RESOURCE MAPPING OF HIGH VALUE CROPS IN CAVITE AND DEVELOPMENT OF THE ALGORITHM FOR DETECTING COCONUT, SUGARCANE, AND RICE USING LIDAR DATA

摘要

included as the most high value crops in the world. The demand for the production of crops is also rising given that food is one of the basic human necessities. The Philippines has a vast number of agricultural resources. However, monitoring is one of the problems in agricultural industry. Due to the fast paced economy and rapid land use and land cover changes; it is mostly important to produce detailed resources maps. This study investigated the prospective of LiDAR data that provides explicit information in delineating land use and land cover. Nevertheless, considering the labor and cost of providing the whole area with LiDAR data might be very challenging; hence, this study developed methodologies to generate maps using LiDAR data and satellite imagery. The optimization of the classification has been applied in image analysis with both qualitative and quantitative measures using Support Vector Machine. The utilization of the features has been described in this study. Furthermore, the study presented the performance of pixel-based and object-based classification. The experiments conducted in six different areas in the province of Cavite. Results show that pixel based algorithm provide higher result than object based given that the classes are in spatially large. Nevertheless, object-based classification provided detailed information with implicit information of the classes in the area.