Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.

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Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

PLoS ONE ◽

10.1371/journal.pone.0232962 ◽

2020 ◽

Vol 15 (5) ◽

pp. e0232962 ◽

Cited By ~ 4

Author(s):

Fiona Ngadze ◽

Kudzai Shaun Mpakairi ◽

Blessing Kavhu ◽

Henry Ndaimani ◽

Monalisa Shingirayi Maremba

Keyword(s):

Burned Area ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Burned Area Mapping ◽

Sentinel 2

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Assessment of Burned Area Mapping Methods for Smoke Covered Sentinel-2 Data

2020 13th International Conference on Communications (COMM) ◽

10.1109/comm48946.2020.9141999 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alexandru-Cosmin Grivei ◽

Corina Vaduva ◽

Mihai Datcu

Keyword(s):

Burned Area ◽

Burned Area Mapping ◽

Sentinel 2

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Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111254 ◽

2019 ◽

Vol 231 ◽

pp. 111254 ◽

Cited By ~ 31

Author(s):

David P. Roy ◽

Haiyan Huang ◽

Luigi Boschetti ◽

Louis Giglio ◽

Lin Yan ◽

...

Keyword(s):

Change Detection ◽

Temporal Change ◽

Burned Area ◽

Landsat 8 ◽

Detection Approach ◽

Burned Area Mapping ◽

Multi Temporal ◽

Combined Sensor ◽

Sentinel 2

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Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

Remote Sensing ◽

10.3390/rs11060622 ◽

2019 ◽

Vol 11 (6) ◽

pp. 622 ◽

Cited By ~ 20

Author(s):

Federico Filipponi

Keyword(s):

Time Series ◽

Spatial Resolution ◽

Satellite Data ◽

High Spatial Resolution ◽

National Level ◽

Burned Area ◽

Commission Error ◽

Burned Areas ◽

Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

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Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

Remote Sensing ◽

10.3390/rs12030522 ◽

2020 ◽

Vol 12 (3) ◽

pp. 522 ◽

Cited By ~ 1

Author(s):

Abdul Qadir ◽

Pinki Mondal

Keyword(s):

Satellite Data ◽

Critical Role ◽

Mixed Forest ◽

Google Earth ◽

Optical Data ◽

Growth Stages ◽

Global Agriculture ◽

Optical Satellite Data ◽

Sentinel 2 ◽

Cropland Mapping

Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this paper, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. We adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using the Google Earth Engine platform. We developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five different AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the SAR-only approach is 90%, whereas that of the combined approach is 93%. Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask.

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A Method to Automatically Detect Changes in Multitemporal Spectral Indices: Application to Natural Disaster Damage Assessment

Remote Sensing ◽

10.3390/rs12172681 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2681 ◽

Cited By ~ 1

Author(s):

Luca Pulvirenti ◽

Giuseppe Squicciarino ◽

Elisabetta Fiori

Keyword(s):

Natural Disaster ◽

Clustering Algorithm ◽

Buffer Zone ◽

Burned Area ◽

Test Case ◽

Management Service ◽

Spectral Indices ◽

Image Objects ◽

Positive Results ◽

Sentinel 2

This paper presents a new method, based on clustering and thresholding, to automatically perform binary change detection in multitemporal spectral indices. The method is denoted as Buffer-From-Cluster Approach (BFCA). To estimate the distributions of changed and unchanged pixels, as needed for the purpose of a reliable thresholding of a spectral index, a clustering algorithm is preliminarily applied to identify image objects possibly corresponding to areas where significant changes occurred. Then, a buffer zone is created around the selected cluster to identify unchanged areas surrounding changed ones. The cluster and the buffer zone are jointly analyzed to estimate the distributions of changed and unchanged pixels and to verify that they can be distinguished from each other. Finally, the results of thresholding and clustering are combined to generate the binary change map. The BFCA has been conceived to map the extent of the areas affected by a natural disaster like wildfire. To validate the proposed method, burned area maps produced by applying the BFCA to spectral indices derived from Sentinel-2 data have been compared to maps produced by the Copernicus Emergency Management Service. For testing the multi-hazard detection capability, the same kind of exercise has been carried out for a flooding test case too. The positive results of the comparison have confirmed the effectiveness of the proposed method.

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Burned-Area Detection in Amazonian Environments Using Standardized Time Series Per Pixel in MODIS Data

Remote Sensing ◽

10.3390/rs10121904 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1904 ◽

Cited By ~ 5

Author(s):

Níckolas Santana ◽

Osmar de Carvalho Júnior ◽

Roberto Gomes ◽

Renato Guimarães

Keyword(s):

Time Series ◽

Seasonal Difference ◽

Thematic Mapper ◽

Burned Area ◽

Amazon Forest ◽

Spectral Indices ◽

Area Index ◽

Commission Errors ◽

Normalized Burn Ratio ◽

Omission Errors

Fires associated with the expansion of cattle ranching and agriculture have become a problem in the Amazon biome, causing severe environmental damages. Remote sensing techniques have been widely used in fire monitoring on the extensive Amazon forest, but accurate automated fire detection needs improvements. The popular Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64 product still has high omission errors in the region. This research aimed to evaluate MODIS time series spectral indices for mapping burned areas in the municipality of Novo Progresso (State of Pará) and to determine their accuracy in the different types of land use/land cover during the period 2000–2014. The burned area mapping from 8-day composite products, compared the following data: near-infrared (NIR) band; spectral indices (Burnt Area Index (BAIM), Global Environmental Monitoring Index (GEMI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR), variation of Normalized Burn Ratio (NBR2), and Normalized Difference Vegetation Index (NDVI)); and the seasonal difference of spectral indices. Moreover, we compared the time series normalization methods per pixel (zero-mean normalization and Z-score) and the seasonal difference between consecutive years. Threshold-value determination for the fire occurrences was obtained from the comparison of MODIS series with visual image classification of Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data using the overall accuracy. The best result considered the following factors: NIR band and zero-mean normalization, obtaining the overall accuracy of 98.99%, commission errors of 32.41%, and omission errors of 31.64%. The proposed method presented better results in burned area detection in the natural fields (Campinarana) with an overall accuracy value of 99.25%, commission errors of 9.71%, and omission errors of 27.60%, as well as pasture, with overall accuracy value of 99.19%, commission errors of 27.60%, and omission errors of 34.76%. Forest areas had a lower accuracy, with an overall accuracy of 98.62%, commission errors of 23.40%, and omission errors of 49.62%. The best performance of the burned area detection in the pastures is relevant because the deforested areas are responsible for more than 70% of fire events. The results of the proposed method were better than the burned area products (MCD45, MCD64, and FIRE-CCI), but still presented limitations in the identification of burn events in the savanna formations and secondary vegetation.

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