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• Evangelista, PH.
• Stohlgren, TJ.
• Morisette, JT.
• Kumar, S
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• Evangelista, PH.
• Stohlgren, TJ.
• Morisette, JT.
• Kumar, S
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• Evangelista, PH.
• Stohlgren, TJ.
• Morisette, JT.
• Kumar, S

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Remote Sens. 2009, 1(3), 519-533; doi:10.3390/rs1030519

Article

Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data

Paul H. Evangelista ^1,* , Thomas J. Stohlgren ² , Jeffrey T. Morisette ²  and Sunil Kumar ¹

¹ Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523- 499, USA ² U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado 80526, USA

* Author to whom correspondence should be addressed.

Received: 23 June 2009; in revised form: 14 August 2009 / Accepted: 21 August 2009 / Published: 31 August 2009

(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)

Download PDF Full-Text [322 KB, uploaded 30 August 2009 18:24 CEST]

Abstract: In this study, we tested the Maximum Entropy model (Maxent) for its application and performance in remotely sensing invasive Tamarix sp. Six Landsat 7 ETM+ satellite scenes and a suite of vegetation indices at different times of the growing season were selected for our study area along the Arkansas River in Colorado. Satellite scenes were selected for April, May, June, August, September, and October and tested in single-scene and time-series analyses. The best model was a time-series analysis fit with all spectral variables, which had an AUC = 0.96, overall accuracy = 0.90, and Kappa = 0.79. The top predictor variables were June tasselled cap wetness, September tasselled cap wetness, and October band 3. A second time-series analysis, where the variables that were highly correlated and demonstrated low predictive strengths were removed, was the second best model. The third best model was the October single-scene analysis. Our results may prove to be an effective approach for mapping Tamarix sp., which has been a challenge for resource managers. Of equal importance is the positive performance of the Maxent model in handling remotely sensed datasets.

Keywords: Landsat 7 ETM+; Maxent; phenology; remote sensing; spatial modeling; Tamarix; time-series

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