Forest Landscape 3d Model Free Download [2021]
Vilma Masaitis
Natural phenomena occur in hierarchies across a wide spectrum of spatiotemporal scales. An equally wide range of models has been developed at each hierarchical level to model these phenomena (Wu et al. 2006). At a given level of hierarchy, a modeling system is composed of interacting components (i.e., lower-level entities) and is itself a component of a larger system (i.e., higher level controls) that influences behavior (Delcourt and Delcourt 1988; Wu and Loucks 1995; Wu 1999; Wu and David 2002; Lischke et al. 2007). Forest landscape models (FLMs) simulate forest landscape processes (FLPs) at landscape scales and stand dynamics as a function of lower-level entities operating at site-scales, while incorporating regional environments (e.g., climate) as higher level controls (Fig. 1). FLPs include seed dispersal and natural and anthropogenic disturbances such as wildfire, windstorm, insect outbreak, disease spread, timber management activities (He et al. 2011).
Upper image is a forest cover of eastern U.S., middle image is a forested landscape in Missouri, and lower images are various forest types within the Eastern U.S. Usually, regional scale predictions (e.g., dynamics of total forest carbon of Eastern U.S.) directly use relationships between processes at stand/site scales (pixels) and climatic drivers at regional scales, completely bypassing landscape processes. The fundamental question is: Are landscape processes necessary in regional scale predictions?
Increasing numbers of studies have shown that fine-scale processes may play a greater role than climate change in affecting forest composition and FLPs may accelerate shifts in species composition by providing regeneration opportunities and altering the competitive balance among tree species (Gustafson et al. 2010; Thompson et al. 2011; Li et al. 2013; Luo et al. 2014; Wang et al. 2015). In addition, studies have found that seed dispersal that links site-scale population (seed abundance and distribution), species biological traits (e.g., dispersal distance), and environmental heterogeneity is fundamental to studying tree species migration and range shifts (Lischke 2005; Thuiller et al. 2008; Doxford and Freckleton 2012; Meier et al., 2012; Corlett and Westcott 2013). FLMs are ideally designed to tackle the key processes discussed above.
Significant theoretical and technological advances have been made in FLMs over the past decade (Scheller and Mladenoff 2007; He 2008; Gustafson 2013). From the theoretical perspective, incorporating quantitative information of biomass, density, and individual trees into landscape models has improved the realism of simulated landscape dynamics, and the inclusion of altered disturbance regimes and nutrient cycling under climate change has addressed key prediction uncertainties. From the technological perspective, computer memory is no longer a severe limitation because of advanced data compressing algorithms and 64-bit operating systems. Furthermore, landscape-scale data sets for model initialization and evaluation are becoming increasingly available through systematic forest inventory and remote sensing. Thus, many FLMs have achieved a new quality of regional-scale predictions by incorporating additional site- and landscape-scale processes. Such predictions allow for comparisons with those by TBMs to reveal the effects of dispersal, disturbance and management, further reducing prediction uncertainties under changing climates.
FLMs have emerged and evolved over the past 20 years. Shifley et al. (2017) provided a comprehensive review that highlighted milestones in the development of forest dynamics models. They pointed out that a great window of opportunity has opened for the development and application of FLMs because past limitations in computing capacity are easing and because data suitable for model calibration or evaluation are becoming more available. Of all the classes of models that simulate FLPs, FLMs are perhaps the most ready to transition to a central role supporting forest management, planning, and policy decisions.
Xiao et al. (2016) compared two versions of the LANDIS FLM family that differ in the main state variables used to represent vegetation: LANDIS PRO simulates density and size of cohorts, whereas LANDIS-II (with biomass extensions) simulates the accumulation and loss of biomass of species cohorts. For early successional species, the two models yielded similar distributions of species abundance and size, but for mid- to late-successional species, the results differed significantly. This highlights the importance of carefully scrutinizing how fine-scale processes are formulated and upscaled to a larger scale in forest landscape modeling.
Several succession modules of the LANDIS FLM family require input variables describing an environment-dependent species growth potential. To understand this potential, the physiological model LINKAGES was adapted and run in ecologically differing areas of the Central Hardwood Region of the USA under current and projected future climate (Dijak et al. 2016). Forest composition and biomass simulated by LINKAGES were plausible with respect to spatial distribution and temporal characteristics of temperature, drought and soil quality. This provides a way to derive LANDIS inputs from LINKAGES results.
Duveneck et al. (2016) used LANDIS II to examine how forest succession will continue to influence future forest conditions under current climate and that projected by four separate global circulation models forced by a high emission scenario (RCP 8.5) in New England. They found that the region will accumulate 34% more forest biomass and succeed to more shade tolerant species. They also found that climate change resulted in a 82% biomass increase. Continued recovery dynamics from historic land use change will have larger impacts than climate change on forest composition in New England. The large increases in biomass simulated under all climate scenarios suggest that climate regulation provided by the eastern forest carbon sink has potential to continue for at least a century.
Boulanger et al. (2016) projected the effects of climate change on the composition and productivity of four landscapes at the southern boreal forest transition zone in Canada. They used LANDIS-II informed by a forest patch model (PICUS). They found that productivity and total biomass would decrease especially for the dominant boreal species. This result differed from Duveneck et al. (2016) and Wang et al. (2016), whose studies showed an overall increase in total biomass under climate change. Sensitivity analyses conducted using a mechanistic model based on physiology first principles suggest that increased respiration loads at very high temperatures more than offset biomass increases predicted under more moderately increased temperatures, which may partially explain these discrepancies. Biomass reduction in boreal regions may also be partially due a lack of temperate species adapted to warmer climates and stronger fire disturbance (Boulanger et al. 2016). They concluded conservation and forest management planning within the southern boreal transition zone should consider both disturbance-and climate-induced changes in forest communities.
LeBrun et al. (2016) linked Bayesian empirical models of bird habitat suitability with the LANDIS-PRO FLM to predict avian abundance under various climate and management scenarios. They were able to characterize bird habitats using density and size information simulated by the model. They found that management actions had a much greater impact on avian abundance than did climate, although greater climate effects may develop beyond the 100-year projection period of the study. Management strategies to increase carbon sequestration or resilience did not produce a strong collateral effect on birds, and climate effects on habitat were significant only for the northern bobwhite.
Loehman et al. (2016) used the mechanistic ecosystem-fire process model FireBGCv2 to model interactions of wildland fire, mountain pine beetle, and white pine blister rust under current and future climates across three diverse study areas for 300 years. They found that while disturbances reduced overall basal area more than other disturbances. They also found that disturbances under future climate had greater effects on landscape basal area than those under the current climate scenarios. Their research highlighted the importance of understanding disturbance interactions because forest responses to wildfires, pathogens, and beetle attacks may offset or exacerbate climate influences under the interacting disturbances.
Loudermilk et al. (2016) used the LANDIS II FLM coupled to the Century carbon dynamics model to assess the effectiveness of fuel reduction treatments in wildfire-prone areas under climate change. They found that over longer periods fuel reductions were successful in terms of reduced fire severity and spread, and increased carbon storage. The latter resulted from increased tree growth after the treatments caused by decreased drought, and a change in species composition toward more drought and fire-tolerant species. This study demonstrates that forest resilience to climate change can be achieved or enhanced by appropriate management measures.
Cary et al. (2016) quantified the relationship between fuel treatment effort (planned burning) and the incidence of moderate-to-high intensity unplanned fire. To maximize generality, they used three landscape-scale fire models in two biomes to evaluate the relative importance of fuel treatment effort, ignition management and weather in reducing the area burned by moderate-to-high intensity wildfire. They found that fuel treatment effort explained less than 7% of the variation in total area burned by unplanned fire across models and biomes. They concluded that effort to control ignitions and housing development in fire-prone environments may be more effective than efforts aimed at broad-area fuel treatment, although constructed assets in a specific locale may be worth protecting with targeted fuel treatments.
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