The Jornada Experiment (JORNEX) on the Jornada Experimental Range in southern New Mexico aims at the description of the surface energy balance of a desert grassland ecosystem. A large volume of both field and remote sensing data has been collected from 1995 to 1998. Airborne Daedalus scanner data with a spatial resolution of 4 m have been used to infer the following land surface characteristics: surface temperature, albedo, and normalized difference vegetation index (NDVI). These land surface characteristics can be used as input for land surface models. However, land surface models work with very coarse grid cells of at least 50 x 50 km, in contrast to high-resolution remote sensing data. Also, land surface models are generally based on nonlinear algorithms. Both restrictions lead to scale problems. One apparent question is how to scale up input remote sensing data to the much coarser resolution of the land surface model. The first step is to derive the length scale of the input land surface characteristics. The length scales of the land surface characteristics have been determined with the following two techniques: autocorrelation and wavelet analysis. Within the Jornada Experimental Range, three different sites with different vegetation characteristics were distinguished: grass, shrub, and a transition site with patches of both grass and shrub. The autocorrelation and wavelet analysis showed similar results for the shrub site. For the grass and transition site the wavelet analysis underestimated the length scale of the surface albedo and temperature. The length scale of the surface albedo was 35, 33, and 10 m for grass, transition, and shrub sites, respectively. The length scale of the surface temperature was 31, 20, and 8 m for grass, transition, and shrub sites, respectively. The length scale of the NDVI was 12, 6, and 5 m for grass, transition, and shrub sites, respectively. These small length scales could hamper the use of low-resolution remote sensing data for deriving input data for land surface models.

VL - 36 UR - files/bibliography/Rango2000-01.pdf AN - JRN00304 N1 - //USDA//LTER III// ER - TY - JOUR T1 - Seasonal changes in fractal landscape surface roughness estimated from airborne laser altimetry data JF - International Journal of Remote Sensing Y1 - 1998 A1 - Pachepsky, Y. A. A1 - J. Ritchie KW - article KW - articles KW - fractal geometry, remote sensing KW - JORNEX, remote sensing KW - journal KW - journals KW - landscape, surface roughness KW - laser altimetry, surface roughness KW - remote sensing, airborne laser altimetry KW - remote sensing, fractal geometry KW - remote sensing, JORNEX KW - remote sensing, surface roughness KW - technique, airborne laser altimetry KW - technique, remote sensing KW - water balance, remote sensing AB - Fractal geometry is a useful tool for the analysis of landscape data. In this study fractal scaling was applied to high-resolution landscape data collected with a profiling laser altimeter. The objective of this work was to assess the persistence of scaling differences over time. Data were collected at the United States Department of Agriculture (USDA), Agriculture Research Service (ARS) Jornada Experimental Range in New Mexico, USA in May and September 1995 and February 1996 over a grass-dominated site, a shrub-dominated site, and a transitional area between shrub- and grass-dominated sites along four transects at each site for each data. Root-mean-square (RMS) roughness was scale-dependent and had more than one range of self-affine scaling. Different numbers of self-affine scaling intervals, boundaries of intervals, and fractal dimensions over these intervals were associated with different land covers. A linearity measure was applied to find intervals of fractal scaling. The number and boundaries of fractal scaling intervals appeared to be persistent over the year. Grass sand shrub sites had two and four linearity intervals respectively. The transitional site had a pattern of scaling that was intermediate between grass and shrub sites. The lowest fractal dimensions at small scales of 6-30 m corresponded to the maximum vegetation in September. VL - 19 N1 - //USDA//NONE// ER - TY - JOUR T1 - Fractal modeling of airborne laser altimetry data JF - Remote Sensing of Environment Y1 - 1997 A1 - Pachepsky, Yakov A1 - J. Ritchie A1 - Gimenex, Daniel KW - article KW - articles KW - JORNEX, remote sensing KW - journal KW - journals KW - landscape, surface roughness KW - laser altimetry, surface roughness KW - remote sensing, airborne laser altimetry KW - remote sensing, JORNEX KW - remote sensing, surface roughness KW - technique, airborne laser altimetry KW - technique, remote sensing KW - water balance, remote sensing AB -Airborne laser altimetry is a remote sensing technique that can provide high resolution data on the roughness of the landscape both for estimating water balance components and for distinguishing between landscapes. Models of the scale-dependent roughness are needed to find scales most appropriate for these purposes. Our objectives were to apply fractal scaling to high-resolution profiling laser altimetry data and to determine fractal parameters fro differentiating land cover. Data were collected at the USDA-ARS Jornada Experimental Range in New Mexico over grass-dominated and shrub-dominated sites along four transects at each site. Scale-dependent root-mean-square (RMS) roughness and data power spectrums were computed from 100,000 data points (~2 km) from each transect. A linearity measure and piecewise linear approximation were applied to find intervals of the fractal scaling. The RMS roughness data had two intervals of self-affine fractal scaling on grass transects and four such intervals on shrub transects. Reduction in the number of data points did not lead to a decrease in roughness but caused a smoothing dependency of fractal dimension on scale. Ten- and hundred-meter scales were appropriate for distinguishing between grass and shrub transects on the basis of fractal dimensions. Published by Elsevier Science Inc.

VL - 61 UR - files/bibliography/JRN00238.pdf AN - JRN00238 N1 - //USDA//LTER III// ER -