Estimating grassland chlorophyll content using remote sensing data at leaf, canopy, and landscape scales

摘要

A small yet promising body of research has been conducted on the use of remote sensing data to retrievenvegetation chlorophyll content for heterogeneous ecosystems at the leaf level; however, the extent to which leaf chlorophyllncontents can be estimated from reflectance measurements at the canopy and landscape scales remain uncertain. The goalnof this study was to develop and evaluate a species percent cover-based chlorophyll content scaling up procedure that aimsnto accurately estimate chlorophyll content at canopy or landscape level. Using both field and QuickBird data collected in anheterogeneous tall grassland located in Ontario, Canada, this study calculated vegetation chlorophyll content at canopynand landscape levels, and it correlated chlorophyll data at leaf, canopy, and landscape levels with a red-edge spectral index.nResults indicated that the relationships between the red-edge index and vegetation chlorophyll content (e.g., chlorophyll a,nchlorophyll b, chlorophyll a u0002 b) were significant at all three scales in the study site. At the landscape level, the speciesnpercent cover-based scaling up chlorophyll was slightly better correlated with the red-edge index than the greenness-basednchlorophyll that was calculated using the ratio of green area to total area as an empirical coefficient, but it was muchnbetter correlated than the site averaging chlorophyll that was directly averaged from leaf level chlorophyll. These resultsnsuggest that inclusion of species percent cover in the scaling up procedure is a more appropriate method for canopy ornlandscape chlorophyll estimation. What we have to keep in mind is that the proposed scaling procedure only takes intonaccount the species composition within a canopy. More canopy information such as standing dead, litter, and soilnbackground should be considered into the scaling tool in the future.