TY - JOUR
T1 - Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland
AU - Rahman, Abdullah F.
AU - Gamon, John A.
AU - Sims, Daniel A.
AU - Schmidts, Miriam
N1 - Funding Information:
We extend our appreciation to the staff at the National Park Service who provided access to the field site, to Rob Green and the rest of the AVIRIS team for supplying a low-altitude image of our field site, and to the Center for Environmental Analysis (CEA-CREST) and the Center for Spatial Analysis and Remote Sensing (CSARS) at Cal State LA for logistical support. Thanks also to the students of Biology 454-LP (Methods and Instrumentation in Environmental Science) for their assistance in the field. We greatly appreciate the comments of three anonymous reviewers. This work was supported by grants from NASA and NSF to J. Gamon.
PY - 2003/2/1
Y1 - 2003/2/1
N2 - Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopy- to watershed-level ecosystem studies of southern California chaparral and grassland vegetation. Three optical reflectance indices, namely, Normalized Difference Vegetation Index (NDVI), Water Band Index (WBI) and Photochemical Reflectance Index (PRI) were used as indicators of biomass, plant water content and photosynthetic activity, respectively. Two geostatistical procedures, the semivariogram and local variance, were used for the spatial scaling analysis of these indices. The results indicate that a pixel size of 6 m or less would be optimal for studying functional properties of southern California grassland and chaparral ecosystems using hyperspectral remote sensing. These results provide a guide for selecting the spatial resolution of future airborne and satellite-based hyperspectral sensors.
AB - Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopy- to watershed-level ecosystem studies of southern California chaparral and grassland vegetation. Three optical reflectance indices, namely, Normalized Difference Vegetation Index (NDVI), Water Band Index (WBI) and Photochemical Reflectance Index (PRI) were used as indicators of biomass, plant water content and photosynthetic activity, respectively. Two geostatistical procedures, the semivariogram and local variance, were used for the spatial scaling analysis of these indices. The results indicate that a pixel size of 6 m or less would be optimal for studying functional properties of southern California grassland and chaparral ecosystems using hyperspectral remote sensing. These results provide a guide for selecting the spatial resolution of future airborne and satellite-based hyperspectral sensors.
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U2 - 10.1016/S0034-4257(02)00107-4
DO - 10.1016/S0034-4257(02)00107-4
M3 - Article
AN - SCOPUS:0037301026
SN - 0034-4257
VL - 84
SP - 192
EP - 207
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 2
ER -