The mangrove forest is a very productive ecosystem, both economically and ecologically. Mangrove forests can protect settlements, buildings, and agriculture on the coast from strong winds or seawater intrusion, support the livelihoods of coastal communities and store high carbon stocks. Regardless of its role, the Taman Hutan Raya Ngurah Rai Mangrove Forest continues to be threatened by human activities. This study aims to monitor changes in the area, health, and water quality of mangrove forests. The data used are Landsat 7 ETM + imagery and Landsat 8 OLI in 2010-2020. We used NDVI vegetation indices to analyze mangrove health and Total Suspended Solid (TSS) to investigate the water quality of the mangrove forest. Temporal water quality parameters are obtained by using the TSS algorithm on Landsat imagery. The results showed variations in mangrove forest area changes, NDVI values, and water quality. In 2010 - 2015 Mangrove Forest Area increase by 117.72 ha but decrease in 2015-2020 by 96.21 ha. Mangrove health was also improved in 2010-2015 but the decline in 2015-2020. Meanwhile, mangrove forest water quality seen from the TSS distribution tends to grow from 2010 to 2020.

Indonesia is a country that has natural geological disasters. One of the most frequent geological disasters in Indonesia is a landslide. Landslides are movements of rock or soil mass due to gravitational forces pulling down accompanied by a driving force on the slopes greater than the innate material. Identification and mapping of landslide potential areas has an important role as an effort to overcome and anticipate landslides. One of methods that can be used to determine potential landslides is the SMORPH method. The SMORPH method will produce a landslide classification based on a matrix between slope angle and slope form. This study aims to analyze the landslides potential distribution area in Kebumen Regency. Landslide identification was carried out at 30 locations of landslide events. Spatial analysis of landslide potential is done by overlaying the slope angle with slope form. The potential level for low landslides has a percentage of 79.54% in the southern and central parts of Kebumen Regency, the level of moderate potential level with a percentage of 8.81% randomly scattered over the Kebumen Regency, and 11.65% of high potential level in the north and southwest of Kebumen Regency.

AbstrakPantai Karst Kabupaten Gunungkidul yang terletak di selatan Pulau Jawa tepatnya di Provinsi Daerah Istimewa Yogyakarta (Pantai Baron, Kukup, Sepanjang, Drini, Krakal, Ngandong, dan Sundak) memiliki potensi ekonomi yang besar. Pantai-pantai tersebut memiliki persamaan dalam hal kondisi geologi (genesa, dan proses pembentukan morfologi pantai), namun untuk setiap pantainya memiliki karakteristik fisik lingkungan yang berbeda-beda. Metode yang digunakan dalam penelitian ini adalah metode analisis spasial deskriptif. Dengan mengetahui dan memahami karakteristik fisik pantai karst tersebut, selanjutnya akan dapat diketahui bagaimana pemanfaatannya yang paling sesuai. AbstractPhysical Characteristics and Utilization oft the Karst Beaches in Kabupaten Gunungkidul. Karsts beaches at Gunungkidul Regency (Baron, Kukup, Sepanjang, Drini, Krakal, Ngandong, and Sundak), located in south of Daerah Istimewa Yogyakarta Province, have economic potential. Even they have similarity in geological condition (genesis and morphological processes), but they have different physical characteristics. Based on result of observation environment on morphological conditions and spatial analysis method, we found the proper utilization of the beaches. Keywords: karst beach, physical characteristics, spatial analysis method, utilization of the beaches PendahuluanIndonesia merupakan negara kepulauan terbesar di dunia, dan memiliki panjang pantai 95.181 km menempati posisi ke-4 setelah Kanada, Amerika Serikat, dan Rusia [1]. Pantai di Indonesia menawarkan beragam keindahan yang bernilai jual tinggi untuk kegiatan pariwisata, olahraga kebaharian, dan sangat potensial bagi pengembangan ekonomi nasional baik karena potensi ruang dan kekayaan alamnya maupun nilai estetikanya. Walaupun memiliki potensi yang besar, kegiatan ekonomi penduduk Indonesia di wilayah pantai masih berorientasi ke daratan [2].Kepadatan penduduk Pulau Jawa adalah yang tertinggi di Indonesia, merupakan ancaman bagi lingkungan pantai yang peka. Pencemaran dan gangguan dapat mempengaruhi pantai sebagai akibat pembangunan perkotaan, permukiman, perikanan dan pelabuhan serta pengrusakan lain yang mungkin ditimbulkan oleh pemanasan global. Kondisi tersebut juga berpotensi terjadi pada pantai di Provinsi Daerah Istimewa Yogyakarta (Prov. DIY) yang terletak di selatan Pulau Jawa. Prov. DIY memiliki kondisi pantai yang berbeda jika dibandingkan kondisi pantai di utara Jawa, baik dalam hal lokasi (menghadap ke Samudera Hindia), geologi dan bathimetri. Informasi akan karakteristik lingkungan pantai sangat diperlukan agar kekayaan alam dapat dimanfaatkan sejalan dengan konsep pembangunan berkelanjutan. Dengan demikian perubahan tataguna ruang diharapkan tidak melebihi daya dukungnya.Pantai di Kabupaten Gunungkidul Prov. DIY, memiliki karakteristik dan potensi ekonomi yang sangat khas dan berbeda dengan daerah lainnya. Pantai-pantai karst di Kabupaten Gunungkidul tepatnya Pantai Objek Wisata Baron, Kukup, Sepanjang, Drini, Krakal, Ngandong, dan Sundak memiliki persamaan dalam hal kond...

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Development and economic growth in an area can cause land cover changes. Penajam Paser Utara Regency, as a new capital candidate, is also predicted to experience in land cover changes. Land cover changes that are not following the land’s potential will cause environmental problems, so it is necessary to predict land cover changes by looking at patterns of land cover changes in the past and the factors that influence it. The purpose of this study is to analyze and predict the land cover change in Penajam Paser Utara Regency in 2031. The method used in this study is modeling using Cellular Automata - Markov. The driving factor of land cover change is used in making prediction models such as distance from the center of activity, distance from the road, distance from the river, elevation, and slope. The prediction land cover changes show that there has been an increase in plantation area and a decrease in forest area, while the development of the built-up area is not visible. The kappa test for predicted land cover showed perfect results. The resulting land cover model can be used to formulate land-use policies.