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Remote Sensing in Natural Resource and Water Environment II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 24580

Special Issue Editors

Prof. Dr. Pingping Luo

E-Mail Website
Guest Editor
School of Water and Environment, Chang’an University, Xi’an 710054, China
Interests: urban flood; flood management; hydrological modeling; water quality analysis; statistical analysis; sustainable water resource management; ecohydrology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiqiang Lyu

E-Mail Website
Guest Editor
School of Water and Environment, Chang’an University, Xi’an 710054, China
Interests: basin hydrological simulation; urban river water pollution control; water resources system analysis and optimal allocation
Special Issues, Collections and Topics in MDPI journals
Dr. Chunying Wang

E-Mail Website
Guest Editor
College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: appropriate agricultural water and nutrient management for improving crop productivity; water use efficiency; reducing non-point-source pollution
Special Issues, Collections and Topics in MDPI journals
Dr. Min Wu

E-Mail Website
Guest Editor
School of Surveying and Mapping Science and technology, Xi’an University of Science and Technology, Xi’an, China
Interests: groundwater; water resources; hydrological simulation
Prof. Dr. Van-Thanh-Van Nguyen

E-Mail Website
Guest Editor
Department of Civil Engineering, McGill University, Montreal, QC H3A 0G4, Canada
Interests: climate change impact and adaptation in water resources; modelling of hydrologic extremes; watershed modelling for sustainable water resource development
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Luiz Borges Chaffe

E-Mail Website
Guest Editor
Department of Sanitary and Environmental Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil
Interests: watered hydrology; process-based distributed hydrological modeling; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The second volume of “Remote Sensing in Natural Resource and Water Environment” aims to continue addressing the challenges faced by natural resources and water environments due to human-generated pollutants. The growing population and increasing risk of pollution have made effective water resource management more important than ever before. To ensure sustainable development, it is crucial to monitor environmental parameters, evaluate the quality of the environment, and predict the dynamics of environmental elements accurately. Remote sensing technology provides a new perspective on hydrological monitoring, water resource ecological protection, and water resource planning and utilization due to its fast detection capacity, wide spatial coverage, and multiple spectral characteristics.

This Special Issue seeks to publish innovative research that utilizes remote sensing techniques in the field of hydrological and water pollution. Specifically, this volume aims to highlight recent advances in the application of remote sensing technology in identifying and monitoring water quality concerns, such as algal blooms, sedimentation, and eutrophication. Additionally, the Issue will use remote sensing techniques to analyze the impacts of climate change on water resources and assess the effectiveness of various remediation methods.

The objective of this Special Issue is to promote sustainable development by utilizing relevant methods of hydrological and water resource planning and management. Authors are encouraged to submit novel methods and views that utilize remote sensing technologies. Potential topics include, but are not limited to, remote sensing inversion simulation, experience method, and sustainable development, to address the current challenges facing natural resources and water environments.

Prof. Dr. Pingping Luo
Dr. Jiqiang Lyu
Dr. Chunying Wang
Dr. Min Wu
Prof. Dr. Van-Thanh-Van Nguyen
Dr. Pedro Luiz Borges Chaffe
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hyperspectral remote sensing in the environment
  • retrieving air pollutant concentrations through remote sensing
  • machine learning algorithms for modeling based on remote sensing data
  • ecological indicators mapping by remote sensing
  • urban stormwater models
  • hydrologic models
  • flood disaster
  • water pollution
  • wastewater treatment
  • water resource management
  • urban-rural management
  • urban planning

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Related Special Issue

Published Papers (15 papers)

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Research

29 pages, 32335 KiB  
Article
Exploring Spatio-Temporal Dynamics of Future Extreme Precipitation, Runoff, and Flood Risk in the Hanjiang River Basin, China
by Dong Wang, Weiwei Shao, Jiahong Liu, Hui Su, Ga Zhang and Xiaoran Fu
Remote Sens. 2024, 16(21), 3980; https://doi.org/10.3390/rs16213980 - 26 Oct 2024
Viewed by 817
Abstract
The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river [...] Read more.
The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river basin (HRB), this study develops a framework for establishing a scientific assessment of spatio-temporal dynamics of future flood risks under multiple future scenarios. In this framework, a GCMs statistical downscaling method based on machine learning is used to project future precipitation, the PLUS model is used to project future land use, the digitwining watershed model (DWM) is used to project future runoff, and the entropy weight method is used to calculate risk. Six extreme precipitation indices are calculated to project the spatio-temporal patterns of future precipitation extremes in the HRB. The results of this study show that the intensity (Rx1day, Rx5day, PRCPTOT, SDII), frequency (R20m), and duration (CWD) of future precipitation extremes will be consistently increasing over the HRB during the 21st century. The high values of extreme precipitation indices in the HRB are primarily located in the southeast and southwest. The future annual average runoff in the upper HRB during the near-term (2023–2042) and mid-term (2043–2062) is projected to decrease in comparison to the baseline period (1995–2014), with the exception of that during the mid-term under the SSP5-8.5 scenario. The high flood risk center in the future will be distributed in the southwestern region of the upper HRB. The proportions of areas with high and medium–high flood risk in the upper HRB will increase significantly. Under the SSP5-8.5 scenario, the area percentage with high flood risk during the future mid-term will reach 24.02%. The findings of this study will facilitate local governments in formulating effective strategic plans for future flood control management. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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25 pages, 11675 KiB  
Article
An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery
by Xiangdong Lei, Jie Jiang, Zifeng Deng, Di Wu, Fangyi Wang, Chengguang Lai, Zhaoli Wang and Xiaohong Chen
Remote Sens. 2024, 16(12), 2246; https://doi.org/10.3390/rs16122246 - 20 Jun 2024
Viewed by 1034
Abstract
Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model [...] Read more.
Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model performance, and the limited retrieval of non-optical active parameters (NOAPs). This study focuses on an urban reservoir, utilizing unmanned aerial vehicle (UAV) multispectral remote sensing and ensemble machine learning (EML) methods to monitor optically active parameters (OAPs, including Chla and SD) and non-optically active parameters (including CODMn, TN, and TP), exploring spatial and temporal variations of WQPs. A framework of Feature Combination and Genetic Algorithm (FC-GA) is developed for feature band selection, along with two frameworks of EML models for WQP estimation. Results indicate FC-GA’s superiority over popular methods such as the Pearson correlation coefficient and recursive feature elimination, achieving higher performance with no multicollinearity between bands. The EML model demonstrates superior estimation capabilities for WQPs like Chla, SD, CODMn, and TP, with an R2 of 0.72–0.86 and an MRE of 7.57–42.06%. Notably, the EML model exhibits greater accuracy in estimating OAPs (MRE ≤ 19.35%) compared to NOAPs (MRE ≤ 42.06%). Furthermore, spatial and temporal distributions of WQPs reveal nitrogen and phosphorus nutrient pollution in the upstream head and downstream tail of the reservoir due to human activities. TP, TN, and Chla are lower in the dry season than in the rainy season, while clarity and CODMn are higher in the dry season than in the rainy season. This study proposes a novel approach to water quality monitoring, aiding in the identification of potential pollution sources and ecological management. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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18 pages, 19464 KiB  
Article
Radar-Based Precipitation Nowcasting Based on Improved U-Net Model
by Youwei Tan, Ting Zhang, Leijing Li and Jianzhu Li
Remote Sens. 2024, 16(10), 1681; https://doi.org/10.3390/rs16101681 - 9 May 2024
Cited by 1 | Viewed by 1748
Abstract
Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water resource scheduling. Based on the structural features of the U-Net model, we proposed the Double Recurrent Residual Attention Gates U-Net (DR2A-UNet) deep-learning model to carry out radar echo extrapolation. The [...] Read more.
Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water resource scheduling. Based on the structural features of the U-Net model, we proposed the Double Recurrent Residual Attention Gates U-Net (DR2A-UNet) deep-learning model to carry out radar echo extrapolation. The model was trained with mean square error (MSE) and balanced mean square error (BMSE) as loss functions, respectively. The dynamic Z-R relationship was applied for quantitative rainfall estimation. The reference U-Net model, U-Net++, and the ConvLSTM were used as control experiments to carry out radar echo extrapolation. The results showed that the model trained by BMSE had better extrapolation. For 1 h lead time, the rainfall nowcasted by each model could reflect the actual rainfall process. DR2A-UNet performed significantly better than other models for intense rainfall, with a higher extrapolation accuracy for echo intensity and variability processes. At the 2 h lead time, the nowcast accuracy of each model was significantly reduced, but the echo extrapolation and rainfall nowcasting of DR2A-UNet were better. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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15 pages, 10245 KiB  
Article
Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites
by Fan Yang, Qi Feng, Yadong Zhou, Wen Li, Xiaoyang Zhang and Baoyin He
Remote Sens. 2024, 16(9), 1491; https://doi.org/10.3390/rs16091491 - 23 Apr 2024
Viewed by 1441
Abstract
Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP [...] Read more.
Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP retrieval model using Sentinel-2 observations and field-measured daily-scale water quality. The model was successfully applied to estimate the temporal and spatial variations of TP concentration in the middle reaches of the Yangtze River (MYR) from 2020 to 2023. Our results show: (1) the model accuracy of TP concentration retrieval with turbidity is significantly higher (R2 = 0.71, MAPE = 15.78%) than that for the model without turbidity (R2 = 0.62, MAPE = 16.38%); (2) the turbidity and TP concentration in the MYR is higher in summer and autumn than in winter and spring; and (3) the turbidity and total phosphorus (TP) concentration of the Yangtze River showed a significant increase after passing through Dongting Lake (p < 0.05). Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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22 pages, 6425 KiB  
Article
Coupling Downscaling and Calibrating Methods for Generating High-Quality Precipitation Data with Multisource Satellite Data in the Yellow River Basin
by Haibo Yang, Xiang Cui, Yingchun Cai, Zhengrong Wu, Shiqi Gao, Bo Yu, Yanling Wang, Ke Li, Zheng Duan and Qiuhua Liang
Remote Sens. 2024, 16(8), 1318; https://doi.org/10.3390/rs16081318 - 9 Apr 2024
Cited by 1 | Viewed by 1335
Abstract
Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the [...] Read more.
Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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22 pages, 15900 KiB  
Article
A Framework Based on LIDs and Storage Pumping Stations for Urban Waterlogging
by Huayue Li, Qinghua Luan, Jiahong Liu, Cheng Gao and Hong Zhou
Remote Sens. 2024, 16(7), 1207; https://doi.org/10.3390/rs16071207 - 29 Mar 2024
Viewed by 1098
Abstract
Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a [...] Read more.
Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a flash flood and high flood peak, storage pumping stations are also needed to drain. Thus, a framework composed of storage pumping stations and Low Impact Developments (LIDs) was proposed based on the topography and the regional upstream and downstream relationships. The water quantity in this framework is applied to YI County in Hebei Province, China. The results showed that individual LIDs effectively reduced runoff volume, with the implementation area being more crucial than the location. Combining storage pumping stations with LIDs significantly reduces peak outflow and delays it by 5 to 51 min. The combined downstream implementation of storage pumping stations and LIDs yielded the most effective results. These findings offer important insights and management strategies for controlling waterlogging in mountainous cities of developing countries. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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21 pages, 4114 KiB  
Article
Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions
by Xiaofeng Wang, Jitao Zhou, Jiahao Ma, Pingping Luo, Xinxin Fu, Xiaoming Feng, Xinrong Zhang, Zixu Jia, Xiaoxue Wang and Xiao Huang
Remote Sens. 2024, 16(5), 751; https://doi.org/10.3390/rs16050751 - 21 Feb 2024
Cited by 2 | Viewed by 1670
Abstract
Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets [...] Read more.
Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), time-expanded climate forecast system reanalysis (CFSR+), the European Centre for Medium-Range Weather Forecast Reanalysis (ERA). and the China Meteorological Forcing Dataset (CMFD), were evaluated by using data from 28 ground-based observations (OBs) in the Source of the Yangtze and Yellow Rivers (SYYR) region and were used as input data for the SWAT model for runoff simulation and performance evaluation, respectively. And, finally, the CMADS was optimized using Integrated Calibrated Multi-Satellite Retrievals for Global Precipitation Measurement (AIMERG) data. The results show that CMFD is the most representative reanalysis data for precipitation characteristics in the SYYR region among the four reanalysis datasets evaluated in this paper, followed by ERA5 and CFSR, while CMADS performs satisfactorily for temperature simulations in this region, but underestimates precipitation. And we contend that the accuracy of runoff simulations is notably contingent upon the precision of daily precipitation within the reanalysis dataset. The runoff simulations in this region do not effectively capture the extreme runoff characteristics of the Yellow River and Yangtze River sources. The refinement of CMADS through the integration of AIMERG satellite precipitation data emerges as a potent strategy for enhancing the precision of runoff simulations. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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21 pages, 7444 KiB  
Article
Surface Subsidence over a Coastal City Using SBAS-InSAR with Sentinel-1A Data: A Case of Nansha District, China
by Huanghao Yu, Binquan Li, Yang Xiao, Jinyan Sun, Cheng Chen, Gaoyang Jin and Huanyu Liu
Remote Sens. 2024, 16(1), 55; https://doi.org/10.3390/rs16010055 - 22 Dec 2023
Cited by 2 | Viewed by 1459
Abstract
The loss of life and property in economically developed areas due to geological hazards caused by intense ground subsidence is incalculable. As one of the fastest growing areas in the Guangdong-Hong Kong-Macao Greater Bay Area, the study of ground subsidence in Nansha will [...] Read more.
The loss of life and property in economically developed areas due to geological hazards caused by intense ground subsidence is incalculable. As one of the fastest growing areas in the Guangdong-Hong Kong-Macao Greater Bay Area, the study of ground subsidence in Nansha will help to provide a scientific basis for urban planning and improve the capacity of monitoring and prevention of ground subsidence. The combination of coastal soft soil foundation and urbanization conditions creates a certain risk of land subsidence. We chose Nansha District, the geographical center of the Greater Bay Area, as the study area to analyze its surface subsidence characteristics in recent years. The 20-view Sentinel-1A data and SBAS-InSAR technique were used to monitor the ground subsidence in Nansha from 2017 to 2023. The main rate of ground subsidence in Nansha ranges from −19.4 to 7.7 mm/yr and is distributed in the urban area, along the rivers, in the construction area, and in the reclamation area. As of 4 May 2023, the average ground settlement in Nansha is 10.05 mm and the maximum settlement can be up to 142.45 mm. The 6-year total settlement at all four settlement intensities is greater than 60 mm, with the highest value exceeding 110 mm. The cumulative settlement increases with time, but inverse settlement and no settlement also occur at points where settlement is severe. For settlement caused by soft soil consolidation, it is recommended that drainage pipes be installed to accelerate drainage as a means of stabilizing settlement. For settlement caused by groundwater extraction and additional loads on the road surface, it is recommended to rationally extract groundwater and reinforce the foundation of the road surface with severe settlement. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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21 pages, 3298 KiB  
Article
Impact of Climate Change and Human Activities to Runoff in the Du River Basin of the Qinling-Daba Mountains, China
by Xiaoying Zhang and Yi He
Remote Sens. 2023, 15(21), 5178; https://doi.org/10.3390/rs15215178 - 30 Oct 2023
Viewed by 1318
Abstract
The hydrological response to climate change and human activities plays a pivotal role in the field of water resource management within a given basin. This study was conducted with a primary focus on the Du River basin, aiming to assess and quantify the [...] Read more.
The hydrological response to climate change and human activities plays a pivotal role in the field of water resource management within a given basin. This study was conducted with a primary focus on the Du River basin, aiming to assess and quantify the impacts of climate change and human activities on changes in runoff patterns. The study utilized the Budyko framework in conjunction with the Soil and Water Assessment Tool (SWAT) model to project future changes in runoff while also employing statistical tests like the Pettitt and Mann–Kendall tests to identify abrupt shifts and monotonic trends in the data. The results shows that (1) The analysis of runoff data spanning from 1960 to 2016 revealed a significant declining trend (p < 0.05) in annual runoff, with an abrupt change point identified in 1994. The multi-year average runoff depth was determined to be 495 mm. (2) According to the Budyko framework, human activities were found to be the dominant driver behind runoff changes, contributing significantly at 74.42%, with precipitation changes contributing 24.81%. (3) The results obtained through the SWAT model simulation indicate that human activities accounted for 61.76% of the observed runoff changes, whereas climate change played a significant but slightly smaller role, contributing 38.24% to these changes. (4) With constant climate conditions considered, the study predicted that runoff will continue to decrease from 2017 to 2030 due to the influence of ongoing and future human activities. However, this downward trend was found to be statistically insignificant (p > 0.1). These findings provide valuable insights into the quantitative contributions of climate change and human activities to runoff changes in the Du River basin. This information is crucial for decision-makers and water resource managers, as it equips them with the necessary knowledge to develop effective and sustainable strategies for water resource management within this basin. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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20 pages, 5841 KiB  
Article
Dynamic Monitoring of Surface Water Bodies and Their Influencing Factors in the Yellow River Basin
by Zikun Zhao, Huanwei Li, Xiaoyan Song and Wenyi Sun
Remote Sens. 2023, 15(21), 5157; https://doi.org/10.3390/rs15215157 - 28 Oct 2023
Cited by 6 | Viewed by 1701
Abstract
Surface water bodies exhibit dynamic characteristics, undergoing variations in size, shape, and flow patterns over time due to numerous natural and human factors. The monitoring of spatial-temporal changes in surface water bodies is crucial for the sustainable development and efficient utilization of water [...] Read more.
Surface water bodies exhibit dynamic characteristics, undergoing variations in size, shape, and flow patterns over time due to numerous natural and human factors. The monitoring of spatial-temporal changes in surface water bodies is crucial for the sustainable development and efficient utilization of water resources. In this study, Landsat series images on the Google Earth Engine (GEE) platform, along with the HydroLAKES and China Reservoir datasets, were utilized to establish an extraction process for surface water bodies from 1986 to 2021 in the Yellow River Basin (YRB). The study aims to investigate the dynamics of surface water bodies and the driving factors within the YRB. The findings reveal an overall expansion tendency of surface water bodies in the YRB between 1986 and 2021. In the YRB, the total area of surface water bodies, natural lakes, and artificial reservoirs increased by 2983.8 km2 (40.4%), 281.1 km2 (11.5%), and 1017.6 km2 (101.7%), respectively. A total of 102 natural lakes expanded, while 23 shrank. Regarding artificial reservoirs, 204 expanded, and 77 shrank. The factors that contributed most to the increase in the surface water bodies were increasing precipitation and reservoir construction, whose contribution rates could reach 47% and 32.6%, respectively. Additionally, the rising temperatures melted permafrost, ice, and snow, positively correlating with water expansion in the upper reaches of the YRB, particularly natural lakes. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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26 pages, 9281 KiB  
Article
A Multi-Scenario Prediction and Spatiotemporal Analysis of the Land Use and Carbon Storage Response in Shaanxi
by Xindong Wei, Shuyuan Zhang, Pingping Luo, Shuomeng Zhang, Huanyuan Wang, Dehao Kong, Yuanyuan Zhang, Yang Tang and Shuo Sun
Remote Sens. 2023, 15(20), 5036; https://doi.org/10.3390/rs15205036 - 20 Oct 2023
Cited by 5 | Viewed by 1994
Abstract
The role of carbon sequestration in terrestrial ecosystems is crucial for achieving carbon neutrality. This study primarily focuses on examining the carbon storage in Shaanxi Province under different land-use scenarios. This study employed the LP-PLUS-InVEST model to explore the characteristics and spatial and [...] Read more.
The role of carbon sequestration in terrestrial ecosystems is crucial for achieving carbon neutrality. This study primarily focuses on examining the carbon storage in Shaanxi Province under different land-use scenarios. This study employed the LP-PLUS-InVEST model to explore the characteristics and spatial and temporal changes in carbon storage across four scenarios (business-as-usual (BUS), ecological protection (EPS), water–energy–food (WEF), and rural revitalization (RRS)) in Shaanxi Province. The results show that from 2000 to 2020, the carbon storage in Shaanxi Province is on a decreasing trend mainly due to the large occupation of ecological land by economic development. EPS has the largest increase in carbon storage under the four scenarios in 2030 and 2060. On the contrary, BUS has a rapid expansion of construction land, which leads to a gradual decreasing trend in carbon storage. WEF has a gradual increasing trend in carbon storage, while RRS has a trend of increasing and then slowly decreasing carbon storage. The spatial distribution trends of carbon storage in all scenarios were similar; high-carbon-reserve areas were mainly distributed in southern and central Shaanxi, which has a better ecological environment and less construction land, while low-value areas were distributed in the Central Shaanxi Plain, which has high land-use intensity. In terms of the stability of carbon reserves, the stable areas are predominantly concentrated in the Qinling Mountains, while the unstable areas are concentrated in the plain urban areas. Specifically, returning cultivated land to forest and grassland is an important initiative to promote the increase in carbon storage in Shaanxi Province. The decrease in carbon storage is mainly affected by strong urban expansion. Our study optimizes the land-use pattern according to the development needs of Shaanxi Province, and promotes the integrated development of ecological protection, food security, and economic development. Guidance is provided to promote regional carbon neutrality. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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19 pages, 6259 KiB  
Article
A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM
by Bingru Tian, Hua Chen, Xin Yan, Sheng Sheng and Kangling Lin
Remote Sens. 2023, 15(18), 4601; https://doi.org/10.3390/rs15184601 - 19 Sep 2023
Viewed by 1196
Abstract
Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations [...] Read more.
Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1° to 0.01° based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gauge observations. Secondly, the fusion model combining ConvLSTM and CBAM explores the spatiotemporal correlation of downscaled satellite precipitation data with environmental co-variables and ground-based observations to correct GPM precipitation. The integrated scheme (CBAM-ConvLSTM) is applied to acquire monthly precipitation at a spatial resolution of 0.01° over Hanjiang River Basin from 2014 to 2018. Comparative analyses of model-based satellite products with in situ observations show that model-based precipitation products have a high-resolution spatial distribution along with high accuracy, which combines the advantages of in situ observations and satellite products. Compared to the original GPM product, the evaluation metric values of the merged precipitation products all improved: the RMSE decreased by 31% while the CC increased from 0.55 to 0.69, the bias decreased from about 25% to less than 1.8%, and the MAE decreased by 27.8% while the KGE increased from 0.28 to 0.52. This two-step scheme provides an effective way to derive a high-resolution and accurate monthly precipitation product for humid regions. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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23 pages, 7615 KiB  
Article
Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China
by Lu Lu, Qiang Xue, Xiaojing Zhang, Changbo Qin and Lizhi Jia
Remote Sens. 2023, 15(18), 4472; https://doi.org/10.3390/rs15184472 - 12 Sep 2023
Cited by 2 | Viewed by 1469
Abstract
Rapidly predicting and revealing the spatiotemporal characteristics and driving factors of land-use changes in carbon storage within megacities under different scenarios is crucial to achieving sustainable development. In this study, Jinan City (JNC) is taken as the study area, and the Markov-FLUS-InVEST model [...] Read more.
Rapidly predicting and revealing the spatiotemporal characteristics and driving factors of land-use changes in carbon storage within megacities under different scenarios is crucial to achieving sustainable development. In this study, Jinan City (JNC) is taken as the study area, and the Markov-FLUS-InVEST model is utilized to predict and analyze the spatiotemporal variation in carbon storage in 2030 under three scenarios, namely, the natural development scenario (S1), the ecological conservation scenario (S2), and the economic development scenario (S3). The drivers of carbon storage changes were identified using an optimal parameter-based geographic detection (OPGD) model. The findings indicate that (1) land use from 2010 to 2018 shows a trend of continuous expansion of construction land and reduction in arable land. (2) The main types of carbon pools were cropland, forest, and grassland, accounting for more than 96% of the total amount. Carbon storage showed a decreasing trend from 2010 to 2018, and the main type of carbon pool that decreased was cropland. The center of gravity of carbon storage increases and decreases was located in the southern Lixia District, and the center of gravity of increase and decrease moved to the southwest by 3057.48 m and 1478.57 m, respectively. (3) From 2018 to 2030, the reductions in carbon stocks were 3.20 × 106 t (S1), 2.60 × 106 t (S2), and 4.26 × 106 t (S3), and the carbon release was about 9 times (S1), 4 times (S2), and 10 times (S3) that of the carbon sink. (4) The contribution of slope (A2) ∩ nighttime light index (B6) and elevation (A1) ∩ nighttime light index (B6) to the regional heterogeneity of carbon stocks was the largest among the interaction drivers. To sum up, this study deepens the simulation of spatial and temporal dynamics of carbon storage under land-use changes in megacities and the related driving mechanism, which can provide the basis for scientific decision-making for cities to conduct territorial spatial planning and ecological protection and restoration. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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23 pages, 9864 KiB  
Article
The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning
by Lili Liu, Zhan Gao, Pingping Luo, Weili Duan, Maochuan Hu, Mohd Remy Rozainy Mohd Arif Zainol and Mohd Hafiz Zawawi
Remote Sens. 2023, 15(18), 4437; https://doi.org/10.3390/rs15184437 - 9 Sep 2023
Cited by 9 | Viewed by 2780
Abstract
Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were [...] Read more.
Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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20 pages, 8141 KiB  
Article
The Impact Mechanism of Climate and Vegetation Changes on the Blue and Green Water Flow in the Main Ecosystems of the Hanjiang River Basin, China
by Ming Kong, Yiting Li, Chuanfu Zang and Jinglin Deng
Remote Sens. 2023, 15(17), 4313; https://doi.org/10.3390/rs15174313 - 1 Sep 2023
Cited by 4 | Viewed by 1379
Abstract
Water resources management and planning traditionally focus on visible liquid or blue water. However, green water also maintains social development and ecosystem services. Therefore, blue and green water should be incorporated into the watershed management system for evaluating water resources. To analyze the [...] Read more.
Water resources management and planning traditionally focus on visible liquid or blue water. However, green water also maintains social development and ecosystem services. Therefore, blue and green water should be incorporated into the watershed management system for evaluating water resources. To analyze the water resources of the Hanjiang River Basin, the SWAT model was set up using long-term and high-precision geographic data. The methods of wavelet analysis and Pearson’s correlation analysis were used to explore the influence mechanism of climate and vegetation changes on the blue and green water flow (BWF and GWF) of the main ecosystems in the basin. The results showed that: (1) The spatial–temporal distribution of the BWF and GWF in the main ecosystems of the basin over the past 50 years was uneven. Forest ecosystems and farmland ecosystems have a greater concentration of water resources in the south, while grassland ecosystems have a greater concentration of water resources in the east. (2) Climate dominates the BWF and GWF changes in the main ecosystems of the basin. The BWF and the precipitation change cycle are synergistic, and the GWF and the temperature change cycle are synergistic. (3) The correlation between vegetation and BWF and GWF in the farmland ecosystem is significant. Vegetation affects the hydrological change process of the BWF and GWF at the microscale. This study can provide data support and scientific rules for ecosystem water resource management in the basin. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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