Detecting Spatial Patterns of Peatland Greenhouse Gas Sinks and Sources with Geospatial Environmental and Remote Sensing Data.

Peatlands play a key role in the circulation of the main greenhouse gases (GHG) - methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). Therefore, detecting the spatial pattern of GHG sinks and sources in peatlands is pivotal for guiding effective climate change mitigation in the land use sector. While geospatial environmental data, which provide detailed spatial information on ecosystems and land use, offer valuable insights into GHG sinks and sources, the potential of directly using remote sensing data from satellites remains largely unexplored. We predicted the spatial distribution of three major GHGs (CH4, CO2, and N2O) sinks and sources across Finland. Utilizing 143 field measurements, we compared the predictive capacity of three different data sets with MaxEnt machine-learning modeling: (1) geospatial environmental data including climate, topography and habitat variables, (2) remote sensing data (Sentinel-1 and Sentinel-2), and (3) a combination of both. The combined dataset yielded the highest accuracy with an average test area under the receiver operating characteristic curve (AUC) of 0.845 and AUC stability of 0.928. A slightly lower accuracy was achieved using only geospatial environmental data (test AUC 0.810, stability AUC 0.924). In contrast, using only remote sensing data resulted in reduced predictive accuracy (test AUC 0.763, stability AUC 0.927). Our results suggest that (1) reliable estimates of GHG sinks and sources cannot be produced with remote sensing data only and (2) integrating multiple data sources is recommended to achieve accurate and realistic predictions of GHG spatial patterns.

A new method to estimate branch biomass from terrestrial laser scanning data by bridging tree structure models.

BACKGROUND AND AIMS: Branch biomass and other attributes are important for estimating the carbon budget of forest stands and characterizing crown structure. As destructive measuring is time-consuming and labour-intensive, terrestrial laser scanning (TLS) as a solution has been used to estimate branch biomass quickly and non-destructively. However, branch information extraction from TLS data alone is challenging due to occlusion and other defects, especially for estimating individual branch attributes in coniferous trees. METHODS: This study presents a method, entitled TSMtls, to estimate individual branch biomass non-destructively and accurately by combining tree structure models and TLS data. The TSMtls method constructs the stem-taper curve from TLS data, then uses tree structure models to determine the number, basal area and biomass of individual branches at whorl level. We estimated the tree structural model parameters from 122 destructively measured Scots pine (Pinus sylvestris) trees and tested the method on six Scots pine trees that were first TLS-scanned and later destructively measured. Additionally, we estimated the branch biomass using other TLS-based approaches for comparison. KEY RESULTS: Tree-level branch biomass estimates derived from TSMtls showed the best agreement with the destructive measurements [coefficient of variation of root mean square error (CV-RMSE) = 9.66 % and concordance correlation coefficient (CCC) = 0.99], outperforming the other TLS-based approaches (CV-RMSE 12.97-57.45 % and CCC 0.43-0.98 ). Whorl-level individual branch attributes estimates produced from TSMtls showed more accurate results than those produced from TLS data directly. CONCLUSIONS: The results showed that the TSMtls method proposed in this study holds promise for extension to more species and larger areas.

Assessing restoration potential of semi-natural grasslands by landscape change trajectories.

Species-rich semi-natural grasslands have rapidly declined and become fragmented in Northern Europe due to ceased traditional agricultural practices and animal husbandry. Restoration actions have been introduced in many places to improve the habitat conditions and increase the area to prevent any further losses of their ecological values. However, given the limited resources and long time span needed for successful restoration, it is essential to target activities on sites having a suitable initial state and where the effects of restoration are most beneficial for the habitat network. In this paper we present a conceptual framework for evaluating the restoration potential of partially overgrown and selectively managed semi-natural grasslands in a moderately transformed agricultural environment in south-western Finland. On the basis of the spatio-temporal landscape trajectory analysis, we construct potential restoration scenarios based on expected semi-natural grassland characteristics that are derived from land productivity, detected grassland continuum, and date of overgrowth. These scenarios are evaluated using landscape metrics, their feasibility is discussed and the effects of potential restoration are compared to the present extent of open semi-natural grasslands. Our results show that landscape trajectory analysis and scenario construction can be valuable tools for the restoration planning of semi-natural grasslands with limited resources. The approach should therefore be considered as an essential tool to find the most optimal restoration sites and to pre-evaluate the effects.

Landscape history improves detection of marginal habitats on semi-natural grasslands.

Semi-natural grassland habitats have markedly declined from their historical coverage, thus causing substantial losses for agricultural biodiversity and establishing a consequent need to spot the remaining habitat patches. These patches are generally remnants of once larger habitat areas, formed by uninterrupted and low-intensity management for centuries, but then later being isolated and fragmented into smaller pieces. In the light of this development, past landscape phases have a crucial role for the present existence of semi-natural grasslands. The importance of historical factors has been indicated in many studies but evaluation of their added value, or actual site-specific effects compared to observations of only the present landscape characteristics, is not generally provided. As data related to the past is often difficult to obtain, tedious to process and challenging to interpret, assessment of its advantages and related effects - or consequences of potential exclusion - would be needed. In this study, we used maximum entropy approach to model the distribution of Fumewort (Corydalis solida) which in the study area is a good indicator of valuable semi-natural habitats. We constructed three different models - one based on only the contemporary environment with expected indicators of habitat stability, one solely on the historical landscape phases and long-term dynamics, and one combining variables from the past and the present. Predictions of the three models were validated and compared with each other, followed by an analysis indicating the similarity of model results with known Fumewort occurrences. Our results indicate that present landscapes may provide workable surrogates to delineate larger core habitats, but utilization of historical data markedly improves the detection of small outlying patches. These conclusions emphasize the importance of previous landscape phases particularly in detecting marginal semi-natural grassland habitats, existing in contemporarily suboptimal conditions and being prone to disappear if no further actions are taken.