Improved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing

Land Use Land Cover (LULC) change detection helps the policy makers to understand the environmental change dynamics to ensure sustainable development. Hence, LULC feature identification has emerged as an important research aspect and thus, a proper and accurate methodology for LULC classification is the need of time. In this study, Landsat-7 satellite data captured by Enhanced Thematic Mapper (ETM+) were used for LULC classification employing the maximum likelihood supervised classification (MLC) algorithm. The study targets the improvement of classification accuracy with the combined use of thermal and spectral information from satellite imagery. Land surface temperature (LST) is sensitive to land surface features and hence can be used to extract information on LULC features. The classification accuracy was found to improve on integrating the thermal information from the thermal band of Landsat ETM+ with spectral information. Two thermal vegetation indices, namely Thermal Integrated Vegetation Index (TLIVI) and Advanced Thermal Integrated Vegetation Index (ATLIVI), proposed in this study showed fairly good correlations (R2 = 0.65 and 0.7, respectively) with the derived surface temperature. These indices based on empirical parameterization of the relationship between surface temperature (Ts) and vegetation indices showed an increase of nearly 6% in the overall accuracy for land-use/land-cover (LULC) classification in comparison to MLC algorithm using Standard False Colour Composite (FCC) satellite image of Landsat ETM+ as reference.