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Remote Sensing Technology Innovation for Sustainable Development Goals

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 4339

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Special Issue Editors

Prof. Dr. Jie Chen

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Guest Editor

Department of Geo-Informatics, Central South University, Changsha 410083, China
Interests: urban sustainable development
Special Issues, Collections and Topics in MDPI journals

Dr. Qian Shi

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Guest Editor

Associate Professor, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Interests: intelligent mining and analysis of remote sensing big data
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Dr. Xiuyuan Zhang

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Guest Editor

Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China
Interests: sustainable cities and community development
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Special Issue Information

Dear Colleagues,

Sustainable development goals (SDGs) encourage humans to assume responsibility for protecting the planet. Therefore, it is essential to understand the interconnectedness of human lifestyles and the planet on global or regional scales. Large-scale and long-time-series earth observations from remotely sensed data play an important role in monitoring the planet and evaluating the progress of the SDGs. These observations can (from satellite, airborne, and in-situ sensors) provide consistent and continuous monitoring of the global state of the atmosphere, ocean, ecosystems, natural resources, the urban environment, etc., and their changes over time. With a foundation of abundant remote sensing data, remote sensing technologies for SDGs research have also flourished in recent years. Therefore, these technologies may applied to advance SDGs, such as the progress of carbon sequestration, estimating forest characteristics, monitoring biodiversity, predicting agricultural yield, assessing the impact of disasters, evaluating the urban environment, managing natural resources, and observing land use changes.

Overall, the explosive growth of earth observation data with higher diversity and dimensionality presents both a great challenge and opportunity for better monitoring the progress of sustainable development. There is thus an urgent demand for the development of innovative and intelligent remote sensing methodologies to mine and utilize reliable and accurate information, facilitating the implementation of SDGs.

Topics of interest for this Special Issue include (but are not limited to):

Sustainable development goals, especially for quantifying SDG 2, 6, 9, 11, 13, 14, and 15 indicators.
Global earth observation/ regional SDG evaluation/ country-level SDG indicator monitoring/ community-level SDG measurement.
Estimating carbon emissions, environmental parameters, land surface and air temperature, agricultural yield, evapotranspiration, and soil moisture.
Global forest cover mapping, cropland mapping, water resources mapping, flood inundation mapping, building footprint extraction, local climate zone mapping, and urban planning.
High spatial resolution/multi-spectral/hyperspectral/SAR remote sensing image processing.
Classification, object detection, change detection, multi-modal data fusion, and downscaling
Long-term time series monitoring and spatiotemporal analysis.

Prof. Dr. Jie Chen
Dr. Qian Shi
Dr. Xiuyuan Zhang
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

remote sensing
sustainable development goals (SDGS)
technology innovation
global mapping
regional survey
resources
environment

Related Special Issue

Innovation and Sustainable Development of Remote Sensing Technology—2nd Edition in Sustainability

Published Papers (3 papers)

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Research

28 pages, 17837 KiB

Open AccessArticle

Simulation-Based Optimization of the Urban Thermal Environment through Local Climate Zones Reorganization in Changsha City, China with the FLUS Model

by Jie Chen, Ruijie Shi, Geng Sun, Ya Guo, Min Deng and Xiuyuan Zhang

Sustainability 2023, 15(16), 12312; https://0-doi-org.brum.beds.ac.uk/10.3390/su151612312 - 12 Aug 2023

Viewed by 826

Abstract

Urbanization leads to changes in surface landscapes, such as the increase in built-up areas and the decrease in natural elements, resulting in local changes in land surface temperature, which often create unusually hot weather and affect livability, especially for mid- and low-latitude cities. Therefore, optimizing urban landscapes and adjusting the thermal environment is especially important to improve comfort and to achieve sustainable urban development. Existing studies on optimizing landscapes have considered mainly horizontal land uses/land covers but ignored their elevation. This study considered local climate zones as basic units to describe three-dimensional landscapes; we measured the relationship between local climate zones and land surface temperature, based on which the research further used a genetic algorithm and future land-use simulation models to optimize the spatial layouts of local climate zones in Changsha, China, considering multiple objectives including adjusting land surface temperature without affecting population carrying capacity, economic development, watershed protection, and forest and grass protection. According to the optimization results, the area of open low-rise buildings increased by 5.98% after optimization, and dense trees decreased by 7.64%; open low-rise buildings were suggested to be newly built in the city center and sparsely buildings should be developed in the surrounding administrative district far away from the city center. The optimization results contributed to a −5.2 °C reduction of average land surface temperature, which could significantly improve the thermal environment under the premise of ensuring the population and economic development levels and thus serves as a novel solution for improving urban landscapes to implement sustainable city development. Full article

(This article belongs to the Special Issue Remote Sensing Technology Innovation for Sustainable Development Goals)

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20 pages, 6481 KiB

Open AccessArticle

Attention Mechanism-Combined LSTM for Grain Yield Prediction in China Using Multi-Source Satellite Imagery

by Fan Liu, Xiangtao Jiang and Zhenyu Wu

Sustainability 2023, 15(12), 9210; https://0-doi-org.brum.beds.ac.uk/10.3390/su15129210 - 07 Jun 2023

Cited by 4 | Viewed by 1227

Abstract

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a dataset. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (convolutional block attention module, CBAM) is added to extract spatial information in different remote sensing bands to improve the efficiency of the model. Long short-term memory (LSTM) neural networks are added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. After the study, it was found that the LSTM model using a combination of multi-source satellite images and an attention mechanism can effectively predict grain yield in China. Furthermore, the proposed model was tested on data from 2018 to 2020 showing an average

R^{2}

of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner. Full article

(This article belongs to the Special Issue Remote Sensing Technology Innovation for Sustainable Development Goals)

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15 pages, 817 KiB

Open AccessArticle

Does Stronger Protection of Intellectual Property Improve Sustainable Development? Evidence from City Data in China

by Ke Mao and Pierre Failler

Sustainability 2022, 14(21), 14369; https://0-doi-org.brum.beds.ac.uk/10.3390/su142114369 - 02 Nov 2022

Cited by 5 | Viewed by 1660

Abstract

Achieving sustainable development goals is a challenge for countries. The core way is to enhance the green total factor productivity. While the literature has examined the various external institutional factors, there is a lack of research on the impact of intellectual property protection (IPP), which is an important external institution. This study adopts the differences-in-differences (DID) model and propensity scores matching (PSM) using the Chinese intellectual property model city policy (IPMP), as a quasi-natural experiment, and Chinese cities’ panel data from 2005 to 2019 to investigate the effect of IPP on sustainable development. The findings demonstrate that: (1) The IPMP significantly increases urban GTFP. (2) Mediation mechanism analyses show that the IPMP can support urban GTFP by fostering technological advancement, boosting human capital, luring foreign direct investment, and modernizing industrial structure. (3) Heterogeneity analysis shows that the Chinese central region, the eastern region, and the region with more fiscal transparency are where the IPMP has the greatest promotion effect on GTFP. Lastly, this study provides several recommendations for the improvement of sustainability in China. Full article

(This article belongs to the Special Issue Remote Sensing Technology Innovation for Sustainable Development Goals)

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