In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R² of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation.

Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R² = 0.81, rRMSE = 10%), basal area (R² = 0.68, rRMSE = 20%), and timber volume (R² = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information.

Context: Mapping the distribution of species, especially those that are endemic and endangered like certain tree species, is a vital step in the effective planning and execution of conservation programs and monitoring efforts. This task gains even more significance as it directly contributes to forest conservation by highlighting the importance of species diversity. Objectives: Our study objective was to assess the detection accuracy of a specific tree using different remote sensing sources and approaches. Methods: Initially, individual trees were identified and classified using a canopy height model derived from UAV data. Next, we carried out the classification of satellite data within the Google Earth Engine. Lastly, we scaled the UAV-RGB dataset to match the spatial resolution of Sentinel-2, which was then employed to train random forest models using the multispectral data from Sentinel-2. Results: For the UAV data, we achieved overall accuracies of 56% for automatically delineated tree crowns and 83% for manually delineated ones. Regarding the second approach using Sentinel-2 data, the classification in the Noor forest yielded an overall accuracy of 74% and a Kappa coefficient of 0.57, while in the Safrabasteh forest, the accuracy was 80% with a Kappa of 0.61. In the third approach, our findings indicate an improvement compared to the second approach, with the overall accuracy and Kappa coefficient of the classification rising to 82% and 0.68, respectively. Conclusions: In this study, it was found that according to the purpose and available facilities, satellite and UAV data can be successfully used to identify a specific tree species.

We outline an approach combining airborne laser scanning (ALS) and imaging spectroscopy (IS) to quantify and assess patterns of tree density (TD) and forest productivity (FP) in a protected heterogeneous alpine forest in the Swiss National Park (SNP). We use ALS data and a local maxima (LM) approach to predict TD, as well as IS data (Airborne Prism Experiment—APEX) and an empirical model to estimate FP. We investigate the dependency of TD and FP on site related factors, in particular on surface exposition and elevation. Based on reference data (i.e., 1598 trees measured in 35 field plots), we observed an underestimation of ALS-based TD estimates of 40%. Our results suggest a limited sensitivity of the ALS approach to small trees as well as a dependency of TD estimates on canopy heterogeneity, structure, and species composition. We found a weak to moderate relationship between surface elevation and TD (R² = 0.18–0.69) and a less pronounced trend with FP (R² = 0.0–0.56), suggesting that both variables depend on gradients of resource availability. Further to the limitations faced in the sensitivity of the applied approaches, we conclude that the combined application of ALS and IS data was convenient for estimating tree density and mapping FP in north-facing forested areas, however, the accuracy was lower in south-facing forested areas covered with multi-stemmed trees.

In this study, the trend of vegetation dynamics in Kermanshah city assessed using NDVI MOD13Q1 product over the time period of 2000–2017. Based on time series imagery the pick of vegetation phenology stage (maximum NDVI) identified, then the trend of vegetation dynamic was investigated using the Ordinary Least Square regression and the Theil-Sen approaches. To generate a pixel-wise trend map, a pixel-based vegetation dynamics was also implemented. A non-parametric Mann-Kendall statistics approach was used to examine a statistically significant trend analysis. The mean maximum NDVI observed for the first half or second half of April. Trend analysis using regression and Theil-Sen methods indicated a no-trend in vegetation fractions. The pixel-based trend assessment using regression showed that a 50% of the study area faced a positive trend and reaming part faced a negative trend. The Theil-Sen method revealed the no-trend for a large majority of area. The Mann-Kendall test indicated that only 20 percent of the area shows a statistically significant trend.