Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots

Attributes that describe forest structure, such as height, canopy cover, volume, and biomass, are required to inform forest inventories and monitoring programs. Light Detection and Ranging (lidar) has been successfully demonstrated as a means to derive a suite of forest structure attributes at the p...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing of environment 2018-10, Vol.216, p.697-714
Main Authors: Matasci, Giona, Hermosilla, Txomin, Wulder, Michael A., White, Joanne C., Coops, Nicholas C., Hobart, Geordie W., Bolton, Douglas K., Tompalski, Piotr, Bater, Christopher W.
Format: Article
Language:English
Subjects:
Biomass
Canopy cover
Dynamic structural analysis
Estimates
Forest ecosystems
Forest management
Forest recovery
Forestry
Forests
Harvesting
Imputation
Landsat
Landsat satellites
Lidar
Remote sensing
Satellite imagery
Sustainability management
Terrestrial ecosystems
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Attributes that describe forest structure, such as height, canopy cover, volume, and biomass, are required to inform forest inventories and monitoring programs. Light Detection and Ranging (lidar) has been successfully demonstrated as a means to derive a suite of forest structure attributes at the plot level; however, these acquisitions are often constrained to limited spatial extents and to a given point in time. Sample based approaches for model development can accommodate the spatial limitations of lidar acquisitions when characterizing large areas. The combination of lidar plot data and time-series satellite imagery is well suited to provide spatially extensive, and temporally dense, information on forest structure and related dynamics over very large areas. In this research, we combine lidar plot-derived information with Landsat pixel-based composites to produce annual forest structure estimates from 1984 to 2016 over 650 million ha of Canada's forest ecosystems using a nearest neighbor imputation approach with a Random Forests-based distance metric. Imputed variables included lidar metrics of height (e.g., mean height, standard deviation of height) and cover, as well as area-based modelled inventory estimates of Lorey's height, basal area, stem volume, and biomass. Models were validated using reserved validation plots, with model R2 ranging from 0.62 to 0.64 for lidar metrics of height and cover, and R2 of 0.67, 0.68, 0.71, and 0.70 for Lorey's height, basal area, volume and biomass, respectively. Unique to this study was the assessment of model extension through time, with model performance for imputing lidar metrics evaluated at the forest stand-level using independent lidar data representing a latitudinal gradient of forest conditions and that was not used in model development. The period evaluated was 2006–2012, with R2 values ranging from 0.36 to 0.66 for height metrics, and 0.47–0.77 for cover metrics. Ultimately, we show how deriving forest structural estimates on an annual basis enables the analysis of both the dynamics and regional trends of undisturbed forest, as well as regenerating stands following stand-replacing disturbances (i.e., fire, harvesting). •We combine lidar-derived forest structure with Landsat spectral data.•The methodology imputes metrics of height and cover as well as inventory attributes.•Wall-to-wall estimates of forest structure were derived across Canada.•Models predicted evolution of forest attributes over time on an
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2018.07.024