Vector assessment model in R3 space for forest value

Abstract

While Forests have enormous potential and value, it is necessary to establish a valuation model to help us make decisions and make sound recommendations. To this end, I have made the following contributions. I collected nine data sets from 12 forests and integrated them into three dimensions. I combined the subjective hierarchical analysis (AHP) and CRITIC objective assignment method to obtain the weights. Using the indicators and data of these three dimensions, I constructed a vector assessment model in R3 space, where the modal length of the spatial vector represents the forest value, the angle with the standard vector (1, 1, 1) represents the space of progress, and the plane established by the two vectors together represents the direction of improvement. In addition, I found a relationship between artificial logging intensity and forest value, and established extreme value points, concluding that appropriate artificial logging helps to improve forest value. Because the modal length of Siberian coniferous forests is 1.008 and the angle of 13 degrees, there is a lot of room for improvement, and I analysed this further. I used the GM (1,1) model to predict the carbon sequestration data of Siberian coniferous forests, and the results are consistent with the analysis results of the three-dimensional vector model I established.