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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 98864

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Prof. Dr. Yuei-An Liou

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Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli District, Taoyuan City 320317, Taiwan
Interests: remoet sensing; hydrometeorological extremes; vulnerability assessment
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Prof. Dr. Steven C. Reising

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Electrical and Computer Engineering Department, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523-1373, USA
Interests: microwave remote sensing of the earth's atmosphere and oceans; earth science measurements from nanosatellites and CubeSats; radiometer and radar systems from GHz to THz frequencies; low-noise monolithic microwave IC design and packaging
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Dr. Yuriy Kuleshov

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Australian Bureau of Meteorology, 700 Collins Street, Docklands, Melbourne, VIC 3008, Australia
Interests: climatology of severe weather phenomena (tropical cyclones, thunderstorms and lightning); climate monitoring and prediction; satellite remote sensing for climate monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chung-Ru Ho

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Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung 202301, Taiwan
Interests: remote sensing; physical oceanography; global change; satellite oceanography
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Dr. Kim-Anh Nguyen

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1. Center for Space and Remote Sensing Research, National Central University No. 300, Jhongda Rd., Jhongli District, Taoyuan City 32001, Taiwan
2. Institute of Geography, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet Rd., Cau Giay, Hanoi, Vietnam
Interests: vulnerability assessment; environmental monitoring; land use/land cover change; urban greenspace; urban heat island
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Evidence of the rapid degradation of the Earth's natural environment is getting stronger each year, and sustaining our planet has become the greatest concern faced by humanity. In 17 Sustainable Development Goals (SDGs) in The 2030 Agenda for Sustainable Development, earth observations have been identified as major contributors to nine of them: 2 (Zero Hunger), 3 (Good Health and Well-being), 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 12 (Sustainable Consumption and Production), 13 (Climate Action), 14 (Life Below Water) and 15 (Life on Land). Achieving the SDGs by turning knowledge into a significant contribution is the critical challenge for research scientists and other subject matter experts throughout the world.

This Special Issue aims to gather original viewpoints and bring up discussions concerning various areas of science of earth observations and environmental health. Innovative techniques/approaches are encouraged to be introduced to foster applications in contemporary practice, along with challenging papers related to the following topics, to be submitted to this Special Issue:

Disasters;
Health;
Energy;
Climate;
Water;
Weather;
Ecosystems;
Agriculture/forestry/fishery;
Biodiversity;
Industry and policy.

Dr. Yuei-An Liou
Dr. Steven C. Reising
Dr. Yuriy Kuleshov
Dr. Chung-Ru Ho
Dr. Kim-Anh Nguyen
Guest Editors

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

4 pages, 176 KiB

Open AccessEditorial

Preface: Earth Observations for Environmental Sustainability for the Next Decade

by Yuei-An Liou, Yuriy Kuleshov, Chung-Ru Ho, Kim-Anh Nguyen and Steven C. Reising

Remote Sens. 2021, 13(15), 2871; https://doi.org/10.3390/rs13152871 - 22 Jul 2021

Viewed by 1897

Abstract

Evidence of the rapid degradation of the Earth’s natural environment has grown in recent years [...] Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

Research

Jump to: Editorial, Review

15 pages, 3997 KiB

Open AccessArticle

Validation of a Primary Production Algorithm of Vertically Generalized Production Model Derived from Multi-Satellite Data around the Waters of Taiwan

by Kuo-Wei Lan, Li-Jhih Lian, Chun-Huei Li, Po-Yuan Hsiao and Sha-Yan Cheng

Remote Sens. 2020, 12(10), 1627; https://doi.org/10.3390/rs12101627 - 19 May 2020

Cited by 17 | Viewed by 3296

Abstract

Basin-scale sampling for high frequency oceanic primary production (PP) is available from satellites and must achieve a strong match-up with in situ observations. This study evaluated a regionally high-resolution satellite-derived PP using a vertically generalized production model (VGPM) with in situ PP. The aim was to compare the root mean square difference (RMSD) and relative percent bias (Bias) in different water masses around Taiwan. Determined using light–dark bottle methods, the spatial distribution of VGPM derived from different Chl-a data of MODIS Aqua (PP_A), MODIS Terra (PP_T), and averaged MODIS Aqua and Terra (PP_A&T) exhibited similar seasonal patterns with in situ PP. The three types of satellite-derived PPs were linearly correlated with in situ PPs, the coefficients of which were higher throughout the year in PP_A&T (r² = 0.61) than in PP_A (r² = 0.42) and PP_T (r² = 0.38), respectively. The seasonal RMSR and bias for the satellite-derived PPs were in the range of 0.03 to 0.09 and −0.14 to −0.39, respectively, which suggests the PP_A&T produces slightly more accurate PP measurements than PP_A and PP_T. On the basis of environmental conditions, the subareas were further divided into China Coast water, Taiwan Strait water, Northeastern upwelling water, and Kuroshio water. The VPGM PP in the four subareas displayed similar features to Chl-a variations, with the highest PP in the China Coast water and lowest PP in the Kuroshio water. The RMSD was higher in the Kuroshio water with an almost negative bias. The PP_A exhibited significant correlations with in situ PP in the subareas; however, the sampling locations were insufficient to yield significant results in the China Coast water. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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18 pages, 8325 KiB

Open AccessArticle

Habitat Suitability Estimation Using a Two-Stage Ensemble Approach

by Jehyeok Rew, Yongjang Cho, Jihoon Moon and Eenjun Hwang

Remote Sens. 2020, 12(9), 1475; https://doi.org/10.3390/rs12091475 - 6 May 2020

Cited by 41 | Viewed by 6509

Abstract

Biodiversity conservation is important for the protection of ecosystems. One key task for sustainable biodiversity conservation is to effectively preserve species’ habitats. However, for various reasons, many of these habitats have been reduced or destroyed in recent decades. To deal with this problem, it is necessary to effectively identify potential habitats based on habitat suitability analysis and preserve them. Various techniques for habitat suitability estimation have been proposed to date, but they have had limited success due to limitations in the data and models used. In this paper, we propose a novel scheme for assessing habitat suitability based on a two-stage ensemble approach. In the first stage, we construct a deep neural network (DNN) model to predict habitat suitability based on observations and environmental data. In the second stage, we develop an ensemble model using various habitat suitability estimation methods based on observations, environmental data, and the results of the DNN from the first stage. For reliable estimation of habitat suitability, we utilize various crowdsourced databases. Using observational and environmental data for four amphibian species and seven bird species in South Korea, we demonstrate that our scheme provides a more accurate estimation of habitat suitability compared to previous other approaches. For instance, our scheme achieves a true skill statistic (TSS) score of 0.886, which is higher than other approaches (TSS = 0.725 ± 0.010). Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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18 pages, 26233 KiB

Open AccessArticle

Temporal Variation and Spatial Structure of the Kuroshio-Induced Submesoscale Island Vortices Observed from GCOM-C and Himawari-8 Data

by Po-Chun Hsu, Chia-Ying Ho, Hung-Jen Lee, Ching-Yuan Lu and Chung-Ru Ho

Remote Sens. 2020, 12(5), 883; https://doi.org/10.3390/rs12050883 - 9 Mar 2020

Cited by 17 | Viewed by 4380

Abstract

Dynamics of ocean current-induced island wake has been an important issue in global oceanography. Green Island, a small island located off southeast of Taiwan on the Kuroshio path was selected as the study area to more understand the spatial structure and temporal variation of well-organized vortices formed by the interaction between the Kuroshio and the island. Sea surface temperature (SST) and chlorophyll-a (Chl-a) concentration data derived from the Himawari-8 satellite and the second generation global imager (SGLI) of global change observation mission (GCOM-C) were used in this study. The spatial SST and Chl-a variations in designed observation lines and the cooling zone transitions on the left and right sides of the vortices were investigated using 250 m spatial resolution GCOM-C data. The Massachusetts Institute of Technology general circulation model (MITgcm) simulation confirmed that the positive and negative vortices were sequentially detached from each other in a few hours. In addition, totals of 101 vortexes from July 2015 to December 2019 were calculated from the 1-h temporal resolution Himawari-8 imagery. The average vortex propagation speed was 0.95 m/s. Totals of 38 cases of two continuous vortices suggested that the average vortex shedding period is 14.8 h with 1.15 m/s of the average incoming surface current speed of Green Island, and the results agreed to the ideal Strouhal-Reynolds number fitting curve relation. Combined with the satellite observation and numerical model simulation, this study demonstrates the structure of the wake area could change quickly, and the water may mix in different vorticity states for each observation station. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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17 pages, 9139 KiB

Open AccessArticle

Evaluation of Satellite Precipitation Estimates over Australia

by Zhi-Weng Chua, Yuriy Kuleshov and Andrew Watkins

Remote Sens. 2020, 12(4), 678; https://doi.org/10.3390/rs12040678 - 19 Feb 2020

Cited by 45 | Viewed by 5791

Abstract

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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20 pages, 7032 KiB

Open AccessArticle

Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China

by Ke Xiong, Basanta Raj Adhikari, Constantine A. Stamatopoulos, Yu Zhan, Shaolin Wu, Zhongtao Dong and Baofeng Di

Remote Sens. 2020, 12(2), 295; https://doi.org/10.3390/rs12020295 - 16 Jan 2020

Cited by 79 | Viewed by 6284

Abstract

Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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19 pages, 5240 KiB

Open AccessArticle

Multiple Kernel Feature Line Embedding for Hyperspectral Image Classification

by Ying-Nong Chen

Remote Sens. 2019, 11(24), 2892; https://doi.org/10.3390/rs11242892 - 4 Dec 2019

Cited by 7 | Viewed by 3010

Abstract

In this study, a novel multple kernel FLE (MKFLE) based on general nearest feature line embedding (FLE) transformation is proposed and applied to classify hyperspectral image (HSI) in which the advantage of multple kernel learning is considered. The FLE has successfully shown its discriminative capability in many applications. However, since the conventional linear-based principle component analysis (PCA) pre-processing method in FLE cannot effectively extract the nonlinear information, the multiple kernel PCA (MKPCA) based on the proposed multple kernel method was proposed to alleviate this problem. The proposed MKFLE dimension reduction framework was performed through two stages. In the first multple kernel PCA (MKPCA) stage, the multple kernel learning method based on between-class distance and support vector machine (SVM) was used to find the kernel weights. Based on these weights, a new weighted kernel function was constructed in a linear combination of some valid kernels. In the second FLE stage, the FLE method, which can preserve the nonlinear manifold structure, was applied for supervised dimension reduction using the kernel obtained in the first stage. The effectiveness of the proposed MKFLE algorithm was measured by comparing with various previous state-of-the-art works on three benchmark data sets. According to the experimental results: the performance of the proposed MKFLE is better than the other methods, and got the accuracy of 83.58%, 91.61%, and 97.68% in Indian Pines, Pavia University, and Pavia City datasets, respectively. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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26 pages, 7069 KiB

Open AccessArticle

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

by Yonghong Zhang, Taotao Ge, Wei Tian and Yuei-An Liou

Remote Sens. 2019, 11(23), 2801; https://doi.org/10.3390/rs11232801 - 27 Nov 2019

Cited by 113 | Viewed by 7098

Abstract

Debris flows have been always a serious problem in the mountain areas. Research on the assessment of debris flows susceptibility (DFS) is useful for preventing and mitigating debris flow risks. The main purpose of this work is to study the DFS in the Shigatse area of Tibet, by using machine learning methods, after assessing the main triggering factors of debris flows. Remote sensing and geographic information system (GIS) are used to obtain datasets of topography, vegetation, human activities and soil factors for local debris flows. The problem of debris flow susceptibility level imbalances in datasets is addressed by the Borderline-SMOTE method. Five machine learning methods, i.e., back propagation neural network (BPNN), one-dimensional convolutional neural network (1D-CNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) have been used to analyze and fit the relationship between debris flow triggering factors and occurrence, and to evaluate the weight of each triggering factor. The ANOVA and Tukey HSD tests have revealed that the XGBoost model exhibited the best mean accuracy (0.924) on ten-fold cross-validation and the performance was significantly better than that of the BPNN (0.871), DT (0.816), and RF (0.901). However, the performance of the XGBoost did not significantly differ from that of the 1D-CNN (0.914). This is also the first comparison experiment between XGBoost and 1D-CNN methods in the DFS study. The DFS maps have been verified by five evaluation methods: Precision, Recall, F1 score, Accuracy and area under the curve (AUC). Experiments show that the XGBoost has the best score, and the factors that have a greater impact on debris flows are aspect, annual average rainfall, profile curvature, and elevation. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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17 pages, 9953 KiB

Open AccessArticle

Consecutive Dual-Vortex Interactions between Quadruple Typhoons Noru, Kulap, Nesat and Haitang during the 2017 North Pacific Typhoon Season

by Yuei-An Liou, Ji-Chyun Liu, Chung-Chih Liu, Chun-Hsu Chen, Kim-Anh Nguyen and James P. Terry

Remote Sens. 2019, 11(16), 1843; https://doi.org/10.3390/rs11161843 - 7 Aug 2019

Cited by 12 | Viewed by 4676

Abstract

This study utilizes remote sensing imagery, a differential averaging technique and empirical formulas (the ‘Liou–Liu formulas’) to investigate three consecutive sets of dual-vortex interactions between four cyclonic events and their neighboring environmental air flows in the Northwest Pacific Ocean during the 2017 typhoon season. The investigation thereby deepens the current understanding of interactions involving multiple simultaneous/sequential cyclone systems. Triple interactions between Noru–Kulap–Nesat and Noru–Nesat–Haitung were analyzed using geosynchronous satellite infrared (IR1) and IR3 water vapor (WV) images. The differential averaging technique based on the normalized difference convection index (NDCI) operator and filter depicted differences and generated a new set of clarified NDCI images. During the first set of dual-vortex interactions, Typhoon Noru experienced an increase in intensity and a U-turn in its direction after being influenced by adjacent cooler air masses and air flows. Noru’s track change led to Fujiwhara-type rotation with Tropical Storm Kulap approaching from the opposite direction. Kulap weakened and merged with Noru, which tracked in a counter-clockwise loop. Thereafter, in spite of a distance of 2000–2500 km separating Typhoon Noru and newly-formed Typhoon Nesat, the influence of middle air flows and jet flows caused an ‘indirect interaction’ between these typhoons. Evidence of this second interaction includes the intensification of both typhoons and changing track directions. The third interaction occurred subsequently between Tropical Storm Haitang and Typhoon Nesat. Due to their relatively close proximity, a typical Fujiwhara effect was observed when the two systems began orbiting cyclonically. The generalized Liou–Liu formulas for calculating threshold distances between typhoons successfully validated and quantified the trilogy of interaction events. Through the unusual and combined effects of the consecutive dual-vortex interactions, Typhoon Noru survived 22 days from 19 July to 9 August 2017 and migrated approximately 6900 km. Typhoon Noru consequently became the third longest-lasting typhoon on record for the Northwest Pacific Ocean. A comparison is made with long-lived Typhoon Rita in 1972, which also experienced similar multiple Fujiwhara interactions with three other concurrent typhoons. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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19 pages, 5427 KiB

Open AccessArticle

Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts

by Yuei-An Liou and Getachew Mehabie Mulualem

Remote Sens. 2019, 11(15), 1828; https://doi.org/10.3390/rs11151828 - 5 Aug 2019

Cited by 73 | Viewed by 9528

Abstract

The recent droughts that have occurred in different parts of Ethiopia are generally linked to fluctuations in atmospheric and ocean circulations. Understanding these large-scale phenomena that play a crucial role in vegetation productivity in Ethiopia is important. In view of this, several techniques and datasets were analyzed to study the spatio–temporal variability of vegetation in response to a changing climate. In this study, 18 years (2001–2018) of Moderate Resolution Imaging Spectroscopy (MODIS) Terra/Aqua, normalized difference vegetation index (NDVI), land surface temperature (LST), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) daily precipitation, and the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) soil moisture datasets were processed. Pixel-based Mann–Kendall trend analysis and the Vegetation Condition Index (VCI) were used to assess the drought patterns during the cropping season. Results indicate that the central highlands and northwestern part of Ethiopia, which have land cover dominated by cropland, had experienced decreasing precipitation and NDVI trends. About 52.8% of the pixels showed a decreasing precipitation trend, of which the significant decreasing trends focused on the central and low land areas. Also, 41.67% of the pixels showed a decreasing NDVI trend, especially in major parts of the northwestern region of Ethiopia. Based on the trend test and VCI analysis, significant countrywide droughts occurred during the El Niño 2009 and 2015 years. Furthermore, the Pearson correlation coefficient analysis assures that the low NDVI was mainly attributed to the low precipitation and water availability in the soils. This study provides valuable information in identifying the locations with the potential concern of drought and planning for immediate action of relief measures. Furthermore, this paper presents the results of the first attempt to apply a recently developed index, the Normalized Difference Latent Heat Index (NDLI), to monitor drought conditions. The results show that the NDLI has a high correlation with NDVI (r = 0.96), precipitation (r = 0.81), soil moisture (r = 0.73), and LST (r = −0.67). NDLI successfully captures the historical droughts and shows a notable correlation with the climatic variables. The analysis shows that using the radiances of green, red, and short wave infrared (SWIR), a simplified crop monitoring model with satisfactory accuracy and easiness can be developed. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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Review

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24 pages, 14128 KiB

Open AccessReview

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

by Swapan Talukdar, Pankaj Singha, Susanta Mahato, Shahfahad, Swades Pal, Yuei-An Liou and Atiqur Rahman

Remote Sens. 2020, 12(7), 1135; https://doi.org/10.3390/rs12071135 - 2 Apr 2020

Cited by 685 | Viewed by 44144

Abstract

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future. Full article

(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade)

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