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Sustainable forest management requires decision support systems to evaluate possible scenarios and anticipate the consequences of decisions. Forest modellers typically develop complex systems of equations to predict the behaviour of forests which makes the use of forest models difficult for end-users in general, affecting transfer of knowledge and technology. To overcome these difficulties and facilitate their practical use, models can be integrated into software to generate user-friendly forest simulators. In this paper we introduce and describe ForestMTIS, a cloud computing compiled and editable open-source project to generate forest simulators which was developed for statistical, non-spatial, deterministic, disaggregated, single species even-aged stand growth and yield models. We demonstrate the use of ForestMTIS based on the development of FlorNExT®, its first practical application, based on a collaborative approach to make growth and yield modelling and sustainable forest management available to a large community of users in the Northeast of Portugal. [ABSTRACT FROM AUTHOR]

Tree height is a key variable in forest monitoring studies and for forest management. However, tree height measurement is time consuming, and the recommended procedure is to use estimates from height-diameter models. Here, we compare height-diameter model forms and approaches for predicting tree height (h) as a function of tree diameter at breast height (d) and additional stand level covariates. Four model forms were evaluated: (i) basic models (which only used d as predictor variable); (ii) generalized models (which used d and stand-level predictor variables); (iii) a mixed-effects model based on the best basic model; and (iv) a mixed-effects model based on the best generalized model. Several alternatives aimed at minimizing height measurement effort were tested in terms of accuracy and applicability. From a practical point of view, the selected generalized model is recommended for estimating the height of maritime pine in Portugal. The results also show that a calibrated basic mixed model provides more accurate results than a basic model locally fitted when the number of h-d observations is limited. [ABSTRACT FROM AUTHOR]