{"title":"通过模拟事前和事后光谱指数,绘制智利中部被烧毁森林的损害图","authors":"M. A. Peña, G. Martínez","doi":"10.4067/s0717-92002021000200205","DOIUrl":null,"url":null,"abstract":"This study estimated the severity of Nilahue-Barahona and Las Máquinas wildfires, occurred in central Chile during the summer of 2016-17, by an empirical-statistical modelling based on preand post-fire arithmetic differences of spectral indices sensitive to vigor, turgor and calcination states of vegetation. By doing this, map of damages were created to aid the efficient management and ecological restoration of disturbed forestry ecosystems. The index differences were calculated from Sentinel-2 satellite images, acquired anually in the summers spanning from 2016 to 2019. The resulting nine index-derived differences were used as predictors of burn severity, field-measured during the summer of 2019 using the CBI (composite burn index) method, into a linear stepwise regression that allowed for selecting those with the highest predictability. CBI yielded low correlations as its calculation includes low vegetation strata largely recovered at the time of the field data collection. However, when overstory field data were used alone, correlations increased (70 % of the data ≥ 0.80, P < 0.05). This was because this stratum was still appreciably damaged during the field campaign, along with its best representation from the image planimetry. The burn severity of both wildfires was mapped using the overstory data as regressand in a model based on NDWIex-ante-2019, NDWIex-ante-2018, NBRex-ante-2018 and NBRex-ante-2017 differences (R 2 ad = 0.77, RMSE = 0.35).","PeriodicalId":55338,"journal":{"name":"BOSQUE","volume":"1 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mapeo del daño en bosques incendiados de Chile central, mediante el modelado de índices espectrales ex-ante y ex-post\",\"authors\":\"M. A. Peña, G. Martínez\",\"doi\":\"10.4067/s0717-92002021000200205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study estimated the severity of Nilahue-Barahona and Las Máquinas wildfires, occurred in central Chile during the summer of 2016-17, by an empirical-statistical modelling based on preand post-fire arithmetic differences of spectral indices sensitive to vigor, turgor and calcination states of vegetation. By doing this, map of damages were created to aid the efficient management and ecological restoration of disturbed forestry ecosystems. The index differences were calculated from Sentinel-2 satellite images, acquired anually in the summers spanning from 2016 to 2019. The resulting nine index-derived differences were used as predictors of burn severity, field-measured during the summer of 2019 using the CBI (composite burn index) method, into a linear stepwise regression that allowed for selecting those with the highest predictability. CBI yielded low correlations as its calculation includes low vegetation strata largely recovered at the time of the field data collection. However, when overstory field data were used alone, correlations increased (70 % of the data ≥ 0.80, P < 0.05). This was because this stratum was still appreciably damaged during the field campaign, along with its best representation from the image planimetry. The burn severity of both wildfires was mapped using the overstory data as regressand in a model based on NDWIex-ante-2019, NDWIex-ante-2018, NBRex-ante-2018 and NBRex-ante-2017 differences (R 2 ad = 0.77, RMSE = 0.35).\",\"PeriodicalId\":55338,\"journal\":{\"name\":\"BOSQUE\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BOSQUE\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.4067/s0717-92002021000200205\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BOSQUE","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.4067/s0717-92002021000200205","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本研究基于植被活力、膨胀和煅烧状态敏感的光谱指数在火灾前后的算法差异,通过经验统计模型估计了2016-17年夏季智利中部Nilahue-Barahona和Las Máquinas野火的严重程度。通过这样做,创建了损害地图,以帮助有效管理和生态恢复受干扰的森林生态系统。这些指数差异是根据2016年至2019年每年夏季采集的Sentinel-2卫星图像计算的。由此产生的9个指数衍生的差异被用作烧伤严重程度的预测因子,在2019年夏季使用CBI(复合烧伤指数)方法进行现场测量,并进入线性逐步回归,从而选择具有最高可预测性的差异。CBI的相关性较低,因为它的计算包括在现场数据收集时大部分恢复的低植被层。然而,当单独使用林场数据时,相关性增加(70%的数据≥0.80,P < 0.05)。这是因为该地层在现场作业中仍然受到明显的破坏,而且它的最佳表现来自图像平面测量。在基于NDWIex-ante-2019、NDWIex-ante-2018、NBRex-ante-2018和NBRex-ante-2017差异(r2 ad = 0.77, RMSE = 0.35)的模型中,将两场野火的烧伤严重程度作为回归数据进行映射。
Mapeo del daño en bosques incendiados de Chile central, mediante el modelado de índices espectrales ex-ante y ex-post
This study estimated the severity of Nilahue-Barahona and Las Máquinas wildfires, occurred in central Chile during the summer of 2016-17, by an empirical-statistical modelling based on preand post-fire arithmetic differences of spectral indices sensitive to vigor, turgor and calcination states of vegetation. By doing this, map of damages were created to aid the efficient management and ecological restoration of disturbed forestry ecosystems. The index differences were calculated from Sentinel-2 satellite images, acquired anually in the summers spanning from 2016 to 2019. The resulting nine index-derived differences were used as predictors of burn severity, field-measured during the summer of 2019 using the CBI (composite burn index) method, into a linear stepwise regression that allowed for selecting those with the highest predictability. CBI yielded low correlations as its calculation includes low vegetation strata largely recovered at the time of the field data collection. However, when overstory field data were used alone, correlations increased (70 % of the data ≥ 0.80, P < 0.05). This was because this stratum was still appreciably damaged during the field campaign, along with its best representation from the image planimetry. The burn severity of both wildfires was mapped using the overstory data as regressand in a model based on NDWIex-ante-2019, NDWIex-ante-2018, NBRex-ante-2018 and NBRex-ante-2017 differences (R 2 ad = 0.77, RMSE = 0.35).
BOSQUEAgricultural and Biological Sciences-Forestry
CiteScore
0.70
自引率
0.00%
发文量
0
期刊介绍:
BOSQUE publishes original works in the field of management and production of forestry resources, wood science and technology, silviculture, forestry ecology, natural resources conservation, and rural development associated with forest ecosystems. Contributions may be articles, rewiews, notes or opinions, Either in Spanish or English.