Burn Area Detection Using Landsat 8 OLI TIRS

Indratmoko, Satria; Rizqihandari, Nurrokhmah

doi:10.1088/1755-1315/338/1/012035

Cited by 10 publications

(7 citation statements)

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“…However, the data collected by the VIIRS sensors is only available from 2012 onwards, and examinations of fire regimes further back in time will therefore have to depend on the coarser-scale MODIS data, or make use of the popular Landsat burned area product. Landsat data, however, have a lower temporal resolution compared to MODIS data (16-days versus daily cycle), and are therefore more suited for assessing post-fire changes in the landscape, rather than monitoring fires (Indratmoko and Rizqihandari 2019). MODIS data are nonetheless valuable for examining the broad features of past fire regimes prior to the introduction of VIIRS satellites.…”

Section: Comparison Of the Datasetsmentioning

confidence: 99%

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A reconstruction of the recent fire regimes of Majete Wildlife Reserve, Malawi, using remote sensing

2021

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Background Fire is an important process that shapes the structure and functioning of African savanna ecosystems, and managers of savanna protected areas use fire to achieve ecosystem goals. Developing appropriate fire management policies should be based on an understanding of the determinants, features, and effects of prevailing fire regimes, but this information is rarely available. In this study, we report on the use of remote sensing to develop a spatially explicit dataset on past fire regimes in Majete Wildlife Reserve, Malawi, between 2001 and 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) images were used to evaluate the recent fire regime for two distinct vegetation types in Majete Wildlife Reserve, namely savanna and miombo. Additionally, a comparison was made between MODIS and Visible Infrared Imager Radiometer Suite (VIIRS) images by separately evaluating selected aspects of the fire regime between 2012 and 2019. Results Mean fire return intervals were four and six years for miombo and savanna vegetation, respectively, but the distribution of fire return intervals was skewed, with a large proportion of the area burning annually or biennially, and a smaller proportion experiencing much longer fire return intervals. Variation in inter-annual rainfall also resulted in longer fire return intervals during cycles of below-average rainfall. Fires were concentrated in the hot-dry season despite a management intent to restrict burning to the cool-dry season. Mean fire intensities were generally low, but many individual fires had intensities of 14 to 18 times higher than the mean, especially in the hot-dry season. The VIIRS sensors detected many fires that were overlooked by the MODIS sensors, as images were collected at a finer scale. Conclusions Remote sensing has provided a useful basis for reconstructing the recent fire regime of Majete Wildlife Reserve, and has highlighted a current mismatch between intended fire management goals and actual trends. Managers should re-evaluate fire policies based on our findings, setting clearly defined targets for the different vegetation types and introducing flexibility to accommodate natural variation in rainfall cycles. Local evidence of the links between fires and ecological outcomes will require further research to improve fire planning.

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Section: Comparison Of the Datasetsmentioning

confidence: 99%

“…Proportion (%) of the area of all fires that burned at different fire return intervals in the entire Majete Wildlife Reserve, and in two broad vegetation types, inMalawi, between 2001 and2019 …”

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confidence: 99%

A reconstruction of the recent fire regimes of Majete Wildlife Reserve, Malawi, using remote sensing

2021

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“…Forest and land fires are common occurrences in South Sumatera Province. This region is among the fire-prone locations in Indonesia (KLHK, 2018) with the highest hotspots and burned area (Ardiansyah et al, 2017) and widespread forest fire regions (Indratmoko & Rizqihandari, 2019), covering 336,798 ha of burned area in 2019 (KLHK, 2021). Based on Terra/AQUA MODIS satellite data, the greatest hotspot is situated in Ogan Komering Ilir (OKI) Regency, also known as the most affected zone (Indratmoko & Rizqihandari, 2019), with peak periods in dry seasons, particularly between August and September.…”

Section: Introductionmentioning

confidence: 99%

“…This region is among the fire-prone locations in Indonesia (KLHK, 2018) with the highest hotspots and burned area (Ardiansyah et al, 2017) and widespread forest fire regions (Indratmoko & Rizqihandari, 2019), covering 336,798 ha of burned area in 2019 (KLHK, 2021). Based on Terra/AQUA MODIS satellite data, the greatest hotspot is situated in Ogan Komering Ilir (OKI) Regency, also known as the most affected zone (Indratmoko & Rizqihandari, 2019), with peak periods in dry seasons, particularly between August and September. However, the fires are known to have instigated haze alongside a significant impact on public health and are strongly related to the peatland incidence where huge carbon materials are released during the burning.…”

Section: Introductionmentioning

confidence: 99%

The Possibility of COVID-19 Pandemic in Eliminating Burning Activities: A Case Study at Ogan Komering Ilir Regency, South Sumatera

Syaufina¹,

Rihuljihad²,

Nurhayati³

2021

jtfm

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Forest and land fires occur almost every year in South Sumatera Province, including at Ogan Komering Ilir (OKI) Regency, mainly due to uncontrolled burning activities. This region has the largest peatland responsible for the adverse haze impacts. The advent of the coronavirus (COVID-19) pandemic in early 2020 has triggered massive consequences across the global communities, including Indonesia. Therefore, large-scale regulations on social restrictions were enacted. The purpose of this study is to analyze the variations in hotspots as forest and land fire indicators before and during the COVID-19 pandemic in OKI as well as to determine the rainfall effects. Daily Terra/Aqua MODIS satellite feeds and rainfall data between January 2018 and December 2020 served as the research materials. Subsequently, the paired t-test and correlation assessment were used to examine the hotspot variations in both datasets, respectively. The results showed significant statistical differences before and during the pandemic. Consequently, social restrictions were assumed to instigate the decline in burning activities. Furthermore, the rainfall demonstrated a vulnerable correlation to the hotspots, indicating that human factor was more pronounced as a fire trigger.

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“…The Thermal Infrared Sensor (TIRS) algorithm which was used to detect fires and SWIR, which was used to detect water stress in vegetation and burned vegetation, will be seen in green, and both will become darker when burning takes place. This method modifies the dNBR (pre-NBR-post-NBR) composite (Indratmoko & Rizqihandar, 2019).…”

Section: Introductionmentioning

confidence: 99%

Monitoring Model of Land Cover Change for the Indication of Devegetation and Revegetation Using Sentinel-2

Arifin¹,

Kartika

2021

IJReSES

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IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (ΔNDVI) and open land index (ΔNBR).The study found that the best thresholds to detect revegetation were ΔNDVI > 0.0309 and ΔNBR < 0.0176 and to detect devegetation ΔNDVI < -0.0206 and ΔNBR > 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions.

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Burn Area Detection Using Landsat 8 OLI TIRS

Cited by 10 publications

References 1 publication

A reconstruction of the recent fire regimes of Majete Wildlife Reserve, Malawi, using remote sensing

A reconstruction of the recent fire regimes of Majete Wildlife Reserve, Malawi, using remote sensing

The Possibility of COVID-19 Pandemic in Eliminating Burning Activities: A Case Study at Ogan Komering Ilir Regency, South Sumatera

Monitoring Model of Land Cover Change for the Indication of Devegetation and Revegetation Using Sentinel-2

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