This data is vegetation type data combined with field surveys; The environmental factor data is driven by climate, soil, terrain, vegetation types, and other data, and the model is constructed using machine learning methods. The data format is GeoTIFF, with a spatial resolution of approximately 30m and a projection of WGS1984_ Albers.
Raw data: vegetation type data from field surveys;
Environmental variable data: Four major categories of environmental variables including terrain, vegetation, climate, and soil were selected as predictive factors.
Data preprocessing: Perform spatial registration and standardization on all multi-source raster data mentioned above. The unified projection coordinate system is WGS1984_ Albers, and the spatial range is cropped to the boundary of the study area. The spatial resolution of all variables is uniformly downscaled to 30 m using resampling techniques, and the format is unified as GeoTIFF to ensure strict spatial matching of multi-source data. Using ArcGIS' Extract Multi Values to Points feature, extract the environmental variable values corresponding to each sample point and construct a high-dimensional dataset of "sample environment features".
Random Forest Model Construction: Stratified Random Sampling is used to divide the dataset into a training set (70%) and a testing set (30%). Build a random forest classification model based on the scikit learn machine learning library in Python environment.
Calculate confusion matrix, Overall Accuracy, Precision, Recall, F1 Score, and Kappa coefficient. The results show that the model has high consistency.
| # | number | name | type |
| 1 | 2022FY100700 | Survey of Permafrost Conditions and Freeze-Thaw Damage in the High-Latitude Regions of Northeast China | Basic Resource Survey Project |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.NIEER.DB7254.2026
10.12072/ncdc.nieer.db7254.2026
