Forest health assessment for geo-environmental planning and management in hilltop mining areas using Hyperion and Landsat data

Abstract

Abstract This work focuses on assessing the health condition of the forest using Hyperspectral and Multispectral satellite imagery and validating it with field spectra data. The field-based spectroradiometer and PCE instrument were used for collection of forest health spectra and measurement of dust accumulation on leaves respectively. In the study area, mining activities have very high potential to induce forestry health and environmental problem which necessitated a proper forest health assessment and its monitoring. The result of the Discrimination analysis (21 spectral wavebands) were used for forest health classification. The result shows that healthy forest parts are found in the upper as well as the lower hilly side of Kiriburu and Meghahatuburu mines. Some healthy pixels are located within 1.5 km from mines because it was situated off the hillside. Furthermore, it also exhibits positive relation amongst different forest health class, distance from mines and foliar dust concentration. Hyperspectral (narrow-bands) Hyperion data used with Vegetation Indices (VIs) model shows better accuracy for forest health assessment (overall accuracy 81.52%, kappa statistic 0.79) than Spectral Angle Mapper (overall accuracy 79.99%, kappa statistic 0.75) as well as Support Vector Machine (overall accuracy 76.53%, kappa statistic 0.71). It was observed that the health assessment accuracy (using SVM algorithm) achieved with Hyperion bands was significantly higher than multispectral (broad-bands) Landsat-OLI data (overall accuracy 67.27%, kappa statistic 0.62). Finally, the forest health assessment results were validated by 60 field sampled spectra data obtained from spectroradiometer. The forest health map obtained provides a guideline for geo-environmental planning and management in mining proximity forest area.