Analisis Perubahan Luas dan Kerapatan Hutan Mangrove menggunakan Citra Landsat 7 ETM+ dan Landsat 8 OLI di Segara Anakan, Cilacap tahun 2003-2018

Coastal ecosystems consist of mangroves, seagrasses and coral reefs. Segara Anakan Lagoon, Cilacap Regency has a huge mangrove ecosystem in coastal areas, but in the last few decades the mangrove area has been continouosly descreasing. Utilization of multitemporal imagery data can be applied to analyze the changes that occur, in this research using two Landsat imagery data with acquisition year 2003 and 2018. The purpose of this research was to determine the area and density changes of mangroves in Segara Anakan, Cilacap from 2003 to 2018 and to identify factors that cause changes in the area and density of mangroves. The method in this research used the multispectral classification of maximum likelihood for land cover data extraction from multitemporal images that had been corrected geometrically and radiometrically. Classification is divided into several sampling classes that focus on mangrove land cover. The classification of mangroves is used as a reference in making the density class of mangrove vegetation with the NDVI (Normalized Difference Vegetation Index) method. Field check is needed to know the percentage of mapping accuracy, which uses the purposive sampling method and confusion matrix table. The results showed data in mangrove area changes that occurred in the Segara Anakan lagoon in 2003 - 2018, including mangrove objects that did not change (still mangrove) for 6257,62 ha. While the degraded/decrease mangroves have result 2765,64 ha and mangrove growth in 2018 amounted to 2576,25 ha. The density of NDVI in the mangrove area in 2003 was dominant in the medium density class with result 5068,17 ha of the total mangrove area, while in 2018 it was still dominated by the medium density class but with decrease amout of 4375,57 ha. Accuracy assesment using confusion matrix for 51 samples data has result percentage value of 86,11% overal accuracy, so it can be expalined that result of the classification is accurate