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21 pages, 32440 KiB

Open AccessArticle

Reference-Based Super-Resolution Method for Remote Sensing Images with Feature Compression Module

by Jiayang Zhang, Wanxu Zhang, Bo Jiang, Xiaodan Tong, Keya Chai, Yanchao Yin, Lin Wang, Junhao Jia and Xiaoxuan Chen

Remote Sens. 2023, 15(4), 1103; https://doi.org/10.3390/rs15041103 - 17 Feb 2023

Cited by 5 | Viewed by 2337

Abstract

High-quality remote sensing images play important roles in the development of ecological indicators’ mapping, urban-rural management, urban planning, and other fields. Compared with natural images, remote sensing images have more abundant land cover along with lower spatial resolutions. Given the embedded longitude and [...] Read more.

High-quality remote sensing images play important roles in the development of ecological indicators’ mapping, urban-rural management, urban planning, and other fields. Compared with natural images, remote sensing images have more abundant land cover along with lower spatial resolutions. Given the embedded longitude and latitude information of remote sensing images, reference (Ref) images with similar scenes could be more accessible. However, existing traditional super-resolution (SR) approaches always depend on increases in network depth to improve performance, which limits the acquisition and application of high-quality remote sensing images. In this paper, we proposed a novel, reference-image-based, super-resolution method with feature compression module (FCSR) for remote sensing images to alleviate the above issue while effectively utilizing high-resolution (HR) information from Ref images. Specifically, we exploited a feature compression branch (FCB) to extract relevant features in feature detail matching with large measurements. This branch employed a feature compression module (FCM) to extract features from low-resolution (LR) and Ref images, which enabled texture transfer from different perspectives. To decrease the impact of environmental factors such as resolution, brightness and ambiguity disparities between the LR and Ref images, we designed a feature extraction encoder (FEE) to ensure accuracy in feature extraction in the feature acquisition branch. The experimental results demonstrate that the proposed FCSR achieves significant performance and visual quality compared to state-of-the-art SR methods. Explicitly, when compared with the best method, the average peak signal-to-noise ratio (PSNR) index on the three test sets is improved by 1.0877%, 0.8161%, 1.0296%, respectively, and the structural similarity (SSIM) index on four test sets is improved by 1.4764%, 1.4467%, 0.0882%, and 1.8371%, respectively. Simultaneously, FCSR obtains satisfactory visual details following qualitative evaluation. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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22 pages, 14984 KiB

Open AccessArticle

Ecological Security Patterns at Different Spatial Scales on the Loess Plateau

by Liangguo Lin, Xindong Wei, Pingping Luo, Shaini Wang, Dehao Kong and Jie Yang

Remote Sens. 2023, 15(4), 1011; https://doi.org/10.3390/rs15041011 - 12 Feb 2023

Cited by 33 | Viewed by 2757

Abstract

The study of ecological security patterns (ESPs) is of great significance for improving the value of ecosystem services and promoting both ecological protection and high-quality socio-economic development. As an important part of the “Loss Plateau-Sichuan-Yunnan Ecological Barrier” and “Northern Sand Control Belt” in [...] Read more.

The study of ecological security patterns (ESPs) is of great significance for improving the value of ecosystem services and promoting both ecological protection and high-quality socio-economic development. As an important part of the “Loss Plateau-Sichuan-Yunnan Ecological Barrier” and “Northern Sand Control Belt” in the national security strategic pattern, there is an urgent need to study the ESPs on the Loess Plateau. Based on a remote sensing dataset, this study identified the ESPs at different spatial scales, and analyzed the similarities and differences of ecological sources, corridors, and key strategic points, so as to better inform the development and implantation of macro and micro ecological protection strategies. When taken as a whole unit, we identified 58 ecological sources (areas with higher levels of ecosystem services) on the Loess Plateau (total area of 57,948.48 km²), along with 134 corridors (total length of 14,094.32 km), 1325 pinch points (total area of 315.01 km²), and 2406 barrier points (total area of 382.50 km²). When splits into ecoregions, we identified 108 sources (total area of 67,892.51 km²), 226 corridors (total length of 13,403.49 km), 2801 pinch points (total area of 851.07 km², and 3657 barrier points (total area of 800.70 km²). Human activities and land use types are the main factors influencing the number and spatial distribution of corridors, ecological pinch points, and barrier points. ESPs constructed at different spatial scales are broadly similar, but significant differences among details were identified. As such, when formulating ecological protection and restoration strategies, the spatial scale should be considered. Moreover, specific programs should be determined based on ESP characteristics to maximize the protection of biodiversity and ecosystem integrity from multiple perspectives and directions. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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18 pages, 200819 KiB

Open AccessArticle

A Numerical Assessment and Prediction for Meeting the Demand for Agricultural Water and Sustainable Development in Irrigation Area

by Qiying Zhang, Hui Qian, Panpan Xu, Rui Liu, Xianmin Ke, Alex Furman and Jiatao Shang

Remote Sens. 2023, 15(3), 571; https://doi.org/10.3390/rs15030571 - 18 Jan 2023

Cited by 2 | Viewed by 1727

Abstract

The demand for agricultural water is a growing problem in irrigated regions across the globe, particularly in arid and semi-arid regions. Changes in the level of groundwater in irrigation districts will affect the flow of surface water connected to the aquifer, which may [...] Read more.

The demand for agricultural water is a growing problem in irrigated regions across the globe, particularly in arid and semi-arid regions. Changes in the level of groundwater in irrigation districts will affect the flow of surface water connected to the aquifer, which may damage the sustainability of water resources and ecosystems. In this study, a two-dimensional unsteady flow model based on MODFLOW was constructed and three scenarios were established to assess the demand for agricultural water in the Jiaokou Irrigation District. The results show that the groundwater in the study area is basically balanced. However, the supply of irrigation water for summer irrigation is insufficient. The results of the model prediction indicate that when groundwater is primarily used for irrigation (scenario 1), the maximum water level decrease is 25 m, which is beyond this limit (15 m). When the ratio of groundwater to surface water is 2:1 for irrigation (scenario 2), the largest decrease in water level is approximately 10 m. Scenario 3 is proposed based on the Hanjiang-to-Weihe River Valley Water Diversion Project to prevent the salinization of soil owing to the rise in water level, and its result shows that the maximum decrease and buried depth are approximately 5 m and above 3 m, respectively, indicating that the scenario is more reasonable and sustainable. These findings provide theoretical guidance to protect water resources and prevent water pollution and should serve as a reference for rationally allocating water resources in other irrigation districts in arid and semi-arid areas. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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21 pages, 8935 KiB

Open AccessArticle

Flood Runoff Simulation under Changing Environment, Based on Multiple Satellite Data in the Jinghe River Basin of the Loess Plateau, China

by Jiqiang Lyu, Shanshan Yin, Yutong Sun, Kexin Wang, Pingping Luo and Xiaolan Meng

Remote Sens. 2023, 15(3), 550; https://doi.org/10.3390/rs15030550 - 17 Jan 2023

Cited by 10 | Viewed by 2411

Abstract

Understanding the hydrological surface condition changes, climate change and their combined impacts on flood runoff are critical for comprehending the hydrology under environmental changes and for solving future flood management challenges. This study was designed to examine the relative contributions of the hydrological [...] Read more.

Understanding the hydrological surface condition changes, climate change and their combined impacts on flood runoff are critical for comprehending the hydrology under environmental changes and for solving future flood management challenges. This study was designed to examine the relative contributions of the hydrological surface condition changes and climate change in the flood runoff of a 45,421-km² watershed in the Loess Plateau region. Statistical analytical methods, including Kendall’s trend test, the Theisen median trend analysis, and cumulative anomaly method, were used to detect trends in the relationship between the climatic variables, the normalized difference vegetation index (NDVI), land use/cover change (LUCC) data, and observed flood runoff. A grid-cell distributed rainfall–runoff model was used to detect the quantitative hydrologic responses to the climatic variability and land-use change. We found that climatic variables were not statistically significantly different (p > 0.05) over the study period. From 1985 to 2013, the cropland area continued to decrease, while the forest land, pastures, and residential areas increased in the Jinghe River Basin. Affected by LUCC and climate change, the peak discharges and flood volumes decreased by 8–22% and 5–67%, respectively. This study can provide a reference for future land-use planning and flood runoff control policy formulation and for revision in the study area. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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17 pages, 27331 KiB

Open AccessArticle

Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai–Tibetan Plateau

by Jiayi Yang, Junjian Fan, Zefan Lan, Xingmin Mu, Yiping Wu, Zhongbao Xin, Puqiong Miping and Guangju Zhao

Remote Sens. 2023, 15(1), 114; https://doi.org/10.3390/rs15010114 - 25 Dec 2022

Cited by 13 | Viewed by 2966

Abstract

Soil organic carbon (SOC) is a critical indicator for the global carbon cycle and the overall carbon pool balance. Obtaining soil maps of surface SOC is fundamental to evaluating soil quality, regulating climate change, and global carbon cycle modeling. However, efficient approaches for [...] Read more.

Soil organic carbon (SOC) is a critical indicator for the global carbon cycle and the overall carbon pool balance. Obtaining soil maps of surface SOC is fundamental to evaluating soil quality, regulating climate change, and global carbon cycle modeling. However, efficient approaches for obtaining accurate SOC information remain challenging, especially in remote or inaccessible regions of the Qinghai–Tibet Plateau (QTP), which is influenced by complex terrains, climate change, and human activities. This study employed field measurements, SoilGrids250m (SOC_250m, a spatial resolution of 250 m × 250 m), and Sentinel-2 images with different machine learning methods to map SOC content in the QTP. Four machine learning methods including partial least squares regression (PLSR), support vector machines (SVM), random forest (RF), and artificial neural network (ANN) were used to construct spatial prediction models based on 396 field-collected sampling points and various covariates from remote sensing images. Our results revealed that the RF model outperformed the PLSR, SVM, and ANN models, with a higher determination coefficient (R² of 0.82 is from the training datasets) and the ratio of performance to deviation (RPD = 2.54). The selected covariates according to the variable importance in projection (VIP) were: SOC_250m, B2, B11, Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), B5, and Soil-Adjusted Total Vegetation Index (SATVI). The predicted SOC map showed an overall decrease in SOC content ranging from 69.30 g·kg⁻¹ in the southeast to 1.47 g·kg⁻¹ in the northwest. Our prediction showed spatial heterogeneity of SOC content, indicating that Sentinel-2 images were acceptable for characterizing the variability of SOC. The findings provide a scientific basis for carbon neutrality in the QTP and a reference for the digital mapping of SOC in the alpine region. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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17 pages, 5930 KiB

Open AccessArticle

Remote Sensing-Supported Flood Forecasting of Urbanized Watersheds—A Case Study in Southern China

by Yu Gu, Yangbo Chen, Huaizhang Sun and Jun Liu

Remote Sens. 2022, 14(23), 6129; https://doi.org/10.3390/rs14236129 - 3 Dec 2022

Cited by 5 | Viewed by 1906

Abstract

Urbanization has significant impacts on watershed hydrology, but previous studies have been confirmatory and not comprehensive; in particular, few studies have addressed the impact of urbanization on flooding in highly urbanized watersheds. In this study, this effect is studied in Chebei Creek, a [...] Read more.

Urbanization has significant impacts on watershed hydrology, but previous studies have been confirmatory and not comprehensive; in particular, few studies have addressed the impact of urbanization on flooding in highly urbanized watersheds. In this study, this effect is studied in Chebei Creek, a highly urbanized watershed in the Pearl River Delta, southern China. Landsat satellite images acquired in 2015 were used to estimate land use and cover changes using the Decision Tree (DT) C4.5 classification algorithm, while the Liuxihe model, a physically based distributed hydrological model (PBDHM), is employed to simulate watershed flooding and hydrological processes. For areas with high degrees of urbanization, the duration of the flood peak is only 1 h, and the flood water level shows steep rises and falls. These characteristics increase the difficulty of flood modeling and forecasting in urbanized areas. At present, hydrological research in urbanized watersheds generally focuses on the quantitative simulation of runoff from urban areas to the watershed, flood flows, peak flood flow, and runoff depth. Few studies have involved real-time flood forecasting in urbanized watersheds. To achieve real-time flood forecasting in urbanized watersheds, PBDHMs and refined underlying surface data based on remote sensing technology are necessary. The Liuxihe model is a PBDHM that can meet the accuracy requirements of inflow flood forecasting for reservoir flood control operations. The accuracies of the two flood forecasting methods used in this study were 83.95% and 97.06%, showing the excellent performance of the Liuxihe model for the real-time flood forecasting of urbanized rivers such as the Chebei Creek watershed. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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17 pages, 4525 KiB

Open AccessArticle

Reconstruction of Historical Land Use and Urban Flood Simulation in Xi’an, Shannxi, China

by Shuangtao Wang, Pingping Luo, Chengyi Xu, Wei Zhu, Zhe Cao and Steven Ly

Remote Sens. 2022, 14(23), 6067; https://doi.org/10.3390/rs14236067 - 30 Nov 2022

Cited by 30 | Viewed by 2530

Abstract

Reconstruction of historical land uses helps to understand patterns, drivers, and impacts of land-use change, and is essential for finding solutions to land-use sustainability. In order to analyze the relationship between land-use change and urban flooding, this study used the Classification and Regression [...] Read more.

Reconstruction of historical land uses helps to understand patterns, drivers, and impacts of land-use change, and is essential for finding solutions to land-use sustainability. In order to analyze the relationship between land-use change and urban flooding, this study used the Classification and Regression Tree (CART) method to extract modern (2017) land-use data based on remote sensing images. Then, the Paleo-Land-Use Reconstruction (PLUR) program was used to reconstruct the land-use maps of Xi’an during the Ming (1582) and Qing (1766) dynasties by consulting and collecting records of land-use change in historical documents. Finally, the Flo-2D model was used to simulate urban flooding under different land-use scenarios. Over the past 435 years (1582–2017), the urban construction land area showed a trend of increasing, while the unused land area and water bodies were continuously decreasing. The increase in urban green space and buildings was 20.49% and 19.85% respectively, and the unused land area changed from 0.32 km² to 0. Urban flooding in the modern land-use scenario is the most serious. In addition to the increase in impervious areas, the increase in building density and the decrease in water areas are also important factors that aggravate urban flooding. This study can provide a reference for future land-use planning and urban flooding control policy formulation and revision in the study area. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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16 pages, 3792 KiB

Open AccessArticle

Research on Sediment Discharge Variations and Driving Factors in the Tarim River Basin

by Zhaoxia Ye, Yaning Chen, Qifei Zhang, Yongchang Liu and Xueqi Zhang

Remote Sens. 2022, 14(22), 5848; https://doi.org/10.3390/rs14225848 - 18 Nov 2022

Cited by 4 | Viewed by 2268

Abstract

Sediment discharge is widely regarded as a critical indicator of soil and water loss. The Mann–Kendall (M-K) test was applied to analyze the trends of temperature, precipitation, annual runoff, annual sediment discharge (ASD), and snow cover area proportion (SCAP). Sensitivity coefficient and contribution [...] Read more.

Sediment discharge is widely regarded as a critical indicator of soil and water loss. The Mann–Kendall (M-K) test was applied to analyze the trends of temperature, precipitation, annual runoff, annual sediment discharge (ASD), and snow cover area proportion (SCAP). Sensitivity coefficient and contribution rate were adopted to assess the sensitivity of ASD to driving factors, and the contribution of driving factors to ASD. The results showed: (1) ASD of the Kaidu River and the Aksu River originating from Tien Shan decreased at rates of 3.8503 × 10⁷ kg per year (p < 0.01) and 47.198 × 10⁷ kg per year, respectively, from 2001 to 2019. The ASD there was also found to be more sensitive to SCAP changes in autumn and winter, respectively. (2) ASD of the Yarkand River and the Yulong Kashgar River originating from the Karakoram Mountains increased at rates of 21.807 × 10⁷ kg per year and 27.774 × 10⁷ kg per year, respectively, during 2001–2019. The ASD there was determined to be more sensitive to annual runoff. (3) In terms of contribution rate, except for the Kaidu River, annual runoff of the other three rivers made the largest contribution. (4) In addition, the proportion of glacial-melt water, slope, glacierization and human activities are also possible factors affecting sediment discharge. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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16 pages, 5201 KiB

Open AccessArticle

Using a Two-Stage Scheme to Map Toxic Metal Distributions Based on GF-5 Satellite Hyperspectral Images at a Northern Chinese Opencast Coal Mine

by Bin Guo, Xianan Guo, Bo Zhang, Liang Suo, Haorui Bai and Pingping Luo

Remote Sens. 2022, 14(22), 5804; https://doi.org/10.3390/rs14225804 - 17 Nov 2022

Cited by 4 | Viewed by 2184

Abstract

Toxic metals have attracted great concern worldwide due to their toxicity and slow decomposition. Although metal concentrations can be accurately obtained with chemical methods, it is difficult to map metal distributions on a large scale due to their inherently low efficiency and high [...] Read more.

Toxic metals have attracted great concern worldwide due to their toxicity and slow decomposition. Although metal concentrations can be accurately obtained with chemical methods, it is difficult to map metal distributions on a large scale due to their inherently low efficiency and high cost. Moreover, chemical analysis methods easily lead to secondary contamination. To address these issues, 110 topsoil samples were collected using a soil sampler, and positions for each sample were surveyed using a global navigation satellite system (GNSS) receiver from a coal mine in northern China. Then, the metal contents were surveyed in a laboratory via a portable X-ray fluorescence spectroscopy (XRF) device, and GaoFen-5 (GF-5) satellite hyperspectral images were used to retrieve the spectra of the soil samples. Furthermore, a Savitzky–Golay (SG) filter and continuous wavelet transform (CWT) were selected to smooth and enhance the soil reflectance. Competitive adaptive reweighted sampling (CARS) and Boruta algorithms were utilized to identify the feature bands. The optimum two-stage method, consisting of the random forest (RF) and ordinary kriging (OK) methods, was used to infer the metal concentrations. The following outcomes were achieved. Firstly, both zinc (Zn) (68.07 mg/kg) and nickel (Ni) (26.61 mg/kg) surpassed the regional background value (Zn: 48.60 mg/kg, Ni: 19.5 mg/kg). Secondly, the optimum model of RF, combined with the OK (RFOK) method, with a relatively higher coefficient of determination (R²) (R² = 0.60 for Zn, R² = 0.30 for Ni), a lower root-mean-square error (RMSE) (RMSE = 12.45 mg/kg for Zn, RMSE = 3.97 mg/kg for Ni), and a lower mean absolute error (MAE) (MAE = 9.47 mg/kg for Zn, MAE = 3.31mg/kg for Ni), outperformed the other four models, including the RF, OK, inverse distance weighted (IDW) method, and the optimum model of RF combined with IDW (RFIDW) method in estimating soil Zn and Ni contents, respectively. Thirdly, the distribution of soil Zn and Ni concentrations obtained from the best-predicted method and the GF-5 satellite hyperspectral images was in line with the actual conditions. This scheme proves that satellite hyperspectral images can be used to directly estimate metal distributions, and the present study provides a scientific base for mapping heavy metal spatial distribution on a relatively large scale. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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16 pages, 2184 KiB

Open AccessArticle

Urban Flood-Related Remote Sensing: Research Trends, Gaps and Opportunities

by Wei Zhu, Zhe Cao, Pingping Luo, Zeming Tang, Yuzhu Zhang, Maochuan Hu and Bin He

Remote Sens. 2022, 14(21), 5505; https://doi.org/10.3390/rs14215505 - 1 Nov 2022

Cited by 22 | Viewed by 5081

Abstract

As a result of urbanization and climate change, urban areas are increasingly vulnerable to flooding, which can have devastating effects on the loss of life and property. Remote sensing technology can provide practical help for urban flood disaster management. This research presents a [...] Read more.

As a result of urbanization and climate change, urban areas are increasingly vulnerable to flooding, which can have devastating effects on the loss of life and property. Remote sensing technology can provide practical help for urban flood disaster management. This research presents a review of urban flood-related remote sensing to identify research trends and gaps, and reveal new research opportunities. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the systematic literature search resulted in 347 documents classified as geography, disaster management application, and remote sensing data utilization. The main results include 1. most of the studies are located in high-income countries and territories and inland areas; 2. remote sensing for observing the environment was more popular than observing the building; 3. the most often applied disaster management activities were vulnerability assessment and risk modeling (mitigation) and rapid damage assessment (response); 4. DEM is often applied to simulate urban floods as software inputs. We suggest that future research directions include 1. coastal urban study areas in non-high-income countries/territories to help vulnerable populations; 2. understudied disaster management activities, which often need to observe the buildings in more urban areas; 3. data standardization will facilitate integration with international standard methods for assessing urban floods. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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21 pages, 10042 KiB

Open AccessArticle

Spatially Non-Stationary Relationships between Changing Environment and Water Yield Services in Watersheds of China’s Climate Transition Zones

by Zhe Cao, Wei Zhu, Pingping Luo, Shuangtao Wang, Zeming Tang, Yuzhu Zhang and Bin Guo

Remote Sens. 2022, 14(20), 5078; https://doi.org/10.3390/rs14205078 - 11 Oct 2022

Cited by 21 | Viewed by 2683

Abstract

Identifying the spatial and temporal heterogeneity of water-related ecosystem services and the mechanisms influencing them is essential for optimizing ecosystem governance and maintaining watershed sustainable development. However, the complex and undiscovered interplay between human activities and natural factors underpins the solutions to the [...] Read more.

Identifying the spatial and temporal heterogeneity of water-related ecosystem services and the mechanisms influencing them is essential for optimizing ecosystem governance and maintaining watershed sustainable development. However, the complex and undiscovered interplay between human activities and natural factors underpins the solutions to the water scarcity and flooding challenges faced by climate transition zone basins. This study used a multiple spatial-scale analysis to: (i) quantify the spatial and temporal variations of the water yield ecosystem service (WYs) of the Wei River Basin (WRB) from 2000 to 2020 using the InVEST model and remote sensing data; and (ii) look at how human activities, climate, topography, and vegetation affect the WYs at the climate transition zone sub-catchment scale using the geographical detector model and multi-scale geographically weighted regression (MGWR). The conclusive research reveals that there would be a gradual increase in WYs between the years 2000 and 2020, as well as a distinct and very different spatial aggregation along the climatic divide. The average yearly precipitation was shown to be particularly linked to the water yield of the WRB. The interplay of human, climatic, plant, and terrain variables has a substantially higher influence than most single factors on the geographical differentiation of WYs. Bivariate enhancement and non-linear enhancement are the most common types of factor interactions. This shows that there are significant interactions between natural and human variables. Our study shows that precipitation and temperature are the main factors that cause WYs in the semi-arid zone. In the semi-humid zone, precipitation and vegetation are the key controlling factors that cause WYs. We provide new perspectives for understanding and optimizing ecosystem management by comparing the drivers of WYS in sub-basins with different climatic conditions. Based on the findings, we recommend that particular attention should be paid to ecosystem restoration practices in watersheds in climatic transition zones. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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20 pages, 6637 KiB

Open AccessArticle

Retrieval of Chlorophyll-a Concentrations Using Sentinel-2 MSI Imagery in Lake Chagan Based on Assessments with Machine Learning Models

by Xuming Shi, Lingjia Gu, Tao Jiang, Xingming Zheng, Wen Dong and Zui Tao

Remote Sens. 2022, 14(19), 4924; https://doi.org/10.3390/rs14194924 - 1 Oct 2022

Cited by 17 | Viewed by 3937

Abstract

Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with [...] Read more.

Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with in-situ measurements to estimate Chl-a in Lake Chagan, which is located in Jilin Province, Northeast China. In this study, the extreme gradient boosting (XGBoost) and Random Forest (RF) models, which had similar performances, were generated by six single bands and six band combinations. The RF model was then selected based on the assessments (R² = 0.79, RMSE = 2.51 μg L⁻¹, MAPE = 9.86%), since its learning of the input features in the model conformed to the bio-optical properties of Case 2 waters. The study considered Chl-a concentrations in Lake Chagan as a seasonal pattern according to the K-Nearest-Neighbors (KNN) classification. The RF model also showed relatively stable performance for three seasons (spring, summer and autumn) and it was applied to map Chl-a in the whole lake. The research presents a more reliable machine learning (ML) model with higher precision than previous empirical models, as shown by the effects of the input features linked with the biological mechanisms of Chl-a. Its robustness was revealed by the temporal and spatial distributions of Chl-a concentrations, which were consistent with in-situ measurements in the map. This research was capable of revealing the current ecological situation in Lake Chagan and can serve as a reference in remote sensing of inland lakes. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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26 pages, 5628 KiB

Open AccessArticle

Controls on Alpine Lake Dynamics, Tien Shan, Central Asia

by Qifei Zhang, Yaning Chen, Zhi Li, Gonghuan Fang, Yanyun Xiang and Yupeng Li

Remote Sens. 2022, 14(19), 4698; https://doi.org/10.3390/rs14194698 - 20 Sep 2022

Cited by 5 | Viewed by 2140

Abstract

The number and area of alpine lakes in Tien Shan (TS) are rapidly growing in response to a warming climate and retreating glaciers. This paper presents a comparative analysis of lake classification and changes by dividing alpine lakes (within a 10 km buffer [...] Read more.

The number and area of alpine lakes in Tien Shan (TS) are rapidly growing in response to a warming climate and retreating glaciers. This paper presents a comparative analysis of lake classification and changes by dividing alpine lakes (within a 10 km buffer of the glacier margins) into four types (supraglacial lakes, proglacial lakes, extraglacial lakes and non-glacial lakes), and subsequently determining the driving forces of change across the TS region from 1990 to 2015. The analysis utilized multiple satellite images and climatic data from gridded data sets and meteorological station observations. The results indicate that the total number and area of glacial lakes continuously increased during the study period, whereas non-glacial lakes intermittently expanded. Specifically, the total number and area of all glacial lakes (supraglacial lakes, proglacial lakes and extraglacial lakes) increased by 45.45% and 27.08%, respectively. Non-glacial lakes, in contrast, increased in quantity and area by 23.92% and 19.01%, respectively. Alpine lakes are closer to glaciers at high altitudes; in fact, some (e.g., proglacial lakes) are connected to glacier termini, and these show the highest expansion speed during the study period. The area of proglacial lakes expanded by 60.32%. Extraglacial lakes expanded by 21.06%. Supraglacial lakes, in marked contrast to the other types, decreased in area by 3.74%. Widespread rises in temperature and glacier wastage were the primary cause of the steady expansion of glacial lakes, particularly those linked to small- and medium-sized glaciers distributed in the Eastern TS where glacial lakes have rapidly increased. Both proglacial and extraglacial lakes expanded by 6.47%/a and 2%/a, respectively, from 1990 to 2015. While these proglacial and extraglacial lakes are located in largely glacierized areas, lakes in the Central TS exhibited the slowest expansion, increasing in area by 1.44%/a and 0.74%/a, respectively. Alterations in non-glacial lake areas were driven by changes in precipitation and varied spatially over the region. This study has substantial implications for the state of water resources under the complex regional changes in climate in the TS and can be used to develop useful water-resource management and planning strategies throughout Central Asia. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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21 pages, 9375 KiB

Open AccessArticle

Assessment of Satellite-Based Precipitation Products for Estimating and Mapping Rainfall Erosivity in a Subtropical Basin, China

by Xianghu Li, Xuchun Ye and Chengyu Xu

Remote Sens. 2022, 14(17), 4292; https://doi.org/10.3390/rs14174292 - 31 Aug 2022

Cited by 3 | Viewed by 1873

Abstract

Rainfall erosivity is an important indicator for quantitatively representing the erosive power of rainfall. This study expanded three satellite-based precipitation products (SPPs) for estimating and mapping rainfall erosivity in a subtropical basin in China and evaluated their performance at different rainfall erosivity intensities, [...] Read more.

Rainfall erosivity is an important indicator for quantitatively representing the erosive power of rainfall. This study expanded three satellite-based precipitation products (SPPs) for estimating and mapping rainfall erosivity in a subtropical basin in China and evaluated their performance at different rainfall erosivity intensities, seasons, and spaces. The results showed that the rainfall erosivity data from GPM-IMERG had the smallest errors compared to the estimates from rain gauge data on monthly and seasonal scales, while data from PERSIANN-CDR and TRMM 3B42 significantly underestimated and slightly overestimated rainfall erosivity, respectively. The three SPPs generally presented different strengths and weaknesses in different seasons. TRMM 3B42 performed best in summer, with small biases, but its performance was less satisfactory in winter. The precision of estimates from GPM-IMERG was higher than that from TRMM 3B42; the biases, especially in winter, were significantly reduced. For different intensities, PERSIANN-CDR overestimated light rainfall erosivity but underestimated heavy rainfall erosivity. In terms of space, TRMM 3B42 and GPM-IMERG correctly presented the spatial pattern of rainfall erosivity. However, PERSIANN-CDR tended to be less skillful in describing its spatial maps. Outcomes of the study provide an insight into the suitability of the SPPs for estimating and mapping rainfall erosivity and suggest possible directions for further improving these products. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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18 pages, 3279 KiB

Open AccessArticle

Community Scale Assessment of the Effectiveness of Designed Discharge Routes from Building Roofs for Stormwater Reduction

by Xiaoran Fu, Dong Wang, Qinghua Luan, Jiahong Liu, Zhonggen Wang and Jiayu Tian

Remote Sens. 2022, 14(13), 2970; https://doi.org/10.3390/rs14132970 - 21 Jun 2022

Cited by 6 | Viewed by 1921

Abstract

Urban flooding is increasing due to climate change and the expansion of impervious land surfaces. Green roofs have recently been identified as effective solutions for mitigating urban stormwater. However, discharge routes that involve receiving catchments of stormwater runoff from roofs to mitigate high [...] Read more.

Urban flooding is increasing due to climate change and the expansion of impervious land surfaces. Green roofs have recently been identified as effective solutions for mitigating urban stormwater. However, discharge routes that involve receiving catchments of stormwater runoff from roofs to mitigate high flows have been limited. Thus, a hydrological model was constructed to investigate the effects of changing discharge routes on stormwater flow. Three hypothetical scenarios were assessed using various combinations of discharge routes and roof types. The reduction effects on outflow and overflow were identified and evaluated across six return periods of designed rainstorms in the Tai Hung Tulip House community in Beijing. The results showed that green roofs, together with the discharge routes connecting to pervious catchments, were effective in reducing peak flow (13.9–17.3%), outflow volume (16.3–27.3%), drainage overflow frequency, and flood duration. Although mitigation can be improved by considering discharge routes, it is limited compared to that achieved by the effects of green roofs. However, integrating green roofs and discharge routes can improve community resilience to rainstorms with longer return periods. These results provide useful information for effective design of future stormwater mitigation and management strategies in small-scale urban areas. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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16 pages, 4888 KiB

Open AccessArticle

Ecological Impacts of Land Use Change in the Arid Tarim River Basin of China

by Yifeng Hou, Yaning Chen, Jianli Ding, Zhi Li, Yupeng Li and Fan Sun

Remote Sens. 2022, 14(8), 1894; https://doi.org/10.3390/rs14081894 - 14 Apr 2022

Cited by 38 | Viewed by 4187

Abstract

Land use/cover change has become an indispensable part of global eco-environmental change research. The Tarim River Basin is the largest inland river basin in China. It is also one of the most ecologically fragile areas in the country, with greening and desertification processes [...] Read more.

Land use/cover change has become an indispensable part of global eco-environmental change research. The Tarim River Basin is the largest inland river basin in China. It is also one of the most ecologically fragile areas in the country, with greening and desertification processes coexisting. This paper analyzes the evolution of land-use/cover change in the Tarim River Basin over the past 30 years based on remote sensing data. The research also explores the contribution of conversion between different land types to the ecological environment by selecting methods, such as transfer matrix and ecological contribution rate. Results indicate that grassland and barren land are the main land types in the region, accounting for 72.46% and 18.87% of the basin area, respectively. From 1990 to 2019, cropland area increased from 33,585.89 km² to 52,436.40 km², an increase of 56.13%, while barren land areas decreased from 781,380.57 km² to 760,783.29 km². Most of the land-use conversion was grassland to other land types and other land types to barren land. Since 1990, the conversion of barren land to grassland and cropland in the basin has led to ecological improvement, whereas the conversion of grassland to cropland has caused deterioration, but with a generally improving trend. It is anticipated that, over the next decade, changes in land types will involve increases in grassland and woodland area, decreases in barren land and cropland, and an overall improvement in the ecological environment in the watershed. Since agriculture and animal husbandry are the main industries in the Tarim River Basin and the land-use structure is dominated by cropland and grassland, several key measures should be implemented. These include improving land use, rationalizing the use of water and soil resources, slowing down the expansion of cropland, and alleviating the contradiction between humans and land, with the ultimate aim of achieving sustainable development of the social economy and ecological environment. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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19 pages, 9123 KiB

Open AccessArticle

Impact of Elevation-Dependent Warming on Runoff Changes in the Headwater Region of Urumqi River Basin

by Zhouyao Zheng, Sheng Hong, Haijun Deng, Zhongqin Li, Shuang Jin, Xingwei Chen, Lu Gao, Ying Chen, Meibing Liu and Pingping Luo

Remote Sens. 2022, 14(8), 1780; https://doi.org/10.3390/rs14081780 - 7 Apr 2022

Cited by 8 | Viewed by 2309

Abstract

Warming in mountainous areas has obvious elevation dependence (warming rate increases with elevation), which deeply impacts runoff change in mountainous areas. This study analysed the influence of elevation-dependent warming on runoff in the headwater region of the Urumqi River Basin (URB) based on [...] Read more.

Warming in mountainous areas has obvious elevation dependence (warming rate increases with elevation), which deeply impacts runoff change in mountainous areas. This study analysed the influence of elevation-dependent warming on runoff in the headwater region of the Urumqi River Basin (URB) based on meteorological data, remote sensing images, and runoff data. Results indicated a significant warming rate in the URB from 1960 to 2019 (0.362 °C/decade; p < 0.01). The temperature increased with an obvious elevation-dependent warming in the URB, especially during winter. Glaciers sharply retreated in the headwater region of the URB under regional warming, and remote-based results showed that glacier areas decreased by 29.45 km² (−57.81%) from the 1960s to 2017. The response of glacier mass balance and meltwater runoff to temperature change has a lag of 3 years in the headwater region of the URB. The elevation-dependent warming of temperature changes significantly impacted glacial meltwater runoff in the URB (R² = 0.49). Rising temperatures altered the glacial meltwater runoff, and the maximum annual runoff of the Urumqi Glacier No. 1 meltwater runoff increased 78.6% in 1990–2017 compared to 1960–1990. During the period of 1960–1996, the total glacial meltwater runoff amounted to 26.9 × 10⁸ m³, accounting for 33.4% of the total runoff during this period, whereas the total glacial meltwater runoff accounted for 51.1% of the total runoff in 1996–2006. Therefore, these results provide a useful reference for exploring runoff changes in mountainous watersheds in the context of elevation-dependent warming. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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17 pages, 5963 KiB

Open AccessArticle

Assessing the Sensitivity of Vegetation Cover to Climate Change in the Yarlung Zangbo River Basin Using Machine Learning Algorithms

by Lizhuang Cui, Bo Pang, Gang Zhao, Chunguang Ban, Meifang Ren, Dingzhi Peng, Depeng Zuo and Zhongfan Zhu

Remote Sens. 2022, 14(7), 1556; https://doi.org/10.3390/rs14071556 - 23 Mar 2022

Cited by 11 | Viewed by 2765

Abstract

Vegetation is a key indicator of the health of most terrestrial ecosystems and different types of vegetation exhibit different sensitivity to climate change. The Yarlung Zangbo River Basin (YZRB) is one of the highest basins in the world and has a wide variety [...] Read more.

Vegetation is a key indicator of the health of most terrestrial ecosystems and different types of vegetation exhibit different sensitivity to climate change. The Yarlung Zangbo River Basin (YZRB) is one of the highest basins in the world and has a wide variety of vegetation types because of its complex topographic and climatic conditions. In this paper, the sensitivity to climate change for different vegetation types, as reflected by the Normalized Difference Vegetation Index (NDVI), was assessed in the YZRB. Three machine learning models, including multiple linear regression, support vector machine, and random forest, were adopted to simulate the response of each vegetation type to climatic variables. We selected random forest, which showed the highest performance in both the calibration and validation periods, to assess the sensitivity of the NDVI to temperature and precipitation changes on an annual and monthly scale using hypothetical climatic scenarios. The results indicated there were positive responses of the NDVI to temperature and precipitation changes, and the NDVI was more sensitive to temperature than to precipitation on an annual scale. The NDVI was predicted to increase by 1.60%–4.68% when the temperature increased by 1.5 °C, while it only changed by 0.06%–0.24% when the precipitation increased by 10% in the YZRB. Monthly, the vegetation was more sensitive to temperature changes in spring and summer. Spatially, the vegetation was more sensitive to temperature increases in the upper and middle reaches, where the existing temperatures were cooler. The time-lag effects of climate were also analyzed in detail. For both temperature and precipitation, Needleleaf Forest and Broadleaf Forest had longer time lags than those of other vegetation types. These findings are useful for understanding the eco-hydrological processes of the Tibetan Plateau. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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19 pages, 125621 KiB

Open AccessArticle

TRDet: Two-Stage Rotated Detection of Rural Buildings in Remote Sensing Images

by Baochai Peng, Dong Ren, Cheng Zheng and Anxiang Lu

Remote Sens. 2022, 14(3), 522; https://doi.org/10.3390/rs14030522 - 22 Jan 2022

Cited by 7 | Viewed by 3200

Abstract

Fast and accurate acquisition of the outline of rural buildings on remote sensing images is an efficient method to monitor illegal rural buildings. The traditional object detection method produces useless background information when detecting rural buildings; the semantic segmentation method cannot accurately segment [...] Read more.

Fast and accurate acquisition of the outline of rural buildings on remote sensing images is an efficient method to monitor illegal rural buildings. The traditional object detection method produces useless background information when detecting rural buildings; the semantic segmentation method cannot accurately segment the contours between buildings; the instance segmentation method cannot obtain regular building contours. The rotated object detection methods can effectively solve the problem that the traditional artificial intelligence method cannot accurately extract the outline of buildings. However, the rotated object detection methods are easy to lose location information of small objects in advanced feature maps and are sensitive to noise. To resolve these problems, this paper proposes a two-stage rotated object detection network for rural buildings (TRDet) by using a deep feature fusion network (DFF-Net) and a pixel attention module (PAM). Specifically, TRDet first fuses low-level location and high-level semantic information through the DFF-Net and then reduces the interference of noise information to the network through the PAM. The experimental results show that the mean average precession (mAP), precision, recall rate, and F1 score of the proposed TRDet are 83.57%, 91.11%, 86.5%, and 88.74%, respectively, which outperform the R2CNN model by 15%, 15.54%, 4.01%, and 9.87%. The results demonstrate that the TRDet can achieve better detection in small rural buildings and dense rural buildings. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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18 pages, 6250 KiB

Open AccessArticle

Quantifying the Contributions of Climate Change and Human Activities to Water Volume in Lake Qinghai, China

by Guoqing Yang, Miao Zhang, Zhenghui Xie, Jiyuan Li, Mingguo Ma, Peiyu Lai and Junbang Wang

Remote Sens. 2022, 14(1), 99; https://doi.org/10.3390/rs14010099 - 26 Dec 2021

Cited by 14 | Viewed by 4464

Abstract

Lake Qinghai has shrunk and then expanded over the past few decades. Quantifying the contributions of climate change and human activities to lake variation is important for water resource management and adaptation to climate change. In this study, we calculated the water volume [...] Read more.

Lake Qinghai has shrunk and then expanded over the past few decades. Quantifying the contributions of climate change and human activities to lake variation is important for water resource management and adaptation to climate change. In this study, we calculated the water volume change of Lake Qinghai, analyzed the climate and land use changes in Lake Qinghai catchment, and distinguished the contributions of climate change and local human activities to water volume change. The results showed that lake water volume decreased by 9.48 km³ from 1975 to 2004 and increased by 15.18 km³ from 2005 to 2020. The climate in Lake Qinghai catchment is becoming warmer and more pluvial, and the changes in land use have been minimal. Based on the Soil and Water Assessment Tool (SWAT), land use change, climate change and interaction effect of them contributed to 7.46%, 93.13% and −0.59%, respectively, on the variation in surface runoff into the lake. From the perspective of the water balance, we calculated the proportion of each component flowing into and out of the lake and found that the contribution of climate change to lake water volume change was 97.55%, while the local human activities contribution was only 2.45%. Thus, climate change had the dominant impact on water volume change in Lake Qinghai. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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26 pages, 8403 KiB

Open AccessArticle

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

by Jun Liu, Jiyan Wang, Junnan Xiong, Weiming Cheng, Huaizhang Sun, Zhiwei Yong and Nan Wang

Remote Sens. 2021, 13(23), 4945; https://doi.org/10.3390/rs13234945 - 5 Dec 2021

Cited by 31 | Viewed by 3692

Abstract

Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These [...] Read more.

Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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18 pages, 5202 KiB

Open AccessArticle

Projected Meteorological Drought over Asian Drylands under Different CMIP6 Scenarios

by Hongwei Li, Zhi Li, Yaning Chen, Yongchang Liu, Yanan Hu, Fan Sun and Patient Mindje Kayumba

Remote Sens. 2021, 13(21), 4409; https://doi.org/10.3390/rs13214409 - 2 Nov 2021

Cited by 26 | Viewed by 4773

Abstract

Asia currently has the world’s largest arid and semi-arid zones, so a timely assessment of future droughts in the Asian drylands is prudent, particularly in the context of recent frequent sandstorms. This paper assesses the duration, frequency, and intensity of drought events in [...] Read more.

Asia currently has the world’s largest arid and semi-arid zones, so a timely assessment of future droughts in the Asian drylands is prudent, particularly in the context of recent frequent sandstorms. This paper assesses the duration, frequency, and intensity of drought events in the Asian drylands based on nine climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results show that a high percentage of land area is experiencing significant drought intensification of 65.1%, 89.9%, and 99.8% under Shared Socioeconomic Pathways (SSP)126, SSP245, and SSP585 scenarios, respectively. Furthermore, the data indicate that future droughts will become less frequent but longer in duration and more intense, with even more severe future droughts predicted for northwest China and western parts of Uzbekistan and Kazakhstan. Drought durations of 10.8 months and 13.4 months are anticipated for the future periods of 2021–2060 and 2061–2100, respectively, compared to the duration of 6.6 months for the historical period (1960–2000). Meanwhile, drought intensity is expected to reach 1.37 and 1.66, respectively, for future events compared to 0.97 for the historical period. However, drought severity under SSP245 will be weaker than that under SSP126 due to the mitigating effect of precipitation. The results of this study can provide a basis for the development of adaptation measures in Asian dryland nations. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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19 pages, 15138 KiB

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Risk Assessment of Urban Floods Based on a SWMM-MIKE21-Coupled Model Using GF-2 Data

by Lidong Zhao, Ting Zhang, Jun Fu, Jianzhu Li, Zhengxiong Cao and Ping Feng

Remote Sens. 2021, 13(21), 4381; https://doi.org/10.3390/rs13214381 - 30 Oct 2021

Cited by 19 | Viewed by 3516

Abstract

Global climate change and rapid urbanization have caused increases in urban floods. Urban flood risk assessment is a vital method for preventing and controlling such disasters. This paper takes the central region of Cangzhou city in Hebei Province as an example. Detailed topographical [...] Read more.

Global climate change and rapid urbanization have caused increases in urban floods. Urban flood risk assessment is a vital method for preventing and controlling such disasters. This paper takes the central region of Cangzhou city in Hebei Province as an example. Detailed topographical information, such as the buildings and roads in the study area, was extracted from GF-2 data. By coupling the two models, the SWMM and MIKE21, the spatial distribution of the inundation region, and the water depth in the study area under different return periods, were simulated in detail. The results showed that, for the different return periods, the inundation region was generally consistent. However, there was a large increase in the mean inundation depth within a 10-to-30-year return period, and the increase in the maximum inundation depth and inundation area remained steady. The comprehensive runoff coefficient in all of the scenarios exceeded 0.8, indicating that the drainage system in the study area is insufficient and has a higher flood risk. The flood risk of the study area was evaluated based on the damage curve, which was obtained from field investigations. The results demonstrate that the loss per unit area was less than CNY 250/m² in each return period in the majority of the damaged areas. Additionally, the total loss was mainly influenced by the damaged area, but, in commercial areas, the total loss was highly sensitive to the inundation depth. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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20 pages, 6283 KiB

Open AccessArticle

Influences of Climate Change and Human Activities on NDVI Changes in China

by Yu Liu, Jiyang Tian, Ronghua Liu and Liuqian Ding

Remote Sens. 2021, 13(21), 4326; https://doi.org/10.3390/rs13214326 - 27 Oct 2021

Cited by 54 | Viewed by 4910

Abstract

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has [...] Read more.

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year⁻¹. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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16 pages, 10589 KiB

Open AccessArticle

Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6

by Yunxia Long, Changchun Xu, Fang Liu, Yongchang Liu and Gang Yin

Remote Sens. 2021, 13(20), 4076; https://doi.org/10.3390/rs13204076 - 12 Oct 2021

Cited by 13 | Viewed by 2657

Abstract

Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global [...] Read more.

Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of p = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What’s more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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21 pages, 5789 KiB

Open AccessArticle

Dynamic Assessment of the Impact of Flood Disaster on Economy and Population under Extreme Rainstorm Events

by Xin Su, Weiwei Shao, Jiahong Liu, Yunzhong Jiang and Kaibo Wang

Remote Sens. 2021, 13(19), 3924; https://doi.org/10.3390/rs13193924 - 30 Sep 2021

Cited by 26 | Viewed by 3876

Abstract

In the context of climate change and rapid urbanization, flood disaster loss caused by extreme rainstorm events is becoming more and more serious. An accurate assessment of flood disaster loss has become a key issue. In this study, extreme rainstorm scenarios with 50- [...] Read more.

In the context of climate change and rapid urbanization, flood disaster loss caused by extreme rainstorm events is becoming more and more serious. An accurate assessment of flood disaster loss has become a key issue. In this study, extreme rainstorm scenarios with 50- and 100-year return periods based on the Chicago rain pattern were designed. The dynamic change process of flood disaster loss was obtained by using a 1D–2D coupled model, Hazard Rating (HR) method, machine learning, and ArcPy script. The results show that under extreme rainstorm events, the direct economic loss and affected population account for about 3% of the total GDP and 16% of the total population, respectively, and built-up land is the main disaster area. In addition, the initial time and the peak time of flood disaster loss increases with an increasing flood hazard degree and decreases with the increase in the return period. The total loss increases with the increase in the return period, and the unit loss decreases with the increase in the return period. Compared with a static assessment, a dynamic assessment can better reveal the development law of flood disaster loss, which has great significance for flood risk management and the mitigation of flood disaster loss. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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16 pages, 2190 KiB

Open AccessArticle

Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis

by Xia Bai, Juliang Jin, Shaowei Ning, Chengguo Wu, Yuliang Zhou, Libing Zhang and Yi Cui

Remote Sens. 2021, 13(18), 3586; https://doi.org/10.3390/rs13183586 - 9 Sep 2021

Cited by 2 | Viewed by 1660

Abstract

Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy [...] Read more.

Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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28 pages, 10008 KiB

Open AccessArticle

Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin

by Jian Wu, Sidong Zeng, Linhan Yang, Yuanxin Ren and Jun Xia

Remote Sens. 2021, 13(16), 3309; https://doi.org/10.3390/rs13163309 - 20 Aug 2021

Cited by 32 | Viewed by 3872

Abstract

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. [...] Read more.

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. Thus, quantitative analysis of the relationship between different land cover types and river water quality contributes to a better understanding of the effects of land cover on water quality, the landscape planning of water quality protection, and integrated water resources management. Based on water quality data of 2006–2018 at 18 typical water quality stations in the Yangtze River basin, this study analyzed the spatial and temporal variation characteristics of water quality by using the single-factor water quality identification index through statistical analysis. Furthermore, the Spearman correlation analysis method was adopted to quantify the spatial-scale and temporal-scale effects of various land uses, including agricultural land (AL), forest land (FL), grassland (GL), water area (WA), and construction land (CL), on the stream water quality of dissolved oxygen (DO), chemical oxygen demand (COD_Mn), and ammonia (NH₃-N). The results showed that (1) in terms of temporal variation, the water quality of the river has improved significantly and the tributaries have improved more than the main rivers; (2) in the spatial variation respect, the water quality pollutants in the tributaries are significantly higher than those in the main stream, and the concentration of pollutants increases with the decrease of the distance from the estuary; and (3) the correlation between DO and land use is low, while that between NH₃-N, COD_Mn, and land use is high. CL and AL have a negative effect on water quality, while FL and GL have a purifying effect on water quality. In particular, AL and CL have a significant positive correlation with pollutants in water. Compared with NH₃-N, COD_Mn has a higher correlation with land use at a larger scale. The results highlight the spatial scale and seasonal dependence of land use on water quality, which can provide a scientific basis for land management and seasonal pollution control. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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16 pages, 9021 KiB

Open AccessArticle

The Water Availability on the Chinese Loess Plateau since the Implementation of the Grain for Green Project as Indicated by the Evaporative Stress Index

by Linjing Qiu, Yuting Chen, Yiping Wu, Qingyue Xue, Zhaoyang Shi, Xiaohui Lei, Weihong Liao, Fubo Zhao and Wenke Wang

Remote Sens. 2021, 13(16), 3302; https://doi.org/10.3390/rs13163302 - 20 Aug 2021

Cited by 10 | Viewed by 2482

Abstract

The vegetation coverage on the Loess Plateau (LP) of China has clearly increased since the implementation of the Grain for Green Project in 1999, but there is a debate about whether the improved greenness was achieved at the expense of the balance between [...] Read more.

The vegetation coverage on the Loess Plateau (LP) of China has clearly increased since the implementation of the Grain for Green Project in 1999, but there is a debate about whether the improved greenness was achieved at the expense of the balance between the supply and demand of water resources. Therefore, developing reliable indicators to evaluate the water availability is a prerequisite for maintaining ecological sustainability and ensuring the persistence of vegetation restoration. This study was designed to evaluate water availability on the LP during 2000–2015, using the evaporative stress index (ESI) derived from a remote sensing dataset. The relative dependences of the ESI on climatic and biological factors (including temperature, precipitation and land cover change) were also analyzed. The results showed that the leaf area index (LAI) in most regions of the LP showed a significant increasing trend (p < 0.05), and larger gradients of increase were mainly detected in the central and eastern parts of the LP. The evapotranspiration also exhibited an increasing trend in the central and eastern parts of the LP, with a gradient greater than 10 mm/year. However, almost the whole LP exhibited a decreased ESI from 2000 to 2015, and the largest decrease occurred on the central and eastern LP, indicating a wetting trend. The soil moisture storage in the 0–289-cm soil profiles showed an increasing trend in the central and eastern LP, and the area with an upward trend enlarged with the soil depth. Further analysis revealed that the decreased ESI on the central and eastern LP mainly depended on the increase in the LAI compared with climatic influences. This work not only demonstrated that the ESI was a useful indicator for understanding the water availability in natural and managed ecosystems under climate change but also indicated that vegetation restoration might have a positive effect on water conservation on the central LP. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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14 pages, 2528 KiB

Open AccessTechnical Note

Attributing Evapotranspiration Changes with an Extended Budyko Framework Considering Glacier Changes in a Cryospheric-Dominated Watershed

by Yaping Chang, Yongjian Ding, Qiudong Zhao and Shiqiang Zhang

Remote Sens. 2023, 15(3), 558; https://doi.org/10.3390/rs15030558 - 17 Jan 2023

Cited by 2 | Viewed by 1801

Abstract

The retreat of glaciers has altered hydrological processes in cryospheric regions and affects water resources at the basin scale. It is necessary to elucidate the contributions of environmental changes to evapotranspiration (ET) variation in cryospheric-dominated regions. Considering the upper reach of the Shule [...] Read more.

The retreat of glaciers has altered hydrological processes in cryospheric regions and affects water resources at the basin scale. It is necessary to elucidate the contributions of environmental changes to evapotranspiration (ET) variation in cryospheric-dominated regions. Considering the upper reach of the Shule River Basin as a typical cryospheric-dominated watershed, an extended Budyko framework addressing glacier change was constructed and applied to investigate the sensitivity and contribution of changes in environmental variables to ET variation. The annual ET showed a significant upward trend of 1.158 mm yr⁻¹ during 1982–2015 in the study area. ET was found to be the most sensitive to precipitation (P), followed by the controlling parameter (w), which reflects the integrated effects of landscape alterations, potential evapotranspiration (ET₀), and glacier change (∆W). The increase in P was the dominant factor influencing the increase in ET, with a contribution of 112.64%, while the decrease in w largely offset its effect. The contributions of P and ET₀ to ET change decreased, whereas that of w increased when considering glaciers using the extended Budyko framework. The change in glaciers played a clear role in ET change and hydrological processes, which cannot be ignored in cryospheric watersheds. These findings are helpful for better understanding changes in water resources in cryospheric regions. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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14 pages, 3433 KiB

Open AccessTechnical Note

Landsat TM/OLI-Based Ecological and Environmental Quality Survey of Yellow River Basin, Inner Mongolia Section

by Wenlong Gao, Shengwei Zhang, Xinyu Rao, Xi Lin and Ruishen Li

Remote Sens. 2021, 13(21), 4477; https://doi.org/10.3390/rs13214477 - 8 Nov 2021

Cited by 42 | Viewed by 3971

Abstract

The monitoring and maintenance of the Inner Mongolia section of the Yellow River Basin is of great significance to the safety and development of China’s Yellow River Economic Belt and to the protection of the Yellow River ecology. In this study, we calculated [...] Read more.

The monitoring and maintenance of the Inner Mongolia section of the Yellow River Basin is of great significance to the safety and development of China’s Yellow River Economic Belt and to the protection of the Yellow River ecology. In this study, we calculated diagnostic values from a total of 520 Landsat OLI/TM remote sensing images of the Yellow River Basin of Inner Mongolia from 2001 to 2020. Using the RSEI and the GEE Cloud Computing Jigsaw, we analyzed the spatial and temporal distribution of diagnostic values representative of the basin’s ecological status. Further, Mantel and Pearson correlations were used to analyze the significance of environmental factors in affecting the ecological quality of cities along the Yellow River within the study area. The results indicated that the overall mean of RSEI values rose at first and then fell. The RSEI grade to land area ratio was calculated to be highest in 2015 (excellent) and worst in 2001. From 2001 to 2020, ecological quality monitoring process of main cities in the Inner Mongolia region of the Yellow River Basin. Hohhot, Baotou, and Linhe all have an RSEI score greater than 0.5, considered average. However, Dongsheng had its best score (0.60, good) in 2005, which then declined and increased to an average rating in 2020. The RSEI value for Wuhai reached excellent in 2010 but then became poor in 2020, dropping to 0.28. The analysis of ecological quality in the city shows that the greenness index (NDVI) carried the most significant impact on the ecological environment, followed by the humidity index (Wet), the dryness index (NDBSI), the temperature index (Lst), land use, and then regional gross product (RGP). The significance of this study is to provide a real-time, accurate, and rapid understanding of trends in the spatial and temporal distribution of ecological and environmental quality along the Yellow River, thereby providing a theoretical basis and technical support for ecological and environmental protection and high-quality development of the Yellow River Basin. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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15 pages, 3569 KiB

Open AccessTechnical Note

Comparison of Hydrological Patterns between Glacier-Fed and Non-Glacier-Fed Lakes on the Southeastern Tibetan Plateau

by Fangdi Sun, Bin He, Caixia Liu and Yuchao Zeng

Remote Sens. 2021, 13(20), 4024; https://doi.org/10.3390/rs13204024 - 9 Oct 2021

Cited by 1 | Viewed by 1893

Abstract

Lakes on the Tibetan Plateau have experienced variations over the last several decades, and the delineation of lake dynamics is favorable for the regional water cycle and can serve as important information for plateau environmental research. This study focused on 57 lakes near [...] Read more.

Lakes on the Tibetan Plateau have experienced variations over the last several decades, and the delineation of lake dynamics is favorable for the regional water cycle and can serve as important information for plateau environmental research. This study focused on 57 lakes near the Tanggula Mountains on the southeastern Tibetan Plateau. Yearly inundations of the lakes in 1989–2019 and altimeter data available for 2003–2020 were integrated to illustrate the changing patterns of glacier-fed and non-glacier-fed lakes. These two groups of lakes presented very similar evolution stages. They both increased in 1989–1992, decreased in 1992–1996, increased rapidly in 1998–2005, and had batch-wise fluctuations since 2005, with respective areas of around 5305.28 and 1636.79 km² in the last decade. The non-glacier-fed lakes were more sensitive to precipitation variation, and glacier-fed lakes were more sensitive to temperature changes. Based on lakes with obvious changes in water level, the whole water storage variations of the studied lakes were 1.90 Gt/y in 2003–2009, including 1.80 Gt/y for glacier-fed lakes and 0.10 Gt/y for non-glacier-fed lakes. The contribution from glacier melting in 2003–2009 amounted to 16.11% of the whole lake volume increase. In 2010–2020, water mass changes were 0.42 Gt/y for glacier-fed lakes and −0.14 Gt/y for non-glacier-fed lakes, respectively. The volume increase of glacier-fed lakes in 2010–2020 was mainly due to the expansion of Selin Co. Selin Co experienced a water increase of about 0.46 Gt/y, and the other glacier-fed lakes experienced a decreasing volume of −0.04 Gt/y. In 2010–2020, 99.43% of the glacier contribution supplied Selin Co. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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