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2 THEORETICALBASIS OF FOREST BUSINESS MANAGEMENTINFORMATION SYSTEMAND PREDICTION MODEL
2.1 Forest Management Information System
2.1.1 Forest information systems
In a report prepared for the European Forest Institute, authors wrote about the ForestInformation System in Europe: The system’s main goal was to solve the problem of the lackof good information about forests in that part of the world. They simply stated thatinformation is a need to assist forest managers to make decisions about a necessary step tobe taken for better forest management (Schuck et al., 2017). The system also was developedin order to provide forest information about European countries. They have found that theEFIS focused on looking for innovative ways of “querying, extracting, processing andvisualizing data”. The EFIS is not a Web service, so it is not possible to have access toinformation from websites.Thesystem presented a challenge in terms offlexibility, it shouldbe able to process a growing number of spatial information.
In 2012, the Global Forest Information Service was launched and its mission is toshare global forest information for government, stakeholders, forest managers and otherswho need to access the forest information. It has allowed the collaboration among differentforest professionals (GFIS, 2018). In “Watershed Forest Management Information System(WFMIS)” Zhang and Barten (2008) have developed a system that helps forester and forestmanagers to efficientlymanage the forest for water resource protection.The system has beendeveloped as an extension of ArcGIS. The developed system has three models, one tospatially differentiate land cover and the characteristics of the site and to their influence onthe water quality. The other sub-model is in charge of evaluating the problem of the existingroad network so that the roads may not pollute the water. The third model aims to analyse Research on Forest Operation’s Management System based on Mapping and PredictionAnalysis
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2.2 Areview on the prediction of forest attributes
Predicting in the forestry industry could be an advantage for the management offorestry because it helps in planning for the future and learning from the past. There aremany methods available for forecasting ranging from simple linear regression, multiplelinear regression to the machine learning algorithms. In this part, we present differentforecasting or prediction methods and we will focus on the regression analysis in particularas it is the one that is used in this project in order to predict the density of the Linkou forestbureau and the area amount that is suitable for tending in the next years.
Different researchers have predicted different forest indicators and using differentmethods. Holmgren (2004) has used laser scanning for predicting forest variables.Peuhkurinen et al. (2018) have conducted a research in order to predict the tree diameterdistribution. They used theAirborne Laser Scanning method to make a tree list.Atree list isa list containing information (density, species, diameter, volume) of each tree in the area ofinterest. Many other types of researches have used remote sensing data in estimating orpredict forest attributes and assessing the carbon storage in forests (Xu et al., 2019).Mcgarrigle et al. (2011) did a research with the purpose of predicting the diameter of smalltrees using the data of trees with larger diameters. Liu and Zhang (2005) have used differentregression models in order to predict forest volume, tree height for a black spruce plantation.
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3 FOMIS SYSTEMANALYSISAND DESIGN........................21
3.1 FOMIS analysis .............................21
3.1.1 System Requirements analysis.......................21
3.1.2 The transaction processing analysis..............................23
4 FOREST OPERATIONS MANAGEMENT INFORMATION SYSTEMIMPLEMENTATION.
