Title: Predicting plot basal area and tree density in mixed-conifer forest from lidar and Advanced Land Imager (ALI) data
Author: Hudak, Andrew T.; Evans, Jeffrey S.; Falkowski, Michael J.; Crookston, Nicholas L.; Gessler, Paul E.; Morgan, Penelope; Smith, Alistair M. S.;
Source: In: Proceedings of the 26th Canadian Symposium on Remote Sensing; Wolfville, Nova Scotia, June 14-16, 2005. CD-ROM. 8 p.
Publication Series: Paper (invited, offered, keynote)
Description: Multispectral satellite imagery are appealing for their relatively low cost, and have demonstrated utility at the landscape level, but are typically limited at the stand level by coarse resolution and insensitivity to variation in vertical canopy structure. In contrast, lidar data are less affected by these difficulties, and provide high structural detail, but are less available due to their comparatively high cost. Two forest structure attributes measured at the plot level, basal area and trees per hectare, were predicted using stepwise multiple regression on 40 predictor variables derived from discrete-return lidar data (2 m post spacing), Advanced Land Imager (ALI) multispectral (30 m resolution) and panchromatic (10 m resolution) images, and geographic X,Y,Z location. Square root and natural logarithm transforms were applied to normalize the positively skewed response variables. Stepwise variable selection used the AIC statistic to guard against overfitting. Models predicting the transformed variables explained 80-93% of variance, based on 20-22 predictor variables. Lidar-derived variables had the most explanatory power; especially height and intensity variables for predicting plot basal area, and cover and intensity variables for predicting tree density. The ALI variables were less useful for predicting these attributes of forest structure, but could prove more helpful for predicting attributes of forest composition.
Keywords: data integration, forest management, northern Idaho, stepwise regression
- 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.
- You may send email to email@example.com to request a hard copy of this publication. (Please specify exactly which publication you are requesting and your mailing address.)
XML: View XML
Hudak, Andrew T.; Evans, Jeffrey S.; Falkowski, Michael J.; Crookston, Nicholas L.; Gessler, Paul E.; Morgan, Penelope; Smith, Alistair M. S. 2005. Predicting plot basal area and tree density in mixed-conifer forest from lidar and Advanced Land Imager (ALI) data. In: Proceedings of the 26th Canadian Symposium on Remote Sensing; Wolfville, Nova Scotia, June 14-16, 2005. CD-ROM. 8 p.
Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility