| dc.description.abstract | Karst area is generally has low agricultural productivity due to having poor soil. Intensive land use occured in the karst region of North Kendeng Mountains can be a threat for land degradation process or desertification in this region. The purpose of this study was to map land use, to assess land degradation by field, to assess vegetation index, to analyze land surface morphometry, and to assess dominant factor causing land degradation. Landsat 8 imagery was interpreted visually for producing land use map, while scoring and interpolation methods were used to assess land degradation degree. Several parameters for assessing it were the quality of vegetation, the rate of deforestation, the percentage of surface rocks, and the level of land management. The method of EVI, SAVI, II, TRVI, and NDVI were used for assessing the vegetation index through ENVI 4.5 software, whereas by SAGA software land surface morphometric has been analyzed, comprising slope steepnes, slope curvature, and surface roughness index. The decision tree analysis used for finding the dominant factor causing the land degradation. The results showed that the land use existing in the study area consists of forest (12.80%), mixed gardens (12.86%), settlement (4.66%), quary (0.13%), and dryland agriculture or tegalan (70.28%). The most dominant crop of tegalan was corn. Based on 71 sample points obtained, it indicated that the land degradation were dominantly taken place in western part of North Kendeng Mountain than the eastern part. According to the vegetation index, TRVI showed the best result for indicating the degraded land as shown of its high average value (0.2) and TRVI also exhibit better contrast color than others. The relationship between land surface morphometry and land degradation showed that the degraded land area was characterized by lower slope steepnes and have concave and not rude topography, otherwise the undegraded land were characterized by high slope steepnes and have convex and rude topography. According to decision tree analysis, TRVI was always on the primary node, while for combination of TRVI and morphometry showed that slope is in the second node. It indicates that TRVI and slope steepnes could be used for indicator for estimating land degradation in karst area. For study area its accuracy reaches 94.00%. | en |
