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

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.

Estimation of Aboveground Vegetation Water Storage in Natural Forests in Jiuzhaigou National Nature Reserve of China Using Machine Learning and the Combination of Landsat 8 and Sentinel-2 Data

Aboveground vegetation water storage (AVWS) is a fundamental ecological parameter of terrestrial ecosystems which participates in plant metabolism, nutrient and sugar transport, and maintains the integrity of the hydraulic system of the plant. The Jiuzhaigou National Nature Reserve (JNNR) is located in the Eastern Tibet Plateau and it is very sensitive to climate change. However, a regional estimate of the AVWS based on observations is still lacking in the JNNR and improving the model accuracy in such mountainous areas is challenging. Therefore, in this study, we combined the Landsat 8 and Sentinel-2 data to estimate AVWS using multivariate adaptive regression splines (MARS), random forest (RF) and extreme gradient boosting (XGBoost) with the linkage of 54 field observations in the JNNR. The results showed that AVWS varied among different forest types. The coniferous forests had the highest AVWS (212.29 ± 84.43 Mg ha−1), followed by mixed forests (166.29 ± 72.73 Mg ha−1) and broadleaf forests (142.60 ± 46.36 Mg ha−1). The average AVWS was 171.2 Mg ha−1. Regardless of the modelling approaches, both Sentinel-2 and Landsat 8 successfully estimated AVWS separately. Prediction accuracy of AVWS was improved by combining Landsat 8 and Sentinel-2 images. Among the three machine learning approaches, the XGBoost model performed best with a model efficiency of 0.57 and root mean square error of 48 Mg ha−1. Predicted AVWS using XGBoost showed a strong spatial pattern of across the study area. The total AVWS was 5.24 × 106 Mg with 67.2% coming from conifer forests. The results highlight the potential of improving the accuracy of AVWS estimation by integrating different optical images and using machine learning approaches in mountainous areas.

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.