Remote Sensing

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6 pages, 188 KiB

Open AccessEditorial

Editorial for Special Issue: “Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors”

by Nan Xu, Yue Ma, Song Li and Debao Tan

Remote Sens. 2024, 16(11), 1821; https://doi.org/10.3390/rs16111821 - 21 May 2024

Viewed by 966

Abstract

In the past few decades, with the advent of climate change, population growth, agricultural irrigation, and industrial development, there have been increasing demands for water resources across the globe, especially in widely distributed arid areas or densely populated areas [...] Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

Research

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23 pages, 68756 KiB

Open AccessArticle

Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters

by Dongzhen Jia, Yu Li, Xiufeng He, Zhixiang Yang, Yihao Wu, Taixia Wu and Nan Xu

Remote Sens. 2023, 15(22), 5406; https://doi.org/10.3390/rs15225406 - 17 Nov 2023

Cited by 2 | Viewed by 1803

Abstract

Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, [...] Read more.

Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (RMSE) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with RMSEs of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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19 pages, 4260 KiB

Open AccessArticle

Boundary-Guided Semantic Context Network for Water Body Extraction from Remote Sensing Images

by Jie Yu, Yang Cai, Xin Lyu, Zhennan Xu, Xinyuan Wang, Yiwei Fang, Wenxuan Jiang and Xin Li

Remote Sens. 2023, 15(17), 4325; https://doi.org/10.3390/rs15174325 - 1 Sep 2023

Cited by 2 | Viewed by 1925

Abstract

Automatically extracting water bodies is a significant task in interpreting remote sensing images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance in processing RSIs, which have been widely used for fine-grained extraction of water bodies. However, it is difficult for the extraction [...] Read more.

Automatically extracting water bodies is a significant task in interpreting remote sensing images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance in processing RSIs, which have been widely used for fine-grained extraction of water bodies. However, it is difficult for the extraction accuracy of CNNs to satisfy the requirements in practice due to the limited receptive field and the gradually reduced spatial size during the encoder stage. In complicated scenarios, in particular, the existing methods perform even worse. To address this problem, a novel boundary-guided semantic context network (BGSNet) is proposed to accurately extract water bodies via leveraging boundary features to guide the integration of semantic context. Firstly, a boundary refinement (BR) module is proposed to preserve sufficient boundary distributions from shallow layer features. In addition, abstract semantic information of deep layers is also captured by a semantic context fusion (SCF) module. Based on the results obtained from the aforementioned modules, a boundary-guided semantic context (BGS) module is devised to aggregate semantic context information along the boundaries, thereby enhancing intra-class consistency of water bodies. Extensive experiments were conducted on the Qinghai–Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. The results demonstrate that the proposed BGSNet outperforms the mainstream approaches in terms of OA, MIoU, F1-score, and kappa. Specifically, BGSNet achieves an OA of 98.97% on the QTPL dataset and 95.70% on the LoveDA dataset. Additionally, an ablation study was conducted to validate the efficacy of the proposed modules. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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20 pages, 3918 KiB

Open AccessArticle

Channel Activity Remote Sensing Retrieval Model: A Case Study of the Lower Yellow River

by Taixia Wu, Zenan Xu, Ran Chen, Shudong Wang and Tao Li

Remote Sens. 2023, 15(14), 3636; https://doi.org/10.3390/rs15143636 - 21 Jul 2023

Cited by 3 | Viewed by 1551

Abstract

Meandering channel migration is a widespread phenomenon in rivers all around the world. Channel activity, which reflects the rate of change of a meandering channel, is calculated by averaging lateral channel migration. Channel migration can create new channels and abandon old ones, with [...] Read more.

Meandering channel migration is a widespread phenomenon in rivers all around the world. Channel activity, which reflects the rate of change of a meandering channel, is calculated by averaging lateral channel migration. Channel migration can create new channels and abandon old ones, with effects on the natural environment. Floods can even lead to excessive rates of channel migration, which can threaten cities or farmland. Remote sensing can detect the spatial and temporal dynamic characteristics of the river channel, taking into account both spatial and temporal resolution, and can help in planning for the safety of the river channel in advance. Previous studies on river channels have suffered from a low accuracy of data, low level of automation, and subjectivity. To overcome these limitations, we propose a channel activity remote sensing retrieval model (CARSM) in this paper. CARSM extracts water using the modified normalized difference water index (MNDWI) combined with Otsu’s method on the Google Earth Engine (GEE) platform, then extracts the channel centerlines via water mask maps using RivWidthCloud, and finally calculates channel activity based on the geometric relationship of the channel centerlines. With more objective extraction results, CARSM can guarantee more than 95% accuracy of channel activity and its high degree of automation can save a lot of labor costs. We use Landsat images to monitor the channel of the Lower Yellow River and calculate the overall and segmental channel activity separately. Our results show that the overall channel activity of the Lower Yellow River has gradually decreased between 1990 and 2020, with decreases of 33.04% and 41.06%, respectively. Analysis of channel activity reveals that the water sediment pattern of the Lower Yellow River changed from siltation to scouring after the completion of Xiaolangdi Reservoir, and the Lower Yellow River is gradually becoming stable. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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18 pages, 4516 KiB

Open AccessArticle

MSAFNet: Multiscale Successive Attention Fusion Network for Water Body Extraction of Remote Sensing Images

by Xin Lyu, Wenxuan Jiang, Xin Li, Yiwei Fang, Zhennan Xu and Xinyuan Wang

Remote Sens. 2023, 15(12), 3121; https://doi.org/10.3390/rs15123121 - 15 Jun 2023

Cited by 9 | Viewed by 2011

Abstract

Water body extraction is a typical task in the semantic segmentation of remote sensing images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional methods in mining visual features; however, due to the inherent convolutional mechanism of the network, spatial details and abstract semantic [...] Read more.

Water body extraction is a typical task in the semantic segmentation of remote sensing images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional methods in mining visual features; however, due to the inherent convolutional mechanism of the network, spatial details and abstract semantic representations at different levels are difficult to capture accurately at the same time, and then the extraction results decline to become suboptimal, especially on narrow areas and boundaries. To address the above-mentioned problem, a multiscale successive attention fusion network, named MSAFNet, is proposed to efficiently aggregate the multiscale features from two aspects. A successive attention fusion module (SAFM) is first devised to extract multiscale and fine-grained features of water bodies, while a joint attention module (JAM) is proposed to further mine salient semantic information by jointly modeling contextual dependencies. Furthermore, the multi-level features extracted by the above-mentioned modules are aggregated by a feature fusion module (FFM) so that the edges of water bodies are well mapped, directly improving the segmentation of various water bodies. Extensive experiments were conducted on the Qinghai-Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. Numerically, MSAFNet reached the highest accuracy on both QTPL and LoveDA datasets, including Kappa, MIoU, FWIoU, F1, and OA, outperforming several mainstream methods. Regarding the QTPL dataset, MSAFNet peaked at 99.14% and 98.97% in terms of F1 and OA. Although the LoveDA dataset is more challenging, MSAFNet retained the best performance, with F1 and OA being 97.69% and 95.87%. Additionally, visual inspections exhibited consistency with numerical evaluations. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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19 pages, 9210 KiB

Open AccessArticle

Stereoscopic Monitoring Methods for Flood Disasters Based on ICESat-2 and Sentinel-2 Data

by Yongqiang Cao, Mengran Wang, Jiaqi Yao, Fan Mo, Hong Zhu, Liuru Hu and Haoran Zhai

Remote Sens. 2023, 15(12), 3015; https://doi.org/10.3390/rs15123015 - 9 Jun 2023

Cited by 3 | Viewed by 1818

Abstract

Climate change has led to an increased frequency of extreme precipitation events, resulting in increased damage from rainstorms and floods. Rapid and efficient flood forecasting is crucial. However, traditional hydrological simulation methods that rely on site distribution are limited by the limited availability [...] Read more.

Climate change has led to an increased frequency of extreme precipitation events, resulting in increased damage from rainstorms and floods. Rapid and efficient flood forecasting is crucial. However, traditional hydrological simulation methods that rely on site distribution are limited by the limited availability of data and cannot provide fast and accurate flood monitoring information. Therefore, this study took the flood event in Huoqiu County in 2020 as an example and proposes a three-dimensional flood monitoring method based on active and passive satellites, which provides effective information support for disaster prevention and mitigation. The experimental results indicated the following: (1) the flood-inundated area was 704.1 km², with the Jiangtang Lake section of the Huaihe River and the southern part of Chengdong Lake being the largest affected areas; (2) water levels in the study area ranged from 15.36 m to 17.11 m, which is 4–6 m higher than the original water level. The highest flood water level areas were the Jiangtang Lake section and the flat area in the south of Chengdong Lake, with Chengdong Lake and the north of Chengxi Lake having the greatest water level increase; (3) the flood water depth was primarily between 4 m and 7 m, with a total flood storage capacity of 2833.47 million m³, with Jiangtang Lake having the largest flood storage capacity; and (4) the rainstorm and flood disaster caused a direct economic loss of approximately CNY 7.5 billion and affected a population of approximately 91 thousand people. Three-dimensional monitoring of floods comprehensively reflects the inundation status of floods and can provide valuable information for flood prediction and management. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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18 pages, 5325 KiB

Open AccessArticle

Denoising and Accuracy Evaluation of ICESat-2/ATLAS Photon Data for Nearshore Waters Based on Improved Local Distance Statistics

by Junfeng Xie, Jincheng Zhong, Fan Mo, Ren Liu, Xiang Li, Xiaomeng Yang and Junze Zeng

Remote Sens. 2023, 15(11), 2828; https://doi.org/10.3390/rs15112828 - 29 May 2023

Cited by 10 | Viewed by 1921

Abstract

The second-generation spaceborne LiDAR-Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which can penetrate a certain depth of water, and is one of the important means to obtain the water depth information of nearshore water. However, [...] Read more.

The second-generation spaceborne LiDAR-Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which can penetrate a certain depth of water, and is one of the important means to obtain the water depth information of nearshore water. However, due to the influence of the atmospheric environment, water quality and color, the system itself and other factors, the photon point cloud introduces survey noise, which restricts the survey accuracy and reliability of nearshore water depth. Therefore, in this study, we presented a photon denoising algorithm for layered processing of submarine surface. Firstly, rough denoising of the original photon data was completed by smoothing filtering. Then, elevation histogram statistics were carried out on the photon data, two peaks of the histogram were fitted by a double Gaussian function, and the intersection of two curves was then taken to separate the water surface and underwater photons. The surface photons were denoised by the DBSCAN clustering algorithm. Then according to the distribution characteristics of underwater signal photons, a single-photon point cloud filtering bathymetric method was proposed based on improved local distance statistics (LDSBM), which was used for fine denoising of underwater point cloud data. Finally, the Gaussian function was used to fit the frequency histogram, and the signal photons were screened to extract the water depth information. In this study, 13 groups of the ATL03 dataset from the Xisha Islands, the St. Thomas and the Acklins Island were used for denoising. The denoising results were compared with the signal photons manually marked and the signal photons extracted by the official built-in method (OM). The experimental results showed that, compared with the official method results of ATL03, the LDSBM had a higher F value (comprehensive evaluation index), with an average of more than 96.70%. In conclusion, the proposed underwater single-photon point cloud filtering bathymetric method was superior to the traditional algorithm and could recover terrain information accurately. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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19 pages, 5728 KiB

Open AccessArticle

Analysis of Spatial and Temporal Variation in Water Coverage in the Sub-Lakes of Poyang Lake Based on Multi-Source Remote Sensing

by Chunyang Wang, Wenying Xie, Tengteng Li, Guiping Wu, Yongtuo Wu, Qifeng Wang, Zhixia Xu, Hao Song, Yingbao Yang and Xin Pan

Remote Sens. 2023, 15(11), 2788; https://doi.org/10.3390/rs15112788 - 26 May 2023

Cited by 3 | Viewed by 1965

Abstract

As the largest freshwater lake in China, Poyang Lake is an internationally important wetland and the largest migratory bird habitat in Asia. Many sub-lakes distributed in the lake basin are seasonal lakes, which have a significant impact on hydro-ecological processes and are susceptible [...] Read more.

As the largest freshwater lake in China, Poyang Lake is an internationally important wetland and the largest migratory bird habitat in Asia. Many sub-lakes distributed in the lake basin are seasonal lakes, which have a significant impact on hydro-ecological processes and are susceptible to various changes. In this study, using multi-source remote sensing data, a continuous time-series construction method of water coverage suitable in Poyang Lake was developed. That method combined the downscaling of the MNDWI (modified normalized difference water index) with the ISODATA (iterative self-organizing data analysis technique algorithm), and its accuracy can be up to 97% in the months when Landsat 8 is available or 87% when it is unavailable. Based on that method, the increasing variation in water coverage was observed in the sub-lakes of Poyang Lake during 2013–2020 to be within a range of 200–690 km² normally. The center of the sub-lakes always remained inundated (>80% inundation frequency), while the surrounding areas were probably kept dry for seven months (except for June to September). The dominant influencing factors of water coverage variations were different in different hydrological periods (wet season and dry–wet season: discharge; dry season: temperature and wind speed; wet–dry season: temperature and precipitation). In addition, “returning farmland to lakes” affected the increase in the water area in the sub-lakes. This study is helpful for the management of water resources and the protection of migratory birds in the Poyang Lake region. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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16 pages, 10746 KiB

Open AccessArticle

Assessment of ICESat-2’s Horizontal Accuracy Using an Iterative Matching Method Based on High-Accuracy Terrains

by Ming Gao, Shuai Xing, Guoping Zhang, Xinlei Zhang and Pengcheng Li

Remote Sens. 2023, 15(9), 2236; https://doi.org/10.3390/rs15092236 - 23 Apr 2023

Cited by 5 | Viewed by 2264

Abstract

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data [...] Read more.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data support for further study. The conventional terrain-matching methods regard the laser ground track as a whole, ignoring the individual differences caused by the interaction of photons during flight. Therefore, a novel terrain-matching method using a two-dimensional affine transformation model was proposed to describe the deformation of laser tracks. The least-square optimizes the model parameters with the high-accuracy terrain data to obtain the best matching result. The results in McMurdo Dry Valley (MDV), Antarctica, and Zhengzhou (ZZ), China, demonstrate that the proposed method can verify geolocation accuracy and indicate that the average horizontal accuracy of ICESat-2 V5 data is about 3.86 m in MDV and 4.67 m in ZZ. It shows that ICESat-2 has good positioning accuracy, even in mountainous areas with complex terrain. Additionally, the random forest (RF) model was calculated to analyze the influence of four factors on geographic location accuracy. The slope and signal-to-noise ratio (SNR) are considered the crucial factors affecting the accuracy of ICESat-2 data. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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18 pages, 4153 KiB

Open AccessArticle

Water Balance Analysis of Hulun Lake, a Semi-Arid UNESCO Wetland, Using Multi-Source Data

by Biao Sun, Zhiyan Yang, Shengnan Zhao, Xiaohong Shi, Yu Liu, Guodong Ji and Jussi Huotari

Remote Sens. 2023, 15(8), 2028; https://doi.org/10.3390/rs15082028 - 11 Apr 2023

Cited by 7 | Viewed by 2024

Abstract

Hulun Lake is the largest lake in northeastern China, and its basin is located in China and Mongolia. This research aims to analyze the dynamic changes in the water volume of Hulun Lake and to estimate the groundwater recharge of the lake during [...] Read more.

Hulun Lake is the largest lake in northeastern China, and its basin is located in China and Mongolia. This research aims to analyze the dynamic changes in the water volume of Hulun Lake and to estimate the groundwater recharge of the lake during the past 60 years. Multi-source data were used, and water-level-data-interpolation extrapolation, water-balance equations, and other methods were applied. The proportion of the contribution of each component to the quantity of water in Hulun Lake during the last 60 years was accurately calculated. Evaporation loss was the main component in the water loss in Hulun Lake. In the last 60 years, the average annual runoff into the lake was about 1.202 billion m³, and it was the factor with the largest variation range and the leading factor affecting the changes in the quantity of water in Hulun Lake. There was groundwater recharge in Hulun Lake for a long period, and the average annual groundwater recharge was about 776 million m³ (excluding leakage). The contribution ratio of the river water, groundwater, and precipitation to the recharging of Hulun Lake was about 5:3:2. The changes in the quantity of water in Hulun Lake are affected by climate change and human activities in China and Mongolia, especially those in Mongolia. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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18 pages, 8442 KiB

Open AccessArticle

A Pre-Pruning Quadtree Isolation Method with Changing Threshold for ICESat-2 Bathymetric Photon Extraction

by Guoping Zhang, Shuai Xing, Qing Xu, Pengcheng Li and Dandi Wang

Remote Sens. 2023, 15(6), 1629; https://doi.org/10.3390/rs15061629 - 17 Mar 2023

Cited by 6 | Viewed by 1619

Abstract

The new generation of spaceborne laser altimeter, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which can simultaneously generate laser reflections on the water surface and underwater, is a potential data source for exploring water depth in nearshore environments. To achieve this scientific [...] Read more.

The new generation of spaceborne laser altimeter, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which can simultaneously generate laser reflections on the water surface and underwater, is a potential data source for exploring water depth in nearshore environments. To achieve this scientific goal, accurate bathymetric photon extraction is needed. This study proposed a pre-pruning quadtree isolation (PQI) method with changing threshold. Firstly, the pre-pruning step is introduced. Photons are transformed into different levels in the isolated quadtree structure according to spatial density. Then, the frequency histogram of photon elevation and isolated level (IL) is generated, the IL thresholds in different depth ranges are calculated by the Otsu method, and the bathymetric photons are extracted. The results in the Culebra archive show that this method achieved a 92.71% F1 score. Noise rate and water depth are the main factors affecting the extraction of sounding photons. When the photon density gradually increases from 2–4 pts/m to 6–8 pts/m, the F1 score of PQI decreases by no more than two percent. In different depth ranges, the extraction results of PQI are also better than those of comparison methods. Therefore, PQI can provide reliable theoretical support for nearshore areas lacking water depth data. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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16 pages, 16773 KiB

Open AccessArticle

Extracting a Connected River Network from DEM by Incorporating Surface River Occurrence Data and Sentinel-2 Imagery in the Danjiangkou Reservoir Area

by Lijie Lu, Lihui Wang, Qichi Yang, Pengcheng Zhao, Yun Du, Fei Xiao and Feng Ling

Remote Sens. 2023, 15(4), 1014; https://doi.org/10.3390/rs15041014 - 12 Feb 2023

Cited by 7 | Viewed by 3318

Abstract

Accurate extraction of river network from the Digital Elevation Model (DEM) is a significant content in the application of a distributed hydrological model. However, the study of river network extraction based on DEM has some limitations, such as location offset, inaccurate parallel channel [...] Read more.

Accurate extraction of river network from the Digital Elevation Model (DEM) is a significant content in the application of a distributed hydrological model. However, the study of river network extraction based on DEM has some limitations, such as location offset, inaccurate parallel channel and short circuit of meandering channels. In this study, we proposed a new enhancement method for NASADEM V001 in the Danjiangkou Reservoir area. We used Surface Water Occurrence (SWO) and Sentinel-2 data to describe vertical limit differences between morphological units to complement actual flow path information from NASADEM data by a stream burning method. The differences between the extracted river network and the actual river network were evaluated in three different geographical regions. Compared with the actual river centerline, the location error of the river network extraction was significantly reduced. The average offset distances between river network extraction and the actual river network were 68.38, 36.99, and 21.59 m in the three test areas. Compared with NASADEM V001, the average offset distances in the three test areas were reduced by 7.26, 40.29, and 42.35%, respectively. To better estimate accuracy, we also calculated and compared the accuracy of the river network based on MERIT Hrdro and HydroSHEDS. The experimental results demonstrated that the method can effectively improve the accuracy of river network extraction and meet the needs of hydrological simulation. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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25 pages, 5109 KiB

Open AccessArticle

Analysis and Correction of Water Forward-Scattering-Induced Bathymetric Bias for Spaceborne Photon-Counting Lidar

by Jian Yang, Yue Ma, Huiying Zheng, Yuanfei Gu, Hui Zhou and Song Li

Remote Sens. 2023, 15(4), 931; https://doi.org/10.3390/rs15040931 - 8 Feb 2023

Cited by 4 | Viewed by 2395

Abstract

The new spaceborne photon-counting lidar, i.e., ICESat-2, has shown great advantages in obtaining nearshore bathymetry at a global scale. The forward-scattering effect in the water column is one of the main error sources in airborne lidar bathymetry (ALB). However, the magnitude of the [...] Read more.

The new spaceborne photon-counting lidar, i.e., ICESat-2, has shown great advantages in obtaining nearshore bathymetry at a global scale. The forward-scattering effect in the water column is one of the main error sources in airborne lidar bathymetry (ALB). However, the magnitude of the bathymetric bias for spaceborne lidars and how can we effectively correct this bias have not been evaluated and are very worthy of investigation. In this study, the forward-scattering effect on spaceborne photon-counting lidar bathymetry is quantitatively modeled and analyzed based on the semi-analytic Monte Carlo simulation method. Meanwhile, an empirical formula for correcting forward-scattering-induced bathymetric bias specific to ICESat-2 is derived. When the water depth exceeds 20 m, this bias cannot be neglected for ICESat-2 even in clear open ocean waters. In two study areas with local in situ measurements (St. Thomas and Hawaii), the bathymetric bias of ICESat-2 in deep waters (>20 m) is corrected from exceeding 50 cm to less than 13 cm using the proposed empirical formula. This study is valuable to evaluate and correct the forward-scattering-induced bias for the existing ICESat-2 and is also fundamental to optimizing the hardware parameters of a possible future photon-counting bathymetric lidar. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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18 pages, 17525 KiB

Open AccessArticle

Monitoring of 35-Year Mangrove Wetland Change Dynamics and Agents in the Sundarbans Using Temporal Consistency Checking

by Zhen Zhang, Md Rasel Ahmed, Qian Zhang, Yi Li and Yangfan Li

Remote Sens. 2023, 15(3), 625; https://doi.org/10.3390/rs15030625 - 20 Jan 2023

Cited by 11 | Viewed by 2888

Abstract

Mangrove wetlands are rapidly being lost due to anthropogenic disturbances and natural processes, such as sea-level rise (SLR), but are also recovering as a result of conservation efforts. Accurate and contemporary mangrove maps to detect their distribution and changes are urgently needed to [...] Read more.

Mangrove wetlands are rapidly being lost due to anthropogenic disturbances and natural processes, such as sea-level rise (SLR), but are also recovering as a result of conservation efforts. Accurate and contemporary mangrove maps to detect their distribution and changes are urgently needed to understand how mangroves respond to global change and develop effective conservation projects. Here, we developed a new change detection algorithm called temporal consistency checking combining annual classification and spectral time series (TCC-CS) for tracking mangrove losses and gains. Specifically, mangrove change events were determined by measuring the deviation of greenness and wetness of candidate change segments from automatically collected mangrove reference samples. By applying to the world’s largest mangrove patches, we monitored the 35-year mangrove trajectory in the Sundarbans from 1988 to 2022 using all available Landsat images on the Google Earth Engine platform. In the Sundarbans, 18,501.89 ha of mangroves have been gained, but these have been offset by losses of 27,009.79 ha, leading to a net mangrove loss of 1.42% (8507.9 ha) in the past 35 years. We further mapped the pixel-level change agents and found that SLR-induced erosion and degradation, instead of human activities, were the major drivers of losses in the Sundarbans. Trend analysis on loss agents indicates that mangrove losses caused by human activities, such as the expansion of croplands and aquaculture ponds, have declined, but SLR is still a persistent threat to mangrove wetlands in this iconic mangrove area. Our study provides a computationally efficient methodology for examining large-scale mangrove changes, and the resultant annual mangrove maps provide strong support for mangrove conservation in the Sundarbans. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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22 pages, 4645 KiB

Open AccessArticle

Run-Length-Based River Skeleton Line Extraction from High-Resolution Remote Sensed Image

by Helong Wang, Dingtao Shen, Wenlong Chen, Yiheng Liu, Yueping Xu and Debao Tan

Remote Sens. 2022, 14(22), 5852; https://doi.org/10.3390/rs14225852 - 18 Nov 2022

Cited by 1 | Viewed by 1611

Abstract

Automatic extraction of the skeleton lines of river systems from high-resolution remote-sensing images has great significance for surveying and managing water resources. A large number of existing methods for the automatic extraction of skeleton lines from raster images are primarily used for simple [...] Read more.

Automatic extraction of the skeleton lines of river systems from high-resolution remote-sensing images has great significance for surveying and managing water resources. A large number of existing methods for the automatic extraction of skeleton lines from raster images are primarily used for simple graphs and images (e.g., fingerprint, text, and character recognition). These methods generally are memory intensive and have low computational efficiency. These shortcomings preclude their direct use in the extraction of skeleton lines from large volumes of high-resolution remote-sensing images. In this study, we developed a method to extract river skeleton lines based entirely on run-length encoding. This method attempts to replace direct raster encoding with run-length encoding for storing river data, which can considerably compress raster data. A run-length boundary tracing strategy is used instead of complete raster matrix traversal to quickly determine redundant pixels, thereby significantly improving the computational efficiency. An experiment was performed using a 0.5 m-resolution remote-sensing image of Yiwu city in the Chinese province of Zhejiang. Raster data for the rivers in Yiwu were obtained using both the DeepLabv3+ deep learning model and the conventional visual interpretation method. Subsequently, the proposed method was used to extract the skeleton lines of the rivers in Yiwu. To compare the proposed method with the classical raster-based skeleton line extraction algorithm developed by Zhang and Suen in terms of memory consumption and computational efficiency, the visually interpreted river data were used to generate skeleton lines at different raster resolutions. The results showed that the proposed method consumed less than 1% of the memory consumed by the classical method and was over 10 times more computationally efficient. This finding suggests that the proposed method has the potential for river skeleton line extraction from terabyte-scale remote-sensing image data on personal computers. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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34 pages, 62588 KiB

Open AccessTechnical Note

Use of ICEsat-2 and Sentinel-2 Open Data for the Derivation of Bathymetry in Shallow Waters: Case Studies in Sardinia and in the Venice Lagoon

by Massimo Bernardis, Roberto Nardini, Lorenza Apicella, Maurizio Demarte, Matteo Guideri, Bianca Federici, Alfonso Quarati and Monica De Martino

Remote Sens. 2023, 15(11), 2944; https://doi.org/10.3390/rs15112944 - 5 Jun 2023

Cited by 4 | Viewed by 2829

Abstract

Despite the high accuracy of conventional acoustic hydrographic systems, measurement of the seabed along coastal belts is still a complex problem due to the limitations arising from shallow water. In addition to traditional echo sounders, airborne LiDAR also suffers from high application costs, [...] Read more.

Despite the high accuracy of conventional acoustic hydrographic systems, measurement of the seabed along coastal belts is still a complex problem due to the limitations arising from shallow water. In addition to traditional echo sounders, airborne LiDAR also suffers from high application costs, low efficiency, and limited coverage. On the other hand, remote sensing offers a practical alternative for the extraction of depth information, providing fast, reproducible, low-cost mapping over large areas to optimize and minimize fieldwork. Satellite-derived bathymetry (SDB) techniques have proven to be a promising alternative to supply shallow-water bathymetry data. However, this methodology is still limited since it usually requires in situ observations as control points for multispectral imagery calibration and bathymetric validation. In this context, this paper illustrates the potential for bathymetric derivation conducted entirely from open satellite data, without relying on in situ data collected using traditional methods. The SDB was performed using multispectral images from Sentinel-2 and bathymetric data collected by NASA’s ICESat-2 on two areas of relevant interest. To assess outcomes’ reliability, bathymetries extracted from ICESat-2 and derived from Sentinel-2 were compared with the updated and reliable data from the BathyDataBase of the Italian Hydrographic Institute. Full article

(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)

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