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We explore the possibility of extending the national forest inventory-based point data of understory presence using region-wide, disparate lidar data for the southeastern USA. For this, we developed a simple inferential model that helps to understand the basic underlying relationships and associations between lidar predictor metrics and forest understory shrub presence over a wide range of forest types and topographic conditions. The model (a least absolute shrinkage and selection operator-based logistic regression model) had fair predictive performance (accuracy = 62%, kappa = 0.23). Hence, we were able to propose a set of biophysically meaningful predictor variables that represent understory (4), canopy (3), topographic conditions (1), and sensor characteristics (1). The single most important predictor variable was the understory layer canopy density, which is the ratio of lidar returns in the understory to those near the ground. Hence, we demonstrate that the interplay of several factors affects understory vegetation condition. Overall, our work highlights the potential value of using lidar to characterize understory conditions. [ABSTRACT FROM PUBLISHER]

Information on foliar macronutrients is required in order to understand plant physiological and ecosystem processes such as photosynthesis, nutrient cycling, respiration and cell wall formation. The ability to measure, model and map foliar macronutrients (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)) at the forest canopy level provides information on the spatial patterns of ecosystem processes (e.g., carbon exchange) and provides insight on forest condition and stress. Imaging spectroscopy (IS) has been used particularly for modeling N, using airborne and satellite imagery mostly in temperate and tropical forests. However, there has been very little research conducted at these scales to model P, K, Ca, and Mg and few studies have focused on boreal forests. We report results of a study of macronutrient modeling using spaceborne IS and airborne light detection and ranging (LiDAR) data for a mixedwood boreal forest canopy in northern Ontario, Canada. Models incorporating Hyperion data explained approximately 90% of the variation in canopy concentrations of N, P, and Mg; whereas the inclusion of LiDAR data significantly improved the prediction of canopy concentration of Ca (R2 = 0.80). The combined used of IS and LiDAR data significantly improved the prediction accuracy of canopy Ca and K concentration but decreased the prediction accuracy of canopy P concentration. The results indicate that the variability of macronutrient concentration due to interspecific and functional type differences at the site provides the basis for the relationship observed between the remote sensing measurements (i.e., IS and LiDAR) and macronutrient concentration. Crown closure and canopy height are the structural metrics that establish the connection between macronutrient concentration and IS and LiDAR data, respectively. The spatial distribution of macronutrient concentration at the canopy scale mimics functional type distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using only IS, only LiDAR or their combination demonstrates the excellent potential for mapping these macronutrients at canopy scales across larger geographic areas into the next decade with the launch of new IS satellite missions and by using spaceborne LiDAR data. [ABSTRACT FROM AUTHOR]

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The amount of chlorophyll in a leaf influences photosynthetic potential and can be an indicator of the overall condition of a plant, including its stress level and nutritional status. Hence, it is important to understand the spatial and temporal variation of chlorophyll concentration. Imaging spectroscopy (IS) has made it possible to estimate chlorophyll at leaf and canopy levels. Spaceborne imaging spectrometers offer the possibility of estimating chlorophyll concentration at larger spatial scales and at lower cost than from direct sampling. We undertook this study in a mixedwood boreal forest to test the robustness of predictive models generated using Hyperion data for predicting chlorophyll concentration of data sets from different locations collected in different years. Among the group of indices tested, the derivative chlorophyll index (DCI) (DCI = D705/D722) and the maximum derivative of the red-edge divided by the derivative of 703 nm (Dmax(680–750))/D703) emerged as the best predictors of chlorophyll concentration across space and through time. When the canopy level chlorophyll predictive models of DCI and Dmax(680–750))/D703derived from Hyperion data were applied to other years’ remote-sensing data acquired by airborne and spaceborne sensors, DCI explained 71%, 63%, and 6% and Dmax(680–750))/D703explained 61%, 54%, and 8% of the variation in chlorophyll in 2002, 2004, and 2008, respectively, with prediction errors ranging from 11.7% to 14.6%. Two-variable models generated using 2005 Hyperion data were not as robust for predicting chlorophyll concentration from other years. Two models were found to explain more than half of the variance in chlorophyll concentration for the 2004 data only. Single and two-variable models applied to 2008 chlorophyll data provided poor predictions. The presence of multiple species creates a gradient in the chlorophyll concentration, which makes it possible to predict chlorophyll concentration. The gradient also affects the performance of predictive models generated using data from a different year. However, differences in sensors may also affect model performance. Our results suggest that predictive models obtained from Hyperion data are robust in predicting chlorophyll concentration within the same site through time and also at different sites across sensors. [ABSTRACT FROM PUBLISHER]

• Harmonic regression coefficients are better predictors of tree canopy cover than median composite components. • Per-pixel prediction variance was obtained from the mean variance of 500 independent model runs. • Harmonic regression variables are less influenced by noise than median composite variables. • Use of all available image data obviates the need for cloud-free scenes near peak growing season. The goal of this study was to evaluate whether harmonic regression coefficients derived using all available cloud-free observations in a given Landsat pixel for a three-year period can be used to estimate tree canopy cover (TCC), and whether models developed using harmonic regression coefficients as predictor variables are better than models developed using median composite predictor variables, the previous operational standard for the National Land Cover Database (NLCD). The two study areas in the conterminous USA were as follows: West (Oregon), bounded by Landsat Worldwide Reference System 2 (WRS-2) paths/rows 43/30, 44/30, and 45/30; and South (Georgia/South Carolina), bounded by WRS-2 paths/rows 16/37, 17/37, and 18/37. Plot-specific tree canopy cover (the response variable) was collected by experienced interpreters using a dot grid overlaid on 1 m spatial resolution National Agricultural Imagery Program (NAIP) images at two different times per region, circa 2010 and circa 2014. Random forest model comparisons (using 500 independent model runs for each comparison) revealed the following (1) harmonic regression coefficients (one harmonic) are better predictors for every time/region of TCC than median composite focal means and standard deviations (across times/regions, mean increase in pseudo R2 of 6.7% and mean decrease in RMSE of 1.7% TCC) and (2) harmonic regression coefficients (one harmonic, from NDVI, SWIR1, and SWIR2), when added to the full suite of median composite and terrain variables used for the NLCD 2011 product, improve the quality of TCC models for every time/region (mean increase in pseudo R2 of 3.6% and mean decrease in RMSE of 1.0% TCC). The harmonic regression NDVI constant was always one of the top four most important predictors across times/regions, and is more correlated with TCC than the NDVI median composite focal mean. Eigen analysis revealed that there is little to no additional information in the full suite of predictor variables (47 bands) when compared to the harmonic regression coefficients alone (using NDVI, SWIR1, and SWIR2; 9 bands), a finding echoed by both model fit statistics and the resulting maps. We conclude that harmonic regression coefficients derived from Landsat (or, by extension, other comparable earth resource satellite data) can be used to map TCC, either alone or in combination with other TCC-related variables. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is an important biophysical parameter used to monitor, model, and manage loblolly pine plantations across the southeastern United States. Landsat provides forest scientists and managers the ability to obtain accurate and timely LAI estimates. The objective of this study was to investigate the relationship between loblolly pine LAI measured in situ (at both leaf area minimum and maximum through two growing seasons at two geographically disparate study areas) and vegetation indices calculated using data from Landsat 7 (ETM+) and Landsat 8 (OLI). Sub-objectives included examination of the impact of georegistration accuracy, comparison of top-of-atmosphere and surface reflectance, development of a new empirical model for the species and region, and comparison of the new empirical model with the current operational standard. Permanent plots for the collection of ground LAI measurements were established at two locations near Appomattox, Virginia and Tuscaloosa, Alabama in 2013 and 2014, respectively. Each plot is thirty by thirty meters in size and is located at least thirty meters from a stand boundary. Plot LAI measurements were collected twice a year using the LI-COR LAI-2200 Plant Canopy Analyzer. Ground measurements were used as dependent variables in ordinary least squares regressions with ETM+ and OLI-derived vegetation indices. We conclude that accurately-located ground LAI estimates at minimum and maximum LAI in loblolly pine stands can be combined and modeled with Landsat-derived vegetation indices using surface reflectance, particularly simple ratio (SR) and normalized difference moisture index (NDMI), across sites and sensors. The best resulting model (LAI = −0.00212 + 0.3329SR) appears not to saturate through an LAI of 5 and is an improvement over the current operational standard for loblolly pine monitoring, modeling, and management in this ecologically and economically important region. [ABSTRACT FROM AUTHOR]

The objective of the current study was to develop methods for estimating the height and horizontal coverage of the forest understorey using airborne Lidar data in three managed pine plantation forest typical of the south eastern USA. The current project demonstrates a two-step approach applied automatically across a given study site extent. The first operation divided the study site extent into a regularly spaced grid (25 × 25 m) and identified the potential height range of the main Loblolly pine canopy layer for each grid-cell through aggregating Lidar return height measurements into a ‘stack’ of vertical height bins describing the frequency of returns by height. Once height bins were created, the resulting vertical distributions were smoothed with a regression curve line function and the main canopy vertical layer was identified through the detection of local maxima and minima. The second operation sub-divided the 25 × 25 m grid-cell into 1 × 1 m horizontal grid, for which height-bin stacks were created for each cell. Vertical features below the main canopy were then identified at this scale in the same manner as in the previous step, and classified as understorey features if they were lower in height than the 25 × 25 m estimate of the main canopy layer. The heights of the tallest understorey and sub-canopy layers were kept, and used to produce a rasterized map of the understorey layer height at the 1 × 1 m scale. Lidar derived estimates of the 25 × 25 m lowest vertical extent of the coniferous canopy correlated highly with field data (R 2 0.87; RMSE 2.1 m). Estimates of understorey horizontal cover ranged from R 2 0.80 to 0.90 (RMSE 6.6–11.7%), and maximum understorey layer height ranged from R 2 0.69 to 0.80 (RMSE 1.6–3.4 m) for the three study sites. The automated method deployed within the current study proved sufficient in determining the presence and absence of vegetation and artificial structures within the understorey portion of the current forest context, in addition to height and horizontal cover to a reasonable accuracy. Issues were encountered within older stands (e.g. more than 30 years old) where understorey deciduous vegetation layers intersected with the coniferous canopy layer, resulting in an underestimation of sub-dominant heights. [ABSTRACT FROM AUTHOR]

Data from four discrete-return airborne laser scanning (ALS) acquisitions and three different sensor types across seven experimentally varied loblolly pine (Pinus taeda L.) plantations were used to test published and novel methodologies in quantifying forest structural attributes within stands, including height to live crown (HTLC; i.e. the lowest vertical canopy extent) of the canopy and the contributions to total plot-level leaf area from understory and overstory canopy vegetation. These ALS data were compared to in situ field measurements to develop ALS-based predictive models of these attributes. The correlation between field- and ALS-modeled HTLC data was strong, with an R2 of 0.79 (p < 0.001). We assessed the ability of eight lidar light penetration indices to estimate effective leaf area index (eLAI) in the field. The best predictor of total (sum of understory and overstory) eLAI produced an R2 of 0.88 (p < 0.001). The independent contributions of overstory and understory components could also be accurately predicted by ALS-derived canopy-only eLAI metrics (R2 = 0.71; p < 0.001) and understory-only metrics (R2 = 0.49; p < 0.001). Two new indices, calculated as the sum of return intensity for each foliar layer and correcting for transmission losses, were developed specifically for the vertical strata related to the understory (BL under) or overstory (BL over). The estimates from BL over were equivalent to the best-performing indices for predicting canopy-only eLAI and the corresponding BL under was superior to other indices for understory eLAI. The broad spatial and temporal extents of the data, as well as the inclusion of pine plantations with differing stand ages, planting densities, understory control, and thinning treatments, suggest the relationships generated from these methods are robust to site and seasonal variability. The results produced from the analysis of multiple acquisitions implies that the methods presented here are transferable across location, time and sensor design, without implementation-specific calibration, at least for structurally similar loblolly pine plantations. • Vertical extent of pine forest overstory automatically defined using LiDAR; • LiDAR estimates of leaf area index (LAI) accurate across seven locations; • Pine forest LAI was best estimated using all returns (counts or intensity); • LAI estimated for separate vertical extents (i.e. overstory and understory). [ABSTRACT FROM AUTHOR]