Abstract
Abstract. Agroforestry is an integrated self-sustainable land use management system that is not only capable of producing food from marginal agricultural land but also capable of maintaining and improving the quality of environment. Accurate assessment of trees on farmlands i.e. agroforestry would help in determining their contribution in meeting timber demand and also in carbon sequestration vis-a-vis climate change mitigation. In the present, high resolution multispectral satellite imagery (LISS-IV) has been used for mapping and estimating agroforestry area in Koraput district of Odisha. Both supervised and Object based Image Analysis (OBIA) classifications methods have been applied. In case of supervised maximum likelihood method, those pixels are fully captured where trees exist, whereas in OBIA captures trees according to their crown shapes. This proved OBIA method to be better in identification of trees on farmlands (scattered trees, boundary, and block plantations) than supervised method. This can lead to accurate estimation of area under trees in scattered form, in linear form and also in patch form. Improved results were obtained in case of OBIA classification with more than 90% accuracy. This research implies that remote sensing provide promising tools for evaluating and mapping of agroforestry at district level. Hence, the proposed approach of using high resolution remote sensing data in conjunction with OBIA method would be promising for mapping agroforestry area.
Highlights
Agroforestry is a land use management system where trees are grown around and among the agricultural crops
Very good accuracy of 91.2% was achieved in agroforestry class by Object based Image Analysis (OBIA) classification
The results obtained by maximum likelihood classifier (MLC) and OBIA methods were compared in case of agroforestry (Figure 5)
Summary
Agroforestry is a land use management system where trees are grown around and among the agricultural crops. Remote Sensing has become an effective tool for mapping and monitoring of agriculture, forestry, and other natural resources. Geo-spatial technologies viz. Geographic Information System (GIS), Global Positioning System (GPS) and satellite Remote Sensing (RS) are being widely used in agriculture, forestry, watershed, natural resource management (Rizvi et al 2013a). The objective of this study was to explore the potential of high resolution data for identification of trees on farmlands and delineation of agroforestry. For this purpose, supervised and object oriented method of classification have been applied and results were compared
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