Integrating mangrove and swamp forests conservation with coastal lowland development; the Banyuasin Sembilang swamps case study, South Sumatra Province, Indonesia

Verheugt, W.J.M.; Purwoko, Agus; Danielsen, Finn; Skov, Henrik; Kadarisman, Riza

doi:10.1016/0169-2046(91)90096-5

Cited by 12 publications

(4 citation statements)

References 2 publications

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“…The conversion of mangroves to aquaculture ponds has been fuelled by governmental support, private sector investment and external assistance from multilateral development agencies such as the World Bank and Asian Development Bank (Siddall et al, 1985;Verheugt et al, 1991). To quote a report of the 1978 Aquaculture Project in Thailand ''The subproject will involve the large-scale development of mangrove swamps into small shrimp/ fish pond holdings .…”

Section: Deforestation and Competing Land Usesmentioning

confidence: 99%

Ethnobiology, socio-economics and management of mangrove forests: A review

et al. 2008

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Section: Deforestation and Competing Land Usesmentioning

confidence: 99%

Ethnobiology, socio-economics and management of mangrove forests: A review

et al. 2008

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“…The South China Sea, close to the Sumatran coast and the Strait of Bangka, is a highly productive ecosystem owing to the shallow conditions (less than 30 m) and the continuous input of nutrient rich sediments from the discharge of the Musi and Banyuasin Rivers [17]. The results obtained from the sea around the of Carat Cape in the form of fish, shrimp, crabs and scallops.…”

Section: Resultsmentioning

confidence: 99%

Valuation of Provisioning Ecosystem Services Carat Cape Mangrove for Sungsang IV Village Community Banyuasin District

Panandi

Mulyana²,

Robiani³

2019

SJE

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Mangrove ecosystem valuation can be used as a reference for decision making for management and conservation of mangrove ecosystems in a sustainable manner. The range of activities and interests conducted in coastal areas is not expected to negatively impact the ecology, economic and social. Carat Cape mangrove areas provide enormous benefits to the surrounding community, especially Sungsang IV village. From the results of the study illustrate the provosioning ecosystem service utilized community Sungsang IV village consists of materials construction (timber and nypa leaves), a source of livelihood or subsistance (nypa fruit, fish, shrimp, crab, and scallops). The total value of provosioning ecosystem services mangrove Carat Cape utilized by 45 respondent the Sungsang IV village community Rp. Rp.1.715.590.000,-per year dominated by income generated by marine of fisheris which is the main livelihood of the fishermen Sungsang IV village reached Rp.1.302.600.000,-per year on 2018.

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“…The condition of mangrove forests in Sembilang National Park is also very dynamic and changes annually [13]. Several groups of mangrove genera can be found at this location, including Avicennia, Rhizopora, Bruguierra, Sonneratia, Ceriops, and Xylocarpus, in which Avicennia and Rhizopora are the dominant genera throughout almost all of Sembilang National Park [9,13,59]. Additionally, many species of Nypa fruticans and shrubs are present [9].…”

Section: Study Areamentioning

confidence: 99%

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

et al. 2022

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Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI+NDSI+DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI+NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI+NDSI+DEM parameters with the number of trees = 100, mtry = all variables (, and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.

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Integrating mangrove and swamp forests conservation with coastal lowland development; the Banyuasin Sembilang swamps case study, South Sumatra Province, Indonesia

Cited by 12 publications

References 2 publications

Ethnobiology, socio-economics and management of mangrove forests: A review

Ethnobiology, socio-economics and management of mangrove forests: A review

Valuation of Provisioning Ecosystem Services Carat Cape Mangrove for Sungsang IV Village Community Banyuasin District

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

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