Leaf Phenology Drives Spatio-Temporal Patterns of Throughfall under a Single Quercus castaneifolia C.A.Mey

Contrasting effects of low-severity fire on stemflow production between coexisting pine and oak trees

As climate change intensifies, fires events are predicted to increase in forest ecosystems. Fire alters the ecosystem structure and consequently, the hydrological cycle. However, little is known about the impacts of forest fire on stemflow. A field experiment was conducted to evaluate the short-term response of stemflow production to low-severity fire in a coniferous and broadleaved mixed forest. Results demonstrated low-severity fire changed stemflow yield and had insignificant effect on the correlation between stemflow efficiency and rainfall or plant morphological variables. In unburned site Quercus acutissima and Pinus massoniana and in burned site Q. acutissima and P. massoniana, stemflow percentage averaged 3.86, 0.37, 1.20, and 0.47 %, whereas funneling ratio averaged 38.8, 4.2, 11.4, and 5.1, respectively. Fire substantially decreased the stemflow percentage and funneling ratio of Q. acutissima (P < 0.05) and slightly enhanced P. massoniana (P > 0.05). The responses of stemflow production to fire differed significantly between oak and pine trees. Fire made Q. acutissima become less effective in funneling rain to the forest ground, which is attributed to that the scaly bark was burned to highly furrowed bark that delivers less water to tree base. Burned P. massoniana was more productive in draining stemflow relative to unburned trees and is attributed to the bark which was still flaky regardless of. Additionally, the higher canopy openness allows more rain to funnel to the trunk. Stemflow efficiency was reduced in response to fire and limited the transfer of water and nutrients from canopy to soil and can reduce the competitiveness of Q. acutissima after fire disturbance.

The Influence of Data Density and Integration on Forest Canopy Cover Mapping Using Sentinel-1 and Sentinel-2 Time Series in Mediterranean Oak Forests

Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes.

The Seasonal Fluctuation of Timber Prices in Hyrcanian Temperate Forests, Northern Iran

Seasonal fluctuations play an important role in the pricing of a timber sale. A good understanding of timber price mechanisms and predictability in the timber market would be very practical for forest owners, managers, and investors, and is crucial for the correct functioning of the timber sector. This research aimed to analyze the effect of sale season on timber (sawlog and lumber) prices of high-value species groups (e.g., oriental beech, chestnut-leaved oak, common alder, velvet maple, and common hornbeam) in the Hyrcanian temperate forests (Northern Iran). The data were collected from official sale documents of the Azarroud Forestry Plan from 1992 to 2015. The relevant data of 592 sale lots at forest roadside were extracted into a data set. Then, the average timber prices (sawlog and lumber) per season/year in quarterly frequency were calculated. In doing so, two-time series of seasonal prices for the sawlog and lumber was obtained. The stationarity of the time series was statistically verified using the augmented Dickey–Fuller test. The effect of sale seasons on timber price was first analyzed using multiple linear regression analysis dummy variables. The results showed that autumn and summer have a significant positive effect on timber prices of 6.5% and 6.1%, respectively. Additionally, the decomposition of time series results showed that the highest prices of the sawlog and lumber were in quarter 3 and quarter 2, respectively, due to an increase in construction activities that picked up in the autumn season. Information about potential price fluctuations will be plausible and allow suppliers and users of sawlogs to adjust their supply and demand. This valuable information can be used in marketing and strategic forest management planning for Hyrcanian temperate forests and other temperate countries with similar conditions.

Changes in Soil’s Chemical and Biochemical Properties Induced by Road Geometry in the Hyrcanian Temperate Forests

Forest roads play an important role in providing access to forest resources. However, they can significantly impact the adjacent soil and vegetation. This study aimed to evaluate the effects of road geometry (RG) on the chemical and biochemical properties of adjacent soils to assist in environmentally friendly forest road planning in mountainous areas. Litter layer, canopy cover, soil organic carbon (SOC) stock, total nitrogen (TN), the activity of dehydrogenase (DHA), and urease (UA) enzymes at a 0–20 cm soil depth were measured by sampling at various distances from the road edge to 100 m into the forest interior. The measurements were done for three road geometries (RG), namely straight, curved, and bent roads, to ensure data heterogeneity and to reflect the main geometric features of the forest roads. Analysis of variance (ANOVA) showed that the effects of RG on the measured variables were statistically significant. Spearman’s correlation test clearly showed a strong positive correlation between environmental conditions, SOC, TN, DHA, and UA for given RGs. Based on piecewise linear regression analysis, the down slope direction of the straight and the inside direction of bent roads accounted for the lowest and highest ranges of ecological effects, respectively. The results of this study contribute to our understanding of the environmental effects brought about by road geometry, which can be important for forest road managers when applying the best management practices.

Spatial Variability and Optimal Number of Rain Gauges for Sampling Throughfall under Single Oak Trees during the Leafless Period

This study examined the spatial variability of throughfall (Tf) and its implications for sampling throughfall during the leafless period of oak trees. To do this, we measured Tf under five single Brant’s oak trees (Quercus brantii var. Persica), in the Zagros region of Iran, spanning a six-month-long study period. Overall, the Tf amounted to 85.7% of gross rainfall. The spatial coefficient of variation (CV) for rainstorm total Tf volumes was 25%, on average, and it decreased as the magnitude of rainfall increased. During the leafless period, Tf was spatially autocorrelated over distances of 1 to 3.5 m, indicating the benefits of sampling with relatively elongated troughs. Our findings highlight the great variability of Tf under the canopies of Brant’s oaks during their leafless period. We may also conclude that the 29 Tf collectors used in the present study were sufficient to robustly estimate tree-scale Tf values within a 10% error of the mean at the 95% confidence level. Given that a ±10% uncertainty in Tf is associated with a ±100% uncertainty in interception loss, this underscores the challenges in its measurement at the individual tree level in the leafless season. These results are valuable for determining the number and placement of Tf collectors, and their expected level of confidence, when measuring tree-level Tf of scattered oak trees and those in forest stands.