气候变化领域集成服务门户(CCPortal): A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series

CCPortal

DOI	10.1139/cjfr-2023-0118
	A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series
	Guindon, Luc; Manka, Francis; Correia, David L. P.; Villemaire, Philippe; Smiley, Byron; Bernier, Pierre; Gauthier, Sylvie; Beaudoin, Andre; Boucher, Jonathan; Boulanger, Yan
发表日期	2024
ISSN	0045-5067
EISSN	1208-6037
英文摘要	Accurate and fine-scale forest data are essential to improve natural resource management, particularly in the face of climate change. Here, we present SCANFI, the Spatialized CAnadian National Forest Inventory, which provides coherent, 30 m resolution 2020 wall-to-wall maps of forest attributes (land cover type, canopy height, crown closure, aboveground tree biomass, and main species composition). These maps were developed using the NFI photo-plot dataset, a systematic regular sample grid of photo-interpreted high-resolution imagery covering all of Canada's non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a multiresponse k-nearest neighbours and random forest imputation method. This tile-level approach revealed the importance of radiometric variables in predicting vegetation attributes, namely winter radiometry, as the large-scale climate gradients were controlled at the tile-level. SCANFI was validated with rigorous cross-validation analyses, which revealed robust model performance for structural attributes (biomass R2 = 0.76; crown closure R2 = 0.82; height R2 = 0.78) and tree species cover (e.g., Douglas fir R2 = 0.60). SCANFI attributes were also validated with several independent external products, ranging from ground plot-based tree species cover (e.g., black spruce R2 = 0.53) to satellite LiDAR height data products (e.g., crown closure R2 = 0.71). SCANFI total aboveground biomass trends also followed those published by other studies. The methodology presented herein can be used to map time series of these attributes, identify the original training points used to make any given prediction, as well as map additional variables associated with the NFI photo-plots that are challenging to map using traditional remote sensing approaches.
英文关键词	Landsat; time series; Canada; forest; NFI
语种	英语
WOS研究方向	Forestry
WOS类目	Forestry
WOS记录号	WOS:001235038200001
来源期刊	CANADIAN JOURNAL OF FOREST RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302996
作者单位	Natural Resources Canada; Canadian Forest Service; Natural Resources Canada; Canadian Forest Service
推荐引用方式 GB/T 7714	Guindon, Luc,Manka, Francis,Correia, David L. P.,et al. A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series[J],2024.
APA	Guindon, Luc.,Manka, Francis.,Correia, David L. P..,Villemaire, Philippe.,Smiley, Byron.,...&Boulanger, Yan.(2024).A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series.CANADIAN JOURNAL OF FOREST RESEARCH.
MLA	Guindon, Luc,et al."A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series".CANADIAN JOURNAL OF FOREST RESEARCH (2024).