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

Paul H Evangelista,Sunil Kumar,Jeffrey T Morisette,Thomas J Stohlgren

doi:10.3390/rs1030519

Paul H Evangelista, Sunil Kumar + Show 2 more

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https://doi.org/10.3390/rs1030519

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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.

Highlights

Mapping invasive plant species has become a high priority for resource managers and researchers across the United States
We explored the application of maximum entropy modeling with remotely sensed data to map the distribution of tamarisk while incorporating strategies that have been previously proven to be effective in vegetation mapping
Our study revealed several important factors that may significantly improve the use of remotely sensed data for detecting tamarisk and other invasive species

Summary

Introduction

Mapping invasive plant species has become a high priority for resource managers and researchers across the United States. Ground surveys are still commonly used for most mapping projects despite intensive labor requirements, associated high economic costs, and incomplete coverage of the landscape [1,2]. Remote sensing has played an important, but limited, role in detecting and mapping invasive plants [3,4,5]. Detecting a specific plant species in forests, rangelands, riparian areas and natural landscapes using remote sensing techniques has proved to be a greater challenge. Large-scale infestations, where invaders are clearly the dominant species and environmental heterogeneity is reduced, tend to be easier to detect remotely [3,8,9]

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