Assessment of tree resources outside forest based on Remote Sensing Satellite Data  Dr. J.K. Rawat IFS, Director, Forest Survey of India, Dehradun Tel: 0135 2756139, Fax: 0135 2759104 Email: fsidir@vsn.com  Sh. Saibal Dasgupta IFS, Director, Forest Survey of India, Dehradun Tel: 0135 2754507, Fax: 0135 2754507 Email: saibaldasgupta@hotmail.com  Sh. Rajesh Kumar ISS, Director, Forest Survey of India, Dehradun Tel: 0135 2755042, Fax: 0135 2754507 Email: rajsus1@rediffmail.com Forest Survey of India, Kaulagarh Road, P.O.:IPE, Dehradun – 248 195 Abstract Assessment of Tree Resources Outside Forest Based on Remote Sensing Satellite Data Forests have gained an important place in international political scenario mainly because of the realization of its role in combating green house gases, carbon storage sink, biodiversity conservation, global warming etc. One of the management practices would be extensive tree growth outside forest areas for providing fuel, fodder and timber to the local people, which will also help in maintaining the ecological balance. Trees outside forests - i.e. trees available on agricultural land, along road, railways, canals, ponds, orchards, parks, gardens and homestead plays many role like forests. They make a critical contribution to sustainable agriculture, food security and rural household economies. Since there exists a large amount of wood resources outside the conventional forests, accurate information about forest resources is a pre-requisite for their proper management. To assess TOF resources, various initiatives have been undertaken world over, following different methodology. Taking advantage of multi spectral property of IRS LISS III and high resolution of corresponding IRS PAN, a methodology of TOF assessment has been developed. LISS III image has been classified into settlement, water bodies, burnt areas, tree cover and agriculture area using appropriate classifier viz. Maximum likelihood. To remove water bodies and dark surface features from PAN images, it was masked using classified image of LISS III. Classified image was visually analyzed with respect to fused images for editing and refinement for inclusion and omissions. Incorporating corrections, final classified image was prepared having three classes in TOF areas, namely, Block, Linear and Scattered. The methodology using digital image processing and geographical information system can be effectively employed using multi spectral and high-resolution satellite imageries to stratify the TOF resources in such a way that the classification system of TOF resource remains valid. In each stratum optimum number of randomly chosen sample points are laid out for ground survey, which will provide estimates of TOF resources. Since, this methodology enables resource-based stratification, it is expected to provide better estimates of TOF resources than the one generated through field survey only. |