INVENTORY OF HIGH VALUE CROPS USING LIDAR DATA AND GIS IN LANAO DEL NORTE PHILIPPINES

Abstract

One of the objectives of Republic Act 8435 or Philippines Agriculture and Fisheries Modernization Act of 1997 is to modernize the agriculture and fisheries sectors by developing it into a technology –supported division. In the Philippines, about 32% of the country's total land areas were agricultural lands. Mapping and delineating the agricultural crops are significant in the implementation of this Act. Moreover, having an updated and accurate inventory of the agricultural resources in an area is critical in the assessment, planning, and development specifically in providing the necessary government support to improve the agricultural system, like Farm-to-Market Roads (FMR), irrigation system, pre- and post-harvest facilities, among others. This study extracted agricultural crops like rice, corn, coconut, banana, and mango present in the province of Lanao Del Norte using Light Detection and Ranging (LiDAR) technology and Geographic Information System (GIS). LiDAR is an active remote sensing system with an illumination source from laser lights. Object-based image analysis (OBIA), multi-level support vector machine (SVM) classifier, and ground truthing were the general procedure in extracting and validating these crops. OBIA allows the segmentation of images derived from LiDAR datasets into meaningful objects. SVM classifier analyzes and identifies the pattern in the attributes of the segmented objects and classifies these objects based on its analysis. 71% of the extracted agricultural areas are planted with coconut, followed by banana with 13%, corn with 8%, rice constitutes 5%, and lastly mango with 3% of the total agricultural area. These extracted crops can now be utilized in updating the agricultural resource accounting of the province. Accuracy assessment was performed using 3,300 validation points gathered from the field and from orthorectified photos. Result of evaluating the accuracy shows that 80-90% of the crops were extracted and classified correctly. The accuracy assessment records an overall accuracy of 0.915 and a Kappa Index of Agreement (KIA) of 0.89147