Growing Stock Volume Estimation Research Articles

A New Forest Growing Stock Volume Estimation Model Based on AdaBoost and Random Forest Model

Forest growing stock volume is a crucial indicator for assessing forest resources. However, contemporary machine learning models used in estimating forest growing stock volume often exhibit fluctuating precision and are confined to specific tree species, lacking universality. This limitation impedes their capacity to provide comprehensive forest survey services. This study designed a novel model for predicting forest growing stock volume named RF-Adaboost. The model represented the inaugural application of the Adaboost algorithm in estimating forest growing stock volume. Additionally, the authors innovatively refined the Adaboost algorithm by integrating Random Forest as its weak learner. To substantiate the model’s effectiveness, the authors designed three data combination schemes at different scales and conducted regression estimation using the RF-Adaboost model, traditional Random Forest, and Adaboost models, respectively. The results indicated that the RF-Adaboost model consistently outperforms others across various data schemes. Furthermore, utilizing a combined data scheme of remote sensing and Continuous Forest Inventory, the RF-Adaboost model demonstrated optimal performance in estimating forest growing stock volume (R2 = 0.81, RMSE = 7.08 m3/site, MAE = 3.36 m3, MAPE = 8%). Finally, the RF-Adaboost model exhibits greater universality, eliminating the need for strict differentiation between tree species. This research presented an efficient and cost-effective approach to estimate forest growing stock, addressing the challenges associated with conventional survey methods.

A comparative analysis of grid-based and object-based modeling approaches for poplar forest growing stock volume estimation in plain regions using airborne LiDAR data

ABSTRACT Poplar (PopulusL.) is one of the most widely distributed tree species planted in the plains of China and plays an important role in wood products and ecological services. Accurate estimation of poplar Growing Stock Volume (GSV) is crucial for better understanding the ecological functions and economic values in plain regions. However, the striped distribution feature of poplar forests in plain regions makes traditional grid-based GSV modeling methods highly uncertain. This research took Lixin County and Yongqiao District as case studies to examine the advantages of using object-based GSV modeling approach over the traditional grid-based approaches for poplar GSV estimation. The canopy height variables and density variables were extracted from airborne LIDAR-derived Canopy Height Model (CHM) data through different grid sizes and segmentation unit for constructing the poplar GSV estimation models using the linear regression. The results indicate that (1) Significantly linear relationships exist between GSV and height percentile variables; (2) The estimation accuracy in Lixin can be effectively improved by incorporating the CHM density variables into height variables, with the coefficient of determination (R 2 ) increasing from 0.46 to 0.71 and Root Mean Square Error (RMSE) decreasing from 20.23 to 14.94 m3/ha when a grid-based approach was implemented at grid size of 26 m by 26 m (plot size). However, CHM density variables have no effect on estimation modeling in Yongqiao district. The patch sizes and shapes considerably affect the selection of modeling variables and accuracy of modeling prediction; (3) The object-based mapping approach outperforms the grid-based approach in solving the mixed plot problem. This is especially valuable in the study areas with striped forest distribution. This study shows that differences in poplar stand structure affect the selection of modeling variables and GSV modeling performance, and an object-based modeling approach is recommended for GSV estimation in the plain areas.

Height-diameter relationships and stem volume equations in young and middle-aged forest stands of Ukraine

Height-diameter (h-d) relationships in forest stands are commonly used in various scientific and practical forestry applications. Accurate h-d models combined with tree stem volume equations are recognised to be effective in growing stock volume estimation. The purpose of the study was threefold: 1) development of a set of mathematical models of the h-d relationship in young and middle-aged forest stands for ten forest-forming species in Ukraine; 2) modelling stem volume in above mentioned forest stands; 3) comparison of established mathematical models with corresponding ones for premature, mature, and overmature forest stands. The study was based on permanent and temporal sample plots data (about 600) established in forest stands during 1950s-2020s within the most forested regions of Ukraine (Polissia, Forest-Steppe, Carpathians). In total, about 10,000 sample trees were measured on the sample plots to accurately estimate their stem volume outside bark. The h-d models demonstrated very similar relationships between stem heights and diameters for most of our species except for spruce and fir in mountain Carpathian forests where the steeper h-d curves were obtained. The study revealed that birch and hornbeam tree stems had the lowest volumes among surveyed species. The results also indicated that tree species tend to have higher volumes (up to 7% for coniferous, and up 10% for aspen and birch forests) in young and middle-aged forest sands than in older ones. For the other species, a statistically significant difference between stem volumes of trees of different ages was not observed. The developed mathematical models can complement the corresponding models for older groups of forest stands since they revealed an important aspect of relationships between the key tree stem parameters. These models are also applicable for a more precise stem volume estimation during thinning operations in the young and middle-aged forests in Ukraine

Growing Stock Volume Estimation for Daiyun Mountain Reserve Based on Multiple Linear Regression and Machine Learning

Remote sensing provides an easy, inexpensive, and rapid method for detecting forest stocks. However, the saturation of data from different satellite sensors leads to low accuracy in estimations of the growing stock volume in natural forests with high densities. Thus, this study added actual data to improve the accuracy. The Daiyun Mountain Reserve was the study area. Landsat 8 operational land imager data were combined with remote sensing data and actual measurements. Multiple linear regression (MLR) and machine learning methods were used to construct a model for estimating the growing stock volume. The decision tree model showed the best fit. By adding the measured data to the model, the saturation could effectively be overcome to a certain extent, and the fitting effect of all the models can be improved. Among the estimation models using only remote sensing data, the normalized difference vegetation index showed the strongest correlation with the model, followed by the annual rainfall and slope. The decision tree model was inverted to produce a map of the accumulation distribution. From the map, the storage volume in the west was lower than that in the east and was primarily confined to the middle-altitude area, consistent with field survey results.

Mapping of Teak (Tectona grandis) Growing Stock Volume based on Sentinel-2 Imagery Classification

Remote sensing utilization for plantation forests management in Java was not yet widely used, whereas the ability of remote sensing data for land cover monitoring and forest resource mapping has been developed, ranging from low resolution imagery for global areas to moderate and high resolution for small scale areas. Data availability and human resources often become obstacles in the application. The emergence of Sentinel satellite images becomes an alternative because the dataset is free access and has moderate spatial resolution and high temporal resolution. This study aims to map the growing stock volume in UGM Educational and Training Forest using Sentinel-2 imagery. Three kinds of classification method based on machine learning algorithms i.e. Random Forest, K-NN and SVM were compared for land cover classification. An NDVI algorithm was also used for mapping the spectral value distribution. Moreover, a stand age distribution which obtained from KHDTK UGM manager were also map. A stand classification map based on land cover types, NDVI value and stand age distribution was created as a basis of growing stock volume estimation. The analysis show that the growing stock volume can be estimated using these method with RMSE 177,8 m3 and MAPE 21,9 m3.

Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation

Many National Forest Inventory (NFI) stakeholders would benefit from accurate estimates at finer geographic scales than most currently implemented in operational estimates using NFI sample data. In the past decade small area estimation techniques have been shown to increase precision in forest inventory estimates by combining field observations and remote-sensing. We sought to demonstrate the potential for improving the precision of forest inventory growing stock volume estimates for counties in United States of North Carolina, Tennessee, and Virginia, by pairing canopy height models from digital aerial photogrammetry (DAP) and field plot data from the United States NFI. Area-level Fay-Herriot estimators were used to avoid the need for precise (GPS) coordinates of field plots. Reductions in standard errors averaging 30% for North Carolina county estimates were observed, with 19% average reductions in standard errors in both Tennessee and Virginia. Accounting for spatial autocorrelation among adjacent counties provided further gains in precision when the three states were treated as a single forest land population; however, analyses conducted one state at a time showed that good results could be achieved without accounting for spatial autocorrelation. Apparent gains in sample sizes ranged from about 65% in Virginia to 128% in North Carolina, compared to the current number of inventory plots. Results should allow for determining whether acquisition of statewide DAP would be cost-effective as a means for increasing the accuracy of county-level forest volume estimates in the United States NFI.

Effects of lidar coverage and field plot data numerosity on forest growing stock volume estimation

ABSTRACT Forest parameter estimation is required to support the sustainable management of forest ecosystems. Currently, forest resource assessment is increasingly linked to auxiliary information obtained from remote sensing (RS) technologies. In forest parameter estimation, airborne laser scanning (ALS) data have been demonstrated to be an invaluable source of information. However, ALS data are often not available for the entire forest area, whereas images from multiple satellite systems offer new opportunities for large-scale forest surveys. This study aims to assess and estimate the contribution of field plot data and ALS data along with Landsat data to the precision of growing stock volume (GSV) estimates. We compared different approaches for model-assisted estimation of mean forest GSV per unit area using different proportions of field sample data, ALS cover data, and wall-to-wall Landsat data. Model-assisted estimators were used with NFI sample data in an Italian study area using 10 RS predictors, specifically the seven Landsat 7 ETM+ bands and three fine-resolution metrics based on ALS-derived canopy height. We found that relative to the standard simple expansion estimator, the model-assisted estimators produced relative efficiency of 1.16 when using only Landsat data and relative efficiencies as great as 1.33 when using increasing levels of ALS coverage.

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

Coniferous Plantations Growing Stock Volume Estimation Using Advanced Remote Sensing Algorithms and Various Fused Data

Spatial distribution prediction of growing stock volume (GSV) for supporting the sustainable management of forest ecosystems, is one of the most widespread applications of remote sensing. For this purpose, remote sensing data were used as predictor variables in combination with ground data obtained from field sample plots. However, with the increase in forest GSV values, the spectral reflectance of remote sensing imagery is often saturated or less sensitive to the GSV changes, making accurate estimation difficult. To improve this, we examined the GSV estimation performance and data saturation of four optical remote sensing image datasets (Landsat 8, Sentinel-2, ZiYuan-3, and GaoFen-2) in the subtropical region of Central South China. First, various feature variables were extracted and three optimization methods were used to select optimal feature variable combinations. Subsequently, k-nearest-neighbor (kNN), random forest regression, and categorical boosting algorithms were employed to build the GSV estimation models, and evaluate the GSV estimation accuracy and saturation. Second, Gram Schmidt (GS) and NNDiffuse pan sharpening (NND) methods were employed to fuse the optimal multispectral images and explore various image fusion schemes suitable for GSV estimation. We proposed an adaptive stacking (AdaStacking) model ensemble algorithm to further improve GSV estimation performance. The results indicated that Sentinel-2 had the highest GSV estimation accuracy exhibiting a minimum relative root mean square error of 20.06% and saturation of 434 m3/ha, followed by GaoFen-2 with a minimum relative root mean square error of 22.16% and a saturation of 409 m3/ha. Among the four fusion images, the NND-B2 image—obtained by fusing the GaoFen-2 green band and Sentinel-2 multispectral image with the NND method—had the best estimation accuracy. The estimated optimal RMSEs of NND-B2 were 24.4% and 16.5% lower than those of GaoFen-2 and Sentinel-2, respectively. Therefore, the fused image data based on GF-2 and Sentinel-2 can effectively couple the advantages of the two images and significantly improve the GSV estimation performance. Moreover, the proposed adaptive stacking model is more effective in GSV estimation than a single model. The GSV estimation saturation value of the AdaStacking model based on NND-B2 was 5.4% higher than that of the KNN-Maha model. The GSV distribution map estimated by AdaStacking model used the NND-B2 dataset corresponded accurately with the field observations. This study provides some insights into the optical image fusion scheme, feature selection, and adaptive modeling algorithm in GSV estimation for coniferous forest.

Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors.

Regression kriging to improve basal area and growing stock volume estimation based on remotely sensed data, terrain indices and forest inventory of black pine forests

Background: The use of satellite imagery to quantify forest metrics has become popular because of the high costs associated with the collection of data in the field.Methods: Multiple linear regression (MLR) and regression kriging (RK) techniques were used for the spatial interpolation of basal area (G) and growing stock volume (GSV) based on Landsat 8 and Sentinel-2. The performance of the models was tested using the repeated k-fold cross-validation method.Results: The prediction accuracy of G and GSV was strongly related to forest vegetation structure and spatial dependency. The nugget value of semivariograms suggested a moderately spatial dependence for both variables (nugget/sill ratio approx. 70%). Landsat 8 and Sentinel-2 based RK explained approximately 52% of the total variance in G and GSV. Root-mean-square errors were 7.84 m2 ha-1 and 49.68 m3 ha-1 for G and GSV, respectively.Conclusions: The diversity of stand structure particularly at the poorer sites was considered the principal factor decreasing the prediction quality of G and GSV by RK.

Forest growing stock volume estimation using optical remote sensing over snow-covered ground: a case study for Sentinel-2 data and the Russian Southern Taiga region

ABSTRACT This paper describes an approach to forest growing stock volume (GSV) estimation based on remotely sensed optical data in red and near-infrared (NIR) bands collected during the period of persistent snow cover. The approach was applied to Sentinel-2 reflectance measurements over forest with snow-covered understory in the north-eastern part of Russian Kostroma region. An in-house dataset with a forest stand-level GSV data was used to approximate GSV-reflectance relationship based on a power function for spruce-dominated, pine-dominated and birch-dominated forests. Highest coefficient of determination (R 2) = 0.84 was obtained for spruce-dominated forest and red band. A cross-validation was performed to estimate the accuracy of a stand-level GSV estimation based on the obtained GSV-reflectance relationship model and Sentinel-2 data. Best results were achieved for pine-dominated forest and NIR band: R 2 = 0.66; root-mean-square error (RMSE) = 58 m3/ha. This GSV estimation approach was validated with an independent dataset of field survey-based GSV measurements at the sample plot level. Validation showed R 2 values comparable to cross-validation results but higher RMSE. Overall Sentinel-2 data tested was found to be informative for GSV estimation; however performance of the described approach varied significantly depending on forest type, spectral band, GSV values range and spatial aggregation level.

Classification of tree species and stock volume estimation in ground forest images using Deep Learning

Tree species classification and estimation of stock volume are two very important tasks in forest management. Currently, Ground Surveys’ Development (GSD) is the basic and most common approach employed by foresters. However, GSD is time-consuming and inefficient as it requires great human effort. In this research, digital cameras have been used, to obtain images of the ground forest. The classification and accumulation of tree species is performed, by considering extracted relevant image information. The purpose of this effort is not only to improve research efficiency, but to reduce the consumption of human and material resources as well. This research uses the UNET network which is pre-trained by the VGG16 model. The aim is to semantically segment the image containing the ground forest and the species and then to accurately identify the number of trees contained in the image. The proportion of the number of pixels in the trunk of each segment is estimated by considering the total number of pixels in the image. The nonlinear mixed effect model is used to estimate the growing stock volume. The differences in the growing stock volume caused by different forest types, are resolved by using the growing stock volume estimation equations, related to different tree species. The experimental results show that the tree species’ classification accuracy in testing is 96.03% and the average IoU (Intersection over Union) is 86%. The R2 and RMSE of the growing stock volume prediction model are equal to 80.70% and 30.539 (m3/ha) respectively. Therefore, it is concluded that the method proposed in this research can be used as an effective tool for tree species’ image segmentation and classification, and that the growing stock volume is predicted accurately by the extracted tree pixel information. The combination of the two approaches provides a new method for forestry ground investigation work.

Improving Estimation Accuracy of Growing Stock by Multi-Frequency SAR and Multi-Spectral Data over Iran’s Heterogeneously-Structured Broadleaf Hyrcanian Forests

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.

Growing stock volume from multi-temporal landsat imagery through google earth engine

Growing stock volume (GSV) is one of the most important variables for forest management and is traditionally estimated from ground measurements. These measurements are expensive and therefore sparse and hard to maintain in time on a regular basis. Remote sensing data combined with national forest inventories constitute a helpful tool to estimate and map forest attributes. However, most studies on GSV estimation from remote sensing data focus on small forest areas with a single or only a few species. The current study aims to map GSV in peninsular Spain, a rather large and very heterogeneous area. Around 50 000 wooded land plots from the Third Spanish National Forest Inventory (NFI3) were used as reference data, whereas more than 8 000 Landsat-5 TM and Landsat-7 ETM + scenes covering both the study period (1997–2007) and area were chosen as a compromise between availability and suitable temporal and spatial resolution to estimate GSV. Google Earth Engine (GEE) was used to handle the huge amount of remotely sensed data. A total of 805 predictors were calculated from Landsat spectral reflectances. Guided regularized random forests algorithm (RF) was used to deal with the arising multicolinearity and identify the most important predictors by comparing with NFI3 plot-level GSV data. As a result, to model the relationship between spectral information and GSV, the original 805 predictors were reduced to only 29 (highlighting texture metrics, vegetation indices and band ratios involving short wave infrared reflectance) while maintaining the accuracy level (R2 ≈ 0.4 and RMSE ≈ 60 m3 ha–1). A 30-m spatial resolution wall-to-wall GSV map over Peninsular Spain was obtained from a standard RF with the 29 selected predictors through GEE. Its accuracy was evaluated against NFI3 province-level GSV data, resulting in R2 ≈ 0.91 and RMSE ≈ 15 m3 ha–1.

Forest Growing Stock Volume Estimation in Subtropical Mountain Areas Using PALSAR-2 L-Band PolSAR Data

Forest growing stock volume (GSV) extraction using synthetic aperture radar (SAR) images has been widely used in climate change research. However, the relationships between forest GSV and polarimetric SAR (PolSAR) data in the mountain region of central China remain unknown. Moreover, it is challenging to estimate GSV due to the complex topography of the region. In this paper, we estimated the forest GSV from advanced land observing satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar (PALSAR-2) full polarimetric SAR data based on ground truth data collected in Youxian County, Central China in 2016. An integrated three-stage (polarization orientation angle, POA; effective scattering area, ESA; and angular variation effect, AVE) correction method was used to reduce the negative impact of topography on the backscatter coefficient. In the AVE correction stage, a strategy for fine terrain correction was attempted to obtain the optimum correction parameters for different polarization channels. The elements on the diagonal of covariance matrix were used to develop forest GSV prediction models through five single-variable models and a multi-variable model. The results showed that the integrated three-stage terrain correction reduced the negative influence of topography and improved the sensitivity between the forest GSV and backscatter coefficients. In the three stages, the POA compensation was limited in its ability to reduce the impact of complex terrain, the ESA correction was more effective in low-local incidence angles area than high-local incidence angles, and the effect of the AVE correction was opposite to the ESA correction. The data acquired on 14 July 2016 was most suitable for GSV estimation in this study area due to its correlation with GSV, which was the strongest at HH, HV, and VV polarizations. The correlation coefficient values were 0.489, 0.643, and 0.473, respectively, which were improved by 0.363, 0.373, and 0.366 in comparison to before terrain correction. In the five single-variable models, the fitting performance of the Water-Cloud analysis model was the best, and the correlation coefficient R2 value was 0.612. The constructed multi-variable model produced a better inversion result, with a root mean square error (RMSE) of 70.965 m3/ha, which was improved by 22.08% in comparison to the single-variable models. Finally, the space distribution map of forest GSV was established using the multi-variable model. The range of estimated forest GSV was 0 to 450 m3/ha, and the mean value was 135.759 m3/ha. The study expands the application potential of PolSAR data in complex topographic areas; thus, it is helpful and valuable for the estimation of large-scale forest parameters.

Evaluation of single-date and multi-seasonal spatial and spectral information of Sentinel-2 imagery to assess growing stock volume of a Mediterranean forest

Accurate growing stock volume (GSV) estimation is essential for forest inventory updating, terrestrial carbon stocks reporting, and ecosystem services assessment. This study investigates the potential of spectral and spatial features derived from single-date and multi-seasonal Sentinel-2 Multi Spectral Instrument (Sentinel-2 MSI) images, for GSV estimation in a Mediterranean region of Northeastern Greece. Original spectral bands, spectral indices, first-order statistics, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, and Local Indicators of Spatial Association (LISA), based on the multi-seasonal and single-date Sentinel-2 MSI imagery, were used for GSV model development using the bagging LASSO algorithm. For both single and multi-date approaches, the spectral indices models were more accurate compared to the respective ones developed with the original Sentinel-2 MSI bands. Also, models based on texture were more efficient than the spectral models. The GLCM measures derived from July image, provided the most accurate single-date estimate of GSV (R2 = 0.89, RMSE = 35.21), while their multi-seasonal counterparts improved slightly the accuracy (R2 = 0.91, RMSE = 32.77). Fusion of spatial and textural information resulted in marginal or no-improvement on the texture model accuracy, however the fused models yielded higher predictive results than the spectral models alone.

Исследование возможностей оценки запасов древесины в лесах Приморского края по данным спутниковой системы Proba-V

Исследование возможностей оценки запасов древесины в лесах Приморского края по данным спутниковой системы Proba-V

Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference

Remotely sensed (RS) data are becoming increasingly important as sources of auxiliary information in forest resource assessments. Data from several satellites providing moderate image resolution are freely available (e.g. Sentinel-2). In addition, very-high-resolution three-dimensional data are available due to the advent of unmanned aerial vehicles (UAV). The increasing availability of auxiliary data offers new opportunities for large-scale forest surveys using UAVs. A recently developed hierarchical model-based mode of inference makes it possible to use hierarchically nested auxiliary data in estimating population properties, such as total or mean biomass or volume, and their corresponding uncertainties in a statistically appropriate manner. In this study, hierarchical model-based inference was used to estimate growing stock volume (GSV; m3ha−1) and its variance using a small sample of field data, a larger sample of UAV data, and wall-to-wall Sentinel-2 data in a study area in SE Norway. The main objective of the study was to compare the performance, in terms of precision, of hierarchical model-based inference (denoted Case C) against two alternative cases. These were (1) model-based inference based on field data and wall-to-wall data, collected either with airborne laser scanning (Case A.1) or Sentinel-2 data (Case A.2), and (2) hybrid inference using a small sample of field data and a larger sample of UAV data (Case B). A second objective was to assess the possibility of reducing the UAV sampling intensity when adopting Case C rather than B, without decreasing the precision of the GSV estimates. The results, calculated as standard error as percentage of the mean (SÊ%), indicated that in case C the precision was of similar magnitude (SÊ%=3.44%) as for Case A.1 (SÊ%=3.69%) and for Case B (SÊ%=3.58%). The standard error of Case A.2 was nearly twice as large (SÊ%=5.81%) as the rest of the cases. The results also indicated possibilities of reducing the UAV sampling intensity without losing precision in cases where wall-to-wall Sentinel-2 data are available (Case C). The same precision for Case C with only five UAV samples was achieved as for Case B with 55 UAV samples. Thus, the study highlights the cost-efficiency of applications of UAV as in Case C and also provides first insights in the use of Sentinel-2 data for GSV estimation in boreal conditions.

The potential of ALOS PALSAR backscatter and InSAR coherence for forest growing stock volume estimation in Central Siberia

Abstract The full potential of ALOS PALSAR L-band interferometric (InSAR) coherence data for the estimation of forest growing stock volume ( GSV ) in the boreal forest has rarely been investigated. Moreover, ALOS PALSAR backscatter and InSAR coherence have yet to be used together to delineate GSV . Due to the observation strategy and the high acquisition success rate over Eurasia, a large amount of high quality ALOS PALSAR L-band data is available over Siberia. Consequently, this paper investigates the capability of ALOS PALSAR backscatter and InSAR coherence for the estimation of GSV in Central Siberia, Russia. The potential of backscatter and coherence are directly compared using the same inventory data. Altogether, 87 PALSAR images are used and eleven forest inventory sites are investigated. Based on this large dataset it was observed that InSAR coherence acquired in frozen conditions offers the highest potential for GSV estimation. The saturation level for single coherence images was on average 230 m 3 /ha, with an average R 2 between coherence and GSV of 0.58. PALSAR backscatter acquired in unfrozen conditions could also estimate GSV ; however, the saturation levels (75–100 m 3 /ha) and the average R 2 (0.42–0.48) were lower. HV backscatter offered only a slightly greater potential than HH backscatter. A simple inversion approach aiming at the delineation of forest GSV maps based on the multitemporal SAR data was developed and applied to five forest inventory sites. This approach combines HV backscatter data acquired in unfrozen conditions and InSAR coherence data acquired in frozen conditions. In general, the produced maps feature a corrected relative RMSE corr of 2 between inventory data and SAR data based maps varied between 0.54 and 0.83.