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"Mangrove memiliki peranan penting bagi ekosistem pesisir, salah satunya penyumbang stok karbon (blue carbon). Isu pemanasan global dapat di kurangi dengan fungsi mangrove dalam penyimpanan karbon dan Kota Surabaya memiliki mangrove yang cukup luas di wilayah pesisir nya. Tetapi ancaman degradasi lahan mangrove di Kota Surabaya diakibatkan oleh aktivitas pembangunan, hal tersebut berkaitan dengan penurunan fungsi mangrove sebagai penyimpan karbon. Tujuan penelitian ini untuk memetakan tutupan lahan dan persebaran stok karbon serta perubahannnya pada tahun 2015 – 2022 di Pesisir Timur Kota Surabaya. Penelitian ini menggunakan citra Sentinel 2 tahun 2015 & 2022. Dalam mengestimasi persebaran dan nilai stok karbon mangrove, analisa dilakukan dengan pendekatan indeks vegetasi yang melalui uji statistik untuk mendapatkan model persebaran. Penghitungan biomassa mangrove lapangan dilakukan dengan cara Non-Destruktif dan menggunakan persamaan alometrik. Hasil penelitian menunjukkan bahwa IPVI sebagai indeks vegetasi yang lebih akurat dibanding TVI dan mRE-SR, dengan nilai korelasi R2 = 0,8072 dan nilai stok karbon mangrove rata rata mengalami pertambahan dari tahun 2015 – 2022 sebesar 50 – 100 kg/piksel. Pertambahan nilai stok karbon mangrove cenderung pada wilayah yang berubah tutupan lahan menjadi mangrove dan kerapatan yang semakin tinggi, sedangkan penurunan nilai stok karbon terjadi pada wilayah yang terjadi degradasi lahan mangrove dan terserang hama.......Mangroves have an important role for coastal ecosystems, one of which is a contributor to carbon stocks (blue carbon). The issue of global warming can be reduced by the function of mangroves in carbon storage and the City of Surabaya has quite extensive mangroves in its coastal areas. But the threat of degradation of mangrove land in the city of Surabaya is caused by development activities, this is related to the decline in the function of mangroves as a carbon store. The purpose of this research is to map land cover and the distribution of carbon stocks and their changes in 2015 – 2022 in the East Coast of Surabaya City. This study used Sentinel 2 imagery for 2015 & 2022. In estimating the distribution and value of mangrove carbon stocks, the analysis was carried out using the vegetation index approach through statistical tests to obtain a distribution model. Calculation of field mangrove biomass was carried out in a non-destructive manner and using allometric equations. The results showed that IPVI as a vegetation index is more accurate than TVI and mRE-SR, with a correlation value of R2 = 0.8072 and the average mangrove carbon stock value has increased from 2015 – 2022 by 50 – 100 kg/pixel. The increase in the value of mangrove carbon stocks tends to be in areas where land cover has changed to mangroves and the density is getting higher, while the decrease in the value of carbon stocks occurs in areas where mangrove land degradation has occurred and pest attacks."

"ABSTRACTSentinel 2 is a satellite launched by a collaboration between The European Commission and the European Space Agency in the Global Monitoring for Environment and Security program. The satellite has a mission to scan the Earth surface simultaneously at an angle of 180 each satellite with a 5 day temporal resolution wilh the same appearance on the equator and has a spatial resolution of 10m, 20m, and 60m. There are multispectral channels including VNIR and SWlR. Four cnannels with 10 m spatia! resolution adapt with SPOT4 or 5 and user comply requirements for land cover classfication. Six channels with 10 m spatial resolution becomes a requirement for other Level 2 processing parameters. Channels with 60m spatial resolution are specified for atmospheric correction and cloud filtering. Based on these specifications, Sentinel 2 can be an alternative for users to obtain image data with spatial, temporal, radiometric, and spectral resolution is better than SPO and Landsat. Sentinental 2 can be downloaded for free nd easy by the general public. The existence of image by Sentinel 2 is expected to be used optimally, especially for remote sensing analysis in marine field."

"Tanaman kelapa sawit merupakan salah satu komoditas yang tumbuh baik di Indonesia dengan nilai komersial tinggi yang membuat permintaan hasil olahan minyak tanaman kelapa sawit semakin meningkat, maka perlu adanya data dan teknologi untuk mengestimasi produktivitas tanaman kelapa sawit secara lebih efisien. Salah satunya dengan penginderaan jauh menggunakan citra Sentinel-2. Penelitian ini bertujuan untuk menganalisis tingkat akurasi algoritma NDVI, ARVI, dan SAVI, variasi spasial serta temporal, dan hubungan kondisi fisik wilayah terhadap estimasi produktivitas tanaman kelapa sawit di Kebun Cikasungka. Produktivitas tanaman kelapa sawit lapangan diregresikan dengan indeks vegetasi dan umur tanaman untuk menghasilkan pemodelan. Berdasarkan pemodelan dengan ketiga algoritma, akurasi model algoritma ARVI memiliki nilai RMSE lebih rendah, yaitu sebesar 421. Estimasi produktivitas tanaman kelapa sawit Kebun Cikasungka bervariasi di tiap bloknya dan tertinggi pada umur tanaman dewasa. Pada tahun 2022 estimasi produktivitas tanaman kelapa sawit bulanan mengalami penurunan dibandingkan tahun 2019 karena adanya alihfungsi lahan. Kondisi fisik wilayah penelitian didominasi oleh lereng curam, jenis tanah Kambisol, dan rata-rata curah hujan bulanan yang tinggi dengan estimasi produktivitas tanaman kelapa sawit sebesar 35.061 Kg/Ha/Bulan menggunakan algortima NDVI dan SAVI serta dengan algoritma ARVI sebesar 35.431 Kg/Ha/Bulan.......Palm oil is one of the commodities that is growing well in Indonesia with a high commercial value which makes the demand for processed palm oil products increase, it is necessary to have data and technology to estimate the productivity of oil palm plants more efficiently. One of them is remote sensing using Sentinel-2 imagery. This study aims to analyze the accuracy of the NDVI, ARVI, and SAVI, spatial and temporal variations, and the relationship of the physical condition area to the estimated productivity of oil palm plants at the Cikasungka Plantation. Oil palm productivity was regressed by vegetation index and plant age to generate a model. Based on modeling, the accuracy of the ARVI model has a lower RMSE value. The estimated productivity of oil palm plants varies in each block with the highest at mature plant age. In 2022, it is estimated that the monthly productivity of oil palm plants will decrease compared to 2019 due to the conversion of land into residential areas. The physical condition reseach area is dominated by steep slopes, Kambisol soil type, and high average monthly rainfall with an estimated productivity of 35.061 Kg/Ha/Month using the NDVI and SAVI and the ARVI of 35.431 Kg /Ha/Month."