Analisis Degradasi Penutup Hutan Di Perkotaan Menggunakan Model Forest Canopy Density Studi Kasus : Kota Bandar Lampung

Salah satu faktor utama terjadinya perubahan iklim yang sedang berlangsung saat ini adalah akibat emisi yang ditimbulkan oleh degradasi hutan, yaitu mencapai sekitar 20% dari seluruh emisi Gas Rumah Kaca (GRK). Di Indonesia, degradasi hutan salah satunya banyak terjadi di kawasan perkotaan, tak terkecuali di Kota Bandar Lampung. Mengingat peran hutan yang begitu vital, banyak bidang-bidang keilmuan yang diaplikasikan untuk mengamati fenomena degradasi hutan, tak terkecuali teknologi penginderaan jauh (inderaja). Salah satu metode pengolahan citra yang sering diaplikasikan untuk mengamati hutan adalah model Forest Canopy Density (FCD). FCD merupakan suatu model yang dikembangkan oleh Atsushi Rikimaru untuk keperluan analisis dan pemantauan perkembangan hutan secara kuantitatif. Dari hasil pengolahan data dan analisis, antara rentang tahun 2009 hingga tahun 2015, Kota Bandar Lampung mengalami degradasi hutan sebesar 1002,75 ha. Meskipun demikian, secara keseluruhan degradasi terjadi pada kawasan budidaya yaitu mencapai 92,03%, sedangkan kawasan lindung hanya terdegradasi sebesar 7,97%. Selain itu, terdapat beberapa wilayah teridentifikasi mengalami peningkatan persentase penutup hutan, diantaranya terdapat pada kawasan hutan, permukiman dan pesisir pantai

Analisis Spasio-temporal Kekeringan Pada Lahan Sawah di Lampung Selatan Berbasis Pengolahan Normalized Difference Drought Index Pada Citra Satelit Landsat 8

Kekeringan pada lahan pertanian merupakan kondisi berkurangnya kandungan air dalam tanah sehingga tidak mampu memenuhi kebutuhan tanaman tertentu dalam periode tertentu. Pada November 2019 Kabupaten Lampung Selatan mengalami kekeringan pertanian yang menyebabkan terjadinya kegagalan panen lahan sawah seluas 1300 Ha. Citra Landsat 8 merupakan salah satu data penginderaan jauh sistem optis yang dapat digunakan untuk mengidentifikasi kekeringan padi menggunakan metode indeks kekeringan. Indeks kekeringan Normalized Difference Drought Index (NDDI) adalah salah satu metode yang digunakan untuk mengetahui tingkat kekeringan suatu wilayah berdasarkan parameter NDVI dan NDWI. Penelitian ini bertujuan untuk mengidentifikasi dan mengestimasi luas kekeringan yang telah terjadi di Kabupaten Lampung Selatan dengan menerapkan indeks NDDI. Hasil dalam penelitian ini menunjukkan bahwa kekeringan yang terjadi di Kabupaten Lampung Selatan pada Juli 2019 sampai Desember 2019 dapat teridentifikasi dengan akurasi sebesar 88,1% dan tingkat kelas kekeringan yang bervariasi. Puncak kekeringan ringan dan kekeringan sedang terjadi pada Juli 2019 dengan luas 10019,43 Ha dan 4539,94 Ha, puncak kekeringan berat dan ekstrem pada Desember 2019 yaitu 1012,26 Ha dan 2463,96 Ha. Peningkatan kekeringan ekstrem pada bulan November dan Desember 2019 diduga karena akumulasi dari rendahnya curah hujan pada beberapa bulan sebelumnya

Monitoring Biochemical Oxygen Demand (BOD) Changes During a Massive Fish Kill Using Multitemporal Landsat-8 Satellite Images in Maninjau Lake, Indonesia

Maninjau Lake is one of Indonesia's lakes for hydroelectric power plants, tourism, and fish farming activities. Some activities around the lake cause pollution, leading to massive fish kill. Therefore, it is necessary to monitor water quality regularly. One of the critical water quality parameters is biochemical oxygen demand (BOD). This study aimed to analyze BOD changes using a remote sensing approach during massive fish kills in Maninjau Lake, Indonesia. Multi-temporal Landsat-8 satellite images are processed to estimate the BOD level based on Wang Algorithm. After that, the estimated BOD value is validated using in situ data measurement. The results of the average BOD concentration that occurred in Lake Maninjau was 1.85 mg/L and showed that R2 was 0.8334, and the standard error was 0.076 between the estimated BOD and in situ data. Furthermore, the average concentration of BOD obtained on 23rd August 2017, 13th December 2017, 30th January 2018, 19th March 2018, and 7th July 2018 are 4.96 mg/L, 4.82 mg/L, 5.31 mg/L, 6.94 mg/L, and 6.60 mg/L, respectively. Increased BOD concentration in January 2018 indicates moderate pollution in the waters. BOD concentration increases after the massive fish kill due to the decaying fish across the lake

Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies

Assessing Potential Climatic and Human Pressures in Indonesian Coastal Ecosystems Using a Spatial Data-Driven Approach

Blue carbon ecosystems are key for successful global climate change mitigation; however, they are one of the most threatened ecosystems on Earth. Thus, this study mapped the climatic and human pressures on the blue carbon ecosystems in Indonesia using multi-source spatial datasets. Data on moderate resolution imaging spectroradiometer (MODIS) ocean color standard mapped images, VIIRS (visible, infrared imaging radiometer suite) boat detection (VBD), global artificial impervious area (GAIA), MODIS surface reflectance (MOD09GA), MODIS land surface temperature (MOD11A2), and MODIS vegetation indices (MOD13A2) were combined using remote sensing and spatial analysis techniques to identify potential stresses. La Niña and El Niño phenomena caused sea surface temperature deviations to reach −0.5 to +1.2 °C. In contrast, chlorophyll-a deviations reached 22,121 to +0.5 mg m−3. Regarding fishing activities, most areas were under exploitation and relatively sustained. Concerning land activities, mangrove deforestation occurred in 560.69 km2 of the area during 2007–2016, as confirmed by a decrease of 84.9% in risk-screening environmental indicators. Overall, the potential pressures on Indonesia’s blue carbon ecosystems are varied geographically. The framework of this study can be efficiently adopted to support coastal and small islands zonation planning, conservation prioritization, and marine fisheries enhancement

Klasifikasi Penginderaan Jauh Berbasis Time Series Menggunakan Multi-Layer Perceptron (MLP) Untuk Pemetaan Jenis Tanaman (Studi Kasus: Desa Girimulyo, Lampung Timur)

Penerapan metode klasifikasi time series penginderaan jauh dengan deep learning merupakan metode inovatif yang mampu mengekstrak informasi penting dari banyaknya data observasi bumi yang beragam dengan cepat dan akurat. Penelitian ini menyajikan metode klasifikasi berbasis time series menggunakan Multi-Layer Perceptron (MLP) untuk pemetaan jenis tanaman sebagai upaya untuk mendukung ketahanan pangan dengan menyediakan produk tutupan lahan berupa peta jenis tanaman. Penelitian ini menggunakan data citra Sentinel-2A dan 150 sampel berupa koordinat titik dari lima kelas yang disimpan dalam bentuk data cube teregulerisasi sebagai dasar informasi untuk pembentukan model klasifikasi menggunakan MLP. Berdasarkan hasil penelitian, jenis tanaman pada Desa Girimulyo diklasifikasikan ke dalam lima kelas klasifikasi yakni kelas jagung dengan luas 22,2 km2, kelas tanaman lain dengan luas 9,9 km2, kelas pisang dengan luas 6,3 km2, kelas kelapa dengan luas 3,7 km2, dan kelas non-tanaman dengan luas 2,8 km2. Hasil penelitian ini menunjukkan bahwa metode klasifikasi yang digunakan efektif untuk memetakan jenis tanaman di wilayah studi dibuktikan dengan nilai overall accuracy dari citra terklasifikasi yang mencapai 83%. Penelitian selanjutnya diharapkan dapat dilakukan dengan jumlah sampel yang lebih banyak pada wilayah studi dengan jenis tanaman yang lebih beragam

Association between Surface Air Temperature And Land Use On The Campus Scale

The increasing trend of global temperature is related to the land use change in the form of urbanization. The impact of land use change to surface air temperature in Indonesia especially in smaller scope in Indonesia have not researched yet. The study area is located on newly built campus and the development of land use change inside campus can be managed carefully. This research aim is to determine which land use affecting high-temperature by using multiple linear regression method with least square approach so that temperature increase can be controlled in which some land uses must be preserved in urbanization. Land use data is interpreted from the photo map of 275 hectare campus. Temperature data is measured by using the digital thermometer three times a day. The method idea is to obtain distinctive contribution of every land use to every temperature measurement point. The contribution follows the inverse distance weighted concept. Surface air temperature measurement points are located with 150 meter interval and centroids of land use polygons are used for association calculation. Temperature measurement shows values between 25.5oC and 35.4oC. Land use with more anthropogenic activities and rubber plantation are the top contributors to high surface air temperature within a day. In the non-built-up land use category, water body increases the temperature in the daytime. Anthropogenic activities and vegetation density within land use is the main factor in increasing the surface air temperature so that it is suggested to plant farm-like vegetation around every built-up land use

Modeling of tsunami run-up using terrain model data based on photogrammetry processing product (case study at Way Muli Village, Rajabasa, South Lampung)

Photogrammetry has become a trend in large-scale mapping today. The ability to produce large-scale geospatial products in a relatively short time and low cost is very beneficial for mapping. The ability of high temporal and spatial resolution also makes photogrammetry used in the disaster mapping process. In this study, the DEM approach from photogrammetry was used for input data in tsunami run-up modeling activities in Way Muli Village. High temporal and spatial capabilities are utilized for the production of surface roughness and elevation which are key parameters for rigorous inundation modeling. The modeled inundation results show that the run-up limit achieved in residential areas is on the main road with a maximum distance of inundation from the shoreline is an average of 80 m. The results obtained can be used by the village government to preparedness in dealing with the tsunami

Potential Loss of Ecosystem Service Value Due to Vessel Activity Expansion in Indonesian Marine Protected Areas

Sustainable Development Goal (SDG) number 14 pertains to the preservation of sustainable marine ecosystems by establishing marine protected areas (MPAs). However, studies have reported massive damage to Indonesian marine ecosystems due to shipping pollution, anchors, and fishing nets. Thus, this study estimated the potential loss of ecosystem service value due to vessel activity expansion in the MPAs of Indonesia. This study was divided into three stages. The first stage is vessel activity expansion zone modeling based on kernel density. The second stage is marine ecosystem service value modeling through semantic harmonization, reclassification, and spatial harmonization. The last stage is the overlay of the vessel expansion zone model, marine ecosystem service value model, and the MPA of Indonesia. The results of this study indicate that the marine neritic zone of Indonesia has an ecosystem service value of USD 814.23 billion, of which USD 159.87 billion (19.63%) are in the MPA. However, the increase in vessel activity that occurred in 2013–2018 could potentially lead to the loss of the ecosystem service value of USD 27.63 billion in 14 protected areas. These results can assist policymakers in determining priority conservation areas based on the threat of vessel activity and value of ecosystem services