Jesslyn F. Brown

TL;DR: The IGBP DISCover global land cover product as mentioned in this paper is an integral component of the Global Land Cover database, which provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface and presents a detailed interpretation of the extent of human development.

Abstract: Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiom- eter (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variabil- ity of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and pre- dicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large- area land cover mapping and monitoring. The utility of re- motely sensed data as input to vegetation mapping is demon- strated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particu- larly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.

TL;DR: In this paper, the authors assess 10 start-of-spring (SOS) methods for North America between 1982 and 2006 and find that SOS estimates were more related to the first leaf and first flowers expanding phenological stages.

TL;DR: A land cover database for the contaminous United States designed for use in a variety of global modeling, monitoring, mapping, and analytical endeavors has been created as discussed by the authors, which consists of a stratification of vegetated and barren land, an unsupervised classificatin of multitemporal greenness data derived from Advanced Very High Resolution Radiometer (AVHRR) imagery collected from March through October 1990, and post-classificatin stratifying of classes into homogeneous land-cover regions using ancillary data.

TL;DR: Gu et al. as discussed by the authors analyzed MODIS normalized difference vegetation index and normalized difference water index (NDWI) data for grassland drought assessment within the central United States, specifically for the Flint Hills of Kansas and Oklahoma.