Forests2014, 5(3), 384-403; doi:10.3390/f5030384 - published online 28 February 2014 Show/Hide Abstract
Abstract: Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for a combined use of ALS and TLS in an inventory was tested and compared to a method without the use of TLS. Single-tree Norway spruce crown biomass was predicted using an ALS-model with training data obtained by TLS. ALS and TLS data were collected for sets of sample trees, including 68 trees with both ALS and TLS data. In total, 29 destructively sampled trees were used to fit a TLS crown biomass model, which then was used to predict crown biomass in a separate set of 68 trees. This dataset was subsequently used to fit an ALS crown biomass model. When validating the model, using a separate dataset with accurately measured crown biomass obtained through destructive sampling, the mean error was 32% of the observed mean biomass. Corresponding crown biomass predictions derived with ALS-predicted diameters and the use of conventional and existing allometric models resulted in a mean error of 35%. Thus, in the present study, a slight improvement, in terms of prediction accuracy, was found when using training data with ground reference values obtained by TLS.
Forests2014, 5(2), 363-383; doi:10.3390/f5020363 - published online 24 February 2014 Show/Hide Abstract
Abstract: The objective of this study was to evaluate the applicability of using a low-density (1–3 points m−2) discrete-return LiDAR (Light Detection and Ranging) for predicting maximum tree height, stem density, basal area, quadratic mean diameter and total volume. The research was conducted at the Penobscot Experimental Forest in central Maine, where a range of stand structures and species composition is present and generally representative of northern Maine’s forests. Prediction models were developed utilizing the random forest algorithm that was calibrated using reference data collected in fixed radius circular plots. For comparison, the volume model used two sets of reference data, with one being fixed radius circular plots and the other variable radius plots. Prediction biases were evaluated with respect to five silvicultural treatments and softwood species composition based on the coefficient of determination (R2), root mean square error and mean bias, as well as residual scatter plots. Overall, this study found that LiDAR tended to underestimate maximum tree height and volume. The maximum tree height and volume models had R2 values of 86.9% and 72.1%, respectively. The accuracy of volume prediction was also sensitive to the plot type used. While it was difficult to develop models with a high R2, due to the complexities of Maine’s forest structures and species composition, the results suggest that low density LiDAR can be used as a supporting tool in forest management for this region.
Forests2014, 5(2), 347-362; doi:10.3390/f5020347 - published online 24 February 2014 Show/Hide Abstract
Abstract: Fusiform rust resistance can involve gene-for-gene interactions where resistance (Fr) genes in the host interact with corresponding avirulence genes in the pathogen, Cronartium quercuum f.sp. fusiforme (Cqf). Here, we identify trees with Fr genes in a loblolly pine population derived from a complex mating design challenged with two Cqf inocula (one gall and 10 gall mixtures). We used single nucleotide polymorphism (SNP) genotypes at sufficient density to ensure linkage between segregating markers and Fr genes identifying SNPs that explained high proportions of variance in disease incidence using BayesCp, that also were significant using Bayesian Association with Missing Data (BAMD) software. Two SNPs mapped near Fr1 and generated significant LOD scores in single marker regression analyses for Fr1/fr1 parent 17 as well as four other parents. One SNP mapped near Fr8 and was significant for parent 28. Two SNPs mapped to linkage groups not previously shown to contain Fr genes and were significant for three parents. Parent 2 showed evidence of Fr gene stacking. Our results suggest that it is feasible to identify trees segregating for Fr genes, and to map Fr genes, based on parental analysis of SNPs that cosegregate with disease incidence in designed resistance screening trials.
Forests2014, 5(2), 325-346; doi:10.3390/f5020325 - published online 21 February 2014 Show/Hide Abstract
Abstract: The ecosite unit in Ontario’s boreal forest ecological land classification system is a polygon of common vegetation type and soil conditions intended to provide a standardized provincial framework to inform meso-scale forestry and planning applications. To determine whether the physical factors used for ecosite classification relate to patterns in ecological function over finer spatial scales, we examined 14 soil properties in replicate boreal forest plots representing eight mineral soil ecosite classes and three organic soil ecosite classes in the Hearst Forest. Despite large differences in vegetation composition, we found few statistically significant differences in properties when compared for individual classes or for more general groupings based on vegetation type and soil texture or expected fertility status. However, some properties (soil organic carbon, total nitrogen, and C:N ratio) were approaching significance in the 0–10 cm depth increment, and there were distinct differences between organic soil and mineral soil sites. Overall, these results suggest few explicit links between ecosystem function and ecosite class at this scale of measurement, highlighting the potential importance of non-steady-state relationships between vegetation species and soil properties in disturbed forests and the potential need for finer-scale characterization to capture patterns in ecosystem function.
Forests2014, 5(2), 309-324; doi:10.3390/f5020309 - published online 21 February 2014 Show/Hide Abstract
Abstract: Many studies have quantified uncertainty in forest carbon (C) storage estimation, but there is little work examining the degree of uncertainty in shrubland C storage estimates. We used field data to simulate uncertainty in carbon storage estimates from three error sources: (1) allometric biomass equations; (2) measurement errors of shrubs harvested for the allometry; and (3) measurement errors of shrubs in survey plots. We also assessed uncertainty for all possible combinations of these error sources. Allometric uncertainty had the greatest independent effect on C storage estimates for individual plots. The largest error arose when all three error sources were included in simulations (where the 95% confidence interval spanned a range equivalent to 40% of mean C storage). Mean C sequestration (1.73 Mg C ha–1 year–1) exceeded the margin of error produced by the simulated sources of uncertainty. This demonstrates that, even when the major sources of uncertainty were accounted for, we were able to detect relatively modest gains in shrubland C storage.
Forests2014, 5(2), 287-308; doi:10.3390/f5020287 - published online 20 February 2014 Show/Hide Abstract
Abstract: The canopy photosynthesis and carbon balance of the subtropical forests are not well studied compared to temperate and tropical forest ecosystems. The main objective of this study was to assess the seasonal dynamics of Normalized Difference Vegetation Index (NDVI) and potential canopy photosynthesis in relation to seasonal changes in leaf area index (LAI), chlorophyll concentration, and air temperatures of NE Argentina subtropical forests throughout the year. We included in the analysis several tree plantations (Pinus, Eucalyptus and Araucaria species) that are known to have high productivity. Field studies in native forests and tree plantations were conducted; stem growth rates, LAI and leaf chlorophyll concentration were measured. MODIS satellite-derived LAI (1 km SIN Grid) and NDVI (250m SIN Grid) from February 2000 to 2012 were used as a proxy of seasonal dynamics of potential photosynthetic activity at the stand level. The remote sensing LAI of the subtropical forests decreased every year from 6 to 5 during the cold season, similar to field LAI measurements, when temperatures were 10 °C lower than during the summer. The yearly maximum NDVI values were observed during a few months in autumn and spring (March through May and November, respectively) because high and low air temperatures may have a small detrimental effect on photosynthetic activity during both the warm and the cold seasons. Leaf chlorophyll concentration was higher during the cold season than the warm season which may have a compensatory effect on the seasonal variation of the NDVI values. The NDVI of the subtropical forest stands remained high and fairly constant throughout the year (the intra-annual coefficient of variation was 1.9%), and were comparable to the values of high-yield tree plantations. These results suggest that the humid subtropical forests in NE Argentina potentially could maintain high canopy photosynthetic activity throughout the year and thus this ecosystem may be a large carbon sink.