Mangroves are one of the most productive ecosystems for human life, marine ecosystems, and coastal areas. Mangrove distribution is a distribution based on specific geographical or administrative boundaries. Kota Langsa is one of the areas that has a good representation of the distribution of mangroves. Therefore, researchers studied the Kota Langsa area because Kota Langsa is one of the areas with the largest and most diverse mangrove ecosystem in Aceh Province. This study examines the mapping of mangrove distribution using Sentinel-2A multispectral imagery with composite images of Red, Green, and Blue. This research uses SNAP software. The research stages consist of radiometric correction, atmospheric correction, and multispectral image classification. The method used in image classification is the maximum likelihood algorithm. The use of the maximum likelihood algorithm is because the maximum likelihood algorithm gives the best results among other algorithms. The development of the research is the distribution of mangroves in Langsa City, covering an area of 4727.35 ha, which is divided into three sub-districts and eleven gampong (kelurahan). The sub-districts that have mangrove distribution are East Langsa District covering an area of 3240.25 Ha (68.55%), Langsa Barat District covering an area of 1486.47 Ha (31.45%), and Langsa Lama District covering an area of 0.63 Ha (0.013).
Mangrove forest is one of the essential components of natural ecosystems. Mangrove forests have various essential functions, such as holding land sediments, tsunamis, and ocean waves, storing large amounts of carbon, and providing other benefits for coastal and land areas. However, the conversion of mangrove forests has reduced and degraded mangrove land. Therefore, monitoring and conserving land changes in mangrove forests must be carried out to determine the effects on land ecosystems and coastal areas. Remote sensing has the spatial ability to analyze changes in mangrove ecosystems in coastal regions temporally because it has the advantage of using satellite imagery data. This study compares the classification method using multiple image sensors to analyze land cover changes in mangrove forests in North Luwu Regency, South Sulawesi, in 2015-2020. The technique used in this research is the classification of Object-Based Image Analysis (OBIA) and the variety of Maximum Likelihood. The results of Sentinel-1 image analysis using Maximum Likelihood provide information on changes in mangrove land cover during 2015-2020 with an area of 449.17 (Ha), while the results of Landsat 8 analysis using (OBIA) provide information on changes in mangrove land cover 596 (Ha).Keywords: Optics, Radar, OBIA, Maximum Likelihood, mangrove
Abstrak. Tanah, air, udara merupakan sumber daya alam utama yang sangat penting dalam kehidupan terutama dibidang pertanian. Oleh karena itu keadaan tanah harus selalu dijaga dan dilestarikan agar dapat dimanfaatkan sesuai dengan fungsinya begitu juga dengan air dan udara yang berpengaruh terhadap pembentukan tanah. Penelitian ini bertujuan untuk mengetahui tingkat permeabilitas tanah terhadap erosi di Kecamatan Kota Jantho Kabupaten Aceh Besar. Metode penelitian menggunakan metode survei yang didasarkan pada hasil pengamatan di lapangan dan analisis tanah di laboratorium, sedangkan analisis spasial menggunakan SIG dengan konsep Interpolasi. Hasil pengamatan di wilayah kajian didapatkan 4 kriteria tingkat permeabilitas yaitu sangat lambat, agak lambat, lambat, dan sedang.Spatial Distribution Of Land Permeability at Kota Jantho Sub-distrik Aceh BesarAbstract. Land, water, air are the most important natural resources in life, especially in agriculture. Therefore the condition of the soil should always be maintained and preserved in order to be utilized in accordance with its function as well as water and air that affect the formation of soil. This study aims to determine the level of soil permeability to erosion in Kecamatan Kota Jantho Kabupaten Aceh Besar. The research method used survey method based on field observation and soil analysis in laboratory, while spatial analysis using GIS with Interpolation concept. The result of observation in the study area found 4 criteria of permeability level that is very slow, somewhat slow, slow, and medium.
Mangroves can store carbon effectively with a value of about 1,023 Mg C/Ha and become one of the richest forests that store 4-20 billion tons of blue carbon globally. Remote sensing imagery can be used to map mangrove surface carbon stocks using radar and optical image sensors. Generally, forest carbon on earth is stored in two places, namely above the surface (Above Ground Carbon, AGC) and below the surface (Below Ground Carbon, BGC). This study aims to estimate the surface carbon stock of mangroves using multisensory imagery using the Random Forest method in the Clungup Mangrove Conservation (CMC) area, Malang Regency, East Java. Four vegetation indices (IRECI, NDI45, NDVI, SAVI), single band, and VV VH polarization were used as predictive variables. Estimating the carbon stock mangrove value using Sentinel-1 imagery produced 2,126 tons of C with R² 0.11. Meanwhile, Sentinel-2 produces an estimated carbon value of 2,025 tons C with an R² of 0.22. The estimation model using Sentinel-2 shows a better evaluation value with a Root Mean Squared Error (RMSE) of 0.89 and a Mean Absolute Error (MAE) of 0.75. The IRECI vegetation index is the most important variable in estimating carbon stocks. The results of the mapping accuracy of the Sentinel-1 model show a value of 34.73% and Sentinel-2 35.03%.Keywords: Mangrove, Carbon, Sentinel-1, Sentinel-2, Random Forest
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