Estimation Of Forest Parameters Research Articles

Trends in Automatic Individual Tree Crown Detection and Delineation—Evolution of LiDAR Data

Automated individual tree crown detection and delineation (ITCD) using remotely sensed data plays an increasingly significant role in efficiently, accurately, and completely monitoring forests. This paper reviews trends in ITCD research from 1990–2015 from several perspectives—data/forest type, method applied, accuracy assessment and research objective—with a focus on studies using LiDAR data. This review shows that active sources are becoming more prominent in ITCD studies. Studies using active data—LiDAR in particular—accounted for 80% of the total increase over the entire time period, those using passive data or fusion of passive and active data comprised relatively small proportions of the total increase (8% and 12%, respectively). Additionally, ITCD research has moved from incremental adaptations of algorithms developed for passive data sources to innovative approaches that take advantage of the novel characteristics of active datasets like LiDAR. These improvements make it possible to explore more complex forest conditions (e.g., closed hardwood forests, suburban/urban forests) rather than a single forest type although most published ITCD studies still focused on closed softwood (41%) or mixed forest (22%). Approximately one-third of studies applied individual tree level (30%) assessment, with only a quarter reporting more comprehensive multi-level assessment (23%). Almost one-third of studies (32%) that concentrated on forest parameter estimation based on ITCD results had no ITCD-specific evaluation. Comparison of methods continues to be complicated by both choice of reference data and assessment metric; it is imperative to establish a standardized two-level assessment framework to evaluate and compare ITCD algorithms in order to provide specific recommendations about suitable applications of particular algorithms. However, the evolution of active remotely sensed data and novel platforms implies that automated ITCD will continue to be a promising technology and an attractive research topic for both the forestry and remote sensing communities.

Improving the precision of dynamic forest parameter estimates using Landsat

Abstract The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well established. When reducing the variance of post-stratification estimates for forest change parameters such as forest growth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is ideally suited for producing such a map. In this study, we generate strata maps based on trajectories of Landsat Thematic Mapper-based normalized difference vegetation index values, with a focus on post-disturbance recovery and recent measurements. These trajectories, from 1985 to 2010, are converted to harmonic regression coefficient estimates and classified according to a hierarchical clustering algorithm from a training sample. The resulting strata maps are then used in conjunction with measured plots to estimate forest status and change parameters in an Alabama, USA study area. These estimates and the variance of the estimates are then used to calculate the estimated relative efficiencies of the post-stratified estimates. Estimated relative efficiencies around or above 1.2 were observed for total growth, total mortality, and total removals, with different strata maps being more effective for each. Possible avenues for improvement of the approach include the following: (1) enlarging the study area and (2) using the Landsat images closest to the time of measurement for each plot. Multitemporal satellite-derived strata maps show promise for improving the precision of change parameter estimates.

Improving the precision of sample-based forest damage inventories through two-phase sampling and post-stratification using remotely sensed auxiliary information.

Many countries have a national forest inventory (NFI) designed to produce statistically sound estimates of forest parameters. However, this type of inventory may not provide reliable results for forest damage which usually affects only small parts of the forest in a country. For this reason, specially designed forest damage inventories are performed in many countries, sometimes in coordination with the NFIs. In this study, we evaluated a new approach for damage inventory where existing NFI data form the basis for two-phase sampling for stratification and remotely sensed auxiliary data are applied for further improvement of precision through post-stratification. We applied Monte Carlo sampling simulation to evaluate different sampling strategies linked to different damage scenarios. The use of existing NFI data in a two-phase sampling for stratification design resulted in a relative efficiency of 50% or lower, i.e., the variance was at least halved compared to a simple random sample of the same size. With post-stratification based on simulated remotely sensed auxiliary data, there was additional improvement, which depended on the accuracy of the auxiliary data and the properties of the forest damage. In many cases, the relative efficiency was further reduced by as much as one-half. In conclusion, the results show that substantial gains in precision can be obtained by utilizing auxiliary information in forest damage surveys, through two-phase sampling, through post-stratification, and through the combination of these two approaches, i.e., post-stratified two-phase sampling for stratification.

Prediction of stem volume in complex temperate forest stands using TanDEM-X SAR data

Abstract Reliable estimations of stem volume are important for sustainable forest management planning as well as for monitoring of global changes. However, the derivation of stem volume in cubic meters per hectare based on traditional sampling-based forest inventories (usually with a repetition rate of ten years) is very expensive, labor-intensive and only available for the minority of the forest areas worldwide. Thus, spaceborne synthetic aperture radar (SAR) data can provide estimations of forest parameters with sufficient spatial and temporal resolution for large areas. Height information extracted from two interferometric dual-polarized TanDEM-X data sets were used to investigate the potential of polarimetric interferometric X-band SAR data for stem volume estimation in the complex forest stands of the Traunstein forest in Southeast Bavaria, Germany. In contrast to other studies of forest parameter estimation from X-band SAR data carried out in boreal or tropical forest stands, the current study investigated stem volume estimation based on X-band SAR data in complex temperate forest stands. A linear regression model based on the allometric relationship of forest height (estimated from SAR data combined with an airborne LiDAR-based Digital Terrain Model) and stem volume per unit area (deduced from traditional forest inventory) was derived. Moreover, the model was extended and thus improved by integrating novel parameters derived from the co-occurrence matrix as surrogates for horizontal forest structure. This linear regression model predicted stem volume at plot (circular plots of 500 m2) level with a coefficient of determination of R2 = 69% and a root mean square error of RMSE = 155 m3 ha− 1 and stand (areas of 1.5 to 6.4 ha) level with R2 = 94% and RMSE = 44 m3 ha− 1 respectively.

TanDEM-X Pol-InSAR Inversion for Mangrove Canopy Height Estimation

This paper presents mangrove canopy height estimations using single- and dual-pol TanDEM-X (TDX) data by means of Pol-InSAR techniques. Using the TDX data for forest applications, the penetration capability of X-band into the volume and the polarimetric diversity of interferometric coherence can create false effects that could sometimes lead to biased three-dimensional (3-D) forest parameter estimation. Moreover, in the case of single-pol TDX acquisition (i.e., one independent complex interferometric coherence), it is not possible to perform the inversion without external topographic information due to the underdetermined problem for Pol-InSAR inversion. To solve these problems, the ground phase in the Pol-InSAR model has been estimated directly from TDX interferograms with an assumption that the underlying topography (i.e., water surface level) over mangroves is flat and negligible. With the estimated ground phase that represents water-level elevation in mangroves, the Pol-InSAR inversion from the single- and dual-pol TDX data did not rely on an external DTM data set. The inversion results were validated against airborne lidar measurements in Campeche, Mexico, and in the Zambezi Delta, Mozambique. The single- and dual-pol inversion results showed a successful inversion performance with a high correlation coefficient (0.851–0.919) and with low RMSEs (1.069–1.727 m). The entire inversion performance quality over mangroves at X-band could reach to 10% height estimation accuracy. In addition to Pol-InSAR inversion results, the location of phase center was checked and assessed. The results showed a surprisingly deep location of phase centers from top mangrove canopy (5–7 m, up to 12.36 m) with a similarity at HH and VV polarization.

Simulation of Interferometric SAR Response for Characterizing Forest Successional Dynamics

The dynamics of scattering phase center height (SPCH) in a paper birch stand throughout its succession following clear cutting was studied to improve the interpretation of interferometric synthetic aperture radar (InSAR) response to forest structure changes. A forest growth model (ZELIG) and a fractal tree model (L-system) were utilized together for simulation of the three-dimensional (3-D) structure changes of the stand. Then, a high-fidelity coherent radar scattering model (CORSM) based on 3-D forest structure was employed to simulate the SPCH dynamics of the birch stand. The simulation experiment shows that correlation between L-band SPCH and forest parameters (Lorey's mean height (hL) and biomass) could be noticeably affected by changing pattern of 3-D forest structure at different successional stages, which should be considered in our future exploration of SPCH for forest parameter estimation.

Mapping forest ecosystem functions for landscape planning in a mountain Natura2000 site, Central Italy

In last decades, numerous efforts have been carried out by the scientific community to assess the multifunctional role of forests in supporting the forest decision-making processes. Recently developed in Italy, the Forest Landscape Management Planning seems to be the most suitable tool to maintain the sustainability balance between forests and local populations. This research identifies and maps different forest ecosystem functions in a mountain Natura2000 Network site by performing a spatial estimation of forest inventory parameters, and implementing a multi-criteria and multi-level approach. Final results on mapping of forest functions show acceptable values of classification accuracy.

Analyzing the Uncertainty of Biomass Estimates From L-Band Radar Backscatter Over the Harvard and Howland Forests

A better understanding of ecosystem processes requires accurate estimates of forest biomass and structure on global scales. Recently, there have been demonstrations of the ability of remote sensing instruments, such as radar and lidar, for the estimation of forest parameters from spaceborne platforms in a consistent manner. These advances can be exploited for global forest biomass accounting and structure characterization, leading to a better understanding of the global carbon cycle. The popular techniques for the estimation of forest parameters from radar instruments, in particular, use backscatter intensity, interferometry, and polarimetric interferometry. This paper analyzes the uncertainty in biomass estimates derived from single-season L-band cross-polarized (HV) radar backscatter over temperate forests of the Northeastern United States. An empirical approach is adopted, relying on ground-truth data collected during field campaigns over the Harvard and Howland Forests in 2009. The accuracy of field biomass estimates, including the impact of the diameter-biomass allometry, is characterized for the field sites. A single-season radar data set from the National Aeronautics and Space Administration Jet Propulsion Laboratory's L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument is analyzed to assess the accuracy of the backscatter-biomass relationships with a theoretical radar error model.

Forest height estimation from mountain forest areas using general model–based decomposition for polarimetric interferometric synthetic aperture radar images

The estimation of forest parameters over mountain forest areas using polarimetric interferometric synthetic aperture radar (PolInSAR) images is one of the greatest interests in remote sensing applications. For mountain forest areas, scattering mechanisms are strongly affected by the ground topography variations. Most of the previous studies in modeling microwave backscattering signatures of forest area have been carried out over relatively flat areas. Therefore, a new algorithm for the forest height estimation from mountain forest areas using the general model–based decomposition (GMBD) for PolInSAR image is proposed. This algorithm enables the retrieval of not only the forest parameters, but also the magnitude associated with each mechanism. In addition, general double- and single-bounce scattering models are proposed to fit for the cross-polarization and off-diagonal term by separating their independent orientation angle, which remains unachieved in the previous model-based decompositions. The efficiency of the proposed approach is demonstrated with simulated data from PolSARProSim software and ALOS-PALSAR spaceborne PolInSAR datasets over the Kalimantan areas, Indonesia. Experimental results indicate that forest height could be effectively estimated by GMBD.

Quantifying the Model-Related Variability of Biomass Stock and Change Estimates in the Norwegian National Forest Inventory

National Forest Inventories (NFIs) provide estimates of forest parameters for national and regional scales. Many key variables of interest, such as biomass and timber volume, cannot be measured directly in the field. Instead, models are used to predict those variables from measurements of other field variables. Therefore, the uncertainty or variability of NFI estimates results not only from selecting a sample of the population but also from uncertainties in the models used to predict the variables of interest. The aim of this study was to quantify the model-related variability of Norway spruce (Picea abies [L.] Karst) biomass stock and change estimates for the Norwegian NFI. The model-related variability of the estimates stems from uncertainty in parameter estimates of biomass models as well as residual variability and was quantified using a Monte Carlo simulation technique. Uncertainties in model parameter estimates, which are often not available for published biomass models, had considerable influence on the model-related variability of biomass stock and change estimates. The assumption that the residual variability is larger than documented for the models and the correlation of within-plot model residuals influenced the model-related variability of biomass stock change estimates much more than estimates of the biomass stock. The larger influence on the stock change resulted from the large influence of harvests on the stock change, although harvests were observed rarely on the NFI sample plots in the 5-year period that was considered. In addition, the temporal correlation between model residuals due to changes in the allometry had considerable influence on the model-related variability of the biomass stock change estimate. The allometry may, however, be assumed to be rather stable over a 5-year period. Because the effects of model-related variability of the biomass stock and change estimates were much smaller than those of the sampling-related variability, efforts to increase the precision of estimates should focus on reducing the sampling variability. If the model-related variability is to be decreased, the focus should be on the tree fractions of living branches as well as stump and roots.

S‐RVoG model for forest parameters inversion over underlying topography

Since the terrain slope cannot be neglected for forest height inversion with polarimetric synthetic aperture radar interferometry (PolInSAR), a sloped random volume over ground (S-RVoG) model is proposed to correct the terrain distortion for forest parameters estimation. A significant model complexity reduction is achieved by aligning the reference frame along the local terrain slope and changing the corresponding radar geometrical configuration. The proposed S-RVoG model inversion promises to provide much more accurate estimation of forest parameters and is validated with L-band PolInSAR data produced by PolSARpro software developed by the European Space Agency (ESA).

Pine plantation structure mapping using WorldView-2 multispectral image

Optical images of different spectral and spatial resolutions continue to provide a reliable source of data for estimating forest inventory parameters. WorldView-2 launched in October 2009 is the first commercial optical satellite to provide high spatial resolution images with eight spectral bands, some of which are new and require investigation for estimation of forest structure parameters. In this study, a WorldView-2 multispectral image has been investigated for mapping pine plantation structural parameters including stand volume, basal area, stocking, mean diameter at breast height (mean DBH), and mean height of trees over a Pinus radiata plantation in New South Wales, Australia. Spectral derivatives including reflectance bands, band ratios, principal components (PCs), and several vegetation indices (VIs) were calculated using four typical bands, including blue, green, red, and near-infrared (NIR1), and all eight bands. Moreover, textural information, including 11 grey-level co-occurrence matrix (GLCM) indices, was extracted using four window sizes and orientations. Several models were developed using the extracted attributes separately to compare the efficiency of the models derived from the attributes of four typical bands and eight bands, as well as to compare between the capability of spectral-based and textural-based models for estimating structural parameters. The results showed that models derived from textural attributes of eight spectral bands provide the best estimates compared to those derived from four typical bands and the models derived from spectral derivatives. Moreover, the mean height and mean DBH with 8% and 13.7% error of estimation, respectively, were estimated more accurately than basal area, stand volume, and stocking, where the error of estimation is up to 30%.

New Approaches for Estimating the Local Point Density and its Impact on Lidar Data Segmentation

This article describes how Light Detection and Ranging (LIDAR) systems have been created for the rapid collection of high density three-dimensional (3D) point cloud data over the past few years. The advent of these systems has reduced the cost and increased the availability of accurate 3D data for diverse applications such as terrain mapping, transportation planning, emergency response, 3D city modeling, heritage documentation, forest parameter estimation, flood hazard mapping, and coastal management. Usually, the original LIDAR point cloud does not comprise semantic information about the type and characteristics of reflecting surfaces. Therefore, the data that has been collected should be processed to extract useful information for the applications mentioned above, such as ground and non-ground classification, Digital Terrain Model (DTM) generation, and building hypothesis generation.

Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data

The paper highlights recent results of forest structure analysis at single tree level based on analyzing airborne full waveform LiDAR data. Single trees are automatically detected by a 3D segmentation technique applied directly to laser point clouds, which uses the normalized cut segmentation combined with a stem detection method. A subsequent classification identifies tree species using salient features that are defined on single 3D tree segments and utilize the additional information extracted from the reflected laser signal by the waveform decomposition. The stem volume and diameter at breast height (DBH) are estimated by a multiple linear regression analysis which uses tree shape parameters derived from the 3D model of the trees. Experiments were conducted in the Bavarian Forest National Park with full waveform LiDAR data. The data were captured with the Riegl LMS Q-560 system at a point density of 25points/m2 under leaf-off and leaf-on conditions. The analysis of waveform data in the tree structure shows that the intensity and pulse width discriminate stem points, crown points and ground points significantly. The unsupervised classification of deciduous and coniferous trees is in the best case 93%. If a supervised classification is applied the accuracy is slightly increased to 95%. Concerning stem volume estimation, in the case of coniferous trees the study shows a low RMSE of about 0.46m3 to 0.43m3 both for the watershed segmentation and the new normalized cut segmentation. In the case of deciduous trees RMSE has increased by 14% in leaf off condition and by 4% in leaf on condition for the normalized cut segmentation. A similar trend can be confirmed for DBH estimation as well, even demonstrating a larger benefit from 3D segmentation. The study results proved that the 3D segmentation approach is not only capable of detecting more small trees in the lower forest layer but also can allow to derive more promising features of single trees used for yielding better performance in species classification and estimation of forest structural parameters, especially for deciduous trees.

Small area estimation of forest attributes in the Norwegian National Forest Inventory

The Norwegian National Forest Inventory (NNFI) provides estimates of forest parameters on national and regional scales by means of a systematic network of permanent sample plots. One of the biggest challenges for the NNFI is the interest in forest attribute information for small sub-populations such as municipalities or protected areas. Frequently, too few sampled observations are available for such small areas to allow estimates with acceptable precision. However, if an auxiliary variable exists that is correlated with the variable of interest, small area estimation (SAE) techniques may provide means to improve the precision of estimates. The study aimed at estimating the mean above-ground forest biomass for small areas with high precision and accuracy, using SAE techniques. For this purpose, the simple random sampling (SRS) estimator, the generalized regression (GREG) estimator, and the unit-level empirical best linear unbiased prediction (EBLUP) estimator were compared. Mean canopy height obtained from a photogrammetric canopy height model (CHM) was the auxiliary variable available for every population element. The small areas were 14 municipalities within a 2,184 km2 study area for which an estimate of the mean forest biomass was sought. The municipalities were between 31 and 527 km2 and contained 1–35 NNFI sample plots located within forest. The mean canopy height obtained from the CHM was found to have a strong linear correlation with forest biomass. Both the SRS estimator and the GREG estimator result in unstable estimates if they are based on too few observations. Although this is not the case for the EBLUP estimator, the estimators were only compared for municipalities with more than five sample plots. The SRS resulted in the highest standard errors in all municipalities. Whereas the GREG and EBLUP standard errors were similar for small areas with many sample plots, the EBLUP standard error was usually smaller than the GREG standard error. The difference between the EBLUP and GREG standard error increased with a decreasing number of sample plots within the small area. The EBLUP estimates of mean forest biomass within the municipalities ranged between 95.01 and 153.76 Mg ha−1, with standard errors between 8.20 and 12.84 Mg ha−1.

A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada

The GEOgraphic Object-Based Image Analysis (GEOBIA) paradigm continues to prove its efficacy in remote sensing image analysis by providing tools which emulate human perception and combine analyst's experience with meaningful image-objects. However, challenges remain in the evolution of this new paradigm as sophisticated methods attempt to deliver on the goal of automated geo-intelligence (i.e., geospatial content within context) from geospatial sources. In order to generate geo-intelligence from a forest scene, this article introduces a GEOBIA framework to estimate canopy height, above-ground biomass (AGB) and volume by combining lidar (light detection and ranging) transects, Quickbird imagery and machine learning algorithms. This framework is comprised three main components: (i) image-object extraction, (ii) lidar transect selection, and (iii) forest parameter generalization. The rational for integrating these methods is to provide a semi-automatic GEOBIA approach from which detailed forest information is obtained at the individual tree crown or small tree cluster level (i.e., mean object size of 0.04ha); while also dramatically reducing airborne lidar data acquisition costs. Analysis is performed over a 16,330ha forested study site in Quebec, Canada. Forest parameter estimation results derived from our GEOBIA framework demonstrate a strong relationship with those using the full lidar cover; where the highest estimates for canopy height (R=0.85; RMSE=3.37m), AGB (R=0.85; RMSE=39.48Mg/ha) and volume (R=0.85; RMSE=52.59m3/ha) were achieved using a lidar transect sample representing only 7.6% of the total study area.

Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel

Estimation of forest structural parameters by field-based data collection methods is both expensive and time consuming. Satellite remote sensing is a low-cost alternative in modeling and mapping structural parameters in large forest areas. The current study investigates the potential of using WordView-2 multispectral satellite imagery for predicting forest structural parameters in a dryland plantation forest in Israel. The relationships between image texture features and the several structural parameters such as Number of Trees (NT), Basal Area (BA), Stem Volume (SV), Clark-Evans Index (CEI), Diameter Differentiation Index (DDI), Contagion Index (CI), Gini Coefficient (GC), and Standard Deviation of Diameters at Breast Heights (SDDBH) were examined using correlation analyses. These variables were obtained from 30m×30m square-shaped plots. The Standard Deviation of Gray Levels (SDGL) as a first order texture feature and the second order texture variables based on Gray Level Co-occurrence Matrix (GLCM) were calculated for the pixels that corresponds to field plots. The results of the correlation analysis indicate that the forest structural parameters are significantly correlated with the image texture features. The highest correlation coefficients were calculated for the relationships between the SDDBH and the contrast of red band (r=0.75, p<0.01), the BA and the entropy of blue band (r=0.73, p<0.01), and the GC and the contrast of blue band (r=0.71, p<0.01). Each forest structural parameter was modeled as a function of texture measures derived from the satellite image using stepwise multi linear regression analyses. The determination coefficient (R2) and root mean square error (RMSE) values of the best fitting models, respectively, are 0.38 and 109.56ha−1 for the NT; 0.54 and 1.79m2ha−1 for the BA; 0.42 and 27.18m3ha−1 for the SV; 0.23 and 0.16 for the CEI; 0.32 and 0.05 for the DDI; 0.25 and 0.06 for the CI; 0.50 and 0.05 for the GC; and 0.67 and 0.70 for the SDDBH. The leave-one-out cross-validation technique was applied for validation of the best-fitted models (R2>0.50). In conclusion, cross-validated statistics confirmed that the structural parameters including the BA, SDDBH, and GC can be predicted and mapped with a reasonable accuracy using the texture features extracted from the spectral bands of WorldView-2 image.

Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations

A new approach for determining the forest leaf area index (LAI) from a geometric-optical model inversion using multisensor observations is developed. For improving the LAI estimate for the forested area on rugged terrain, a priori information on tree height and the spectra of four scene components of a geometric-optical mutual shadowing (GOMS) model are extracted from airborne light-detection and ranging (LiDAR) data and optical remote sensing data with high spatial resolution, respectively. The slope and aspect of the study area are derived from digital elevation model data. These extracted parameters are applied in an inversion to improve the estimates of forest canopy structural parameters in a GOMS model. For the field investigation, a bidirectional reflectance factor data set of needle forest pixels is collected by combining moderate-resolution-imaging-spectroradiometer (MODIS) and multiangle-imaging-spectroradiometer (MISR) multiangular remote sensing observations. Then, forest canopy parameters are inverted based on the GOMS model. Finally, the LAI of the forest canopy of each pixel is estimated from the retrieved structural parameters and validated by field measurements. The results indicate that the accuracy of forest canopy LAI estimates can be improved by combining observations of passive multiangle and active remote sensors.

An application-oriented automated approach for co-registration of forest inventory and airborne laser scanning data

Airborne laser scanning (ALS) data can be used for downscaling point-based forest inventory (FI) measurements to obtain spatially distributed estimates of forest parameters at a more detailed, local scale. Such downscaling algorithms usually consist of a direct coupling between selected FI parameters and ALS data collected at the field sampling locations. Thus, precise co-registration between FI and ALS data is an essential preprocessing step to obtain accurate predictive relationships. This paper presents a new, automated co-registration approach that searches iteratively for the best match between an ALS-based canopy height model and the tree positions and heights measured during the FI. While the basic principle of the algorithm applies to various types of FI sampling configurations, the co-registration approach was developed specifically to take into account the tree selection criterion posed by angle count sampling. The angle count sampling method only includes trees that at a given distance from the sample plot centre have a minimum required diameter at breast height (DBH). This tree selection criterion leads to maximum plot radii and number of inventoried trees that strongly vary from sample plot to sample plot. In the automated co-registration procedure, several criteria (e.g. the occurrence of more than one spatial cluster of minimum residuals and a predominance of deciduous trees in a sample plot) were used to detect possible uncertain solutions and to reduce post-processing efforts by an image operator. Model calibration and validation were based on national forest inventory (NFI) and ALS data from the Austrian federal state of Vorarlberg. Transferability and robustness of the approach was verified using an independent local FI. The results show that 68% of the NFI sample plots and 74% of the local FI plots could be automatically co-registered to a location at a distance of less than 5.0 m from the reference location. The maximum difference of 5.0 m used for marking a solution as correct was based on the relatively small influence that deviations of up to this value have on ALS-based predictions of biophysical forest variables at a stand level. The quality flagging criteria adopted were very successful in identifying uncertain solutions; only one out of 153 co-registered sample plots with a deviation from the reference data set greater than 5.0 m was not identified as uncertain. Applying the automatically co-registered sample plots in calibration of a growing stock model provided estimates that were clearly superior to those obtained with the original plot positions and even slightly outperformed those based on manual co-registration. As the algorithm developed will be part of an operational processing chain for Austrian NFI data, it has a high practical relevance.

Forest canopy height retrieval using LiDAR data, medium-resolution satellite imagery and kNN estimation in Aberfoyle, Scotland

The most common form of forest stand parameter estimation is undertaken based on field-based forest inventory data. Parameter estimations are implemented at a range of scales depending on the user needs and requirements. Due to time and cost constraints, these approaches are sample based, with measurements collected using sampling frames, which tend to be sparsely distributed when inventories are carried out across a country (e.g. national forest inventories) or region (Matthews and Mackie, 2006). Commonly, forest managers require detailed information on individual stands in order to plan silvicultural management strategies. Such information is typically derived from a stand-based inventory, where detailed measurements from several plots are collected within each stand, providing more precise forest parameter estimates. In recent decades, forest practitioners have increasingly integrated remote sensing data as a means of assisting and complementing forest inventories. Aerial photography has been used extensively to estimate forest attributes, which include species identification, tree height, crown diameter and the delineation of forest boundaries (Tuominen and Pekkarinen, 2005). Top height is an important forest parameter that is used in the UK and Ireland to estimate Yield Class (annual production potential) and as an indicator of standing biomass. Using remote sensing, tree height can be measured using high-resolution stereoscopic aerial photos, with photogrammetric techniques to measure the lengths of shadows projected onto level open ground (Kovats, 1997). However, these techniques are dependent on a number of factors that include level open ground and the need to determine the sun elevation and latitude, which consequently limits the operational use of these techniques. However, a more effective approach to determine tree heights in forests is by using parallax measurements from stereoscopic photographs (Schut and Van Wijk, 1965). More recently, research has been conducted that combines space-borne satellite imagery with forest inventory Forest canopy height retrieval using LiDAR data, medium-resolution satellite imagery and kNN estimation in Aberfoyle, Scotland