http://mcfns.net/index.php/Journal/issue/feedMathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)2023-05-12T19:44:00+00:00Chris Cieszewskimail@cjci.netOpen Journal Systems<p>The mission of MCFNS is to publish peer-reviewed basic and applied research in Mathematical and Computational Forestry and Natural-Resource Sciences. This research can include analytical solutions, proofs, derivations, software developments, and simulations, in forest management, growth and yield modeling, and other natural resource-related studies. Journal items are published collectively as part of an issue with its Table of Contents biannually, currently in <strong>April</strong> and <strong>October</strong>. </p>http://mcfns.net/index.php/Journal/article/view/15.1Nearest-tree and Variable Polygon Sampling2023-05-03T04:40:35+00:00Kim Ileskiles@island.netHTML Source EditorWord Wrap Sampling a nearest neighbor is often presented as a Hansen-Hurwitz or Horvitz-Thompson estimation process. This may not be the most informative viewpoint, and measuring the probability of selection is not necessary. The measurement of the nearest object as a “depth” over the selection area can be done by a sampling process, and the total estimated without the polygon areas. The process is unbiased, quite general, and easy to understand. It can be extended to more than just the nearest object to a sample point and to many different polygon shapes. This paper is an extension, simplification and generalization of an earlier paper in this journal (Iles, K. 2009 “Nearest-tree" estimations - A discussion of their geometry, MCFNS 1(2), pages 47 51.) , and does not require a random orientation or weighting for the direction of measurement from the tree to the polygon border.2023-04-30T00:00:00+00:00Copyright (c) 2023 Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)http://mcfns.net/index.php/Journal/article/view/15.2Parameter Estimation And Data-Driven Method For Forest Fire Prediction2023-05-12T19:44:00+00:00Xingdong Lilixd@nefu.edu.cnChun Tangchun_tang@nefu.edu.cnMingxian Zhangdljdzmx@nefu.edu.cnShiyu Zhangzhangshiyu666@126.comSanping Linefulisp@163.comYangwei Wangnefuwyw@163.comShufa Sunnefussf@163.comJiuqing Liudljdljq@163.comImprovement in the accuracy of the forest fire prediction model is essential to properly instruct firefighting forces. The input parameters of traditional prediction method cannot be adjusted in real-time, so the forecasting accuracy will decrease over time. To solve this problem, the forest fire prediction system based on parameter estimation and data-driven method is proposed in this paper. First, two dynamic parameters based on empirical values, rate of fire spread and main spreading direction, and multi-sensor data are input to a forward prediction model based on the Huygens principle to generate the predicted fireline for the current time. Secondly, the difference between the predicted and observed firelines is minimized by the Grey wolf optimization algorithm, which derives the optimal dynamic parameters. Finally, the optimal parameters and the current multi-sensor data are input into the prediction model to achieve accurate prediction of the fireline. The burn experiment was designed, and the feasibility of the system was verified by real fire data. The results indicate that a fire prediction system that quickly calibrates dynamic input parameters is developed and can achieve real-time accurate fire predictions.2023-04-30T00:00:00+00:00Copyright (c) 2023 Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)http://mcfns.net/index.php/Journal/article/view/15.3Solutions to the Base-Age Variant Models2023-05-04T00:07:22+00:00Chris J. Cieszewskimail@cjci.net<p>Self-referencing models predict the value of Y at age t as a function of both t and a snapshot observation of Y=Y0 at t=t0, which implicitly integrates the entire environment affecting the development of Y. Common examples of such models are site-dependent height over age models, or site index models, hereafter referred to as site models. These models are often developed using pooled cross-sectional and longitudinal data and describe families of multiple curve shapes.</p><p>It is advantageous to formulate these models as algebraic difference equations, which can be referred to as "dynamic equations," with their reference variables describing the environment or site quality. For example, in height modeling, site models predict height as a function of age and a height at a base-age known as the site index.</p><p>The base-age specific modeling ideology suggests that curves generated by these models are unique to a particular selection of base-age, at which the input data or site index is defined during the estimation of model parameters. Base-age variant models are designed to capture some of the patterns of curves corresponding to different base-ages through a single formula. The curves generated by this approach vary with base-ages and with various methods in which the models can be applied.</p><p>However, the available base-age variant models have been limited in their usage to avoid inconsistent predictions and cannot be considered equations in the algebraic sense since they can show that 1=0. To address this issue, I present a mathematical approach that leads to the derivation of a new type of proper base-age invariant equations, which can be applied in various alternative ways for the same purpose as the base-age variant models, but without creating mathematical inconsistencies.</p>2023-04-30T00:00:00+00:00Copyright (c) 2023 Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)