Identifikasi Sebaran Spasial dan Kerapatan Mangrove Gili Lawang menggunakan Citra Landsat 9 OLI-2/TIRS-2

Prasetyo, Andrie Ridzki; Valentino, Niechi; Hadi, Muhammad Anwar

doi:10.29303/jstl.v9i2.450

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Gultom et al menunjukkan bahwa sebaran mangrove di masing-masing wilayah amatan perlu dimonitoring secara berkala untuk mengidentifikasi kerapatan hutan mangrove berdasarkan jenis serta faktor-faktor yang memengaruhi pertumbuhan mangrove tersebut [24]. Disisi lain, Prasetyo et al menunjukkan bahwa penginderaan jarak jauh melalui model NDVI dapat digunakan untuk mengidentifikasi sebaran spasial dan kerapatan hutan mangrove [25].…”

Section: Ndvi 2013 Ndvi 2018 Ndvi 2023unclassified

Analisis Indeks Vegetasi dan Perancangan Sistem Monitoring Kawasan Ekowisata Mangrove Berbasis Hyper Spectral of Remote Sensing dan Design Thinking Framework

Singgalen

2023

josh

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One of the challenges in decision-making for determining the direction of mangrove ecotourism development policies and programs on Tagalaya Island is the availability of mangrove databases at each observation station and remote sensing models used to identify mangrove forest density. This research offers the idea of combining the hyperspectral method of remote sensing with the information system design method design thinking framework. Meanwhile, the location of this research is the mangrove ecotourism area of Tagalaya Island, North Halmahera Regency, North Maluku Province, Indonesia. The findings of this study demonstrate the need to use remote sensing to locate and evaluate spatial data, particularly index values based on NDVI models for each Tagalaya Island observation station. The first and second observation stations are shown to be included in the unusual group by the computation results of the 2023 raster data in the mangrove area of Tagalaya Island. The third observation station is also included in the medium category. The monitoring information system for the Tagalaya mangrove ecotourism region is also a solution to the mangrove database challenge in deciding the direction of Tagalaya mangrove ecotourism policies and activities (SIMOKE Tagalaya). Thus, the problems and solutions encountered in enhancing the monitoring program of mangrove ecotourism areas on Tagalaya Island become relevant and contextual when applying the hyperspectral method of remote sensing and information system design using a design thinking framework.

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Section: Ndvi 2013 Ndvi 2018 Ndvi 2023unclassified

Analisis Indeks Vegetasi dan Perancangan Sistem Monitoring Kawasan Ekowisata Mangrove Berbasis Hyper Spectral of Remote Sensing dan Design Thinking Framework

Singgalen

2023

josh

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“…Nilai indeks kehijauan vegetasi mendekati 1 mengindikasikan area dengan vegetasi yang rapat. Namun tingginya nilai IKVm tidak selalu menunjukkan bahwa objek tersebut merupakan mangrove yang padat (Prasetyo et al, 2023).…”

Section: Hubungan Cadangan Karbon Tegakan Dengan Nilai Ikvmunclassified

Estimasi Simpanan Karbon Tegakan Menggunakan Citra Sentinel-2A Pada Kawasan Mangrove Labuan Tereng Kabupaten Lombok Barat

Hambali,

Ichsan,

Valentino

et al. 2023

jstl

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The primary worry in addressing climate change problems is the elevation in global temperatures resulting from the growing levels of CO2 emissions in the atmosphere. Mangrove ecosystems contribute to the "blue carbon" plan which is capable of storing carbon well, this research was conducted to assess carbon storage within the mangrove forest ecosystem by combining Sentinel-2A satellite imagery with on-site field measurements. The data analysis findings indicate the presence of six distinct mangrove varieties, namely R. mucronata, A. marina, R. apiculata, S. alba, E. agallocha, and C. decandra. The R. mucronata type is the type that dominates the mangrove area with an average carbon amount of 122.1 tonnes/ha. Correlation analysis shows a strong relationship between IKVm and mangrove forest carbon stocks, with a correlation coefficient value of 80%. In the regression model, the power model provides the best equation for estimating carbon stocks with a coefficient of determination value of 64.4% giving a model equation of y = 109.51x1.2381. Analysis of image carbon reserves obtained the lowest value, namely 0.02-10.46 tonnes/ha which was in the very rare vegetation density type and the highest carbon reserve value was 58.30-59.02 tonnes/ha in the very high density class.

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Identifikasi Sebaran Spasial dan Kerapatan Mangrove Gili Lawang menggunakan Citra Landsat 9 OLI-2/TIRS-2

Cited by 2 publications

References 19 publications

Analisis Indeks Vegetasi dan Perancangan Sistem Monitoring Kawasan Ekowisata Mangrove Berbasis Hyper Spectral of Remote Sensing dan Design Thinking Framework

Analisis Indeks Vegetasi dan Perancangan Sistem Monitoring Kawasan Ekowisata Mangrove Berbasis Hyper Spectral of Remote Sensing dan Design Thinking Framework

Estimasi Simpanan Karbon Tegakan Menggunakan Citra Sentinel-2A Pada Kawasan Mangrove Labuan Tereng Kabupaten Lombok Barat

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