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Pollen production for 13 urban North American tree species: Allometric equations for tree trunk diameter and crown area.

ABSTRACT: Estimates of airborne pollen concentrations at the urban scale would be useful for epidemiologists, land managers, and allergy sufferers. Mechanistic models could be well suited for this task, but their development will require data on pollen production across cities, including estimates of pollen production by individual trees. In this study, we developed predictive models for pollen production as a function of trunk size, canopy area, and height, which are commonly recorded in tree surveys or readily extracted from remote sensing data. Pollen production was estimated by measuring the number of flowers per tree, the number of anthers per flower, and the number of pollen grains per anther. Variability at each morphological scale was assessed using bootstrapping. Pollen production was estimated for the following species: Acer negundo, Acer platanoides, Acer rubrum, Acer saccharinum, Betula papyrifera, Gleditsia triacanthos, Juglans nigra, Morus alba, Platanus x acerfolia, Populus deltoides, Quercus palustris, Quercus rubra, and Ulmus americana. Basal area predicted pollen production with a mean R² of 0.72 (range: 0.41 - 0.99), whereas canopy area predicted pollen production with a mean R² of 0.76 (range: 0.50 - 0.99). These equations are applied to two tree datasets to estimate total municipal pollen production and the spatial distribution of street tree pollen production for the focal species. We present some of the first individual-tree based estimates of pollen production at the municipal scale; the observed spatial heterogeneity in pollen production is substantial and can feasibly be included in mechanistic models of airborne pollen at fine spatial scales.

SUBMITTER: Katz DSW

PROVIDER: S-EPMC7748260 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Biomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (Pinus contorta), ponderosa pine (P. ponderosa), and Douglas-fir (Pseudotsuga menziesii) in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation-errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n?=?285). RESULTS:Tree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30-75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25-70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean [% RMSE]?=?50%). This is likely due to low-sample size (10-20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE?=?97%) and nationwide (2.2 billion Mg; % RMSE?=?94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg; % RMSE?=?165%). CONCLUSIONS:Allometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.

Project description:An international data science challenge, called National Ecological Observatory Network-National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

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Pollen production for 13 urban North American tree species: Allometric equations for tree trunk diameter and crown area.

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