|Title||Detecting change in grasslands using measures of spatial dependence with Landsat TM data|
|Publication Type||Journal Article|
|Year of Publication||1993|
|Journal||Remote Sensing of Environment|
Spatial dependence is a fundamental structure of spatial data that can be readily measured. Change in landscapes can be monitored effectively using measures of spatial dependence. Scale of fluctuation analysis estimates the dimensional extent to which data are significantly autocorrelated by observing the behavior of sample variance under extended local averaging. Algorithms to estimate the one-dimensional scale of fluctuation (correlation length) and two-dimensional scale fluctuation (correlation area) in image data are described. The approach is demonstrated with an analysis of a 9-year TM image series of a tallgrass prairie preserve, Konza Prairie Research Natural Area, to assess the impact of bison on the spatial patterning of vegetation.