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High-Resolution and Large-Scale Assessment of Environmental Parameters Using Remote Sensing...
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High-Resolution and Large-Scale Assessment of Environmental Parameters Using Remote Sensing Data

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 14317

Special Issue Editors

Dr. Niko Lukač

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: simulation and modelling; remote sensing; parallel computing
Special Issues, Collections and Topics in MDPI journals
Dr. Marko Bizjak

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: environmental simulation; computational geometry; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Data acquisition by large-scale Earth Observation (EO) using remote sensing (e.g., optical imagery, SAR, and LiDAR) and in-situ sensing has increased more than tenfold in the past few years. This enables new opportunities for better decision-making and monitoring capabilities of microclimate parameters within urban and rural environments. Environmental simulations using EO data are now among the most promising solutions to assess complex environmental parameters more accurately in spatial and temporal dimensions. These simulations provide a foundation for predictive and prescriptive analytics, and therefore yield substantial improvements in our understanding of extreme environmental phenomena, microclimate patterns, and anthropogenic effects on the environment. Due to their importance, these environmental issues have also been raised within the UN Sustainable Development Goals for good health and well-being (Goal 3), as well as for sustainable cities and communities (Goal 11).  However, various state-of-the-art environmental simulations and modelling algorithms still provide insufficient spatial resolution when being utilized over large-scale areas.

In this Special Issue, we invite various researchers to share their state-of-the-art environmental simulations and modelling algorithms, as well as their applications over different end-user domains, by utilizing both high-resolution and large-scale remote sensing data. The Special Issue is not limited to environmental simulations and can include their further interconnection with spatial analysis and modelling, geovisualisation, geocomputation, and big data analytics. 

Assist. Prof. Dr. Niko Lukač
Dr. Marko Bizjak
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1700 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

  • environmental parameters assessment
  • environmental simulations
  • environmental modelling
  • spatial analysis and modelling
  • geovisualization
  • geocomputation
  • big data analytics

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Published Papers (4 papers)

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Research

19 pages, 3407 KiB  
Article
A Multi-Criteria Evaluation of the Urban Ecological Environment in Shanghai Based on Remote Sensing
by Yuxiang Yan, Xianwen Yu, Fengyang Long and Yanfeng Dong
ISPRS Int. J. Geo-Inf. 2021, 10(10), 688; https://doi.org/10.3390/ijgi10100688 - 13 Oct 2021
Cited by 6 | Viewed by 2231
Abstract
The urban ecological environment is related to human health and is one of the most concerned issues nowadays. Hence, it is essential to detect and then evaluate the urban ecological environment. However, the conventional manual detection methods have many limitations, such as the [...] Read more.
The urban ecological environment is related to human health and is one of the most concerned issues nowadays. Hence, it is essential to detect and then evaluate the urban ecological environment. However, the conventional manual detection methods have many limitations, such as the high cost of labor, time, and capital. The aim of this paper is to evaluate the urban ecological environment more conveniently and reasonably, thus this paper proposed an ecological environment evaluation method based on remote sensing and a projection pursuit model. Firstly, a series of criteria for the urban ecological environment in Shanghai City are obtained through remote sensing technology. Then, the ecological environment is comprehensively evaluated using the projection pursuit model. Lastly, the ecological environment changes of Shanghai City are analyzed. The results show that the average remote sensing ecological index of Shanghai in 2020 increased obviously compared with that in 2016. In addition, Jinshan District, Songjiang District, and Qingpu District have higher ecological environment quality, while Hongkou District, Jingan District, and Huangpu District have lower ecological environment quality. In addition, the ecological environment of all districts has a significant positive spatial autocorrelation. These findings suggest that the ecological environment of Shanghai has improved overall in the past five years. In addition, Hongkou District, Jingan District, and Huangpu District should put more effort into improving the ecological environment in future, and the improvement of ecological environment should consider the impact of surrounding districts. Moreover, the proposed weight setting method is more reasonable, and the proposed evaluation method is convenient and practical. Full article
(This article belongs to the Special Issue High-Resolution and Large-Scale Assessment of Environmental Parameters Using Remote Sensing Data)
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17 pages, 3112 KiB  
Article
Vegetation Coverage Prediction for the Qinling Mountains Using the CA–Markov Model
by Lu Cui, Yonghua Zhao, Jianchao Liu, Huanyuan Wang, Ling Han, Juan Li and Zenghui Sun
ISPRS Int. J. Geo-Inf. 2021, 10(10), 679; https://doi.org/10.3390/ijgi10100679 - 8 Oct 2021
Cited by 11 | Viewed by 2770
Abstract
The Qinling Mountains represent the dividing line of the natural landscape of north-south in China. The prediction on vegetation coverage is important for protecting the ecological environment of the Qinling Mountains. In this paper, the data accuracy and reliability of three vegetation index [...] Read more.
The Qinling Mountains represent the dividing line of the natural landscape of north-south in China. The prediction on vegetation coverage is important for protecting the ecological environment of the Qinling Mountains. In this paper, the data accuracy and reliability of three vegetation index data (GIMMS NDVI, SPOT NDVI, and MODIS NDVI) were compared at first. SPOT, NDVI, and MODIS NDVI were used for calculating the vegetation coverage in the Qinling Mountains. Based on the CA–Markov model, the vegetation coverage grades in 2008, 2010, and 2013 were used to simulate the vegetation coverage grade in 2025. The results show that the grades of vegetation coverage of the Qinling Mountains calculated by SPOT, NDVI, and MODIS NDVI are highly similar. According to the prediction results, the grade of vegetation coverage in the Qinling Mountains has a rising trend under the guidance of the policy, particularly in urban areas. Most of the vegetation coverage transit from low vegetation coverage to middle and low vegetation coverage. The grades of the vegetation coverage, which were predicted by the CA–Markov model using SPOT, NDVI, and MODI NDVI, are consistent in spatial distribution and temporal variation. Full article
(This article belongs to the Special Issue High-Resolution and Large-Scale Assessment of Environmental Parameters Using Remote Sensing Data)
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21 pages, 5366 KiB  
Article
High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network
by Yajie Shi, Chao Ren, Zhiheng Yan and Jianmin Lai
ISPRS Int. J. Geo-Inf. 2021, 10(9), 623; https://doi.org/10.3390/ijgi10090623 - 17 Sep 2021
Cited by 12 | Viewed by 3168
Abstract
Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation [...] Read more.
Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)’s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products. Full article
(This article belongs to the Special Issue High-Resolution and Large-Scale Assessment of Environmental Parameters Using Remote Sensing Data)
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17 pages, 3997 KiB  
Article
Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China
by Ting Zhang, Ruiqing Yang, Yibo Yang, Long Li and Longqian Chen
ISPRS Int. J. Geo-Inf. 2021, 10(7), 475; https://doi.org/10.3390/ijgi10070475 - 10 Jul 2021
Cited by 42 | Viewed by 4934
Abstract
The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method [...] Read more.
The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method where the impact of RSEI indicators on the eco-environment quality and the seasonal change of RSEI are examined and considered. The northern Chinese municipal city of Tianjin was selected as a case study to test the proposed method. Landsat images acquired in spring, summer, autumn, and winter were obtained and processed for three different years (1992, 2005, and 2018) for a multitemporal analysis. Results from the case study show that both the contributions of RSEI indicators to eco-environment quality and RSEI values vary with the season and that such seasonal variability should be considered by normalizing indicator measures differently and using more representative remote-sensing images, respectively. The assessed eco-environment quality of Tianjin was, overall, improving owing to governmental environmental protection measures, but the damage caused by rapid urban expansion and sea reclamation in the Binhai New Area still needs to be noted. It is concluded that our proposed urban eco-environment quality assessment method is viable and can provide a reliable assessment result that helps gain a more accurate understanding of the evolution of the urban eco-environment quality over seasons and years. Full article
(This article belongs to the Special Issue High-Resolution and Large-Scale Assessment of Environmental Parameters Using Remote Sensing Data)
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