Special Issue "Advances in Geo-Information for Environmental Forensics and Environmental Risk Management in the Anthropocene"

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

Deadline for manuscript submissions: closed (30 June 2017).

Special Issue Editor

Guest Editor
Prof. Dr. Jason K. Levy Website E-Mail
Disaster Preparedness and Emergency Management, University of Hawaii, Kapolei, HI 96707, USA
Interests: disaster risk governance; sustainable hazard mitigation; stochastic and statistical hydrology; sociohydrology; fluvial and marine disasters; global climate change, computational intelligence for water management; hydrologic resilience; process-based modeling of coupled human–water systems; inundation; economics of water resources management; drought

Special Issue Information

Dear Colleagues,

The Anthropocene is considered to be a new epoch, which is fundamental when thinking about environmental forensics, risk management, and climate change. This Special Issue offers a new way of exploring the significance of geospatial information the epoch of the Anthropocene, a time when humans confront the limits of our ability to manipulate, dominate, and control natural systems. Many environmental academics, government officials, business leaders, and practitioners have begun to examine the environmental ethics, policies, and politics of geo-information in a changing biosphere, focusing on global processes and effects. In particular, the Special Issue will examine the use of remote sensing (both satellite and aerial photography) and other geomatic tools for environmental forensics and risk management, including land use planning and change, forestry, agriculture, wetlands and watersheds, and emergency response. A wide range of papers will be considered. A multi-disciplinary approach is encouraged.

While weather satellites have long been used for environmental applications for the past half-century, ETM+, MODIS, and other satellite sensors, possess the ability to revolutionize the fields of environmental forensics and risk management in order to understand the complex root causes of environmental challenges and disasters. For example, disaster management scholars are increasingly using ETM+ data to monitor floods, droughts, beach erosion, and volcanic activity over time. In addition, the U.S. Environmental Protection Agency (EPA) use innovative satellite and aerial remote tools to support emergency response to hazardous material release, while forestry applications for passive remote sensors include environmental tree surveys, monitoring clear-cut operations, and observing successional forest growth. Finally, there has been considerable interest in recent years in the use of satellites to better understand, monitor, and enforce multilateral environmental agreements (MEAs), such as the Kyoto Protocol.

Prof. Dr. Jason K. Levy
Guest Editor

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. 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 1000 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 hazards
  • satellite sensors
  • risk management
  • disaster science
  • climate change
  • anthropocene

Published Papers (10 papers)

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

Research

Open AccessArticle
Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea
ISPRS Int. J. Geo-Inf. 2017, 6(7), 210; https://doi.org/10.3390/ijgi6070210 - 08 Jul 2017
Abstract
The equal division of tropical cyclone (TC) trajectory method, the mass moment of the TC trajectory method, and the mixed regression model method are clustering algorithms that use space and shape information from complete TC trajectories. In this article, these three clustering algorithms [...] Read more.
The equal division of tropical cyclone (TC) trajectory method, the mass moment of the TC trajectory method, and the mixed regression model method are clustering algorithms that use space and shape information from complete TC trajectories. In this article, these three clustering algorithms were applied in a TC trajectory clustering analysis to identify the TCs that affected the South China Sea (SCS) from 1949 to 2014. According to their spatial position and shape similarity, these TC trajectories were classified into five trajectory classes, including three westward straight-line movement trajectory clusters and two northward re-curving trajectory clusters. These clusters show different characteristics in their genesis position, heading, landfall location, TC intensity, lifetime and seasonality distribution. The clustering results indicate that these algorithms have different characteristics. The equal division of the trajectory method provides better clustering result generally. The approach is simple and direct, and trajectories in the same class were consistent in shape and heading. The regression mixture model algorithm has a solid theoretical mathematical foundation, and it can maintain good spatial consistency among trajectories in the class. The mass moment of the trajectory method shows overall consistency with the equal division of the trajectory method. Full article
Show Figures

Figure 1

Open AccessArticle
Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents
ISPRS Int. J. Geo-Inf. 2017, 6(6), 168; https://doi.org/10.3390/ijgi6060168 - 07 Jun 2017
Cited by 6
Abstract
Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 images and calculate vegetation greenness (i.e., normalized difference vegetation index [...] Read more.
Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 images and calculate vegetation greenness (i.e., normalized difference vegetation index (NDVI)), canopy structure (i.e., enhanced vegetation index (EVI)), and canopy water content (i.e., normalized difference water index (NDWI))-related variables. Our proposed methods consisted of: (i) evaluating the relationships between PAN band (0.503–0.676 µm) with a spatial resolution of 15 m and individual MS bands of Landsat-8 from blue (i.e., acquiring in the range 0.452–0.512 µm), green (i.e., 0.533–0.590 µm), red (i.e., 0.636–0.673 µm), near infrared (NIR: 0.851–0.879 µm), shortwave infrared-I (SWIR-I: 1.566–1.651 µm), and SWIR-II (2.107–2.294 µm) bands with a spatial resolution of 30 m; (ii) determining the suitable individual MS bands to be enhanced into the spatial resolution of the PAN band; and (iii) calculating several vegetation greenness and canopy moisture indices (i.e., NDVI, EVI, NDWI-I, and NDWI-II) at 15 m spatial resolution and subsequent validation using their equivalent-values at a spatial resolution of 30 m. Our analysis revealed that strong linear relationships existed between the PAN and most of the MS individual bands of interest except NIR. For example, r2 values were 0.86–0.89 for blue band; 0.89–0.95 for green band; 0.84–0.96 for red band; 0.71–0.79 for SWIR-I band; and 0.71–0.83 for SWIR-II band. As a result, we performed smoothing filter-based intensity modulation method of pan-sharpening to enhance the spatial resolution of 30 m to 15 m. In calculating the vegetation indices, we used the enhanced MS images and resampled the NIR to 15 m. Finally, we evaluated these indices with their equivalents at 30 m spatial resolution and observed strong relationships (i.e., r2 values in the range 0.98–0.99 for NDVI, 0.95–0.98 for EVI, 0.98–1.00 for NDWI). Full article
Show Figures

Figure 1

Open AccessArticle
Management System for Dam-Break Hazard Mapping in a Complex Basin Environment
ISPRS Int. J. Geo-Inf. 2017, 6(6), 162; https://doi.org/10.3390/ijgi6060162 - 01 Jun 2017
Cited by 1
Abstract
Flood disasters from dam breaks cause serious loss of human life and immense damage to infrastructure and economic stability. The application of Geographic Information System technology integrated with hydrological modeling for mapping flood-inundated areas and depth can play a momentous role in further [...] Read more.
Flood disasters from dam breaks cause serious loss of human life and immense damage to infrastructure and economic stability. The application of Geographic Information System technology integrated with hydrological modeling for mapping flood-inundated areas and depth can play a momentous role in further minimizing the risk and possible damage. In the present study, base terrain data, hydrological data, and dam engineering data were integrated using the MIKE-21 dam-break model to analyze flood routing under the most serious scenarios. A deterministic approach was used to calculate the hydraulic elements of dam breakage during a flood. Additionally, the hydraulic elements generated by the MIKE-21 dam-break model (a modelling system for estuaries, coastal waters, and seas)—including flood depth, submersion time, and flow direction—were integrated with a digital elevation model of the site downstream of the dam in order to map the possible affected areas. Using an empirical model in addition to using the superimposition of dam flood calculation results and the social and economic survey data, dam damage assessment was implemented. In accordance with a relevant standard, the flood risk mapping guidelines and a set of client/server structures were developed for a management system for dam-break hazard mapping of the Foziling reservoir. The simulation data and the study results can provide a scientific basis for emergency management of the reservoir and provide a socio-economic framework for downstream areas. Full article
Show Figures

Figure 1

Open AccessArticle
An Integrated Approach for Monitoring and Information Management of the Guanling Landslide (China)
ISPRS Int. J. Geo-Inf. 2017, 6(3), 79; https://doi.org/10.3390/ijgi6030079 - 12 Mar 2017
Cited by 6
Abstract
Landslide triggered by earthquake or rainstorm often results in serious property damage and human casualties. It is, therefore, necessary to establish an emergency management system to facilitate the processes of damage assessment and decision-making. This paper has presented an integrated approach for mapping [...] Read more.
Landslide triggered by earthquake or rainstorm often results in serious property damage and human casualties. It is, therefore, necessary to establish an emergency management system to facilitate the processes of damage assessment and decision-making. This paper has presented an integrated approach for mapping and analyzing spatial features of a landslide from remote sensing images and Digital Elevation Models (DEMs). Several image interpretation tools have been provided for analyzing the spatial distribution and characteristics of the landslide on different dimensions: (1D) terrain variation analysis along the mass movement direction and (3D) morphological analysis. In addition, the results of image interpretation can be further discussed and adjusted on an online cooperating platform, which was built to improve the coordination of all players involved in different phases of emergency management, e.g., hazard experts, emergency managers, and first response organizations. A mobile-based application has also been developed to enhance the data exchange and on-site investigation. Our pilot study of Guanling landslide shows that the presented approach has the potential to facilitate the phases of landslide monitoring and information management, e.g., hazard assessment, emergency preparedness, planning mitigation, and response. Full article
Show Figures

Figure 1

Open AccessArticle
An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping
ISPRS Int. J. Geo-Inf. 2017, 6(1), 18; https://doi.org/10.3390/ijgi6010018 - 16 Jan 2017
Cited by 11
Abstract
Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses [...] Read more.
Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance. Full article
Show Figures

Figure 1

Open AccessArticle
Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau
ISPRS Int. J. Geo-Inf. 2016, 5(12), 238; https://doi.org/10.3390/ijgi5120238 - 16 Dec 2016
Cited by 19
Abstract
The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully-affected areas detection is the basic work in this region for gully erosion assessment and monitoring. For the first time, an unmanned aerial vehicle (UAV) was applied to [...] Read more.
The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully-affected areas detection is the basic work in this region for gully erosion assessment and monitoring. For the first time, an unmanned aerial vehicle (UAV) was applied to extract gully features in this region. Two typical catchments in Changwu and Ansai were selected to represent loess tableland and loess hilly regions, respectively. A high-powered quadrocopter (md4-1000) equipped with a non-metric camera was used for image acquisition. InPho and MapMatrix were applied for semi-automatic workflow including aerial triangulation and model generation. Based on the stereo-imaging and the ground control points, the highly detailed digital elevation models (DEMs) and ortho-mosaics were generated. Subsequently, an object-based approach combined with the random forest classifier was designed to detect gully-affected areas. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The overall extraction accuracy in Changwu and Ansai achieved was 84.62% and 86.46%, respectively, which indicated the potential of the proposed workflow for extracting gully features. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research. Full article
Show Figures

Figure 1

Open AccessArticle
Gully Erosion Mapping and Monitoring at Multiple Scales Based on Multi-Source Remote Sensing Data of the Sancha River Catchment, Northeast China
ISPRS Int. J. Geo-Inf. 2016, 5(11), 200; https://doi.org/10.3390/ijgi5110200 - 04 Nov 2016
Cited by 15
Abstract
This research is focused on gully erosion mapping and monitoring at multiple spatial scales using multi-source remote sensing data of the Sancha River catchment in Northeast China, where gullies extend over a vast area. A high resolution satellite image (Pleiades 1A, 0.7 m) [...] Read more.
This research is focused on gully erosion mapping and monitoring at multiple spatial scales using multi-source remote sensing data of the Sancha River catchment in Northeast China, where gullies extend over a vast area. A high resolution satellite image (Pleiades 1A, 0.7 m) was used to obtain the spatial distribution of the gullies of the overall basin. Image visual interpretation with field verification was employed to map the geometric gully features and evaluate gully erosion as well as the topographic differentiation characteristics. Unmanned Aerial Vehicle (UAV) remote sensing data and the 3D photo-reconstruction method were employed for detailed gully mapping at a site scale. The results showed that: (1) the sub-meter image showed a strong ability in the recognition of various gully types and obtained satisfactory results, and the topographic factors of elevation, slope and slope aspects exerted significant influence on the gully spatial distribution at the catchment scale; and (2) at a more detailed site scale, UAV imagery combined with 3D photo-reconstruction provided a Digital Surface Model (DSM) and ortho-image at the centimeter level as well as a detailed 3D model. The resulting products revealed the area of agricultural utilization and its shaping by human agricultural activities and water erosion in detail, and also provided the gully volume. The present study indicates that using multi-source remote sensing data, including satellite and UAV imagery simultaneously, results in an effective assessment of gully erosion over multiple spatial scales. The combined approach should be continued to regularly monitor gully erosion to understand the erosion process and its relationship with the environment from a comprehensive perspective. Full article
Show Figures

Figure 1

Open AccessArticle
A GIS Study of the Influences of Warm Ocean Eddies on the Intensity Variations of Tropical Cyclones in the South China Sea
ISPRS Int. J. Geo-Inf. 2016, 5(10), 169; https://doi.org/10.3390/ijgi5100169 - 23 Sep 2016
Abstract
This study presented the spatial distribution patterns of tropical cyclones (TCs) in the South China Sea (SCS) and discussed the possible influences of average sea surface temperature (SST) and the size of warm ocean eddies on changes in the intensity of TCs passing [...] Read more.
This study presented the spatial distribution patterns of tropical cyclones (TCs) in the South China Sea (SCS) and discussed the possible influences of average sea surface temperature (SST) and the size of warm ocean eddies on changes in the intensity of TCs passing over them. Between 1993 and 2013, the SCS has experienced 233 TCs, of which 134 have interacted with warm ocean eddies. The results of fuzzy c-means (FCM) clustering showed that these TCs are mainly located in the northern portion of the SCS. After interacting with warm ocean eddies, TCs may intensify, remain at the same intensity, or weaken. For intensifying TCs, the enhancements range from 0 to 3 m/s only; however, this level of TC intensity enhancement is statistically significant at p<0.05. Further statistical analyses show that warm ocean eddies with a higher-than-average SST and a larger ratio between the size of the warm ocean eddies and the radius of the TC maximum wind may help intensify passing TCs. Full article
Show Figures

Figure 1

Open AccessArticle
Evaluation of Multiple Classifier Systems for Landslide Identification in LANDSAT Thematic Mapper (TM) Images
ISPRS Int. J. Geo-Inf. 2016, 5(9), 164; https://doi.org/10.3390/ijgi5090164 - 13 Sep 2016
Cited by 2
Abstract
Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for [...] Read more.
Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for landslide risk management, remote sensing usage does have a few drawbacks. The aforementioned events commonly occur at high steep slope regions, frequently associated with shadow occurrence in satellite images, which impairs the identification process and results in low accuracy classifications. In this sense, this paper aims to evaluate the accuracy of different ensembles of multiple classifier systems (MCSs) for landslide scar identification. A severe landslide event on a steep slope with a high rainfall rate area in the southeast region of Brazil was chosen. Ten supervised classifiers were used to identify this severe event and other possible features for the LANDSAT thematic mapper (TM) from June of 2000. The results were evaluated, and nine MCSs were constructed based on the accuracy of the classifiers. Voting was applied through the ensemble method, coupled with contextual analysis and random selection tie-breaker methods. Accuracy was evaluated for each classification ensemble, and a progressive enhancement in the ensemble accuracy was noted as the least accurate classifiers were removed. The best accuracy for landslide identification emerged from the ensemble of the three most accurate classification results. In summary, MCS application generally improved the classification quality and led to fewer omission errors, coupled with a better classification percentage for the ‘landslide’ class. However, the MCS ensemble algorithm selection must be customized to the purpose of the classification. It is crucial to assess single accuracy indicators of each algorithm to ascertain those with the most consistent performance regarding the final results. Full article
Show Figures

Figure 1

Open AccessArticle
Assessment on the Impact of Arable Land Protection Policies in a Rapidly Developing Region
ISPRS Int. J. Geo-Inf. 2016, 5(5), 69; https://doi.org/10.3390/ijgi5050069 - 16 May 2016
Cited by 5
Abstract
To investigate the effect of arable land protection policies in China, a practical framework that integrates geographic information systems (GIS), soil quality assessment and landscape metrics analysis was employed to track and analyze arable land transformations and landscape changes in response to rampant [...] Read more.
To investigate the effect of arable land protection policies in China, a practical framework that integrates geographic information systems (GIS), soil quality assessment and landscape metrics analysis was employed to track and analyze arable land transformations and landscape changes in response to rampant urbanization within the Ningbo region (China) from 2005 to 2013. The results showed that arable land loss and degradation have continued, despite the development of a comprehensive legal framework for arable land protection. The implementation of arable land protection policies is judged to be effective, but not entirely successful, because it guarantees the overall amount of arable land but does not consider soil quality and spatial distribution. In addition, there are distinct variations in arable land change dynamics between two temporal intervals. From 2005–2009, the transformation of arable land was diversified, with intensified conversion among arable land, built-up land, water and orchards. Moreover, many new arable land parcels were adjacent to built-up land, and are in danger of being occupied again through urban sprawl. By 2009–2013, most of the arable land was occupied by urban expansion, whereas a majority of newly increased arable land was reclaimed from coastal tideland. Although the newly increased arable land was contiguous and far from the urban area, it is of poor quality and has limited use. The permanent loss of high-quality arable land due to intensified urban sprawl may threaten sustainable development and food security on a larger scale. Full article
Show Figures

Figure 1

Back to TopTop