Fire occurrence zones: kernel density estimation of historical wildfire ignitions at the national level, Greece

doi:10.1080/17445647.2014.908750

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Fire occurrence zones: kernel density estimation of historical wildfire ignitions at the national level, Greece

Journal Article•DOI•

Fire occurrence zones: kernel density estimation of historical wildfire ignitions at the national level, Greece

Nikos Koutsias¹, Panagiotis Balatsos², Kostas Kalabokidis³•Institutions (3)

University of Patras¹, Indian Ministry of Environment and Forests², University of the Aegean³

29 May 2014-Journal of Maps (Taylor & Francis)-Vol. 10, Iss: 4, pp 630-639

TL;DR: In this article, the authors used kernel density surfaces to construct fire occurrence zones in Greece using historical wildland fire ignition observations at national level in Greece, where the observed distribution was statistically significantly different than the expected one that arises under complete spatial randomness.

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Abstract: The focus of our study was to create a Main Map of fire occurrence zones from historical wildland fire ignition observations at national level in Greece using a Kernel Density estimation procedure. Kernel density estimation, a non parametric statistical method for estimating probability densities, has been widely used for home range estimation in wildlife ecology. It has the advantage of directly producing density estimates that are not influenced by grid size and localization effects. Furthermore, it produces densities of any shape and analyzes any data distributed multi-modally or non-normally. Under this perspective, kernel density surfaces have been created to construct fire occurrence zones. Their observed distribution was statistically significantly different than the expected one that arises under complete spatial randomness. A smoothing effect is certainly observed when increasing the bandwidth size of the kernel density interpolation. Excluding the kernel size of 1000 meters, then the results do ...

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Journal Article•DOI•

Fire occurrence zoning from local to global scale in the European Mediterranean basin: implications for multi-scale fire management and policy

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Nikos Koutsias, Britta Allgöwer, Kostas Kalabokidis, Giorgos Mallinis, Panagiotis Balatsos, Johann G. Goldammer¹ - Show less +2 more•Institutions (1)

Max Planck Society¹

26 Apr 2016-Iforest - Biogeosciences and Forestry

TL;DR: In this article, the authors proposed and evaluated a relatively new concept for fire occurrence zoning based on documented historical fire records, which creates continuous kernel density surfaces based on wildland fire ignition observations and compares the observed with the expected distribution of the number of fires within these zones using a Monte Carlo randomization test.

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Abstract: This study proposes and evaluates a relatively new concept for fire occurrence zoning based on documented historical fire records. The proposed method creates continuous kernel density surfaces based on wildland fire ignition observations. Kernels have the advantage of directly producing density estimates that are not influenced by grid size or localization effects. Within this scheme, kernel density surfaces have been created and reclassified to construct fire occurrence zones at local to global scales in the Mediterranean Basin. Specifically, fire occurrence zones were created for the European scale (European Mediterranean Basin), national scale (Greece), regional scale (Peloponnese, Greece) and local scale (Chalkidiki, Greece). To evaluate fire occurrence zones, we compared the observed with the expected distribution of the number of fires within these zones using a Monte Carlo randomization test, finding that these numbers were statistically different in all cases. The deviations observed from the expected distributions towards the high occurrence zone indicated their successful assessment and value. In this paper, we further discuss their potential role and use for multi-scale fire management and policy in a European context.

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27 citations

Cites background or methods from "Fire occurrence zones: kernel densi..."

...4) based on the kernel density interpolation, result from the work of Koutsias et al. (2005) and Koutsias et al. (2014)....
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...3), based on the kernel density interpolation, were taken from the kernel density map of Greece which is the result of the work by Koutsias et al. (2005) and Koutsias et al. (2014)....
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Journal Article•DOI•

Quantifying economic losses from wildfires in black pine afforestations of northern Spain

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Fermín J. Alcasena¹, Michele Salis², Nicholas J. Nauslar³, A. Eduardo Aguinaga, Cristina Vega-García¹ - Show less +1 more•Institutions (3)

University of Lleida¹, Central Maine Community College², Desert Research Institute³

01 Dec 2016-Forest Policy and Economics

TL;DR: In this paper, the authors implemented a fire risk assessment framework that combines spatially-explicit burn probabilities, post-fire mortality models and public auction timber prices, to estimate expected economic losses from wildfires in 155 black pine stands covering about 450ha in the Juslapena Valley of central Navarra, northern Spain.

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26 citations

Journal Article•DOI•

Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014

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Quanfeng Jin¹, Xiangqing Ma¹, Guangyu Wang², Xiajie Yang¹, Futao Guo¹ - Show less +1 more•Institutions (2)

Fujian Agriculture and Forestry University¹, University of British Columbia²

06 Dec 2017-Journal of Environmental Sciences-china

TL;DR: The emissions of pollutants released from crop residue burning were found to be spatially variable, with the burning of crop residue mainly occurring in Northeast, North and South China, and pollutants were mostly concentrated in the central and eastern regions of China.

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Abstract: Based on satellite image data and China's Statistical Yearbooks (2000 to 2014), we estimated the total mass of crop residue burned, and the proportion of residue burned in the field vs. indoors as domestic fuel. The total emissions of various pollutants from the burning of crop residue were estimated for 2000-2014 using the emission factor method. The results indicate that the total amount of crop residue and average burned mass were 8690.9Tg and 4914.6Tg, respectively. The total amount of emitted pollutants including CO2, CO, NOx, VOCs, PM2.5, OC (organic carbon), EC (element carbon) and TC (total carbon) were 4212.4-8440.9Tg, 192.8-579.4Tg, 4.8-19.4Tg, 18.6-61.3Tg, 18.8-49.7Tg, 6.7-31.3Tg, 2.3-4.7Tg, and 8.5-34.1Tg, respectively. The emissions of pollutants released from crop residue burning were found to be spatially variable, with the burning of crop residue mainly occurring in Northeast, North and South China. In addition, pollutant emissions per unit area (10 km × 10 km) were mostly concentrated in the central and eastern regions of China. Emissions of CO2, NOx, VOCs, OC and TC were mainly from rice straw burning, while burning of corn and wheat residues contributed most to emissions of CO, PM2.5 and EC. The increased ratio of PM2.5 emissions from crop residue burning to the total emitted from industry during the study period is attributed to the implementation of strict emissions management policies in Chinese industry. This study also provides baseline data for assessment of the regional atmospheric environment.

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23 citations

Journal Article•DOI•

Spatial distribution of grassland fires at the regional scale based on the MODIS active fire products

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Zhengxiang Zhang¹, Zhiqiang Feng², Hongyan Zhang¹, Jianjun Zhao¹, Shan Yu, Wala Du - Show less +2 more•Institutions (2)

Northeast Normal University¹, University of Edinburgh²

23 Feb 2017-International Journal of Wildland Fire

TL;DR: In this article, kernel density estimation (KDE) was applied to grassland fire events in the eastern Inner Mongolia of China, based on MODIS Terra and Aqua daily active fire data from 2001 to 2014.

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Abstract: Grassland fires are major disturbances to ecosystems and economies around the world. Therefore, research on the spatial patterns of grassland fires is important for understanding the dynamics of fire occurrence and providing evidence for fire prevention and management. One of the problems in grassland fire risk analysis is that historically observed fire data are generally in the point format, with imprecise positions, whereas other influencing factors are often expressed in continuous areal units. To minimise the influences of inaccurate locations and grid size, density estimates can be produced using kernel density estimation (KDE) – a nonparametric statistical method for estimating probability densities. This method has been widely used to convert historical fire data into continuous surfaces. In this study, KDE was applied to grassland fire events in the eastern Inner Mongolia of China, based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily active fire data from 2001 to 2014. The bandwidth choice was based on the mean random distance method. Annual and seasonal kernel density maps were produced, showing that the spatial patterns of grassland fire events remained temporally consistent. These results were used to create grassland fire risk zones on the basis of the mean density values in the study area. Grassland fire prevention and planning may focus on high-risk areas identified using this method.

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19 citations

Cites methods from "Fire occurrence zones: kernel densi..."

...163 Kernel density estimation, a non-parametric statistical method for estimating probability densities, 164 has been widely used for home range estimation in wildlife ecology and for forest fire risk 165 assessment (Amatulli et al., 2007; Boer et al., 2009; Koutsias et al., 2014; Kuter et al., 2011)....
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...…Kernel density estimation, a non-parametric statistical method for estimating probability densities, 164 has been widely used for home range estimation in wildlife ecology and for forest fire risk 165 assessment (Amatulli et al., 2007; Boer et al., 2009; Koutsias et al., 2014; Kuter et al., 2011)....
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Journal Article•DOI•

How do industrial land price and environmental regulations affect spatiotemporal variations of pollution-intensive industries? Regional analysis in China

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Mathilde Hoyer¹•Institutions (1)

Soochow University (Suzhou)¹

01 Jan 2022-Journal of Cleaner Production

TL;DR: Li et al. as mentioned in this paper investigated the impacts of industrial land price and environmental regulations on the distribution of pollution-intensive industries (PIIs) and found that PIIs tend to relocate to the less developed area of western China.

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17 citations

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References

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Book•DOI•

Density estimation for statistics and data analysis

[...]

Bernard W. Silverman

01 Jan 1986

TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.

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Abstract: Introduction. Survey of Existing Methods. The Kernel Method for Univariate Data. The Kernel Method for Multivariate Data. Three Important Methods. Density Estimation in Action.

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15,499 citations

Journal Article•DOI•

Kernel methods for estimating the utilization distribution in home-range studies

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B. J. Worton

01 Feb 1989-Ecology

TL;DR: Kernel methods are of flexible form and can be used where simple parametric models are found to be inappropriate or difficult to specify and give alternative approaches to the Anderson (1982) Fourier transform methods.

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Abstract: In this paper kernel methods for the nonparametric estimation of the utilization distribution from a random sample of locational observations made on an animal in its home range are described. They are of flexible form, thus can be used where simple parametric models are found to be inappropriate or difficult to specify. Two examples are given to illustrate the fixed and adaptive kernel approaches in data analysis and to compare the methods. Various choices for the smoothing parameter used in kernel methods are discussed. Since kernel methods give alternative approaches to the Anderson (1982) Fourier transform methods, some comparisons are made.

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3,949 citations

"Fire occurrence zones: kernel densi..." refers background or methods in this paper

...Kernel density estimation, a non parametric statistical method for estimating probability densities, has been widely used for home range estimation in wildlife ecology (Seaman & Powell, 1996; Tufto, Andersen, & Linnell, 1996; Worton, 1989)....
[...]
...Narrow bandwidths allow nearby observations to dominate the density estimate, while wide bandwidths favor distant locations (Seaman and Powell, 1996; Worton, 1989)....
[...]
...The bivariate kernel density estimator is mathematically defined as (Seaman & Powell, 1996; Silverman, 1986; Worton, 1989): f̂ (x) = 1 nh2 ∑n i=1 K (x − Xi) h { } where n is the number of points, h is the smoothing parameter or the bandwidth, K is a kernel density function, x is a vector of…...
[...]
...The bivariate kernel density estimator is mathematically defined as (Seaman & Powell, 1996; Silverman, 1986; Worton, 1989):...
[...]
...In addition to the choice of the kernel type, which might not be so important, the choice of the smoothing parameter is very critical since it controls the amount of variation of the estimates (Worton, 1989)....
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Book•

Interactive Spatial Data Analysis

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Trevor C. Bailey, Anthony C. Gatrell

29 Jun 1995

TL;DR: A: Introduction 1. Spatial data analysis 2. Computers and Spatial Data Analysis B: The Analysis of Data Associated with Points 3. Methods Relating to Point Patterns 4. Methodsrelating to Marked Point Patterns 5. MethodsRelating to a Continuously Varying Attribute Sampled at Points.

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Abstract: A: Introduction 1. Spatial Data Analysis 2. Computers and Spatial Data Analysis B: The Analysis of Data Associated with Points 3. Methods Relating to Point Patterns 4. Methods Relating to Marked Point Patterns 5. Methods Relating to a Continuously Varying Attribute Sampled at Points C: The Analysis of Data Associated with Areas 6. Univariate Analysis of Area Data 7. Analysis of Relationships Between Attributes of Areas 8. Multivariate Methods of Area Data D: The Analysis of Data Associated with Lines 9. Network Analysis 10. Spatial Interaction Models

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2,168 citations

Journal Article•DOI•

An Evaluation of the Accuracy of Kernel Density Estimators for Home Range Analysis

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D. Erran Seaman, Roger A. Powell

01 Oct 1996-Ecology

TL;DR: Computer simulations are used to compare the area and shape of kernel density estimates to the true area andshape of multimodal two—dimensional distributions and show the fixed kernel gave area estimates with very little bias and the cross—validated fixed kernel also gave surface estimates with the lowest error.

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Abstract: Kernel density estimators are becoming more widely used, particularly as home range estimators. Despite extensive interest in their theoretical properties, little empirical research has been done to investigate their performance as home range estimators. We used computer simulations to compare the area and shape of kernel density estimates to the true area and shape of multimodal two—dimensional distributions. The fixed kernel gave area estimates with very little bias when least squares cross validation was used to select the smoothing parameter. The cross—validated fixed kernel also gave surface estimates with the lowest error. The adaptive kernel overestimated the area of the distribution and had higher error associated with its surface estimate. See full-text article at JSTOR

...read moreread less

1,661 citations

"Fire occurrence zones: kernel densi..." refers background or methods in this paper

...Narrow bandwidths allow nearby observations to dominate the density estimate, while wide bandwidths favor distant locations (Seaman and Powell, 1996; Worton, 1989)....
[...]
...The bivariate kernel density estimator is mathematically defined as (Seaman & Powell, 1996; Silverman, 1986; Worton, 1989): f̂ (x) = 1 nh2 ∑n i=1 K (x − Xi) h { } where n is the number of points, h is the smoothing parameter or the bandwidth, K is a kernel density function, x is a vector of…...
[...]
...Furthermore, it produces densities of any shape and analyzes any data distributed multi-modally or non-normally (Seaman & Powell, 1996)....
[...]
...Kernel density estimation, a non parametric statistical method for estimating probability densities, has been widely used for home range estimation in wildlife ecology (Seaman & Powell, 1996; Tufto, Andersen, & Linnell, 1996; Worton, 1989)....
[...]

Journal Article•DOI•

Spatial point pattern analysis and its application in geographical epidemiology

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Anthony C. Gatrell¹, Trevor C. Bailey², Peter J. Diggle¹, Barry Rowlingson¹•Institutions (2)

Lancaster University¹, University of Exeter²

01 Jan 1996-Transactions of the Institute of British Geographers

TL;DR: Methods for exploring spatial variation in disease risk, spatial and space-time clustering, and the raised incidence of disease around suspected point sources of pollution are examined.

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Abstract: This paper reviews a number of methods for the exploration and modelling of spatial point patterns with particular reference to geographical epidemiology (the geographical incidence of disease). Such methods go well beyond the conventional ‘nearest-neighbour’ and ‘quadrat’ analyses which have little to offer in an epidemiological context because they fail to allow for spatial variation in population density. Correction for this is essential if the aim is to assess the evidence for ‘clustering’ of cases of disease. We examine methods for exploring spatial variation in disease risk, spatial and space-time clustering, and we consider methods for modelling the raised incidence of disease around suspected point sources of pollution. All methods are illustrated by reference to recent case studies including child cancer incidence, Burkitt’s lymphoma, cancer of the larynx and childhood asthma. An Appendix considers a range of possible software environments within which to apply these methods. The links to modern geographical information systems are discussed.

...read moreread less

743 citations

"Fire occurrence zones: kernel densi..." refers background in this paper

...Kernel estimation is an extension of the ‘moving window’ concept where the fixed-size window is replaced by a three-dimensional function (Gatrell et al., 1996)....
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