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Regional Climate Change and Application of Remote Sensing

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 23 October 2024 | Viewed by 11582

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Dr. Jun Qin

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State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: data assimilation; climate modeling; deep learning; remote sensing inversion

Dr. Hou Jiang

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China
Interests: remote sensing; vegetation recovery; surface solar radiation; cloud motion; land cover changes; climate impacts
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Dear Colleagues,

Climate change is one of the biggest challenges of the 21st century. Evidence of climate change comes from various phenomena, such as global warming, changes in spatio-temporal patterns in precipitation, more frequent and intense extreme events, and so on. Widespread adverse impacts and related damages to human and natural systems have been driven by climate change, for instance, changes in vegetation phenology, declines in key ecosystem services, and a reduction in food and water security, as well as negative impacts on human health and economics. To mitigate such negative effects, some measures are being implemented under the Paris Agreement. Developing renewable energy resources to build low-carbon energy systems, and restoring vegetation to increase carbon sinks, are two typical examples. The details of these measures in practice usually differ among regions due to the differences in regional climate change trends and the underlying mechanisms. Remote sensing has made significant progress in understanding climate change by quantifying the state and variability of the atmosphere, land and ocean. The unprecedented and fine spatial coverage of satellite observations not only contributes to the continuous monitoring of climate change and associated impacts across the globe, but also the quantitative assessment and comparison of ongoing measures at a regional scale. It is believed that remote sensing will have increasing value in coping with climate change and achieving sustainable development.

This Special Issue aims to seek innovative solutions related to regional climate change based on remote sensing and from a sustainable perspective. Any advances or insights on the use of remote sensing to address regional issues in climate change are encouraged. Topics may include (but are not limited to) the following:

Satellite-based monitoring of extreme weather events, including droughts, wildfires, land and ocean heat waves, floods, etc.
Remote sensing surveys of biological responses to climate change, in terms of physiology, growth, abundance, geographic placement and shifting seasonal timing, etc.
Analysis of various losses induced by climate change based on remote sensing big data, looking at local species, economic livelihoods, food security and nutrition, etc.
Evaluation of the capacity of nature and humanity to adapt to climate change using remote sensing data; for example, in terms of the potential of renewable energy resources.

We look forward to receiving your contributions.

Dr. Jun Qin
Dr. Hou Jiang
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. Sustainability 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 2400 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 change
sustainable development
remote sensing big data
biodiversity
renewable energy
extreme weather events
human health

Published Papers (9 papers)

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Research

24 pages, 10403 KiB

Open AccessArticle

Spatio-Temporal Variation in Landforms and Surface Urban Heat Island in Riverine Megacity

by Namita Gorai, Jatisankar Bandyopadhyay, Bijay Halder, Minhaz Farid Ahmed, Altaf Hossain Molla and Thomas M. T. Lei

Sustainability 2024, 16(8), 3383; https://doi.org/10.3390/su16083383 - 18 Apr 2024

Viewed by 538

Abstract

Rapid urbanization and changing climatic procedures can activate the present surface urban heat island (SUHI) effect. An SUHI was considered by temperature alterations among urban and rural surroundings. The urban zones were frequently warmer than the rural regions because of population pressure, urbanization, vegetation insufficiency, industrialization, and transportation systems. This investigation analyses the Surface-UHI (SUHI) influence in Kolkata Municipal Corporation (KMC), India. Growing land surface temperature (LST) may cause an SUHI and impact ecological conditions in urban regions. The urban thermal field variation index (UTFVI) served as a qualitative and quantitative barrier to the SUHI susceptibility. The maximum likelihood approach was used in conjunction with supervised classification techniques to identify variations in land use and land cover (LULC) over a chosen year. The outcomes designated a reduction of around 1354.86 Ha, 653.31 Ha, 2286.9 Ha, and 434.16 Ha for vegetation, bare land, grassland, and water bodies, correspondingly. Temporarily, from the years 1991–2021, the built-up area increased by 4729.23 Ha. The highest LST increased by around 7.72 °C, while the lowest LST increased by around 5.81 °C from 1991 to 2021. The vegetation index and LST showed a negative link, according to the correlation analyses; however, the built-up index showed an experimentally measured positive correlation. This inquiry will compel the administration, urban planners, and stakeholders to observe humanistic activities and thus confirm sustainable urban expansion. Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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21 pages, 17710 KiB

Open AccessArticle

A Morphing-Based Future Scenario Generation Method for Stochastic Power System Analysis

by Yanna Gao, Hong Dong, Liujun Hu, Zihan Lin, Fanhong Zeng, Cantao Ye and Jixiang Zhang

Sustainability 2024, 16(7), 2762; https://doi.org/10.3390/su16072762 - 27 Mar 2024

Viewed by 386

Abstract

As multiple wind and solar photovoltaic farms are integrated into power systems, precise scenario generation becomes challenging due to the interdependence of power generation and future climate change. Future climate data derived from obsolete climate models, featuring diminished accuracy, less-refined spatial resolution, and a limited range of climate scenarios compared to more recent models, are still in use. In this paper, a morphing-based approach is proposed for generating future scenarios, incorporating the interdependence of power generation among multiple wind and photovoltaic farms using copula theory. The K-means method was employed for scenario generation. The results of our study indicate that the average annual variations in dry-bulb temperature (DBT), global horizontal irradiance (GHI), and wind speed (WS) are projected to increase by approximately 0.4 to 1.9 °C, 7.5 to 20.4 W/m², and 0.3 to 1.7 m/s, respectively, in the forthcoming scenarios of the four considered Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). It seems that accumulated maximum wind electricity output (WEO) and solar electricity output (SEO) will increase from 0.9% to 7.3% and 1.1% to 6.8%, respectively, in 2050. Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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15 pages, 7745 KiB

Open AccessArticle

Multi-Attention Network for Sewage Treatment Plant Detection

by Yue Shuai, Jun Xie, Kaixuan Lu and Zhengchao Chen

Sustainability 2023, 15(7), 5880; https://doi.org/10.3390/su15075880 - 28 Mar 2023

Cited by 1 | Viewed by 1235

Abstract

As an important facility for effectively controlling water pollution discharge and recycling waste water resources, accurate sewage treatment plant extraction is very important for protecting quality, function, and sustainable development of the water environment. However, due to the presence of rectangular and circular treatment facilities in sewage treatment plants, the shapes are diverse and the scales are different, resulting in the poor performance of conventional object detection algorithms. This paper proposes a multi-attention network (MANet) for sewage treatment plants using remote sensing images. MANet consists of three major components: a light backbone used to obtain multi-scale features, a channel and spatial attention module that realizes the feature representation of the channel dimension and spatial dimension, and a scale attention module to obtain scale-aware features. The results from the extensive experiments performed on the sewage treatment plant dataset suggest that our proposed MANet exhibits a superior performance compared with other competing methods. Meanwhile, we used a well-trained model to predict the sewage treatment plant from the GF-2 data for the Beijing area. By comparing the results with the data of manually obtained sewage treatment plants, our method can achieve an accuracy of 80.1% while maintaining the recall rate at a high level (90.4%). Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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18 pages, 17436 KiB

Open AccessArticle

A Convolutional Neural Network for Coastal Aquaculture Extraction from High-Resolution Remote Sensing Imagery

by Jinpu Deng, Yongqing Bai, Zhengchao Chen, Ting Shen, Cong Li and Xuan Yang

Sustainability 2023, 15(6), 5332; https://doi.org/10.3390/su15065332 - 17 Mar 2023

Cited by 2 | Viewed by 1286

Abstract

Aquaculture has important economic and environmental benefits. With the development of remote sensing and deep learning technology, coastline aquaculture extraction has achieved rapid, automated, and high-precision production. However, some problems still exist in extracting large-scale aquaculture based on high-resolution remote sensing images: (1) the generalization of large-scale models caused by the diversity of remote sensing in breeding areas; (2) the confusion of breeding target identification caused by the complex background interference of land and sea; (3) the boundary of the breeding area is difficult to extract accurately. In this paper, we built a comprehensive sample database based on the spatial distribution of aquaculture, and expanded the sample database by using confusing land objects as negative samples. A multi-scale-fusion superpixel segmentation optimization module is designed to solve the problem of inaccurate boundaries, and a coastal aquaculture network is proposed. Based on the coastline aquaculture dataset that we labelled and produced ourselves, we extracted cage culture areas and raft culture areas near the coastline of mainland China based on high-resolution remote sensing images. The overall accuracy reached 94.64% and achieved a state-of-the-art performance. Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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16 pages, 4161 KiB

Open AccessArticle

Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations

by Hou Jiang, Ning Lu and Xuecheng Wang

Sustainability 2023, 15(4), 3380; https://doi.org/10.3390/su15043380 - 13 Feb 2023

Viewed by 1590

Abstract

Developing rooftop photovoltaic (PV) has become an important initiative for achieving carbon neutrality in China, but the carbon reduction potential assessment has not properly considered the spatial and temporal variability of PV generation and the curtailment in electricity dispatch. In this study, we propose a technical framework to fill the gap in assessing carbon reduction potential through remote sensing data-driven simulations. The spatio-temporal variations in rooftop PV generations were simulated on an hourly basis, and a dispatch analysis was then performed in combination with hourly load profiles to quantify the PV curtailment in different scenarios. Our results showed that the total rooftop PV potential in China reached 6.5 PWh yr⁻¹, mainly concentrated in the eastern region where PV generation showed high variability. The carbon reduction from 100% flexible grids with 12 h of storage capacity is close to the theoretical maximum, while without storage, the potential may be halved. To maximize the carbon reduction potential, rooftop PV development should consider grid characteristics and regional differences. This study has important implications for the development of rooftop PV and the design of carbon-neutral pathways based on it. Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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12 pages, 61320 KiB

Open AccessArticle

Resource-Based Port Material Yard Detection with SPPA-Net

by Xiaoyong Zhang, Rui Xu, Kaixuan Lu, Zhihang Hao, Zhengchao Chen and Mingyong Cai

Sustainability 2022, 14(24), 16413; https://doi.org/10.3390/su142416413 - 08 Dec 2022

Cited by 1 | Viewed by 949

Abstract

Since the material yard is a crucial place for storing coal, ore, and other raw materials, accurate access to its location is of great significance to the construction of resource-based ports, environmental supervision, and investment and operating costs. Its extraction is difficult owing to its small size, variable shape, and dense distribution. In this paper, the SPPA-Net target detection network was proposed to extract the material yard. Firstly, a Dual-Channel-Spatial-Mix Block (DCSM-Block) was designed based on the Faster R-CNN framework to enhance the feature extraction ability of the location and spatial information of the material yard. Secondly, the Feature Pyramid Network (FPN) was introduced to improve the detection of material yards with different scales. Thirdly, a spatial pyramid pooling self-attention module (SPP-SA) was established to increase the global semantic information between material yards and curtail false detection and missed detection. Finally, the domestic GF-2 satellite data was adopted to conduct extraction experiments on the material yard of the port. The results demonstrated that the detection accuracy of the material yard reached 88.7% when the recall rate was 90.1%. Therefore, this study provided a new method for the supervision and environmental supervision of resource-based port material yards. Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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38 pages, 90815 KiB

Open AccessArticle

A Prior Semantic Network for Large-Scale Landcover Change of Landsat Imagery

by Xuan Yang, Yongqing Bai, Pan Chen, Cong Li, Kaixuan Lu and Zhengchao Chen

Sustainability 2022, 14(20), 13167; https://doi.org/10.3390/su142013167 - 13 Oct 2022

Viewed by 1399

Abstract

Landcover change can reflect changes in the natural environment and the impact of human activities. Remotely sensed big data with large-scale and multi-temporal key characteristics provide the data support for landcover change information extraction. The development of deep learning provides technical method support for information extraction from remotely sensed big data. However, the current mainstream deep learning change detection methods only establish the changing relationship between two phases of images. They cannot directly extract the ground object categories before and after the change. It is easily affected by pseudo-changes caused by the color difference of multi-temporal images, resulting in many false detections. In this paper, we propose a prior semantic network and a difference enhancement block module to establish prior guidance and constraints on changing features to solve the pseudo-change problem. We propose a semantic-change integrated single-task network, which can simultaneously extract multi-temporal landcover classification and landcover change. On the self-made, large-scale multi-temporal Landsat dataset, we have performed multi-temporal landcover change information extraction, reaching an overall accuracy of 83.1% and achieving state-of-the-art performance. Finally, we thoroughly analyzed the landcover change results in the study area from 2005 to 2020. Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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16 pages, 4661 KiB

Open AccessArticle

Crop Identification and Analysis in Typical Cultivated Areas of Inner Mongolia with Single-Phase Sentinel-2 Images

by Jing Tang, Xiaoyong Zhang, Zhengchao Chen and Yongqing Bai

Sustainability 2022, 14(19), 12789; https://doi.org/10.3390/su141912789 - 07 Oct 2022

Cited by 4 | Viewed by 1581

Abstract

The Hetao Plain and Xing’an League are the major cultivated areas and main grain-producing areas in Inner Mongolia, and their crop planting structure significantly affects the grain output and economic development in Northern China. Timely and accurate identification, extraction, and analysis of typical crops in Xing’an League and Hetao Plain can provide scientific guidance and decision support for crop planting structure research and food security in ecological barrier areas in Northern China. The pixel samples and the neighborhood information were fused to generate a spectral spatial dataset based on single-phase Sentinel-2 images. Skcnn_Tabnet, a typical crop remote sensing classification model, was built at the pixel scale by adding the channel attention mechanism, and the corn, sunflower, and rice in the Hetao Plain were quickly identified and studied. The results of this study suggest that the model exhibits high crop recognition ability, and the overall accuracy of the three crops is 0.9270, which is 0.1121, 0.1004, and 0.0874 higher than the Deeplabv3+, UNet, and RF methods, respectively. This study confirms the feasibility of the deep learning model in the application research of large-scale crop classification and mapping and provides a technical reference for achieving the automatic national crop census. Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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22 pages, 6888 KiB

Open AccessArticle

Land-Use-Based Runoff Yield Method to Modify Hydrological Model for Flood Management: A Case in the Basin of Simple Underlying Surface

by Chaowei Xu, Hao Fu, Jiashuai Yang, Lingyue Wang and Yizhen Wang

Sustainability 2022, 14(17), 10895; https://doi.org/10.3390/su141710895 - 31 Aug 2022

Cited by 3 | Viewed by 1266

Abstract

The study of runoff under the influence of human activities is a research hot spot in the field of water science. Land-use change is one of the main forms of human activities and it is also the major driver of changes to the runoff process. As for the relationship between land use and the runoff process, runoff yield theories pointed out that the runoff yield capacity is spatially heterogeneous. The present work hypothesizes that the distribution of the runoff yield can be divided by land use, which is, areas with the same land-use type are similar in runoff yield, while areas of different land uses are significantly different. To prove it, we proposed a land-use-based framework for runoff yield calculations based on a conceptual rainfall–runoff model, the Xin’anjiang (XAJ) model. Based on the framework, the modified land-use-based Xin’anjiang (L-XAJ) model was constructed by replacing the yielding area (f/F) in the water storage capacity curve of the XAJ model with the area ratio of different land-use types (L/F; L is the area of specific land-use types, F is the whole basin area). The L-XAJ model was then applied to the typical cultivated–urban binary land-use-type basin (Taipingchi basin) to evaluate its performance. Results showed great success of the L-XAJ model, which demonstrated the area ratio of different land-use types can represent the corresponding yielding area in the XAJ model. The L-XAJ model enhanced the physical meaning of the runoff generation in the XAJ model and was expected to be used in the sustainable development of basin water resources. Full article

(This article belongs to the Special Issue Regional Climate Change and Application of Remote Sensing)

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