Name: integrating remote sensing with species distribution models; mapping tamarisk invasions using the software for assisted habitat modeling (sahm)
Uploaded: 2016-10-11T15:00:00.000+00:00
Duration: 12 min 26 s
Description: 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

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.野外数据采集<ol><li>导出字段的数据来自于2005年和2006年17全州调查由红柳联盟收集了矢量多边形数据红柳。 注:通过密集的地面调查，在现场技术人员映射的所有红柳使用全球定位系统（GPS）和航拍照片沿着阿肯色河站获得的数据。 </li><li>这些多边形内，生成随机的499点（ 即派驻）来训练模型。放置两个约束集的随机点:（1）各点需要为"30米任何多边形边，以确保它是红柳内站在一个陆地卫星5 TM分辨率;和（2）所需的每个点可以从任何相邻点≥60米，以确保在整个研究站点健壮分布和最小化空间自相关。 </li><li>在三列的MS Excel文件编译现场数据与标题标有"反应"中，"X"，和"Y"，这里的响应值是:（1）对存在，UTM东向为X，以及为Y.保存该文件.csv格式用于SAHM（FieldData模块）UTM北向。 </li><li>生成红柳联盟的红柳多边形内的多边形作为独立的测试数据外额外的100随机点和另一个100个随机点来评估模型结果。以.csv格式保存该文件用于SAHM（FieldData模块）。 </li></ol> 2.预测变量<ol><li>下载陆地卫星5表面反射L4-5 TM图片来自美国地质调查局的全球可视化浏览器/ E​​arthExplorer（路径32，排34）（http://earthexplorer.usgs.gov/）。这些场景包括由科罗拉多红柳联盟在2005年和2006年17取样的程度。为了确定用于模型的几个月里，选择场景是基本上无云（ 即 &lt;10％的云层），并代表Source认为有区别的柽柳候个电子;这些都是2004年10月，2005年4月，2005年5月，2005年6月，2005年7月，2005年9月，2006年4月，2006年5月，2006年6月，2006年7月，2006年8月，2006年9月和2006年11月。 </li><li>请从遥感指数衍生工具 https://github.com/rander38/Remote-Sensing-Indices-Derivation-Tool。 </li><li>运行该工具的Python脚本，无论是GDAL或ArcPy中的版本;建议GDAL。 </li><li>选择适当的卫星传感器，期望的指数，并设定输入的图像文件并在文件将被存储在输出文件夹（ 图1）。我们出口的各个频段和使用的NDVI，SAVI，和缨帽亮度，绿色，以及从每一个Landsat TM的场景衍生湿度指数。请注意，任何指标可以被修改或改变Sensors_Formulas_RSIDT.ini文件加入。 </li></ol><img alt="图1"SRC ="/文件/ ftp_upload / 54578 / 54578fig1.jpg"/&gt; 图1.遥感指数衍生工具的GUI。 <ol start="5"><li>运行该工具，并在ArcMap v直观地验证输出文件。10.0（ESRI，雷德兰兹，CA）或其他地理信息系统软件。 </li></ol> 3.软件辅助建模居（SAHM）（图2） <img alt="图1" src="/files/ftp_upload/54578/54578fig2.jpg" /> 图2.整个SAHM工作流程包括输入数据，预处理，初步的模型分析和决策，相关模型，和输出过程。 <ol><li>在https://my.usgs.gov/catalog/RAM/SAHM运行SAHM，来自美国地质调查局网站先下载文件（包括VisTrails）。请参阅用户手册在同一网站上下载和安装SAHM详细说明。请注意，该网站也有一个SAHM tutoriaL和附加说明的数据。 </li><li>为了开发红柳物种分布模型，使用随包下载SAHM_tutorial_2.0.vt文件（在SAHM例子文件夹）。在历史视图中，选择独立位置的工作流程。其它工作流的例子，可以选择并取决于研究的目标;描述提供与每个。选择管道 。 </li><li>设置输出文件夹，通过将包，然后SAHM&gt; 更改会话文件夹 。整个工作流开发过程中，每一个步骤和选项的详细描述可通过选择位于SAHM查看器画面的右侧的文件标签被发现。在下列方法中列出的所有模块可在SAHM窗口的SAHM标签下的左侧找到。 </li><li>接着，直接SAHM到将成为字段数据用来训练的物种分布模型。 <ol><li>点击TemplateLayer模块。浏览到将被用作掩模，并以限定分析的投影，细胞的大小和程度的栅格。 </li><li>点击在工作流的左侧FieldData模块。浏览到.csv（ 即 training.csv）字段数据模块中的现场数据（存在点或有无分）的文件。 </li><li>点击PredictorListFile模块上浏览到.csv文件列表（ 例如 ，包含完整路径预测的所有文件，在使用建模参考用户手册）。 </li></ol></li><li>接下来，进行预处理步骤。 <ol><li>单击FieldDataQuery模块上，并且响应柱与列标题填补反应（ 即 ，在FieldData.csv的列名）中，X和Y列。 </li><li>点击MDSBuilder模块 。设置backgroundPointField 10000。 注:如果您使用的品种存在和不存在数据被模仿，你并不需要更改backgroundPointField;你将包括在该领域Data.csv的响应（0）这些位置。它是可选的设置backgroundProbSurf如果希望通过指向值的范围从0到100的光栅表面，以限制的区域内的背景点选择（这些值代表了一个随机生成的将被保留下来应该它落入一个概率特定的细胞）。在这项研究中，使用具有100值的backgroundProbSurf一个5000米缓冲器阿肯色河和0为地区，这种缓冲液（基于由红柳联合取样的总面积）的外侧范围内。 </li></ol></li><li>要使用，然后指定物种分布建模算法。 <ol><li>需要注意的是BoostedRegressionTree，GLM， 火星和镭ndomForest模块在独立的地点工作流程已经建立。在MAXENT模块添加到工作流程，测试所有五种型号。它连接到CovariateCorrelationAndSelection模块。 注:为所有型号的默认设置开始;这些可以根据研究目的（详情请参阅模型文档）进行修改。 </li><li>添加ModelOutputViewer模块，并将其连接到MAXENT模块;改变柱5和行为1。ModelOutputViewer产生一个可用于模型的结果进行比较的电子表格。 </li><li>点击OutputName模块上，然后键入子文件夹名称。 </li></ol></li><li>接下来，添加创建模型输出的合奏的模块。这个模块产生两个输出映射;愉快，包括输出的平均连续概率和与模型的数量的计数的第二带正仓元概率。 <ol><li>一个EnsembleBuilder模块添加到工作流程。这是可选的设置阈值度量;本次研究中，选择AUC和0.75设为默认的阈值。这将确保只有机型的AUC值大于或等于0.75包括在合奏地图输出。连接BoostedRegressionTree，GLM，MARS， 随机森林 和MAXENT模块的EnsembleBuilder。 </li></ol></li><li>接着，直接模式，以独立的测试数据。 <ol><li>点击其他FieldData模块（工作流的右侧）并浏览到包含模型验证数据的.csv文件。这些都是在协议的步骤1.4中产生的200有无分。 </li><li>点击FieldDataQuery模块上，确保响应，x和y列相匹配的列我n个字段数据.csv格式。 </li><li>添加ApplyModel模块，并将其连接到MAXENT模块。添加ModelOutputViewer模块，并将其连接到这个ApplyModel模块;更改列5和行1.在菜单中，选择套餐 - &gt; SAHM - &gt; 更改处理模式 。既然你要运行一个以上的型号，选择单一机型顺序（N - 1每个内核）。这将通过以多个计算机的核心优势加速模型的执行时间。 </li></ol></li><li>接下来，执行物种分布模型。 <ol><li> 保存文件.vt，然后点击执行 。 </li><li>当CovariateCorrelationAndSelection插件（ 图3）显示，去选择每个相关对变量，其中相关系数为一的| r | ≥0.7（基于％ 越轨从单变量干事解释<html>源化此小部件（ 图3）和知情生态决定的左侧看到的添加剂模型;在这项研究中的优先级的至少一个协变量的选择每个月捕获红柳候变化）。在观察窗图解的数量可以通过在数字输入（默认为8）和点击更新来改变。 </li><li>敲定协后，选择在CovariateCorrelationAndSelection小部件（ 图3）的底部确定。在这项研究中，以下9个变量被保留: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和Nov_19_2006_SAVI。该物种分布模型将选择确定后执行。 </li></ol></li></ol>JPG"/&gt; 图3.协变量的相关性和选择SAHM接口。 <ol start="10"><li>结果输出。 注:该机型完成后，VisTrails电子表格出现模型比较（ 图4） <ol><li>比较AUC地块，文本输出，响应曲线，校准，混淆矩阵，残差和响应曲线模型之间。 注:从SAHM模型输出包括CovariateCorrelationOutputMDS，混淆矩阵，残差图，校正曲线，模型评估的情节，变量重要性的情节，响应曲线的文件夹，扩大输出文件夹bin地图，乱七八糟的地图，MOD地图，概率地图，残图，和一个output.txt的;看到SAHM用户指南，详细的描述。 </li></ol></li></ol><img alt="图4" src="/files/ftp_upload/54578/54578fig4.jpg" /> 图4. VisTrails电子表格可以用来评估模型输出。这是训练数据的AUC模型比较;从左至右分别为车型是BRT，GLM，MARS，RF和Maxent模型。

<ol>
	<li>Evangelista, P. H., Stohlgren, T. J., Morisette, J. T., Kumar, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+invasive+tamarisk+(Tamarix):+a+comparison+of+single-scene+and+time-series+analyses+of+remotely+sensed+data.">Mapping invasive tamarisk (Tamarix): a comparison of single-scene and time-series analyses of remotely sensed data.</a> Remote Sensing. 1, 519-533 (2009).</li><li>DiTomaso, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact,+biology,+and+ecology+of+saltcedar+(Tamarix+spp.)+in+the+southwestern+United+States.">Impact, biology, and ecology of saltcedar (Tamarix spp.) in the southwestern United States.</a> Weed Technology. 12, 326-336 (1998).</li><li>Evangelista, P., Kumar, S., Stohlgren, T., Crall, A., Newman, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+above-ground+biomass+of+Tamarisk+ramosissima+in+the+Arkansas+River+Basin+of+Southeastern+Colorado,+USA.">Modeling above-ground biomass of Tamarisk ramosissima in the Arkansas River Basin of Southeastern Colorado, USA.</a> Western North American Naturalist. 67 (4), 503-509 (2007).</li><li>Hirano, A., Madden, M., Welch, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hyperspectral+image+data+for+mapping+wetland+vegetation.">Hyperspectral image data for mapping wetland vegetation.</a> Wetlands. 23 (2), 436-448 (2003).</li><li>Ge, S., Carruthers, R., Gong, P., Herrera, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texture+analysis+for+mapping+Tamarix+pariviflora+using+aerial+photographs+along+Cache+Creek,+California.">Texture analysis for mapping Tamarix pariviflora using aerial photographs along Cache Creek, California.</a> Environmental Monitoring and Assessment. 114, 65-83 (2006).</li><li>Hamada, Y., Stow, D. A., Coulter, L. L., Jafolla, J. C., Hendricks, L. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+tamarisk+species+(Tamarisk+spp.)+in+riparian+habitats+of+Southern+California+using+high+spatial+resolution+hyperspectral+imagery.">Detecting tamarisk species (Tamarisk spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery.</a> Remote Sensing of Environment. 109, 237-248 (2007).</li><li>York, P., Evangelista, P., Kumar, S., Graham, J., Flather, C., Stohlgren, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Habitat+Overlap+Analysis+derived+from+Maxent+for+Tamarisk+and+the+Southwestern+Willow+Flycatcher.">A Habitat Overlap Analysis derived from Maxent for Tamarisk and the Southwestern Willow Flycatcher.</a> Frontiers of Earth Science. 5 (2), 120-129 (2011).</li><li>Myneni, R. B., Ramakrishna, R., Nemani, R., Running, S. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimation+of+global+leaf+area+index+and+absorbed+par+using+radiative+transfer+models.">Estimation of global leaf area index and absorbed par using radiative transfer models.</a> Geoscience and Remote Sensing. 35 (6), 1380-1393 (1997).</li><li>Todd, S. W., Hoffer, R. M., Milchunas, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biomass+estimation+on+grazed+and+ungrazed+rangelands+using+spectral+indices.">Biomass estimation on grazed and ungrazed rangelands using spectral indices.</a> International Journal of Remote Sensing. 19 (3), 427-438 (1998).</li><li>Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+vegetation+systems+in+the+Great+Plains+with+ERTS.">Monitoring vegetation systems in the Great Plains with ERTS.</a> Proceedings of the Third Earth Resources Technology Satellite-1 Symposium. , (1974).</li><li>Huete, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Soil-Adjusted+Vegetation+Index+(SAVI).">A Soil-Adjusted Vegetation Index (SAVI).</a> Remote Sensing of Environment. 25, 295-309 (1988).</li><li>Kauth, R. J., Thomas, G. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+tasselled+cap+-+a+graphic+description+of+the+spectral-temporal+development+of+agricultural+crops+as+seen+in+Landsat.">The tasselled cap - a graphic description of the spectral-temporal development of agricultural crops as seen in Landsat.</a> Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. , 41-51 (1976).</li><li>Cohen, W. B., Spies, T. A., Fiorella, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+the+age+and+structure+of+forests+in+a+multi-ownership+landscape+of+western+Oregon,+USA.">Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, USA.</a> International Journal of Remote Sensing. 16, 721-746 (1995).</li><li>Jin, S., Sader, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+time+series+tasselled+cap+wetness+and+the+normalized+difference+moisture+index+in+detecting+forest+disturbances.">Comparison of time series tasselled cap wetness and the normalized difference moisture index in detecting forest disturbances.</a> Remote Sensing of Environment. 94 (3), 364-372 (2005).</li><li>Crist, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+TM+Tasseled+Cap+equivalent+transformation+for+reflectance+factor+data.">A TM Tasseled Cap equivalent transformation for reflectance factor data.</a> Remote Sensing of Environment. 17 (3), 301-306 (1985).</li><li>Lindauer, I. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+the+plant+communities+of+the+South+Platte+and+Arkansas+River+drainages+in+eastern+Colorado.">A comparison of the plant communities of the South Platte and Arkansas River drainages in eastern Colorado.</a> The Southwestern Naturalist. 28 (3), 249-259 (1983).</li><li>Guisan, A., Zimmerman, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+habitat+distribution+models+in+ecology.">Predictive habitat distribution models in ecology.</a> Ecological Modeling. 135, 147-186 (2000).</li><li>Friedman, J. H., Hastie, T., Tibshirani, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Additive+logistic+regression:+a+statistical+view+of+boosting.">Additive logistic regression: a statistical view of boosting.</a> Annals of Statistics. 28 (2), 337-407 (2000).</li><li>Breiman, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Random+forests.">Random forests.</a> Machine Learning. 45 (1), 5-32 (2001).</li><li>Friedman, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multivariate+adaptive+regression+splines.">Multivariate adaptive regression splines.</a> Annals of Statistics. 19 (1), 1-141 (1991).</li><li>McCullagh, P., Nelder, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Generalized Linear Models, 2nd ed. , (1989).</li><li>Phillips, S. J., Anderson, R. P., Schapire, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Maximum+entropy+modeling+of+species+geographic+distributions.">Maximum entropy modeling of species geographic distributions.</a> Ecological Modelling. 190 (3-4), 231-259 (2006).</li><li>Araujo, M. B., New, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ensemble+forecasting+of+species+distributions.">Ensemble forecasting of species distributions.</a> Trends in Ecology and Evolution. 22, 42-47 (2007).</li><li>Elith, J., Graham, C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Do+they?+How+do+they?+Why+do+they+differ?+On+finding+reasons+for+differing+performances+of+species+distribution+models.">Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models.</a> Ecography. 32, 66-77 (2009).</li><li>Freire, J., Silva, C., Callahan, S., Santos, E., Schedegger, C., Moreau, L., Foster, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Managing+rapidly-evolving+scientific+workflows.">Managing rapidly-evolving scientific workflows.</a> International Provenance and Annotation Workshop (IPAW). , 10-18 (2006).</li><li>Morisette, J. T., Jarnevich, C. S., Holcombe, T. R., Talbert, C. B., Ignizio, D., Talbert, M. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=VisTrails+SAHM:+visualization+and+workflow+management+for+species+habitat+modeling.">VisTrails SAHM: visualization and workflow management for species habitat modeling.</a> Ecography. 36 (2), 129-135 (2013).</li><li>Fielding, A. H., Bell, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+of+methods+for+the+assessment+of+prediction+errors+in+conservation+presence/absence+models.">A review of methods for the assessment of prediction errors in conservation presence/absence models.</a> Environmental Conservation. 24, 38-49 (1997).</li><li>Allouche, O., Tsoar, A., Kadmon, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+accuracy+of+species+distribution+models:+prevalence,+kappa+and+the+true+skill+statistic+(TSS).">Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS).</a> Journal of Applied Ecology. 43 (6), 1223-1232 (2006).</li><li>Stohlgren, T. J., Ma, P., Kumar, S., Rocca, M., Morisette, J., Jarnevich, C. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ensemble+habitat+mapping+of+invasive+plant+species.">Ensemble habitat mapping of invasive plant species.</a> Risk Analysis. 30, 224-235 (2010).</li><li>Dormann, C. F., Purschke, O., Marquez, J. R. G., Lautenbach, S., Schrader, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Components+of+uncertainty+in+species+distribution+analysis:+A+case+study+of+the+great+grey+shrike.">Components of uncertainty in species distribution analysis: A case study of the great grey shrike.</a> Ecology. 89, 3371-3386 (2008).</li><li>Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., Thuiller, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+consensus+methods+in+predictive+species+distribution+modelling.">Evaluation of consensus methods in predictive species distribution modelling.</a> Diversity and Distributions. 15, 59-69 (2009).</li></ol>

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

我们的结果表明拟合BRT，RF，MARS，GLM和Maxent模型与红柳存在点和时间序列遥感的Landsat卫星影像数据可在景观区分红柳，是一种有效的替代传统的单一场景分类方法。这是从我们的结果六月是我们研究范围内检测红柳一个特别重要的时间明确;此同意与2009年1斯塔等。这表明六月旱涝是红柳发生在这方面基于一个Maxent模型模型拟合以时间序列陆地卫星图像的最重要的预测。 被纳入BRT，RF，火星和Maxent模型的模型，其他光谱指数和乐队可以从土壤基质，其他落叶乔木，包括三叶（ 杨树 ）和柳树进一步区分红柳（ 柳树 ），或灌溉农业的是在较低的共同阿肯色河流域。其他GIS图层，如地形，土壤类型和气候数据也被视为协变量和包含在这些模型中，但我们建议保持这些到最低限度，如果目的是检测当前的物种分布的景观，而不是预测的潜力发生或适宜的栖息地。 为我们的研究测试的模型提供了强有力的分析能力以及评估结果的多个选项。让所有在单一框架内，这些相关模型，如SAHM的，允许正规化和建模过程的记录温顺。前和响应和预测变量的后处理被标准化在SAHM，允许更好和更有效的模型进行比较，而工作流记录的分析提供便利的修改，迭代和复制的每一个步骤。 合奏映射的目的是几个相关模型的优势结合起来，同时尽量减少在W任何一个模型30 eakness。我们认为，这是在我们的研究的情况;然而，我们告诫说是表现不佳（ 即下预测或过度预测）模型可以削弱整体效果。在文献中的有限使用合奏映射已有利的结果，但大多数这些方法试图"预测"物种发生，而不是"检测"。此外，乐团映射允许不确定性的不同的建模方法之间的视觉评估，确定示范协议的水平。最常见的是建模方法的选择（ 例如 ，GLM对BRT）是对模型的结果，而不是在建模过程中其他决定，如位置数据的不确定性最大的31量化的影响。虽然我们相信，我们最好的红柳地图是所有五种模式是一致的，进一步的测试和使用Ensemble映射的各种方法建议（ 例如 ，通过AUC加权）32 &lt;/ SUP&gt;，并通过独立的野外观测的最佳验证。总之，这些方法可以很容易地适合于使用衍生于SAHM一个给定的研究区域的环境变量其他物种的分布进行建模。

在整个美国西南部滨河湿地生态系统正由红柳的入侵的威胁（ 柽柳 ），欧亚大陆在1800年1引入了非本土木本灌木。红柳具有许多生理机制，允许类利用水资源，外竞争本地物种，并改变生态系统过程1-2。评估环境影响，并制定有效的控制策略映射红柳分布是为资源管理者高度优先事项。虽然地面调查仍经常使用，它们是不切实际的非常大的地区，由于人力，时间，和物流相关成本。 卫星遥感起到了红柳侵扰检测和映射一个重要的，但有限，角色。传统的分类分析和遥感软件有边际的成功3-5。最近的几项研究具备检测利用遥感数据1,6入侵植物探索非传统的方法。红柳，像许多外来入侵植物，展品在整个生长季节，从本地河岸物种的物候的不同物候的变化。在一些地区，例如，红柳叶出一些原生河岸植物面前，红柳保留其枝叶比其他本地物种更长的时间。通过使用光谱波段，并从整个生长季节时间序列卫星数据得出光谱指数，我们可以区分基于这些物候差异1,6原生植物红柳。建立在Evangelista 等人的工作。 2009年1月，在这项研究中，我们从时间序列的Landsat 5专题制图仪（TM）的卫星图像纳入各个波段1-7和衍生归一化植被指数（NDVI），土壤调节植被指数（SAVI）和缨帽从这些频带转换。归differen策植被指数（NDVI）是用于估计植被的生物量，篷盖，和叶面积指数8-9中最常用的光谱指数之一，并且是可见的（红色）和近之间的比率的一个非线性变换红外波段10。土壤调节植被指数（SAVI）是用来减少土壤背景对植被指数11影响的修改NDVI。缨帽变换进行加权六个陆地卫星频带的复合材料成测量土壤亮度（缨帽，频带1），植被绿色（缨帽，频带2），和土壤/植被湿润（缨帽，带3）和三个正交频段经常被用来区分植被组合物，龄级，和结构12-14。我们使用了克里斯特（1985）15日报道，为所有缨帽变换系数。 在这项研究中，我们测试五个品种分布模型与时间序列光谱波段和蔬菜从陆地卫星5 TM衍生etation指数映射沿下阿肯色河红柳在科罗拉多州东南部，美国。阿肯色河，跨越2364公里（1,469英里），是密苏里州的密西西比系的第二大支流。它涵盖分水岭在科罗拉多落基山脉的源头435123公里2（168002英里2）。从其在2965米起源，阿肯色州大幅下降的高程，拉平普韦布洛，CO附近，并通过农田和短草草原蜿蜒。这条河是受季节性洪水，并依靠在洛矶福特，拉詹塔，和拉马尔市政和农业用水，持续到堪萨斯州，俄克拉何马州，阿肯色州和它流入密西西比河之前。红柳首次在阿肯色河由R. Niedrach观察到1913年近16拉马尔的当今镇。今天，据估计，柽柳覆盖普韦布洛和堪萨斯州林间100多平方公里即，与另外60 平方公里 ，沿阿肯色河17条支流。研究区域包括灌溉沟渠，湿地，农田等几个支流汇流;所有伴有不同程度的红柳为患。牧场和农业是主要的土地利用毗邻主要包括苜蓿，干草，玉米和小麦的河岸走廊。 物种分布模型依赖于地理参考事件（ 例如，纬度，经度）来确定一个物种的发生与环境18之间的关系。环境数据可以包括多个遥感和其他空间层。我们测试了五种分布模型包括升压回归树（BRT）19，随机森林（RF）20，多元自适应回归样条（MARS）21，广义线性模型（GLM）22，和Maxent模型23。这五个MOD埃尔算法是最常用的物种分布建模之间，并且许多研究已证明其功效24-25。我们使用的软件辅助建模居（SAHM）诉2.0模块来执行五款机型，其中包含在VisTrails v.2.2.2 26可视化和处理软件。有几个优点使用SAHM为比较建模。除了形式化和建模过程的记录温顺，SAHM允许用户与个别，有不同的接口，软件和文件格式27多个物种分布模型的算法工作。 SAHM产生一致的门槛独立和阈值相关的评价指标，评价模型的性能。其中之一是地区根据ROC曲线（AUC），计算结果为背景，从28歧视存在的模型的能力的门槛独立指标。一个AUC VAL0.5或更低UE表示模型的预测并不比随机更好或更坏; 0.5和0.70之间的值表示性能差;和值从0.70到1.0增加表明逐步更高的性能。另一个指标是百分之正确分类（PCC），即重灵敏度和特异性基于度量的用户定义的阈值的阈值依赖度量;灵敏度测量观察派驻人员划分为适宜和特异性的百分比衡量的背景地点列为不适宜的百分比。然而，另一个指标是真正的技术统计（TSS =灵敏度+特异- 1），这使更多的重量比特异性模型的灵敏度，值的范围-1到1，其中值&gt; 0表示比29的机会更好的模型性能之间。 使用模式输出映射红柳，我们构建二进制分类使用均衡的灵敏度和特异性来定义在p阈resence与否红柳。然后，这些个体模型推导出的地图进行相加，形成一个集成的地图30。乐团地图结合个别物种分布模型的预测产生分类图，排列测试的模型的集体协议。例如，一个合奏细胞值表示只有一个型号划分该小区为合适的栖息地，而五个值指示所有五款车型分类的小区作为合适的栖息地。一个优点这种方法是合奏的地图产生较低的平均误差比任何单独的模型。它也允许用户直观地比较测试每个模型的性能。我们的总体目标是提供的这些方法可适合于物种对景观的电流分布进行建模的详细描述。

<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)

入侵植物物种的早期检测是对自然资源和生态系统过程的保护管理是至关重要的。使用用于映射侵入植物分布卫星遥感的正变得越来越普遍，但是传统的成像软件和分类方法已被证明是不可靠的。在这项研究中，我们测试和评估采用沿阿肯色河东南部科罗拉多五个品种分布模型技术契合卫星遥感数据映射侵入柽柳（ 柽柳 ）。测试的模型包括升压回归树（BRT），随机森林（RF），多元自适应回归样条（MARS），广义线性模型（GLM）和Maxent模型。这些分析采用了新开发的软件包叫做软件辅助建模居（SAHM）进行。所有型号都带有499的存在点10000伪缺席点，并预测变量acqu受训来自八个月期间陆地卫星5专题制图仪（TM）传感器，红外发光二极管，从使用的物候差异检测原生河岸植被区分红柳。从陆地卫星的场景中，我们使用各个波段和计算的归一化植被指数（NDVI），土壤，调节植被指数（SAVI），并加盖流苏转换。确定对景观当前红柳分配所有五款车型成功的基础上独立的门槛和阈值相关的评价指标与位置无关的数据。为了说明模型的具体差异，我们生产的所有五款车型的合奏地图输出高亮和协议方面不确定性的领域。我们的研究结果表明物种分布模型的有效性在分析遥感数据和测绘合奏的效用，并展示在预处理SAHM的能力，执行多个复杂的模型。

<html>基于独立测试数据集BRT，RF，MARS，GLM和Maxent模型的统计评价显示所有五个模型检测红柳表现相对较好;有模型中的阈值的独立和阈值取决于评价指标相差不大。 AUC值均&gt; 0.88，％的正确分类值均&gt; 77％，敏感性和特异性均&gt; 0.77和TSS均&gt; 0.54（表1）。二元模型输出的合奏透露沿阿肯色河（图5）地区多模协议。残局（多元环境的相似性面）每个模型映射输出指示研究区的现有环境下以及取样（图6），进一步提高我们的集成方法的信心。 方法"&gt; <tbody><tr><td>模型</td><td> AUC </td><td> PCC </td><td>灵敏度</td><td>特异性</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>火星</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> 表1。独立的阈值（AUC）和阈值依赖（PCC，敏感性，特异性和TSS）评价指标BRT，RF，MARS，GLM和Maxent模型模型适合独立的测试<html>红柳存在和不存在的数据集。 <img alt="图5" src="/files/ftp_upload/54578/54578fig5.jpg" /> 图5.合奏结果在ArcGIS结合BRT，GLM，MARS，RF和Maxent模型二进制输出地图。区域通过的模型数目在协议着色，从0（无颜色）至5（红色）。注意在预测西北角的彩色区域;这条线是陆地卫星图像的假象;因此，模型结果应该在这个区域谨慎服用。 <a href="https://www.jove.com/files/ftp_upload/54578/54578fig5large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图6" src="/files/ftp_upload/54578/54578fig6.jpg" /> 图6.多元环境相似表面（MESS）输出。目标="_空白"&gt;点击此处查看该图的放大版本。 使用九个预测的，2006年6月30日的亮度是所有五个模型（表2）的最重要的变量。这是基于逐步赤池信息标准由GLM保留的唯一变量（AIC;这是在SAHM GLM模型选择默认值），不过需要注意的这款机型还包括这个变量的平方项是很重要的。 RF和Maxent模型默认保留所有变量。 <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td>预报器</td><td> BRT </td><td> RF </td><td>火星</td><td> GLM </td><td> MAXENT </td></tr><tr><td> 2006年7月30日亮度</td><td> 41.60 </td><td> 34.11 </td><td> 76.78 </td><td> 100 </td><td> 67.27 </td></tr><tr><td> 2006年8月31日四级</td><td> 6.35 </td><td> 5.87 </td><td> 5.16 </td><td> 0 </td><td> 2.82 </td></tr><tr><td> 2005年6月9日SAVI </td><td> 13.67 </td><td> 14.09 </td><td> 9.14 </td><td> 0 </td><td> 9.75 </td></tr><tr><td> 2005年4月22日亮度</td><td> 6.29 </td><td> 6.30 </td><td> 0 </td><td> 0 </td><td> 0.43 </td></tr><tr><td> 2004年10月28日NDVI </td><td> 5.66 </td><td> 8.25 </td><td> 0 </td><td> 0 </td><td> 2.94 </td></tr></tbody></table> 表2.每个模型预测的相对重要性。

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.

整合与物种分布模型遥感;映射红柳入侵使用软件进行辅助建模居（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

Nanyang Technological University

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JoVE Journal

Environment

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

13.2K 次观看.  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.

视频: 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.

一种优化富集技术的隔离<em&gt;节杆菌</em&gt;噬菌体种从土壤样品隔离

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

科研

环境科学

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.

协议的鲁棒抗除草剂试验在不同杂草

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.

温室棉二斑叶螨危害的遥感评价

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.

紫外辐射或等离子体照射表面的粉尘充电和动员实验方法

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.

利用主成分扩展地形模型绘制土壤再分配和土壤有机碳

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.

农药在田间对病媒蚊子的影响

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.

使用快速检测策略早期检测蓝藻花和相关氰毒素

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

化学

基于无人机遥感数据的入侵植物生物量评估

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.

作者聚焦:用于高效入侵植物生物量估算的无人机遥感

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.

基于计算机视觉的入侵植物生物量估算

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 ...

整合与物种分布模型遥感;映射红柳入侵使用软件进行辅助建模居（SAHM）

摘要

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此视频中的章节

一种优化富集技术的隔离

协议的鲁棒抗除草剂试验在不同杂草

温室棉二斑叶螨危害的遥感评价

紫外辐射或等离子体照射表面的粉尘充电和动员实验方法

利用主成分扩展地形模型绘制土壤再分配和土壤有机碳

农药在田间对病媒蚊子的影响

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

使用快速检测策略早期检测蓝藻花和相关氰毒素

基于计算机视觉的入侵植物生物量估算

基于计算机视觉的入侵植物生物量估算