Determination of Forest Health using Remote Sensing Techniques in Gashaka-Gumti National Park, Northeast, Nigeria

This study was conducted in order to determine the health status of forest vegetation in Gashaka-Gumti National Park. Landsat images were downloaded from the USGS website. The images were pre-processed using radiometric correction since the reflectance values were needed for computing spectral indices, the digital numbers were converted to radiance and reflectance, and analysis was carried out using a raster calculator. The range of NDVI, GNDVI, ARVI and MSI were used for health assessment. Utilizing NDVI, GNDVI, ARVI, and MSI as assessment tools revealed moderate to good health in most forest regions, with higher ARVI, GNDVI, and NDVI indicating healthier vegetation and elevated MSI values suggesting areas under moisture stress. the average values of NDVI, GNDVI, ARVI, and MSI over three decades indicate a potential decline in overall vegetation health, reduced green vegetation, changes in vegetation conditions, and a decrease in moisture stress, suggesting a potential increase in greening and photosynthetic activities in plants. These trends highlight the dynamic nature of the forest ecosystem over the studied period. Positive correlations between ARVI, GNDVI, and NDVI across years indicate a consistent vegetation pattern, while negative correlations with MSI suggest potential inverse relationships, providing valuable insights into forest health dynamics. Higher values of ARVI, GNDVI, and NDVI generally signify healthier vegetation, whereas elevated MSI values may indicate areas experiencing moisture stress, emphasizing the importance of monitoring these indices for sustainable forest management. The study recommends the sustained use of NDVI, GNDVI, ARVI, and MSI for forest health monitoring in the study area, implementing integrated pest management based on identified stress conditions, utilize spatial maps for strategic timber harvest planning, developing climate-resilient management considering moisture stress, and invest in research for enhanced assessment precision and understanding of ecosystem dynamics.