LeWangsaltcedar 定量遥感班课件

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1、利用多分辨率遥感数据进行侵入物种 利用多分辨率遥感数据进行侵入物种 传播建模 王乐 副教授(终身教授),研究生主任 纽约州立大学布法罗分校地理系 纽约州立大学布法罗分校地理系Tamarisk (Saltcedar) () Tamarisk is a large shrub to small tree native to Africa and small tree native to Africa and Eurasia. It was introduced in the western U.S. in 1837 as “ornamental vegetation“ and for wind a

2、nd erosion control. USDA tamarisk is Credit: NASA Tamarisk covers 1.5 Million Acres in West USDA: tamarisk is one of the most harmful invasive species in the Three tamarisk species: T. Parviflora, Th i i( C h i invasive species in the nation. NASA: Earth Science T.chinensis (Chinese Tamarisk) Enterp

3、rise: Invasive species is one ofHuman-induced Anderson et al., 1993 High Spatial resolution Fuller, 2005, Turner et al., 2003 Hyperspectral Remote Sensing Underwood et al 2003 ONeill 2000 Lass Underwood et al., 2003, O Neill, 2000, Lass and Prather, 2004 Study Site: Forgotten River River bank taken

4、up by saltcedar up by saltcedar Leaf off Leafoff Mesquite Research Question 1 Q What have been the annual expansion of saltcedar p and the dynamics of associated native species within the period 1993-2003? Prior observations: Saltcedar expansion rate about 20km of river length Saltcedar expansion ra

5、te about 20km of river length per year (Graf 1978). Greatest differential success of saltcedar during drought years (Cleverly et al. 1997) Hypotheses: Hypotheses: Saltcedar has actively displaced native species Advancement reinforced during drought years Advancement reinforced during drought yearsRe

6、search Question 2 Q How has the spatial and geographical context p ggp influenced the saltcedar spread? Prior observations: Positive correlation with minimum annual temperature and low elevations (Friedman et al 2005) and low elevations (Friedman et al. 2005) Hypothesis: Hypothesis: Accesibility to

7、the river have promoted its advance into adjacent upland habitats Fragmentation of native forrest influences saltcedar spreadMethodology gy Geocomputation Geocomputation Continuous Cellular Automata (CA) Artificial Neural Networks (ANN) Remote Sensing Hi h S ti l (Q i kBi d) d H t l (AISA) High Spat

8、ial (QuickBird) and Hyperspectral (AISA) remote sensing classification at species level Saltcedar Density mapping through Landsat TM/ETM+ yp pgg GIS Proximity analysis Data management and visualizationSix Field Campaigns pg Oct 8-10 2005: drive through most of the counties Oct. 8 10,2005: drive thro

9、ugh most of the counties along the Rio Grande river Nov. 17-19,2005: collect GPS ground truthing data in Nov. 17 19,2005: collect GPS ground truthing data in Presidio, TX Dec. 17-19,2005: collect additional GPS readings g and measure field hyperspectral data to calibrate AISA acquisition Mar. 29,200

10、6: collect field hyperspectral data June 26-30, 2006: collect field hyperspectral dt tt tdi tit hB iBd t i l data, start a new study site in the Big Bend national park Dec 10 2006 collect field h perspectral data LAI Dec.10, 2006: collect field hyperspectral data, LAI measurementsGround Truthing (GP

11、S with 1m Accuracy) g( y)Field hyperspectral measurements yp p1. Species ClassificationWithin-Class VariationField Spectra and AISA Spectra pp Field Spectra AISA Spectra pAISA (1 m, 62 bands) Dec. 23, 2005 QuickBird (0.61 m, 4 bands) D 8 2004 Dec. 23, 2005 Dec. 8, 2004Classification scheme Level 4 Level 2 Water Water Road Soil Roof Sand Sa d Gravel Herbaceous Green Hb Herbaceous Herbaceous Dry Wetland Giant Saltcedar Saltcedar Dry Saltcedear Green Saltcedar Mi t Mesquite Mesquite Poverty Weed Mysterious Bush Creosote bush Creosote bush Mashweed

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