Forest stand target attribute prediction

A system for predicting a forest stand target attribute, the system comprising:

• at least one processing unit;
• at least one memory;
• wherein the at least one memory stores program instructions that, when executed by the at least one processing unit cause the system to:
• obtain direct indicator data about forest stands, the direct indicator data comprising at least one of forest inventory estimates, airborne laser scan data, field measurement data, optical, hyperspectral or radar satellite data, and aerial image data obtain indirect indicator data about the forest stands, the indirect indicator data providing data that helps to explain growth of trees in the forest stands, the indirect indicator data comprising at least one of silvicultural data, geographical data, geological data, historical weather and climate data;
• obtain empirical measurement data about the forest stands, the empirical measurement data comprising at least one of harvester machine data, X-ray data from saw mills, saw mill data, pulp mill data and integrated mills data;
• divide the forest stands into a grid composed of geographically non-overlapping cells, each cell being bound by geographical coordinates, the grid comprising a plurality of grid layers;
• determine values of a forest stand target attribute for a first set of cells of a grid layer on the empirical measurement data, wherein the forest stand target attribute refers to any attribute that is measurable for a forest stand and characterizes the forest stand;
• determine values of a plurality of input variables for a second set of cells of the remaining grid layers based on the direct indicator data and the indirect indicator data so that cells of each remaining grid layer comprise values associated with the corresponding same input variable, the second set of cells geographically corresponding to the first set of cells;
• convert the grid layers to grid-specific feature vectors so that each grid-specific feature vector corresponds to a single cell of the grid;
• apply a supervised machine learning algorithm for the forest stand target attribute to generate a trained model for the forest stand target attribute based on the grid-specific feature vectors;
• determine values of the plurality of input variables for a given cell of the remaining grid layers based on the direct indicator data and the indirect indicator data;
• construct an input feature vector for the given cell based on the values of the plurality of input variables for the given cell;
• and
• predict the value of the forest stand target attribute for the given cell based on the input feature vector and the trained model for the forest stand target attribute.