Geoinformatics Application in Land use Dynamics and its Biogeophysical Impacts on Macro Climate-A Case Study

Climate change is one of the major concern in the present century, extensive multidisciplinary research is required to evaluate the cause and consequences of climate change for a better adaptation and mitigation policies. Present research focused on identifying the impact of land use dynamics on Angul climate, especially the biogeophysical-land use induced impacts using geoinformatics. Climate data from 1901 to 2015 is statistically analysed to estimate the trend in study area climate. Nonparametric Mann-Kendall rank test, Pearson r correlation, Kendall correlation, Auto-Regressive Integrated Moving Average and Analysis of Variance is applied to detect the diurnal, monthly, seasonal, annual changes in temperature, precipitation, vapour pressure, potential evapotranspiration and crop evapotranspiration. Statistical analysis estimated a blend of increasing and decreasing trend in Angul’s climate variables and recorded a negative correlation between temperature and rainfall. The temperature increased drastically over the last three decades, the average minimum and maximum temperature of 1901 is 20°C - 31°C and increased to 23°C - 33°C in 2015. Auto- Regressive Integrated Moving Average model projected a significant rise in seasonal temperature and reduction in rainfall, which is considered to be an early warning for the upcoming extreme climate events. Maximum likelihood (Bayesian Variation) parametric decision approach applied to estimate the changes in land use and land cover from 1972 to 2016. Significant reduction in vegetation (19%) and agricultural land use (18%) is estimated. Mining area increased to 325%, settlement area and dry land area increased to 56 %, 36% respectively. Twenty vegetation indices including Leaf Area Index, Improved Modified Chlorophyll Absorption Ratio Index 2, Soil and Atmospherically Resistant Vegetation Index, Improved Modified Triangular Vegetation Index-2, Vegetation Fraction, Normalized Difference Vegetation Index and Transformed Vegetation Index is applied to estimate the vegetation impacts on climate. Vegetation indices recorded a significant reduction vegetation conditions and its impact on climate. Reduction in vegetation increases the rate of surface temperature, albedo and carbon dioxide emission from the earth surface. Surface temperature estimation from the satellite images using NDVI based emissivity techniques identified an increasing trend in Angul surface temperature. Maximum temperature registered in agricultural land, dry land, mining and settlement area. Negative correlation identified between the vegetation and surface temperature. Albedo estimated from the Landsat and Ocean Color Monitor-2 satellite images. Shortwave Landsat albedo estimation techniques used for Landsat images and narrowband to broadband albedo estimation method applied for Ocean Color Monitor-2 images to estimate the albedo. Maximum albedo values observed over agriculture, mining, settlement and dry land areas. Change detection analysis of surface water carried out using normalised difference water index and identified a significant reduction in surface water, especially in the central part of the study area. The results also delineated the water filled mining areas, cause to environmental degradation. Triangulated irregular network and digital elevation model created to analyse the topography and geomorphological characteristics of the study area. Detailed slope analysis carried out to estimate the slope condition of the study area. Flow direction and flow accumulation studies conducted to understand the water flow direction and accumulation points. Watershed analysis carried out for the study area and three watersheds with pour point delineated