Stand density management decision-support program for simulating multiple thinning regimes within black spruce plantations

2003 ◽  
Vol 38 (1) ◽  
pp. 45-53 ◽  
Author(s):  
P.F. Newton
Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 448
Author(s):  
Peter F. Newton

The objectives of this study were to develop a stand density management decision-support software suite for boreal conifers and demonstrate its potential utility in crop planning using practical deployment exemplifications. Denoted CPDSS (CroPlanner Decision-support Software Suite), the program was developed by transcribing algorithmic analogues of structural stand density management diagrams previously developed for even-aged black spruce (Picea mariana (Mill) BSP.) and jack pine (Pinus banksiana Lamb.) stand-types into an integrated software platform with shared commonalities with respect to computational structure, input requirements and generated numerical and graphical outputs. The suite included 6 stand-type-specific model variants (natural-origin monospecific upland black spruce and jack pine stands, mixed upland black spruce and jack pine stands, and monospecific lowland black spruce stands, and plantation-origin monospecific upland black spruce and jack pine stands), and 4 climate-sensitive stand-type-specific model variants (monospecific upland black spruce and jack pine natural-origin and planted stands). The underlying models which were equivalent in terms of their modular structure, parameterization analytics and geographic applicability, were enabled to address a diversity of crop planning scenarios when integrated within the software suite (e.g., basic, extensive, intensive and elite silvicultural regimes). Algorithmically, the Windows® (Microsoft Corporation, Redmond, WA, USA) based suite was developed by recoding the Fortran-based algorithmic model variants into a collection of VisualBasic.Net® (Microsoft Corporation, Redmond, WA, USA) equivalents and augmenting them with intuitive graphical user interfaces (GUIs), optional computer-intensive optimization applications for automated crop plan selection, and interactive tabular and charting reporting tools inclusive of static and dynamic stand visualization capabilities. In order to address a wide range of requirements from the end-user community and facilitate potential deployment within provincially regulated forest management planning systems, a participatory approach was used to guide software design. As exemplified, the resultant CPDSS can be used as an (1) automated crop planning searching tool in which computer-intensive methods are used to find the most appropriate precommercial thinning, commercial thinning and (or) initial espacement (spacing) regime, according to a weighted multivariate scoring metric reflective of attained mean tree size, operability status, volumetric productivity, and economic viability, and a set of treatment-related constraints (e.g., thresholds regarding intensity and timing of thinning events, and residual stocking levels), as specified by the end-user, or (2) iterative gaming-like crop planning tool where end-users simultaneously contrast density management regimes using detailed annual and rotational volumetric yield, end-product and ecological output measures, and (or) an abbreviate set of rotational-based performance metrics, from which they determine the most applicable crop plan required for attaining their specified stand-level objective(s). The participatory approach, modular computational structure and software platform used in the formulation of the CPDSS along with its exemplified utility, collectively provides the prerequisite foundation for its potential deployment in boreal crop planning.


2003 ◽  
Vol 33 (3) ◽  
pp. 490-499 ◽  
Author(s):  
P F Newton

The objectives of this study were to (i) quantify the prediction error associated with estimating density (N (stems/ha)), quadratic mean diameter (Dq (cm)), basal area (G (m2/ha)), total volume (Vt (m3/ha)), and merchantable volume (Vm (m3/ha)) using a stand density management decision-support program (SDMDSP) developed for black spruce (Picea mariana (Mill.) BSP) plantations and (ii) given objective i, assess model adequacy by examining the relationship between prediction error and model input variables (prediction period, site index, initial density, and number of thinning treatments) by yield variate. Specifically, the SDMDSP was evaluated by comparing its yield predictions with corresponding measured values (n = 44) within 19 black spruce plantations. The resultant tolerance intervals indicated that 95% of the relative errors associated with future predictions would be within the following limits 95% of the time (minimum–maximum): (i) –27.3 to 29.7% for N, (ii) –26.1 to 14.3% for Dq, (iii) –48.3 to 26.1% for G, (iv) –64.3 to 37.7% for Vt, and (v) –87.0 to 73.0% for Vm. Graphical analysis indicated that errors for Vt and Vm were associated with the data from thinned plantations. This result is discussed within the context of residual stand structure variation and response delay from which recommendations for model improvement are derived.


2014 ◽  
Vol 962-965 ◽  
pp. 663-667 ◽  
Author(s):  
Yuri Sukhanov ◽  
Victor Lukashevich ◽  
Anton Sokolov ◽  
Alexey Pekkoev

The forestry specialists should be acquainted with modern information technologies and should be able to use modern software tools for solving various problems of forest industry. One of such modern software tools is the program MOTTI, which supports the solving of forest planning tasks at stand-level. The article presents the results of the adaptation of the MOTTI program to the conditions of Republic of Karelia, as well as the experience of its implementation for educational purposes.


2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.


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