Land Suitability Assessment for Cassava Production in Indonesia Using GIS, Remote Sensing, and Multi-Criteria Analysis

Abstract

Sustainable land use is essential for increasing the production of cassava as a diversified crop for ensuring food security in Indonesia. Understanding the spatial factors and criteria is required for locating suitable production areas to increase cassava production. In this study, a spatial model was developed to assess the suitability of land for supporting sustainable cassava production. The model was divided into three stages considering different criteria. First, satellite digital images were processed from Landsat-4 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 satellites to create vector data layers and a normalized difference vegetation index (NDVI) database. Second, a spatial analysis was performed to identify highly suitable areas for cassava production using a geographical information system (GIS) and the multi-criteria analysis including the analytical hierarchy process (AHP) and the analytical network process (ANP). Third, a sustainability evaluation was conducted based on land suitability information for a study period of 5 years. Land suitability assessment was performed to increase cassava production. We found that 43.11% (11,094 ha) of the study area was highly suitable for cassava production, whereas 30.87% (8233 ha) was moderately suitable and 9.83% (2623 ha) was marginally suitable with incorporating AHP analysis. Moreover, 17.69% (4718 ha) of the land was occupied by residents and settlements. On the other hand, ANP analysis also conducted to confirm the AHP results. Although many decision problems are studied through the AHP, however as the novelty in this study, ANP have added the better decision judgment based on the expert opinions. This research recommended that the integrated approach of GIS based on multi-criteria can be extended with satellite remote sensing vegetation datasets to assess the regional production and site-specific management of cassava crops.