Natural forest regeneration in Chernobyl Exclusion Zone: predictive mapping and model diagnostics
2021
Matsala, Maksym | Bilous, Andrii | Myroniuk, Viktor | Diachuk, Petro | Burianchuk, Maksym | Zadorozhniuk, Roman
Following the nuclear disaster of 1986, forests have established throughout the abandoned agricultural landscapes within Chernobyl Exclusion Zone (ChEZ). However, they are yet to be monitored properly. Their biometrical parameters need a robust assessment considering climate change mitigation potential and wildfire-induced risks. To predict basal area (BA) and growing stock volume (GSV) of these forests using spatially explicit approach, we utilized Sentinel-2 satellite data and three types of machine learning models (k-Nearest Neighbors (k-NN), Random Forest (RF) and Gradient Boosting Machine (GBM)). Root mean square error among all models ranged between 5.2 m² ha⁻¹ (49% of the mean) and 7.2 m² ha⁻¹ (71% of the mean), derived for BA by the GBM and k-NN models, respectively. While total and mean estimates of forest attributes were quite similar within an entire ChEZ, GBM approach outperformed other methods by predicting GSV more precisely when compared to local reference data. At the same time, k-NN approach has shown better performance in terms of preserving the initial empirical distribution and semivariation patterns. We concluded that k-NN method should be used for the spatial predictions of forest attributes, however, with a specific focus given on the training data set quality and profound model validation.
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