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

Sentinel-2 imagery can identify forest and land fires in underground parts, surface fires, and crown fires. The dNBR and RBR spectral indices on Sentinel-2 images proved accurate in identifying. This study analyzed the index value for burned area mapping in wetland areas using Sentinel-2 imagery data in 2019 and hotspot data from the MODIS data. The indices used to identify the burned area and the severity of the fire was the differenced normalized burn ratio (dNBR) and relativized burn ratio (RBR). Visual validation tests were performed by comparing RGB composite images to check the appearance before and after combustion with dNBR and RBR results. The dNBR value accuracy was 91.5%, and for a kappa, the accuracy was 89.58%. The RBR accuracy was 92.9%, and the kappa accuracy was 0.91. The results confirmed that in the Banjarbaru area, RBR was more accurate in identifying burned areas than dNBR; both indices can be used for burned area mapping in wetland areas.

[1]  D. Rosadi,et al.  Spatiotemporal Patterns of Burned Areas Based on the Geographic Information System for Fire Risk Monitoring , 2021, International Journal of Forestry Research.

[2]  Sahel Mahdavi,et al.  Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform , 2021, Remote. Sens..

[3]  D. Rosadi,et al.  Prediction of Forest Fire Occurrence in Peatlands using Machine Learning Approaches , 2020, 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI).

[4]  V. S. K. Vanama,et al.  Burn area mapping in Google Earth Engine (GEE) cloud platform: 2019 forest fires in eastern Australia , 2020, 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC).

[5]  Luis Roman Carrasco,et al.  Spatial correlates of forest and land fires in Indonesia , 2020, International Journal of Wildland Fire.

[6]  The Destructive Impact of Burned Peatlands to Physical and Chemical Properties of Soil , 2020, Acta Montanistica Slovaca.

[7]  Rosa Lasaponara,et al.  On the Mapping of Burned Areas and Burn Severity Using Self Organizing Map and Sentinel-2 Data , 2020, IEEE Geoscience and Remote Sensing Letters.

[8]  Khalifah Insan Nur Rahmi,et al.  Pemanfaatan Data Sentinel-2 untuk Analisis Indeks Area Terbakar (Burned Area) , 2020 .

[9]  Luigi Saulino,et al.  Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data , 2020, Remote. Sens..

[10]  J. Armesto,et al.  Event-Based Integrated Assessment of Environmental Variables and Wildfire Severity through Sentinel-2 Data , 2019, Forests.

[11]  K. Kovács EVALUATION OF BURNED AREAS WITH SENTINEL-2 USING SNAP: THE CASE OF KINETA AND MATI, GREECE, JULY 2018 , 2019, Geographia Technica.

[12]  José A. Sobrino,et al.  Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection , 2019, Forests.

[13]  A. Teodoro,et al.  A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data , 2019, Environments.

[14]  윤형진,et al.  Detection of Forest Fire and NBR Mis-classified Pixel Using Multi-temporal Sentinel-2A Images , 2019 .

[15]  R. Lasaponara,et al.  On the Use of Satellite Sentinel 2 Data for Automatic Mapping of Burnt Areas and Burn Severity , 2018, Sustainability.

[16]  George P. Petropoulos,et al.  Determining the use of Sentinel-2A MSI for wildfire burning & severity detection , 2018, International Journal of Remote Sensing.

[17]  Ronald J. Hall,et al.  Variability and drivers of burn severity in the northwestern Canadian boreal forest , 2018 .

[18]  Alfonso Fernández-Manso,et al.  Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[19]  G. Mallinis,et al.  Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece , 2018 .

[20]  Gabriel Navarro,et al.  Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[21]  David P. Roy,et al.  Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination , 2016, Remote. Sens..

[22]  Alfonso Fernández-Manso,et al.  SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Harald van der Werff,et al.  Sentinel-2 for Mapping Iron Absorption Feature Parameters , 2015, Remote. Sens..

[24]  Harald van der Werff,et al.  Potential of ESA's Sentinel-2 for geological applications , 2014 .

[25]  Carol Miller,et al.  A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio , 2014, Remote. Sens..

[26]  Rokhmatuloh,et al.  PENGEMBANGAN MODEL IDENTIFIKASI DAERAH BEKAS KEBAKARAN HUTAN DAN LAHAN (BURNED AREA) MENGGUNAKAN CITRA MODIS DI KALIMANTAN (MODEL DEVELOPMENT OF BURNED AREA IDENTIFICATION USING MODIS IMAGERY IN KALIMANTAN) , 2013 .

[27]  John F. Caratti,et al.  FIREMON: Fire Effects Monitoring and Inventory System , 2012 .

[28]  Chengquan Huang,et al.  Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest , 2011 .

[29]  J. Keeley Fire intensity, fire severity and burn severity: a brief review and suggested usage , 2009 .

[30]  Emilio Chuvieco,et al.  Global Impacts of Fire , 2009 .

[31]  S. Escuin,et al.  Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images , 2008 .

[32]  Christiane Schmullius,et al.  Comparative assessment of CORINE2000 and GLC2000: Spatial analysis of land cover data for Europe , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Joanne C. White,et al.  Identifying and Describing Forest Disturbance and Spatial Pattern: Data Selection Issues and Methodological Implications , 2006 .

[34]  B. Paulo,et al.  Post-Fire Vegetation Regeneration. The Case Study of the "Massif de l'Etoile" Fire. , 2004 .

[35]  Carl H. Key,et al.  Landscape Assessment ( LA ) Sampling and Analysis Methods , 2004 .

[36]  Yoram J. Kaufman,et al.  An Enhanced Contextual Fire Detection Algorithm for MODIS , 2003 .

[37]  D. Riaño,et al.  Fuel loads and fuel type mapping , 2003 .

[38]  S. Running,et al.  Remote Sensing of Forest Fire Severity and Vegetation Recovery , 1996 .