Figure 4 - uploaded by Ignacio Borlaf
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Boxplot representing the VV backscatter IOR for grassland, bare areas, and needleleaf forests at both sites: Mean and median values (green triangle, orange line) and inter quantile-ranges (whiskers) for 5–95%.
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Spaceborne remote sensing can track ecosystems changes thanks to continuous and systematic coverage at short revisit intervals. Active remote sensing from synthetic aperture radar (SAR) sensors allows day and night imaging as they are not affected by cloud cover and solar illumination and can capture unique information about its targets. However, S...
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Citations
... Using the DEMs for radiometric terrain correction, they found the best compensation for the incidence angle when using the SRTM DEM. Borlaf-Mena et al. [31] compare the similarity of S-1 scenes from different viewing angles for different landcover using DEMs of SRTM, Advanced Land Observing Satellite (ALOS) World 3D, and TDX. They used the inter-orbit range (IOR, explained in III-B3), measuring the range of intensities from different relative orbits per pixel, using a DTM generated from Aerial Laser Scanning (ALS) data as a reference and noticed a considerable dependence of backscatter intensity on the choice of the DEM. ...
... 3) Across-orbit Consistency Analysis: Finally, we investigate which DEM improves the consistency of the SAR data across different orbits by calculating the monthly averaged IOR with, on average, 5 scenes per orbit [31]. We examine the usually applied approach of using a single DEM to preprocess all relative orbits. ...
... The number of orbits compared and the period for averaging the backscatter intensities impact the IOR and must be consistent for direct comparisons. With differing pixel spacing, number of compared orbits, AOI size and averaging period, our IORs are not directly comparable to the results presented by [31]. Figure 5 shows clearly that seasonal-mean intensity images were most consistent within one and between different midrange orbits (117 (asc) and 66 (desc)) in VH polarization when preprocessed with the P50 DEM and least consistent when using the less detailed SRTM DEM. ...
Microwave scattering from forests generates pixel geolocation shifts in Synthetic aperture radar (SAR) data that require an adequate representation within digital elevation models (DEM) for preprocessing. We analyze the impact of DEM properties on the radiometry and geolocation of radiometric terrain corrected (RTC) Copernicus Sentinel-1 imagery of forests to improve consistency in backscatter intensities for time series analyses. To account for the penetration depth of the C-Band sensor, we approximate the structure of stands in a temperate deciduous forest using height percentiles from Aerial Laser Scanning (ALS) point clouds in the Hainich National Park, Germany. Comparing the RTC results obtained using DEMs of SRTM, Copernicus, and ALS DEMs, the latter reduces topographically induced errors, resulting in visibly smaller effects from topography and spatially shifted information. Based on the P50 ALS vegetation elevation, results show homogeneous intensities within the same orbit and reduce variance from 2.4 dB
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to 1.2 dB
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in the difference of mid-range data from ascending and descending azimuth directions. Over forest, we observe lower intensities on sensor-facing and increased intensities on away-facing slopes and correlations with the illuminated pixel area (IPA) and local incidence angle. We reduce this bias with linear regressions of intensity on IPA. ALS DEMs in RTC and the proposed regression correction increase the consistency of images across orbits, measured by the inter-orbit range, throughout the selected year at our study site. We suggest the proposed method applies to other areas, requiring further testing under different forest types and topography.
... Although the topographic effect was corrected using topographic normalization during our pre-processing to correct the location of each pixel [33], slope steepness and slope direction had a complex effect on radar backscattering [62]. Topographic normalization in pre-processes cannot completely remove the impact of terrain in complex terrains, because the DEM products (e.g., SRTM or TanDEM-X DEM) used in topographic normalization may not be able to normalize SAR backscatter radiometrically under complex topographic conditions [62,63]. ...
Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.
... The InSAR technique estimates surface height by comparing phase differences between two coherent SAR image pairs acquired at slightly different sensor positions. While the high spatial resolution of SAR imagery can guarantee independent elevation measurements on a dense grid of sample points with the technique, phase-based measurements at microwave frequencies ensure all-weather and time capability to achieve DEM height accuracies of a few metres (Borlaf-Mena et al. 2020;Richards and Member 2006). Although the technique may not have been popular, probably due to the limited access to SAR data, the European Space Agency's (ESA) open access to continuous large-achieved focused Single Look Complex (SLC) Sentinel-1 SAR data containing phase components of the radar signal since 2014 and the provision of the Sentinel Application Platform (SNAP) for seamless processing of Sentinel products has opened up new opportunities for the generation of InSAR DEMs across the globe. ...
Generation of digital elevation models (DEM) over densely vegetated humid tropics using Sentinel-1 data and Interferometry synthetic aperture radar (InSAR) technique is revised based on recommended optimum perpendicular baselines, and accuracy assessment based on coherence cells is introduced. Six pairs of Sentinel-1 data within the optimum perpendicular baseline of 150 − 400 m and different temporal baselines acquired between 2015 and 2021 were processed using Sentinel Application Platform. Results revealed: that due to the extreme difficulty of achieving good coherence, interferograms are characterized by noise, generated DEMs are dominated by artefacts, higher elevation accuracy can be achieved within coherence cells, and within the recommended optimum perpendicular baseline range, elevation accuracy within coherence cells is mainly determined by the perpendicular baseline compared to temporal baseline. Therefore, this study can provide practical guide for InSAR generated DEMs evaluation in densely vegetated environments, and remote sensing method for regional-scale measurement and mapping of spots heights.
... Mapping Deforestation in Permanent Forest Reserve of Peninsular Malaysia with Multi-Temporal Sar Imagery and U-Net Based Semantic Segmentation, pp.,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] ...
... Mapping Deforestation in Permanent Forest Reserve of Peninsular Malaysia with Multi-Temporal Sar Imagery and U-Net Based Semantic Segmentation, pp., [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] ...
Deforestation is the long-term or permanent conversion of forest land to other uses, such as agriculture, mining, and urban development. As a result, deforestation has catastrophic consequences for the environment, including the loss of biodiversity, disruption of clean water supplies, and the acceleration of climate change. According to statistics, the deforestation trend in developing countries is at an alarming rate including Malaysia where plantation activities are the primary cause of forest loss. Recent anecdotal studies have demonstrated the effectiveness of the deep learning-based (DL) approach in producing deforestation maps. However, there are limited studies concentrating on DL approach for synthetic aperture radar (SAR) imaging due to complexity of the computational concepts of the method. The SAR imagery can be challenging to interpret but its all-weather and all-day capability can be critical in forest monitoring compared to optical imagery. Thus, in this study, we propose to map deforestation areas in Permanent Forest Reserve (HSK) using multi-temporal Sentinel-1 SAR data. Deep learning-based U-Net was employed to classify the SAR imagery as forest and non-forest due to its semantic segmentation capabilities. The experiment results showed that the proposed deep learning-based technique successfully achieved 0.993 of intersection over union (IoU) and 0.980 of overall accuracy (OA). Also, we explained the entire procedure from beginning to end as simple as possible for beginners to comprehend. In brief, the findings of this study have the potential to improve monitoring of damaged HSK areas, prioritize the restoration of the affected forest areas and protecting the forest lands from illegal deforestation activities.
... In our study, our choice was driven by the use of the CNES generic processing chain which can be used worldwide and the availability of the 30 m SRTM DEM model. In the framework of surface type classification, [40] have investigated the influence of the DEM employed for terrain normalization of backscatter and coherence data. The authors show that high-resolution TanDEM-X DEM (20 or 30 m resolution) was the global DEM providing the largest reduction of terrain induced variability. ...
In this study, we develop a novel method to automatically detect areas of snow avalanche debris using a color space segmentation technique applied to SAR image time series through January 2018 in the Swiss Alps. Debris avalanche zones are detected assuming that these areas are characterised by a significant and localised increase in SAR signal relative to the surrounding environment. We undertake a sensitivity study by calculating debris products by varying the D-M reference images (a stable reference image taken several weeks before the event). We examine the results according to the direction of the orbit, the characteristics of the terrain (slope, altitude, orientation) and also by evaluating the relevance of the detection with the help of an independent SPOT database ([1]) including 18,737 avalanche events. Small avalanches are not detected by SAR images and depending on the orientation of the terrain some avalanches are not detected by either the ascending or the descending orbit. The detection results vary with the reference image; best detection results are obtained with some selected individual dates with almost 70 % of verified avalanche events using the ascending orbit.
... Notice that Sentinel-1B satellite started to consistently acquire images over the area after May 2019. For the flatter terrain at the tropical site, the use of data from one relative orbit (54) was considered sufficient. SAR processing at this site was based on the NASADEM height data [31,32]. ...
... However, misclassifications were still observed and may be related to the inclusion of 2017 data (with the related noise problem) in the training sample. Other possible sources of error were the under-correction of slopes facing the sensor [23,49,54] and the elongation of the path traversed within the forest canopy on backslopes [42]. Such errors may be alleviated if topographic information is included (orientation, slope, incidence angle, etc.) [19]. ...
This study tested the ability of Sentinel-1 C-band to separate forest from other common land use classes (i.e., urban, low vegetation and water) at two different sites. The first site is characterized by temperate forests and rough terrain while the second by tropical forest and near-flat terrain. We trained a support vector machine classifier using increasing feature sets starting from annual backscatter statistics (average, standard deviation) and adding long-term coherence (i.e., coherence estimate for two acquisitions with a large time difference), as well as short-term (six to twelve days) coherence statistics from annual time series. Classification accuracies using all feature sets was high (>92% overall accuracy). For temperate forests the overall accuracy improved by up to 5% when coherence features were added: long-term coherence reduced misclassification of forest as urban, whereas short-term coherence statistics reduced the misclassification of low vegetation as forest. Classification accuracy for tropical forests showed little differences across feature sets, as the annual backscatter statistics sufficed to separate forest from low vegetation, the other dominant land cover. Our results show the importance of coherence for forest classification over rough terrain, where forest omission error was reduced up to 11%.
... As for the limitations of this study, the most important is related to the design of NFIs, which are not optimized for calibrating and validating remote sensing products [49], the large differences between the amount of data collected by the two SAR sensors, which limited a like-for-like comparison, and the available DEM (SRTM DEM) used for terrain normalization as a more precise DEM (e.g., Lidar based or Tandem-X DEM) allows for improved scattering area estimation reducing the effect of topography on the backscatter and thus improving the retrieval of the target biophysical characteristic [34,51]. Table A1. ...
While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors.
We assessed the nature and spatial and temporal patterns of deformation over the Miami Park bluffs on the eastern margin of Lake Michigan and investigated the factors controlling its observed deformation. Our approach involved the following steps: (1) extracting bluff deformation rates (velocities along the line of sight of the satellite) using a stack of Sentinel-1A radar imagery in ascending acquisition geometry acquired between 2017 and 2021 and applying the Intermittent Small Baseline Subset (ISBAS) InSAR time series analysis method; (2) generating high-resolution (5 cm) elevation models and orthophotos from temporal unmanned aerial vehicle (UAV) surveys acquired in 2017, 2019, and 2021; and (3) comparing the temporal variations in mass wasting events to other relevant datasets including the ISBAS-based bluff deformation time series, lake level (LL) variations, and local glacial stratigraphy. We identified areas witnessing high line-of-sight (LOS) deformation rates (up to −21 mm/year) along the bluff from the ISBAS analysis and seasonal deformation patterns associated with freeze-thaw cycles, suggesting a causal effect. The acceleration of slope failures detected from field and UAV acquisitions correlated with high LLs and intensified onshore wave energy in 2020. The adopted methodology successfully predicts landslides caused by freezes and thaws of the slope face by identifying prolonged slow deformation preceding slope failures, but it does not predict the catastrophic landslides preceded by short-lived LOS deformation related to LL rise.
Keywords: landslide monitoring; Sentinel-1; DInSAR; Intermittent Small Baseline Subset (ISBAS); Michigan
This paper presents an effort to evaluate the generated digital elevation model (DEM) from an active sensor onboard satellite of Sentinel-1A and from aerial photos taken using an unmanned aerial vehicle (UAV). The objective is to compare the quality of generated DEM and review the processes for disaster mitigation and prevention plans application. The radar data acquisition used in this study is pair of SLC-type radar data. The interferogram is processed from the coherence and the phase of complex data of the pair radar imageries. Meanwhile, aerial photography was taken within the smaller urban area in Padang City. The photogrammetry process to generate the DEM was conducted using the structure from motion (SfM) technique. The quality and procedures are reviewed by comparing the DEM products with other publicly available DEM data from DEMNAS, SRTM, and AW3D. This study found that generating the DEM from Sentinel-1 interferometry SAR is a challenging process. The product is unmatched and has lower quality compared to available DEM data due to several identified factors. In contrast, high computational cost photogrammetry produced good quality DEM if sufficient ground control points (GCP) were set.
