Integrated AN INTEGRATED APPROACH FOR NATURAL RESOURCES MONITORING USING GEO-INFORMATICS AND CA

In last few decades, it was observed that land use land cover (LULC) changes are more extensive and occurring at faster pace to meet the developmental demands of ever increasing population. Such unplanned development and growth is leading to adverse impacts on natural resources like degradation of land resources, reduction in vegetation cover, loss of agricultural land, loss of forest, over-exploitation of water resources and environmental degradation. Correct assessment and monitoring of natural resources including land, water and vegetation are prerequisites for sustainable land use planning and optimum utilisation of other natural resources. Geo-spatial technologies like remote sensing, satellite based positioning and mapping, laser based data collection and Geographical Information System (GIS) are very effective in systematic data collection and monitoring of natural resources through LULC change detection. The current study presents integration of geo-spatial technologies and Cellular Automata (CA) - based mathematical modelling for monitoring of natural resources through assessment of LULC changes over a period. Multi-spectral satellite data for different years have been processed to extract historical LULC information and parameterisation of CA based LULC change detection model i.e., SLEUTH. Further, LULC changes and change in natural resources have been predicted for the year 2030 using calibrated model. The study has been found to be successful in demonstrating the use of geo-spatial technologies and SLEUTH in simulating the LULC changes and assessment of natural resources. The study reveals future changes in natural resources which can help planners and authorities to take proactive measures for their sustainable development.

. These issues are critical for sustainable environment. To mitigate adverse impacts of such unplanned LULC changes and degradation of natural resources, systematic data collection and LULC monitoring at different temporal is crucial. Further, natural resources data collection, inventorying and monitoring is very essential for their optimum planning, development and utilisation. Geomatics, in the current digital era, is the leading technology which observes earth's surface remotely through sensors, stores, processes and analyses land surfaces, temporarily, very effectively (Jat et al., 2008). Moreover, integration of geo-spatial technologies with modelling approaches may be helpful in giving LULC change on a continuous basis and also can predict future landscape changes (Clarke et al., 2007;Saxena et al., 2016, Jat et al., 2017. Various (Lambin et al., 2000). Expert system and Artificial Neural Network (ANN)-based models are the product of computer technologies, developed to introduce dynamism and multiple explanatory variables in to the model. The Cellular Automata (CA)-based models were developed as a powerful tool in simulating LULC changes by introducing stochasticity, complexity and dynamism into the model (Clarke, 2008) and became very successful. Among various CA-based models, SLEUTH model was prominently applied for urban growth modelling and found very successful (Clarke and Gaydos, 1998;Jat et al., 2017).
However, its applicability and performance for LULC change monitoring are still to be tested.

The model incorporates socio-economic and bio-
Journal of Rural Development,Vol.37,No. (2), April-June:2018 physical factors which majorly are responsible for LULC change (Saxena et al., 2016). The model utilises five coefficients (i.e. diffusion, breed, spread, slope resistant and road gravity) which form four rules (diffusive, new spreading centre, edge and road influenced growth) to derive LULC change (Clarke and Gaydos 1998;Clarke, 2008 3. LULC and natural resource prediction (up to year 2030) using SLEUTH model.

Methodology
The study used different types of data obtained from multiple sources like government and private organisations. Present study used different types of data and methods of geospatial techniques and cellular automata.
Input Data: Present study utilises seven years of multi-spectral Landsat satellite data (i.e. 1989, 1994, 1997, 2000, 2002, 2005 and 2009), an AutoCAD map for digitising transportation layer, topographic maps, town plan map for digitising exclusion layer, DEM of 1 meter spatial resolution for preparing slope and hillshade map.
Salient details of input data listed in Table 1. Satellite Imagery (1989, 1994, 1997, 28.5 meter resolution 2000, 2002, 2005 and 2009)    Accuracy percentage and kappa statistics were found to be acceptable for such a medium resolution data. The classified outputs are shown in Figure 4.

GIS Database Creation: GIS layers like slope,
LULC maps, exclusion layer, urban maps (1989,1994,1997,2000,2002,2005 Table 3.  Table 4. Also, open land was greatly reduced from    increased urban growth can be clearly seen ( Figure 7). open land will be declining in near future also and these will gradually be transformed into settlement or built-up activities. The study is successfully implemented in the study area which is quantifying and giving simulated maps that will help the analyst to plan land and natural resources in such a manner that sustainable development goal can be achieved.