Water catchment critical level identification for revitalization planning of the Luk Ulo watershed, Central Java, Indonesia

The Luk Ulo River watershed, the largest river in Kebumen Regency, owns some environmental issues, including its declining quality due to land degradation, land conversion, rapid urbanization, and mining of sand and rock for building material. Rapid population growth also triggers the change of the land functions from forest to built-up area. This massive land-use changing lead to the watershed decreasing potential to absorb a large amount of water optimally, since most of the rainwater will flow to the surface due to reduced forest as a barrier to the rate of surface water. Therefore, watershed potential and actual identification in term of water resources availability is urgently needed. The development of remote sensing technology and geographic information systems has a possibility to study the spatial pattern of water catchment areas in a wide range. This study aims to determine the potential and actual of water catchment areas in the Luk Ulo watershed. The potential of water catchment obtained from the result of overlay from rainfall, soil type (Secondary data) and slope data which derived from DEM SRTM 30 meters. The result of the potential water catchment area is overlaid with land use information extracted from Sentinel-2A imagery through visual interpretation and resulting the actual potential of water catchment. The results show that the condition of the potential natural water catchment area in the Luk Ulo watershed is dominated by moderate infiltration potential with an area of 42,462.87 Ha (65.170). Furthermore, for actual potential of water catchment areas dominated by good and quite critical conditions with an area of 24,979.85 Ha (38.23) and 22,896.96 Ha (35.80) 24,758.53 Ha (38.14). This research contributes to the potential assessment of watershed revitalization planning, especially to provide the estimation extent area followed by its spatial distribution. Data validation using field observation and secondary data will be ideal for future study to measure the model accuracy followed by giving us local knowledge of the study area. © 2021 SPIE.