Fire has a vast influence on the climatic balance, and the Global Climate Observing System (GCOS) considers it an Essential Climate Variable (ECV). Remote sensing data is a powerful source of information for burned area detection and thus for estimating greenhouse gases (GHGs) emissions from fires. Currently, most burned area products are based on optical images. However, cloud cover independent Synthetic Aperture Radar (SAR) datasets are increasingly exploited for burned area mapping. This study assessed temporal indices based on temporal backscatter coefficient to understand their suitability for burned area detection. The analysis was carried out using the random forests machine learning classifier, which provides a rank for each independent variable used as input. Depending on land cover type, soil moisture, and topographic conditions, remarkable differences were observed between the temporal backscatter based indices.
Remote sensing-derived maps contain errors. The magnitude of such errors is evaluated through accuracy assessment based on sub-sampling the total ‘population’ (i.e. map). Several strategies have been proposed to define the optimal sampling design leading to a statistically robust accuracy assessment. In this work, a stratified random sampling approach as proposed by Padilla et al.1 was applied to validate two burned area (BA) products as part of the ESA’s Firecci project: a SAR-based product generated from S1 data and the MODIS MCD64. The sampling design considers sample allocation as a function of burned area proportion inside each biome. In our study the sampling size was computed as suggested by Olofsson et al.2. The objective of this study was to assess to which extent a reduction in the sampling size influences the accuracy metrics. The validation was carried out for BA detected from Sentinel-1 as well as a MODIS based-product, the MCD64, generated for the year 2017 in the Amazon region (8M km2). The reference BA dataset was generated using optical time-series acquired by the Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2 MSI sensors. The BA products were validated three times: i) over n = 44 sample units (as computed from Olofsson et al.2; ii) considering a sample size of n/2, and iii) considering a sample size of n/4, to test the sensitivity of the accuracy assessment to changes in the sample size over the tropics.
The results showed that halving the sample size while maintaining the stratified allocation method, yielded similar results when compared to the original sample size (differences in OE and CE did not exceed 5% in any of the products, while differences in DC did not exceed 2%). For a sample size of n/4, the validation results were more unstable (differences in DC reached up to 9% and confidence intervals were higher). The results provide evidence for the optimal sampling size for the accuracy assessment of different BA products over the Amazon basin.
Worldwide, about 2.1 PgC are released every year from biomass burning. Due to its importance for climate modelling, several products were developed to map burned areas (BA) at global levels. Most of these products are based on medium or low-resolution optical sensors which are rather insensitive to small size fires. Moreover, frequent cloud cover and smog may hinder BA detection using optical sensors. To mitigate such shortcomings, BA may be derived from high resolution radar backscatter time-series. This study analyses the results of a locally adaptive BA detection algorithm based on data acquired by the ESA’s C-band synthetic aperture radar (SAR) Sentinel-1 A and B satellites. Sentinel-1 time series were analysed to understand the backscatter coefficient variation over burned and unburned areas. In addition, the analysis was extended to areas where the detection algorithm was affected by commission and omission errors. The study was carried out at 17 sites globally distributed. The analysis revealed shortcomings of the proposed algorithm particularly over areas where fire does not significantly decrease the cross-polarized backscatter. Over such areas, the backscatter change in the co-polarized channel could provide additional information that may overcome the lack of pre to post fire dynamic range of the VH polarization. In fact, for more than 50% of the study sites the change in VV polarization was peaked over undetected burned areas (omission errors). Furthermore, the VV polarization may help reducing commission errors as similar backscatter changes over burned and unburned areas was observed over less tiles (47%) when compared to the VH polarization (70%).
Prescribed burning is a technique applied to control fire risk, and it has been used in the forests of Western Australia since the 1960s. Synthetic Aperture Radar (SAR) data are sensitive to vegetation structural changes and may detect changes in understory vegetation particularly when the upper forest canopy remains largely unaffected, as it is often the case for prescribed burns. In this study, the ability of the Radar Burn Ratio (RBR), a SAR index that measures the degree of change between pre- and post-event radar backscatter, to appraise fire efficiency in prescribed burns was assessed. Data acquired by the L-band PALSAR-2 sensor, onboard the ALOS-2 satellite, were analysed to study the relationship between radar backscatter coefficient and prescribed burns carried out in eucalypt forests in Western Australia. A previously proposed framework was adapted to evaluate burn impacts in different environmental conditions (dry, wet and mean) using HV and HH polarizations as well as the RFDI (Radar Forest Degradation Index). A linear relationship between RBR and fire severity was found for HV polarization and RFDI confirming previous results observed for wildfires. RBRHV in dry environmental conditions yielded the most accurate estimates of fire impact (OA = 77.8 %; k = 0.67). RBRHH showed higher ability to differentiate between severity classes in wet conditions while the RBRRFDI showed an intermediate behavior. HH polarization and RFDI showed special ability for burn area detection in wet conditions. The results showed that it is possible to estimate the impact of prescribed burns using SAR data. As such, SAR data could contribute and assess the effectiveness of fire policy in Western Australia and similar environments.
Fire is considered an essential climate variable (ECV) by the Global Climate Observing System (GCOS). Remote sensing is often used to detect the burned areas and subsequently estimate CO2 emissions from wildfires. Most burned area mapping approaches are based on optical images. However, cloud cover independent radar datasets are increasingly employed for burned area detection. This study presents results related to a burned area detection algorithm based on temporal series of backscatter images. The algorithm was developed in the frame of the ESA’s Fire cci project. During the development, it was observed that large temporal differences exist between the fire date and the date when significant changes of the C-band backscatter coefficient occurred. In this contribution we analysed this temporal decorrelation. The aim was to quantify it and try to understand the reasons behind it and thus improve the C-band SAR based mapping algorithm.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.