Title :
Precision and accuracy of EO-1 Advanced Land Imager (ALI) data for semiarid vegetation studies
Author :
Elmore, Andrew James ; Mustard, John Fraser
Author_Institution :
Dept. of Geol. Sci., Brown Univ., Providence, RI, USA
fDate :
6/1/2003 12:00:00 AM
Abstract :
Landsat Thematic Mapper (TM) data and spectral mixture analysis have been used to estimate vegetation green cover in the Great Basin, western United States, to ±4.0% green cover (%GC). In this paper, we compare estimates of percent green cover derived from EO-1 Advanced Land Imager (ALI) data to estimates derived from field-based analyses and to results derived from Landsat Enhanced Thematic Mapper plus (ETM+) data. These analyses define the precision and accuracy of ALI and ETM+ for making quantitative measurements of earth for semiarid ecological studies. The benefits of using ALI were not observed in the calculated uncertainty values (±5.61%GC and ±6.15%GC for ETM+ and ALI, respectively). However, ALI did not return as many negative green cover estimates and exhibited lower spatial variance in regions of low green cover. These results were attributed to the better signal to noise and data precision inherent to the ALI sensor, and not to the increased number of multispectral bands. ALI was found to be internally inconsistent in that the third sensor chip assembly image swath contained multispectral band coregistration errors. This caused a less than 25%GC error in the ALI estimate of percent green cover along large vegetation gradients.
Keywords :
forestry; geophysical techniques; vegetation mapping; 400 to 2500 nm; ALI; Advanced Land Imager; EO-1; Great Basin; IR; USA; United States; accuracy; geophysical measurement technique; green cover; hyperspectral remote sensing; infrared; precision; quantitative measurements; satellite remote sensing; semiarid land; spectral mixture analysis; vegetation mapping; visible; Data analysis; Earth; Image analysis; Image sensors; Remote sensing; Satellites; Sensor phenomena and characterization; Spectral analysis; State estimation; Vegetation mapping;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2003.813132