A review of forest visualization and forest fire simulation technology research

Huai Yongjian; Meng Qingkuo; Ma Tianrong; Xu Haifeng; Zhao Xi; Cheng Mingzhi; Huang Xinyuan

doi:10.11834/jig.230016

Pattern Recognition and Intelligent Visualization | Views : 0 下载量: 528 CSCD: 0

PDF
Export
Share
Collection
Album

A review of forest visualization and forest fire simulation technology research
Vol. 28, Issue 6, Pages: 1891-1908(2023)
Received：11 January 2023，

Revised：2023-02-28，

Published：16 June 2023
DOI： 10.11834/jig.230016
稿件说明：

移动端阅览

淮永建，孟庆阔，马天容，徐海峰，赵曦，程明智，黄心渊. 2023. 森林场景可视化和林火模拟仿真技术研究综述. 中国图象图形学报， 28(06):1891-1908 DOI： 10.11834/jig.230016.

Huai Yongjian， Meng Qingkuo， Ma Tianrong， Xu Haifeng， Zhao Xi， Cheng Mingzhi， Huang Xinyuan. 2023. A review of forest visualization and forest fire simulation technology research. Journal of Image and Graphics， 28(06):1891-1908 DOI： 10.11834/jig.230016.

摘要

森林是生态环境系统的重要组成部分。随着气候变暖，恶劣气候气象条件造成全球森林火灾频繁发生，给国民经济和消防救援带来巨大挑战，森林火灾已成为全球主要的自然灾害。因此，森林场景可视化建模、3维场景仿真、林火模拟仿真、火场复现、预测和灾害评估成为林业虚拟仿真研究热点。本文对树木形态结构建模技术、森林场景大规模重建和实时渲染、森林场景可视化、林火模型和林火模拟仿真等前沿技术和算法进行综述。对相关的林木、植被的形态结构表达和真实感可视化建模方法进行归纳分类，并对不同可视化方法的算法优劣、复杂度、实时渲染效率和适用场景进行讨论。基于规则的林木建模方法和基于林分特征的真实场景重建方法对大规模森林场景重建技术进行分类，基于物理模型、经验模型和半经验模型对森林火灾的林火模型、单木林火、多木林火模拟和蔓延进行总结，对影响林火蔓延的不同环境气象因子（如地形地貌、湿度、可燃物等）和森林分布对林火发生、扩散和蔓延的影响进行分析，对不同算法的优劣进行对比、分析和讨论，对森林场景可视化和林火模拟仿真技术未来的发展方向、存在问题和挑战进行展望。本文为基于森林真实场景的森林火灾模拟仿真和数字孪生沉浸式互动模拟系统的构建提供了理论方法基础，该平台可以实现森林场景快速构建、不同火源林火模拟、火场蔓延模拟仿真以及不同气象影响条件的火场预测，可对森林火场救援指挥、火场灾害评估和火场复原提供可视化决策支持。

Abstract

Forest is a sort of highly complex and important ecosystem and it is still challenged by such severe forest fires. To strengthen the information-driven ability of forest fire emergency management decision-making， it is required to build a forest fire virtual simulation platform in terms of virtual reality and visualization technology. Therefore， our review analysis is focused on a virtual reality and visualization-based forest fire virtual simulation platform for exploring the forest scene visualization and forest fire simulation technology， and it seeks to trace forest scenes， multi-source forest fire simulation， and its fire scene further. In order to provide visual decision-making support for fire rescue command and fire disaster assessment， information-driven forest fire emergency management decision-making is improved further. First， we discuss and analyze the highly realistic 3D visualization reconstruction technology of real forest landscapes， which is beneficial for a forest model library and realize highly realistic 3D visualization reconstruction of forest landscape areas. To establish a forest fire spread model and reflect the forest fire combustion process and pyrolysis-physical characteristics accurately and its combustion process-oriented real-time visual simulation， it is essential to develop the application of forest fire simulation technology and explore the mechanism of forest fire spread. The related methods are categorized into forest scene visualization and forest fire simulation technology， and the research growth of it is summarized as well. Forest scene visualization methods can be divided into mechanism-based tree modeling methods， including L system-based and custom sketch-based or interactive modeling methods， and forest stand characteristics-based natural scene reconstruction methods. That is， real-world forest stands feature data like images and point clouds are used to reconstruct forest trees. The two types of methods mentioned above are mostly used to construct single tree models as the theoretical basis for large-scale forest scene reconstruction. The mechanism-based tree modeling method can be used to balance the tree structure intuitively and flexibly， and it is more suitable for plant growth design， immersive creation， and other related fields； the forest stand characteristics-based real scene reconstruction method has high fidelity， and it is suitable for vegetation quantification， small-scale ecosystem simulation， and its contexts. Forest fire simulation technology is divided into three categories： physical model， empirical model， and semi-empirical model. The physical model can be well used to illustrate the physical and chemical reactions in the burning process of the forest fire and show the changes in the flame. The empirical model is used for the relevant data obtained from the experiment for mathematical fitting， which can simulate the forest fire spreading status in some typical scenarios， and the accuracy of the direction and rate of forest fire spread is guaranteed. For the semi-empirical model， the physical and chemical reactions in the process of forest fire spread and the statistical analysis methods in specific experiments used are considered at the same time， which reduces the computing cost of physical simulation and enables real-time simulation further. In addition， forest fire simulation can be a key aspect of immersive forest fire simulation and interactive fire extinguishing simulation research， and real forest scene visualization is on the basis of other related research. Real-time generation of realistic forest scenes is beneficial for the simulation of the forest fire. The immersion of the corresponding senses can be improved by constructing real forest scenes to approach the real situation of forest fires， and complex terrain and meteorological conditions can be simulated and visualized as well. Experience and realistic scene roaming can be one of the application scenarios for forest scene visualization technology. We link researches of 1） the growth of forest scene visualization and the forest fire simulation technology， 2） an immersive three-dimensional visual simulation of forest fire scene， and various forest fire ignition methods related to forest fire spread， and 3） fire extinguishing simulation together. Furthermore， most researches are focused on the forest fire spread model coupled in relevance to atmospheric， ecological， and hydrological models since a real and credible forest fire cannot be completely simulated only by forest scene visualization and forest fire simulation technology. To aid fire departments to alleviate deployment decisions， both the forest scene visualization and forest fire simulation technologies can be applied in the field of forest firefighting， strategic planning， and resource allocation via simulation-based planning methods. Finally， we summarize the relevance of forest scene visualization and forest fire simulation technology. Three types of models of forest fire spreading， physical， empirical， and semi-empirical are introduced in detail， in which task-based forest scene visualization into tree modeling methods are related to forest stand characteristics-based reconstruction methods， and the theoretical basis， scope of application， pros and， cons of forest scene visualization and forest fire simulation technology are explained as well. At the same time， it is essential to resolve some application problems in terms of large-scale scene rendering technology and forest fire protection， and a virtual reality and visualization technology-driven basis are offered for future construction of forest fire virtual simulation. The development trend of forest scene visualization and forest fire simulation technology is predicted further.

关键词

Keywords

references

Anderson D H ， Catchpole E A ， De Mestre N J and Parkes T . 1982 . Modelling the spread of grass fires . The ANZIAM Journal ， 23 （ 4 ）： 451 - 466 ［ DOI： 10.1017/S0334270000000394 http://dx.doi.org/10.1017/S0334270000000394 ］

Andrews P L ， Cruz M G and Rothermel R C . 2013 . Examination of the wind speed limit function in the Rothermel surface fire spread model . International Journal of Wildland Fire ， 22 （ 7 ）： 959 - 969 ［ DOI： 10.1071/WF12122 http://dx.doi.org/10.1071/WF12122 ］

Ascoli D ， Vacchiano G ， Motta R and Bovio G . 2015 . Building Rothermel fire behaviour fuel models by genetic algorithm optimisation . International Journal of Wildland Fire ， 24 （ 3 ）： 317 - 328 ［ DOI： 10.1071/WF14097 http://dx.doi.org/10.1071/WF14097 ］

Bakhshaii A and Johnson E A . 2019 . A review of a new generation of wildfire-atmosphere modeling . Canadian Journal of Forest Research ， 49 （ 6 ）： 565 - 574 ［ DOI： 10.1139/cjfr-2018-0138 http://dx.doi.org/10.1139/cjfr-2018-0138 ］

Bao G B ， Li H J ， Zhang X P ， Che W J and Jaeger M . 2011 . Realistic real-time rendering for large-scale forest scenes // 2011 IEEE International Symposium on VR Innovation . Singapore， Singapore ： IEEE： 217 - 223 ［ DOI： 10.1109/ISVRI.2011.5759637 http://dx.doi.org/10.1109/ISVRI.2011.5759637 ］

Bart R R ， Kennedy M C ， Tague C L and McKenzie D . 2020 . Integrating fire effects on vegetation carbon cycling within an ecohydrologic model . Ecological Modelling ， 416 ： # 108880 ［ DOI： 10.1016/j.ecolmodel.2019.108880 http://dx.doi.org/10.1016/j.ecolmodel.2019.108880 ］

Barton J ， Gorte B ， Eusuf M S R S and Zlatanova S ， 2020 . A voxel-based method to estimate near-surface and elevated fuel from dense lidar point cloud for hazard reduction burning // Proceedings of 2020 ISPRS Annals of the Photogrammetry， Remote Sensing and Spatial Information Sciences . Sydney， Australia ： ISPRS： 3 - 10 ［ DOI： 10.5194/isprs-annals-VI-3-W1-2020-3-2020 http://dx.doi.org/10.5194/isprs-annals-VI-3-W1-2020-3-2020 ］

Bournez E ， Landes T ， Najjar G ， Kastendeuch P ， Ngao J and Saudreau M . 2019 . Sensitivity of simulated light interception and tree transpiration to the level of detail of 3D tree reconstructions . Urban Forestry and Urban Greening ， 38 ： 1 - 10 ［ DOI： 10.1016/j.ufug.2018.10.016 http://dx.doi.org/10.1016/j.ufug.2018.10.016 ］

Bournez E ， Landes T ， Saudreau M ， Kastendeuch P and Najjar G . 2017 . From TLS point clouds to 3D models of trees： a comparison of existing algorithms for 3D tree reconstruction // Proceedings of 2017 International Archives of the Photogrammetry， Remote Sensing and Spatial Information Sciences . Nafplio， Greece ： ISPRS： 113 - 120 ［ DOI： 10.5194/isprs-archives-XLII-2-W3-113-2017 http://dx.doi.org/10.5194/isprs-archives-XLII-2-W3-113-2017 ］

Bucksch A ， Lindenbergh R and Menenti M . 2010 . SkelTre： robust skeleton extraction from imperfect point clouds . The Visual Computer ： International Journal of Computer Graphics ， 26 （ 10 ）： 1283 - 1300 ［ DOI： 10.1007/s00371-010-0520-4 http://dx.doi.org/10.1007/s00371-010-0520-4 ］

Cai Z Y ， Shi H Y ， Zhao H J ， Li T Q ， Wang X Y and Zhou Y M . 2022 . Construction and simulation of amphibious aircraft fire-fighting flight simulation system . Acta Aeronautica et Astronautica Sinica ， 40 ： 1 - 16

蔡志勇，石含玥，赵红军，李天琦，王希宇，周尧明 . 2022 . 水陆两栖飞机灭火飞行仿真系统构建与仿真 . 航空学报， 40 ： 1 - 16 ［ DOI： 10.7527/S1000-6893.2022.27036 http://dx.doi.org/10.7527/S1000-6893.2022.27036 ］

Calders K ， Adams J ， Armston J ， Bartholomeus H ， Bauwens S ， Bentley L P ， Chave J ， Danson F M ， Demol M ， Disney M ， Gaulton R ， Krishna Moorthy S M ， Levick S R ， Saarinen N ， Schaaf C ， Stovall A ， Terryn L ， Wilkes P and Verbeeck H . 2020 . Terrestrial laser scanning in forest ecology： expanding the horizon . Remote Sensing of Environment ， 251 ： # 112102 ［ DOI： 10.1016/j.rse.2020.112102 http://dx.doi.org/10.1016/j.rse.2020.112102 ］

Calders K ， Newnham G ， Burt A ， Murphy S ， Raumonen P ， Herold M ， Culvenor D ， Avitabile V ， Disney M ， Armston J and Kaasalainen M . 2015 . Nondestructive estimates of above‐ground biomass using terrestrial laser scanning . Methods in Ecology and Evolution ， 6 （ 2 ）： 198 - 208 ［ DOI： 10.1111/2041-210X.12301 http://dx.doi.org/10.1111/2041-210X.12301 ］

Cao W ， Chen D ， Shi Y F ， Cao Z and Xia S B . 2021 . Progress and prospect of LiDAR point clouds to 3D tree models . Geomatics and Information Science of Wuhan University ， 46 （ 2 ）： 203 - 220

曹伟，陈动，史玉峰，曹震，夏少波 . 2021 . 激光雷达点云树木建模研究进展与展望 . 武汉大学学报（信息科学版）， 46 （ 2 ）： 203 - 220 ［ DOI： 10.13203/j.whugis20190275 http://dx.doi.org/10.13203/j.whugis20190275 ］

Chakraborty M ， Khot L R ， Sankaran S and Jacoby P W . 2019 . Evaluation of mobile 3D light detection and ranging based canopy mapping system for tree fruit crops . Computers and Electronics in Agriculture ， 158 ： 284 - 293 ［ DOI： 10.1016/j.compag.2019.02.012 http://dx.doi.org/10.1016/j.compag.2019.02.012 ］

Chen X J ， Neubert B ， Xu Y Q ， Deussen O and Kang S B . 2008 . Sketch-based tree modeling using Markov random field // Proceedings of 2008 ACM SIGGRAPH Asia 2008 Papers . Singapore， Singapore ： ACM： #109 ［ DOI： 10.1145/1457515.1409062 http://dx.doi.org/10.1145/1457515.1409062 ］

Chi S D ， Lim Y H ， Lee J K ， Lee J S ， Hwang S C and Song B H . 2003 . A simulation-based decision support system for forest fire fighting // Proceedings of the 8th Congress of the Italian Association for Artificial Intelligence . Pisa， Italy ： Springer： 487 - 498 ［ DOI： 10.1007/978-3-540-39853-0_40 http://dx.doi.org/10.1007/978-3-540-39853-0_40 ］

Clark T L ， Coen J and Latham D . 2004 . Description of a coupled atmosphere-fire model . International Journal of Wildland Fire ， 13 （ 1 ）： 49 - 63 ［ DOI： 10.1071/WF03043 http://dx.doi.org/10.1071/WF03043 ］

Cluzeau C ， Dupouey J L and Courbaud B . 1995 . Polyhedral representation of crown shape. A geometric tool for growth modelling . Annales des Sciences Forestières ， 52 （ 4 ）： 297 - 306 ［ DOI： 10.1051/forest：19950401 http://dx.doi.org/10.1051/forest：19950401 ］

Coen J L ， Cameron M ， Michalakes J ， Patton E G ， Riggan P J and Yedinak K M . 2013 . WRF-Fire： coupled weather-wildland fire modeling with the weather research and forecasting model . Journal of Applied Meteorology and Climatology ， 52 （ 1 ）： 16 - 38 ［ DOI： 10.1175/JAMC-D-12-023.1 http://dx.doi.org/10.1175/JAMC-D-12-023.1 ］

Colaço A F ， Trevisan R G ， Molin J P ， Rosell-Polo J R and Escol􀅣 A . 2017 . A method to obtain orange crop geometry information using a mobile terrestrial laser scanner and 3D modeling . Remote Sensing ， 9 （ 8 ）： # 763 ［ DOI： 10.3390/rs9080763 http://dx.doi.org/10.3390/rs9080763 ］

Cristal I ， Ameztegui A ， Gonz􀅡lez-Olabarria J R and Garcia-Gonzalo J . 2019 . A decision support tool for assessing the impact of climate change on multiple ecosystem services . Forests ， 10 （ 5 ）： # 440 ［ DOI： 10.3390/f10050440 http://dx.doi.org/10.3390/f10050440 ］

Delagrange S ， Jauvin C and Rochon P . 2014 . PypeTree： a tool for reconstructing tree perennial tissues from point clouds . Sensors ， 14 （ 3 ）： 4271 - 4289 ［ DOI： 10.3390/s140304271 http://dx.doi.org/10.3390/s140304271 ］

Dey T K and Sun J . 2006 . Defining and computing curve-skeletons with medial geodesic function //Sheffer A and Polthier K， eds. Eurographics Symposium on Geometry Processing. ［s.l.］： The Eurographics Association： 143 - 152 ［ DOI： 10.2312/SGP/SGP06/143-152 http://dx.doi.org/10.2312/SGP/SGP06/143-152 ］

Dimitropoulos K ， Köse K ， Grammalidis N and Cetin E . 2010 . Fire detection and 3D fire propagation estimation for the protection of cultural heritage areas // 2010 ISPRS Technical Commission VIII Symposium Networking the World with Remote Sensing . Kyoto， Japan ：［s.n.］： 620 - 625

Dowdy A J ， Fromm M D and McCarthy N . 2017 . Pyrocumulonimbus lightning and fire ignition on Black Saturday in southeast Australia . Journal of Geophysical Research： Atmospheres ， 122 （ 14 ）： 7342 - 7354 ［ DOI： 10.1002/2017JD026577 http://dx.doi.org/10.1002/2017JD026577 ］

Du S L ， Lindenbergh R ， Ledoux H ， Stoter J and Nan L L . 2019 . AdTree： accurate， detailed， and automatic modelling of laser-scanned trees . Remote Sensing ， 11 （ 18 ）： # 2074 ［ DOI： 10.3390/rs11182074 http://dx.doi.org/10.3390/rs11182074 ］

Ervilha A R ， Pereira J M C and Pereira J C F . 2017 . On the parametric uncertainty quantification of the Rothermel’s rate of spread model . Applied Mathematical Modelling ， 41 ： 37 - 53 ［ DOI： 10.1016/j.apm.2016.06.026 http://dx.doi.org/10.1016/j.apm.2016.06.026 ］

Fan G P ， Nan L L ， Chen F X ， Dong Y Q ， Wang Z M ， Li H and Chen D Y . 2020 . A new quantitative approach to tree attributes estimation based on LiDAR point clouds . Remote Sensing ， 12 （ 11 ）： # 1779 ［ DOI： 10.3390/rs12111779 http://dx.doi.org/10.3390/rs12111779 ］

Filippi J B ， Bosseur F ， Mari C and Lac C . 2018 . Simulation of a large wildfire in a coupled fire-atmosphere model . Atmosphere ， 9 （ 6 ）： # 218 ［ DOI： 10.3390/atmos9060218 http://dx.doi.org/10.3390/atmos9060218 ］

Fons W L . 1946 . Analysis of fire spread in light forest fuels . Journal of Agricultural Research ， 72 （ 3 ）： 92 - 121

Forestry Canada Fire Danger Group . 1992 . Development and Structure of the Canadian Forest Fire Behavior Prediction System. Information Report ST-X-3 . Forestry Canada， Science and Sustainable Development Directorate

Gao Y ， Skutsch M ， Paneque-G􀅡lvez J and Ghilardi A . 2020 . Remote sensing of forest degradation： a review . Environmental Research Letters ， 15 （ 10 ）： # 103001 ［ DOI： 10.1088/1748-9326/abaad7 http://dx.doi.org/10.1088/1748-9326/abaad7 ］

Grasso P and Innocente M S . 2020 . Physics-based model of wildfire propagation towards faster-than-real-time simulations . Computers and Mathematics with Applications ， 80 （ 5 ）： 790 - 808 ［ DOI： 10.1016/j.camwa.2020.05.009 http://dx.doi.org/10.1016/j.camwa.2020.05.009 ］

Grishin A M . 1996 . General mathematical model for forest fires and its applications . Combustion， Explosion and Shock Waves ， 32 （ 5 ）： 503 - 519 ［ DOI： 10.1007/BF01998573 http://dx.doi.org/10.1007/BF01998573 ］

Grishin A M . 1997 . Mathematical Modeling Forest Fire and New Methods Fighting Them . Tomsk， Russia ： Publishing House of Tomsk University

Guo Q H ， Su Y J ， Hu T Y ， Guan H C ， Jin S C ， Zhang J ， Zhao X X ， Xu K X ， Wei D J ， Kelly M and Coops N C . 2021 . Lidar boosts 3D ecological observations and modelings： a review and perspective . IEEE Geoscience and Remote Sensing Magazine ， 9 （ 1 ）： 232 - 257 ［ DOI： 10.1109/MGRS.2020.3032713 http://dx.doi.org/10.1109/MGRS.2020.3032713 ］

Hackenberg J ， Spiecker H ， Calders K ， Disney M and Raumonen P . 2015 . SimpleTree — An efficient open source tool to build tree models from TLS clouds . Forests ， 6 （ 11 ）： 4245 - 4294 ［ DOI： 10.3390/f6114245 http://dx.doi.org/10.3390/f6114245 ］

Hädrich T ， Banuti D T ， Pałubicki W ， Pirk S and Michels D L . 2021 . Fire in paradise： mesoscale simulation of wildfires . ACM Transactions on Graphics ， 40 （ 4 ）： # 163 ［ DOI： 10.1145/3450626.3459954 http://dx.doi.org/10.1145/3450626.3459954 ］

Huang H Y ， Tang L Y ， Li J W and Chen C C . 2012 . Simulation and visualization of forest fire growth in an integrated 3D virtual geographical environment——a preliminary study // Proceedings of the 20th International Conference on Geoinformatics . Hong Kong， China ： IEEE： 1 - 6 ［ DOI： 10.1109/Geoinformatics.2012.6270344 http://dx.doi.org/10.1109/Geoinformatics.2012.6270344 ］

Huang J W ， Lucash M S ， Scheller R M and Klippel A . 2021 . Walking through the forests of the future： using data-driven virtual reality to visualize forests under climate change . International Journal of Geographical Information Science ， 35 （ 6 ）： 1155 - 1178 ［ DOI： 10.1080/13658816.2020.1830997 http://dx.doi.org/10.1080/13658816.2020.1830997 ］

Isokane T ， Okura F ， Ide A ， Matsushita Y and Yagi Y . 2018 . Probabilistic plant modeling via multi-view image-to-image translation // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 2906 - 2915 ［ DOI： 10.1109/CVPR.2018.00307 http://dx.doi.org/10.1109/CVPR.2018.00307 ］

Jain P ， Coogan S C P ， Subramanian S G ， Crowley M ， Taylor S and Flannigan M D . 2020 . A review of machine learning applications in wildfire science and management . Environmental Reviews ， 28 （ 4 ）： 478 - 505 ［ DOI： 10.1139/er-2020-0019 http://dx.doi.org/10.1139/er-2020-0019 ］

Janoutov􀅡 R ， Homolov􀅡 L ， Malenovský Z ， Hanuš J ， Lauret N and Gastellu-Etchegorry J P . 2019 . Influence of 3D spruce tree representation on accuracy of airborne and satellite forest reflectance simulated in DART . Forests ， 10 （ 3 ）： # 292 ［ DOI： 10.3390/f10030292 http://dx.doi.org/10.3390/f10030292 ］

Kang Q J . 2022 . Research progress on forest fire spread . Forest Investigation Design ， 51 （ 3 ）： 27 - 32

康庆江 . 2022 . 林火蔓延研究进展 . 林业勘查设计， 51 （ 3 ）： 27 - 32 ［ DOI： 10.3969/j.issn.1004-2180.2022.03.009 http://dx.doi.org/10.3969/j.issn.1004-2180.2022.03.009 ］

Larini M ， Giroud F ， Porterie B and Loraud J C . 1998 . A multiphase formulation for fire propagation in heterogeneous combustible media . International Journal of Heat and Mass Transfer ， 41 （ 6/7 ）： 881 - 897 ［ DOI： 10.1016/S0017-9310（97）00173-7 http://dx.doi.org/10.1016/S0017-9310（97）00173-7 ］

Li B S ， Kałużny J ， Klein J ， Michels D L ， Pałubicki W ， Benes B and Pirk S . 2021 . Learning to reconstruct botanical trees from single images . ACM Transactions on Graphics ， 40 （ 6 ）： # 231 ［ DOI： 10.1145/3478513.3480525 http://dx.doi.org/10.1145/3478513.3480525 ］

Lindenmayer A . 1968 . Mathematical models for cellular interactions in development I. Filaments with one-sided inputs . Journal of Theoretical Biology ， 18 （ 3 ）： 280 - 299 ［ DOI： 10.1016/0022-5193（68）90079-9 http://dx.doi.org/10.1016/0022-5193（68）90079-9 ］

Liu Y N ， Hussaini M Y and Ökten G . 2015 . Global sensitivity analysis for the Rothermel model based on high-dimensional model representation . Canadian Journal of Forest Research ， 45 （ 11 ）： 1474 - 1479 ［ DOI： 10.1139/cjfr-2015-0148 http://dx.doi.org/10.1139/cjfr-2015-0148 ］

Liu Z H ， Shen C ， Li Z ， Weng T Y ， Deussen O ， Cheng Z L and Wang D X . 2019 . Interactive modeling of trees using VR devices // Proceedings of 2019 International Conference on Virtual Reality and Visualization （ICVRV） . Hong Kong， China ： IEEE： 69 - 75 ［ DOI： 10.1109/ICVRV47840.2019.00020 http://dx.doi.org/10.1109/ICVRV47840.2019.00020 ］

Lu Y . 2011 . Research and Implementation of Key Technologies for Large-Scale Forest Scene Modeling and Renderin . Chengdu ： University of Electronic Science and Technology of China （卢宇 . 2011. 大规模森林场景建模与渲染关键技术的研究与实现 . 成都：电子科技大学）

Magney T S ， Eitel J U ， Griffin K L ， Boelman N T ， Greaves H E ， Prager C M ， Logan B A ， Zheng G ， Ma L X ， Fortin E A ， Oliver R Y and Vierling L A . 2016 . LiDAR canopy radiation model reveals patterns of photosynthetic partitioning in an Arctic shrub . Agricultural and Forest Meteorology ， 221 ： 78 - 93 ［ DOI： 10.1016/j.agrformet.2016.02.007 http://dx.doi.org/10.1016/j.agrformet.2016.02.007 ］

Makowski M ， Hädrich T ， Scheffczyk J ， Michels D L ， Pirk S and Pałubick W . 2019 . Synthetic silviculture： multi-scale modeling of plant ecosystems . ACM Transactions on Graphics ， 38 （ 4 ）： # 131 ［ DOI： 10.1145/3306346.3323039 http://dx.doi.org/10.1145/3306346.3323039 ］

Mao X M and Xu W X . 1991 . Study on calculation method of forest fire spreading speed . Journal of Meteorology and Environment ， 7 （ 1 ）： 9 - 13

毛贤敏，徐文兴 . 1991 . 林火蔓延速度计算方法的研究 . 辽宁气象， 7 （ 1 ）： 9 - 13

Masinda M M ， Sun L ， Wang G Y and Hu T X . 2021 . Moisture content thresholds for ignition and rate of fire spread for various dead fuels in northeast forest ecosystems of China . Journal of Forestry Research ， 32 （ 3 ）： 1147 - 1155 ［ DOI： 10.1007/s11676-020-01162-2 http://dx.doi.org/10.1007/s11676-020-01162-2 ］

Moreno A ， Posada J ， Segura Á ， Arbelaiz A and García-Alonso A . 2014 . Interactive fire spread simulations with extinguishment support for virtual reality training tools . Fire Safety Journal ， 64 （ 2 ）： 48 - 60 ［ DOI： 10.1016/j.firesaf.2014.01.005 http://dx.doi.org/10.1016/j.firesaf.2014.01.005 ］

Moreno A ， Segura A ， Zlatanova S ， Posada J and García-Alonso A . 2012 . Introducing GIS-based simulation tools to support rapid response in wildland fire fighting . WIT Transactions on Ecology and the Environment ， 158 ： 163 - 174 ［ DOI： 10.2495/FIVA120141 http://dx.doi.org/10.2495/FIVA120141 ］

Muñoz-Esparza D ， Kosović B ， Jiménez P A and Coen J L . 2018 . An accurate fire‐spread algorithm in the weather research and forecasting model using the level‐set method . Journal of Advances in Modeling Earth Systems ， 10 （ 4 ）： 908 - 926 ［ DOI： 10.1002/2017MS001108 http://dx.doi.org/10.1002/2017MS001108 ］

Okabe M ， Owada S and Igarashi T . 2006 . Interactive design of botanical trees using freehand sketches and example-based editing // Proceedings of the ACM SIGGRAPH 2006 Courses . Boston， USA ： ACM： #1185779 ［ DOI： 10.1145/1185657.1185779 http://dx.doi.org/10.1145/1185657.1185779 ］

Okura F . 2022 . 3D modeling and reconstruction of plants and trees： a cross-cutting review across computer graphics， vision， and plant phenotyping . Breeding Science ， 72 （ 1 ）： 31 - 47 ［ DOI： 10.1270/jsbbs.21074 http://dx.doi.org/10.1270/jsbbs.21074 ］

Pais C ， Carrasco J ， Martell D L ， Weintraub A and Woodruff D L . 2021 . Cell2Fire： a cell-based forest fire growth model to support strategic landscape management planning . Frontiers in Forests and Global Change ， 4 ： # 692706 ［ DOI： 10.3389/ffgc.2021.692706 http://dx.doi.org/10.3389/ffgc.2021.692706 ］

Pałubicki W ， Makowski M ， Gajda W ， Hädrich T ， Michels D L and Pirk S . 2022 . Ecoclimates： climate-response modeling of vegetation . ACM Transactions on Graphics ， 41 （ 4 ）： # 155 ［ DOI： 10.1145/3528223.3530146 http://dx.doi.org/10.1145/3528223.3530146 ］

Pastor E ， Z􀅡rate L ， Planas E and Arnaldos J . 2003 . Mathematical models and calculation systems for the study of wildland fire behaviour . Progress in Energy and Combustion Science ， 29 （ 2 ）： 139 - 153 ［ DOI： 10.1016/S0360-1285（3 http://dx.doi.org/10.1016/S0360-1285（3 ）# 00017 - 0 ］

Pirk S ， Jarząbek M ， Hädrich T ， Michels D L and Palubicki W . 2017 . Interactive wood combustion for botanical tree models . ACM Transactions on Graphics ， 36 （ 6 ）： # 197 ［ DOI： 10.1145/3130800.3130814 http://dx.doi.org/10.1145/3130800.3130814 ］

Quan L ， Tan P ， Zeng G ， Yuan L ， Wang J D and Kang S B . 2006 . Image-based plant modeling // Proceedings of the ACM SIGGRAPH 2006 Papers . Boston， USA ： ACM： 599 - 604 ［ DOI： 10.1145/1179352.1141929 http://dx.doi.org/10.1145/1179352.1141929 ］

Raumonen P ， Kaasalainen M ， Åkerblom M ， Kaasalainen S ， Kaartinen H ， Vastaranta M ， Holopainen M ， Disney M and Lewis P . 2013 . Fast automatic precision tree models from terrestrial laser scanner data . Remote Sensing ， 5 （ 2 ）： 491 - 520 ［ DOI： 10.3390/rs5020491 http://dx.doi.org/10.3390/rs5020491 ］

Razavi-Termeh S V ， Sadeghi-Niaraki A and Choi S-M . 2020 . Ubiquitous GIS-based forest fire susceptibility mapping using artificial intelligence methods . Remote Sensing ， 12 （ 10 ）： # 1689 ［ DOI： 10.3390/rs12101689 http://dx.doi.org/10.3390/rs12101689 ］

Ren F E ， Liu T and Yang L . 2021 . 3D reconstruction of a single plant leaf image . Journal of Image and Graphics ， 26 （ 11 ）： 2713 - 2722

任非儿，刘通，杨龙 . 2021 . 单幅植物叶片图像的3维重建 . 中国图象图形学报， 26 （ 11 ）： 2713 - 2722 ［ DOI： 10.11834/jig.200475 http://dx.doi.org/10.11834/jig.200475 ］

Séro-Guillaume O and Margerit J . 2002 . Modelling forest fires. Part I： a complete set of equations derived by extended irreversible thermodynamics . International Journal of Heat and Mass Transfer ， 45 （ 8 ）： 1705 - 1722 ［ DOI： 10.1016/S0017-9310（01）00248-4 http://dx.doi.org/10.1016/S0017-9310（01）00248-4 ］

Shlyakhter I ， Rozenoer M ， Dorsey J and Teller S . 2001 . Reconstructing 3D tree models from instrumented photographs . IEEE Computer Graphics and Applications ， 21 （ 3 ）： 53 - 61 ［ DOI： 10.1109/38.920627 http://dx.doi.org/10.1109/38.920627 ］

Song H S and Lee S H . 2017 . Effects of wind and tree density on forest fire patterns in a mixed-tree species forest . Forest Science and Technology ， 13 （ 1 ）： 9 - 16 ［ DOI： 10.1080/21580103.2016.1262793 http://dx.doi.org/10.1080/21580103.2016.1262793 ］

Sullivan A L . 2009a . Wildland surface fire spread modelling， 1990-2007.1： physical and quasi-physical models . International Journal of Wildland Fire ， 18 （ 4 ）： 349 - 368 ［ DOI： 10.1071/WF06143 http://dx.doi.org/10.1071/WF06143 ］

Sullivan A L . 2009b . Wildland surface fire spread modelling， 1990-2007.2： empirical and quasi-empirical models . International Journal of Wildland Fire ， 18 （ 4 ）： 369 - 386 ［ DOI： 10.1071/WF06142 http://dx.doi.org/10.1071/WF06142 ］

Tan P ， Fang T ， Xiao J X ， Zhao P and Quan L . 2008 . Single image tree modeling . ACM Transactions on Graphics ， 27 （ 5 ）： # 108 ［ DOI： 10.1145/1409060.1409061 http://dx.doi.org/10.1145/1409060.1409061 ］

Tan P ， Zeng G ， Wang J D ， Kang S B and Quan L . 2007 . Image-based tree modeling // Proceedings of the ACM SIGGRAPH 2007 Papers . San Diego， USA ： ACM： #1276486 ［ DOI： 10.1145/1275808.1276486 http://dx.doi.org/10.1145/1275808.1276486 ］

Tang X Y ， Meng X Y and Yi H R . 2002 . Review and prospect of researohes on forest fire spreading models and simulating method . Journal of Beijing Forestry University ， 24 （ 1 ）： 87 - 91

唐晓燕，孟宪宇，易浩若 . 2002 . 林火蔓延模型及蔓延模拟的研究进展 . 北京林业大学学报， 24 （ 1 ）： 87 - 91 ［ DOI： 10.3321/j.issn：1000-1522.2002.01.019 http://dx.doi.org/10.3321/j.issn：1000-1522.2002.01.019 ］

Thompson D . 1992 . On Growth and Form （Canto）//J. Bonner， eds . Cambridge ： Cambridge University Press ［ DOI： 10.1017/CBO9781107325852 http://dx.doi.org/10.1017/CBO9781107325852 ］

Tymstra C ， Bryce R W ， Wotton B M ， Taylor S W and Armitage O B . 2010 . Development and Structure of Prometheus： the Canadian Wildland Fire Growth Simulation Model. Information Report NOR-X-417 . Natural Resources Canada， Canadian Forest Service， Northern Forestry Centre

Verroust A and Lazarus F . 1999 . Extracting skeletal curves from 3D scattered data // Proceedings of International Conference on Shape Modeling and Applications . Aizu-Wakamatsu， Japan ： IEEE： 194 - 201 ［ DOI： 10.1109/SMA.1999.749340 http://dx.doi.org/10.1109/SMA.1999.749340 ］

Weise D R and Biging G S . 1997 . A qualitative comparison of fire spread models incorporating wind and slope effects . Forest Science ， 43 （ 2 ）： 170 - 180 ［ DOI： 10.1093/forestscience/43.2.170 http://dx.doi.org/10.1093/forestscience/43.2.170 ］

Williams B J ， Song B ， Chou C Y ， Williams T M and Hom J . 2011 . Software applications to three-dimensional visualization of forest landscapes —— A case study demonstrating the use of visual nature studio （VNS） in visualizing fire spread in forest landscapes //Li C， Lafortezza R and Chen J Q， eds. Landscape Ecology in Forest Management and Conservation . Berlin， Heidelberg， Germany ： Springer： 148 - 175 ［ DOI： 10.1007/978-3-642-12754-0_7 http://dx.doi.org/10.1007/978-3-642-12754-0_7 ］

Xu H ， Wang C C ， Shen X S and Zlatanova S . 2021 . 3D tree reconstruction in support of urban microclimate simulation： a comprehensive literature review . Buildings ， 11 （ 9 ）： # 417 ［ DOI： 10.3390/buildings11090417 http://dx.doi.org/10.3390/buildings11090417 ］

You J W ， Huai Y J ， Nie X Y and Chen Y Y . 2022 . Real-time 3D visualization of forest fire spread based on tree morphology and finite state machine . Computers and Graphics ， 103 ： 109 - 120 ［ DOI： 10.1016/j.cag.2022.01.009 http://dx.doi.org/10.1016/j.cag.2022.01.009 ］

Yuan Q and Huai Y J . 2021 . Immersive sketch-based tree modeling in virtual reality . Computers and Graphics ， 94 ： 132 - 143 ［ DOI： 10.1016/j.cag.2020.12.001 http://dx.doi.org/10.1016/j.cag.2020.12.001 ］

Zeide B and Pfeifer P . 1991 . A method for estimation of fractal dimension of tree crowns . Forest Science ， 37 （ 5 ）： 1253 - 1265 ［ DOI： 10.1093/forestscience/37.5.1253 http://dx.doi.org/10.1093/forestscience/37.5.1253 ］

Zhang H G ， Liang Z H ， Liu H J ， Wang R and Liu Y A . 2020 . Ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue —— A case study of dynamic optimization problems . Engineering Applications of Artificial Intelligence ， 90 ： # 103517 ［ DOI： 10.1016/j.engappai.2020.103517 http://dx.doi.org/10.1016/j.engappai.2020.103517 ］

Zhang X T ， Liu P S and Wang X F . 2020 . Research on improvement of Wang Zhengfei’s forest fire spread model . Shandong Forestry Science and Technology ， 50 （ 1 ）： 1 - 6 ， 40

张晓婷，刘培顺，王学芳 . 2020 . 王正非林火蔓延模型改进研究 . 山东林业科技， 50 （ 1 ）： 1- 6 ， 40 ［ DOI： 10.3969/j.issn.1002-2724.2020.01.002 http://dx.doi.org/10.3969/j.issn.1002-2724.2020.01.002 ］

Zheng Z ， Huang W ， Li S N and Zeng Y N . 2017 . Forest fire spread simulating model using cellular automaton with extreme learning machine . Ecological Modelling ， 348 ： 33 - 43 ［ DOI： 10.1016/j.ecolmodel.2016.12.022 http://dx.doi.org/10.1016/j.ecolmodel.2016.12.022 ］

Alert me when the article has been cited

提交

暂无数据