Global forest management data at a 100m resolution for the year 2015: region-specific models

Buchhorn, M. & Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342 (2022). Global forest management data at a 100m resolution for the year 2015: region-specific models. 10.5281/zenodo.5849150.

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Abstract

The global Forest Management data map for year 2015 (see DOI 10.5281/zenodo.4541512) was produced using a set of region-specific Random Forest Classifier models.

These models are trained on and applied to each region defined in the Global Biome Cluster layer (see DOI 10.5281/zenodo.5848609). They can be run with Python's scikit-learn Random Forest Classifier and the Python joblib package.

The model information is provided in three folders:

training data (.csv files) for each model in the right Remote Sensing band order, including lat, lon of the location, and the class [coded as number]
training parameters: random forest classifier parameters and used PROBA-V metrics bands (.ini files) to train the model with the given training data, after the 5folder cross-validation and optimization
models (.joblib.z files) for each biome. The model names includes the identifier code from the global Biome Cluster layer.

Item Type:	Data
Additional Information:	Creative Commons Attribution 4.0 International
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Novel Data Ecosystems for Sustainability (NODES)
Depositing User:	Luke Kirwan
Date Deposited:	15 Dec 2022 15:21
Last Modified:	15 Dec 2022 15:21
URI:	https://pure.iiasa.ac.at/18515

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