Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI + NDSI + DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI + NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root ($\surd \mathrm{k}$), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI + NDSI + DEM parameters with the number of trees = 100, mtry = all variables ($\mathrm{k}$), and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.

Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.

Development gaps between regions are still a problem that occurs in several countries, including Indonesia. This condition causes an area to lag behind other regions, which ultimately results in poverty and low welfare. This is not in line with the concept of sustainable development, which emphasizes "no one left behind" with the existence of areas that are still left behind. This study aims to analyze the sustainability status of the underdeveloped regions in Nusa Tenggara Timur (NTT) Province, Indonesia, from economic, social, and ecological aspects and formulate alleviation strategies. The data used in this study was collected through secondary data obtained from the Ministry of Village Development of Disadvantaged Regions and Transmigration (Kemendesa), as well as statistical data from the Central Bureau of Statistics (BPS) Indonesia. Data was analyzed using Rapid Appraisal for Fisheries (RAPFISH) to determine its sustainability status and determine the best strategy for alleviating underdeveloped areas. The results showed that the economic dimension has a bad sustainability status compared to the other two dimensions, where there are only 3 districts with good status. Meanwhile, there are 7 districts with good status in the social dimension and 11 districts with good status in the ecological dimension. On the other hand, there are underdeveloped districts that have good sustainability in two dimensions at once. The most influential attributes of sustainability in each dimension include electrification, school participation, and disasters. The strategy for alleviating disadvantaged areas is through mainstreaming intervention programs in disadvantaged districts that have good sustainability on two dimensions and have the highest scores on each dimension. In addition, alleviation optimization is carried out through development planning, which leads to the most influential attributes in each dimension of sustainability.

1. Mangrove forests are known to play a crucial role in supporting the life of coastal and marine ecosystems, considered to be the most productive systems on earth (Conservation International 2023,...

Indonesia had a large diversity of coastal ecosystems. One part of the them is the coral reef. The concept of mapping coral reef ecosystems has been outlined in the RSNI document about the mapping of shallow marine waters. The aim of this study is to map shallow marine waters using the 1981 and 2006 lyzenga methods. The mapping was made based on three classes including coral reef, mixed seagrass and macroalgae, and substrate. The location of the study was conducted at Pemuteran Beach, Bali. The data used were Landsat 8 imagery acquisition on 14 April 2018. Stages of data processing include atmospheric correction, radiometric correction, pansharpening, masking, cropping, and water column correction and classification. Water column correction used the Lyzenga 1981 and 2006. Classification methods to distinguish objects of shallow marine waters using the unsupervised method. The results showed differences in the results of extraction of shallow marine waters information using the Lyzenga 1981 with the 2006 Lyzenga method. The extraction results with the Lyzenga 2006 method provide more detailed information in identifying the three classes of shallow marine waters. Indonesia memiliki keanekaragaman ekosistem pesisir yang cukup besar. Salah satu bagian dari ekosistem tersebut adalah ekosistem terumbu karang. Konsep pemetaan ekosistem terumbu karang telah dituangkan dalam RSNI tentang pemetaan habitat dasar perairan laut dangkal. Tujuan penelitian ini adalah untuk melakukan pemetaan habitat perairan laut dangkal dengan menggunakan metode lyzenga 1981 dan 2006. Pemetaan tersebut dibuat berdasarkan tiga kelas diantaranya: kelas terumbu karang, kelas campuran padang lamun dan makro alga, serta kelas substrat dasar. Lokasi penelitian dilaksanakan di Pantai Pemuteran, Bali. Data yang digunakan adalah citra Landsat 8 akuisisi 14 April 2018. Tahapan pengolahan data meliputi, koreksi atmosferik, koreksi radiometrik, proses pansharpening, proses masking darat air, cropping, serta koreksi kolom air serta klasifikasi. Koreksi kolom air menggunakan metode Lyzenga 1981 dan 2006. Klasifikasi untuk membedakan obyek habitat perairan laut dangkal menggunakan metode unsupervised . Hasil penelitian menunjukkan adanya perbedaan hasil ekstraksi informasi habitat perairan laut dangkal menggunakan metode Lyzenga 1981 dengan metode Lyzenga 2006. Hasil ekstraksi dengan metode Lyzenga 2006 memberikan informasi yang lebih detail dalam mengidentifikasi tiga kelas habitat perairan laut dangkal tersebut.