<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author><author><style face="normal" font="default" size="100%">Hargrove, W. W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Potential NDVI as a baseline for monitoring ecosystem functioning</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Remote Sensing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">AVHRR</style></keyword><keyword><style  face="normal" font="default" size="100%">California</style></keyword><keyword><style  face="normal" font="default" size="100%">GAP</style></keyword><keyword><style  face="normal" font="default" size="100%">managed areas</style></keyword><keyword><style  face="normal" font="default" size="100%">NDVI</style></keyword><keyword><style  face="normal" font="default" size="100%">net primary production</style></keyword><keyword><style  face="normal" font="default" size="100%">NPP</style></keyword><keyword><style  face="normal" font="default" size="100%">Oregon</style></keyword><keyword><style  face="normal" font="default" size="100%">regression tree analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">time integrated NDVI</style></keyword><keyword><style  face="normal" font="default" size="100%">Washington</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2000</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">&lt;Go to ISI&gt;://000084681200014</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">401-407</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Baseline data are needed to determine the overall magnitude and direction of change in ecosystem functioning. This letter presents an approach to estimate potential NDVI from environmental variables and training data of actual NDVI in nature reserves. Patterns of deviations of actual NDVI from the baseline generally correspond with land-use types in the western United States.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author><author><style face="normal" font="default" size="100%">Bueno, M. J.</style></author><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author><author><style face="normal" font="default" size="100%">Cassidy, K. M.</style></author><author><style face="normal" font="default" size="100%">Driese, K. L.</style></author><author><style face="normal" font="default" size="100%">Kagan, J. S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Map-guided classification of regional land-cover with multi-temporal AVHRR data</style></title><secondary-title><style face="normal" font="default" size="100%">Photogrammetric Engineering and Remote Sensing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">accuracy assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">AVHRR</style></keyword><keyword><style  face="normal" font="default" size="100%">gap analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Intermountain Semidesert ecoregion</style></keyword><keyword><style  face="normal" font="default" size="100%">map-guided classification</style></keyword><keyword><style  face="normal" font="default" size="100%">National Vegetation Classification Standards</style></keyword><keyword><style  face="normal" font="default" size="100%">NVCS</style></keyword><keyword><style  face="normal" font="default" size="100%">remote sensing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1998</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">&lt;Go to ISI&gt;://000075109200012</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">64</style></volume><pages><style face="normal" font="default" size="100%">831-838</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Cartographers often need to use information in existing land-cover maps when compiling regional or global maps, but there are no standardized techniques for using such data effectively. An iterative, map-guided classification approach was developed to compile a spatially and thematically consistent, seamless land-cover map of the entire Intermountain Semi-Desert ecoregion from a set of semi-independent subregional maps derived by various methods. A multi-temporal dataset derived from AVHRR data was classified using the subregional maps as training data. The resulting regional map attempted to meet the guidelines of the proposed National Vegetation Classification Standards for classification at the alliance level. The approach generally improved the spatial properties of the regional mapping, while maintaining the thematic detail of the source maps. The methods described may be useful in many situations where mapped information exists but is incomplete, compiled by different methods, or is based on inconsistent classification systems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stoms, D. M.</style></author><author><style face="normal" font="default" size="100%">Bueno, M. J.</style></author><author><style face="normal" font="default" size="100%">Davis, F. W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Viewing geometry of AVHRR image composites derived using multiple criteria</style></title><secondary-title><style face="normal" font="default" size="100%">Photogrammetric Engineering and Remote Sensing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">AVHRR</style></keyword><keyword><style  face="normal" font="default" size="100%">cloud removal</style></keyword><keyword><style  face="normal" font="default" size="100%">compositing</style></keyword><keyword><style  face="normal" font="default" size="100%">NDVI</style></keyword><keyword><style  face="normal" font="default" size="100%">Normalized Difference Vegetation Index</style></keyword><keyword><style  face="normal" font="default" size="100%">satellite zenith angle</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1997</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">&lt;Go to ISI&gt;://A1997XC40700004</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">63</style></volume><pages><style face="normal" font="default" size="100%">681-689</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The U. S. Geological Survey currently generates composites of AVHRR imagery based on a single objective--maximizing the Normalized Difference Vegetation Index--as a means of reducing cloud contamination. Our research supports the findings of others that in some cases, NDVI is maximized at the expense of optimal viewing geometry; that is, satellite zenith angles are often further off-nadir than necessary to ensure cloud-free viewing. We explore various compositing methods by systematically varying weights on NDVI, satellite zenith angle, and maximum apparent temperature. A test composite of California from September 1990 appears to be superior to the maximum NDVI and maximum apparent temperature composites in several respects. First, the satellite zenith angle distribution is more closely clustered about nadir, which minimizes atmospheric path length, spatial distortion, and bidirectional reflectance effects. Second, neighboring pixels are more frequently selected with similar viewing geometry and atmospheric conditions.</style></abstract></record></records></xml>