CLIMATE REGULATING ECOSYTEM SERVICES OF THE FOREST LANDSCAPE IN THE UKRAINIAN CARPATHIANS

Abstract

Ecosystems provide multiple services for humans. Among them, a group of supporting and regulating ecosystem services is often less recognized by people as benefit and has been less studied by researchers. Amid various manifestations of climate change, more attention has been paid to particular subset of this group of services called climate regulating. Despite these there still few quantitative studies in this field. Trying to fill this research gap we conducted a study aimed at exploration of relation between climate regulating ecosystem services and their spatial determinants in the forest landscape within Ukrainian Carpathians. For that we chose the territory within Rakhiv and Tsiachiv districts in Transcarpathian region which represents all diversity of forest mountain ecosystems. For this study we used information on land surface temperature (LST) extracted from Landsat 8 thermal band for summer season of 2015. In order to account for vertical thermal gradient in mountains the LST data underwent normalization and in further analysis a dependent variable we employed normalized LST (nLST). Set of independent variables included geomorphometric indicators (altitude, slope, aspect, TPI) and data on forest cover (disturbance, density, dominant species, and disturbance in the neighborhood). For key study area of Velykyi watershed of 4059 ha we additionally used data on forest biomass and tree age. In general, all forest ecosystems in present research have been divided into three distinct classes – “natural”, “disturbed” and “other” forests. Using boosted regression trees method we built three statistical models for each of the forest classes called “global” models. Also we developed 12 “local” models that showed the link between nLST and analyzed independent variables within each altitudinal bioclimatic zone with considering also forest class. Three separate statistical models have been built for each of the forest classes for key study area. Our results suggest that both maximum and mean values of nLST within particular altitudinal bioclimatic zone are the lowest in “natural” forests and the highest in “disturbed” ones.. The statistical model performance based on the variance explained indicator ranged from 32 to 74 %, whilst for models for key study area it was between 77 and 89 %. The set of influential variables for different forest classes varied substantially, but the most often they included aspect, forest density and elevation despite of normalization applied before. In models created for class “disturbed” forests between 19 and 35 % of all explained variance has been contributed by variable indicating time of disturbance. In “local” models for class “natural” forests we revealed gradual decrease of influence of the geomorphometric indicators (elevation, slope, and TPI) when move from warmer to cooler altitudinal zones while for topographic aspect and forest density the trends were just the opposite. In case of key study area a wood stock and tree age variables along with elevation and aspect were amongst the most influential ones. We can conclude that depending on the state of naturalness of forest ecosystems they have different climate regulating potential which might be severely depleted by human and natural disturbances.
 Keywords: forest landscape, ecosystem services, remote sensing, climate regulation, climate change, Landsat satellite images, Ukrainian Carpathians.