Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multitemporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two urbanization indicators calculated in this study viz. Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.
<p><strong>Abstract.</strong> Crop discrimination and acreage play a vital role in interpreting the cropping pattern, statistics of the produce and market value of each product. Sultan Battery is an area where a large amount of irrigated and rainfed paddy crops are grown along with Rubber, Arecanut and Coconut. In addition, the northern region of Sultan Battery is covered with evergreen and deciduous forest. In this study, the main objective is to evaluate the performance of optical and Synthetic Aperture Radar (SAR)-optical hybrid fusion imageries for crop discrimination in Sultan Bathery Taluk of Wayanad district in Kerala. Seven land use classes such as paddy, rubber, coconut, deciduous forest, evergreen forest, water bodies and others land use (e.g., built-up, barren etc.) were selected based on literature review and local land use classification policy. Both Sentinel-2A (optical) and sentinel-1A (SAR) satellite imageries of 2017 for Kharif season were used for classification using three machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Classification and Regression Trees (CART). Further, the performance of these techniques was also compared in order to select the best classifier. In addition, spectral indices and textural matrices (NDVI, GLCM) were extracted from the image and best features were selected using the sequential feature selection approach. Thus, 10-fold cross-validation was employed for parameter tuning of such classifiers to select best hyperparameters to improve the classification accuracy. Finally, best features, best hyperparameters were used for final classification and accuracy assessment. The results show that SVM outperforms the RF and CART and similarly, Optical+SAR datasets outperforms the optical and SAR satellite imageries. This study is very supportive for the earth observation scientists to support promising guideline to the agricultural scientist, policy-makers and local government for sustainable agriculture practice.</p>
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