Search Results (3)

Scaling leaf-level optical signals to the canopy level is essential for airborne and satellite-based forest monitoring. In evergreen trees, biophysical and optical traits may change as foliage ages. This study aims to evaluate the effect of age in Norway spruce needle on biophysical trait-prediction based on laboratory leaf-level spectra. Mature Norway spruce trees were sampled at forest stands in ten headwater catchments with different soil properties. Foliage biophysical traits (pigments, phenolics, lignin, cellulose, leaf mass per area, water, and nitrogen content) were assessed for three needle-age classes. Complementary samples for needle reflectance and transmittance were measured using an integrating sphere. Partial least square regression (PLSR) models were constructed for predicting needle biophysical traits from reflectance—separating needle age classes and assessing all age classes together. The ten study sites differed in soil properties rather than in needle biophysical traits. Optical properties consistently varied among age classes; however, variation related to the soil conditions was less pronounced. The predictive power of PLSR models was needle-age dependent for all studied traits. The following traits were predicted with moderate accuracy: needle pigments, phenolics, leaf mass per area and water content. PLSR models always performed better if all needle age classes were included (rather than individual age classes separately). This also applied to needle-age independent traits (water and lignin). Thus, we recommend including not only current but also older needle traits as a ground truth for evergreen conifers with long needle lifespan. Full article

Central European forests suffered from severe, large-scale atmospheric depositions of sulfur and nitrogen due to coal-based energy production during the 20th century. High deposition of acid compounds distorted soil chemistry and had negative effects on forest physiology and growth. Since 1994, continuous data on atmospheric deposition and stream runoff fluxes have provided evidence of ecosystem recovery from acidification. In this study, we combined for the first time mass budget data (sulfur deposition and total dissolved inorganic nitrogen (DIN) export) from the GEOMON monitoring network of headwater catchments with annual trajectories of vegetation indices derived from Landsat remote sensing observations. Time series of selected vegetation indices was constructed from Landsat 5, 7, and 8 using Google Earth Engine. Linear regression between the field data and vegetation indices was analyzed using R software. Biogeochemical responses of the forested catchment to declining acid deposition (driven by SO₂ emission reduction) were consistent across all catchments covering various forest stands from different regions of the Czech Republic. Significant correlations were found with total sulfur depositions, suggesting that the forests are continuously and consistently prospering from reductions in acid deposition. Disturbance index (DI) was the only vegetation index that was well-related to changes in forest cover associated with salvage loggings (due to the forest decline) during the 1980s and 1990s. A significant relationship (R² = 0.82) was found between the change in DI and DIN export in stream water. Regrowth of young forests in these highly affected areas tracks the most pronounced changes in total DIN export, suggesting a prominent role of vegetation in nitrogen retention. With the Landsat-derived DI, we could map decennial changes in forest disturbances beyond the small scale of the catchments to the regional level (demonstrated here for two protected landscape areas). This analysis showed the peak in forest disturbances to have occurred around the mid-1990s, followed by forest recovery and regrowth. Despite the improvement in forest ecosystem functioning over the past three decades in mountainous areas, emerging threats connected to changing climate will shape forest development in the near future. Full article

Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m²) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region. Full article