Taman Nasional Ujung Kulon (TNUK) merupakan taman nasional tertua yang berada di Pulau Jawa dan diresmikan sebagai salah satu warisan dunia oleh UNESCO untuk melindungi satwa terancam punah yaitu badak jawa (Rhinoceros sondaicus). Akan tetapi, adanya area pertanian padi milik masyarakat setempat di dalam kawasan TNUK yang merupakan salah satu ancaman yang dapat mengakibatkan terfragmentasinya kawasan hutan TNUK. Hal ini diproyeksikan akan berdampak terhadap upaya perlindungan habitat badak jawa serta satwa terancam punah lainya. Oleh karena itu, teknologi geospasial dilibatkan dalam proses identifikasi area pertanian di dalam kawasan konservasi TNUK. Pada penelitian ini menggunakan sumber citra Sentinel-2 MultiSpectral Instrument (MSI) dan proses analisisnya melalui platform berbasis cloud computing Google Earth Engine (GEE). Area pertanian diidentifikasi menggunakan algoritma machine learning berupa Random Forest (RF) dan algoritma Indeks seperti MNDVI, EVI, SAVI, IBI, ARVI, SLAVI, NDBI, LSWI, MNDWI, dan ANDWI. Klasifikasi menunjukan bahwa terdapat 1.556,82 ha (2,54%) lahan pertanian padi milik masyarakat yang tumpang tindih dengan batas kawasan hutan konservasi TNUK. Nilai akurasi yang didapatkan dari integrasi data geospasial ini berkisar di angka 93,00 (OA) dan 0,87 (KS) sehingga dapat mengestimasi luasan ekspansi area pertanian dengan tepat. Area pertanian padi ini menjadi permasalahan yang sangat serius terutama pihak TNUK dan masyarakat setempat. Oleh karena itu, permasalahan ini membutuhkan solusi yang mempertimbangkan fungsi dari taman nasional dan kesejahteran masyarakat setempat terutama para petani di dalam kawasan TNUK. Diharapkan dari penelitian ini dapat menjadi bahan pertimbangan bagi pemerintah setempat dan sebagai referensi bagi penelitian selanjutnya.

Mangrove ecosystem is a very potential area, generally located in ecoton areas (a combination of intertidal and supratidal areas), where there is interaction between waters (sea, brackish water, and rivers) with land areas. Indonesia, especially the Banten and West Java regions, have vast mangrove areas and are currently under threat of land conversion. Moreover, mapping the distribution of mangrove forests using the Google Earth Engine platform based on Cloud Computing is less published. Therefore, this research was conducted by introducing the distribution of mangrove forests which involved the Random Forest (RF) classification algorithm method, and looking for the best modification of the index. The combination test was carried out by involving the NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, and LSWI indexes. There is a distribution of mangroves in three provinces (West Java, Banten, and Jakarta) which are 933.54 ha (8.372%), 1,537.89 ha (18.231%), and 8,184.82 ha (73.397%). Of the 70 combination tests, the LSWI index (K13, Type-A) is the combination with the lowest accuracy rate of 58.45% (Overal Accuracy) and 39.59 (Kappa statistic), and the combination of K23 (SAVI-MNDWI-IBI) is a combination the best are 96.48% and 92.79. The results and recommendations in this study are expected to be used as a reference in determining policies for the protection of mangrove areas and a reference for further research

The Ujung Kulon National Park (UKNT) is one of the national parks on the island of Java and has an essential role in saving endemic species in Indonesia. As a form of national park conservation effort, the completeness of LULC spatial data is a primary database that is indispensable in determining national park management policies. Therefore, this research was conducted to map the LULC (Land Use - Land Cover) in the forest landscape with high heterogeneity in UKNT. Sentinel-2 MSI (Multispectral Instrument) image data were classified using the Random Forest (RF) classification algorithm and tested using 11 index algorithms. The classification process takes place on a cloud computing-based geospatial platform, Google Earth Engine (GEE). This test resulted in 10 LULC classes; water had the broadest percentage of 45.44%. Meanwhile, the primary forest has an area of 21,868.41 or about 19.53% of the total area of the national park. However, there are some discrepancies in the spatial information generated by this classification process, so it is considered necessary to evaluate the combination of indexes, training data, and classification algorithms to limit the classification area. Therefore, this study is expected to be considered for further research related to LULC in high-heterogeneity landscapes.

Every year, land use in Indonesia has increased, both for settlements, agriculture, and other uses that are used to meet the needs of human life for certain purposes. Cijengkol Village is one of the agricultural development villages in Subang Regency and is affected by topography, resulting in different types of land use. This mapping aimed to provide information related to the classification of land use for settlements, agriculture, plantations, fields, and others in Cijengkol Village. Land use mapping was carried out in this village to reveal the distribution of land use so that it could be taken into consideration, as well as directions for determining spatial planning by the local government. Therefore, this mapping was carried out by involving the Sentinel-2 MultiSpectral Instrument (MSI) image data source and processed using a cloud computing-based Google Earth Engine (GEE) platform. Six spectral scoring index algorithms exist the Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Specific Leaf Area Vegetation Index (SLAVI), Index-Based Built-Up Index (IBI), Normalized Difference Built-up Index (NDBI), and the Normalized Difference Water Index (NDWI). The results of the random forest (RF) classification algorithm resulted in six types of land use with percentages, namely mixed gardens (39.69%), agriculture (34.08%), homogeneous gardens (13.57%), residential (10.58%), open land (2.09%), and water bodies (0.001%). Image classification in this mapping also produces an accuracy rate of 82.43% (Overall Accuracy) and 0.78 (Kappa Statistics). The results of this research are of a good level of accuracy, so it is hoped that this research will become a database for the local village government and become a reference for further research.