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Remote Sensing
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Earth Observations for Environmental Sustainability for the Next Decade Ⅱ
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Earth Observations for Environmental Sustainability for the Next Decade Ⅱ

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 2023) | Viewed by 7422

Special Issue Editors

Prof. Dr. Yuei-An Liou

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Yuriy Kuleshov

E-Mail Website
Guest Editor
Australian Bureau of Meteorology, 700 Collins Street, Docklands, Melbourne, VIC 3008, Australia
Interests: satellite remote sensing; climate change; hazardous natural phenomena; tropical cyclones; precipitation enhancement
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chung-Ru Ho

E-Mail Website
Guest Editor
Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung 202301, Taiwan
Interests: remote sensing; physical oceanography; global change; satellite oceanography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Steven C. Reising

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Kim-Anh Nguyen

E-Mail Website
Guest Editor
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,

The Earth's natural environment is clearly degrading, with natural disasters intensifying all over the world in recent years, such as droughts, forest fires, cyclones/typhoons/hurricanes, and heat waves. The sustainability of our planet is becoming the most urgent issue and greatest concern faced by humanity. Among the 17 Sustainable Development Goals (SDGs) put forth by the UN in the 2030 Agenda for Sustainable Development, this Special Issue focuses on the following: 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 these SDGs by using acquired knowledge to make significant contributions is a critical challenge for research scientists and other experts throughout the world.

This Special Issue, “Earth Observations for Environmental Sustainability for the Next Decade Ⅱ,” aims to gather original viewpoints and initiate discussions on various areas within the science of observations about Earth’s environmental health. Regarding the papers submitted to this Special Issue, we encourage those introducing innovative techniques or approaches to foster applications in contemporary practice, as well as challenging papers related to the following topics:

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

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

Manuscript Submission Information

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

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

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

Keywords

  • earth observation
  • remote sensing
  • disaster
  • climate
  • weather
  • agriculture

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

20 pages, 5789 KiB  
Article
Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data
by Mubarak Mammel, Muhamad Naimullah, Ali Haghi Vayghan, Jhen Hsu, Ming-An Lee, Jun-Hong Wu, Yi-Chen Wang and Kuo-Wei Lan
Remote Sens. 2022, 14(12), 2932; https://doi.org/10.3390/rs14122932 - 19 Jun 2022
Cited by 11 | Viewed by 2923
Abstract
The environmental characteristics of the Taiwan Strait (TS) have been linked to variations in the abundance and distribution of greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically valuable species in ecosystems, and its spatial distribution patterns are [...] Read more.
The environmental characteristics of the Taiwan Strait (TS) have been linked to variations in the abundance and distribution of greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically valuable species in ecosystems, and its spatial distribution patterns are pivotal to fisheries management and conservation. However, the relationship between the catch rates of S. dumerili and the environmental changes and their impact on fish communities remains undetermined in the TS. The goal of this study was to determine the spatiotemporal distribution pattern of S. dumerili with environmental characteristics in the TS from south to north (20°N–29°N and 115°E–127°E), applying generalized additive models (GAMs) and spatiotemporal fisheries data from logbooks and voyage data recorders from Taiwanese fishing vessels (2014–2017) as well as satellite-derived remote sensing environmental data. We used the generalized linear model (GLM) and GAM to analyze the effect of environmental factors and catch rates. The predictive performance of the two statistical models was quantitatively assessed by using the root mean square difference. Results reveal that the GAM outperforms the GLM model in terms of the functional relationship of the GAM for generating a reliable predictive tool. The model selection process was based on the significance of model terms, increase in deviance explained, decrease in residual factor, and reduction in Akaike’s information criterion. We then developed a species distribution model based on the best GAMs. The deviance explained indicated that sea surface temperature, linked to high catch rates, was the key factor influencing S. dumerili distributions, whereas mixed layer depth was the least relevant factor. The model predicted a relatively high S. dumerili catch rate in the northwestern region of the TS in summer, with the area extending to the East China Sea. The target species is strongly influenced by biophysical environmental conditions, and potential fishing areas are located throughout the waters of the TS. The findings of this study showed how S. dumerili populations respond to environmental variables and predict species distributions. Data on the habitat preferences and distribution patterns of S. dumerili are essential for understanding the environmental conditions of the TS, which can inform future priorities for conservation planning and management. Full article
(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade Ⅱ)
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21 pages, 2830 KiB  
Article
A Comparison of Various Correction and Blending Techniques for Creating an Improved Satellite-Gauge Rainfall Dataset over Australia
by Zhi-Weng Chua, Yuriy Kuleshov, Andrew B. Watkins, Suelynn Choy and Chayn Sun
Remote Sens. 2022, 14(2), 261; https://doi.org/10.3390/rs14020261 - 7 Jan 2022
Cited by 15 | Viewed by 2831
Abstract
Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global [...] Read more.
Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology’s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of individual datasets leads to potential applicability in other regions of the world. Full article
(This article belongs to the Special Issue Earth Observations for Environmental Sustainability for the Next Decade Ⅱ)
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