In this study, four models describing the interferometric coherence of the forest vegetation layer are proposed and compared with the TanDEM-X data. Our focus is on developing tools for hemiboreal forest height estimation from single-pol interferometric SAR measurements, suitable for wide area forest mapping with limited a priori information. The multi-temporal set of 19 TanDEM-X interferometric pairs and the 90th percentile forest height maps are derived from Airborne LiDAR Scanning (ALS), covering an area of 2211 ha of forests over Estonia. Three semi-empirical models along with the Random Volume over Ground (RVoG) model are examined for applicable parameter ranges and model performance under various conditions for over 3000 forest stands. This study shows that all four models performed well in describing the relationship between forest height and interferometric coherence. Use of an advanced model with multiple parameters is not always justified when modeling the volume decorrelation in the boreal and hemiboreal forests. The proposed set of semi-empirical models, show higher robustness compared to a more advanced RVoG model under a range of seasonal and environmental conditions during data acquisition. We also examine the dynamic range of parameters that different models can take and propose optimal conditions for forest stand height inversion for operationally-feasible scenarios.

Airborne laser scanning (ALS) data and digital hemispherical photos (DHP) from 93 sample plots in Laeva test site, Estonia, were used to study effects of phenology and scan angle on the ALS-based canopy cover (CC_ALS) estimates. The relative share of first returns (P_1/A) for 6185 forest stands was analysed. The CC_ALS was calculated using different height thresholds and echoes, and was compared with the CC estimates based on DHP (CC_DHP) and crown model (CC_RCrown). The first of many echoes-based canopy cover estimate (CC_{ALS,1.3_1}) saturated at values greater than 80%. The strongest correlation of CC_DHP was found with CC_{ALS,1.3_A} using all echoes and a 1.3 m height break (R² = 0.81, RMSE = 11.8%). Correcting the estimate for view nadir angle did not improve the correlation of CC_{ALS,1.3_A} with CC_DHP. The CC_RCrown had a weak correlation (R² < 0.25) with CC_ALS and with CC_DHP. The P_1/A was not influenced by tree species composition, but by phenology, stand relative density and forest height; however, CC_ALS was not dependent on stand height. Foliage phenology had a substantial effect on CC_ALS and CC_DHP. In dense mixed-species forests, we recommend to use all returns for canopy cover estimation.

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R² > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R² = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

Repeated airborne laser scanning (ALS) measurements during leaf-on and leaf-off phenophases were studied. A 15 km × 15 km test site located in northern Estonia was used that included a reference set of stands, and 870 stands with thinning carried out before, between, and after two ALS flights. The decrease in ALS-based canopy cover estimate (CC_ALS) caused by thinning was similar for the leaf-off and leaf-on phenophases, and for different height thresholds. The point cloud height percentile (H_Px) values increased in almost all thinned stands, and the increase was present for the leaf-off and leaf-on phenophases. ALS point cloud metrics (skewness, kurtosis, mode, and canopy relief ratio) showed no response to thinning (p-value >0.05). Stand-dominating species had no significant influence on H_Px increment or CC_ALS change using the leaf-on data (p-value >0.05). The minimum height filter for pulse return selection had a substantial influence on H_Px increment in stands thinned between the two ALS measurements. Ground points are usually excluded from H_Px calculation, but for stand-level analyses, their inclusion can provide additional information.

Airborne laser scanning (ALS) based standing wood volume models were analysed in two contrasting test sites with different forest types in Estonia. In Aegviidu test site main tree species are Scots pine and Norway spruce and Laeva test site is mainly dominated by deciduous species. ALS data measurements were carried out for Aegviidu in 2008 and for Laeva in 2013. Approximately 450 sample plots were established additionally to the forest inventory dataset in both test sites. Exclusive to the sample plots, 46 stands were measured in 2012 in Aegviidu for stand level model. The sample plot-based model standard error in Aegviidu was S_e = 59.8 m³/ha (22%) and in Laeva S_e = 69.2 m³/ha (29%). The stand-level model based on 46 measured stands from Aegviidu had S_e = 38.4 m³/ha. Based on the models a cross-validation between the two test sites was carried out and systematic differences between the two test sites were found. The reasons are related to differences in optical properties of trees, crown shapes, flight configuration and canopy cover even though the sample plot based models included ALS-based canopy cover variable. The ALS-based wood volume estimate was also compared to forest inventory (FI) data and systematically larger estimates compared to FI dataset in both test sites were found. This average systematic error increased substantially (by 100 m³/ha) for stands with volume over 250 m³/ha. It was also detected that a model developed on small point clouds drawn for sample plots may produce systematic errors when applied to stand-level point clouds.

The goal of this study was to predict the need for commercial thinning using airborne lidar data (ALS) with random forest (RF) machine learning algorithm. Two test sites (with areas of 14,750 km² and 12,630 km²) were used with a total of 1053 forest stands from southwestern Estonia and 951 forest stands from southeastern Estonia. The thinnings were predicted based on the ALS measurements in 2019 and 2017. The two most important ALS metrics for predicting the need for thinning were the 95th height percentile and the canopy cover. The prediction accuracy based on validation stands was 93.5% for southwestern Estonia and 85.7% for southeastern Estonia. For comparison, the general linear model prediction accuracy was less for both test sites—92.1% for southwest and 81.8% for southeast. The selected important predictive ALS metrics differed from those used in the RF algorithm. The cross-validation of the thinning necessity models of southeastern and southwestern Estonia showed a dependence on geographic regions.