Single-picture reconstruction and rendering of trees for plausible vegetation synthesis

State-of-the-art approaches for tree reconstruction either put limiting constraints on the input side (requiring multiple photographs, a scanned point cloud or intensive user input) or provide a representation only suitable for front views of the tree. In this paper we present a complete pipeline for synthesizing and rendering detailed trees from a single photograph with minimal user effort. Since the overall shape and appearance of each tree is recovered from a single photograph of the tree crown, artists can benefit from georeferenced images to populate landscapes with native tree species. A key element of our approach is a compact representation of dense tree crowns through a radial distance map. Our first contribution is an automatic algorithm for generating such representations from a single exemplar image of a tree. We create a rough estimate of the crown shape by solving a thin-plate energy minimization problem, and then add detail through a simplified shape-from-shading approach. The use of seamless texture synthesis results in an image-based representation that can be rendered from arbitrary view directions at different levels of detail. Distant trees benefit from an output-sensitive algorithm inspired on relief mapping. For close-up trees we use a billboard cloud where leaflets are distributed inside the crown shape through a space colonization algorithm. In both cases our representation ensures efficient preservation of the crown shape. Major benefits of our approach include: it recovers the overall shape from a single tree image, involves no tree modeling knowledge and minimal authoring effort, and the associated image-based representation is easy to compress and thus suitable for network streaming.Peer ReviewedPostprint (author's final draft

Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data

Tropical forests are huge reservoirs of terrestrial carbon and are experiencing rapid degradation and deforestation. Understanding forest structure proves vital in accurately estimating both forest biomass and also the natural disturbances and remote sensing is an essential method for quantification of forest properties and structure in the tropics. Our objective is to examine canopy vegetation profiles formulated from discrete return LIght Detection And Ranging (lidar) data and examine their usefulness in estimating forest structural parameters measured during a field campaign. We developed a modeling procedure that utilized hypothetical stand characteristics to examine lidar profiles. In essence, this is a simple method to further enhance shape characteristics from the lidar profile. In this paper we report the results comparing field data collected at La Selva, Costa Rica (10° 26′ N, 83° 59′ W) and forest structure and parameters calculated from vegetation height profiles and forest structural modeling. We developed multiple regression models for each measured forest biometric property using forward stepwise variable selection that used Bayesian information criteria (BIC) as selection criteria. Among measures of forest structure, ranging from tree lateral density, diameter at breast height, and crown geometry, we found strong relationships with lidar canopy vegetation profile parameters. Metrics developed from lidar that were indicators of height of canopy were not significant in estimating plot biomass (p-value = 0.31, r2 = 0.17), but parameters from our synthetic forest model were found to be significant for estimating many of the forest structural properties, such as mean trunk diameter (p-value = 0.004, r2 = 0.51) and tree density (p-value = 0.002, r2 = 0.43). We were also able to develop a significant model relating lidar profiles to basal area (p-value = 0.003, r2 = 0.43). Use of the full lidar profile provided additional avenues for the prediction of field based forest measure parameters. Our synthetic canopy model provides a novel method for examining lidar metrics by developing a look-up table of profiles that determine profile shape, depth, and height. We suggest that the use of metrics indicating canopy height derived from lidar are limited in understanding biomass in a forest with little variation across the landscape and that there are many parameters that may be gleaned by lidar data that inform on forest biometric properties

Estimating Tropical Forest Structure Using a Terrestrial Lidar

Forest structure comprises numerous quantifiable biometric components and characteristics, which include tree geometry and stand architecture. These structural components are important in the understanding of the past and future trajectories of these biomes. Tropical forests are often considered the most structurally complex and yet least understood of forested ecosystems. New technologies have provided novel avenues for quantifying biometric properties of forested ecosystems, one of which is LIght Detection And Ranging (lidar). This sensor can be deployed on satellite, aircraft, unmanned aerial vehicles, and terrestrial platforms. In this study we examined the efficacy of a terrestrial lidar scanner (TLS) system in a tropical forest to estimate forest structure. Our study was conducted in January 2012 at La Selva, Costa Rica at twenty locations in a predominantly undisturbed forest. At these locations we collected field measured biometric attributes using a variable plot design. We also collected TLS data from the center of each plot. Using this data we developed relative vegetation profiles (RVPs) and calculated a series of parameters including entropy, Fast Fourier Transform (FFT), number of layers and plant area index to develop statistical relationships with field data.We developed statistical models using a series of multiple linear regressions, all of which converged on significant relationships with the strongest relationship being for mean crown depth (r2 = 0.88, p \u3c 0.001, RMSE = 1.04 m). Tree density was found to have the poorest significant relationship (r2 = 0.50, p \u3c 0.01, RMSE = 153.28 n ha-1). We found a significant relationship between basal area and lidar metrics (r2 = 0.75, p \u3c 0.001, RMSE = 3.76 number ha-1). Parameters selected in our models varied, thus indicating the potential relevance of multiple features in canopy profiles and geometry that are related to field-measured structure. Models for biomass estimation included structural canopy variables in addition to height metrics. Our work indicates that vegetation profiles from TLS data can provide useful information on forest structure

The Effect of Plant Water Storage on Water Fluxes within the Coupled Soil–Plant System

In addition to buffering plants from water stress during severe droughts, plant water storage (PWS) alters many features of the spatio-temporal dynamics of water movement in the soil–plant system. How PWS impacts water dynamics and drought resilience is explored using a multi-layer porous media model

A New General Allometric Biomass Model

To implement monitoring and assessment of national forest biomass, it is becoming the trend to develop generalized single-tree biomass models suitable for large scale forest biomass estimation. Considering that the theoretical biomass allometric model developed by West et al. [1,2] was statistically different from the empirical one, the two parameters in the most commonly used biomass equation M=aDb were analyzed in this paper. Firstly, based on the knowledge of geometry, the theoretical value of parameter b was deduced, i.e., b=7/3(~2.33), and the comparison with many empirical studies conducted throughout the globe indicated that the theoretical parameter could describe soundly the average allometric relationship between aboveground biomass M and D (diameter on breast height). Secondly, using five datasets of aboveground biomass which consisted of 1441 M-D pairs of sample trees, the new general biomass allometric model was validated. Finally, the relationship between parameter a and wood density p was analyzed, and the linear regression was developed. The new model, which is not only simple but also species-specific, offers a feasible approach on establishment of generalized biomass models for regional and national forest biomass estimation

Managing understory light conditions in boreal mixedwoods through variation in the intensity and spatial pattern of harvest: A modelling approach

In the context of partial harvesting, adequately managing post-harvest light conditions are essential to obtain a desired composition of tree species regeneration. The objective of this study was to determine how varying the intensity and spatial pattern of harvest would affect understory light conditions in boreal mixedwood stands of northwestern Quebec using the spatially explicit SORTIE-ND light model. The model was evaluated based on comparisons of observed and predicted light levels in both mapped and un-mapped plots. In mapped plots, reasonably accurate predictions of the overall variation in light levels were obtained, but predictions tended to lack spatial precision. In un-mapped plots, SORTIE-ND accurately predicted stand-level mean GLI (Gap Light Index) under a range of harvest intensities. The model was then used to simulate nine silvicultural treatments based on combinations of three intensities of overstory removal (30%, 45% and 60% of basal area) and three harvest patterns (uniform, narrow strips, large gaps). Simulations showed that increasing overstory removal had less impact on light conditions with uniform harvests, and a more marked effect with more aggregated harvest patterns. Whatever the harvest intensity, uniform cuts almost never created high light conditions (GLI > 50%). Gap cuts, on the other hand, resulted in up to 40% of microsites receiving GLI > 50%. Our results suggest that either a 30% strip or gap cut or a 45–60% uniform partial harvest could be used to accelerate the transition from an aspen dominated composition to a mixedwood stand because both types of cut generate the greatest proportion of moderately low light levels (e.g., 15–40% GLI). These light levels tend to favour an accelerated growth response among shade-tolerant conifers, while preventing excessive recruitment of shade-intolerant species. A better understanding of how spatial patterns of harvest interact with tree removal intensity to affect understory light conditions can provide opportunities for designing silvicultural prescriptions that are tailored to species’ traits and better suited to meet a variety of management objectives