基本信息来源于合作网站,原文需代理用户跳转至来源网站获取       
摘要:
Background: The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas.However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58°and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2,and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age.Results: The best modelling strategy was to fit independent linear regression models to each observed site index(SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6%to 26%) for Sl-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between - 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years.Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively.Conclusions: Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.
推荐文章
Forest carbon storage in Guizhou Province based on field measurement dataset
Forest carbon storage
Field measurement dataset
Karst landform
Low carbon storage of woody debris in a karst forest in southwestern China
Secondary forest
Fine woody debris
Coarse woody debris
Dead wood
Karst
Subtropical China
基于Sentinel-2影像的四川木里森林火灾监测
遥感
'哨兵二号'卫星
大气修正植被指数
燃烧指数
森林火灾
Low net primary productivity of dominant tree species in a karst forest, southwestern China: first e
Biomass increment
Tree ring
Girth measurement
Karst evergreen and deciduous broadleaved forest
Allometric functions
内容分析
关键词云
关键词热度
相关文献总数  
(/次)
(/年)
文献信息
篇名 Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data
来源期刊 森林生态系统(英文版) 学科
关键词
年,卷(期) 2020,(4) 所属期刊栏目
研究方向 页码范围 793-806
页数 14页 分类号
字数 语种 英文
DOI
五维指标
传播情况
(/次)
(/年)
引文网络
引文网络
二级参考文献  (0)
共引文献  (0)
参考文献  (27)
节点文献
引证文献  (0)
同被引文献  (0)
二级引证文献  (0)
1999(1)
  • 参考文献(1)
  • 二级参考文献(0)
2002(2)
  • 参考文献(2)
  • 二级参考文献(0)
2003(2)
  • 参考文献(2)
  • 二级参考文献(0)
2006(2)
  • 参考文献(2)
  • 二级参考文献(0)
2008(1)
  • 参考文献(1)
  • 二级参考文献(0)
2009(1)
  • 参考文献(1)
  • 二级参考文献(0)
2010(1)
  • 参考文献(1)
  • 二级参考文献(0)
2011(1)
  • 参考文献(1)
  • 二级参考文献(0)
2012(4)
  • 参考文献(4)
  • 二级参考文献(0)
2013(1)
  • 参考文献(1)
  • 二级参考文献(0)
2014(3)
  • 参考文献(3)
  • 二级参考文献(0)
2015(1)
  • 参考文献(1)
  • 二级参考文献(0)
2016(2)
  • 参考文献(2)
  • 二级参考文献(0)
2017(3)
  • 参考文献(3)
  • 二级参考文献(0)
2019(2)
  • 参考文献(2)
  • 二级参考文献(0)
2020(0)
  • 参考文献(0)
  • 二级参考文献(0)
  • 引证文献(0)
  • 二级引证文献(0)
引文网络交叉学科
相关学者/机构
期刊影响力
森林生态系统(英文版)
季刊
2095-6355
10-1166/S
大16开
北京市海淀区清华东路35号
1994
eng
出版文献量(篇)
805
总下载数(次)
0
论文1v1指导