Skip to Main Content
Predicting forest successional stages using mutitemporal Landsat imagery with forest inventory and analysis dataAuthor(s): Weiguo Liu; Conghe Song; Todd A. Schroeder; Warren B. Cohen
Source: International Journal of Remote Sensing
Publication Series: Scientific Journal (JRNL)
View PDF (2.91 MB)
DescriptionForest succession is an important ecological process that has profound biophysical, biological and biogeochemical implications in terrestrial ecosystems. Therefore, information on forest successional stages over an extensive forested landscape is crucial for us to understand ecosystem processes, such as carbon assimilation and energy interception. This study explored the potential of using Forest Inventory and Analysis (FIA) plot data to extract forest successional stage information from remotely sensed imagery with three widely used predictive models, linear regression (LR), decision trees (DTs) and neural networks (NNs). The predictive results in this study agree with previous findings that multitemporal Landsat Thematic Mapper (TM) imagery can improve the accuracy of forest successional stage prediction compared to models using a single image. Because of the overlap of spectral signatures of forests in different successional stages, it is difficult to accurately separate forest successional stages into more than three broad age classes (young, mature and old) with reasonable accuracy based on the age information of FIA plots and the spectral data of the plots from Landsat TM imagery. Given the mixed spectral response of forest age classes, new approaches need to be explored to improve the prediction of forest successional stages using FIA data.
- 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.)
- 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.
CitationLiu, Weiguo; Song, Conghe; Schroeder, Todd A.; Cohen, Warren B. 2008. Predicting forest successional stages using mutitemporal Landsat imagery with forest inventory and analysis data. International Journal of Remote Sensing. 29(13): 3855-3872.
KeywordsLandsat, secondary succession
- Data fusion of Landsat TM and IRS images in forest classification
- Quantifying tropical dry forest type and succession: substantial improvement with LiDAR
- Forest/non-forest stratification in Georgia with Landsat Thematic Mapper data
XML: View XML