useful for developing vegetation indices such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), especially in broadleaf forests (Heumann et al 2007(Heumann et al , 2013Anees et al 2013;Zhang et al 2017;Barka et al 2019;Ebinne et al 2020).…”

A Review on Remote Sensing Based Forest Vegetation Health Assessment: Bangladesh Perspective

“…[Source: Compiled from Kamran and Khorrami, 2022;Roshani et al, 2022;Kumari and Asok, 2017;Mahato et al, 2020;Mitra et al, 2021;Ebinne et al, 2020;Ishtiaque et al, 2016;Meng et al, 2016;Mandal et al, 2022. ]…”