Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest

Asy'Ari, Rahmat; Rahmawati, Azelia Dwi; Sa'diyya, Naifa; Gustawan, Ardya Hwardaya; Setiawan, Yudi; Zamani, Neviaty P.; Pramulya, Rahmat

doi:10.29244/jpsl.12.1.97-111

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Therefore, the built-up land class is found in all resorts in UKNT. In the case of mangroves, this study found 1,897.88 ha, whereas, in previous studies, the area of mangroves in this national park was 547.34 ha (Asy'Ari et al, 2022). This is considered necessary for an evaluation of the LULC classification process.…”

Section: Constraints and Limitations: Index Assessment And Classifica...contrasting

confidence: 52%

High Heterogeneity LULC Classification in Ujung Kulon National Park, Indonesia: A Study Testing 11 Indices, Random Forest, Sentinel-2 MSI, and GEE-based Cloud Computing

Asy'Ari

Taufik²,

Umamayse³

et al. 2023

CELEBES Agricultural

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The Ujung Kulon National Park (UKNT) is one of the national parks on the island of Java and has an essential role in saving endemic species in Indonesia. As a form of national park conservation effort, the completeness of LULC spatial data is a primary database that is indispensable in determining national park management policies. Therefore, this research was conducted to map the LULC (Land Use - Land Cover) in the forest landscape with high heterogeneity in UKNT. Sentinel-2 MSI (Multispectral Instrument) image data were classified using the Random Forest (RF) classification algorithm and tested using 11 index algorithms. The classification process takes place on a cloud computing-based geospatial platform, Google Earth Engine (GEE). This test resulted in 10 LULC classes; water had the broadest percentage of 45.44%. Meanwhile, the primary forest has an area of 21,868.41 or about 19.53% of the total area of the national park. However, there are some discrepancies in the spatial information generated by this classification process, so it is considered necessary to evaluate the combination of indexes, training data, and classification algorithms to limit the classification area. Therefore, this study is expected to be considered for further research related to LULC in high-heterogeneity landscapes.

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Section: Constraints and Limitations: Index Assessment And Classifica...contrasting

confidence: 52%

High Heterogeneity LULC Classification in Ujung Kulon National Park, Indonesia: A Study Testing 11 Indices, Random Forest, Sentinel-2 MSI, and GEE-based Cloud Computing

Asy'Ari

Taufik²,

Umamayse³

et al. 2023

CELEBES Agricultural

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“…The NDVI value of mangroves in the Dong Rui commune is greater when compared to the NDVI value of the Can Gio mangrove forest region, a typical mangrove forest area in southern Vietnam (Hoa et al 2020). Dong Rui commune mangroves have NDVI and GNDVI values that are comparable to those found on Sumatra's eastern coast (NDVI = 0.738 and GNDVI = 0.641) (Samsuri et al 2021) and greater than those found in the Banten, Jakarta, and West Java Ecotone Zones (NDVI: 0.39 -0.61 and GNDVI: 0.25 -0.48) (Asy' Ari et al 2022).…”

Section: Discussionmentioning

confidence: 93%

Analysis Of The Mangrove Structure In The Dong Rui Commune Based On Multispectral Unmanned Aerial Vehicle Image Data

Ngo,

Quoc,

Dang

et al. 2024

GES

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Mangroves are one of the most important types of wetlands in coastal areas and perform many different functions. Assessing the structure and function of mangroves is a premise for the management, monitoring and development of this most diverse and vulnerable ecosystem. In this study, the unmanned aerial vehicle (UAV) Phantom 4 Multispectral was used to analyse the structure of a mangrove forest area of approximately 50 hectares in Dong Rui commune, Tien Yen district, Quang Ninh Province – one of the most diverse wetland ecosystems in northern Vietnam. Based on the visual classification method combined with the results of field taxonomic sampling, a mangrove tree classification map was established for UAV with three species, Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, achieving an overall accuracy = 86.28%, corresponding to a Kappa coefficient =0.84. From the images obtained from the UAV, we estimated and developed maps and assessed the difference in tree height and four vegetation indices, including the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), enhanced vegetation index (EVI), and green chlorophyll index (GCI), for three mangrove plant species in the flying area. Bruguiera gymnorrhiza and Rhizophora stylosa reach an average height of 4 to 5 m and are distributed mainly in high tide areas. Meanwhile, Kandelia obovata has a lower height (ranging from 2 to 4 m), distributed in low-tide areas, near frequent flows. This study confirms the superiority of UAV with red edge and near-infrared wave bands in classifying and studying mangrove structures in small-scale areas.

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“…One area with mangrove ecotourism potential is the beach KSS, Tangerang Regency, Banten (Kurniawansyah et al, n.d.). In 1996 it was stated that there was a mangrove forest area of 487.5 in Tangerang Regency (Asy'Ari et al, 2022). However, as time goes by, there is a decrease in the area of mangrove forests and only 182.14 ha remain (Marlianingrum et al, 2021); (TW et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

The Potential of Mangrove Ecosystems as A Sustainable Ecotourism Product in The Mangrove Area of Kss Beach, Tangerang District

Halim,

Setiawan

2023

SLJIL

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Mangrove forests have ecological, economic, research, educational and ecotourism functions. The decline in mangrove forest land has resulted in an increase in carbon in Indonesia and the loss of habitat for various types of marine biota and natural resources. Therefore, mangrove ecotourism is one way that can have a positive impact on protecting and preserving the environment and can improve the economy of the surrounding community. One of the potentials for mangrove ecotourism that can be developed is located at KSS Beach, Tangerang Regency. This research uses qualitative research by conducting observations, interviews and distributing questionnaires to the public and visitors at KSS beach. KSS Beach fulfills four elements in ecotourism, namely the element of education, the element of understanding, the element of conservation of natural resources and the element of increasing local community income, namely having mangrove forest exploration activities, mangrove education and mangrove planting. KSS Beach has three types of mangroves, namely Avicenniaceae, Rhizophoraceae, and Sonneratiaceae which can be a processed mangrove product that has sales value among the community. Therefore, KSS Beach requires further development and assistance from the government so that it can become a mangrove ecotourism that is widely known by the community.

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Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest

Cited by 5 publications

References 39 publications

High Heterogeneity LULC Classification in Ujung Kulon National Park, Indonesia: A Study Testing 11 Indices, Random Forest, Sentinel-2 MSI, and GEE-based Cloud Computing

High Heterogeneity LULC Classification in Ujung Kulon National Park, Indonesia: A Study Testing 11 Indices, Random Forest, Sentinel-2 MSI, and GEE-based Cloud Computing

Analysis Of The Mangrove Structure In The Dong Rui Commune Based On Multispectral Unmanned Aerial Vehicle Image Data

The Potential of Mangrove Ecosystems as A Sustainable Ecotourism Product in The Mangrove Area of Kss Beach, Tangerang District

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