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Remote Sensing for Climate Change II
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Remote Sensing for Climate Change 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 (15 July 2024) | Viewed by 5837

Special Issue Editor

Dr. Xander Wang

E-Mail Website
Guest Editor
School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada
Interests: regional climate modeling; climate downscaling; hydrological modeling and flooding risk analysis; energy systems modeling under climate change; climate change impact assessment and adaptation studies; GIS; spatial modeling and analysis; big data analysis and visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Observations from weather stations have been widely used to study climate change over a long period of time. However, due to the scarcity of point-based weather observations, our understanding of the Earth’s changing climate is very limited. This impedes the advancement in our knowledge of the Earth’s climate system and our capability to develop well-suited climate models to simulate future climate change, which further results in considerable uncertainties associated with future climate projections. How to quantify and minimize these uncertainties is thus becoming one of the most challenging issues to be addressed for climate change impact assessment and adaptation studies. Remote sensing offers a new way of observing the Earth’s climate system with continuous and high-resolution spatial coverage through satellite-based, aircraft-based, or drone-based sensor technologies. This can significantly improve our understanding of climate change and its potential impacts at global, regional, and local scales. The data collected with remote sensing technologies can also be used to validate our climate models, improve our knowledge of the physical and dynamical processes of the climate system, and help us to project future climate change and its impacts with minimized uncertainties.

Based on the success of the 1st volume, the 2nd volume of this Special Issue will continue to focus on the latest research advances in remote sensing technologies and their applications for observing, understanding, modeling, visualizing, and communicating climate change and the potential impacts on agriculture, water, air quality, energy, land use/cover, flood, drought, wildfire, urban infrastructure, ecosystem, human health, glaciers, permafrost, ice sheet, sea level rise, etc. Submissions in the form of research articles, reviews, perspectives, and case studies are all welcome.

Dr. Xander Wang
Guest Editor

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

  • climate monitoring
  • climate modeling
  • climate change impact assessment
  • climate change adaptation
  • climate risk visualization
  • remote sensing
  • drone technologies
  • land cover/use change
  • flooding
  • droughts
  • ecosystems
  • agriculture
  • coastal hazards

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Published Papers (3 papers)

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Research

20 pages, 6363 KiB  
Article
Temporal-Difference Graph-Based Optimization for High-Quality Reconstruction of MODIS NDVI Data
by Shengtai Ji, Shuxin Han, Jiaxin Hu, Yuguang Li and Jing-Cheng Han
Remote Sens. 2024, 16(15), 2713; https://doi.org/10.3390/rs16152713 - 24 Jul 2024
Viewed by 590
Abstract
The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud [...] Read more.
The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud contamination, posing challenges for accurate observation. In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. Regarding spatio-temporal NDVI data as a time-varying graph signal, the developed method utilized an optimization algorithm to maximize the spatial smoothness of temporal differences while preserving the spatial NDVI pattern. This approach was further evaluated by reconstructing MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m Grid (MOD13Q1) products over Northwest China. Through quantitative comparison with a previous state-of-the-art method, the Savitzky–Golay (SG) filter method, the obtained results demonstrated the superior performance of the TDG method, and highly accurate results were achieved in both the temporal and spatial domains irrespective of noise types (positively-biased, negatively-biased, or linearly-interpolated noise). In addition, the TDG-based optimization approach shows great robustness to noise intensity within spatio-temporal NDVI data, suggesting promising prospects for its application to similar datasets. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change II)
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34 pages, 6717 KiB  
Article
Estimating Carbon Dioxide Emissions from Power Plant Water Vapor Plumes Using Satellite Imagery and Machine Learning
by Heather D. Couture, Madison Alvara, Jeremy Freeman, Aaron Davitt, Hannes Koenig, Ali Rouzbeh Kargar, Joseph O’Connor, Isabella Söldner-Rembold, André Ferreira, Jeyavinoth Jeyaratnam, Jordan Lewis, Colin McCormick, Tiffany Nakano, Charmaine Dalisay, Christy Lewis, Gabriela Volpato, Matthew Gray and Gavin McCormick
Remote Sens. 2024, 16(7), 1290; https://doi.org/10.3390/rs16071290 - 6 Apr 2024
Viewed by 2188
Abstract
Combustion power plants emit carbon dioxide (CO2), which is a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, latency, and granularity. To fill this gap and greatly increase the number of power plants [...] Read more.
Combustion power plants emit carbon dioxide (CO2), which is a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, latency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant water vapor plumes as proxy signals to estimate electric power generation and CO2 emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, then an aggregation model predicted plant utilization over a 30-day period. Lastly, emission factors specific to region, fuel, and plant technology were used to convert the estimated electricity generation into CO2 emissions. Models were trained with reported hourly electricity generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed the baseline approaches. In validating our model results against available generation and emissions reported data, we calculated the root mean square error as 1.75 TWh (236 plants across 17 countries over 4 years) and 2.18 Mt CO2 (207 plants across 17 countries over 4 years), respectively. Ultimately, we applied our ML method to plants that constitute 32% of global power plant CO2 emissions, as estimated by Climate TRACE, averaged over the period 2015–2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is made freely available to the public to support global emissions reduction. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change II)
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18 pages, 17657 KiB  
Article
Assessing the Potential Impacts of Climate Change on Current Coastal Ecosystems—A Canadian Case Study
by Quan Van Dau, Xiuquan Wang, Mohammad Aminur Rahman Shah, Pelin Kinay and Sana Basheer
Remote Sens. 2023, 15(19), 4742; https://doi.org/10.3390/rs15194742 - 28 Sep 2023
Cited by 6 | Viewed by 2094
Abstract
Understanding how climate change affects coastal ecosystems is one of the most important elements in determining vulnerability and resilience for long-term ecosystem management in the face of the increasing risk of coastal hazards (e.g., sea level rise, coastal flooding, and storm surge). This [...] Read more.
Understanding how climate change affects coastal ecosystems is one of the most important elements in determining vulnerability and resilience for long-term ecosystem management in the face of the increasing risk of coastal hazards (e.g., sea level rise, coastal flooding, and storm surge). This research attempts to undertake a study on the ecosystem–climate nexus in the Canadian province of Prince Edward Island (PEI). Cloud-based remote sensing techniques with Google Earth Engine (GGE) are utilized to identify ecosystem changes over time. In addition, the effects of coastal flooding and storm surge ecosystems under different climate scenarios are examined. The results suggest a reduction in the forest (3%), open water or marsh component (9%), salt water (5%), no open water or marsh component (3%), and salt or brackish marsh (17%) ecosystems from 2013 to 2022. Dune and beach exhibit a non-uniform distribution across the period because of variations in natural processes, with an upward trend ranging from 0% to 11%. Approximately 257 km2 (9.4%) of PEI’s ecosystems would be affected by extreme coastal flooding (scenario 4), compared to 142 km2 (5.2%), 155 km2 (5.7%), and 191 km2 (7%) in scenarios 1, 2, and 3, respectively. Under a 4 m storm surge scenario, around 223 km2 (8.2%) of PEI’s ecosystems would be flooded, compared to 61 km2 (2.2%), 113 km2 (4.1%), and 168 km2 (6.1%) under 1 m, 2 m, and 3 m scenarios, respectively. The findings from this research would enable policymakers to take necessary actions to sustain ecosystem services in PEI while confronting the impacts of climate change. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change II)
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