SUMMARYTree roots, exposed by hillslope erosion in the Piceance basin of Colorado, were inspected to determine average net erosion rates during the last four centuries. Twenty pinyon pine and juniper root sections were obtained from each of five sites in this region. The date when a root was first exposed by erosion was determined, for 83 of the sections collected, by one of three methods: 1. time of initial cambium dieback; 2. interpretation of annual ring growth pattern; and 3. the earliest occurrence of reaction wood.Analysis indicated no significant difference in erosion rates between the five study sites. However, a strong difference in erosion rates was noted between north-facing (0.56 mm/yr) and south-facing (1.18 mm/yr) slopes. No significant difference in erosion rates were found between various south-facing aspects and local steepness of slopes. Significant differences were noted between erosion rate and the length of time the root was exposed t o erosion. Rates of erosion on south-facing slopes in the pinyon-juniper community during the last four centuries are as follows:period (years ago) 0-99 100-199 200-299 300-399 erosion rate (mm/yr)

[1] Automated snow maps over North America have been produced at the National Environmental Satellite Data and Information Service (NESDIS) of the National Oceanic and Atmospheric Administration (NOAA) since 1999. The developed snow-mapping system is based on observations in the visible, middle infrared, infrared, and microwave spectral bands from operational geostationary and polar orbiting meteorological satellites and generates daily maps of snow cover at a spatial resolution of 4 km. Recently, the existing snow-mapping technique was extended to derive the fractional snow cover. To obtain snow fraction, we use measurements of the Imager instrument on board Geostationary Operational Environmental Satellite (GOES). The algorithm treats every cloud-clear image pixel as a ''mixed scene'' consisting of a combination of snow-covered and snow-free land surface. To determine the portion of the pixel that is covered with snow, we employ a linear mixture approach, which relies on the Imager measurements in the visible spectral band. The estimated accuracy of subpixel snow fraction retrievals is about 10%. In this paper, we present a description of the snow cover and snow fraction mapping algorithms. Application of the developed algorithms over North America for three winter seasons from 1999-2000 to 2001-2002 has shown that the spatial distribution of the fractional snow cover over areas affected by seasonal snow closely corresponds to the distribution of the forest cover. The fraction of snow in the middle of the winter season generally varied from 100% over croplands, grasslands, and other nonforested areas to 20-30% over dense boreal forests. The snow fraction over dense boreal forests exhibited a slight intraseason variability; however, no obvious correlation of these changes with snowfalls was noticed. Over areas with no or sparse tree vegetation cover (croplands, grasslands), snow fraction showed a noticeable correlation with snow depth for snow depths up to 35-40 cm.

Abstract:The National Operational Hydrologic Remote Sensing Center (NOHRSC) of the National Oceanic and Atmospheric Administration's (NOAA's) National Weather Service (NWS) provides daily satellite-derived snow cover maps to support the NWS Hydrologic Services Program covering the coterminous USA and Alaska. This study compared the NOHRSC snow cover maps with new automated snow cover maps produced by the National Environmental Satellite, Data, and Information Service (NESDIS) and the snow cover maps created from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The purpose of this paper is to demonstrate and account for the differences that occur between the three different snow cover mapping techniques. Because each of these snow cover products uses data from different sensors at different resolutions, the data were degraded to the coarsest relevant resolution. In both comparisons, forest canopy density was examined as a possible explanatory factor to account for those differences. NOHRSC snow cover maps were compared with NESDIS snow cover maps for 32 different dates from November 2000 to February 2001. NOHRSC snow cover maps were also compared with MODIS snow cover maps in the Pacific Northwest and the Great Plains for 18 days and 21 days, respectively, between March and June 2001. In the first comparison, where the NOHRSC product (¾1 km) was degraded to match the resolution of the NESDIS data (¾5 km), the two products showed an average agreement of 96%. Forest canopy density data provided only weak explanation for the differences between the NOHRSC and the NESDIS snow cover maps. In the second comparison, where the MODIS product (¾500 m) was degraded to match the resolution of the NOHRSC product for two sample areas, the agreement was 94% in the study area in the Pacific Northwest, and 95%