Search Results (7)

The increase in environmental and human-related changes (e.g., increase in the carbon cycle flux of plants) has increased the dynamism of ecosystems. Examining fluctuations in net primary production (NPP) is very important in adopting correct strategies for ecosystem management. The current study explores the spatiotemporal variations in NPP and its association with agricultural droughts in Iran’s ecosystems over 20 years (2000–2020). Mann–Kendall and Sen’s slope methods in each pixel were used to track changes in trends. Drought upsets the terrestrial carbon cycle balance. In this study, Vegetation Health Index (VHI) used to assess drought that extracted from different bands of images satellite. Then, the relationship between NPP rates and agricultural droughts was investigated through running Pearson correlation. The results demonstrated that Iran’s annual share of carbon sequestration is 1.38 kg*C/m²/year. The highest carbon sequestration rate was recorded in Caspian Hyrcanian forests. In contrast, the lowest rate was observed in the Arabian Desert and East Sahero-Arabian xeric shrublands in southwestern Iran. Moreover, the highest photosynthesis variations were recorded in Arabian Desert and East Sahero-Arabian xeric shrublands and Tigris–Euphrates alluvial salt marsh, while the lowest changes were registered in Badghyz and Karabil. In total, 34.2% of the studied pixels showed a statistically significant rising or falling trend. Sen’s slope estimator demonstrated that the sharpest negative trend in carbon sequestration belonged to Caspian Hyrcanian mixed forests (−12.24 g*C/m²/year), while the sharpest positive trend was observed in Azerbaijan shrub desert and steppe (12.29 g*C/m²/year). The results of the Pearson correlation revealed significant correlations between NPP and VHI in different ecosystems with coefficients ranging from −0.93 to 0.95. The largest area with a positive correlation (33.97%) belonged to the Zagros Mountains forest steppe. Identification of areas with the greatest carbon sequestration changes could result in prioritizing varied ecosystems management for carbon sequestering. It can be also utilized in environmental planning such as scaling up ecosystem values or estimating current and past ecological capacity. Full article

Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility. Full article

Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1’s polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R² and RMSE in a suggested GA fitness function. We calculated R², RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model’s stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation. Full article

Working time studies, work productivity, and cost assessments of forest logging are of interest to forest managers and planners. These aspects are particularly important in salvage logging, because of difficulties due to irregularly positioned fallen trees in forest areas, and due to particular aspects related to the absence of work planning. In this research, system productivity and the cost of salvage logging are analyzed for two mountain forests managed with close-to nature-silviculture: the Hyrcanian forest, using extraction by a skidder, and the Camaldoli forest, using extraction by tractors. The system productivity of salvage logging by skidders and tractors was calculated as 1.54 and 0.81 m³·h⁻¹, respectively. In contrast to common logging, system productivity was about 6- to 15-fold lower in salvage logging. The effective cost consumptions for the skidder and tractor were calculated as $72.57 and $118.62 USD·m⁻³, respectively. For both yards, winching time increased due to increasing winching distance and winching load volume. The same result was determined for skidding time in relation to load volume and distance. The possible cost decreases for the skidder and tractor were calculated as 2.6% and 4.3%, respectively. The results revealed that operational costs for extracting wind-fallen trees are higher than for traditional standing-trees extraction for both situations studied. In both cases, the harvesting costs were higher than the timber price by 10% to 30%. Therefore, extraction of wind-fallen trees has no economic justification in these forests. Full article

The unique Hyrcanian (Caspian) forests of northern Iran provide vital ecosystem services for local and global communities. We assess the status and trends of key ecosystem services in this region where native forest conversion has accelerated to make way for housing and farm development. This is a mountainous forested area that is valuable for both conservation and multiple human uses including recreation and farming. It contains globally significant natural habitats for in situ conservation of biological diversity. A rapid, qualitative, and participatory approach was used including interviews with local households and experts in combination with assessment of land use/cover remote sensing data to identify and map priority ecosystem services in the Geographic Information System (GIS). Based on the interests of the beneficiaries, eight priority services (food production, water supply, raw materials, soil conservation, water regulation, climate regulation, biodiversity, and recreation) were identified and mapped. The results indicate the current typical spatial distribution of the provided services based on structural characteristics of the study landscape and their changing trends through a comparison of past, present and future land use, and land cover. Although food production and recreation have greatly increased in recent decades, the other services, in particular timber production, biodiversity, and water purification and supply are being gradually lost. The results of this study and of others elsewhere should raise awareness of ecosystem service status and trends and the value of examining these since they provide much of the information to inform natural resources policy and decision making. The declines in supply of key ecosystem services both within and outside the protected area are creating conflicts within communities as well as impacting on the integrity of the area and careful planning and conservation is required to provide win-win opportunities. Full article

The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R² = 0.59; RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R² = 0.76; RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest’s physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions. Full article

Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (i.e., maximum likelihood classification (MLC)) as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA) = 65%). This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%). However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%). The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%). However, this shows a slight improvement compared to the ETM+ classification (OA = 84%). The machine learning classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data. Full article