Predicting plot basal area and tree density in mixed-conifer forest from lidar and Advanced Land Imager (ALI) data

A. T. Hudak, J. S. Evans, M. J. Falkowski, N. L. Crookston, P. E. Gessler, P. Morgan, A. M.S. Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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 (10m 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Canadian Symposium on Remote Sensing
Pages201-208
Number of pages8
StatePublished - Dec 1 2005
Event26th Canadian Symposium on Remote Sensing - Wolfville, NS, Canada
Duration: Jun 14 2005Jun 16 2005

Other

Other26th Canadian Symposium on Remote Sensing
CountryCanada
CityWolfville, NS
Period6/14/056/16/05

Keywords

  • Data integration
  • Forest management
  • Northern Idaho
  • Stepwise regression

Fingerprint Dive into the research topics of 'Predicting plot basal area and tree density in mixed-conifer forest from lidar and Advanced Land Imager (ALI) data'. Together they form a unique fingerprint.

  • Cite this

    Hudak, A. T., Evans, J. S., Falkowski, M. J., Crookston, N. L., Gessler, P. E., Morgan, P., & Smith, A. 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 (pp. 201-208)