Utilizing Sentinel-2 Data for Mapping Burned Areas in Banjarbaru Wetlands, South Kalimantan Province

Arisanty, Deasy; Ramadhan, Muhammad Feindhi; Angriani, Parida; Muhaimin, Muhammad; Saputra, Aswin Nur; Hastuti, Karunia Puji; Rosadi, Dedi

doi:10.1155/2022/7936392

Cited by 8 publications

(7 citation statements)

References 31 publications

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“…This research was conducted in the Banjarbaru area. The Banjarbaru area was chosen as the research area because fires often occur in this region [13,[15][16][17][18]. In addition, the existence of Syamsudin Noor International Airport is a top priority in handling land fires in the South Kalimantan region of Indonesia.…”

Section: Methodsmentioning

confidence: 99%

The value of local wisdom for peatland fire risk reduction in the wetland area of South Kalimantan

Arisanty,

Hastuti,

Aristin

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Mitigation of land fires that occur in wetland areas, especially in peatlands, continues to be addressed by the community in Banjarbaru, South Kalimantan Province. Local wisdom that applies in the community, which includes local knowledge, local value, local skills, local sources, and regional society solidarity, is expected to reduce the risk of land fires. The research objective is to analyze the value of local wisdom for peatland fire reduction in the wetland area of South Kalimantan. The respondents interviewed were 30 from the Community and Fire Care Community or Masyarakat Peduli Api (MPA) members. Local wisdom prevailing in the community can reduce the risk of peatland fires. The community has the local knowledge, local value, local skills, local sources, and local society solidarity in managing peatlands. Peatlands managed by communities are less likely to burn than unmanaged lands. The tapulikampar system is a local wisdom in the Banjar community to prepare agricultural peatlands without burning. The Banjar community also has good skills in managing peatlands, which are not easily damaged and prone to burning. Cooperation between community groups in MPA is also well implemented in monitoring land fires and suppression efforts. The community makes maximum efforts so that peatlands are not damaged, and the risk of fire can be reduced because potential resources on peatlands must be passed on to the next generation.

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Section: Methodsmentioning

confidence: 99%

The value of local wisdom for peatland fire risk reduction in the wetland area of South Kalimantan

Arisanty,

Hastuti,

Aristin

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Banjarmasin City is South Kalimantan Province's capital, with the highest population density compared to other cities in South Kalimantan. Tis city is a wetland area with a tidal swamp [34,35].…”

Section: Methodsmentioning

confidence: 99%

The Effect of Built-Up Area Density and Vegetation Density on Surface Temperature in Banjarmasin City

Adyatma

Muhaimin

Arisanty

et al. 2022

International Journal of Forestry Research

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Banjarmasin City continues to develop rapidly. Malls and settlements are the newly built-up area that has reduced vegetation cover leading to changes in surface temperature in Banjarmasin City. Analysis of temperature changes is needed to determine the effect of increasing built-up areas and decreasing vegetation cover. The surface temperature can be detected and analyzed using satellite imagery. The study aimed to analyze the built-up area and vegetation density index and their effect on changes in surface temperature in Banjarmasin City from 2015 to 2019. We employed remote sensing and surveys to monitor and detect regional changes in urban areas due to rapid development. Built-up areas can be mapped using the Normalized Difference Built-Up Index (NDBI) algorithm, and vegetation density can be mapped using the Normalized Difference Vegetation Index (NDVI) algorithm. The correlation value between building density and surface temperature in 2015 was 0.826, and in 2019, it was 0.969. This means that the NDBI of the built-up area density in 2015 and 2019 strongly correlates with surface temperature. Correlation values between the vegetation density score and the surface temperature were -0.860 in 2015 and -0.949 in 2019. All correlation results are negative, which means that the vegetation density has an inverse ratio to surface temperature; in other words, high vegetation density causes low surface temperature.

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“…For S2, we calculate it using the wavelengths of bands B8 and B12 [45]. A high NBR value generally indicates good vegetation, while a low NBR value indicates empty land and recently burned sites, as burned areas have high reflectance in the SWIR band and low reflectance in the NIR band [46,47]. It is calculated using Equation (1):…”

mentioning

confidence: 99%

“…dNBR is the differenced normalized burn ratio. It is calculated to estimate the severity of the fire [46], by subtracting the post-fire NBR image from the pre-fire NBR image, as follows:…”

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confidence: 99%

Improving the Accuracy of Random Forest Classifier for Identifying Burned Areas in the Tangier-Tetouan-Al Hoceima Region Using Google Earth Engine

Badda,

Cherif,

Boulaassal

et al. 2023

Remote Sensing

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Forest fires have become a major concern in the northern parts of Morocco, particularly in the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the environment and human lives. To address this pressing issue, this study proposes an approach that utilizes remote sensing (RS) and machine learning (ML) techniques to detect burned areas in the TTA region within the Google Earth Engine platform. The study focuses on burned areas resulting from forest fires in three specific locations in the TTA region that have experienced such fires in recent years, namely Tangier-Assilah in 2017, M’diq Fnideq in 2020, and Chefchaouen in 2021. In our study, we extensively explored multiple combinations of spectral indices, such as normalized burn ratio (dNBR), normalized difference vegetation index (dNDVI), soil-adjusted vegetation index (dSAVI), and burned area index (dBAI), in conjunction with Sentinel-2 (S2) satellite images. These combinations were employed within the Random Forest (RF) algorithm, allowing us to draw important conclusions. Initially, we assess the individual effectiveness of the dNBR index, which yields accuracy rates of 83%, 90%, and 82% for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Recognizing the need for improved outcomes, we expand our analysis by incorporating spectral indices and S2 bands. However, the results obtained from this expanded combination lack consistency and stability across different locations. While Tangier-Assilah and M’diq Fnideq experience accuracy improvements, reaching 95% and 88%, respectively, the inclusion of Sentinel bands has an adverse effect on Chefchaouen, resulting in a decreased accuracy of 87%. To achieve optimal accuracy, our focus shifted towards the combination of dNBR and the other spectral indices. The results were truly remarkable, with accuracy rates of 96%, 97%, and 97% achieved for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Our decision to prioritize the spectral indices was based on the feature importance method, which highlights the significance of each feature in the classification process. The practical implications of our study extend to fire management and prevention in the TTA region. The insights gained from our analysis can inform the development of effective policies and strategies to mitigate the impact of forest fires. By harnessing the potential of RS and ML techniques, along with the utilization of spectral indices, we pave the way for enhanced fire monitoring and response capabilities in the region.

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Utilizing Sentinel-2 Data for Mapping Burned Areas in Banjarbaru Wetlands, South Kalimantan Province

Cited by 8 publications

References 31 publications

The value of local wisdom for peatland fire risk reduction in the wetland area of South Kalimantan

The value of local wisdom for peatland fire risk reduction in the wetland area of South Kalimantan

The Effect of Built-Up Area Density and Vegetation Density on Surface Temperature in Banjarmasin City

Improving the Accuracy of Random Forest Classifier for Identifying Burned Areas in the Tangier-Tetouan-Al Hoceima Region Using Google Earth Engine

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