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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]

Abstract: Remote sensing has demonstrated wide applicability in the area of estimating and mapping forest physical and structural features. Focus in recent years has been directed towards measuring the biophysical/physiological character of forest ecosystems in order to estimate and predict forest ecosystem health and sustainability. The following reviews the relationship between forest condition and reflectance; remote-sensing measurements (and derivatives) that provide biophysical/ physiological information; and the potential of hyperspectral sensors in the measurement of these parameters. [ABSTRACT FROM AUTHOR]

Abstract: The adaptation of a national/continental-scale model of carbon uptake to the local-scale using highly detailed airborne discrete lidar and hyperspectral data was investigated at a boreal forest mixedwood site in northern Ontario. Spatially explicit maps of canopy chlorophyll concentration and the fraction of photosynthetically active radiation absorbed by the canopy (FPAR) under direct and diffuse radiative conditions were developed for five time periods in 2004 (i.e., winter (January 1–May 31), leaf-out (June 1–20), summer (June 21–July 31 and August 1–31) and senescence (September 1–30)). The maps were used in conjunction with meteorological measurements, such as incident total and diffuse PAR, global solar radiation, and air temperature, taken on a flux tower which is located at the center of the study. Distinct spatial patterns in FPAR, a result of changes in canopy chlorophyll concentration, are increasingly evident throughout the season because of the variation in species groupings within the 1-km radius surrounding the flux tower. In general, the model shows a good correlation to flux-tower-measured gross ecosystem productivity (GEP) (r 2 =0.70 for 10-day averages throughout the year), with the largest deviations occurring in June–July. Species-level data shows the influence of a large homogenous patch of black spruce on the tower measured GEP and demonstrates the contribution of white birch, by far the most dominant species, to the total GEP for the area. [Copyright &y& Elsevier]

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]

Abstract: Understanding the influence of within-pixel land cover heterogeneity is essential for the extrapolation of measured and modeled CO2 fluxes from the canopy to regional scales using remote sensing. Airborne light detection and ranging (lidar) was used to estimate spatial and temporal variations of gross primary production (GPP) across a jack pine chronosequence of four sites in Saskatchewan, Canada for comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product. This study utilizes high resolution canopy structural information obtained from airborne lidar to bridge gaps in spatial representation between plot, eddy covariance (EC), and MODIS estimates of vegetation GPP. First we investigate linkages between canopy structure obtained from measurements and light response curves at a jack pine chronosequence during the growing season of 2004. Second, we use the measured canopy height and foliage cover inputs to create a structure-based GPP model (GPPLandsberg) which was tested in 2005. The GPP model is then run using lidar data (GPPLidar) and compared with eight-day cumulative MODIS GPP (GPPMODIS) and EC observations (GPPEC). Finally, we apply the lidar GPP model at spatial resolutions of 1 m to 1000 m to examine the influence of within-pixel heterogeneity and scaling (or pixel aggregation) on GPPLidar. When compared over eight-day cumulative periods throughout the 2005 growing season, the standard deviation of differences between GPPlidar and GPPMODIS were less than differences between either of them and GPPEC at all sites. As might be expected, the differences between pixel aggregated GPP estimates are most pronounced at sites with the highest levels of spatial canopy heterogeneity. The results of this study demonstrate one method for using lidar to scale between eddy covariance flux towers and coarse resolution remote sensing pixels using a structure-based Landsberg light curve model. [Copyright &y& Elsevier]