Special Issue "Remote Sensing and Health Problems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (10 December 2020).

Special Issue Editors

Dr. Weiwei Sun
E-Mail Website
Guest Editor
Department of Geography and Spatial Information Techniques, Ningbo University, 818 Fenghua Road, Ningbo 315201, China
Interests: remote sensing of coastal wetland; hyperspectral remote sensing of coastal zone
Special Issues and Collections in MDPI journals
Dr. Weiyue Li
E-Mail Website
Guest Editor
Institute of Urban Studies, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, China
Interests: image classification; satellite precipitation evaluation; climate change; urbanization and sustainability; natural hazard
Prof. Dr. Marco Scaioni
E-Mail Website1 Website2
Guest Editor
Department of architecture, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy
Interests: photogrammetry; geomatics for geosciences; remote sensing of the built environment
Special Issues and Collections in MDPI journals
Dr. Jiangtao Peng
E-Mail Website
Guest Editor
Faculty of Mathematics and Statistics, 368 Youyi Avenue, Wuchang District, Wuhan 430062, China
Interests: hyperspectral imagery; deep learning; transfer learning; image classification; health monitoring
Special Issues and Collections in MDPI journals
Prof. Jialin Li
E-Mail
Guest Editor
Department of Geography and Spatial Information Techniques, Ningbo University, 818 Fenghua Road, Ningbo 315211, Zhejiang, China
Interests: remote sensing; wetland protection; health analysis; evolution and modelling; natural resource monitoring

Special Issue Information

Dear Colleagues,

With global climate change and the development of urbanization, good health is one of the important indicators to evaluate natural and human social sustainable development. Currently, robust and intelligent algorithms have been developed for multispectral and hyperspectral imagery processing. Long-term valuable information derived from remotely sensed data and heath models have been playing an increasingly important role in understanding the relationship between health and eco-environmental factors. In addition, remote sensing and associated geo-statistical techniques also have particular potential applications in human health and urban sustainability.

This Special Issue of Applied Science aims to report the latest algorithms and applications for environmental and human health. We invite you to submit your recent research on remote sensing applications to health problems, particularly addressing the following topics:

  1. Advanced image processing methods (e.g., feature extraction, data mining, artificial intelligence, etc.) for health identification;
  2. Remote sensing monitoring for environmental (e.g., atmosphere, vegetation, river, ocean, coast, soil, etc.) health observations;
  3. Urban health (e.g., human health, food safety, water quality, etc.) and sustainable development;
  4. Human factors (e.g., ozone depletion, greenhouse effect, urban heat island, impervious surface, etc.) and health problems;
  5. Health modelling and geo-statistical analysis;
  6. Climate change and health problems.

Dr. Weiwei Sun
Dr. Weiyue Li
Prof. Marco Scaioni
Dr. Jiangtao Peng
Prof. Jialin Li
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 papers will be 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. Applied Sciences 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 2000 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
  • methods and applications
  • environmental factors
  • urban health
  • health modelling
  • long-term analysis
  • climate change
  • sustainable development

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Spatiotemporal Analysis of Land Use and Land Cover (LULC) Changes and Precipitation Trends in Shanghai
Appl. Sci. 2020, 10(21), 7897; https://doi.org/10.3390/app10217897 - 07 Nov 2020
Cited by 1 | Viewed by 838
Abstract
The impacts of anthropogenic land use and land cover (LULC) changes on the spatiotemporal distribution of precipitation in megacities have been highlighted in studies on urban climate change. In this study, we conducted a quantitative analysis of urban growth on the impact on [...] Read more.
The impacts of anthropogenic land use and land cover (LULC) changes on the spatiotemporal distribution of precipitation in megacities have been highlighted in studies on urban climate change. In this study, we conducted a quantitative analysis of urban growth on the impact on precipitation in Shanghai, China. We considered four periods of LULC data in 1979, 1990, 2000 and 2010, in addition to the long-term (1979–2010) trend of daily precipitation. The results indicate that the trend in precipitation exhibit different characteristics for urban (Ur), outskirt of urban (OUr) and outer suburb (OS) regions. Most Ur regions had an upward trend in annual and extreme precipitation during 1979–2010, while annual precipitation for the OUr and OS regions exhibited a decreasing trend. From 1979 to 2010, the areas of fastest expansion were located in the OUr region. The OS region, far away from the central area, had a relatively lower rate of change. In addition, OUr regions with rapid LULC changes exhibited higher increasing trends in annual and daily extreme precipitation, which is critical for the identification of frequent precipitation areas and the reliable projection of further changes. Full article
(This article belongs to the Special Issue Remote Sensing and Health Problems)
Show Figures

Graphical abstract

Article
Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data
Appl. Sci. 2019, 9(21), 4620; https://doi.org/10.3390/app9214620 - 30 Oct 2019
Cited by 3 | Viewed by 878
Abstract
This study focuses on the retrieval of chemical oxygen demand (COD) in the Baiyangdian area in North China, using a modified capsule network. Herein, the capsule model was modified to analyze the regression relationship between 1-D hyperspectral data and COD values. The results [...] Read more.
This study focuses on the retrieval of chemical oxygen demand (COD) in the Baiyangdian area in North China, using a modified capsule network. Herein, the capsule model was modified to analyze the regression relationship between 1-D hyperspectral data and COD values. The results indicate there is a statistically significant correlation between COD and the hyperspectral data. The accuracy of the capsule network was compared with the results obtained from using a traditional back-propagation neural network (BP) method. The capsule network achieved superior accuracy with fewer iterations, compared with the BP algorithm. An R2 value of 0.78 was obtained against measured COD values retrieved using the capsule network method, compared with a value of 0.42 for the BP algorithm retrievals. This suggests the capsule network method has great potential to solve regression problems in the field of remote sensing. Full article
(This article belongs to the Special Issue Remote Sensing and Health Problems)
Show Figures

Figure 1

Article
A Probabilistic Hyperspectral Imagery Restoration Method
Appl. Sci. 2019, 9(12), 2529; https://doi.org/10.3390/app9122529 - 21 Jun 2019
Viewed by 815
Abstract
Hyperspectral image (HSI) restoration is an important task of hyperspectral imagery processing, which aims to improve the performance of the subsequent HSI interpretation and applications. Considering HSI is always influenced by multiple factors—such as Gaussian noise, stripes, dead pixels, etc.—we propose an HSI-oriented [...] Read more.
Hyperspectral image (HSI) restoration is an important task of hyperspectral imagery processing, which aims to improve the performance of the subsequent HSI interpretation and applications. Considering HSI is always influenced by multiple factors—such as Gaussian noise, stripes, dead pixels, etc.—we propose an HSI-oriented probabilistic low-rank restoration method to address this problem. Specifically, we treat the expected clean HSI as a low-rank matrix. We assume the distribution of complex noise obeys a mixture of Gaussian distributions. Then, the HSI restoration problem is casted into solving the clean HSI from its counterpart with complex noise. In addition, considering the rank number need to be assigned manually for existing low-rank based HSI restoration method, we propose to automatically determine the rank number of the low-rank matrix by taking advantage of hyperspectral unmixing. Experimental results demonstrate HSI image can be well restored with the proposed method. Full article
(This article belongs to the Special Issue Remote Sensing and Health Problems)
Show Figures

Figure 1

Article
An Improved Gradient Boosting Regression Tree Estimation Model for Soil Heavy Metal (Arsenic) Pollution Monitoring Using Hyperspectral Remote Sensing
Appl. Sci. 2019, 9(9), 1943; https://doi.org/10.3390/app9091943 - 12 May 2019
Cited by 20 | Viewed by 1693
Abstract
Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of [...] Read more.
Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of hyperspectral inversion models. To resolve the problem, a gradient boosting regression tree (GBRT) hyperspectral inversion algorithm for heavy metal (Arsenic (As)) content in soils based on Spearman’s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CARS) is proposed in this paper. Firstly, the CARS algorithm is used to roughly select the original spectral data. Second derivative (SD), Gaussian filtering (GF), and min-max normalization (MMN) pretreatments are then used to improve the correlation between the spectra and As in the characteristic band enhancement stage. Finally, the low-correlation bands are removed using the SCA method, and a subset with absolute correlation values greater than 0.6 is retained as the optimal band subset after each pretreatment. For the modeling, the five most representative characteristic bands were selected in the Honghu area of China, and the nine most representative characteristic bands were selected in the Daye area of China. In order to verify the generalization ability of the proposed algorithm, 92 soil samples from the Honghu and Daye areas were selected as the research objects. With the use of support vector machine regression (SVMR), linear regression (LR), and random forest (RF) regression methods as comparative methods, all the models obtained a good prediction accuracy. However, among the different combinations, CARS-SCA-GBRT obtained the highest precision, which indicates that the proposed algorithm can select fewer characteristic bands to achieve a better inversion effect, and can thus provide accurate data support for the treatment and recovery of heavy metal pollution in soils. Full article
(This article belongs to the Special Issue Remote Sensing and Health Problems)
Show Figures

Figure 1

Article
Seasonal and Intra-Annual Patterns of Sedimentary Evolution in Tidal Flats Impacted by Laver Cultivation along the Central Jiangsu Coast, China
Appl. Sci. 2019, 9(3), 522; https://doi.org/10.3390/app9030522 - 03 Feb 2019
Cited by 2 | Viewed by 1120
Abstract
Human activities such as the rapid development of marine aquaculture in the central Jiangsu coast have had a marked impact on the tidal flat morphology. This research focuses on characterizing the spatial expansion of laver cultivation and its influence on the sedimentary evolution [...] Read more.
Human activities such as the rapid development of marine aquaculture in the central Jiangsu coast have had a marked impact on the tidal flat morphology. This research focuses on characterizing the spatial expansion of laver cultivation and its influence on the sedimentary evolution of tidal flats in the central Jiangsu coast. First, seasonal digital elevation models (DEMs) were established using 160 satellite images with medium resolution. Then, laver aquaculture regions were extracted from 50 time-series satellite images to calculate the area and analyze the spatial distribution and expansion of these areas. Finally, seasonal and intra-annual sedimentary evolution patterns of both aquaculture and non-aquaculture regions were determined using the constructed DEMs. Our results show that aquaculture regions have gradually expanded to the north and peripheral domains of the entire sand ridge since 1999 and by 2013, the seaward margins of each sandbank developed into dense cultivation regions. Additionally, the aquaculture regions increased from 11.99 km2 to 295.28 km2. The seasonal sedimentary evolution patterns indicate that deposition occurs during the winter and erosion during the summer. Thus, the aquaculture regions experience deposition in certain elevation intervals during the laver growing period and in the non-growing period, alluvial elevation intervals in the aquaculture regions are eroded and erosive ones are deposited in order to maintain the balance between scouring and silting. The sedimentary evolution of each sandbank is heterogeneous due to their different locations and the difference in sediment transport. The intra-annual evolution pattern is characterized by deposition in the high tidal flats and erosion in low ones. Hydrodynamic conditions and laver cultivation dominate partial sedimentary evolution, which gradually shapes the beach surface. Full article
(This article belongs to the Special Issue Remote Sensing and Health Problems)
Show Figures

Figure 1

Back to TopTop