Mapping forest fire risk zones with spatial data and principal component analysis

2006 ◽  
Vol 49 (S1) ◽  
pp. 140-149 ◽  
Author(s):  
Dong Xu ◽  
Guofan Shao ◽  
Limin Dai ◽  
Zhanqing Hao ◽  
Lei Tang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Forest Fire ◽  
Spatial Data ◽  
Principal Component ◽  
Fire Risk ◽  
Component Analysis ◽  
Risk Zones ◽  
Forest Fire Risk

scholarly journals Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: a case study in the mountainous terrain of Northeast India

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Ridalin Lamat ◽  
Mukesh Kumar ◽  
Arnab Kundu ◽  
Deepak Lal
Keyword(s):  
High Risk ◽  
Forest Fire ◽  
Analytical Hierarchy Process ◽  
Fire Risk ◽  
Control Measures ◽  
Fire Control ◽  
Risk Zones ◽  
Hierarchy Process ◽  
Forest Fire Risk ◽  
Very High

AbstractThis study presents a geospatial approach in conjunction with a multi-criteria decision-making (MCDM) tool for mapping forest fire risk zones in the district of Ri-Bhoi, Meghalaya, India which is very rich in biodiversity. Analytical hierarchy process (AHP)-based pair-wise comparison matrix was constructed to compare the selected parameters against each other based on their impact/influence (equal, moderate, strong, very strong, and extremely strong) on a forest fire. The final output delineated fire risk zones in the study area in four categories that include very high-risk, high-risk, moderate-risk, and low-risk zones. The delineated fire risk zones were found to be in close agreement with actual fire points obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) fire data for the study area. Results indicated that Ri-Bhoi’s 804.31 sq. km. (32.86%) the area was under ‘very high’ fire susceptibility. This was followed by 583.10 sq. km. (23.82%), 670.47 sq. km. (27.39%), and 390.12 sq. km. (15.93%) the area under high, moderate, and low fire risk categories, respectively. These results can be used effectively to plan fire control measures in advance and the methodology suggested in this study can be adopted in other areas too for delineating potential fire risk zones.

scholarly journals The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

2021 ◽  
Vol 13 (24) ◽  
pp. 13859
Author(s):  
Shu Wu
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Forest Fire ◽  
Forest Fires ◽  
Inner Mongolia ◽  
Information Diffusion ◽  
Principal Component ◽  
Component Analysis ◽  
Expected Number

As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest.

scholarly journals Principal Component Analysis on Spatial Data: An Overview

2013 ◽  
Vol 103 (1) ◽  
pp. 106-128 ◽  
Author(s):  
Urška Demšar ◽  
Paul Harris ◽  
Chris Brunsdon ◽  
A. Stewart Fotheringham ◽  
Sean McLoone
Keyword(s):  
Principal Component Analysis ◽  
Spatial Data ◽  
Principal Component ◽  
Component Analysis

A Method for Spatial Data Registration Based on PCA-ICP Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 1033-1036 ◽  
Author(s):  
Shi Jun He ◽  
Shi Ting Zhao ◽  
Fan Bai ◽  
Jia Wei
Keyword(s):  
Principal Component Analysis ◽  
Basic Principle ◽  
Spatial Data ◽  
Laser Scanning ◽  
Principal Component ◽  
Component Analysis ◽  
3D Laser Scanning ◽  
Icp Algorithm ◽  
Data Registration ◽  
Registration Algorithm

The spatial data which acquired by 3D laser scanning is huge, aiming at the iteration time is long with classic ICP algorithm, a improved registration algorithm of spatial data ICP algorithm which based on principal component analysis (PCA) is proposed in this paper (PCA-ICP), the basic principle and steps of PCA-ICP algorithm are given. The experiment results show that this method is feasible and the iterative time of PCA-ICP algorithm is shorter than classical ICP algorithm.

A computer-system that classifies the prefectures of Greece in forest fire risk zones using fuzzy sets

2002 ◽  
Vol 4 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Lazaros S. Iliadis ◽  
Anastasios K. Papastavrou ◽  
Panagiotis D. Lefakis
Keyword(s):  
Fuzzy Sets ◽  
Computer System ◽  
Forest Fire ◽  
Fire Risk ◽  
Risk Zones ◽  
Forest Fire Risk ◽  
Of Greece

scholarly journals Mapping the Forest Fire Risk Zones Using Artificial Intelligence with Risk Factors Data

2022 ◽  
Author(s):  
Volkan Sevinc
Keyword(s):  
Artificial Intelligence ◽  
High Risk ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
Geographical Information ◽  
Data Set ◽  
Risk Zone ◽  
Risk Zones ◽  
Forest Fire Risk

Abstract Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of nine human-induced factors, three natural factors, and a temperature factor causing forest fires. Moreover, an artificial intelligence method, k-means, clustering algorithm was employed in preparation of the fire risk zone map. Turkey was selected as the study area as there are social and economic varieties among its zones. Therefore, the forestry zones in Turkey were separated into three groups as low, moderate, and high-risk categories and a map was provided for these risk zones. The map reveals that the forestry zones on the west coast of Turkey are under high risk of forest fire while the moderate risk zones mostly exist in the southeastern zones. The zones located in the interior parts, in the east, and on the north coast of Turkey have comparatively lower forest fire risks.

scholarly journals Utilisation of GIS and remote sensing for forest fire risk zone mapping at Tram Chim National Park

2015 ◽  
Vol 18 (4) ◽  
pp. 221-235
Author(s):  
Minh Thanh Vu ◽  
Hien Thi Thu Le
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Rainy Season ◽  
National Park ◽  
Fire Risk ◽  
Dry Season ◽  
Risk Zone ◽  
Gis And Remote Sensing ◽  
Risk Zones ◽  
Forest Fire Risk

Identification of areas of high fire risk is extremely important task in fire prevention and fire fighting. This study focuses on utilizing GIS and remote sensing to predict highest forest fire risk zones at Tram Chim National Park. Forest fire risk index was calculated based on forest-fire causing factors. The factors consist of landcover density and types, distance to water and settlements, surface temperature and leaf wetness index. And then, two forest fire risk maps were completed, one of them represented the fire risk in the rainy season in 2013, the other performed the fire risk in the dry season 2014. High fire risk zones locate mostly at the edge of the park where the bionass is rich and are near settlements. According to this fire risk computing, in the rainy season, area of high fire risk zone was 1,014.65 ha, about 14 % natural areas of Tram Chim National Park. In additional, in the dry season, high forest fire risk zones was 3,344.65 ha, and there is no safety zone. Results of the research contribute to the forest protecting at Tram Chim National Park and over the country.

Reigniting GIS's Application in Ecotourism

2019 ◽  
pp. 1478-1492
Author(s):  
Sovik Mukherjee
Keyword(s):  
Principal Component Analysis ◽  
Spatial Data ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Secondary Source ◽  
Tourist Attractions ◽  
Potential Index ◽  
Travel Agencies ◽  
Euclidean Distance Function

The chapter brings out a brief note on the tourist attractions, hotels and lodges, NGOs/travel agencies operating in that region, railway/bus stations, land use profile, etc. in the Sundarban area of West Bengal in conjunction with exploring the potential of ecotourism using GIS and some secondary source data. Moving onto the analysis part, by making use of geo-spatial data, the attributes of ecotourism potential in the Sundarbans has been explored. The author makes use of the Euclidean distance mechanism and principal component analysis to rank the ecotourism sites in Sunderbans (i.e., based on the construction of ecotourism potential index [EPI]). The novelty of the chapter lies in comparing the ranks obtained by constructing the EPI following the principal component analysis and the Euclidean distance function. It needs to be mentioned here that these tourist spots have been selected based on the information collected on the inflow of both domestic and foreign tourists to these spots. The chapter concludes by discussing the future scope of research in this regard.

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