globalchange  > 气候减缓与适应
DOI: 10.3390/rs11040463
WOS记录号: WOS:000460766100093
论文题名:
Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science
作者: Boisvenue, Celine; White, Joanne C.
通讯作者: Boisvenue, Celine
刊名: REMOTE SENSING
ISSN: 2072-4292
出版年: 2019
卷: 11, 期:4
语种: 英语
英文关键词: forests ; forest modeling ; carbon
WOS关键词: LANDSAT TIME-SERIES ; TANDEM-X INSAR ; OF-THE-ART ; ABOVEGROUND BIOMASS ; CLIMATE-CHANGE ; INCREASING CO2 ; USE EFFICIENCY ; BOREAL FOREST ; SOIL CARBON ; LIDAR
WOS学科分类: Remote Sensing
WOS研究方向: Remote Sensing
英文摘要:

Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes.


Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/129616
Appears in Collections:气候减缓与适应

Files in This Item:

There are no files associated with this item.


作者单位: Nat Resources Canada, Pacific Forestry Ctr, Canadian Forest Serv, 506 West Burnside Rd, Victoria, BC V8Z 1M5, Canada

Recommended Citation:
Boisvenue, Celine,White, Joanne C.. Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science[J]. REMOTE SENSING,2019-01-01,11(4)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Boisvenue, Celine]'s Articles
[White, Joanne C.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Boisvenue, Celine]'s Articles
[White, Joanne C.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Boisvenue, Celine]‘s Articles
[White, Joanne C.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.