Multiple equidistant belt technique for width function estimation through a two-segmented-distance strategy DOI
Pengfei Wu, Jintao Liu,

Meiyan Feng

et al.

Environmental Modelling & Software, Journal Year: 2023, Volume and Issue: 171, P. 105865 - 105865

Published: Oct. 28, 2023

Language: Английский

Automatic Detection of Ditches and Natural Streams from Digital Elevation Models Using Deep Learning DOI Creative Commons
Mariana Dos Santos Toledo Busarello, Anneli Ågren, Florian Westphal

et al.

Computers & Geosciences, Journal Year: 2025, Volume and Issue: unknown, P. 105875 - 105875

Published: Jan. 1, 2025

Language: Английский

Citations

2
Elevation-dependent patterns of temporally asymmetrical vegetation response to climate in an alpine basin on the Qinghai-Tibet Plateau DOI Creative Commons

Tianke Bai,

Jintao Liu, Hu Liu

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 159, P. 111736 - 111736

Published: Feb. 1, 2024

The temporally asymmetric effects of vegetation growth response to climate, e.g., time-lag, time-accumulation, and their combination effects, have been widely reported. However, elevation-dependent spatial distribution patterns in alpine basins the Qinghai-Tibet Plateau remain unclear, especially different seasons. This study investigated temporal precipitation temperature throughout whole growing season its two subphases (growth phase senescence phase) Lhasa River Basin (LRB). results showed that responds climate all seasons with obvious time-accumulation relatively weak during season, there is no significant lag both temperature, while accumulation about 3 ∼ 4 weeks 2 weeks, respectively. Comparatively, are enhanced phase, a similar 6-week 4-week for LRB subdivided into four elevation zones, each hydrothermal condition characteristic. Across it found stronger water-deficit stress on shorter longer precipitation, heat-deficit leads which determined by vegetation's demands ambient environment. These findings shed light relationship between provide potential theoretical support conservation basins.

Language: Английский

Citations

5
Machine Learning and Deep Learning in Remote Sensing Data Analysis DOI
Hankui K. Zhang,

Shi Qiu,

Ji Won Suh

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

5
Reply on RC1 DOI Creative Commons
Kaifeng Peng

Published: Jan. 16, 2025

Abstract. Rivers play important roles in ecological biodiversity, shipping trade and the carbon cycle. Owing to human disturbances extreme climates recent decades, river extents have altered frequently dramatically. The development of sequential fine-scale extent datasets, which could offer strong data support for protection, management sustainable use, is urgently needed. A literature review revealed that annual datasets with fine spatial resolutions are generally unavailable China. To address this issue, first Sentinel-derived China dataset (CRED) from 2016 2023 was produced our study. We water maps by combining dynamic world (DW), ESRI global land cover (EGLC) multiple index detection rule (MIWDR). For DW MIWDR time series, mode algorithm, calculates most common values, used generate yearly maps. Then, an object-based hierarchical decision tree based on geometric features auxiliary developed extract rivers data. results indicated overall accuracies (OAs) CRED were greater than 96.0 % 2023. user (UAs), producer (PAs) F1 scores exceeded 95.3 %, 91.3 93.7 respectively. further intercomparison shared similar patterns wetland map East Asia (EA_Wetlands), use/cover change (CNLUCC) covers (CWaC) correlation coefficients (R) 0.75. Moreover, outperformed three terms small mapping misclassification reduction. area statistics 44,948.78 km2 2023, mostly distributed coastal provinces From areas characterized initial increase, followed a decrease then slight increase. Spatially, decreased located mainly Southeast China, whereas increased Central Northeast In general, explicitly delineated dynamics provide good foundation improving ecology management. publicly available at https://doi.org/10.5281/zenodo.13841910 (Peng et al., 2024a).

Language: Английский

Citations

0
Reply on RC2 DOI Creative Commons
Kaifeng Peng

Published: Jan. 16, 2025

Abstract. Rivers play important roles in ecological biodiversity, shipping trade and the carbon cycle. Owing to human disturbances extreme climates recent decades, river extents have altered frequently dramatically. The development of sequential fine-scale extent datasets, which could offer strong data support for protection, management sustainable use, is urgently needed. A literature review revealed that annual datasets with fine spatial resolutions are generally unavailable China. To address this issue, first Sentinel-derived China dataset (CRED) from 2016 2023 was produced our study. We water maps by combining dynamic world (DW), ESRI global land cover (EGLC) multiple index detection rule (MIWDR). For DW MIWDR time series, mode algorithm, calculates most common values, used generate yearly maps. Then, an object-based hierarchical decision tree based on geometric features auxiliary developed extract rivers data. results indicated overall accuracies (OAs) CRED were greater than 96.0 % 2023. user (UAs), producer (PAs) F1 scores exceeded 95.3 %, 91.3 93.7 respectively. further intercomparison shared similar patterns wetland map East Asia (EA_Wetlands), use/cover change (CNLUCC) covers (CWaC) correlation coefficients (R) 0.75. Moreover, outperformed three terms small mapping misclassification reduction. area statistics 44,948.78 km2 2023, mostly distributed coastal provinces From areas characterized initial increase, followed a decrease then slight increase. Spatially, decreased located mainly Southeast China, whereas increased Central Northeast In general, explicitly delineated dynamics provide good foundation improving ecology management. publicly available at https://doi.org/10.5281/zenodo.13841910 (Peng et al., 2024a).

Language: Английский

Citations

0
Enhanced River Connectivity Assessment Across Larger Areas Through Deep Learning With Dam Detection DOI
Xiao Zhang, Qi Liu, Dongwei GUI

et al.

Hydrological Processes, Journal Year: 2025, Volume and Issue: 39(1)

Published: Jan. 1, 2025

ABSTRACT Monitoring river connectivity across large regions is essential for understanding hydrological processes and environmental management. However, comprehensive assessments of are often hindered by inaccurate dam databases, which biased towards larger dams while overlooking smaller or low‐head dams. To enhance the accuracy assessments, we developed three advanced convolutional neural networks (CNNs; YOLOv5, Advance‐You Only Look Once [YOLO], Faster R‐CNN) to accurately classify evaluate using high‐resolution (1 m) remote sensing imagery. The evaluation results showed that Advance‐YOLO performs best with an average mean precision (mAP) 86.6%, R‐CNN mediocrely mAP 77.9%. Applying well‐trained model in Tarim River Basin (China), one largest inland basins around globe, found there currently 135 total on its sources. Conversely, existing public database underestimates 85.9% Notably, a 14.3% decline over past decade, current density four source rivers 1.12 per 10 000 km 2 . overestimated 83.9%. here enhances assessment areas long period, thereby fostering more research effective water resource

Language: Английский

Citations

0
Predicting the thickness of alpine meadow soil on headwater hillslopes of the Qinghai-Tibet Plateau DOI Creative Commons
Xiaole Han, Jintao Liu, Pengfei Wu

et al.

Geoderma, Journal Year: 2025, Volume and Issue: 456, P. 117271 - 117271

Published: March 26, 2025

Language: Английский

Citations

0
Characteristics of the water extent and width of endorheic Tibetan Plateau rivers revealed by Sentinel-2 DOI

Fanxuan Zeng,

Kai Liu, Yongquan Zhao

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133191 - 133191

Published: March 1, 2025

Language: Английский

Citations

0
DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery DOI
Zhen Hao, Giles M. Foody, Yong Ge

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 638, P. 131512 - 131512

Published: June 15, 2024

Language: Английский

Citations

3
Extracting an accurate river network: Stream burning re-revisited DOI Creative Commons
Qiuyang Chen, Simon M. Mudd, Mikaël Attal

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 312, P. 114333 - 114333

Published: July 29, 2024

Extracting river networks that are both accurate and topologically connected is important for applications involve correct routing of material, example water sediment, through such networks. We combined sediment extraction using radar multispectral imagery from Sentinel-1 Sentinel-2 to create masks over a range study areas. These were then used condition topographic Digital Elevation Models (DEMs) by lowering the elevation pixels with present, in process known as stream burning. examined how burning could improve accuracy extracted identified most effective method optimal results. find deeper depths improved accuracy, diminishing returns: we suggest 40 50 meters. improves humid temperate landscapes, but arid landscapes should be burned only pixels. significantly better on COP30 global dataset compared NASADEM dataset, mainly due time collection. The AW3D30 DEM FABDEM datasets have accuracies just below DEM.

Language: Английский

Citations

3