Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing

This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 × 2.3 m spatial resolution), collected over the U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R 2 > 0.80. The use of REPs failed to accurately predict LAI (R 2 < 0.2). The use of the MTMF-derived metrics (matched

[1]  J. J. Colls,et al.  Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks , 2004 .

[2]  C. Daughtry,et al.  Plant Litter and Soil Reflectance , 2000 .

[3]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[4]  Jungho Im,et al.  Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments , 2009 .

[5]  Jean-Michel Poggi,et al.  Boosting and instability for regression trees , 2006, Comput. Stat. Data Anal..

[6]  F. Baret,et al.  Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data : Principles and validation , 2006 .

[7]  Angela Harris,et al.  A multi-scale remote sensing approach for monitoring northern peatland hydrology: present possibilities and future challenges. , 2009, Journal of environmental management.

[8]  Simon D. Jones,et al.  Remote sensing of nitrogen and water stress in wheat , 2007 .

[9]  G. Guyot,et al.  Utilisation de la Haute Resolution Spectrale pour Suivre L'etat des Couverts Vegetaux , 1988 .

[10]  J. Boardman,et al.  Mapping target signatures via partial unmixing of AVIRIS data: in Summaries , 1995 .

[11]  Do.  Evaluation of Subsurface Engineered Barriers at Waste Sites , 1998 .

[12]  M. Cho,et al.  A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method , 2006 .

[13]  Stuart R. Phinn,et al.  Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach , 2011, Remote. Sens..

[14]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[15]  J. R. Jensen,et al.  Remote Sensing Agricultural Crop Type for Sustainable Development in South Africa , 2006 .

[16]  N. Coops,et al.  Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications. , 2009 .

[17]  M. Hodgson,et al.  Object-Based Land Cover Classification Using High-Posting-Density LiDAR Data , 2008 .

[18]  Edward P. Glenn,et al.  Rapid nitrate loss from a contaminated desert soil , 2005 .

[19]  Geneva G. Belford,et al.  Instability of decision tree classification algorithms , 2001, KDD.

[20]  C. Mao,et al.  Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.) , 2003 .

[21]  A. Nonomura,et al.  Impact of land use and land cover changes on the ambient temperature in a middle scale city, Takamatsu, in Southwest Japan. , 2009, Journal of environmental management.

[22]  Craig H. Benson,et al.  Sustainable Covers for Uranium Mill Tailings, USA: Alternative Design, Performance, and Renovation , 2009 .

[23]  John R. Jensen,et al.  A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .

[24]  P. Curran,et al.  Technical Note Grass chlorophyll and the reflectance red edge , 1996 .

[25]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[26]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[27]  K. Soudani,et al.  Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .

[28]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .

[29]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[30]  S. Ustin,et al.  The role of environmental context in mapping invasive plants with hyperspectral image data , 2008 .

[31]  A. Viña,et al.  Mapping understory vegetation using phenological characteristics derived from remotely sensed data , 2010 .

[32]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[33]  Craig H. Benson,et al.  Water Balance Covers for Waste Containment: Principles and Practice , 2010 .

[34]  Johannes R. Sveinsson,et al.  Multiple classifiers applied to multisource remote sensing data , 2002, IEEE Trans. Geosci. Remote. Sens..

[35]  Barry Haack,et al.  Visible and Infrared Remote Imaging of Hazardous Waste: A Review , 2010, Remote. Sens..

[36]  J. G. Lyon,et al.  Hyperspectral Remote Sensing of Vegetation , 2011 .

[37]  Uwe Stilla,et al.  Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification , 2011, Remote. Sens..

[38]  P. M. Hansena,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[39]  Ronald J. P. Lyon,et al.  Influence of rock-soil spectral variation on the assessment of green biomass , 1985 .

[40]  J. D. Herman,et al.  A temporal and spatial resolution remote sensing study of a Michigan Superfund site , 1994 .

[41]  J. Townshend,et al.  A stepwise regression tree for nonlinear approximation: Applications to estimating subpixel land cover , 2003 .

[42]  Dar A. Roberts,et al.  Mapping Canadian boreal forest vegetation using pigment and water absorption features derived from the AVIRIS sensor , 2001 .

[43]  Jungho Im,et al.  Population estimation based on multi-sensor data fusion , 2010 .

[44]  Yuri Knyazikhin,et al.  Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .

[45]  Jungho Im,et al.  Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification , 2010 .

[46]  Amy E. Parker Williams,et al.  Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering , 2002 .

[47]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[48]  J. A. Tullis,et al.  A Remote Sensing and GIS-assisted Spatial Decision Support System for Hazardous Waste Site Monitoring , 2009 .

[49]  Jacob T. Mundt,et al.  Hyperspectral data processing for repeat detection of small infestations of leafy spurge , 2005 .

[50]  José M. C. Pereira,et al.  Land-cover Mapping in the Brazilian Amazon Using SPOT-4 Vegetation Data and Machine Learning Classification Methods , 2006 .

[51]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[52]  R. Jackson,et al.  Spectral response of a plant canopy with different soil backgrounds , 1985 .

[53]  Jungho Im,et al.  Characterization of Forest Crops with a Range of Nutrient and Water Treatments Using AISA Hyperspectral Imagery , 2012 .

[54]  Karsten Schulz,et al.  Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet , 2010, Remote. Sens..

[55]  John C. Stormont,et al.  Method to Estimate Water Storage Capacity of Capillary Barriers , 1998 .

[56]  P. Curran,et al.  A new technique for interpolating the reflectance red edge position , 1998 .

[57]  John R. Jensen,et al.  Object‐based change detection using correlation image analysis and image segmentation , 2008 .

[58]  Niklaus E. Zimmermann,et al.  Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods , 2006 .

[59]  Robert K. Vincent.  Remote sensing for solid waste landfills and hazardous waste sites , 1994 .

[60]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[61]  Edward P. Glenn,et al.  Natural bioremediation of a nitrate-contaminated soil-and-aquifer system in a desert environment , 2008 .

[62]  P. Gong,et al.  Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia , 2002 .

[63]  Jonathan Cheung-Wai Chan,et al.  Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data , 2000 .

[64]  D. Roberts,et al.  A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery , 2002 .

[65]  Edward P. Glenn,et al.  Growth and water and nitrate uptake patterns of grazed and ungrazed desert shrubs growing over a nitrate contamination plume , 2006 .

[66]  Lindi J. Quackenbush,et al.  Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data , 2012 .

[67]  Wooil M. Moon,et al.  On the use of consensus algorithms to address variability in the results of neural network classifications: preliminary tests involving two northern study areas , 2005 .

[68]  D. Moore,et al.  Natural and Enhanced Attenuation of Soil and Groundwater at the Monument Valley, Arizona, DOE Legacy Waste Site—10281 , 2010 .

[69]  S. Ustin,et al.  A Comparison of Spatial and Spectral Image Resolution for Mapping Invasive Plants in Coastal California , 2007, Environmental management.

[70]  Pamela L. Nagler,et al.  Scaling sap flux measurements of grazed and ungrazed shrub communities with fine and coarse‐resolution remote sensing , 2008 .