Investigation of the Factors Affecting Artificial Seed Sowing Success and Seedling Survival in Pinus brutia Natural Stands in Middle Elevations of Central Cyprus

The aim of this study was to analyze the germination of Pinus brutia Ten. seeds, in the field, in relation to factors such as period of sowing, light environment, and watering, in sites of different productivity in the middle elevations in central Cyprus. Two sowing experiments were conducted in three sites of different productivity. In the first experiment P. brutia seed sowing took place in February 2009 in two sowing environments which were gap and under canopy environments. The shade conditions in those environments were determined using hemispherical photographs. Also, the influence of watering on the seed germination was checked. In the second experiment, which was established in the same areas as in the first experiment, the seed sowing took place in December 2009. However, in this case, no watering was applied during the germination period. Moreover, the survival of the seedlings from both sowing periods were monitored up to the end of 2010. During the period of monitoring, the influence of watering was checked. The germination rates of seeds from the February sowing were very low. On the contrary, from the December sowing, the germination rates of seeds were very high in both sowing environments in all studied sites. In the case of seedling survival from the February and December sowing, mortality rates were relatively high in all sites except from the under-canopy sowing environment where watering was applied in the medium productivity site. From the December sowing, from a practical point of view, the number of survived seedlings, in all the plots of the three sites can be considered adequate for the successful regeneration of P. brutia.

Application of LiDAR Data for the Modeling of Solar Radiation in Forest Artificial Gaps—A Case Study

Artificial canopy gaps (forest openings) are frequently used as an element of regeneration cutting. The development of regeneration in gaps can be controlled by selecting a relevant size and shape for the gap, which will regulate the radiation microclimate inside it. Based on the size and shape of a gap computer models can assess where solar radiation is decreased or eliminated by the surrounding canopy. The accuracy of such models to a large extent depends on how the modeled shape of a gap matches the actual shape of the gap. The aim of this study was to compare the results of modeling solar radiation availability by applying Solar Radiation Tools (SRT) that use a different digital surface model (DSM) for a description of the shape of a studied gap, with the results of the analysis of 27 hemispherical photographs. The three-dimensional gap shape was approximated with the use of simple geometrical prisms or airborne laser scanning (LiDAR) data. The impact of two variations of exposure (automatic and manual underexposure) and two variations of automatic thresholding on the congruence of SRT and Gap Light Analyzer (GLA) results were studied. Taking into account information on differences in height between trees surrounding the gap enhanced the results of modeling. The best results were obtained when the boundary of the gap base estimated from LiDAR was expanded in all directions by a value close to a mean radius of the crowns of surrounding trees. Modeling of radiation conditions on the gap floor based on LiDAR data by an SRT program is efficient and more time effective than taking hemispherical photographs. The proposed solution can be successfully applied as a trustworthy source of information about light conditions in gaps, which is needed for management decision-making in silviculture.

Model-based local thresholding for canopy hemispherical photography

Canopy hemispherical photography (HP) is widely used to estimate forest structural variables. To achieve good results with HP, a classification algorithm is needed to produce binary images to accurately estimate the gap fraction. Our aim was to develop a local thresholding method for binarizing carefully acquired hemispherical photographs. The method was implemented in the R package “caiman”. Working with photographs of artificial structures and using a linear model, our method turns the cumbersome problem of finding the optimal threshold value into a simpler one, which is estimating the digital number (DN) of the sky. Using hemispherical photographs of a deciduous forest, we compared our method with several standard and state-of-the-art binarization techniques. Our method was as accurate as the best-tested binarization techniques, regardless of the exposure, as long as it was between 0 and 2 stops over the open sky auto-exposure. Moreover, our method did not require knowing the exact relative exposure. Intending to balance accuracy and practicality, we mapped the sky DN using the values extracted from gaps. However, we discussed whether a more accurate but less practical way to map sky DN could provide, along with our method, a new benchmark.

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE) at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index) and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth). The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle). The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.

Can terrestrial laser scanners (TLSs) and hemispherical photographs predict tropical dry forest succession with liana abundance?

Tropical dry forests (TDFs) are ecosystems with long drought periods, a mean temperature of 25 °C, a mean annual precipitation that ranges from 900 to 2000 mm, and that possess a high abundance of deciduous species (trees and lianas). What remains of the original extent of TDFs in the Americas remains highly fragmented and at different levels of ecological succession. It is estimated that one of the main fingerprints left by global environmental and climate change in tropical environments is an increase in liana coverage. Lianas are non-structural elements of the forest canopy that eventually kill their host trees. In this paper we evaluate the use of a terrestrial laser scanner (TLS) in combination with hemispherical photographs (HPs) to characterize changes in forest structure as a function of ecological succession and liana abundance. We deployed a TLS and HP system in 28 plots throughout secondary forests of different ages and with different levels of liana abundance. Using a canonical correlation analysis (CCA), we addressed how the VEGNET, a terrestrial laser scanner, and HPs could predict TDF structure. Likewise, using univariate analyses of correlations, we show how the liana abundance could affect the prediction of the forest structure. Our results suggest that TLSs and HPs can predict the differences in the forest structure at different successional stages but that these differences disappear as liana abundance increases. Therefore, in well known ecosystems such as the tropical dry forest of Costa Rica, these biases of prediction could be considered as structural effects of liana presence. This research contributes to the understanding of the potential effects of lianas in secondary dry forests and highlights the role of TLSs combined with HPs in monitoring structural changes in secondary TDFs.