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GIS for Safety & Security Management

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

Deadline for manuscript submissions: closed (15 December 2018) | Viewed by 81064

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Special Issue Editors

Dr. Igor Ivan

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Guest Editor

Department of Geoinformatics, VSB-Technical University of Ostrava, 17. listopadu 15, 708 00 Ostrava, Czech Republic
Interests: quantitative geography; spatial statistics; visualisation; crime and fear mapping; transport accessibility
Special Issues, Collections and Topics in MDPI journals

Dr. Jan Caha

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Guest Editor

Assistant Professor, Department of Regional Development and Public Administration, Faculty of Regional Development and International Studies, Mendel University in Brno, Brno, Czech Republic
Interests: fuzzy sets, logic, and arithmetic in geography; uncertainty; visibility analyses; data science; open source software

Dr. Jaroslav Burian

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Guest Editor

Assistant Professor, Department of Geoinformatics, Faculty of Science, Palacký University Olomouc, Olomouc, Czech Republic
Interests: geoinformatics in human geography; urban and spatial planning; urban modeling; spatial analyses

Special Issue Information

Dear Colleagues,

In a turbulent world, many threats and challenges are endangering society. Important questions regarding how geospatial technologies can help to protect people, information, critical infrastructure, property, and environments, can be raised. These questions can include the following: How to build and utilize smart cities to be resilient to possible threats? How to divide the responsibility and development of safe and security management among the state and local governments, rescue and security organizations, and GI community?

Current and future GI-technologies are often requested to provide accurate and up-to-date geospatial data gathered from advanced technologies, including new satellite missions, UAV, UGV, sensor networks, mobile phones, social networks, crowdsourcing, and others. Tracking and predicting mobility of people, means, and sources, effective data collection, and fast processing are key features of future geospatial systems for security management. Such data might provide new valuable insights into many spatial processes. Processing of these highly diverse data sources, as well as big geodata, is still a relatively new topic for GIscience. The utilization of such datasets requires implementation of new techniques for risk and hazard optimization, dealing with uncertainty, improvements of modeling and simulations within the context spatial analyses as well as it requires enhancements of spatial visualizations and cartographic capabilities for event management.

Improvement of GI-technologies should support the collaboration of emergency services, law enforcement, military, and intelligence staff, as well as central and local governance, researchers, and society.

The main topics considered for this issue include, but are not limited to:

natural hazards,
man-made hazards (including large technical failures like blackouts),
security threats (terrorisms, national security, crime, intelligence services),
local risks (traffic accidents, fires, and other dwelling accidents),
software tools for support of safe and security management.

Dr. Igor Ivan
Dr. Jan Caha
Dr. Jaroslav Burian
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.

Published Papers (14 papers)

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19 pages, 3969 KiB

Open AccessArticle

Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method

by Mengmeng Hao, Dong Jiang, Fangyu Ding, Jingying Fu and Shuai Chen

ISPRS Int. J. Geo-Inf. 2019, 8(3), 133; https://doi.org/10.3390/ijgi8030133 - 07 Mar 2019

Cited by 34 | Viewed by 7061

Abstract

In recent years, various types of terrorist attacks have occurred which have caused worldwide catastrophes. The ability to proactively detect and even predict a potential terrorist risk is critically important for government agencies to react in a timely manner. In this study, a method of geospatial statistics was used to analyse the spatio-temporal evolution of terrorist attacks on the Indochina Peninsula. The machine learning random forest (RF) method was adopted to predict the potential risk of terrorist attacks on the Indochina Peninsula on a spatial scale with 15 driving factors. The RF model performed well with AUC values of 0.839 [95% confidence interval of 0.833–0.844]. The map of the potential distribution of terrorist attack risk was obtained with a 0.05×0.05-degree (approximately 5×5 km) resolution. The results indicate that Thailand is the most dangerous area for terrorist attacks, especially southern Thailand, Bangkok and its surrounding cities. Middle Cambodia and the northern and southern parts of Myanmar are also high-risk areas. Other areas are relatively low risk. This study provides the hotspots for terrorist attacks on a more fine-grained geographical unit. Meanwhile, it shows that machine learning algorithms (e.g., RF) combined with GIS have great potential for simulating the risk of terrorist attacks. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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14 pages, 5139 KiB

Open AccessArticle

QRA-Grid: Quantitative Risk Analysis and Grid-based Pre-warning Model for Urban Natural Gas Pipeline

by Shuang Li, Chengqi Cheng, Guoliang Pu and Bo Chen

ISPRS Int. J. Geo-Inf. 2019, 8(3), 122; https://doi.org/10.3390/ijgi8030122 - 01 Mar 2019

Cited by 14 | Viewed by 4163

Abstract

With the increasing use and complexity of urban natural gas pipelines, the occurrence of accidents owing to leakage, fire, explosion, etc., has increased. Based on Quantitative Risk Analysis (QRA) models and Geographic Information System (GIS) technology, we put forward a quantitative risk simulation model for urban natural gas pipeline, combining with a multi-level grid-based pre-warning model. We develop a simulation and pre-warning model named QRA-Grid, conducting fire and explosion risk assessment, presenting the risk by using a grid map. Experiments show that by using the proposed method, we can develop a fire and explosion accident pre-warning model for gas pipelines, and effectively predict areas in which accidents will happen. As a result, we can make a focused and forceful policy in areas which have some potential defects in advance, and even carry out urban planning once more, rebuilding it to prevent the risk. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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16 pages, 9477 KiB

Open AccessArticle

Detection of Microrelief Objects to Impede the Movement of Vehicles in Terrain

by Filip Dohnal, Martin Hubacek and Katerina Simkova

ISPRS Int. J. Geo-Inf. 2019, 8(3), 101; https://doi.org/10.3390/ijgi8030101 - 26 Feb 2019

Cited by 14 | Viewed by 2666

Abstract

Relief of terrain as a part of the landscape greatly affects the possibilities of vehicles moving off the road. The main influence on the movement is the slope of terrain and the occurrence of microrelief objects. While the slope limits can be easily modeled in the GIS environment, it is difficult to express the effect of the microrelief on the possibilities of moving vehicles. The aim of this work was to find procedures for identification of impassable microrelief objects using GIS tools and precise digital elevation models. Technical parameters defining the ability of a vehicle to overcome microrelief objects are known and these are mainly defined by the dimensions of the vehicle such as a wheel base, a ground clearance, approach angle, and others. Large-scale digital elevation models have not been able to reliably express the location and shape of microrelief objects until recently. Their accuracy of height in nodes achieved meter or decimeter values. The change occurred with the use of airborne laser scanning technology for digital elevation model creation. The accuracy of models created using this technology achieves centimeter values. These can be used for detection of microrelief objects. One of these models is the DMR5 from the territory of the Czech Republic. Its declared total mean height error is 0.18 meters. This model, together with the GIS tools and the technical parameters of individual vehicles, was used to search for such microrelief objects that act as a barrier to movement. Procedures for detecting impassable microrelief objects were created by ArcGIS tools. Modeling tools and mathematical methods were used to create procedures for detection of microrelief objects. These have been applied to selected locations in the Czech Republic. Raster layers representing individual impassable microrelief objects are the result of modeling. The modeling results were verified in the terrain using military vehicles. Field tests confirmed the high reliability of the proposed procedure. Therefore, the calculation process was optimized and will be introduced in the future as one of the input calculations of the complex model of passability in the Army of the Czech Republic. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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20 pages, 10126 KiB

Open AccessArticle

Estimating the Available Sight Distance in the Urban Environment by GIS and Numerical Computing Codes

by Marco Bassani, Nives Grasso, Marco Piras and Lorenzo Catani

ISPRS Int. J. Geo-Inf. 2019, 8(2), 69; https://doi.org/10.3390/ijgi8020069 - 30 Jan 2019

Cited by 7 | Viewed by 4151

Abstract

The available sight distance (ASD) is that part of the roadway ahead which is visible to the driver, and should be of sufficient length to allow a vehicle traveling at the designated speed to stop before reaching a stationary object in its path. It is fundamental in assessing road safety of a project or on an existing road section. Unfortunately, an accurate estimation of the available sight distance is still an issue on existing roads, above all due to the lack of information regarding the as-built condition of the infrastructure. Today, the geomatics field already offers different solutions for collecting 3D information about environments at different scales, integrating multiple sensors, but the main issue regarding existing mobile mapping systems (MMSs) is their high cost. The first part of this research focused on the use of a low-cost MMS as an alternative for obtaining 3D information about infrastructure. The obtained model can be exploited as input data of specific algorithms, both on a GIS platform and in a numerical computing environment to estimate ASD on a typical urban road. The aim of the investigation was to compare the performances of the two approaches used to evaluate the ASD, capturing the complex morphology of the urban environment. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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13 pages, 3646 KiB

Open AccessArticle

Do Crash Barriers and Fences Have an Impact on Wildlife–Vehicle Collisions?—An Artificial Intelligence and GIS-Based Analysis

by Raphaela Pagany and Wolfgang Dorner

ISPRS Int. J. Geo-Inf. 2019, 8(2), 66; https://doi.org/10.3390/ijgi8020066 - 30 Jan 2019

Cited by 10 | Viewed by 4095

Abstract

Wildlife–vehicle collisions (WVCs) cause significant road mortality of wildlife and have led to the installation of protective measures along streets. Until now, it has been difficult to determine the impact of roadside infrastructure that might act as a barrier for animals. The main deficits are the lack of geodata for roadside infrastructure and georeferenced accidents recorded for a larger area. We analyzed 113 km of road network of the district Freyung-Grafenau, Germany, and 1571 WVCs, examining correlations between the appearance of WVCs, the presence or absence of roadside infrastructure, particularly crash barriers and fences, and the relevance of the blocking effect for individual species. To receive infrastructure data on a larger scale, we analyzed 5596 road inspection images with a neural network for barrier recognition and a GIS for a complete spatial inventory. This data was combined with the data of WVCs in GIS to evaluate the infrastructure’s impact on accidents. The results show that crash barriers have an effect on WVCs, as collisions are lower on roads with crash barriers. In particular, smaller animals have a lower collision share. The risk reduction at fenced sections could not be proven as fenced sections are only available at 3% of the analyzed roads. Thus, especially the fence dataset must be validated by a larger sample number. However, these preliminary results indicate that the combination of artificial intelligence and GIS may be used to analyze and better allocate protective barriers or to apply it in alternative measures, such as dynamic WVC risk-warning. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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18 pages, 2552 KiB

Open AccessArticle

The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach

by Lu Wang, Gabby Lee and Ian Williams

ISPRS Int. J. Geo-Inf. 2019, 8(1), 51; https://doi.org/10.3390/ijgi8010051 - 21 Jan 2019

Cited by 24 | Viewed by 15701

Abstract

Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and violent crime among different socio-economic stratums and across space by examining the neighbourhood socioeconomic conditions and individual characteristics of offenders associated with crime in the city of Toronto, which consists of 140 neighbourhoods. Despite being the largest urban centre in Canada, with a fast-growing population, Toronto is under-studied in crime analysis from a spatial perspective. In this study, both property and violent crime data sets from the years 2014 to 2016 and census-based Ontario-Marginalisation index are analysed using spatial and quantitative methods. Spatial techniques such as Local Moran’s I are applied to analyse the spatial distribution of criminal activity while accounting for spatial autocorrelation. Distance-to-crime is measured to explore the spatial behaviour of criminal activity. Ordinary Least Squares (OLS) linear regression is conducted to explore the ways in which individual and neighbourhood demographic characteristics relate to crime rates at the neighbourhood level. Geographically Weighted Regression (GWR) is used to further our understanding of the spatially varying relationships between crime and the independent variables included in the OLS model. Property and violent crime across the three years of the study show a similar distribution of significant crime hot spots in the core, northwest, and east end of the city. The OLS model indicates offender-related demographics (i.e., age, marital status) to be a significant predictor of both types of crime, but in different ways. Neighbourhood contextual variables are measured by the four dimensions of the Ontario-Marginalisation Index. They are significantly associated with violent and property crime in different ways. The GWR is a more suitable model to explain the variations in observed property crime rates across different neighbourhoods. It also identifies spatial non-stationarity in relationships. The study provides implications for crime prevention and security through an enhanced understanding of crime patterns and factors. It points to the need for safe neighbourhoods, to be built not only by the law enforcement sector but by a wide range of social and economic sectors and services. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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21 pages, 14246 KiB

Open AccessArticle

Threat of Pollution Hotspots Reworking in River Systems: Case Study of the Ploučnice River (Czech Republic)

by Jitka Elznicová, Tomáš Matys Grygar, Jan Popelka, Martin Sikora, Petr Novák and Michal Hošek

ISPRS Int. J. Geo-Inf. 2019, 8(1), 37; https://doi.org/10.3390/ijgi8010037 - 16 Jan 2019

Cited by 6 | Viewed by 3776

Abstract

As fluvial pollution may endanger the quality of water and solids transported by rivers, mapping and evaluation of historically polluted fluvial sediments is an urgent topic. The Ploučnice River and its floodplain were polluted by local uranium mining from 1971–1989. We have studied this river since 2013 using a combination of diverse methods, including geoinformatics, to identify pollution hotspots in floodplains and to evaluate the potential for future reworking. Archival information on pollution history and past flooding was collected to understand floodplain dynamics and pollution heterogeneity. Subsequently, a digital terrain model based on laser scanning data and data analysis were used to identify the sites with river channel shifts. Finally, non-invasive geochemical mapping was employed, using portable X-ray fluorescence and gamma spectrometers. The resulting datasets were processed with geostatistical tools. One of the main outputs of the study was a detailed map of pollution distribution in the floodplain. The results showed a relationship between polluted sediment deposition, past channel shifts and floodplain development. We found that increased concentration of pollution occurred mainly in the cut-off meanders and lateral channel deposits from the mining period, the latter in danger of reworking (reconnecting to the river) in the coming decades. Full article

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12 pages, 18232 KiB

Open AccessArticle

Air Pollution Dispersion Modelling Using Spatial Analyses

by Jan Bitta, Irena Pavlíková, Vladislav Svozilík and Petr Jančík

ISPRS Int. J. Geo-Inf. 2018, 7(12), 489; https://doi.org/10.3390/ijgi7120489 - 19 Dec 2018

Cited by 9 | Viewed by 3865

Abstract

Air pollution dispersion modelling via spatial analyses (Land Use Regression—LUR) is an alternative approach to the standard air pollution dispersion modelling techniques in air quality assessment. Its advantages are mainly a much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate more factors affecting pollutant’s concentration than standard dispersion models. The goal of the study was to model the PM₁₀ particles dispersion via spatial analyses in the Czech–Polish border area of the Upper Silesian industrial agglomeration and compare the results with the results of the standard Gaussian dispersion model SYMOS’97. The results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover were included in the LUR model, the LUR model results improved significantly (65% determination coefficient) to a level comparable with the Gaussian model. A hybrid approach of combining the Gaussian model with the LUR gives superior quality of results (86% determination coefficient). Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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23 pages, 2551 KiB

Open AccessArticle

New Trends in Using Augmented Reality Apps for Smart City Contexts

by Pravesh Yagol, Francisco Ramos, Sergio Trilles, Joaquín Torres-Sospedra and Francisco J. Perales

ISPRS Int. J. Geo-Inf. 2018, 7(12), 478; https://doi.org/10.3390/ijgi7120478 - 14 Dec 2018

Cited by 57 | Viewed by 9331

Abstract

The idea of virtuality is not new, as research on visualization and simulation dates back to the early use of ink and paper sketches for alternative design comparisons. As technology has advanced so the way of visualizing simulations as well, but the progress is slow due to difficulties in creating workable simulations models and effectively providing them to the users. Augmented Reality and Virtual Reality, the evolving technologies that have been haunting the tech industry, receiving excessive attention from the media and colossal growing are redefining the way we interact, communicate and work together. From consumer application to manufacturers these technologies are used in different sectors providing huge benefits through several applications. In this work, we demonstrate the potentials of Augmented Reality techniques in a Smart City (Smart Campus) context. A multiplatform mobile app featuring Augmented Reality capabilities connected to GIS services are developed to evaluate different features such as performance, usability, effectiveness and satisfaction of the Augmented Reality technology in the context of a Smart Campus. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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12 pages, 1501 KiB

Open AccessFeature PaperArticle

Identification of Experimental and Control Areas for CCTV Effectiveness Assessment—The Issue of Spatially Aggregated Data

by Adam Dąbrowski, Piotr Matczak, Andrzej Wójtowicz and Michael Leitner

ISPRS Int. J. Geo-Inf. 2018, 7(12), 471; https://doi.org/10.3390/ijgi7120471 - 07 Dec 2018

Cited by 6 | Viewed by 4201

Abstract

Progress in surveillance technology has led to the development of Closed-Circuit Television (CCTV) systems in cities around the world. Cameras are considered instrumental in crime reduction, yet existing research does not unambiguously answer the question whether installing them affects the number of crimes committed. The quasi-experimental method usually applied to evaluate CCTV systems’ effectiveness faces difficulties with data quantity and quality. Data quantity has a bearing on the number of crimes that can be conclusively inferred using the experimental procedure. Data quality affects the level of crime data aggregation. The lack of the exact location of a crime incident in the form of a street address or geographic coordinates hinders the selection procedure of experimental and control areas. In this paper we propose an innovative method of dealing with data limitations in a quasi-experimental study on the effectiveness of CCTV systems in Poland. As police data on crime incidents are geocoded onto a neighborhood or a street, we designed a method to overcome this drawback by applying similarity measures to time series and landscape metrics. The method makes it possible to determine experimental (test) and control areas which are necessary to conduct the study. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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21 pages, 3374 KiB

Open AccessArticle

GIS-Assisted Prediction and Risk Zonation of Wildlife Attacks in the Chitwan National Park in Nepal

by Aleš Ruda, Jaromír Kolejka and Thakur Silwal

ISPRS Int. J. Geo-Inf. 2018, 7(9), 369; https://doi.org/10.3390/ijgi7090369 - 07 Sep 2018

Cited by 17 | Viewed by 7181

Abstract

Population growth forces the human community to expand into the natural habitats of wild animals. Their efforts to use natural sources often collide with wildlife attacks. These animals do not only protect their natural environment, but in the face of losing the potential food sources, they also penetrate in human settlements. The research was situated in the Chitwan National Park (CNP) in Nepal, and the aim of this study was to investigate possible geospatial connections between attacks of all kinds of animals on humans in the CNP and its surroundings between 2003 and 2013. The patterns of attacks were significantly uneven across the months, and 89% of attacks occurred outside the park. In total, 74% attacks occurred in the buffer zone forests and croplands within 1 km from the park. There was a strong positive correlation among the number of victims for all attacking animals with a maximum of one victim per 4 km², except elephant and wild boar. The density of bear victims was higher where the tiger and rhino victims were lower, e.g., in the Madi valley. The data collected during this period did not show any signs of spatial autocorrelation. The calculated magnitude per unit area using the kernel density, together with purpose-defined land use groups, were used to determine five risk zones of wildlife attacks. In conclusion, it was found that the riskiest areas were locations near the forest that were covered by agricultural land and inhabited by humans. Our research results can support any local spatial decision-making processes for improving the co-existence of natural protection in the park and the safety of human communities living in its vicinity. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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19 pages, 4476 KiB

Open AccessArticle

On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors

by Xun Zhang, Min Jin, Jingying Fu, Mengmeng Hao, Chongchong Yu and Xiaolan Xie

ISPRS Int. J. Geo-Inf. 2018, 7(9), 354; https://doi.org/10.3390/ijgi7090354 - 27 Aug 2018

Cited by 13 | Viewed by 5615

Abstract

Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. We used the data of terrorist attacks in Southeast Asia from 1970 to 2016, and comprehensively considered 17 influencing factors, including socioeconomic and natural resource factors. The improved recommendation algorithm is used to build a spatial risk assessment model of terrorist attacks, and the effectiveness is tested. The model trained in this study is tested with precision, recall, and F-Measure. The results show that, when the threshold is 0.4, the precision is as high as 88%, and the F-Measure is the highest. We assess the spatial risk of the terrorist attacks in Southeast Asia through experiments. It can be seen that the southernmost part of the Indochina peninsula and the Philippines are high-risk areas and that the medium-risk and high-risk areas are mainly distributed in the coastal areas. Therefore, future anti-terrorism measures should pay more attention to these areas. Full article

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27 pages, 7437 KiB

Open AccessArticle

The Influence of the Shape and Size of the Cell on Developing Military Passability Maps

by Krzysztof Pokonieczny and Albina Mościcka

ISPRS Int. J. Geo-Inf. 2018, 7(7), 261; https://doi.org/10.3390/ijgi7070261 - 03 Jul 2018

Cited by 17 | Viewed by 3436

Abstract

The necessity to divide the analysed area into basic elements, regardless of the administrative division (cells or pixels, also called primary fields), and use them to prepare thematic maps emerged as early as by the end of the 19th century. The automation of map development processes brought a new approach to the function of cells, which made them a carrier that facilitates information processing, and presenting the results of analyses in the form of studies that very often function only in spatial information systems or on the Internet. Cells are currently used to conduct a series of advanced spatial analyses in practically all areas of application. The aim of the presented research was to analyse the influence of the shape and size of cells on the terrain classification results for the purposes of developing military passability maps. The research used the automatic terrain classification method, based on calculating the index of passability, calculated for cells of square, triangular, and hexagonal shapes and of different sizes, ranging from 100 m to 10,000 m. Indices of passability were determined basing on parameters derived from land cover elements that exist in the area of each of the adopted cells. Because of the fact that passability maps are mainly developed for military purposes, the study used a standardised vector spatial database—VMap Level 2. The studies have demonstrated that, if the surface areas of cells are identical, their shapes do not have a significant influence on the resulting passability map. The authors have also determined the sizes of cells that should be adopted for developing passability maps on various levels of accuracy, and, as a consequence, for being used on various levels of command of military troops. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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Review

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28 pages, 13942 KiB

Open AccessReview

Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks

by Premysl Stych, Josef Lastovicka, Radovan Hladky and Daniel Paluba

ISPRS Int. J. Geo-Inf. 2019, 8(2), 71; https://doi.org/10.3390/ijgi8020071 - 31 Jan 2019

Cited by 10 | Viewed by 3887

Abstract

This study focused on the evaluation of forest vegetation changes from 1992 to 2015 in the Low Tatras National Park (NAPANT) in Slovakia and the Sumava National Park in Czechia using a time series (TS) of Landsat images. The study area was damaged by wind and bark beetle calamities, which strongly influenced the health state of the forest vegetation at the end of the 20th and beginning of the 21st century. The analysis of the time series was based on the ten selected vegetation indices in different types of localities selected according to the type of forest disturbances. The Landsat data CDR (Climate Data Record/Level 2) was normalized using the PIF (Pseudo-Invariant Features) method and the results of the Time Series were validated by in-situ data. The results confirmed the high relevance of the vegetation indices based on the SWIR bands (e.g., NDMI) for the purpose of evaluating the individual stages of the disturbance (especially the bark beetle calamity). Usage of the normalized Landsat data Climate Data Record (CDR/Level 2) in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability of the corrected data. Full article

(This article belongs to the Special Issue GIS for Safety & Security Management)

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