Skip to Main Content
Estimation of changes of forest structural attributes at three different spatial aggregation levels in Northern California using multitemporal LiDARAuthor(s): Francisco Mauro; Martin Ritchie; Brian Wing; Bryce Frank; Vicente Monleon; Hailemariam Temesgen; Andrew Hudak
Source: Remote Sensing. 11: 923.
Publication Series: Scientific Journal (JRNL)
Station: Pacific Southwest Research Station
View PDF (3.0 MB)
DescriptionAccurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key to quantify greenhouse gas (GHG) emissions/sequestration. We compared two different modeling strategies to estimate changes in V, BA and B, at three different spatial aggregation levels using auxiliary information from two light detection and ranging (LiDAR) flights. The study area is Blacks Mountains Experimental Forest, a ponderosa pine dominated forest in Northern California for which two LiDAR acquisitions separated by six years were available. Analyzed strategies consisted of (1) directly modeling the observed changes as a function of the LiDAR auxiliary information (δ-modeling method) and (2) modeling V, BA and B at two different points in time, including a term to account for the temporal correlation, and then computing the changes as the difference between the predicted values of V, BA and B for time two and time one. We analyzed predictions and measures of uncertainty at three different level of aggregation (i.e., pixels, stands or compartments and the entire study area). Results showed that changes were very weakly correlated with the LiDAR auxiliary information. Both modeling alternatives provided similar results with a better performance of the δ-modeling for the entire study area; however, this method also showed some inconsistencies and seemed to be very prone to extrapolation problems. The y-modeling method, which seems to be less prone to extrapolation problems, allows obtaining more outputs that are flexible and can outperform the δ-modeling method at the stand level. The weak correlation between changes in structural attributes and LiDAR auxiliary information indicates that pixel-level maps have very large uncertainties and estimation of change clearly requires some degree of spatial aggregation; additionally, in similar environments, it might be necessary to increase the time lapse between LiDAR acquisitions to obtain reliable estimates of change.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationMauro, Francisco; Ritchie, Martin; Wing, Brian; Frank, Bryce; Monleon, Vicente; Temesgen, Hailemariam; Hudak, Andrew. 2019. Estimation of changes of forest structural attributes at three different spatial aggregation levels in Northern California using multitemporal LiDAR. Remote Sensing. 11: 923.
Keywordsforest structure change, EBLUP, small area estimation, multitemporal LiDAR and stand-level estimates
- Aggregating pixel-level basal area predictions derived from LiDAR data to industrial forest stands in North-Central Idaho
- Temporal transferability of LiDAR-based imputation of forest structure attributes
- Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data
XML: View XML