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Mean topographic slope ( • ) and confidence interval (95%) as derived from the Shuttle Radar Topography Mission (SRTM) by land cover classes.
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Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in radar-based burned areas monitoring depend on a better understanding of the temporal decorrelation ef...
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... the Shrubs class, fire severity was the most important although it was closely followed by Slope and variations in the vegetation water content between post-and pre-fire images as estimated through the dNDWI. Variable importance by land cover classes was related to the mean topographic slope (Table 2). Aspect and Slope showed high importance over Herbaceous and Shrubs classes which covered higher topographic slopes when compared to the Crops and Forests classes ( Table 2). ...Context 2
... importance by land cover classes was related to the mean topographic slope (Table 2). Aspect and Slope showed high importance over Herbaceous and Shrubs classes which covered higher topographic slopes when compared to the Crops and Forests classes ( Table 2). The reduced importance of Aspect and Slope on the decorrelation process for the Crops class was explained by its location on low lying terrain when compared to the remaining land cover classes ( Figure 6). ...Similar publications
Soil total nitrogen (STN) is a crucial component of the ecosystem’s nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distrib...
Previous wetlands studies have thoroughly verified the usefulness of data from synthetic aperture radar (SAR) sensors in various acquisition modes. However, the effect of the processing parameters in wetland classification remains poorly explored. In this study, we investigated the influence of speckle filters and decomposition methods with differe...
Assessing crop lodging at the regional scale is an important requirement for breeding lodging-resistant varieties and harvest planning. Accurately and continuously estimating crop lodging area from remote sensing data remains challenging due to the high randomness scattering signal of SAR images and the insufficient number of applicable optical ima...
In the past decade, high spatial resolution Synthetic Aperture Radar (SAR) sensors have provided information that contributed significantly to cropland monitoring. However, the specific configurations of SAR sensors (e.g., band frequency, polarization mode) used to identify land-use types remains underexplored. This study investigates the contribut...
In this study we investigate the potential for using Synthetic Aperture Radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone. Using in situ measurements collected in 2017 we calculated the proportion of both live and defoliated tree crown for 165 $10 m \times 10 m$ ground plots along six...
Citations
... After a fire event, the leaves are charred if not completely burned, thus not contributing to the volume scattering as much as healthy leaves [48,49]. As explained in [50], diverging trends in C-band post-fire backscattering can also be attributed to vegetation death changing the structure of the canopy, leaving dry branches and a minimum number of dead leaves. ...
This study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km2 in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired between January 2018 and December 2020 were assigned to five classes depending on the Drought Code (DC) scenario over several unburned and burned sites with total (>90%) forest canopy cover. Confounding variables such as tree cover and incidence angle were accounted for by masking using specific thresholds. The following results are discussed: (a) C-VV and C-VH backscatter values are inversely correlated (R2 = 0.324 to 0.438 −p < 0.001) with local incidence angle over canopies; (b) correlation is significantly stronger over very wet scenarios (DC class = 0 to 1); (c) C-VV and C-VH backscatter values can discriminate wet to dry forest environments, but they are less sensitive to the transition between dry (DC classes = 1 to 10, 10 to 100) and extremely dry environments (DC classes = 100 to 1000); (d) C-VH is more sensible than C-VV to capture burnt canopy; and (e) the C-VH polarization captures post-fire recovery after an average minimum period of 360 days after the fire event, although with less distinction for extremely wet soils. We conclude that C-band VH backscatter intensity decreases from wet to dry canopy conditions, that this behavior of the backscatter signal with respect to canopy dryness is lost after a fire event, and that after one year it is recovered.
... In addition to optical data, synthetic aperture radar (SAR) active sensors have achieved good performance in burned area mapping and fire effect estimation (Belenguer-Plomer, Chuvieco, and Tanase 2019;De Luca, Silva, and Modica 2021;Donezar et al. 2019;Gimeno, San-Miguel-Ayanz, and Schmuck 2004;Kurum 2015;Lasaponara and Tucci 2019;Lehmann et al. 2015;Pepe et al. 2018;Stroppiana et al. 2015;Tanase et al. 2020Tanase et al. , 2011Zhang et al. 2019). The Earth's microwave backscatter is affected by variations in the structural parts and dielectric permittivity of the surface, triggered by vegetation cover, shape, size, and orientation of the canopy scatterers, soil structure, and moisture content modifications, making it a suitable system for discriminating alterations on the Earth's surface. ...
Wildland fires are among the main factors affecting the surrounding territory in terms of ecological and socioeconomic changes at different temporal and spatial scales. In the Mediterranean environment, although fire can positively influence some bio-physical dynamics of habitats, it acts as a pressing disturbance on ecosystems when the severity, spatial scale, and/or frequency are high, thereby determining their degradation. Therefore, knowing and mapping the accurate quantitative spatial distribution of all areas affected by fire during an entire high-frequency fire season and on a relatively large scale (regional/national scale) is an essential step to initialize the numerous subsequent effect monitoring analyses that can be carried out. This work proposes a reliable and open-access workflow to map burned areas on regional and national scales during the entire fire season. To achieve this, we integrated optical (Sentinel-2, S2) and Synthetic Aperture Radar (SAR; Sentinel-1, S1) free high spatial and temporal resolution data into a multitemporal composite criterion. Open-source software and Python-based libraries were used to develop the workflow. In particular, the second-lowest near infra-red (NIR) image composite (secMinNIR) criterion, based on the retrieval of the second minimum values that the NIR values reached in each pixel during the entire time frame considered, was applied to cloud-free S2 imagery to optimize the separability between burned and unburned areas. Subsequently, a second temporal composite criterion was developed and applied to the S1 time series, relying on the SAR capacity to detect vegetation fire-induced structural and humidity changes. It was based on retrieving the S1 pixel value of the first next (or the same) date to the corresponding date of the pixel value previously found by secMinNIR. The burned area map was created using an object-based geographic analysis (GEOBIA) process, using two optical and SAR composite images as input layers. The large-scale mean-shift (LSMS) algorithm was employed to segment the image, while the random forest (RF) classifier was the machine-learning model used to perform supervised classification. GEOBIA-based burned area classification was also performed using only the optical composite. The resulting accuracy values were compared using the precision (p), recall (r), and F-score accuracy metrics. The classification achieved high accuracy levels (F-score value greater than 0.9) in both cases (S1+ S2, 0.956; S2, 0.914), highlighting the increased effectiveness of this approach in detecting burned areas, heterogeneous in terms of amplitude, and affected site-specific characteristics that occurred during the fire season. Although the use of only optical data is sufficient to map the fire-affected areas early, some commission errors, represented by small regions scattered over the entire study area, remain, proving that the integration of SAR data improves the quality of the obtained results.
... The use of fully polarimetric C-and L-band SAR data has been investigated in (Tanase et al., 2013) for studying to what extent polarimetric target decomposition components are affected by the forest burn severity. The polarimetric coherence matrix has been studied in (Tanase et al., 2010), and, finally, the role of the temporal decorrelation of the Sentinel-1Cband interferograms has been analysed in (Belenguer-Plomer et al., 2019b). ...
This paper aims at exploiting the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for the detection of forest disturbances due to fires. The study focuses on the forested part of Angola that was largely affected by fires during summer 2019. The data collected in the area by the NASA Cyclone GNSS (CyGNSS)
constellation have been downloaded and processed. As reference data for developing and testing the retrieval algorithms, the ESA Climate Change Initiative (CCI) decadal burned areas maps have been considered. After evaluating the sensitivity of the GNSS-R observables, namely Signal to Noise Ratio and Equivalent Reflectivity, to
the forest disturbances, some retrieval algorithms based on different machine learning techniques have been implemented, tested, and compared with a simple approach based on the temporal gradient of the GNSS-R observables. In details, classifiers based on Support Vector Machines (SVM) and Random Forests (RF) have been implemented for discriminating burned/not burned pixels of the images and regressors based on Artificial
Neural Networks (ANN), RF and Support Vector Regressors (SVR) have been implemented for estimating the fraction of burned areas within a pixel. All these algorithms allowed a satisfactory identification of the burned areas with respect to the reference data, enabling the generation of maps every ten days. These results represent the first observations of forest disturbances due to fires using spaceborne GNSS-Reflectometry.
... However, there is still remarkable uncertainty within the community regarding the worldwide burned area extent in cropland areas. Currently, there are very few studies that have provided a global or regional comparison of burned area products for crop residues that have burned areas smaller than 100 ha, which results in large detection errors due to the relatively coarse spatial resolution of these data [13]. Therefore, additional analysis of crop residue burning is needed to provide new insights into the relative performance of burned area products. ...
Burning crop residues is a common way to remove them during the final stages of crop ripening in China. To conduct research effectively, it is critical to reliably and quantitatively estimate the extent and location of a burned area. Here, we investigated three publicly available burned area products—MCD64A1, FireCCI 5.1, and the Copernicus Burnt Area—and evaluated their relative performance at estimating total burned areas for cropland regions in China between 2015 and 2019. We compared these burned area products at a fine spatial and temporal scale using a grid system comprised of three-dimensional cells spanning both space and time. In general, the Copernicus Burnt Area product detected the largest annual average burned area (37,095.1 km2), followed by MCD64A1 (21,631.4 km2) and FireCCI 5.1 (12,547.99 km2). The Copernicus Burnt Area product showed a consistent pattern of monthly burned areas during the study period, whereas MCD64A1 and FireCCI 5.1 showed frequent changes in monthly burned area peaks. The greatest spatial differences between all three products occurred in Northeast and North China, where cultivated land is concentrated. The burned area detected by Copernicus in Xinjiang Province was larger than that detected by the other two products. In conclusion, we found that all three products underestimated the amount of crop residues present in a burned area. This limits the ability of end users to understand fire-related impacts and burned area characteristics, and hinders them in making an informed choice of which product is most appropriate for their application.
... These methods have known limits: there is often a delay between a change in the vegetation structure and/or cover and a change in the SAR backscattering. Due to the SAR sensitivity to changes in the vegetation/soil moisture content and surface roughness, vegetation change is difficult to detect or may be detected with a delay (Belenguer-Plomer et al., 2019;Reiche et al., 2018a;Ruiz-Ramos et al., 2018;Watanabe et al., 2018). Moreover, if the revisit time of the satellite is too low, vegetation may partially recover after being cut, hindering the detection of the cut (Numbisi and Van Coillie, 2020;Reiche et al., 2021). ...
The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.
... As soil moisture changes the importance of C-band VV and VH polarisations when distinguishing between burned and unburned areas (Van Zyl et al., 2011;Belenguer-Plomer et al., 2019a, 2019b, BA mapping based on Sentinel-1 datasets shall take into account more reliable information on soil moisture as ancillary global products become available at higher spatial resolutions. Current global products (i.e., SMAP at 9 km or CCI soil moisture at 0.25 • ) are not accurate enough for such purposes. ...
In this paper, we present an in-depth analysis of the use of convolutional neural networks (CNN), a deep learning method widely applied in remote sensing-based studies in recent years, for burned area (BA) mapping combining radar and optical datasets acquired by Sentinel-1 and Sentinel-2 on-board sensors, respectively. Combining active and passive datasets into a seamless wall-to-wall cloud cover independent mapping algorithm significantly improves existing methods based on either sensor type. Five areas were used to determine the optimum model settings and sensors integration, whereas five additional ones were utilised to validate the results. The optimum CNN dimension and data normalisation were conditioned by the observed land cover class and data type (i.e., optical or radar). Increasing network complexity (i.e., number of hidden layers) only resulted in rising computing time without any accuracy enhancement when mapping BA. The use of an optimally defined CNN within a joint active/passive data combination allowed for (i) BA mapping with similar or slightly higher accuracy to those achieved in previous approaches based on Sentinel-1 (Dice coefficient, DC of 0.57) or Sentinel-2 (DC 0.7) only and (ii) wall-to-wall mapping by eliminating information gaps due to cloud cover, typically observed for optical-based algorithms.
... Estimation of soil moisture involved the testing of empirical models to present a correlation between measured soil moisture and predicted soil moisture derived from environmental covariates and backscattering coefficients with the aid of the ordinary least square (OLS) linear regression function [45]. Many researchers established empirical relationships to estimate soil moisture, and these models revealed that there is a fundamental relation between soil moisture and the backscattering coefficient [45][46][47][48][49]. ...
Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.
Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum-spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user's accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbour filter increased the detection performance to a user's accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4 ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories.
