Is there Chornobyl nuclear accident signature in Scots pine radial growth and its climate sensitivity?

The extensive radioactive fallout resulting from the 1986 Chornobyl accident caused tree death near the nuclear power plant and perturbed trees communities throughout the whole Chornobyl exclusion zone. Thirty years into the post-accident period, the radiation continues to exert its fatal effects on the surviving trees. However, to what extent the continuous multi-decadal radiation exposure has affected the radial tree growth and its sensitivity to climate variation remains unascertained. In this comparative study, we measure the Scots pine radial growth and quantify its response to climate at two sites along the western track of the nuclear fallout that received significantly different doses of radiation in 1986. The common features of the two sites allow us to disentangle and intercompare the effects of sub-lethal and moderate radiation doses on the pine's growth and climatic sensitivity. We extend the response function analysis by making the first use of the Full-Duration at Half-Maximum FDHM method in dendrochronology and apply the double-moving window approach to detect the main patterns of the growth-to-climate relationships and their temporal evolution. The stand exposed to sub-lethal radiation shows a significant radial growth reduction in 1986 with a deflection period of one year. The stand exposed to moderate radiation, in contrast, demonstrates no significant decrease in growth either in 1986 or in the following years. Beyond the radiation effects, the moving response function and FDHM enabled us to detect several mutual patterns in the growth-to-climate relationships, which are seemingly unrelated to the nuclear accident. To advance our predictive understanding of the response of forest ecosystems to a massive radioactive contamination, future studies should include quantitative wood anatomy techniques.Impact de la gestion forestière et du changement climatique sur le microclimat en sous-boisImpact de la gestion forestière et du changement climatique sur le microclimat en sous-boi

Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k-nearest neighbors (k-NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ~99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k-NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors (k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k-NN method allowed us to estimate growing stock volume with an accuracy of 3 m3 ha−1 and for live biomass of about 2 t ha−1 over the study area