Remote Sensing and GIS techniques for forest resource monitoring and agriculture potentiality

Remote Sensing (RS) and Geographic Information System (GIS) has demonstrated itself as a very powerful tool in forest and agricultural research and natural resource management. This study proposes an empirical methodology for analyzing and mapping of forest resource and agriculture potentiality using the RS and GIS techniques. The study is carried out over Morobe province in the Papua New Guinea. The forest resource monitoring, mapping and change analysis have been carried out using two sets of satellite data during 2005 to 2010 based on hybrid maximum-Normalized Differential Vegetation Index (NDVI) and minimum-red compositing technique. The paper also examines multi-criteria decision approach to determine rice cultivation suitability based on different variables, like topography, physical and chemical soil properties, climate and land accessibility that are mandatory inputs to land suitability model. These parameters are generated from Shuttle Radar Topography Mission (SRTM) data, soil data base, monthly and annual temperature and rainfall data, respectively. ArcGIS v-10 and Erdas Imagine v-11 model builder are used to construct the index model for agriculture suitability analysis. The entire study area has been classified into five categories of rice suitability. The result indicates that only four percent (4%) land can be demarcated as `very high' and twenty one percent (21%) as `high' suitability categories in the study area and the spatial expanse of all the five categories within the province are mapped and displayed.