Using AVIRIS data and multiple-masking techniques to map urban forest tree species

Tree type and species information are critical parameters for urban forest management, benefit cost analysis and urban planning. However, traditionally, these parameters have been derived based on limited field samples in urban forest management practice. In this study we used high-resolution Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data and multiple-spectral masking techniques to identify and map urban forest trees. Trees were identified based on their spectral character difference in AVIRIS data. The use of multiple-masking techniques shift the focus to the target land cover types only, thus reducing confounding noise during spectral analysis. The results were checked against ground reference data and by comparison to tree information in an existing geographical information system (GIS) database. At the tree type level, mapping was accomplished with 94% accuracy. At the tree species level, the average accuracy was 70% but this varied with both tree type and species. Of the four evergreen tree species, the average accuracy was 69%. For the 12 deciduous tree species, the average accuracy was 70%. The relatively low accuracy for several deciduous species was due to small tree size and overlapping among tree crowns at the 3.5 m spatial resolution of AVIRIS data. This urban forest tree species mapping method has the potential to increase data update intervals and accuracy while reducing costs compared to field sampling or other traditional methods.

[1]  P. Vellinga,et al.  The urban environment , 2001 .

[2]  D. H. Card.  Using known map category marginal frequencies to improve estimates of thematic map accuracy , 1982 .

[3]  R. Rowntree.  Ecology of the urban forest—Introduction to part II , 1984 .

[4]  J. McBride,et al.  Presettlement forest structure as a factor in urban forest development , 1986 .

[5]  Philip J. Howarth,et al.  Multispectral Classification of Land Use at the Rural-Urban Fringe Using Spot Data , 1988 .

[6]  Manfred Ehlers.  Remote Sensing And Geographic Information Systems: Towards Integrated Spatial Information Processing , 1990 .

[7]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[8]  T. Avery,et al.  Fundamentals of Remote Sensing and Airphoto Interpretation , 1992 .

[9]  E. Gregory McPherson,et al.  Assessing the Benefits and Costs of the Urban Forest , 1992, Arboriculture & Urban Forestry.

[10]  Leo Newland,et al.  Using spot satellite data to map impervious cover for urban runoff predictions , 1993 .

[11]  C. S. B. Grimmond,et al.  Surface description for urban climate studies: A gis based methodology , 1994 .

[12]  T. J. Lyons,et al.  Estimation of land surface parameters using satellite data , 1995 .

[13]  C. Arnold,et al.  IMPERVIOUS SURFACE COVERAGE: THE EMERGENCE OF A KEY ENVIRONMENTAL INDICATOR , 1996 .

[14]  J. C. Stevens,et al.  Measuring and analyzing urban tree cover , 1996 .

[15]  J. D. Tarpley,et al.  The comparison of vegetation index and surface temperature composites for urban heat-island analysis , 1996 .

[16]  George Alan Blackburn,et al.  An ecological survey of deciduous woodlands using airborne remote sensing and geographical information systems (GIS). , 1997 .

[17]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[18]  James A. Brass,et al.  An Integration of Remote Sensing, GIs, and Information Distribution for Wildfire Detection and Management , 1998 .

[19]  Li-hua Xia,et al.  Measurement of Rapid Agricultural Land Loss in the Pearl River Delta with the Integration of Remote Sensing and GIS , 1998 .

[20]  M. Murthy,et al.  EFFICIENT FOREST RESOURCES MANAGEMENT THROUGH GIS AND REMOTE SENSING , 1998 .

[21]  D. Young,et al.  An Integration of Remote Sensing and GIS to Examine the Responses of Shrub Thicket Distributions to Shoreline Changes on Virginia Barrier Islands , 1998 .

[22]  Susan L. Ustin,et al.  XIA-1 CHARACTERIZATION OF THE STRUCTURE AND SPECIES COMPOSITION OF URBAN TREES USING HIGH RESOLUTION AVIRIS DATA , 1999 .

[23]  J. Boardman.  PRECISION GEOCODING OF LOW ALTITUDE AVIRIS DATA: LESSONS LEARNED IN 1998 , 1999 .

[24]  E. Mcpherson,et al.  Equations for Predicting Diameter, Height, Crown Width, and Leaf Area of San Joaquin Valley Street Trees , 2001, Arboriculture & Urban Forestry.

[25]  S. Franklin,et al.  Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia , 2001 .

[26]  S. Ustin,et al.  Mapping successional boreal forests in interior central Alaska , 2001 .

[27]  K. Price,et al.  Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas , 2002 .

[28]  M. Ridd,et al.  A SUBPIXEL CLASSIFIER FOR URBAN LAND-COVER MAPPING BASED ON A MAXIMUM-LIKELIHOOD APPROACH AND EXPERT SYSTEM RULES , 2002 .

[29]  Ruiliang Pu,et al.  Crown closure estimation of oak savannah in a dry season with Landsat TM imagery: Comparison of various indices through correlation analysis , 2003 .

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

[31]  T. Carlson,et al.  Satellite and ground-based microclimate and hydrologic analyses coupled with a regional urban growth model , 2003 .

[32]  Christiane Weber,et al.  Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area , 2003 .

[33]  W. B. Clapham.  Continuum-based classification of remotely sensed imagery to describe urban sprawl on a watershed scale , 2003 .

[34]  R. Hall,et al.  Discrimination of conifer height, age and crown closure classes using Landsat-5 TM imagery in the Canadian Northwest Territories , 2003 .

[35]  S. Ustin,et al.  Mapping nonnative plants using hyperspectral imagery , 2003 .

[36]  E. Gregory McPherson,et al.  Evaluation of four methods for estimating leaf area of isolated trees , 2003 .

[37]  Qingfu Xiao,et al.  Rainfall interception by Santa Monica's municipal urban forest , 2002, Urban Ecosystems.

[38]  E. Mcpherson,et al.  Quantifying urban forest structure, function, and value: the Chicago Urban Forest Climate Project , 1995, Urban Ecosystems.