A BSTRACTThe study evaluates the potential of satellite remote sensing technology for detection, mapping and monitoring of diseased rubber plantation affected by Corynespora and Gloeospormm fungi, which causes leaf spot and leaf fall. Multi-date satellite data of IRS-IC have been analyzed adopting enhancement and classification techniques to identify and extract information on the spatial extent and distribution of healthy and diseased rubber plants with an accuracy of 90%. The diseased rubber plantations have shown considerable reduction in the near-infrared reflectance followed by a rise in the reflectance in red and short wave infrared. Vegetation index images generated for different periods have shown the progress of disease incidence, severity and recovery of rubber plantations after fungicidal spraying. The study has demonstrated the use of remote sensing technology in identifying and delineating diseased rubber plantations. Early detection of the disease would be of immense value for taking up necessary control measures and minimize the loss.
Coffee is the second most traded commodity in the world and its production has implications in both international and domestic economy. It is an important commercial crop of India and hence, reliable acreage and production estimation is most essential for taking up policy decisions. The coffee growing regions in India are mainly confined to the traditional South Indian states (Karnataka, Kerala and Tamil Nadu) and partly in non-traditional regions (Andhra Pradesh and Odisha) while to a smaller extent in North-Eastern states. Interpretation and mapping of coffee plantations using satellite data is quite challenging due to the diverse and complex cultivation practices. In the present study, multi-resolution and multi-source data was utilized for mapping of coffee plantations in the country. Temporal LISS-III (24.0m) data was used for characterizing the phenology of coffee and other competing plantation crops for selection of optimal high resolution satellite (HRS) datasets. Accordingly, Cartosat-1 (2.5m) and Resourcesat LISS-IV multispectral (5.0m) datasets corresponding to February-April months were utilized. The spectral signature of coffee plantations is determined by the age category of coffee plantations, varietal difference, density & composition of shade trees along with terrain features like slope and aspect. The plantations manifested in different tones of red and mottled texture on the multispectral image. Object oriented classification approach showed encouraging results in homogenous & contiguous areas but showed poor mapping accuracy in heterogeneous regions due to complex spectral signature and varying texture. Thus, a combination of digital and visual interpretation techniques were used for mapping of coffee plantations depending on the suitability. Feature space optimization function was used for selection of object parameters and 14 image features consisting of mean spectral values, standard deviation, NDVI, geometry and contextual parameters were used for classification of coffee plantations using Support Vector Machine (SVM). In case of small holdings and heterogeneous areas, interactive visual interpretation of HRS data at 1:5,000 scale using tone, texture, shape and terrain characteristics was carried out for mapping of coffee plantations with the help of ground truth and field experience of Liaison Officials of Coffee Board. Post-interpretation field verification/validation of the interpreted maps was carried out for the accuracy assessment and the overall mapping accuracy of better than 90.0 per cent was achieved in the study. Total area under coffee plantations was about 4.41 lakh ha (excluding N-E states). This is the first study in the country to generate coffee map at national level for creation of baseline geospatial database which could be updated periodically. Suitability analysis using pedo-climatic and terrain parameters is being carried to promote the coffee cultivation in the nontraditional regions. Further research efforts are necessary for varietal discrimination and modelling of...
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