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
  • [1] Modeling of spatial pattern and influencing factors of cultivated land quality in Henan Province based on spatial big data
    Wang, Hua
    Zhu, Yuxin
    Wang, Jinghao
    Han, Hubiao
    Niu, Jiqiang
    Chen, Xueye
    [J]. PLOS ONE, 2022, 17 (04):
  • [2] Modeling Spatial Uncertainty for Locally Uncertain Data
    Savelyeva, Elena
    Utkin, Sergey
    Kazakov, Sergey
    Demyanov, Vasyliy
    [J]. GEOENV VII - GEOSTATISTICS FOR ENVIRONMENTAL APPLICATIONS, 2010, 16 : 295 - +
  • [3] Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification
    Pham, Tuan D.
    [J]. PLOS ONE, 2014, 9 (08):
  • [4] A memory-free spatial additive mixed modeling for big spatial data
    Murakami, Daisuke
    Griffith, Daniel A.
    [J]. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE, 2020, 3 (01) : 215 - 241
  • [5] A memory-free spatial additive mixed modeling for big spatial data
    Daisuke Murakami
    Daniel A. Griffith
    [J]. Japanese Journal of Statistics and Data Science, 2020, 3 : 215 - 241
  • [6] Spatial data uncertainty for location modeling: Ghost blocks and their implications
    Grubesic, Tony H.
    Wei, Ran
    Helderop, Edward
    [J]. APPLIED GEOGRAPHY, 2024, 166
  • [7] Integrating big social data, computing and modeling for spatial social science
    Ye, Xinyue
    Huang, Qunying
    Li, Wenwen
    [J]. CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE, 2016, 43 (05) : 377 - 378
  • [8] Computationally efficient joint species distribution modeling of big spatial data
    Tikhonov, Gleb
    Duan, Li
    Abrego, Nerea
    Newell, Graeme
    White, Matt
    Dunson, David
    Ovaskainen, Otso
    [J]. ECOLOGY, 2020, 101 (02)
  • [9] Bayesian Modeling Approach in Big Data Contexts: an Application in Spatial Epidemiology
    Orozco-Acosta, Erick
    Adin, Aritz
    Ugarte, Maria Dolores
    [J]. 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020), 2020, : 749 - 750
  • [10] Statistical modeling of spatial big data: An approach from a functional data analysis perspective
    Giraldo, Ramon
    Dabo-Niang, Sophie
    Martinez, Sergio
    [J]. STATISTICS & PROBABILITY LETTERS, 2018, 136 : 126 - 129