Intermountain plant community classification using Landsat TM and SPOT HRV data.

Rangeland plant communities of the Intermountain West differ in their ecology and management requirements. Successful management of extensive areas at plant community-level resolution first requires an efficient, cost-effective means of plant community classification and mapping. We evaluated the influence of image acquisition date and satellite imaging system on the accuracy of plant community maps created from multispectral satellite imagery of Reynolds Creek Experimental Watershed (RCEW) (234 km2) in southwestern Idaho. Maps delineating 6 native and 2 non-native Intermountain plant communities were created from Landsat 5 TM and SPOT 3 HRV data using a maximum likelihood classification procedure. Map accuracy was assessed using ground reference points. Maps created from satellite data acquired during dry-down (early August) had higher overall accuracy (average = 70.5%) than from data acquired during peak growth (early June) (average = 54.4%). Overall accuracy of maps generated by Landsat (average = 60.1%) and SPOT (average = 65.5%) were statistically similar. Given their broad spatial coverages (3,600 to 31,450 km2 scene(-1), respectively), moderate resolutions (20 to 30 m pixels, respectively), and potential to provide high classification accuracies, the SPOT 3 HRV and Landsat 5 TM satellite systems were well-suited for classifying plant communities in the Reynolds Creek Watershed and similar areas of the Intermountain West. Practical procedures for plant community classification and map accuracy assessment are presented for use by natural resource managers. DOI:10.2458/azu_jrm_v54i2_clark

[1]  Paul T. Tueller,et al.  Remote sensing technology for rangeland management applications. , 1989 .

[2]  K. Price,et al.  In situ narrow‐band reflectance characteristics of cover components in sagebrush‐steppe , 1998 .

[3]  J. Franklin,et al.  Reflectance of vegetation and soil in Chihuahuan desert plant communities from ground radiometry using SPOT wavebands , 1993 .

[4]  S. Gerstl,et al.  Radiation physics and modelling for off-nadir satellite sensing of non-Lambertian surfaces , 1986 .

[5]  Mark E. Jakubauskas,et al.  Multitemporal characterization and mapping of montane sagebrush communities using Indian IRS LISS‐II imagery , 1998 .

[6]  Steven W. Running,et al.  Community type differentiation using NOAA/AVHRR data within a sagebrush-steppe ecosystem , 1993 .

[7]  Douglas Stow,et al.  Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico , 1993 .

[8]  Steven E. Franklin,et al.  Radiometric processing of aerial and satellite remote-sensing imagery , 1995 .

[9]  R. Jackson,et al.  Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands , 1987 .

[10]  T. N. Shiflet.  Rangeland cover types of the United States. , 1994 .

[11]  Philip H. Swain,et al.  Remote Sensing: The Quantitative Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[13]  Nonparametric modeling of radiance in hill country , 1988 .

[14]  John R. Dymond.  Nonparametric Modeling of Radiance in Hill Country for Digital Classification of Aerial Photographs , 1992 .

[15]  Giles M. Foody,et al.  The effects of viewing geometry on image classification , 1988 .

[16]  G. Pickup,et al.  Remote‐Sensing‐Based Condition Assessment for Nonequilibrium Rangelands Under Large‐Scale Commercial Grazing , 1994 .

[17]  M. S. Moran,et al.  Obtaining Surface Reflectance Factors from Atmospheric and View Angle Corrected SPOT-1 HRV Data , 1990 .

[18]  J. Blaisdell.  Seasonal Development and Yield of Native Plants on the Upper Snake River Plains and their Relation to Certain Climatic Factors , 1958 .