A novel Landsat-based automated mapping of marsh wetland in the headwaters of the Brahmaputra, Ganges and Indus Rivers, southwestern Tibetan Plateau

Liu, Qionghuan; Zhang, Yili; Liu, Linshan; Wang, Zhaofeng; Nie, Yong; Rai, Mohan

doi:10.1016/j.jag.2021.102481

Cited by 6 publications

(5 citation statements)

References 72 publications

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“…Some authors have demonstrated that wetland mapping, which is based on Google Earth Engine (GEE), feature optimization, and a random forest (RF) model (GFORF), provides new perspectives on studying them. The results obtained showed that the overall accuracy for wetland data was 82.73%, 83.16%, 82.47%, and 88.14% across the interval 1990-2010 [69]. As a result of improving the accuracy of wetland mapping, it is possible to identify the dynamics of wetlands, which justifies the implementation of ecological services and protection measures in this category of study [69].…”

Section: Discussionmentioning

confidence: 95%

“…The results obtained showed that the overall accuracy for wetland data was 82.73%, 83.16%, 82.47%, and 88.14% across the interval 1990-2010 [69]. As a result of improving the accuracy of wetland mapping, it is possible to identify the dynamics of wetlands, which justifies the implementation of ecological services and protection measures in this category of study [69].…”

Section: Discussionmentioning

confidence: 95%

“…Wetlands not only affect local hydrology and ecosystems but also regulate human environmental conditions [69]. Some authors have demonstrated that wetland mapping, which is based on Google Earth Engine (GEE), feature optimization, and a random forest (RF) model (GFORF), provides new perspectives on studying them.…”

Section: Discussionmentioning

confidence: 99%

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Identification of Vegetation Surfaces and Volumes by Height Levels in Reservoir Deltas Using UAS Techniques—Case Study at Gilău Reservoir, Transylvania, Romania

Rus,

Șerban,

Brețcan

et al. 2024

Sustainability

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The hydrophilic vegetation from reservoir deltas sustains rapid expansions in surface and important increases in vegetal mass against a background of a significant influx of alluvium and nutrients from watercourses. It contributes to reservoir water quality degradation and reservoir silting due to organic residues. In this paper, we propose an evaluation method of two-dimensional and three-dimensional parameters (surfaces and volumes of vegetation), using the combined photogrammetric techniques from the UAS category. Raster and vector data—high-resolution orthophotoplan (2D), point cloud (pseudo-LIDAR) (3D), points that defined the topographic surface (DTM—Digital Terrain Model (3D) and DSM—Digital Surface Model (3D))—were the basis for the realization of grid products (a DTM and DSM, respectively). After the successive completion of the operations within the adopted workflow (data acquisition, processing, post-processing, and their integration into GIS), after the grid analysis, the two proposed variables (topics) of this research, respectively, the surface of vegetation and its volume, resulted. The data acquisition area (deriving grids with a centimeter resolution) under the conditions of some areas being inaccessible using classical topometric or bathymetric means (low depth, the presence of organic mud and aquatic vegetation, etc.) has an important role in the reservoirs’ depth dynamics and reservoir usage. After performing the calculations in the abovementioned direction, we arrived at results of practical and scientific interest: Cut Volume = 196,000.3 m3, Cut 2D Surface Area = 63,549 m2, Fill Volume = 16.59998 m3, Fill 2D Surface Area = 879.43 m2, Total Volume Between Surfaces = 196,016.9 m3. We specify that this approach does not aim to study the vegetation’s diversity but to determine its dimensional components (surface and volume), whose organic residues participate in mitigating the reservoir functions (water supply, hydropower production, flash flood attenuation capacity, etc.).

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 95%

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Identification of Vegetation Surfaces and Volumes by Height Levels in Reservoir Deltas Using UAS Techniques—Case Study at Gilău Reservoir, Transylvania, Romania

Rus,

Șerban,

Brețcan

et al. 2024

Sustainability

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“…6 Remote sensing is a key technology used for wetlands monitoring and automatically extracting wetland information. 7 Although there are many challenges arising from the sophisticated hydrological conditions in the wetlands, remote sensing techniques are increasingly being used in wetland research at different scales. [8][9][10] Hu et al 11 combined unmanned/unoccupied aerial vehicle remote sensing techniques with deep learning for wetland mapping in the playa wetland, United States, with very high accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with remote sensing technology, traditional field-based techniques are too expensive and more labor-intensive 6 . Remote sensing is a key technology used for wetlands monitoring and automatically extracting wetland information 7 . Although there are many challenges arising from the sophisticated hydrological conditions in the wetlands, remote sensing techniques are increasingly being used in wetland research at different scales 8…”

Section: Introductionmentioning

confidence: 99%

Wetland mapping based on feature optimization and Google Earth Engine in Zhalong Nature Reserve

Huang

2022

J. Appl. Rem. Sens.

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Wetlands have very important and irreplaceable ecological service functions in protecting biodiversity and maintaining ecological balance. Due to the unique hydrological characteristics of wetlands, large-scale wetland mapping methods are usually time-consuming, labor intensive, and expensive. With the rise of big data and cloud computing platforms, there are opportunities for long time series and large-scale spatial data processing that improve largescale wetland mapping. In this study, the Sentinel-1, Sentinel-2, and digital elevation model derived from the Google Earth Engine were used to extract the wetland extent in the Zhalong Nature Reserve based on the Jeffries-Matusita (JM) distance for feature optimization and random forest. Research shows the following: (1) The JM distance for each feature showed that the optical features had the highest separation in all five features and (2) the classification after feature optimization performed best. User accuracy (UA) and producer accuracy (PA) for wetland reached 89.64% and 83.00%, respectively, and overall accuracy reached 90.76%. After feature optimization, PA and UA of wetland were each increased 4.26% and 1.07%, respectively, and the number of features was reduced from 46 to 28. The method used in this study is highly accurate in wetland mapping and can effectively avoid the problem of data redundancy.

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Continuous change detection outperforms traditional post-classification change detection for long-term monitoring of wetlands

Demarquet,

Rapinel,

Gore

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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A novel Landsat-based automated mapping of marsh wetland in the headwaters of the Brahmaputra, Ganges and Indus Rivers, southwestern Tibetan Plateau

Cited by 6 publications

References 72 publications

Identification of Vegetation Surfaces and Volumes by Height Levels in Reservoir Deltas Using UAS Techniques—Case Study at Gilău Reservoir, Transylvania, Romania

Identification of Vegetation Surfaces and Volumes by Height Levels in Reservoir Deltas Using UAS Techniques—Case Study at Gilău Reservoir, Transylvania, Romania

Wetland mapping based on feature optimization and Google Earth Engine in Zhalong Nature Reserve

Continuous change detection outperforms traditional post-classification change detection for long-term monitoring of wetlands

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