Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
| Autor: | Nicholas E. Young, Thomas J. Stohlgren, Paul H. Evangelista, Colin Talbert, Ryan Anderson, Jeffrey T. Morisette, Amanda M. West, Catherine S. Jarnevich, Marian Talbert |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0106 biological sciences
Generalized linear model Ensemble models 010504 meteorology & atmospheric sciences General Chemical Engineering Species distribution 010603 evolutionary biology 01 natural sciences Normalized Difference Vegetation Index General Biochemistry Genetics and Molecular Biology Issue 116 Ecosystem 0105 earth and related environmental sciences Remote sensing Riparian zone geography geography.geographical_feature_category Multivariate adaptive regression splines Ensemble forecasting Invasive species General Immunology and Microbiology Tamaricaceae Tamarisk General Neuroscience Species distribution model Software for Assisted Habitat Modeling (SAHM) Models Theoretical Random forest Thematic Mapper Remote Sensing Technology Environmental science Introduced Species Landsat Software Environmental Sciences |
| Zdroj: | Journal of Visualized Experiments : JoVE |
| ISSN: | 1940-087X |
| DOI: | 10.3791/54578-v |
| Popis: | Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|