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Highlights • Multi-seasonal optical imagery and ALS was compared for modelling forest attributes. • Optical and ALS intensity data convey distinct information about forest structure. • ALS data alone exhibited strong predictive power for stem density. • The synergy of both data sources led to more accurate model of BA and species mixture. • Sentinel-2 A imagery holds great potential for enhancing ALS-based forest inventories. Abstract Advanced forest resource inventory (FRI) information is of critical importance for sustainable forest management. FRIs are dependent on remote sensing data and processing methods, along with field calibration/validation to generate cost-effective options for modelling forest inventory and biophysical variables over large areas. The objective of this study was to examine the impact of combining multi-seasonal multispectral satellite imagery with airborne laser scanning (ALS) data for estimating basal area, species mixture and stem density for an uneven-aged tolerant hardwood forest in Ontario, Canada. Using random forest (RF) regression as a non-parametric diagnostic technique, three multispectral optical sensors (i.e., Landsat-5 TM, Sentinel-2 A and WorldView-2) were compared to examine the most cost-effective sensor configuration for modelling FRI variables. The contribution of spectral predictors derived from these optical sensors as well as ALS height and intensity metrics were evaluated using RF variable importance. As part of our variable selection framework, all predictor variables were grouped into relatively independent clusters using a hierarchical variable clustering technique, which revealed the distinctiveness between information contained in spectral predictors, height- and intensity-based metrics. This indicates that ALS intensity data carry unique information complementary to passive near-infrared data for forest characterization. ALS data alone did not result in accurate models for basal area and species mixture, but predictive accuracies were improved significantly with the addition of spectral predictors. Compared to single-date images, multi-seasonal imagery proved to be more accurate for modelling FRI variables, especially when combined with ALS data. Despite its limited spatial resolution, Sentinel-2 A was found to be the most cost-effective image source for enhancing ALS-based FRI models. Using variables identified by the variable selection procedure, best subsets regression outperformed the RF models developed for diagnostic analysis, resulting in a suite of accurate and parsimonious predictive models, with coefficients of determination of 0.73, 0.90 and 0.67, for basal area, species mixture, and stem density, respectively. [ABSTRACT FROM AUTHOR]

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This study investigates the potential of discrete return light detection and ranging (lidar) data to characterize forest succession in a mixed mature forest in central Ontario using indices applied to the lidar point cloud. Derived indices include statistical indices, predicted Lorey's height (R² = 0,86; RSME = 2.36 m) and quadratic mean diameter- at-breast-height (R² = 0.68; RMSE = 1.21 cm), canopy density indices and an information theory based complexity index. To assess how well these indices are able to capture the vertical structure of forest stands, they are compared to Oliver and Larson's (1996) four stages of forest stand development. Best subsets regressions indicated that no single index is able to separate all four stages adequately. However, the predicted Lorey's height index is optimal for separating early from mid succession stages (p <.0001) and a combination of height and complexity indices performed best to discriminate between mid- and late-succession stages (p <.0001). [ABSTRACT FROM AUTHOR]

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Sets of spectral, spectral-spatial, textural, and geomorphometric variables derived from high spatial resolution Compact Airborne Spectrographic Imager (CASI) and elevation data are tested to determine their ability to discriminate landscape-scale forest ecosystem classes for a study area in northern Ontario, Canada. First, linear discriminant analysis for various spectral and spectral-spatial variables indicated that a spatial resolution of approximately 6 m was optimal for discriminating six landscape-scale forest ecosystem classes. Second, texture features, using second-order spatial statistics, significantly improved discrimination of the classes over the original reflectance data. Finally, addition of terrain descriptors improved discrimination of the six forest ecosystem classes. It has been demonstrated that,in a low- to moderate-relief boreal environment, addition of textural and terrain variables to high-resolution CASI reflectance data provides improved discrimination of forest ecosystem classes. [ABSTRACT FROM AUTHOR]