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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: 31 December 2024 | Viewed by 18246

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Special Issue Editors

Prof. Dr. Zhongjing Wang

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Guest Editor

1. Institute of Hydrology & Water Resources, Tsinghua University, Beijing 100084, China
2. State Key Lab (Breeding Base) of Land Degradation and Ecological Restoration in Northwest China, Ningxia University, Yinchuan 750021, China
Interests: hydrology and water resources; hydrological remote sensing

Dr. Lu Zhang

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Guest Editor

Fonorary Fellow, CSIRO Land and Water, Canberra, ACT 2601, Australia
Interests: ecohydrology; water resources; hydroclimate

Dr. Lei Wang

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Guest Editor

School of Ecology and Environment, Ningxia University, Yinchuan 750021, China
Interests: remote sensing of eco-environment; vegetation and ecological remote sensing

Special Issue Information

Dear Colleagues,

Water is the key element of ecosystems and profoundly impacts ecosystem structures, environmental quality, and ecological services in different climatic regions and at different scales. Our understanding of these processes at the regional scale is somewhat limited due to a lack of regional information. Remote sensing provides a powerful tool to help us understand the status and processes controlling ecosystem services at large scales. Many remote sensing-based methods have been developed to measure fluxes in the soil-vegetation-atmosphere interface and provide regional information on water cycles, resources evolution, landscape patterns, ecological processes, and ecohydrology variability and vulnerability. However, the use of remote sensing techniques in environmental monitoring requires ground truthing and validation. Therefore, it is important to test remote sensing techniques to understand their strengths and limitations for eco-hydro-environmental studies. Through case studies, we will be able to quantify the uncertainties of remote sensing techniques to help us better understand ecohydrological interactions and dynamic stressors in order to develop strategies toward sustainable development goals.

This Special Issue focuses on remote sensing applications for providing key information on ecology, hydrology, and environment, such as precipitation, evapotranspiration, vegetation, water pollution, land degradation, carbon reduction, and disaster assessment over different scales. Both innovative methodologies and successful case studies are expected.

In this Special Issue, we are seeking review and research papers exploring remote sensing techniques in environmental monitoring and assessment. In particular, we invite articles exploring the following themes (but not limited to them):

Remote sensing for landscape pattern /vegetation evolution modelling and assessment.
Remote sensing for hydrology and water resources evaluation and simulation.
Remote sensing for ecohydrology and eco-environmental processes and dynamics .
Remote sensing for soil salinity assessment and prediction.
Remote sensing for natural disaster monitoring and mitigation.

Prof. Dr. Zhongjing Wang
Dr. Lu Zhang
Dr. Lei Wang
Guest Editors

Manuscript Submission Information

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Keywords

multi-source remote sensing
eco-hydrological processes
landscape patterns
ecological processes
spatial and temporal variability
arid and semi-arid zones

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Published Papers (6 papers)

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Research

23 pages, 3530 KiB

Open AccessArticle

Changes in Land Use and Ecosystem Service Values of Dunhuang Oasis from 1990 to 2030

by Fan Yi, Qiankun Yang, Zhongjing Wang, Yonghua Li, Leilei Cheng, Bin Yao and Qi Lu

Remote Sens. 2023, 15(3), 564; https://doi.org/10.3390/rs15030564 - 17 Jan 2023

Cited by 14 | Viewed by 2232

Abstract

Maintaining the integrity and stability of oasis ecosystems is an important topic in the field of ecological research. Assessment of ecosystem services and their changes can provide important support for the sustainable development of oases. This study took the Dunhuang oasis in the hyper-arid area as the research object and used 1990, 2010, and 2020 Landsat series satellite images to complete the land use interpretation by random forest classification. Then we estimated the ecosystem services value (ESV) by using benefit transfer method, and predicted the trend of ecosystem service value changes under three scenarios using the Analytic Hierarchy Process method and the patch generation land use simulation model (AHP-PLUS model). The results showed that the vegetation areas of the Dunhuang Oasis first decreased and then increased during 1990–2020. The decrease was largely due to the expansion of built-up land and farmland, and the increase was mainly contributed by the implementation of ecological protection policies. The path of changes in the ESV of the Dunhuang Oasis during 1990–2020 was well consistent with that of vegetation areas, with a maximum of 9068.15×10⁶ yuan (in 1990) and a minimum of 6271.46×10⁶ yuan (in 2010). Spatial autocorrelation analysis showed that urbanization reduced ESV, and the implementation of ecological policies enhanced ESV. The ESV of the Dunhuang Oasis for the year 2030 under the ecological conservation scenario could reach 7631.07×10⁶ yuan, which is 381.1×10⁶ yuan higher that under the economic development scenario. The ecological conservation scenario is the optimal option to achieve sustainable development of the Dunhuang Oasis. We suggested that the government should continuously enhance the protection of forests and waterbodies, reasonably restrict production and domestic water consumption, and efficiently increase the proportion of ecological water consumption. In addition, this study improved the evaluation method of oasis ESV based on the proportion of Normalized Difference Vegetation Index (NDVI) of grasslands with different coverage, which is important for improving the environment in arid areas. Full article

(This article belongs to the Special Issue Remote Sensing for Eco-Hydro-Environment)

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18 pages, 6444 KiB

Open AccessArticle

Influence of DEM Resolution on the Hydrological Responses of a Terraced Catchment: An Exploratory Modelling Approach

by João Rocha, André Duarte, Sérgio Fabres, Ana Quintela and Dalila Serpa

Remote Sens. 2023, 15(1), 169; https://doi.org/10.3390/rs15010169 - 28 Dec 2022

Cited by 7 | Viewed by 3499

Abstract

Terracing is widely used as an effective soil and water conservation practice in sloped terrains. Physically based hydrological models are useful tools for understanding the hydrological response of terraced catchments. These models typically require a DEM as input data, whose resolution is likely to influence the model accuracy. The main objective of the present work was to investigate how DEM resolution affects the accuracy of terrain representations and consequently the performance of SWAT hydrological model in simulating streamflow for a terraced eucalypt-dominated catchment (Portugal). Catchment´s hydrological responses were analyzed based on two contrasting topographic scenarios: terraces and no terrace, to evaluate the influence of terraces. To this end, different SWAT models were set up using multi-resolution DEMs (10 m, 1 m, 0.5 m, 0.25 m and 0.10 m) based on photogrammetric techniques and LiDAR data. LiDAR-derived DEMs (terraces scenario) improved topographic surface and watershed representation, consequently increasing the model performance, stage hydrographs and flow-duration curves accuracy. When comparing the contrasting topographic scenarios, SWAT simulations without terraces (10 m and 1 m DEMs) produced a more dynamic and rapid hydrological response. In this scenario, the streamflow was 28% to 36% higher than SWAT simulations accounting for the terraces, which corroborates the effectiveness of terraces as a water conservation practice. Full article

(This article belongs to the Special Issue Remote Sensing for Eco-Hydro-Environment)

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20 pages, 7687 KiB

Open AccessArticle

Accessible Remote Sensing Data Mining Based Dew Estimation

by Ying Suo, Zhongjing Wang, Zixiong Zhang and Steven R. Fassnacht

Remote Sens. 2022, 14(22), 5653; https://doi.org/10.3390/rs14225653 - 9 Nov 2022

Cited by 1 | Viewed by 1863

Abstract

Dew has been considered a supplementary water resource as it constitutes an important water supply in many ecosystems, especially in arid and semiarid areas. Remote sensing allows large-scale surface observations, offering the possibility to estimate dew in such arid and semiarid regions. In this study, by screening and combining different remote sensing variables, we obtained a well-performing monthly scale dew yield estimation model based on the support vector machine (SVM) learning method. Using daytime and nighttime land surface temperatures (LST), the normalized difference vegetation index (NDVI), and three emissivity bands (3.929–3.989 µm, 10.780–11.280 µm, and 11.770–12.270 µm) as the model inputs, the simulated site-scale monthly dew yield achieved a correlation coefficient (CC) of 0.89 and a root mean square error (RMSE) of 0.30 (mm) for the training set, and CC = 0.59 and RMSE = 0.55 (mm) for the test set. Applying the model to the Heihe River Basin (HRB), the results showed that the annual dew yield ranged from 8.83 to 20.28 mm/year, accounting for 2.12 to 66.88% of the total precipitation, with 74.81% of the area having an annual dew amount of 16 to 19 mm/year. We expanded the model application to Northwest China and obtained a dew yield of 5~30 mm/year from 2011 to 2020, indicating that dew is a non-negligible part of the water balance in this arid area. As a non-negligible part of the water cycle, the use of remote sensing to estimate dew can provide better support for future water resource assessment and analysis. Full article

(This article belongs to the Special Issue Remote Sensing for Eco-Hydro-Environment)

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17 pages, 4362 KiB

Open AccessArticle

Inversion of Different Cultivated Soil Types’ Salinity Using Hyperspectral Data and Machine Learning

by Pingping Jia, Junhua Zhang, Wei He, Ding Yuan, Yi Hu, Kazem Zamanian, Keli Jia and Xiaoning Zhao

Remote Sens. 2022, 14(22), 5639; https://doi.org/10.3390/rs14225639 - 8 Nov 2022

Cited by 14 | Viewed by 3171

Abstract

Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson’s correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK_{400 nm} was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R² = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing for Eco-Hydro-Environment)

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20 pages, 9240 KiB

Open AccessArticle

Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity

by Pingping Jia, Junhua Zhang, Wei He, Yi Hu, Rong Zeng, Kazem Zamanian, Keli Jia and Xiaoning Zhao

Remote Sens. 2022, 14(11), 2602; https://doi.org/10.3390/rs14112602 - 28 May 2022

Cited by 27 | Viewed by 3610

Abstract

An accurate estimation of soil electrical conductivity (EC) using hyperspectral techniques is of great significance for understanding the spatial distribution of solutes and soil salinization. Although spectral transformation has been widely used in data pre-processing, the performance of different pre-processing techniques (or combination methods) on different models of the same data set is still ambiguous. Moreover, extremely randomized trees (ERT) and light gradient boosting machine (LightGBM) models are new learning algorithms with good generalization performance (soil moisture and above-ground biomass), but are less studied in estimating soil salinity in the visible and near-infrared spectra. In this study, 130 soil EC data, soil measured hyperspectral data, topographic factors, conventional salinity indices such as Salinity Index 1, and two-band (2D) salinity indices such as ratio indices, were introduced. The five spectral pre-processing methods of standard normal variate (SNV), standard normal variate and detrend (SNV-DT), inverse (1/OR) (OR is original spectrum), inverse-log (Log(1/OR) and fractional order derivative (FOD) (range 0–2, with intervals of 0.25) were performed. A gradient boosting machine (GBM) was used to select sensitive spectral parameters. Models (extreme gradient boosting (XGBoost), LightGBM, random forest (RF), ERT, classification and regression tree (CART), and ridge regression (RR)) were used for inversion soil EC and model validation. The results reveal that the two-dimensional correlation coefficient highlighted EC more effectively than the one-dimensional. Under SNV and the second order derivative, the two-dimensional correlation coefficient increased by 0.286 and 0.258 compared to the one-dimension, respectively. The 13 characteristic factors of slope, NDI, SI-T, RI, profile curvature, DOA, plane curvature, SI (conventional), elevation, Int2, aspect, S1 and TWI provided 90% of the cumulative importance for EC using GBM. Among the six machine models, the ERT model performed the best for simulation (R² = 0.98) and validation (R² = 0.96). The ERT model showed the best performance among the EC estimation models from the reference data. The kriging map based on the ERT simulation showed a close relationship with the measured data. Our study selected the effective pre-processing methods (SNV and the 2 order derivative) using one- and two-dimensional correlation, 13 important factors and the ERT model for EC hyperspectral inversion. This provides a theoretical support for the quantitative monitoring of soil salinization on a larger scale using remote sensing techniques. Full article

(This article belongs to the Special Issue Remote Sensing for Eco-Hydro-Environment)

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20 pages, 8426 KiB

Open AccessArticle

Effect of the Shadow Pixels on Evapotranspiration Inversion of Vineyard: A High-Resolution UAV-Based and Ground-Based Remote Sensing Measurements

by Saihong Lu, Junjie Xuan, Tong Zhang, Xueer Bai, Fei Tian and Samuel Ortega-Farias

Remote Sens. 2022, 14(9), 2259; https://doi.org/10.3390/rs14092259 - 7 May 2022

Cited by 13 | Viewed by 2556

Abstract

Due to the proliferation of precision agriculture, the obstacle of estimating evapotranspiration (ET) and its components from shadow pixels acquired from remote sensing technology should not be neglected. To accurately detect shaded soil and leaf pixels and quantify the implications of shadow pixels on ET inversion, a two-year field-scale observation was carried out in the growing season for a pinot noir vineyard. Based on high-resolution remote sensing sensors covering visible light, thermal infrared, and multispectral light, the supervised classification was applied to detect shadow pixels. Then, we innovatively combined the normalized difference vegetation index with the three-temperature model to quantify the proportion of plant transpiration (T) and soil evaporation (E) in the vineyard ecosystem. Finally, evaluated with the eddy covariance system, we clarified the implications of the shadow pixels on the ET estimation and the spatiotemporal patterns of ET in a vineyard system by considering where shadow pixels were presented. Results indicated that the shadow detection process significantly improved reliable assessment of ET and its components. (1) The shaded soil pixels misled the land cover classification, with the mean canopy cover ignoring shadows 1.68–1.70 times more often than that of shaded area removal; the estimation accuracy of ET can be improved by 4.59–6.82% after considering the effect of shaded soil pixels; and the accuracy can be improved by 0.28–0.89% after multispectral correction. (2) There was a 2 °C canopy temperature discrepancy between sunlit leaves and shaded leaves, meaning that the estimation accuracy of T can be improved by 1.38–7.16% after considering the effect of shaded canopy pixels. (3) Simultaneously, the characteristics showed that there was heterogeneity of ET in the vineyard spatially and that E and T fluxes accounted for 238.05 and 208.79 W·m⁻², respectively; the diurnal variation represented a single-peak curve, with a mean of 0.26 mm/h. Our findings provide a better understanding of the influences of shadow pixels on ET estimation using remote sensing techniques. Full article

(This article belongs to the Special Issue Remote Sensing for Eco-Hydro-Environment)

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