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Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA

文献类型：期刊论文

作者Kelly, Maggi1; Su, Yanjun2,3; Di Tommaso, Stefania; Fry, Danny L.; Collins, Brandon M.; Stephens, Scott L.; Guo, Qinghua2,3
刊名REMOTE SENSING
出版日期2018
卷号10期号:1
ISSN号2072-4292
关键词wildfire burn probability crown fire forest fuels Sierra Nevada Lidar error
DOI10.3390/rs10010010
文献子类Article
英文摘要Light detection and ranging (Lidar) data can be used to create wall-to-wall forest structure and fuel products that are required for wildfire behavior simulation models. We know that Lidar-derived forest parameters have a non-negligible error associated with them, yet we do not know how this error influences the results of fire behavior modeling that use these layers as inputs. Here, we evaluated the influence of error associated with two Lidar data products-canopy height (CH) and canopy base height (CBH)-on simulated fire behavior in a case study in the Sierra Nevada, California, USA. We used a Monte Carlo simulation approach with expected randomized error added to each model input. Model 1 used the original, unmodified data, Model 2 incorporated error in the CH layer, and Model 3 incorporated error in the CBH layer. This sensitivity analysis showed that error in CH and CBH did not greatly influence the modeled conditional burn probability, fire size, or fire size distribution. We found that the expected error associated with CH and CBH did not greatly influence modeled results: conditional burn probability, fire size, and fire size distributions were very similar between Model 1 (original data), Model 2 (error added to CH), and Model 3 (error added to CBH). However, the impact of introduced error was more pronounced with CBH than with CH, and at lower canopy heights, the addition of error increased modeled canopy burn probability. Our work suggests that the use of Lidar data, even with its inherent error, can contribute to reliable and robust estimates of modeled forest fire behavior, and forest managers should be confident in using Lidar data products in their fire behavior modeling workflow.
学科主题Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
出版地BASEL
WOS关键词MIXED-CONIFER FORESTS ; SURFACE FUEL MODELS ; WAVE-FORM LIDAR ; FIRE BEHAVIOR ; AIRBORNE LIDAR ; SIMULATING FIRE ; LADDER FUELS ; MANAGEMENT ; PARAMETERS ; PREDICTION
语种英语
出版者MDPI
WOS记录号WOS:000424092300009
资助机构USDA Forest Service Region 5 ; USDA Forest Service Pacific Southwest Research StationUnited States Department of Agriculture (USDA)United States Forest Service ; US Fish and Wildlife ServiceUS Fish & Wildlife Service ; California Department of Water Resources ; California Department of Fish and Wildlife ; California Department of Forestry and Fire Protection ; Sierra Nevada Conservancy
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/20381]  
专题植被与环境变化国家重点实验室
作者单位1.[Kelly, Maggi; Di Tommaso, Stefania; Fry, Danny L.; Stephens, Scott L.] Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA
2.Univ Calif Berkeley, Div Agr & Nat Resources UCANR, Berkeley, CA 94720 USA
3.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
4.Univ Calif Merced, Sch Engn, Sierra Nevada Res Inst, Merced, CA 95343 USA
5.Collins, Brandon M.] Univ Calif Berkeley, Ctr Fire Res & Outreach, Berkeley, CA 94720 USA
推荐引用方式
GB/T 7714		 Kelly, Maggi,Su, Yanjun,Di Tommaso, Stefania,et al. Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA[J]. REMOTE SENSING,2018,10(1).
APA Kelly, Maggi.,Su, Yanjun.,Di Tommaso, Stefania.,Fry, Danny L..,Collins, Brandon M..,...&Guo, Qinghua.(2018).Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA.REMOTE SENSING,10(1).
MLA Kelly, Maggi,et al."Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA".REMOTE SENSING 10.1(2018).

入库方式： OAI收割

来源：植物研究所

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