A Method for SRTM DEM Elevation Error Correction in Forested Areas Using ICESat-2 Data and Vegetation Classification Data

Li, Yi; Fu, Haiqiang; Zhu, Jianjun; Wu, Ke‐Fu; Yang, Pan-feng; Wang, Li; Gao, Shijuan

doi:10.3390/rs14143380

Cited by 19 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only minor changes between the HAND prior case (a) and the non-informed prior case (b) were observed at this study site. Since the area is densely vegetated, the quality of the SRTM DEM to represent ground topography is in question [52,53]. As observed, most areas have relatively similar and relatively higher HAND values (c) outside the drainage network compared to the other sites, possibly confirming the tree canopy height issue.…”

Section: Suriname and Belizementioning

confidence: 86%

Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference

Tupas,

Roth,

Bauer-Marschallinger

et al. 2023

Water

View full text Add to dashboard Cite

Sentinel-1-based flood mapping works well but with well-known issues over rugged terrain. Applying exclusion masks to improve the results is common practice in unsupervised and global applications. One such mask is the height above the nearest drainage (HAND), which uses terrain information to reduce flood lookalikes in SAR images. The TU Wien flood mapping algorithm is one operational workflow using this mask. Being a Bayesian method, this algorithm can integrate auxiliary information as prior probabilities to improve classifications. This study improves the TU Wien flood mapping algorithm by introducing a HAND prior function instead of using it as a mask. We estimate the optimal function parameters and observe the performance in flooded and non-flooded scenarios in six study sites. We compare the flood maps generated with HAND and (baseline) non-informed priors with reference CEMS rapid mapping flood extents. Our results show enhanced performance by decreasing false negatives at the cost of slightly increasing false positives. In utilizing a single parametrization, the improved algorithm shows potential for global implementation.

show abstract

Section: Suriname and Belizementioning

confidence: 86%

Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference

Tupas,

Roth,

Bauer-Marschallinger

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…For densely vegetated areas and highly mountainous regions the artefacts in the DEM need to be corrected. Following methods proposed by [26][27] [28] can be used to remove the artefacts before proceeding to next step. Afterwards the elevation points that falls under each flood boundary is extracted and corresponding percentiles are computed.…”

Section: Flood Depth Calculation 1 Extraction Of Surface Water Levelsmentioning

confidence: 99%

Flood Depth Estimation in Agricultural Lands From L and C-Band Synthetic Aperture Radar Images and Digital Elevation Model

Surampudi

Kumar

2023

IEEE Access

View full text Add to dashboard Cite

Flood depth is one of the important attributes in damage assessment especially in areas where agro-based activities takes place. Assessing impacted region turns out to be exceptionally difficult in the case of low-lying catchments with arable lands, which inundates due to extreme precipitation and flood modelling becomes impossible. Under these circumstances, SAR derived flood maps are valuable in removing different constraints related to flood modelling with high degree of profundity. Depth estimation from non-contact based methods requires, flood boundary as a primary input. However, detecting flood depth in emerging vegetation poses a complex challenge in terms of boundary estimation due to composite signatures that are manifested on SAR data. In this paper, a new approach is proposed to extract flood boundaries by fusing SAR data of two different frequencies, which are sensitive to water level changes. In the first step, wavelet fusion is applied to combine L-and C-band SAR data followed by Otsu"s segmentation method to extract varying levels of flood boundaries. In the second step, SRTM Digital Elevation Model (DEM) with 30m horizontal resolution is used on each boundary for statistical analysis based on which water surface levels are extracted. In the final step, depth levels are calculated from the water surface elevation and DEM. Floodwater Depth Estimation tool (FwDET) derived depth measurements are used to calibrate the statistical thresholds for derived flood depths. The study carried out on 2016 Assam flood event shows maximum flood depth of 1.56 meters in the selected study area and the results are verified with evidence-based ground truth collections which showed RMSE error of 0.25 meters from the measured values.INDEX TERMS DEM statistical analysis, Flood depth, SAR, water surface levels wavelet fusion.

show abstract

“…At present, the accuracy of the DEM can be validated through various methods, primarily by comparing and evaluating it against high-precision ground control points [61], high-precision DEM models generated from airborne LiDAR point cloud data [62], and satellite-borne LiDAR height measurements [63,64], which serve as reference data. Given the complex environmental conditions and the absence of ground control point data and high-precision airborne radar data for reference in the study area, this paper employs ICESat-2 satellite altimetry data along with NASA DEM and ALOS DEM to evaluate the accuracy of the InSAR DEM.…”

Section: Insar Dem Accuracy Assessmentmentioning

confidence: 99%

The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin

Yang,

Du,

et al. 2023

Water

View full text Add to dashboard Cite

Quantifying the effects of alpine GMB (Glacier Mass Balance) on river runoff is an important content of climate change. Uncertainty exists in GMB monitoring when applying remote-sensing technology. There are several reasons for these uncertainties, such as terrain deviation co-registration among different topographic data, the mismatch between GSE (Glacier Surface Elevation) from satellite monitoring and the GMB that comprises the physical glacier properties, the driving factors of GMB, and the response patterns of the runoff within the basin. This paper proposed a method based on the ridge line co-registration of DEMs (Digital Elevation Models), and the Tailan River basin, which is a typical glacier melt runoff recharge basin located in the southern Tianshan Mountains, was selected. Abnormal values in GSE changes were removed using ice thickness data, and the GSE results were optimized based on the regularity of the GSE change with altitude to estimate the GMB. The driving factors of the GMB and the response characteristics of the runoff in the basin were also explored. The results showed that the accuracy of the optimized GSE results across different periods has improved by more than 25%. The mean annual thinning value of GSE in the basin from 2000 to 2022 was −0.25 ± 0.02 m·a−1, corresponding to a GMB value of −0.30 ± 0.02 m w.e.a−1, indicating a consistent GMB loss state. Combined with climate data, the glaciers in the basin were impacted by rising temperatures, and the smallest increase in annual precipitation in the basin was insufficient to compensate for the GMB loss. Moreover, in the past 22 years, glacier meltwater accounts for 46.15% of the total runoff in the Tailan River basin.

show abstract

A Method for SRTM DEM Elevation Error Correction in Forested Areas Using ICESat-2 Data and Vegetation Classification Data

Cited by 19 publications

References 27 publications

Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference

Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference

Flood Depth Estimation in Agricultural Lands From L and C-Band Synthetic Aperture Radar Images and Digital Elevation Model

The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin

Contact Info

Product

Resources

About