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Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change

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

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 28066

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Special Issue Editors

Prof. Dr. Yongshuo Fu

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Guest Editor

College of Water Sciences, Beijing Normal University, Beijing 100875, China
Interests: vegetation phenology; climate change; ecohydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Xuan Zhang

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Guest Editor

College of Water Sciences, Beijing Normal University, Beijing 100875, China
Interests: drought; extreme climate; eco-hydrology; hydrological simulation
Special Issues, Collections and Topics in MDPI journals

Dr. Senthilnath Jayavelu

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Guest Editor

Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
Interests: deep learning; reinforcement learning; optimizations; multiagent systems; materials informatics; remote sensing
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Dr. Shengli Tao

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Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Interests: ecology; forest; water; lidar; microwave
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Dr. Xuesong Zhang

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Agricultural Research Service, Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Beltsville, MD 20705, USA
Interests: agriculture; carbon cycle; hydrology; remote sensing
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Special Issue Information

Dear Colleagues,

Climate change, especially in the form extreme climates such as drought and heat waves, has profoundly influenced the terrestrial water cycle and vegetation growth and subsequently affected the fluvial geomorphology pattern, carbon and energy balance, as well as water safety and food security. Identifying the extent to which hydrology and vegetation respond to the ongoing climate change and investigating the mechanisms behind these changes will not only help to address the negative effects of climate change but also to provide effective adaptive measures. Therefore, it is essential to explore the changes of hydrology and vegetation under climate changes at the basin and regional scale, or even at the global scale. With the development of high-resolution satellites and unmanned aerial vehicles (UAVs), the capacity of remote sensing to monitor changes in hydrology and vegetation has been significantly improved.

The purpose of this Special Issue is to present new research advances on the applications of remote sensing techniques such as multi/hyperspectral and light detection and ranging (LiDAR) from satellites and UAVs for monitoring the changes of hydrology and vegetation under climate change. Contributions focusing on applications in hydrology and vegetation, both algorithmic and methodological, are invited. In particular, new approaches and novel contributions such as fusion methods, knowledge extraction, machine learning, and deep learning methods are of interest, specifically studies based on multispectral and hyperspectral LiDAR data from UAV platforms.

This Special Issue of Remote Sensing invites papers related to new technological advancements in the application of remote sensing techniques in the domains of hydrology and vegetation. The following specific topics are suggested recommendations:

Hydrology and vegetation mapping and change detection (multi/hyper-spectral, LiDAR);
Vegetation response to extreme drought;
Water quality monitoring (multi/hyperspectral, RS);
Vegetation health monitoring;
Phenotyping estimation and disease detection of forest;
Time-series analysis monitoring for agriculture and forest;
Machine learning and deep learning;
Novel methods for phenotyping from UAV imagery (e.g., leaf nitrogen, leaf area index, or biomass);
Reconstruction of structure of forest using LiDAR;
Fluvial network topology and its climatic dependence.

Dr. Yongshuo Fu
Dr. Xuan Zhang
Dr. Senthilnath Jayavelu
Dr. Shengli Tao
Dr. Xuesong Zhang
Guest Editors

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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

Hydrology and ecohydrology
Water cycle
UAV remote sensing
Microwave and Lidar remote sensing
Forest ecology
Phenology extraction
Yield prediction
Climate dynamics
Vegetation dynamic
Modeling climate change
Machine learning and deep learning
River basin geometry and topology

Published Papers (12 papers)

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Research

19 pages, 4881 KiB

Open AccessArticle

Integrated UAV-Based Multi-Source Data for Predicting Maize Grain Yield Using Machine Learning Approaches

by Yahui Guo, Xuan Zhang, Shouzhi Chen, Hanxi Wang, Senthilnath Jayavelu, Davide Cammarano and Yongshuo Fu

Remote Sens. 2022, 14(24), 6290; https://doi.org/10.3390/rs14246290 - 12 Dec 2022

Cited by 7 | Viewed by 1901

Abstract

Increases in temperature have potentially influenced crop growth and reduced agricultural yields. Commonly, more fertilizers have been applied to improve grain yield. There is a need to optimize fertilizers, to reduce environmental pollution, and to increase agricultural production. Maize is the main crop in China, and its ample production is of vital importance to guarantee regional food security. In this study, the RGB and multispectral images, and maize grain yields were collected from an unmanned aerial vehicle (UAV) platform. To confirm the optimal indices, RGB-based vegetation indices and textural indices, multispectral-based vegetation indices, and crop height were independently applied to build linear regression relationships with maize grain yields. A stepwise regression model (SRM) was applied to select optimal indices. Three machine learning methods including: backpropagation network (BP), random forest (RF), and support vector machine (SVM) and the SRM were separately applied for predicting maize grain yields based on optimal indices. RF achieved the highest accuracy with a coefficient of determination of 0.963 and root mean square error of 0.489 (g/hundred-grain weight). Through the grey relation analysis, the N was the most correlated indicator, and the optimal ratio of fertilizers N/P/K was 2:1:1. Our research highlighted the integration of spectral, textural indices, and maize height for predicting maize grain yields. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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22 pages, 4796 KiB

Open AccessArticle

Developing an Automated Python Surface Energy Balance System (PySEBS) Software for Calculating Actual Evapotranspiration-Software Development and Application Case in Jilin Province, China

by Haipeng Liu, Feng Huang, Yingxuan Li, Pinpin Ren, Gary W. Marek, Beibei Ding, Baoguo Li and Yong Chen

Remote Sens. 2022, 14(21), 5629; https://doi.org/10.3390/rs14215629 - 07 Nov 2022

Viewed by 1962

Abstract

In this study, Python Surface Energy Balance System (PySEBS) software was developed in the Python 2.7 programming language for continuous calculation of actual evapotranspiration (ET_a) at regional scales. The software is based on the Surface Energy Balance System (SEBS) model, which uses basic meteorological data, MODIS remote sensing data, and Digital Elevation Model (DEM) data as the original input data and finally outputs daily-scale ET_a in the form of raster data with a spatial resolution of 1 km × 1 km. To verify the reliability of the PySEBS model, the ET_a of spring maize during the growing season in Jilin Province, China, from 2001 to 2020 was calculated and analyzed in this study and compared with the results of similar studies by others. The findings showed that the PySEBS model has a reasonable accuracy in estimating ET_a within ±15% and is a robust model that can achieve the continuous calculation of ET_a at a regional scale. Therefore, PySEBS software is a useful tool for regional irrigation scheduling and water resources management. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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21 pages, 5210 KiB

Open AccessArticle

What Are Contemporary Mexican Conifers Telling Us? A Perspective Offered from Tree Rings Linked to Climate and the NDVI along a Spatial Gradient

by Marín Pompa-García, Eduardo D. Vivar-Vivar, José A. Sigala-Rodríguez and Jaime R. Padilla-Martínez

Remote Sens. 2022, 14(18), 4506; https://doi.org/10.3390/rs14184506 - 09 Sep 2022

Cited by 4 | Viewed by 1837

Abstract

Forest structure and composition have changed rapidly worldwide, presenting tendencies towards an increasing proportion of younger trees. From chronologies of tree-ring indices (TRI) and the reconstruction of the basal area increment (BAI), a dendroecological study was conducted from the perspective of the radial growth of twelve contemporary conifer species in a highly diverse region of the planet. From an elevational perspective, the TRI were associated with climate and the NDVI, while the BAI was also modeled as a potential proxy for forest productivity. Climate affects the species differently according to elevation: at 1900 m asl, Pinus caribaea, P. oocarpa and P. jeffreyi presented the lowest sensitivities to climate and drought. For their part, species occupying the intermediate part of the gradient (1901–3000 m asl), such as P. engelmannii, P. patula, P. johannis and P. maximartinezii, were very sensitive to maximum temperature (TMax), precipitation (PP) and drought during the winter–spring period. Finally, of the species distributed on the upper part of the gradient (>3000 m asl), only Abies religiosa was associated with TMax and drought; Juniperus deppeana, A. hickelii and P. hartwegii did not seem to be vulnerable to drought. Complementarily, we found significant differences in the BAI as a function of elevation, with the sites at 1001–1500 m asl presenting higher BAI. The results suggest that the growth in these forests is impacted by droughts and follows a distinct spatial pattern, with greater restriction found in mid-elevation forests. Consistent implications are also observed in BAI trends. For its part, the NDVI demonstrated a decreasing tendency in greenness from south to north, although no elevation pattern was evident. The combined proxies utilized here produced parameters that improve our understanding of forest growth and should be considered in vegetation dynamics models in order to reduce their uncertainty in the face of climate vulnerability. These forests must be sustainably managed, and it is therefore crucial to determine the influence of ecological variables on their growth. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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19 pages, 3534 KiB

Open AccessArticle

Deep Learning-Based Water Quality Retrieval in an Impounded Lake Using Landsat 8 Imagery: An Application in Dongping Lake

by Hanwen Zhang, Baolin Xue, Guoqiang Wang, Xiaojing Zhang and Qingzhu Zhang

Remote Sens. 2022, 14(18), 4505; https://doi.org/10.3390/rs14184505 - 09 Sep 2022

Cited by 10 | Viewed by 3006

Abstract

Attempts have been made to incorporate remote sensing techniques and in situ observations for enhanced water quality assessments. Estimations of nonoptical indicators sensitive to water environment changes, however, have not been fully studied, mainly due to complex nonlinear relationships between the observed values and surface reflectance. In this study, we applied a novel deep learning approach driven by a range of spectral properties to retrieve 6-year changes in water quality variables, i.e., Chl-a, BOD, TN, CODMn, NH3-N, and TP, on a monthly basis between 2013 and 2018 at Dongping Lake, an impounded lake located in the Yellow River in China. Band arithmetic was used to compute 26 predictors from Landsat 8 OLI imagery for model inputs. The results showed generally strong agreement between in situ and ConvLSTM-derived lake variables, generating R² of 0.92, 0.88, 0.84, 0.80, 0.83, and 0.77 for TN, NH3-N, CODMn, Chl-a, TP, and BOD, which suggest good performance of the developed model. We then used statistical analysis to identify the spatial and temporal heterogeneity. The framework established in this study has applications in effective water quality monitoring and serves as an alarming tool for water-environment management in the complex inland lake waters. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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19 pages, 4201 KiB

Open AccessArticle

Structural Characteristics of Endorheic Rivers in the Tarim Basin

by Yichu Wang, Danlu Liu, Enhang Liang and Jinren Ni

Remote Sens. 2022, 14(18), 4502; https://doi.org/10.3390/rs14184502 - 09 Sep 2022

Cited by 6 | Viewed by 1982

Abstract

Endorheic rivers as landlocked systems with no hydrological connections to marine environments are suffering from water and ecosystem crisis worldwide, yet little is known about their structural characteristics with complex geomorphic and climatic dependence. Based on the river networks identified from 30 m resolution digital elevation models and surface water dynamic information derived from Landsat images, we investigate the hierarchical characteristics of 60 sub-basins in the Tarim Basin, the largest endorheic river basin in China. In the Tarim River basin, endorheic rivers exhibit a self-similarity only in the range of stream-orders 1–4, compared to the range of stream-orders 1–5 observed in exorheic rivers, owning to the limited stream power to maintain the similar aggregation of rivers in the arid regions. Moreover, the Tarim River networks demonstrate lower bifurcation ratio (2.48), length ratio (2.03), fractal dimension (1.38), and drainage density (0.24 km⁻¹) in representative sub-basins, with a significant decay in median values compared with those derived from exohreic rivers at similar scales, suggesting sparser and imperfect developed branching river networks in endorheic basins. Further analysis on the Tarim reveals that endorheic river structure is more related to glacier extent (r = 0.67~0.84), potential evapotranspiration (r = 0.63~0.81), and groundwater type index (r = 0.64~0.73), which is essentially different from the structure of exorheic river represented by the Yellow River largely controlled by surface runoff, precipitation, and vegetation coverage. This study stresses the differences in intrinsic structural characteristics and extrinsic drivers of endorheic and exorheic rivers and highlights the necessity of differentiated strategies for endorheic river management in fragile ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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16 pages, 7277 KiB

Open AccessArticle

Spatiotemporal Variation of Evapotranspiration on Different Land Use/Cover in the Inner Mongolia Reach of the Yellow River Basin

by Xiaojing Zhang, Guoqiang Wang, Baolin Xue, Yuntao Wang and Libo Wang

Remote Sens. 2022, 14(18), 4499; https://doi.org/10.3390/rs14184499 - 09 Sep 2022

Cited by 7 | Viewed by 1558

Abstract

The accurate estimation of global evapotranspiration (ET) is essential to understanding the water cycle and land–atmosphere feedbacks in the Earth system. This study focused on the Inner Mongolia Reach of the Yellow River Basin, a typical arid and semi-arid region. Although there are many remote sensing ET datasets, many of the ET algorithms have not considered the impact of soil moisture, especially in water-limited areas. In this paper, the new PT-JPL model, which incorporates soil moisture into ET simulation, is used to improve the accuracy of ET simulation in water-limited areas. The simulation value is evaluated using two Hobq Desert eddy-covariance towers and the Penman–Monteith–Leuning version 2 (PML-V2) dataset. The new PT-JPL model shows the most significant improvements in water-limited regions; the coefficient of determination can reach 0.826, and the RMSE can reduce to 9.645 W/m². Soil evaporation is central to the actual ET increase in the study area. Implementing ecological restoration projects reduced the exposed area of land in the study area and reduced the rate of total ET effectively. Furthermore, the most advanced machine learning local interpretation algorithm—the TreeExplainer-based Shapley additive explanation (SHAP) method—was used to identify the driving factors of ET capacity under different land use types. Temperature, NDVI, and root zone soil moisture were the main environmental factors causing ET changes in different plants. Meanwhile, temperature and root zone soil moisture had a noticeable coupling effect, except for grassland. Furthermore, a threshold effect of temperature to ET was found, and the value is 25, 30, and 30 °C in the forest, grassland, and cropland, respectively. This study provides an essential reference for accurately describing the ET characteristics of arid and semi-arid regions to achieve the efficient management of water resources. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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26 pages, 10687 KiB

Open AccessArticle

Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery

by Alireza Taheri Dehkordi, Mohammad Javad Valadan Zoej, Hani Ghasemi, Mohsen Jafari and Ali Mehran

Remote Sens. 2022, 14(18), 4491; https://doi.org/10.3390/rs14184491 - 08 Sep 2022

Cited by 8 | Viewed by 2400

Abstract

Within water resources management, surface water area (SWA) variation plays a vital role in hydrological processes as well as in agriculture, environmental ecosystems, and ecological processes. The monitoring of long-term spatiotemporal SWA changes is even more critical within highly populated regions that have an arid or semi-arid climate, such as Iran. This paper examined variations in SWA in Iran from 1990 to 2021 using about 18,000 Landsat 5, 7, and 8 satellite images through the Google Earth Engine (GEE) cloud processing platform. To this end, the performance of twelve water mapping rules (WMRs) within remotely-sensed imagery was also evaluated. Our findings revealed that (1) methods which provide a higher separation (derived from transformed divergence (TD) and Jefferies–Matusita (JM) distances) between the two target classes (water and non-water) result in higher classification accuracy (overall accuracy (OA) and user accuracy (UA) of each class). (2) Near-infrared (NIR)-based WMRs are more accurate than short-wave infrared (SWIR)-based methods for arid regions. (3) The SWA in Iran has an overall downward trend (observed by linear regression (LR) and sequential Mann–Kendall (SQMK) tests). (4) Of the five major water basins, only the Persian Gulf Basin had an upward trend. (5) While temperature has trended upward, the precipitation and normalized difference vegetation index (NDVI), a measure of the country’s greenness, have experienced a downward trend. (6) Precipitation showed the highest correlation with changes in SWA (r = 0.69). (7) Long-term changes in SWA were highly correlated (r = 0.98) with variations in the JRC world water map. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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20 pages, 5353 KiB

Open AccessArticle

Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

by Chong Li, Xuan Zhang, Guodong Yin, Yang Xu and Fanghua Hao

Remote Sens. 2022, 14(14), 3307; https://doi.org/10.3390/rs14143307 - 08 Jul 2022

Cited by 9 | Viewed by 1879

Abstract

The characteristics of the drought propagation from meteorological drought (MD) to agricultural drought (AD) differ in various climatic and underlying surface conditions. However, how these factors affect the process of drought propagation is still unclear. In this study, drought propagation and influencing factors were investigated in an arid region of Northeast Asia (ARNA) during 1982–2014. Based on run theory, the drought characteristics were detected using the standardized precipitation index (SPI) and standardized soil moisture index (SMI), respectively. The propagation time from MD to AD was investigated, and the influence factors were identified. Results demonstrated that five clusters (C1–C5) based on land cover distribution were further classified by the K-means cluster algorithm to discuss the spatial and seasonal propagation variation. MD and AD in ARNA became more severe during the study period in all five clusters. The propagation times from MD to AD in all five clusters were shorter (1–3 months) in summer and autumn and longer (5–12 months) in spring and winter. This result suggested that the impact of vegetation on the seasonal drought propagation time was more obvious than that of the spatial drought propagation time. Precipitation and vegetation were the major impactors of AD in spring, summer and autumn (p < 0.05). The impact of precipitation on AD was more noticeable in summer, while vegetation mainly influenced AD in spring and autumn. The research also found that drought propagation time had a negative relationship (p < 0.05) with precipitation, evapotranspiration, soil moisture and NDVI in this region, which indicated that a rapid hydrological cycle and vegetation can shorten the propagation time from MD to AD. This study can help researchers to understand the drought propagation process and the driving factors to enhance the efficiency of drought forecasting. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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18 pages, 61848 KiB

Open AccessArticle

Water Deficit May Cause Vegetation Browning in Central Asia

by Haichao Hao, Yaning Chen, Jianhua Xu, Zhi Li, Yupeng Li and Patient Mindje Kayumba

Remote Sens. 2022, 14(11), 2574; https://doi.org/10.3390/rs14112574 - 27 May 2022

Cited by 7 | Viewed by 1921

Abstract

There is consistent evidence of vegetation greening in Central Asia over the past four decades. However, in the early 1990s, the greening temporarily stagnated and even for a time reversed. In this study, we evaluate changes in the normalized difference vegetation index (NDVI) based on the long-term satellite-derived remote sensing data systems of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI from 1981 to 2013 and MODIS NDVI from 2000 to 2020 to determine whether the vegetation in Central Asia has browned. Our findings indicate that the seasonal sequence of NDVI is summer > spring > autumn > winter, and the spatial distribution pattern is a semicircular distribution, with the Aral Sea Basin as its core and an upward tendency from inside to outside. Around the mid-1990s, the region’s vegetation experienced two climatic environments with opposing trends (cold and wet; dry and hot). Prior to 1994, NDVI increased substantially throughout the growth phase (April–October), but this trend reversed after 1994, when vegetation began to brown. Our findings suggest that changes in vegetation NDVI are linked to climate change induced by increased CO₂. The state of water deficit caused by temperature changes is a major cause of the browning turning point across the study area. At the same time, changes in vegetation NDVI were consistent with changes in drought degree (PDSI). This research is relevant for monitoring vegetation NDVI and carbon neutralization in Central Asian ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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23 pages, 5542 KiB

Open AccessArticle

Nitrogen and Phosphorus Retention Risk Assessment in a Drinking Water Source Area under Anthropogenic Activities

by Yuexin Zheng, Qianyang Wang, Xuan Zhang, Jingshan Yu, Chong Li, Liwen Chen and Yuan Liu

Remote Sens. 2022, 14(9), 2070; https://doi.org/10.3390/rs14092070 - 26 Apr 2022

Cited by 2 | Viewed by 1916

Abstract

Excessive nitrogen (N) and phosphorus (P) input resulting from anthropogenic activities seriously threatens the supply security of drinking water sources. Assessing nutrient input and export as well as retention risks is critical to ensuring the quality and safety of drinking water sources. Conventional balance methods for nutrient estimation rely on statistical data and a huge number of estimation coefficients, which introduces uncertainty into the model results. This study aimed to propose a convenient, reliable, and accurate nutrient prediction model to evaluate the potential nutrient retention risks of drinking water sources and reduce the uncertainty inherent in the traditional balance model. The spatial distribution of pollutants was characterized using time-series satellite images. By embedding human activity indicators, machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were constructed to estimate the input and export of nutrients. We demonstrated the proposed model’s potential using a case study in the Yanghe Reservoir Basin in the North China Plain. The results indicate that the area information concerning pollution source types was effectively established based on a multi-temporal fusion method and the RF classification algorithm, and the overall classification low-end accuracy was 92%. The SVM model was found to be the best in terms of predicting nutrient input and export. The determination coefficient (R²) and Root Mean Square Error (RMSE) of N input, P input, N export, and P export were 0.95, 0.94, 0.91, and 0.93, respectively, and 32.75, 5.18, 1.45, and 0.18, respectively. The low export ratios (2.8–3.0% and 1.1–2.2%) of N and P, the ratio of export to input, further confirmed that more than 97% and 98% of N and P, respectively, were retained in the watershed, which poses a pollution risk to the soil and the quality of drinking water sources. This nutrient prediction model is able to improve the accuracy of non-point source pollution risk assessment and provide useful information for water environment management in drinking water source regions. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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15 pages, 5380 KiB

Open AccessArticle

Impact of Extreme Climate on the NDVI of Different Steppe Areas in Inner Mongolia, China

by Kuan Chen, Genbatu Ge, Gang Bao, Liga Bai, Siqin Tong, Yuhai Bao and Luomeng Chao

Remote Sens. 2022, 14(7), 1530; https://doi.org/10.3390/rs14071530 - 22 Mar 2022

Cited by 13 | Viewed by 2263

Abstract

The frequency of extreme climate events has increased resulting in major changes to vegetation in arid and semi-arid areas. We selected 12 extreme climate indices and used trend analysis and multiple linear regression models to analyze extreme climate trends in steppe areas of Inner Mongolia and their impact on the normalized difference vegetation index (NDVI). From 1998 to 2017, the NDVI of the Inner Mongolia steppe increased overall; however, there was a small area of decrease. Extreme climate indices related to warming exhibited increasing trends, particularly in the desert steppe. Although the extreme precipitation index did not change significantly overall, it increased in the northeastern and southwestern regions of the study area and decreased in the central region. The established model showed that the extreme climate explained the highest NDVI variation in desert steppe (R² = 0.413), followed by typical steppe (R² = 0.229), and meadow steppe (R² = 0.109). In desert steppe, TX90P (warm days index) had the greatest impact; in typical steppe, R10 (number of heavy precipitation days index) had the greatest impact; in meadow steppe, R95P (very wet days index) had the greatest impact. This study offered new insights into dynamic vegetation changes in steppe areas of Inner Mongolia and provided a scientific basis for implementing environmental protection strategies. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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20 pages, 4901 KiB

Open AccessEditor’s ChoiceArticle

Decadal Lake Volume Changes (2003–2020) and Driving Forces at a Global Scale

by Yuhao Feng, Heng Zhang, Shengli Tao, Zurui Ao, Chunqiao Song, Jérôme Chave, Thuy Le Toan, Baolin Xue, Jiangling Zhu, Jiamin Pan, Shaopeng Wang, Zhiyao Tang and Jingyun Fang

Remote Sens. 2022, 14(4), 1032; https://doi.org/10.3390/rs14041032 - 21 Feb 2022

Cited by 14 | Viewed by 3803

Abstract

Lakes play a key role in the global water cycle, providing essential water resources and ecosystem services for humans and wildlife. Quantifying long-term changes in lake volume at a global scale is therefore important to the sustainability of humanity and natural ecosystems. Yet, such an estimate is still unavailable because, unlike lake area, lake volume is three-dimensional, challenging to be estimated consistently across space and time. Here, taking advantage of recent advances in remote sensing technology, especially NASA’s ICESat-2 satellite laser altimeter launched in 2018, we generated monthly volume series from 2003 to 2020 for 9065 lakes worldwide with an area ≥ 10 km². We found that the total volume of the 9065 lakes increased by 597 km³ (90% confidence interval 239–2618 km³). Validation against in situ measurements showed a correlation coefficient of 0.98, an RMSE (i.e., root mean square error) of 0.57 km³ and a normalized RMSE of 2.6%. In addition, 6753 (74.5%) of the lakes showed an increasing trend in lake volume and were spatially clustered into nine hot spots, most of which are located in sparsely populated high latitudes and the Tibetan Plateau; 2323 (25.5%) of the lakes showed a decreasing trend in lake volume and were clustered into six hot spots—most located in the world’s arid/semi-arid regions where lakes are scarce, but population density is high. Our results uncovered, from a three-dimensional volumetric perspective, spatially uneven lake changes that aggravate the conflict between human demands and lake resources. The situation is likely to intensify given projected higher temperatures in glacier-covered regions and drier climates in arid/semi-arid areas. The 15 hot spots could serve as a blueprint for prioritizing future lake research and conservation efforts. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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