Use of empirically based land use dynamics model with climate and socio-economic parameters in a rain-fed agricultural area

Vidhya R.; Manonmani R.

doi:10.1117/12.2028715

24 October 2013 Use of empirically based land use dynamics model with climate and socio-economic parameters in a rain-fed agricultural area

Vidhya R., Manonmani R.

Author Affiliations +

Proceedings Volume 8893, Earth Resources and Environmental Remote Sensing/GIS Applications IV; 889311 (2013) https://doi.org/10.1117/12.2028715
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

The changes in the agricultural patterns and the forest land are one important indicators of serious socio economic and environmental management implications. The main challenge for the agriculture sector from the rainfed areas is to sustain present livelihood conditions and to secure future demands and typically they are more fragile to the changing climates and demands. Remote Sensing and Geographical Information System coupled with empirical modeling is recognized as a powerful and effective tool in detecting land use change and predict future changes. In this study, modeling and prediction of agricultural land use change across space and time using Logistic Regression and Geographically Weighted Regression in dry land region is attempted as the land use and land cover change is the function of interacting factors which are known as drivers, agents and determinants. The land use pertaining to 1985,1995 and 2005 for the study area were generated to study and model the land use dynamics. The drivers for land us change based on the factors such as climatological data and socio economics data were identified and a model for changes land use using geographically weighted regression and binary logistic regression methods . The models were validated with the help of land use map generated with remote sensing data.

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Vidhya R. and Manonmani R. "Use of empirically based land use dynamics model with climate and socio-economic parameters in a rain-fed agricultural area", Proc. SPIE 8893, Earth Resources and Environmental Remote Sensing/GIS Applications IV, 889311 (24 October 2013); https://doi.org/10.1117/12.2028715

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KEYWORDS

Agriculture

Data modeling

Remote sensing

Climatology

Geographic information systems

Binary data

Climate change

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