The authors would like to thank the U.S. Geological Survey, Natural Resource Ecology Laboratory at Colorado State University, Colorado State Forest Service and the Tamarisk Coalition for logistical support, data, use of facilities and expertise. Additionally, we thank Shelly Simmons, Lane Carter, John Moore, and Chandra Reed for their contributions to this work. Thomas J. Stohlgren was partially supported by the Bioenergy Alliance Network of the Rockies (BANR), USDA UV-B Monitoring and Research Program and USDA CSREES/NRI 2008-35615-04666. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

1. Field Data Collection
<ol>
	<li>Derive field data for tamarisk from a vector polygon dataset collected by the Tamarisk Coalition in a state-wide survey in 2005 and 200617. 
	NOTE: The data were obtained through an intensive ground survey, where field technicians mapped all tamarisk stands along the Arkansas River using Global Positioning Systems (GPS) and aerial photographs.</li>
	<li>Within these polygons, generate 499 random points (i.e., presences) to train the models. Place two sets of constraints on the random points: (1) each point is required to be &#62; 30 m from any polygon edge to ensure it is within the tamarisk stand at a Landsat 5 TM resolution; and (2) each point is required to be &#8805; 60 m from any neighboring point, to ensure a robust distribution across the study site and minimize spatial autocorrelation.</li>
	<li>Compile field data in a MS Excel file in three columns with the headings labeled &#34;response&#34;, &#34;X&#34;, and &#34;Y&#34;, where the response values are (1) for presence, UTM easting for X, and UTM northing for Y. Save this file in .csv format for use in SAHM (FieldData module).</li>
	<li>Generate an additional 100 random points within the Tamarisk Coalition&#39;s tamarisk polygons and another 100 random points outside the polygons as independent test data to evaluate model results. Save this file in .csv format for use in SAHM (FieldData module).</li>
</ol>
2. Predictor Variables
<ol>
	<li>Download Landsat 5 surface reflectance L4-5 TM images (Path 32, Row 34) from the U.S. Geological Survey&#39;s Global Visualization Viewer/EarthExplorer (http://earthexplorer.usgs.gov/). The scenes include the extent sampled by the Colorado Tamarisk Coalition in 2005 and 200617. To determine the months used for the models, select scenes that are largely cloud-free (i.e., &#60; 10% cloud cover) and representative of months thought to have distinguishable tamarisk phenology; these were October 2004, April 2005, May 2005, June 2005, July 2005, September 2005, April 2006, May 2006, June 2006, July 2006, August 2006, September 2006, and November 2006.</li>
	<li>Download the Remote Sensing Indices Derivation Tool from 
	https://github.com/rander38/Remote-Sensing-Indices-Derivation-Tool.</li>
	<li>Run the Python script in the tool, either GDAL or Arcpy version; GDAL is recommended.</li>
	<li>Select the appropriate satellite sensor, desired indices, and set the input image file and the output folder where files will be stored (Figure 1). We exported the individual bands and used the NDVI, SAVI, and Tasseled Cap brightness, greenness, and wetness indices derived from each of the Landsat TM scenes. Note that any indices can be modified or added by altering the Sensors_Formulas_RSIDT.ini file.</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/54578/54578fig1.jpg" /> 
Figure 1. Remote Sensing Indices Derivation Tool GUI.
<ol start="5">
	<li>Run the tool and verify the output files visually in ArcMap v. 10.0 (ESRI, Redlands, CA) or another geographic information system software.</li>
</ol>
3. Software for Assisted Habitat Modeling (SAHM) (Figure 2)
<img alt="Figure 1" src="/files/ftp_upload/54578/54578fig2.jpg" /> 
Figure 2. The entire SAHM workflow includes input data, preprocessing, preliminary model analysis and decision, correlative models, and output routines.
<ol>
	<li>For running SAHM, first download files (including VisTrails) from the U.S. Geological Survey website at https://my.usgs.gov/catalog/RAM/SAHM. Refer to the User&#8217;s Guide at the same website for detailed instructions on downloading and installing SAHM. Note that the website also has a SAHM tutorial and data for additional instructions.</li>
	<li>To develop the tamarisk species distribution models, use the SAHM_tutorial_2.0.vt file that comes with the package download (in the SAHM examples folder). Within the History view, select the Independent locations workflow. The other workflow examples can be selected and depend on study objectives; descriptions are provided with each. Select Pipeline.</li>
	<li>Set the output folder, by going to Packages and then SAHM &#62; Change Session Folder. Throughout the workflow development process, detailed descriptions of each step and options may be found by selecting the Documentation tab located on the right side of the SAHM viewer screen. All modules listed in the following methods can be found on the left side of the SAHM window under the SAHM tab.</li>
	<li>Next, direct SAHM to the field data that will be used to train species distribution models.
	<ol>
		<li>Click on the TemplateLayer module. Browse to the raster that will be used as a mask and to define the projection, cell size and extent of the analyses.</li>
		<li>Click on the FieldData module in the left side of the workflow. Browse to .csv (i.e., training.csv) file of field data (presence points or presence and absence points) within the field data module.</li>
		<li>Click on the PredictorListFile module and browse to .csv file list (e.g., file containing the full path to all predictors to use in the model- refer to user guide).</li>
	</ol>
	</li>
	<li>Next, conduct preprocessing steps.
	<ol>
		<li>Click on the FieldDataQuery module and fill in response column with column heading for response (i.e., the column names in the FieldData.csv), X and Y columns.</li>
		<li>Click on the MDSBuilder module. Set the backgroundPointField to 10,000. 
		NOTE: If you are using presence and absence data for the species being modeled, you do not need to change the backgroundPointField; you would include these locations with a response (0) in the Field Data.csv. It is optional to set a backgroundProbSurf if you want to constrain background point selection within an area by pointing to a raster surface with values ranging from 0 to 100 (these values represent the probability that a randomly generated point will be retained should it fall within a particular cell). For this study, use a backgroundProbSurf with values of 100 within a 5,000 m buffer of the Arkansas River and 0 for areas outside this buffer (based on the total area sampled by the tamarisk coalition).</li>
	</ol>
	</li>
	<li>Next, specify the species distribution modeling algorithms to be used.
	<ol>
		<li>Note that the BoostedRegressionTree, GLM, MARS, and RandomForest modules are already set up in the Independent locations workflow. Add the MAXENT module to the workflow to test all five models. Connect it to the CovariateCorrelationAndSelection module. 
		NOTE: Begin with default settings for all models; these can be modified based on study objectives (see model documentation for more details).</li>
		<li>Add a ModelOutputViewer module and connect it to the MAXENT module; change column to 5 and row to 1. The ModelOutputViewer produces a spreadsheet that can be used for model result comparisons.</li>
		<li>Click on the OutputName module and type a subfolder name.</li>
	</ol>
	</li>
	<li>Next, add a module that creates an ensemble of model outputs. This module produces two output maps; one with the average continuous probability of all the included outputs and a second with the count of the number of models with a positive binary probability.
	<ol>
		<li>Add an EnsembleBuilder module to the workflow. It is optional to set a Threshold Metric; for this study, choose AUC and leave the Threshold Value at the default value of 0.75. This will ensure that only models with an AUC value greater than or equal to 0.75 are included in the ensemble map outputs. Connect the BoostedRegressionTree, GLM, MARS, RandomForest, and MAXENT modules to the EnsembleBuilder.</li>
	</ol>
	</li>
	<li>Next, direct the models to the independent test data.
	<ol>
		<li>Click on the other FieldData module (right side of the workflow) and browse to the .csv file that contains model validation data. These are the 200 presence and absence points generated in step 1.4 of the PROTOCOL.</li>
		<li>Click on the FieldDataQuery module and make sure the response, x, and y columns match the columns in the field data .csv.</li>
		<li>Add an ApplyModel module and connect it to the MAXENT module. Add a ModelOutputViewer module and connect it to this ApplyModel module; change the column to 5 and the row to 1. In the menu, select Packages -&#62; SAHM -&#62; Change Processing Mode. Since you want to run more than one model, select single models sequentially (n - 1 cores each). This will expedite the execution time of the models by taking advantage of multiple computer cores.</li>
	</ol>
	</li>
	<li>Next, execute the species distribution models.
	<ol>
		<li>Save the file .vt and then click on Execute.</li>
		<li>When the CovariateCorrelationAndSelection widget (Figure 3) appears, de-select one of each correlated pair of variables where the correlation coefficient is |r| &#8805; 0.7 (based on % Deviance Explained from a univariate generalized additive model seen in on the left side of this widget (Figure 3) and informed ecological decisions; in this study prioritize the selection of at least one covariate for each month to capture phenological variation in tamarisk). The Number of Plots in the viewing window can be changed by typing in a number (the default is 8) and clicking on Update.</li>
		<li>After finalizing covariate selection, select OK at the bottom of the CovariateCorrelationAndSelection widget (Figure 3). For this study, the following 9 variables were retained: July_30_2006_Brightness, June_09_2005_SAVI, Sept_16_2006_SAVI, May_24_2005_B4, Oct_28_2004_NDVI, April_22_2005_Brightness, April_09_2006_SAVI, Aug_31_2006_B4, and Nov_19_2006_SAVI. The species distribution models will execute after selecting OK.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" src="/files/ftp_upload/54578/54578fig3.jpg" /> 
Figure 3. Covariate correlation and selection SAHM interface.
<ol start="10">
	<li>Results output. 
	NOTE: After the models have completed, a VisTrails Spreadsheet appears for model comparison (Figure 4)
	<ol>
		<li>Compare AUC plots, text outputs, response curves, calibration, confusion matrix, residuals, and response curves across models. 
		NOTE: Output from SAHM models include a CovariateCorrelationOutputMDS, confusion matrix, residual plot, calibration plot, model evaluation plot, variable importance plot, a response curves folder, an expanded output folder, bin map, mess map, MoD map, prob map, residual map, and an output.txt; see the SAHM User Guide for more description.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 4" src="/files/ftp_upload/54578/54578fig4.jpg" /> 
Figure 4. VisTrails spreadsheets can be used to evaluate model output. This is the AUC model comparison for the training data; from left to right the models are BRT, GLM, MARS, RF, and Maxent, respectively.

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The authors have no competing financial interests or conflict of interest.

Our results demonstrate fitting BRT, RF, MARS, GLM, and Maxent with presence points for tamarisk and a time-series of remotely sensed Landsat satellite imagery data can distinguish tamarisk on the landscape and is an effective alternative to traditional single-scene classification methods. It is clear from our results that June is a particularly important time for detecting tamarisk within our study area; this agrees with Evangelista et al. 20091 which indicated June Wetness was the most important predictor for tamarisk occurrence in this area based on a Maxent model fit with a time series of Landsat imagery.

The other spectral indices and bands that were included in the BRT, RF, MARS, and Maxent models may further distinguish tamarisk from soil substrate, other deciduous trees including cottonwood (Populus spp.) and willow (Salix spp.), or irrigated agriculture that is common in the lower Arkansas River basin. Other GIS layers, such as topography, soil types, or climate data could also be considered as covariates and included in these models, but we recommend keeping these to a minimum if the goal is to detect current species distribution on the landscape rather than predict potential occurrence or suitable habitat.
The models tested for our research provided strong analytical capacity and multiple options for evaluating results. Having all of these correlative models within a single framework, such as SAHM, allows the formalization and tractable recording of the modeling process. Pre- and post-processing of response and predictor variables are standardized in SAHM, allowing better and efficient model comparisons, while workflows record each step of the analyses facilitating modification, iteration and replication.
Ensemble mapping aims to combine the strengths of several correlative models, while minimizing the weakness of any one model30. We believe this was the case in our study; however, we caution that models that underperform (i.e., under-predict or over-predict) can weaken the overall results. The limited use of ensemble mapping in the literature has had favorable results, but most of these approaches have attempted to &#34;predict&#34; species occurrence rather than &#34;detect.&#34; Furthermore, ensemble mapping allows for a visual assessment of uncertainty among the different modeling methods, identifying levels of model agreement. Most often it is the choice of modeling method (e.g., GLM versus BRT) that has greatest quantifiable impact on model results rather than other decisions in the modeling process such as location data uncertainty31. Although we believe that our best tamarisk map is where all five models are in agreement, further testing and using various methods of ensemble mapping is recommended (e.g., weighted by AUC)32, and best validated through independent field observations. In summary, these methods can easily be tailored to model the distribution of other species using environmental variables derived for a given study region in SAHM.

Riparian and wetland ecosystems throughout the southwestern United States are being threatened by the invasion of tamarisk (Tamarix spp.), a non-native woody shrub introduced from Eurasia in the 1800s1. Tamarisk has many physiological mechanisms that allow the genus to exploit water resources, out-compete native species, and alter ecosystem processes1-2. Mapping tamarisk distributions for assessing environmental impacts and formulating effective control strategies are high priorities for resource managers. Although ground surveys remain regularly used, they are impractical for extremely large areas due to the associated costs of labor, time, and logistics.
Satellite remote sensing has played an important, but limited, role in the detection and mapping of tamarisk infestations. Conventional classification analyses and remote sensing software have had marginal success3-5. Several recent studies have explored non-traditional approaches to detect invasive plants using remote sensing data1,6. Tamarisk, like many invasive plants, exhibits phenological variation throughout the growing season that differs from native riparian species&#39; phenology. In some areas, for example, tamarisk leaf-out is before some native riparian plants, and tamarisk retains its foliage longer than other native species. By using spectral bands and spectral indices derived from a time-series of satellite data throughout the growing season, we can distinguish tamarisk from native plants based on these phenological differences1,6. Building on the work of Evangelista et al. 20091, in this study we incorporated individual bands 1-7 from a time-series of Landsat 5 Thematic Mapper (TM) satellite imagery and derived normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and tasseled cap transformations from these bands. Normalized difference vegetation index (NDVI) is one of the most commonly used spectral indices for estimating vegetation biomass, canopy cover, and leaf area indices8-9, and is a non-linear transformation of the ratio between the visible (red) and near-infrared bands10. Soil-adjusted vegetation index (SAVI) is a modified NDVI used to minimize the effects of soil background on vegetation indices11. Tasseled cap transformations are weighted composites of the six Landsat bands into three orthogonal bands that measure soil brightness (tasseled cap, band 1), vegetation greenness (tasseled cap, band 2), and soil/vegetation wetness (tasseled cap, band 3) and are often used to distinguish vegetation composition, age class, and structure12-14. We used the coefficients reported in Crist (1985)15 for all tasseled cap transformations.
In this study, we test five species distribution models with a time-series of spectral bands and vegetation indices derived from Landsat 5 TM to map tamarisk along the lower Arkansas River in southeastern Colorado, USA. The Arkansas River, spanning 2,364 km (1,469 mi), is the second largest tributary in the Missouri-Mississippi system. Its watershed covers 435,123 km2 (168,002 mi2) with headwaters in the Colorado Rocky Mountains. From its origin at 2,965 m, the Arkansas drops considerably in elevation, leveling out near Pueblo, CO, and meandering through agricultural lands and short-grass prairie. The river is subject to seasonal flooding and is relied on for municipal and agricultural water use in Rocky Ford, La Junta, and Lamar, before continuing into Kansas, Oklahoma, and Arkansas where it flows into the Mississippi River. Tamarisk was first observed on the Arkansas River by R. Niedrach in 1913 near the present-day town of Lamar16. Today, it has been estimated that tamarisk covers more than 100 km2 between Pueblo and the Kansas state line, with an additional 60 km2 along the tributaries of the Arkansas River17. The study area includes irrigation ditches, wetlands, agricultural land, and the confluences of several tributaries; all with varying degrees of tamarisk infestation. Ranching and agriculture are the primary land-uses adjacent to the riparian corridors consisting largely of alfalfa, hay, corn, and winter wheat.
Species distribution models rely on geo-referenced occurrences (i.e., latitude, longitude) to identify relationships between a species&#39; occurrence and its environment18. The environmental data can include multiple remote sensing and other spatial layers. The five species distribution models we tested include boosted regression trees (BRT)19, random forests (RF)20, multivariate adaptive regression splines (MARS)21, a generalized linear model (GLM)22, and Maxent23. These five model algorithms are among the most commonly employed for species distribution modeling, and a number of studies have demonstrated their efficacy24-25. We used the Software for Assisted Habitat Modeling (SAHM) v. 2.0 modules to execute the five models, which are contained in VisTrails v.2.2.226 visualization and processing software. There are several advantages to using SAHM for comparative modeling. In addition to the formalization and tractable recording of modeling processes, SAHM allows users to work with multiple species distribution model algorithms that, individually, have disparate interfaces, software and file formatting27. SAHM produces consistent threshold-independent and threshold-dependent evaluation metrics to evaluate model performance. One of these is Area Under the Receiver Operating Characteristic Curve (AUC), a threshold independent metric that evaluates ability of a model to discriminate presence from background28. An AUC value of 0.5 or less indicates model predictions are not better or worse than random; values between 0.5 and 0.70 indicate poor performance; and values increasing from 0.70 to 1.0 indicate progressively higher performance. Another metric is percent correctly classified (PCC), a threshold dependent metric that weighs sensitivity and specificity based on a user-defined threshold metric; sensitivity measures the percentage of observed presences classified as suitable and specificity measures the percentage of background locations classified as unsuitable. Yet another metric is True Skill Statistic (TSS = sensitivity + specificity - 1), which places more weight on model sensitivity than specificity, with values ranging between -1 and 1 where values &#62; 0 indicate better model performance than chance29.
To map tamarisk using model output, we constructed binary classifications using the threshold that equalizes sensitivity and specificity to define the presence or absence of tamarisk. These individual model derived maps were then summed to create an ensemble map30. Ensemble maps combine the predictions of individual species distribution models to produce a classified map that ranks the collective agreement of the models tested. For example, an ensemble cell value of one indicates that only one model classified that cell as suitable habitat, whereas a value of five indicates that all five models classified the cell as suitable habitat. One advantage to this approach is that ensemble maps yield a lower mean error than any individual model. It also allows users to visually compare the performance of each model tested. Our overall goal was to provide a detailed description of these methods that can be tailored to model the current distribution of species on the landscape.

<table><tbody><tr><td>Earth Explorer</td><td>USGS</td><td>http://earthexplorer.usgs.gov</td><td>Open Access: Yes</td></tr><tr><td>Remote Sensing Indices Derivation Tool</td><td>github</td><td>https://github.com/rander38/Remote-Sensing-Indices-Derivation-Tool</td><td>Open Access: Yes</td></tr><tr><td>Software for Assisted Habitat Modeling</td><td>USGS</td><td>https://my.usgs.gov/catalog/RAM/SAHM</td><td>Open Access: Yes</td></tr><tr><td>ArcGIS v.10.3&amp;nbsp;</td><td>Esri</td><td>https://www.arcgis.com/features/</td><td>Open Access: No</td></tr></tbody></table>

integrating remote sensing with species distribution models; mapping tamarisk invasions using the software for assisted habitat modeling (sahm)

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.

Statistical evaluations of BRT, RF, MARS, GLM, and Maxent based on the independent test dataset indicated all five models performed relatively well in detecting tamarisk; there was little difference between threshold independent and threshold dependent evaluation metrics among models. AUC values were &#62; 0.88, percent correctly classified values were&#160;&#62; 77%, sensitivities and specificities were&#160;&#62; 0.77, and TSS were&#160;&#62; 0.54 (Table 1). An ensemble of binary model outputs revealed much model agreement in areas along the Arkansas River (Figure 5). The MESS (multivariate environmental similarity surface) map outputs for each model indicated the available environment of the study area was well sampled (Figure 6), further increasing our confidence in the ensemble approach.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Model</td>
			<td>AUC</td>
			<td>PCC</td>
			<td>Sensitivity</td>
			<td>Specificity</td>
			<td>TSS</td>
		</tr>
		<tr>
			<td>BRT</td>
			<td>0.91</td>
			<td>85</td>
			<td>0.85</td>
			<td>0.85</td>
			<td>0.70</td>
		</tr>
		<tr>
			<td>RF</td>
			<td>0.92</td>
			<td>85</td>
			<td>0.85</td>
			<td>0.85</td>
			<td>0.70</td>
		</tr>
		<tr>
			<td>MARS</td>
			<td>0.90</td>
			<td>82</td>
			<td>0.82</td>
			<td>0.82</td>
			<td>0.64</td>
		</tr>
		<tr>
			<td>GLM</td>
			<td>0.88</td>
			<td>77</td>
			<td>0.77</td>
			<td>0.77</td>
			<td>0.54</td>
		</tr>
		<tr>
			<td>Maxent</td>
			<td>0.92</td>
			<td>84</td>
			<td>0.83</td>
			<td>0.84</td>
			<td>0.67</td>
		</tr>
	</tbody>
</table>
Table 1. Threshold independent (AUC) and threshold dependent (PCC, Sensitivity, Specificity, and TSS) evaluation metrics for BRT, RF, MARS, GLM, and Maxent models fit to an independent test dataset of tamarisk presence and absence.
<img alt="Figure 5" src="/files/ftp_upload/54578/54578fig5.jpg" /> 
Figure 5. Ensemble results combining BRT, GLM, MARS, RF, and Maxent binary output maps in ArcGIS. Areas are colored by number of models in agreement, from 0 (no color) to 5 (red). Note the colored area in the northwest corner of the prediction; this line is an artifact of Landsat imagery; therefore model results should be taken with caution in this region. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54578/54578fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/54578/54578fig6.jpg" /> 
Figure 6. Multivariate environmental similarity surface (MESS) output. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54578/54578fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Of the nine predictors used, June 30 2006 Brightness was the most important variable for all five models (Table 2). This was the only variable retained by GLM based on stepwise Akaike information criterion (AIC; this is the default for GLM model selection in SAHM), however it is important to note this model also included a squared term of this variable. RF and Maxent retain all variables by default.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Predictor</td>
			<td>BRT</td>
			<td>RF</td>
			<td>MARS</td>
			<td>GLM</td>
			<td>Maxent</td>
		</tr>
		<tr>
			<td>July 30 2006 Brightness</td>
			<td>41.60</td>
			<td>34.11</td>
			<td>76.78</td>
			<td>100</td>
			<td>67.27</td>
		</tr>
		<tr>
			<td>August 31 2006 Band 4</td>
			<td>6.35</td>
			<td>5.87</td>
			<td>5.16</td>
			<td>0</td>
			<td>2.82</td>
		</tr>
		<tr>
			<td>June 09 2005 SAVI</td>
			<td>13.67</td>
			<td>14.09</td>
			<td>9.14</td>
			<td>0</td>
			<td>9.75</td>
		</tr>
		<tr>
			<td>April 22 2005 Brightness</td>
			<td>6.29</td>
			<td>6.30</td>
			<td>0</td>
			<td>0</td>
			<td>0.43</td>
		</tr>
		<tr>
			<td>October 28 2004 NDVI</td>
			<td>5.66</td>
			<td>8.25</td>
			<td>0</td>
			<td>0</td>
			<td>2.94</td>
		</tr>
	</tbody>
</table>
Table 2. Relative importance of predictors in each model.

Watch this Scientific Journal Video about Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM at JoVE.com

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

We demonstrate the utility of remotely sensed data and the newly developed Software for Assisted Habitat Modeling (SAHM) in predicting invasive species occurrence on the landscape. An ensemble of predictive models produced highly accurate maps of tamarisk (Tamarix spp.) invasion in Southeastern Colorado, USA when assessed with subsequent field validations.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite ...

integrating-remote-sensing-with-species-distribution-models-mapping

Universidad San Sebastian

Research

JoVE Journal

Environment

13.2K Views.  Colorado State University. We demonstrate the utility of remotely sensed data and the newly developed Software for Assisted Habitat Modeling (SAHM) in predicting invasive species occurrence on the landscape. An ensemble of predictive models produced highly accurate maps of tamarisk (Tamarix spp.) invasion in Southeastern Colorado, USA when assessed with subsequent field validations.

Video: Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

An Optimized Enrichment Technique for the Isolation of Arthrobacter Bacteriophage Species from Soil Sample Isolates

Bacteriophage isolation from environmental samples has been performed for decades using principles set forth by pioneers in microbiology. The isolation of phages infecting Arthrobacter hosts has been limited, perhaps due to the low success rate of many previous isolation techniques, resulting in an underrepresented group of Arthrobacter phages available for study. The enrichment technique described here, unlike many others, uses a filtered extract free of contaminating bacteria as the base for indicator bacteria growth, Arthrobactersp. KY3901, specifically. By first removing soil bacteria the target phages are not hindered by competition with native soil bacteria present in initial soil samples. This enrichment method has resulted in dozens of unique phages from several different soil types and even produced different types of phages from the same enriched soil sample isolate. The use of this procedure can be expanded to most nutrient rich aerobic media for the isolation of phages in a vast diversity of interesting host bacteria.

An Optimized Enrichment Technique for the Isolation of Arthrobacter Bacteriophage Species from Soil Sample Isolates

We present an enrichment protocol for the isolation of bacteriophages infecting bacteria in the Arthrobacter genus. This enrichment protocol produces fast and reproducible results for the isolation and amplification of Arthrobacter phages from soil isolates.

Bacteriophage isolation from environmental samples has been performed for decades using principles set forth by pioneers in microbiology. The isolation of ...

Protocols for Robust Herbicide Resistance Testing in Different Weed Species

Robust protocols to test putative herbicide resistant weed populations at whole plant level are essential to confirm the resistance status. The presented protocols, based on whole-plant bioassays performed in a greenhouse, can be readily adapted to a wide range of weed species and herbicides through appropriate variants. Seed samples from plants that survived a field herbicide treatment are collected and stored dry at low temperature until used. Germination methods differ according to weed species and seed dormancy type. Seedlings at similar growth stage are transplanted and maintained in the greenhouse under appropriate conditions until plants have reached the right growth stage for herbicide treatment. Accuracy is required to prepare the herbicide solution to avoid unverifiable mistakes. Other critical steps such as the application volume and spray speed are also evaluated. The advantages of this protocol, compared to others based on whole plant bioassays using one herbicide dose, are related to the higher reliability and the possibility of inferring the resistance level. Quicker and less expensive in vivo or in vitro diagnostic screening tests have been proposed (Petri dish bioassays, spectrophotometric tests), but they provide only qualitative information and their widespread use is hindered by the laborious set-up that some species may require. For routine resistance testing, the proposed whole plant bioassay can be applied at only one herbicide dose, so reducing the costs.

A robust and flexible approach to confirm herbicide resistance in weed populations is presented. This protocol allows the herbicide resistance levels to be inferred and applied to a wide range of weed species and herbicides with minor adaptations.

Robust protocols to test putative herbicide resistant weed populations at whole plant level are essential to confirm the resistance status. The presented ...

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton

The objective of this study was to evaluate a ground-based multispectral optical sensor as a remote sensing tool to assess foliar damage caused by the two-spotted spider mite (TSSM), Tetranychus urticae Koch, on greenhouse grown cotton. TSSM is a polyphagous pest which occurs on a variety of field and horticultural crops. It often becomes an early season pest of cotton in damaging proportions as opposed to being a late season innocuous pest in the mid-southern United States. Evaluation of acaricides is important for maintaining the efficacy of and preventing resistance to the currently available arsenal of chemicals and newly developed control agents. Enumeration of spider mites for efficacy evaluations is laborious and time consuming. Therefore, subjective visual damage rating is commonly used to assess density of spider mites. The NDVI (Normalized Difference Vegetation Index) is the most widely used statistic to describe the spectral reflectance characteristics of vegetation canopy to assess plant stress and health consequent to spider mite infestations. Results demonstrated that a multispectral optical sensor is an effective tool in distinguishing varying levels of infestation caused by T. urticae on early season cotton. This remote sensing technique may be used in lieu of a visual rating to evaluate insecticide treatments.

This manuscript describes a multispectral optical sensor that effectively detected damage to early season cotton artificially infested with varying densities of the two-spotted spider mite populations.

The objective of this study was to evaluate a ground-based multispectral optical sensor as a remote sensing tool to assess foliar damage caused by the ...

Experimental Methods of Dust Charging and Mobilization on Surfaces with Exposure to Ultraviolet Radiation or Plasmas

Electrostatic dust transport has been hypothesized to explain a number of observations of unusual planetary phenomena. Here, it is demonstrated using three recently developed experiments in which dust particles are exposed to thermal plasma with beam electrons, beam electrons only, or ultraviolet (UV) radiation only. The UV light source has a narrow bandwidth in wavelength centered at 172 nm. The beam electrons with the energy of 120 eV are created with a negatively biased hot filament. When the vacuum chamber is filled with the argon gas, a thermal plasma is created in addition to the electron beam. Insulating dust particles of a few tens of microns in diameter are used in the experiments. Dust particles are recorded to be lofted to a height up to a few centimeters with a launch speed up to 1 m/s. These experiments demonstrate that photo and/or secondary electron emission from a dusty surface changes the charging mechanism of dust particles. According to the recently developed &#34;patched charge model&#34;, the emitted electrons can be re-absorbed inside microcavities between neighboring dust particles below the surface, causing the accumulation of enhanced negative charges on the surrounding dust particles. The repulsive forces between these negatively charged particles may be large enough to mobilize and lift them off the surface. These experiments present the advanced understanding of dust charging and transport on dusty surfaces, and laid a foundation for future investigations of its role in the surface evolution of airless planetary bodies.

Dust charging and mobilization is demonstrated in three experiments with exposure to thermal plasma with beam electrons, beam electrons only, or ultraviolet (UV) radiation only. These experiments present the advanced understanding of electrostatic dust transport and its role in shaping the surfaces of airless planetary bodies.

Electrostatic dust transport has been hypothesized to explain a number of observations of unusual planetary phenomena. Here, it is demonstrated using ...

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.

Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it ...

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Efficacy of public health pesticides targeting nuisance and disease-vector insects such as mosquitoes, sand flies, and filth-breeding flies is not uniform across ecological zones. To best protect public and veterinary health from these insects, the environmental limitations of pesticides need to be investigated to inform effective use of the most appropriate pesticide formulations and techniques. We have developed a research program to evaluate combinations of pesticides, pesticide application equipment, and application techniques in hot-arid desert, hot-humid tropical, warm and cool temperate, and urban locations to derive pesticide use guidelines specific to target insect and environment. To these ends we designed a system of protocols to support efficient, cost-effective, portable, and standardized evaluation of a diverse range of pesticides and equipment across multiple environments. At the core of these protocols is the use of an array of small cages with colony-reared sentinel mosquitoes (adults and immatures) and sand flies (adults), strategically arranged in natural habitats and exposed to pesticide spray. Spatial and temporal patterns of pesticide efficacy are derived from percent mortality in sentinel cages, then mapped and visualized in a geographic information system. Maps of sentinel mortality data may be statistically compared to evaluate relative efficacy of a pesticide across multiple environments, or to study multiple pesticides in a single environment. Protocols may be modified to accommodate a variety of scenarios, including, for example, the vertical orientation of sentinels in canopy habitats or simultaneous testing of ground and aerial application methods.

The efficacy of public health pesticides targeting nuisance and disease-vector insects is not uniform across different ecological zones. Here we present a system of techniques using captive vector insects as sentinels for pesticide efficacy to derive electronic maps supporting the standard evaluation of pesticides across multiple environments.

Efficacy of public health pesticides targeting nuisance and disease-vector insects such as mosquitoes, sand flies, and filth-breeding flies is not uniform ...

Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation

Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for maintaining global ecosystem services. Monitoring vegetation in these ecosystems is required to assess rangeland health, to gauge habitat suitability for wildlife and domestic livestock, to combat invasive weeds, and to elucidate temporal environmental changes. Although rangeland ecosystems cover vast areas, traditional monitoring techniques are often time-consuming and cost-inefficient, subject to high observer bias, and often lack adequate spatial information. Image-based vegetation monitoring is faster, produces permanent records (i.e., images), may result in reduced observer bias, and inherently includes adequate spatial information. Spatially balanced sampling designs are beneficial in monitoring natural resources. A protocol is presented for implementing a spatially balanced sampling design known as balanced acceptance sampling (BAS), with imagery acquired from ground-level cameras and unmanned aerial systems (UAS). A route optimization algorithm is used in addition to solve the &#8216;travelling salesperson problem&#8217; (TSP) to increase time and cost efficiency. While UAS images can be acquired 2&#8211;3x faster than handheld images, both types of images are similar to each other in terms of accuracy and precision. Lastly, the pros and cons of each method are discussed and examples of potential applications for these methods in other ecosystems are provided.

The protocol presented in this paper utilizes route optimization, balanced acceptance sampling, and ground-level and unmanned aircraft system (UAS) imagery to efficiently monitor vegetation in rangeland ecosystems. Results from images obtained from ground-level and UAS methods are compared.

Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for ...

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of cyanobacteria and related cyanotoxins in water samples and in an organic matrix, such as bivalve extracts. FDS combines remote/proximal sensing techniques with analytical/bioinformatics analyses. Sampling spots are chosen through multi-disciplinary, multi-scale, and multi-parametric monitoring in a three-dimensional physical space, including remote sensing. Microscopic observation and taxonomic analysis of the samples are performed in the laboratory setting, which allows for the identification of cyanobacterial species. Samples are then extracted with organic solvents and processed with LC-MS/MS. Data obtained by MS/MS are analyzed using a bioinformatic approach using the online platform Global Natural Products Social (GNPS) to create a network of molecules. These networks are analyzed to detect and identify toxins, comparing data of the fragmentation spectra obtained by mass spectrometry with the GNPS library. This allows for the detection of known toxins and unknown analogues that appear related in the same molecular network.

A fast-multidisciplinarystrategy for early detection of cyanobacterial blooms and associated cyanotoxins is described here. It allows for the detection of cyanobacteria and related cyanotoxins in water samples and in organic matrices, such as bivalve samples, in 24 h.

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of ...

Chemistry

Invasive Plant Biomass Assessment by Unmanned Aerial Vehicle Remote Sensing Data

Computer Vision-Based Biomass Estimation for Invasive Plants

We report on the detailed steps of a method to estimate the biomass of invasive plants based on UAV remote sensing and computer vision. To collect samples from the study area, we prepared a sample square assembly to randomize the sampling points. An unmanned aerial camera system was constructed using a drone and camera to acquire continuous RGB images of the study area through automated navigation. After completing the shooting, the aboveground biomass in the sample frame was collected, and all correspondences were labeled and packaged. The sample data was processed, and the aerial images were segmented into small images of 280 x 280 pixels to create an image dataset. A deep convolutional neural network was used to map the distribution of Mikania micrantha in the study area, and its vegetation index was obtained. The organisms collected were dried, and the dry weight was recorded as the ground truth biomass. The invasive plant biomass regression model was constructed using the K-nearest neighbor regression (KNNR)&#160;by extracting the vegetation index from the sample images as an independent variable and integrating it with the ground truth biomass as a dependent variable. The results showed that it was possible to predict the biomass of invasive plants accurately. An accurate spatial distribution map of invasive plant biomass was generated by image traversal, allowing precise identification of high-risk areas affected by invasive plants. In summary, this study demonstrates the potential of combining unmanned aerial vehicle remote sensing with&#160;machine learning techniques to estimate invasive plant biomass. It contributes significantly to the research of new technologies and methods for real-time monitoring of invasive plants and provides technical support for intelligent monitoring and hazard assessment at the regional scale.

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

We report detailed procedures for an invasive plant biomass estimation method that utilizes data obtained from unmanned aerial vehicle (UAV) remote sensing to assess biomass and capture the spatial distribution of invasive species. This approach proves highly beneficial for conducting hazard assessment and early warning of invasive plants.

We report on the detailed steps of a method to estimate the biomass of invasive plants based on UAV remote sensing and computer vision. To collect samples ...

Engineering

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.
Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods3,4,9,10,13-15,17-19,22,23,25. In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model7 with a multiobjective evolutionary algorithm SPEA226, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

This work demonstrates an integration of a water quality model with an optimization component utilizing evolutionary algorithms to solve for optimal (lowest-cost) placement of agricultural conservation practices for a specified set of water quality improvement objectives. The solutions are generated using a multi-objective approach, allowing for explicit quantification of tradeoffs.

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals ...

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm