Modeling potential distribution and above-ground biomass of Scots pine (Pinus sylvestris L.) forests in the Inner Anatolian Region, Türkiye.

Abstract

Scots pine (Pinus sylvestris L.) holds a substantial position as a tree species designated for biomass energy within European forests, covering a significant part of Türkiye's forests. We used the machine learning technique, namely, maximum entropy (MaxEnt), to estimate the suitable areas for Scots pine and to investigate its potential future distribution under various climate change scenarios in Inner Anatolian Region, Türkiye. The distribution data of Scots pine was utilized, and a set of 20 variables was chosen from spectral, topographic, and bioclimatic datasets to train the MaxEnt model. A map depicting the potential distribution of Scots pine in the area was generated, and alterations in its spatial distribution under SSP2-4.5 and SSP5-8.5 climate change scenarios were predicted. The results showed that the most effective factors for the distribution of Scots pine in the region were normalized difference vegetation index (NDVI), Red band of the imagery, and Bio19 variables, and the contribution percentages were 45.6%, 18.5%, and 18.1%, respectively. Current conditions have indicated that 81.11% of the region is not suitable for Scots pine. Highly suitable areas for Scots pine constituted 0.88% of the total area in the east and southeast parts of the region. Considering the SSP2-4.5 and SSP5-8.5 scenarios, it has been determined that there may be a partial increase in highly suitable areas. The above-ground biomass (AGB) data generated based on potential distribution areas were predicted between 0.04 and 168.76 t ha-1, and the areas with dense biomass over 120 t ha-1 were identified in the west, north, and northeast parts of the region. While actual AGB of Scots pine was 6.92 MT, its potential AGB was estimated 125.93 MT in total area. The difference may well be attributed to the wide potential distribution of Scots pine stands in the area apart from the current forest lands. Nevertheless, this research contributes to the holistic management of forests and provides substantial values for formulating well-suited silvicultural interventions, developing sustainable forest management strategies, and furthering research aimed at estimating biomass reserves.