Forests are experiencing an environment that changes much faster than during the past several hundred years. In addition, the abiotic factors determining forest dynamics vary depending on its location. Forest modeling thus faces the new challenge of supporting forest management in the context of environmental change. This review focuses on three types of models that are used in forest management: empirical (EM), process-based (PBM) and hybrid models. Recent approaches may lead to the applicability of empirical models under changing environmental conditions, such as (i) the dynamic state-space approach, or (ii) the development of productivity-environment relationships. Twenty-five process-based models in use in Europe were analyzed in terms of their structure, inputs and outputs having in mind a forest management perspective. Two paths for hybrid modeling were distinguished: (i) coupling of EMs and PBMs by developing signal-transfer environment-productivity functions; (ii) hybrid models with causal structure including both empirical and mechanistic components. Several gaps of knowledge were identified for the three types of models reviewed.The strengths and weaknesses of the three model types differ and all are likely to remain in use. There is a trade-off between how little data the models need for calibration and simulation purposes, and the variety of input-output relationships that they can quantify. PBMs are the most versatile, with a wide range of environmental conditions and output variables they can account for. However, PBMs require more data making them less applicable whenever data for calibration are scarce. EMs, on the other hand, are easier to run as they require much less prior information, but the aggregated representation of environmental effects makes them less reliable in the context of environmental changes. The different disadvantages of PBMs and EMs suggest that hybrid models may be a good compromise, but a more extensive testing of these models in practice is required.
We invent here in this manuscript new tree describing parameters which can be derived from a QSM. QSMs are topological ordered cylinder models of trees which describe the branching structure completely. All new invented parameters have in common, that their defining point of view looks from the direction of the tips and not from the root along the tree. The reason here is simple, diameter relations are stronger when they rely on a distance measure to the tip(s) rather than on a distance measure to the root. In the traditional branch order for example diferent sized branches are contained, but in the reverse branch order this problem is barely exiting anymore. The pipe model theory (PMT) is a theory adapted to deciduous trees, based on the allometric scaling theory. And according to the PMT the count of new growth units can serve as a proxy to predict the sapwood area at the query cylinders cross section. By multiplying this area proxy with the cylinder length we receive a proxy for the sapwood volume contained. We name the sapwood volume of the whole subbranch VesselVolume. The sapwood volume finally serves as the predictor for the basic allometry function to predict the diameter/radius of combined sap and heartwood at the query point. For validation we use QSMs produced from an open point cloud data set of tree clouds with SimpleForest software. We compare the QSM volume against the harvested reference data for 66 felled trees. We also found QSM data of TreeQSM, a competitive and broadly accepted QSM modeling tool. Our RMSE was less than 40 % of the TreeQSM RMSE. And for other error measures, the r2 adj. and the CCC, the relative improvement looked even better with reaching only 27 % and 21 % of the TreeQSM errors respectively. With the invention of this filter we improve tree volume prediction capabilities utilizing QSMs. Additionally, we run numerical tests against the West Brown Enquist (WBE) model predictions using our filtered QSMs.
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