Uncertainty in the modeling of spatial big data on a pattern of bushfires holes

被引:1
|
作者
Stein, Alfred [1 ]
Tolpekin, Valentyn A. [1 ]
机构
[1] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands
关键词
forest fire; fractal dimension; hole; NDVI; remote sensing; spatial variation; vegetation patches; SOUTH-EASTERN AUSTRALIA; WILDFIRE SUPPRESSION; POINT PATTERNS; FIRE SEVERITY; FOREST-FIRES; PERSPECTIVE;
D O I
10.1111/nrm.12180
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper focuses on the presence of vegetation patches, called holes remaining after forest fires. Holes are of interest to explore because their vegetation is affected by severe temperature stress nearby, although they can serve as an agent to regenerate a forest after the burn. Further, it is interesting to know why holes emerge at all, while little if anything is known about their structure and distribution in space. A statistical analysis of their presence and abundance and a spatial statistical analysis to analyze their positions was done within four forest fire footprints. Fractal dimension of the holes was compared to that of the forest fire footprint, whereas remote sensing imagery was used to identify the normalized difference vegetation index (NDVI) of the patches before and after the fire. Results showed that the fractal dimension of the holes is lower than that of the forest fire footprint, and that the NDVI is slowly recovering to the original NDVI. Differences with the NDVI of the surrounding areas remain large. We concluded that patches of vegetation after a forest fire are interesting to study, providing clues of why unburned patches occur despite the fire presence nearby, how they can be characterized spatially and how the vegetation composition responds to such nearby fire. The Recommendations for Resource ManagersForest fires affect the forests, and have an effect on the population living within the forest and close to it. A forest fire commonly leaves behind a large number of unburnt vegetation patches. In this study we call them holes. These holes have been under severe heat and smoke pressure, but have similar tree species and forest structure as the original forest. They serve as the starting point to regenerate the forest. The primary implications for resource management are as follows: A better understanding of where they are, and how they are composed may help to understand the behavior of a fire. Their characterization may help to better understand the relation between vegetation as a fuel for forest fire. Their biodiversity will improve the fire spread modeling of burns that are carried out for management of a forest stand.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] Modeling of spatial geological objects at a priori uncertainty of a geological-geophysical data complex
    Goltsman, FM
    Kalinina, TB
    [J]. FIZIKA ZEMLI, 1996, (02): : 82 - 89
  • [42] Data Multiverse: The Uncertainty Challenge of Future Big Data Analytics
    Tudoran, Radu
    Nicolae, Bogdan
    Brasche, Goetz
    [J]. SEMANTIC KEYWORD-BASED SEARCH ON STRUCTURED DATA SOURCES, IKC 2016, 2017, 10151 : 17 - 22
  • [43] Guest editorial: big spatial data
    Vatsavai, Raju
    Chandola, Varun
    [J]. GEOINFORMATICA, 2016, 20 (04) : 797 - 799
  • [44] Guest editorial: big spatial data
    Raju Vatsavai
    Varun Chandola
    [J]. GeoInformatica, 2016, 20 : 797 - 799
  • [45] TWITTER AS A SOURCE OF BIG SPATIAL DATA
    Kocich, David
    Horak, Jiri
    [J]. INFORMATICS, GEOINFORMATICS AND REMOTE SENSING CONFERENCE PROCEEDINGS, SGEM 2016, VOL I, 2016, : 921 - 928
  • [46] Big data, spatial optimization, and planning
    Cao, Kai
    Li, Wenwen
    Church, Richard
    [J]. ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE, 2020, 47 (06) : 941 - 947
  • [47] Spatial analysis in the era of big data
    Zhang, Xiaoxiang
    [J]. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 2014, 39 (06): : 655 - 659
  • [48] SPATIAL GEOMARKETING POWERED BY BIG DATA
    Shaytura, S., V
    Feoktistova, V. M.
    Minitaeva, A. M.
    Olenev, L. A.
    Chulkov, V. O.
    Kozhaev, Y. P.
    [J]. TURISMO-ESTUDOS E PRATICAS, 2020,
  • [49] Clustering Algorithms for Spatial Big Data
    Schoier, Gabriella
    Gregorio, Caterina
    [J]. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2017, PT IV, 2017, 10407 : 571 - 583
  • [50] Spatial big data and anxieties of control
    Leszczynski, Agnieszka
    [J]. ENVIRONMENT AND PLANNING D-SOCIETY & SPACE, 2015, 33 (06): : 965 - 984