Growing concern over the status of global and regional bioenergy resources has necessitated the analysis and monitoring of land cover and land use parameters on spatial and temporal scales. The knowledge of land cover and land use is very important in understanding natural resources utilization, conversion and management. Land cover, land use intensity and land use diversity are land quality indicators for sustainable land management. Optimal management of resources aids in maintaining the ecosystem balance and thereby ensures the sustainable development of a region. Thus, the sustainable development of a region requires a synoptic ecosystem approach in the management of natural resources that relates to the dynamics of natural variability and the effects of human intervention on key indicators of biodiversity and productivity. Spatial and temporal tools such as Remote Sensing (RS), Geographic Information System (GIS) and Global Positioning System (GPS) provide spatial data at regular intervals with the functionalities of a decision support system to help in visualization, querying, analysis, etc., which would aid in sustainable management of natural resources.

RS data and GIS technologies play an important role in spatially evaluating bioresource availability and demand. This paper explores various land cover and land use techniques that could be used for bioresources monitoring considering the spatial data of Kolar district, Karnataka, India. Slope and distancebased vegetation indices are computed for qualitative and quantitative assessment of land cover using remote spectral measurements. Different-scale mapping of land use pattern in Kolar district is done using supervized classification approaches. Slope-based vegetation indices show area under vegetation which range from 47.65% to 49.05%, while distance-based vegetation indices show its range from 40.40% to 47.41%. Land use analyses using maximum likelihood classifier, indicate that 46.69% is agricultural land, 42.33% is wasteland (barren land), 4.62% is built up, 3.07% is plantation, 2.77% is natural forest and 0.53% is water bodies. The comparative analysis of various classifiers indicate that the Gaussian maximum likelihood classifier has least errors. The computation of taluk-wise bioresource status shows that Chikballapur taluk has better availability of resources compared to other taluks in the district.

Comparative Assessment of Techniques for Bioresource Monitoring Using Gis and Remote Sensing, sustainable, management, natural, spatial, vegetation, analysis, likelihood, monitoring, bioresource, distancebased, classifier, monitoring, status, System, ecosystem, temporal, Differentscale, ensures, evaluating, functionalities, Geographic