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]

Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require "scaling-up" from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas. [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]

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]

Studies the use of NAVSTAR Global Positioning System (GPS) for updating databases. Accuracy assessment and error modelling; Correction of GPS data for changes in urban land use; Accuracies achieved by NAVSTAR GPS; Road network accuracy assessment.

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]

Abstract: The purpose of this study was to estimate the fraction of photosynthetically active radiation absorbed by the canopy (fPAR) from point measurements to airborne lidar for hierarchical scaling up and assessment of the Moderate Resolution Imaging Spectroradiometer (MODIS) fPAR product within a “medium-sized” (7 km×18 km) watershed. Nine sites across Canada, containing one or more (of 11) distinct species types and age classes at varying stages of regeneration and seasonal phenology were examined using a combination of discrete pulse airborne scanning Light Detection And Ranging (lidar) and coincident analog and digital hemispherical photography (HP). Estimates of fPAR were first compared using three methods: PAR radiation sensors, HP, and airborne lidar. HP provided reasonable estimates of fPAR when compared with radiation sensors. A simplified fractional canopy cover ratio from lidar based on the number of within canopy returns to the total number of returns was then compared with fPAR estimated from HP at 486 geographically registered measurement locations. The return ratio fractional cover method from lidar compared well with HP-derived fPAR (coefficient of determination=0.72, RMSE=0.11), despite varying the lidar survey configurations, canopy structural characteristics, seasonal phenologies, and possible slight inaccuracies in location using handheld GPS at some sites. Lidar-derived fractional cover estimates of fPAR were ∼10% larger than those obtained using HP (after removing wood components), indicating that lidar likely provides a more realistic estimate of fPAR than HP when compared with radiation sensors. Finally, fPAR derived from lidar fractional cover was modelled at 1 m resolution and averaged over 99 1 km areas for comparison with MODIS fPAR. The following study is one of the first to scale between plot measurements and MODIS pixels using airborne lidar. [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]

Carbon dioxide, water vapour, and energy fluxes vary spatially and temporally within forested environments. However, it is not clear to what extent they vary as a result of variability in the spatial distribution of biomass and elevation. The following study presents a new methodology for extracting changes in the structural characteristics of vegetation and elevation within footprint areas, for direct comparison with eddy covariance (EC) CO2 flux concentrations. The purpose was to determine whether within-site canopy structure and local elevation influenced CO2 fluxes in a mature jack pine (Pinus banksiana Lamb.) forest located in Saskatchewan, Canada. Airborne light detection and ranging (lidar) was used to extract tree height, canopy depth, foliage cover, and elevation within 30 min flux footprints. Within-footprint mean structural components and elevation were related to 30 min mean net ecosystem productivity (NEP) and gross ecosystem production (GEP). NEP and GEP were modeled using multiple regression, and when compared with measured fluxes, almost all periods showed improvements in the prediction of flux concentration when canopy structure and elevation were included. Increased biomass was related to increased NEP and GEP in June and August when the ecosystem was not limited by soil moisture. On a daily basis, fractional cover and elevation had varying but significant influences on CO2 fluxes. [ABSTRACT FROM AUTHOR]

Abstract: The spatial variability of the fraction of photosynthetically active radiation absorbed by the canopy (fPAR) was characterized for a heterogeneous boreal mixedwood forest site located in northern Ontario, Canada, based on relationships found between fPAR and light detection and ranging (lidar) data over different canopy architectures. Estimates of fPAR were derived from radiation measurements made above the canopy at a flux tower and below-canopy radiation was measured across a range of species compositions and canopy architectures. Airborne lidar data were used to characterize spatial variability of canopy structure around the flux tower and a map of mean canopy chlorophyll concentration was derived from airborne hyperspectral imagery. Different volumes of lidar points for the locations directly above each photosynthetically active radiation (PAR) sensor were examined to determine if there is an optimal method of relating lidar returns to estimated fPAR values. The strongest correlations between mean lidar height and fPAR occurred when using points that fell within a theoretical cone which originated at the PAR sensor having a solid angle α =55°. For diffuse conditions, the correlation (r) between mean lidar height versus fPAR×chlorophyll was stronger than between mean lidar height versus fPAR by 8% for mean daily fPAR and from 10 to 20% for diurnal fPAR, depending on solar zenith angle. For direct light conditions, the relationship was improved by 12% for mean daily fPAR and 12–41% for diurnal relationships. Linear regression models of mean daily fPAR×chlorophyll versus mean lidar height were used in conjunction with gridded lidar data and the canopy chlorophyll map to generate maps of mean daily fPAR for direct and diffuse sunlight conditions. Site average fPAR calculated from these maps was 0.79 for direct light conditions and 0.78 for diffuse conditions. When compared to point estimates of mean daily fPAR calculated on the tower, the average fPAR was significantly lower than the point estimate. Subtracting the direct sunlight fPAR map from the diffuse sunlight fPAR map revealed a distinct spatial pattern showing that areas with open canopies and relatively low chlorophyll (e.g., black spruce patches) have a higher fPAR under direct sunlight conditions, while closed canopies with higher chlorophyll (e.g., deciduous species) absorb more PAR under diffuse conditions. These findings have implications for scaling from point measurements at flux towers to larger resolution satellite imagery and addressing local scale heterogeneity in flux tower footprints. [Copyright &y& Elsevier]