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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 51551

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Prof. Dr. Weicheng Wu

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Key Laboratory of Digital Land and Resources, East China University of Technology, Nanchang 330013, China
Interests: environmental remote sensing; land resource mapping; land degradation; multi-biome biomass; natural hazard risk zoning and machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yalan Liu

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Guest Editor

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, North of 20A, Datun Road, Chaoyang District, Beijing 100101, China
Interests: intelligent remote sensing information extraction for natural resource and environment, including land cover/land use change, disaster monitoring and assessment, and key technologies of space information integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geo-information technology, including remote sensing, GIS, has been playing a more and more important role in environmental monitoring, land resource quantification and mapping, natural hazard damage and risk assessment, urbanization and other land use change monitoring and modeling. New advancements and innovations have been achieved especially with the emergence of big data mining and machine learning including deep learning techniques. It is hence the objective of this Special Issue to provide a platform for worldwide experts in these fields to present and share their new research approaches and outcomes to promote the advancement of geo-information technology. This Special Issue will cover the following topics:

Remote sensing-based machine learning and big data mining technique
Land resource mapping and land cover change tracking
Natural hazard damage assessment and risk zoning
Land degradation and dust storm assessment
Coastal environmental problem analysis
Deformation monitoring and early warning by radar, InSAR and GPS

Prof. Dr. Weicheng Wu
Prof. Dr. Yalan Liu
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. 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

Remote Sensing
Geo-information
Big Data mining
Machine learning
Land resource mapping
Hazard damage and risk
Land degradation
Dust storm

Published Papers (11 papers)

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Editorial

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4 pages, 161 KiB

Open AccessEditorial

Editorial for the Special Issue: “Integrated Applications of Geo-Information in Environmental Monitoring”

by Weicheng Wu and Yalan Liu

Remote Sens. 2022, 14(17), 4251; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174251 - 29 Aug 2022

Cited by 1 | Viewed by 3818

Abstract

Geo-information technology has been playing an increasingly important role in environmental monitoring in recent decades. With the continuous improvement in the spatial resolution of remote sensing images, the diversification of sensors and the development of processing packages, applications of a variety of geo-information, in particular, multi-resolution remote sensing and geographical data, have become momentous in environmental research, including land cover change detection and modeling, land degradation assessment, geohazard mapping and disaster damage assessment, mining and restoration monitoring, etc. In addition, machine learning algorithms such as Random Forests (RF) and Convolutional Neural Networks (CNN) have improved and deepened the applications of geo-information technology in environmental monitoring and assessment. The purpose of this Special Issue is to provide a platform for communication of high-quality research in the world in the domain of comprehensive application of geo-information technology. It contains 10 high-level scientific papers on the following topics such as desertification monitoring, governance of mining areas, identification of marine dynamic targets, extraction of buildings, and so on. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

Research

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24 pages, 3394 KiB

Open AccessArticle

Assessment of the Effectiveness of Sand-Control and Desertification in the Mu Us Desert, China

by Jie Li, Weicheng Wu, Xiao Fu, Jingheng Jiang, Yixuan Liu, Ming Zhang, Xiaoting Zhou, Xinxin Ke, Yecheng He, Wenjing Li, Cuimin Zhou, Yuan Li, Yifei Song, Hongli Yang and Qihong Tu

Remote Sens. 2022, 14(4), 837; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040837 - 10 Feb 2022

Cited by 8 | Viewed by 5274

Abstract

The first successful sand-control was achieved in the Mu Us Desert by local people in the 1950–1960s, and their experience and approach have been extended to the whole Ordos and Northern China since then. The objective of this paper is to assess comprehensively the effectiveness of sand-control in 15 counties in and around Mu Us using multitemporal satellite images and socioeconomic data. After atmospheric correction, Landsat TM and OLI images were harnessed for land cover classification based on the ground-truth data and for derivation of the GDVI (generalized difference vegetation index) to extract the biophysical changes of the managed desert and desertification. Climatic, socioeconomic, environmental and spatial factors were selected for coupling analysis by multiple linear and logistic regression models to reveal the driving forces of desertification and their spatial determinants. The results show that from 1991 to 2020, 8712 km² or 63% of the desert has been converted into pastures and shrublands with a greenness increase of 0.3509 in GDVI; the effectiveness of sand-control is favored by the rational agropastoral activities and policies; though desertification occurs locally, it is associated with both climatic and socioeconomic factors, such as wind speed, precipitation, water availability, distance to roads and animal husbandry. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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23 pages, 10437 KiB

Open AccessArticle

High-Resolution Mining-Induced Geo-Hazard Mapping Using Random Forest: A Case Study of Liaojiaping Orefield, Central China

by Yaozu Qin, Li Cao, Ali Darvishi Boloorani and Weicheng Wu

Remote Sens. 2021, 13(18), 3638; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183638 - 11 Sep 2021

Cited by 11 | Viewed by 5554

Abstract

Mining-induced geo-hazard mapping (MGM) is a critical step for reducing and avoiding tremendous losses of human life, mine production, and property that are caused by ore mining. Due to the restriction of the survey techniques and data sources, high-resolution MGM remains a big challenge. To overcome this problem, in this research, such an MGM was conducted using detailed geological exploration and topographic survey data as well as Gaofen-1 satellite imagery as multi-source geoscience datasets and machine learning technique taking Liaojiaping Orefield, Central China as an example. First, using Gaofen-1 panchromatic and multispectral (PMS) sensor data and Random Forest (RF) non-parametric ensemble classifier, a seven-class land cover map was generated for the study area with an overall accuracy (OA) and Kappa coefficient (KC) of 99.69% and 98.37%, respectively. Next, several environmental drivers including land cover, topography (aspect and slope), lithology, distance from fault, elevation difference between surface and underground excavation, and the difference of spectral information from PMS multispectral data of different years were integrated as predictors to construct an RF-based MGM model. The constructed model showed an excellent prediction performance, with an OA of 98.53%, KC of 97.06%, and AUC of 0.998, and the 85.60% of the observed geo-disaster that have occurred in the predicted high susceptibility class (encompassing 2.82% of the study area). The results suggested that the changes in environmental factors in the high susceptibility areas can be used as indicators for monitoring and early-warning of the geo-disaster occurrence. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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23 pages, 11579 KiB

Open AccessArticle

Towards Sustainable Management of Mussel Farming through High-Resolution Images and Open Source Software—The Taranto Case Study

by Carmine Massarelli, Ciro Galeone, Ilaria Savino, Claudia Campanale and Vito Felice Uricchio

Remote Sens. 2021, 13(15), 2985; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152985 - 29 Jul 2021

Cited by 5 | Viewed by 2487

Abstract

This research activity, conducted in collaboration with the Aero-Naval Operations Department of the Guardia di Finanza of Bari as part of the Special Commissioner for urgent measures of reclamation, environmental improvements and redevelopment of Taranto’s measurement, is based on the use of a high-resolution airborne sensor, mounted on board a helicopter to identify and map all in operation and abandoned mussel farming in the first and second inlet of Mar Piccolo. In addition, factors able to compromise the environmental status of the Mar Piccolo ecosystem were also evaluated. The methodological workflow developed lets extract significant individual frames from the captured video tracks, improves images by applying five image processing algorithms, georeferences the individual frames based on flight data, and implements the processed data in a thematic Geographical Information System. All mussel farms, in operation and derelict, all partially submerged and/or water-coated invisible to navigation poles and other elements such as illegal fishing nets and marine litter on the seabed up to about 2 m deep, have been identified and mapped. The creation of an instant, high-precision cartographic representation made it possible to identify the anthropogenic pressures on the Mar Piccolo of Taranto and the necessary actions for better management of the area. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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24 pages, 21190 KiB

Open AccessArticle

The Use of InSAR Phase Coherence Analyses for the Monitoring of Aeolian Erosion

by Jung-Rack Kim, Cheng-Wei Lin and Shih-Yuan Lin

Remote Sens. 2021, 13(12), 2240; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122240 - 08 Jun 2021

Cited by 10 | Viewed by 3076

Abstract

Aeolian erosion occurring in sand deserts causes significant socio-economical threats over extensive areas through mineral dust storm generation and soil degradation. To monitor a sequence of aeolian erosion in a sand desert area, we developed an approach fusing a set of remote sensing data. Vegetation index and Interferometric Synthetic Aperture Radar (InSAR) phase coherence derived from space-borne optical/SAR remote sensing data were used. This scheme was applied to Kubuqi Desert in Inner Mongolia where the effects of activity to combat desertification could be used to verify the outcome of the approach. We first established time series phase coherence and conducted a functional operation based on principal component analysis (PCA) to remove uncorrelated noise. Then, through decomposition of vegetation effect, where a regression model together with the Enhanced Vegetation Index (EVI) was employed, we estimated surface migration caused by aeolian interaction, that is, the aeolian erosion rate (AER). AER metrics were normalized and validated by additional satellite and ground data. As a result, the spatiotemporal migration of the target environment, which certainly induced dust storm generation, was traced and analyzed based on the correlations among surface characteristics. It was revealed that the derived AER successfully monitored the surface changes that occurred before and after the activities to combat desertification in the target area. Employing the established observation scheme, we expect a better understanding of the aeolian process in sand deserts with enhanced spatio-temporal resolution. In addition, the scheme will be beneficial for the evaluation of combating desertification activities and early warning of dust storm generations. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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19 pages, 7012 KiB

Open AccessArticle

Mining and Restoration Monitoring of Rare Earth Element (REE) Exploitation by New Remote Sensing Indicators in Southern Jiangxi, China

by Lifeng Xie, Weicheng Wu, Xiaolan Huang, Penghui Ou, Ziyu Lin, Wang Zhiling, Yong Song, Tao Lang, Wenchao Huangfu, Yang Zhang, Xiaoting Zhou, Xiao Fu, Jie Li, Jingheng Jiang, Ming Zhang, Zhenjiang Zhang, Yaozu Qin, Shanling Peng, Chongjian Shao and Yonghui Bai

Remote Sens. 2020, 12(21), 3558; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213558 - 30 Oct 2020

Cited by 20 | Viewed by 6179

Abstract

Rare earth elements (REEs) are widely used in various industries. The open-pit mining and chemical extraction of REEs in the weathered crust in southern Jiangxi, China, since the 1970s have provoked severe damages to the environment. After 2010, different restorations have been implemented by various enterprises, which seem to have a spatial variability in both management techniques and efficiency from one mine to another. A number of vegetation indices, e.g., normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and atmospherically resistant vegetation index (ARVI), can be used for this kind of monitoring and assessment but lack sensitivity to subtle differences. For this reason, the main objective of this study was to explore the possibility to develop new, mining-tailored remote sensing indicators to monitor the impacts of REE mining on the environment and to assess the effectiveness of its related restoration using multitemporal Landsat data from 1988 to 2019. The new indicators, termed mining and restoration assessment indicators (MRAIs), were developed based on the strong contrast of spectral reflectance, albedo, land surface temperature (LST) and tasseled cap brightness (TCB) of REE mines between mining and postmining restoration management. These indicators were tested against vegetation indices such as NDVI, EVI, SAVI and generalized difference vegetation index (GDVI), and found to be more sensitive. Of similar sensitivity to each other, one of the new indicators was employed to conduct the restoration assessment of the mined areas. Six typically managed mines with different restoration degrees and management approaches were selected as hotspots for a comparative analysis to highlight their temporal trajectories using the selected MRAI. The results show that REE mining had experienced a rapid expansion in 1988–2010 with a total mined area of about 66.29 km² in the observed counties. With implementation of the post-2010 restoration measures, an improvement of varying degrees in vegetation cover in most mines was distinguished and quantified. Hence, this study with the newly developed indicators provides a relevant approach for assessing the sustainable exploitation and management of REE resources in the study area. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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17 pages, 4940 KiB

Open AccessArticle

Application Study on Double-Constrained Change Detection for Land Use/Land Cover Based on GF-6 WFV Imageries

by Jingxian Yu, Yalan Liu, Yuhuan Ren, Haojie Ma, Dacheng Wang, Yafei Jing and Linjun Yu

Remote Sens. 2020, 12(18), 2943; https://doi.org/10.3390/rs12182943 - 11 Sep 2020

Cited by 5 | Viewed by 2338

Abstract

As a new satellite sensor of the GaoFen (GF) series, GF-6 Wide Field of View (WFV) with the resolution of 16 m has the characteristics of wide coverage, high-frequency imaging and has four new bands of two red-edge, yellow, and purple compared with GF-1 WFV. In order to test the validity of the supplementary bands of GF-6WFV data for change detection of land use/land cover (LULC), this study applied the Double-constrained Change Detection Method (DCDM) that uses the double constraints (change vector intensity and correlation coefficient) for change detection on object-level. According to two GF-6WFV imageries acquired in the Xiong’an New Area in June of 2018 and 2019, feature analysis was performed to determine whether the new bands are helpful to detect the change of LULC first. Then, by coupling these selected features, the intensity of change vector and correlation coefficient were used as the double constraints to perform the change detection. The study demonstrates that the relevant features of the two red-edge bands can achieve the overall accuracy of 89% for change detection of LULC and improved by 2% comparing with using the corresponding temporal GF-1WFV data, while the purple and yellow bands cannot provide enough effective information for this detection. This study can provide theoretical support for the in-depth applications of GF-6 WFV data products in the change detection fields and has explored its applicability and potential in resource and environment monitoring, it is helpful to the further applications. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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24 pages, 6891 KiB

Open AccessArticle

Coastline Vulnerability Assessment through Landsat and Cubesats in a Coastal Mega City

by Majid Nazeer, Muhammad Waqas, Muhammad Imran Shahzad, Ibrahim Zia and Weicheng Wu

Remote Sens. 2020, 12(5), 749; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050749 - 25 Feb 2020

Cited by 37 | Viewed by 11074

Abstract

According to the Intergovernmental Panel on Climate Change (IPCC), global mean sea levels may rise from 0.43 m to 0.84 m by the end of the 21st century. This poses a significant threat to coastal cities around the world. The shoreline of Karachi (a coastal mega city located in Southern Pakistan) is vulnerable mainly due to anthropogenic activities near the coast. Therefore, the present study investigates rates and susceptibility to shoreline change using a 76-year multi-temporal dataset (1942 to 2018) through the Digital Shoreline Analysis System (DSAS). Historical shoreline positions were extracted from the topographic sheets (1:250,000) of 1942 and 1966, the medium spatial resolution (30 m) multi-sensor Landsat images of 1976, 1990, 2002, 2011, and a high spatial resolution (3 m) Planet Scope image from 2018, along the 100 km coast of Karachi. The shoreline was divided into two zones, namely eastern (25 km) and western (29 km) zones, to track changes in development, movement, and dynamics of the shoreline position. The analysis revealed that 95% of transects drawn for the eastern zone underwent accretion (i.e., land reclamation) with a mean rate of 14 m/year indicating that the eastern zone faced rapid shoreline progression, with the highest rates due to the development of coastal areas for urban settlement. Similarly, 74% of transects drawn for the western zone experienced erosion (i.e., land loss) with a mean rate of −1.15 m/year indicating the weathering and erosion of rocky and sandy beaches by marine erosion. Among the 25 km length of the eastern zone, 94% (23.5 km) of the shoreline was found to be highly vulnerable, while the western zone showed much more stable conditions due to anthropogenic inactivity. Seasonal hydrodynamic analysis revealed approximately a 3% increase in the average wave height during the summer monsoon season and a 1% increase for the winter monsoon season during the post-land reclamation era. Coastal protection and management along the Sindh coastal zone should be adopted to defend against natural wave erosion and the government must take measures to stop illegal sea encroachments. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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16 pages, 12149 KiB

Open AccessArticle

Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images

by Haojie Ma, Yalan Liu, Yuhuan Ren, Dacheng Wang, Linjun Yu and Jingxian Yu

Remote Sens. 2020, 12(2), 260; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020260 - 11 Jan 2020

Cited by 49 | Viewed by 4432

Abstract

Effective extraction of disaster information of buildings from remote sensing images is of great importance to supporting disaster relief and casualty reduction. In high-resolution remote sensing images, object-oriented methods present problems such as unsatisfactory image segmentation and difficult feature selection, which makes it difficult to quickly assess the damage sustained by groups of buildings. In this context, this paper proposed an improved Convolution Neural Network (CNN) Inception V3 architecture combining remote sensing images and block vector data to evaluate the damage degree of groups of buildings in post-earthquake remote sensing images. By using CNN, the best features can be automatically selected, solving the problem of difficult feature selection. Moreover, block boundaries can form a meaningful boundary for groups of buildings, which can effectively replace image segmentation and avoid its fragmentary and unsatisfactory results. By adding Separate and Combination layers, our method improves the Inception V3 network for easier processing of large remote sensing images. The method was tested by the classification of damaged groups of buildings in 0.5 m-resolution aerial imagery after the earthquake of Yushu. The test accuracy was 90.07% with a Kappa Coefficient of 0.81, and, compared with the traditional multi-feature machine learning classifier constructed by artificial feature extraction, this represented an improvement of 18% in accuracy. Our results showed that this improved method could effectively extract the damage degree of groups of buildings in each block in post-earthquake remote sensing images. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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13 pages, 27340 KiB

Open AccessTechnical Note

Simulation of a Wideband Radar Echo of a Target on a Dynamic Sea Surface

by Wang-Qiang Jiang, Liu-Ying Wang, Xin-Zhuo Li, Gu Liu and Min Zhang

Remote Sens. 2021, 13(16), 3186; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163186 - 11 Aug 2021

Cited by 4 | Viewed by 1498

Abstract

Unlike a generally rough ground surface, the sea surface varies over time. To analyze the impact of the motion of sea waves on the synthetic aperture radar (SAR) image of a target, the wideband echo simulation method based on a frequency domain is used. For the wideband echo, the electromagnetic (EM) scattering properties of the main frequency components are analyzed by the simulation method. Based on the EM scattering properties, the echo can be accurately simulated by using the inverse fast Fourier transformation (IFFT). Combined with the flight path of the radar, the echo of each pulse can be simulated to obtain the SAR image. The correct evaluation of the EM scattering properties is indispensable to the acquisition of an accurate SAR image. For complex targets, such as ships, the multiple scattering effects have a significant influence on the EM scattering properties. Thus, a rectangular wave beam-based geometrical optics and physical optics (GO/PO) method is introduced to calculate the EM scattering properties, which is more efficient than the traditional GO/PO. The GO/PO method is suitable to simulate SAR images in which the EM scattering properties of each pulse need to be calculated. With these methods, the SAR images of the target on the sea surface are simulated. Based on the comparison of the SAR images between a static and dynamic sea surface, it is found that the region corresponding to the target is blurred and the texture of the dynamic sea is blurrier. The impact of multiple scattering and sea wave motion on target recognition are also analyzed with the SAR images that were generated under different conditions. Some strong scattering points appear when multiple scattering effects are considered. It is also found that the texture of the SAR images, corresponding to the sea surface, changes with the synthetic aperture time. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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15 pages, 7696 KiB

Open AccessLetter

Improved Anchor-Free Instance Segmentation for Building Extraction from High-Resolution Remote Sensing Images

by Tong Wu, Yuan Hu, Ling Peng and Ruonan Chen

Remote Sens. 2020, 12(18), 2910; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182910 - 08 Sep 2020

Cited by 28 | Viewed by 4236

Abstract

Building extraction from high-resolution remote sensing images plays a vital part in urban planning, safety supervision, geographic databases updates, and some other applications. Several researches are devoted to using convolutional neural network (CNN) to extract buildings from high-resolution satellite/aerial images. There are two major methods, one is the CNN-based semantic segmentation methods, which can not distinguish different objects of the same category and may lead to edge connection. The other one is CNN-based instance segmentation methods, which rely heavily on pre-defined anchors, and result in the highly sensitive, high computation/storage cost and imbalance between positive and negative samples. Therefore, in this paper, we propose an improved anchor-free instance segmentation method based on CenterMask with spatial and channel attention-guided mechanisms and improved effective backbone network for accurate extraction of buildings in high-resolution remote sensing images. Then we analyze the influence of different parameters and network structure on the performance of the model, and compare the performance for building extraction of Mask R-CNN, Mask Scoring R-CNN, CenterMask, and the improved CenterMask in this paper. Experimental results show that our improved CenterMask method can successfully well-balanced performance in terms of speed and accuracy, which achieves state-of-the-art performance at real-time speed. Full article

(This article belongs to the Special Issue Integrated Applications of Geo-Information in Environmental Monitoring)

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