Landsat-5 TM and lidar fusion for sub-pixel juniper tree cover estimates in a Western Rangeland

Temuulen Sankey, Nancy Glenn

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Pinyon-juniper woodlands comprise the third most common land-cover type in the United States and have been documented to have drastically increased both in density and extent in recent decades. We explored Landsat-5 TM and Light Detection and Ranging (lidar) data, individually and fused together, for estimating sub-pixel juniper cover. Linear spectral unmixing (LSU), Constrained Energy Minimization (CEM), and Mixture Tuned Matched Filtering (MTMF) techniques were compared along with spectral-lidar fusion approaches. None of the Landsat-5 TM-derived estimates were significantly correlated with field-measured juniper cover (n = 100), while lidar-derived estimates were strongly correlated (R2= 0.74, p-value<0.001). Fusion of these estimates produced superior results to both classifications individually (R2= 0.80, p-value<0.001). The MTMF technique performed best, while a multiple regression-based fusion was the best approach to combining the two data sources. Future studies can use the best sub-pixel classification and fusion approach to quantify changes in associated ecosystem properties such as carbon.

Original languageEnglish (US)
Pages (from-to)1241
Number of pages1
JournalPhotogrammetric Engineering and Remote Sensing
Volume77
Issue number12
DOIs
StatePublished - Dec 2011
Externally publishedYes

ASJC Scopus subject areas

  • Computers in Earth Sciences

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