Search

This study investigates the potential of lidar and hyperspectral data for prediction of canopy chlorophyll (Chl) and carotenoid concentrations for a spatially complex boreal mixedwood. First, canopy scale application of hyperspectral reflectance and derivative indices are used to estimate Chl concentration. Second, lidar data analyses is conducted to identify structural metrics related to Chl concentration. Third, lidar metrics and hyperspectral indices are combined to determine if Chl concentration estimates can be improved further. Of the hyperspectral indices considered, only the derivative chlorophyll index (DCI) and the red-edge inflection point (λp) are shown to be good predictors of Chl concentration when mixed-species plots are included in the analysis (i.e., for total chlorophyll concentration (a+b), r 2 = 0.79, RMSE = 4.6 µg cm-2 and r 2 = 0.78, RMSE = 4.5 µg cm-2 for DCI and λp, respectively). Integrating mean lidar first return heights for the 25th percentile with the hyperspectral DCI index further strengthens the relationship to canopy Chl concentration (i.e., for Chl(a+b), r 2 = 0.84, RMSE = 3.5 µg cm-2). Maps of total chlorophyll concentration for the study site reveal distinct spatial patterns that are indicative of the spatial distribution of species at the site. [ABSTRACT FROM AUTHOR]

The aim of this paper is to determine how classification-scheme information content influences remote sensing classification accuracies. Two important informational constructs in environmental science are 'process" and "pattern". In remote sensing these are analogous to 'function" and 'structure', 'land use" and "land cover", or "informational" and "spectral" classes. The objective of this research was to test the hypothesis that structure-based classes result in extraction of more accurate information than do function-based classes. Two hierarchical. 19-class schemes, one functional, the other structural, were developed for application with Satellite pour ["Observation de la Terre (SPOT) multispectral data for a watershed in North Sulawesi, Indonesia. Eight of the 19 classes were shared between the two schemes since these constituted equally valid functional and structural classes. Results indicate that there is no significant difference in classification accuracy between the functional and structural classifications as a whole (Khat=82.2% and 84.9%, respectively). However, comparison of the two sub-matrices associated with the 11 non-shared classes showed significantly higher accuracies for the structural classes (Khat=91.0%) than for the functional classes (Khat = 84.1%). thereby supporting the original hypothesis. Results demonstrate that careful consideration is required when developing function-based classes for the extraction of thematic information from remote sensor data. [ABSTRACT FROM AUTHOR]

Changes in vegetation have been observed in areas of the Arctic due to changing climate. This study examines a normalized difference vegetation index (NDVI) time series (2004–2018) of high spatial resolution satellite data (i.e., IKONOS, WorldView-2, WorldView-3) to determine if vegetation abundance has changed over the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut. Image data were corrected to top-of-atmosphere reflectance and normalized for time series analysis using the pseudo-invariant feature (PIF) method. Percent vegetation cover measurements and indices derived from local climate data (growing degree days base 5 °C; GDD5) were used to contextualize NDVI trends in different vegetation types and within active layer detachments (ALDs). NDVI showed similar patterns within the different vegetation types and across the ALDs. There was no significant change in NDVI nor in GDD5 over time. However, there were statistically significant (p < 0.05) relationships between the GDD5 and NDVI for all vegetation types. Using field measurements with high spatial resolution remote sensing data helps link changes in NDVI with changes to vegetation and earth surface processes. The challenges of integrating high spatial resolution satellite data from different sensors in a time series analysis are also discussed. [ABSTRACT FROM AUTHOR]