Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates

Adaptive single tree detection methods using airborne laser scanning (ALS) data were investigated and validated on 40 large plots sampled from a structurally heterogeneous boreal forest dominated by Norway spruce and Scots pine. Under the working assumption of having uniformly distributed tree locations, area-based stem number estimates were used to guide tree crown delineation from rasterized laser data in two ways: (1) by controlling the amount of smoothing of the canopy height model and (2) by obtaining an appropriate spatial resolution for representing the forest canopy. Single tree crowns were delineated from the canopy height models (CHMs) using a marker-based watershed algorithm, and the delineation results were assessed using a simple tree crown delineation algorithm as a reference method (‘RefMeth’). Using the proposed methods, approximately 46–50% of the total number of trees were detected, while approximately 5–6% false positives were found. The detection rate was, in general, higher for Scots pine than for Norway spruce. The accuracy of individual tree variables (total height and crown width) extracted from the laser data was compared with field-measured data. The individual tree heights were better estimated for deciduous tree species than for the coniferous species Norway spruce and Scots pine. The estimation of crown diameters for Scots pine and deciduous species achieved comparable accuracy, being better than for Norway spruce. The proposed methodology has the potential for easy integration with operational laser scanner-based stand inventories.     

Identifying species of individual trees using airborne laser scanner

Individual trees can be detected using high-density airborne laser scanner data. Also, variables characterizing the detected trees such as tree height, crown area, and crown base height can be measured. The Scandinavian boreal forest mainly consists of Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.), and deciduous trees. It is possible to separate coniferous from deciduous trees using near-infrared images, but pine and spruce give similar spectral signals. Airborne laser scanning, measuring structure and shape of tree crowns could be used for discriminating between spruce and pine. The aim of this study was to test classification of Scots pine versus Norway spruce on an individual tree level using features extracted from airborne laser scanning data. Field measurements were used for training and validation of the classification. The position of all trees on 12 rectangular plots (50×20 m²) were measured in field and tree species was recorded. The dominating species (>80%) was Norway spruce for six of the plots and Scots pine for six plots. The field-measured trees were automatically linked to the laser-measured trees. The laser-detected trees on each plot were classified into species classes using all laser-detected trees on the other plots as training data. The portion correctly classified trees on all plots was 95%. Crown base height estimations of individual trees were also evaluated (r=0.84). The classification results in this study demonstrate the ability to discriminate between pine and spruce using laser data. This method could be applied in an operational context. In the first step, a segmentation of individual tree crowns is performed using laser data. In the second step, tree species classification is performed based on the segments. Methods could be developed in the future that combine laser data with digital near-infrared photographs for classification with the three classes: Norway spruce, Scots pine, and deciduous trees.     

Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics

Forest inventories based on single-tree interpretation of airborne laser scanning (ALS) data often rely on an allometric estimation chain in which inaccuracies in the estimates of the diameter at breast height (DBH) propagate to other characteristics of interest such as the stem volume. Our purpose was to test nearest neighbor imputation by the k-Most Similar Neighbor (k-MSN) and the Random Forest (RF) methods for the simultaneous estimation of species, DBH, height and stem volume using ALS data. The predictors included computational alpha shape metrics and variables based on the height and intensity distributions in the ALS data. Separate data sets covering 1898 and 1249 dominant to intermediate trees in a typical Scandinavian stand structure were used for training and validation, respectively. RF proved to be a flexible method with an ability to handle 1846 predictors with no need for their reduction. Classification of Scots pine, Norway spruce and deciduous trees showed an accuracy of 78%, and the estimates of DBH, height and volume had root mean square errors of 13%, 3%, and 31%, respectively, when evaluated against the validation data. The two selection strategies implemented here reduced the number of candidate variables effectively without any substantial effect on the accuracy relative to the use of all predictors. Differences in k-MSN and RF imputations were marginal when the reduced sets of variables were used. Estimation accuracies could be maintained practically unchanged with only 12.5% of the initial reference data (237 trees), provided the distribution of the observations was similar in the reference and target data. Since we used information collected in the field for extracting the ALS point clouds for individual trees, our results represent an optimal case and should nevertheless be validated against automated tree delineation.     

Estimating spruce and pine biomass with interferometric X-band SAR

The primary aim of this study was to investigate the suitability of interferometric X-band SAR (InSAR) for inventory of boreal forest biomass. We investigated the relationship between SRTM X-band InSAR height and above-ground biomass in a study area in southern Norway. We generated biomass reference data for each SRTM pixel from a field inventory in combination with airborne laser scanning (ALS). One set of forest inventory plots served for calibrating ALS based biomass models, and another set of field plots was used to validate these models. The biomass values obtained in this way ranged up to 250 t/ha at the stand level. The relationship between biomass and InSAR height was linear, no apparent saturation effect was present, and the accuracy was high (RMSE = 19%). The relationship differed between Norway spruce and Scots pine, where an increase in InSAR height of 1 m corresponded to an increase in biomass of 9.9 and 7.0 t/ha, respectively. Using a high-quality terrain model from ALS enabled biomass to be estimated with a higher accuracy as compared to using a terrain model from topographic maps. Interferometric X-band SAR appears to be a promising method for forest biomass monitoring.     

Influence of terrain model smoothing and flight and sensor configurations on detection of small pioneer trees in the boreal-alpine transition zone utilizing height metrics derived from airborne scanning lasers

It has been suggested that airborne laser scanning (ALS) with high point densities could be used to monitor changes in the alpine tree line. The overall goal of this study was to assess the influence of ALS sensor and flight configurations on the ability to detect small trees in the alpine tree line and on the estimation of their heights. The study was conducted in a sub-alpine/alpine environment in southeast Norway. 342 small trees (0.11-5.20 m tall) of Norway spruce, Scots pine, and downy birch were precisely georeferenced and measured in field. ALS data acquired with two different instruments and at different flying altitudes (700-1130 m a.g.l.) with different pulse repetition frequencies (100, 125, and 166 kHz) were collected with a point density of all echoes of 7.7-11.0 m^− 2. For each acquisition, three different terrain models were used to process the ALS point clouds in order to assess the effects of different preprocessing parameters on the ability to detect small trees. Regardless of acquisition and terrain model, positive height values were found for 91% of the taller trees (> 1 m). For smaller trees (< 1 m), 29-61% of the trees displayed positive height values. For the lowest repetition frequencies (100 and 125 kHz) in particular, the portion of trees with positive laser height values increased significantly with increasing terrain smoothing. For the highest repetition frequency there were no differences between smoothing levels, likely because of large ALS measurement errors at low laser pulse energy levels causing a large portion of the laser echoes to be discarded during terrain modeling. Error analysis revealed large commission errors when detecting small trees. The commissions consisted of objects like terrain structures, rocks, and hummocks having positive height values. The magnitude of commissions ranged from 709 to 8948% of the true tree numbers and tended to increase with increasing levels of terrain smoothing and with acquisitions according to increasing point densities. The accuracy of tree height derived from the ALS data indicated a systematic underestimation of true tree height by 0.35 to 1.47 m, depending on acquisition, terrain model, and tree species. The underestimation also increased with increasing tree height. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.16-0.57 m. Because there are significant effects of sensor and flight configurations on tree height estimation, field calibration of tree heights at each point of time is required when using airborne lasers for tree growth monitoring.     

Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data

The objective of this study was to identify candidate features derived from airborne laser scanner (ALS) data suitable to discriminate between coniferous and deciduous tree species. Both features related to structure and intensity were considered. The study was conducted on 197 Norway spruce and 180 birch trees (leaves on conditions) in a boreal forest reserve in Norway. The ALS sensor used was capable of recording multiple echoes. The point density was 6.6 m^− 2. Laser echoes located within the vertical projection of the tree crowns, which were assumed to be circular and defined according to field measurements, were attributed to three categories: “first echoes of many”, “single echoes”, or “last echoes of many echoes”. They were denoted FIRST, SINGLE, and LAST, respectively. In tree species classification using ALS data features should be independent of tree heights. We found that many features were dependent on tree height and that this dependency influenced selection of candidate features. When we accounted for this dependency, it was revealed that FIRST and SINGLE echoes were located higher and LAST echoes lower in the birch crowns than in spruce crowns. The intensity features of the FIRST echoes differed more between species than corresponding features of the other echo categories. For the FIRST echoes the intensity values tended to be higher for birch than spruce. When using the various features for species classification, maximum overall classification accuracies of 77% and 73% were obtained for structural and intensity features, respectively. Combining candidate features related to structure and intensity resulted in an overall classification accuracy of 88%.     

Subpixel mapping of snow cover in forests by optical remote sensing

Forest represents a challenging problem for snow-cover mapping by optical satellite remote sensing. To investigate reflectance variability and to improve the mapping of snow in forested areas, a method for subpixel mapping of snow cover in forests (SnowFrac) has been developed. The SnowFrac method is based on linear spectral mixing modelling of snow, trees and snow-free ground. The focus has been on developing a physically based reflectance model which uses a forest-cover map as prior information. The method was tested in flat terrain covered by spruce, pine and birch forests, close to the Jotunheimen region of South Norway. Experiments were carried out using a completely snow-covered Landsat Thematic Mapper (TM) scene, aerial photos and in situ reflectance measurements. A detailed forest model was photogrammetrically derived from the aerial photos. Modelled and observed TM reflectances were compared. In the given situation, the results demonstrate that snow and individual tree species, in addition to cast shadows on the snow surface from single trees, are the most influencing factors on visible and near-infrared reflectance. Modelling of diffuse radiation reduced by surrounding trees slightly improve the results, indicating that this effect is less important. The best results are obtained for pine forest and mixed pine and birch forest. Future work will focus on deriving a simplified reflectance model suitable for operational snow-cover mapping in forests.     

Using airborne laser scanning to monitor tree migration in the boreal-alpine transition zone

The boreal tree line is expected to advance upwards into the mountains and northwards into the tundra due to global warming. The major objective of this study was to find out if it is possible to use high-resolution airborne laser scanner data to detect very small trees — the pioneers that are pushing the tree line up into the mountains and out onto the tundra. The study was conducted in a sub-alpine/alpine environment in southeast Norway. A total of 342 small trees of Norway spruce, Scots pine, and downy birch with tree heights ranging from 0.11 to 5.20 m were precisely georeferenced and measured in field. Laser data were collected with a pulse density of 7.7 m^− 2. Three different terrain models were used to process the airborne laser point cloud in order to assess the effects of different pre-processing parameters on small tree detection. Greater than 91% of all trees > 1 m tall registered positive laser height values regardless of terrain model. For smaller trees (< 1 m), positive height values were found in 5-73% of the cases, depending on the terrain model considered. For this group of trees, the highest rate of trees with positive height values was found for spruce. The more smoothed the terrain model was, the larger the portion of the trees that had positive laser height values. The accuracy of tree height derived from the laser data indicated a systematic underestimation of true tree height by 0.40 to 1.01 m. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.11-0.73 m. Commission errors, i.e., the detection of terrain objects — rocks, hummocks — as trees, increased significantly as terrain smoothing increased. Thus, if no classification of objects into classes like small trees and terrain objects is possible, many non-tree objects with a positive height value cannot be separated from those actually being trees. In a monitoring context, i.e., repeated measurements over time, we argue that most other objects like terrain structures, rocks, and hummocks will remain stable over time while the trees will change as they grow and new trees are established. Thus, this study indicates that, given a high laser pulse density and a certain density of newly established trees, it would be possible to detect a sufficient portion of newly established trees over a 10 years period to claim that tree migration is taking place.     

The effect of crown shape on the reflectance of coniferous stands

The Kuusk-Nilson forest reflectance model was used to study the effect of crown shape on the reflectance of Scots pine and Norway spruce stands. In the first part of the study, we examined spruce and pine stands with an age range of 20-100 years and compared their simulated hemispherical-directional reflectance factors (HDRFs) at nadir in red (661 nm), NIR (838 nm) and MIR (1677 nm) when crowns were modeled as ellipsoids or cones. In all the cases, when a stand was modeled with conical crowns, it had a smaller reflectance factor than the same stand with ellipsoidal crowns.To analyze the sensitivity of HDRF on crown shape, in the second part of the study we simulated the angular distributions of HDRF of two pine stands with different leaf area index (LAI) and canopy closure values at 661 nm assuming four different crown shapes (cone, cylinder, ellipsoid, and cylinder bottom, cone top) and separated the components forming the HDRF. Considerable difference in the HDRF between the four crown shapes was observed: The larger the crown volume, the higher the canopy reflectance at similar LAI and canopy closure. A comparison of the two stands revealed that in denser stands (with a higher canopy closure) single scattering from tree crowns was responsible for the difference in HDRF between the different crown shapes, whereas in stands with a smaller canopy closure the single scattering from ground dominated the HDRF. Finally, the role of crown shape for the retrieval of LAI by inversion from remotely sensed data is discussed.     

A GIS-based decision support system for risk assessment of wind damage in forest management

In this study a GIS-based decision support system (DSS) was built for assessing the short- and long-term risk of wind damage in boreal forests. This was done by integrating a forest growth model SIMA and a mechanistic wind damage model HWIND into geographical information system software (ArcGIS 8.2) as a toolbar (DLL) using ArcObjects in ArcGIS and Visual Basic 6. In this DSS complex problems are solved within program so that forest gaps, edge stands and edges are automatically tracked when the forest structure changes over time as a result of forest growth dynamics and management. This DSS can be used to assess the risk of wind damage to Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and birch (Betula spp.) stands, regarding the number of stands and area at risk and length of vulnerable edges of these risk stands at certain critical wind speed classes (i.e. corresponding the maximum wind speed a tree/stand can resist). This DSS can helps forest managers to analyse and visualise (charts, maps) the possible effects of forest management, such as clear-cuts, on both the immediate and long-term risks of wind damage at both stand and regional level.     

Changes in satellite-measured reflectances caused by thinning cuttings in Boreal forest

Reflectance changes caused by thinning cuttings in northern Sweden were measured using multitemporal Landsat TM data. The test area was dominated by Scots pine (Pinus sylvestris) mixed with Norway spruce (Picea abies) and birch (Betula spp). Landsat TM images from eight sequential summers were relatively calibrated pairwise using regression functions. For each combination of images from different years, local relative changes in digital numbers (DNs) were obtained from the differences between measured data from the late image and a prediction of these data based on the early image. These changes were scaled into units of reflectance using the radiative transfer code 5S. Between 19% and 57% of the basal area was removed in the thinning cuttings. This caused an increase of between 0.001 and 0.004 reflectance in the visible bands and around 0.01 in the middle infrared bands. The best single bands for discrimination of thinned stands from normally developed stands were, in order, TM5, TM7, and TM3. The reflectance changes in these bands increased with thinning grade and with the proportion of pine in the stand. In TM4, the thinning cuttings caused a decrease in reflectance that, to a large degree, could be explained by the proportion of deciduous forest removed. In detected thinning cuttings, around 50% of the variance in the thinning grade, basal area cut, or stem volume cut, could be explained by regression functions based on the TM data only. The reflectance changes caused by thinning cuttings were, in most bands, reduced to half between 4 and 5 years after cutting. The method used for calibration of local changes in DN to changes in reflectance allows data from different image acquisitions to be compared; thus it also may be suitable for creating a knowledge base containing reflectance changes caused by cuttings and local damages in forest.     

Mapping LAI in a Norway spruce forest using airborne laser scanning

In this study we demonstrate how airborne laser scanning (ALS) can be applied to map effective leaf area index (LAI_e) in a spruce forest, after being calibrated with ground based measurements. In 2003 and 2005, ALS data and field estimates of LAI_e were acquired in a Norway spruce forest in SE Norway. We used LI-COR's LAI-2000® Plant canopy analyzer (“LAI-2000”) and hemispherical images (“HI”) for field based estimates of LAI_e. ALS penetration rate calculated from first echoes and from first and last echoes was strongly related to field estimates of LAI_e. We fitted regression models of LAI_e against the log-transformed inverse of the ALS penetration rate, and in accordance with the Beer-Lambert law this produced a linear, no-intercept relationship. This was particularly the case for the LAI-2000, having R² values > 0.9. The strongest relationship was obtained by selecting ALS data from within a circle around each plot with a radius of 0.75 times the tree height. We found a slight difference in the relationship for the two years, which can be attributed to the differences in the ALS acquisition settings. The relationship was valid across four age classes of trees representing different stages of stand development, except in one case with newly regenerated stands which most likely was an artifact. Using LAI_e based on HI data produced weaker relationships with the ALS data. This was the case even when we simulated LAI-2000 measurements based on the HI data.     

Generation of crown bulk density for Pinus sylvestris L. from lidar

The use of lidar data to estimate critical variables needed for modeling wildfire behavior was tested on a Scots pine forest (Pinus sylvestris L.) in central Spain. Lidar data accurately estimated crown bulk density at the plot level (r²=0.80). Lidar data could be used to directly estimate crown volume (r²=0.92) and foliage biomass (r²=0.84), which together produced better results than directly fitting the lidar data to crown bulk density. Incorporating equations that relate tree diameter at breast height and other forest parameters improved estimates of foliage biomass. Individual tree level analyses were not completely successful due to difficulty in accurately assigning laser pulses to the correct tree (r²=0.14).     

Estimating above-ground biomass in young forests with airborne laser scanning

Total above-ground biomass of spruce, pine and birch was estimated in three different field datasets collected in young forests in south-east Norway. The mean heights ranged from 1.77 to 9.66 m. These field data were regressed against metrics derived from canopy height distributions generated from airborne laser scanner (ALS) data with a point density of 0.9–1.2 m^?2. The field data consisted of 79 plots with size 200–232.9 m² and 20 stands with an average size of 3742 m². Total above-ground biomass ranged from 2.27 to 90.42 Mg ha^?1. The influences of (1) regression model form, (2) canopy threshold value and (3) tree species on the relationships between biomass and ALS-derived metrics were assessed. The analysed model forms were multiple linear models, models with logarithmic transformation of the response and explanatory variables, and models with square root transformation of the response. The different canopy thresholds considered were fixed values of 0.5, 1.3 and 2.0 m defining the limit between laser canopy echoes and below-canopy echoes. The proportion of explained variability of the estimated models ranged from 60% to 83%. Tree species had a significant influence on the models. For given values of the ALS-derived metrics related to canopy height and canopy density, spruce tended to have higher above-ground biomass values than pine and deciduous species. There were no clear effects of model form and canopy threshold on the accuracy of predictions produced by cross validation of the various models, but there is a risk of heteroskedasticity with linear models. Cross validation revealed an accuracy of the root mean square error (RMSE) ranging from 3.85 to 13.9 Mg ha^?1, corresponding to 22.6% to 48.1% of mean field-measured biomass. It was concluded that airborne laser scanning has a potential for predicting biomass in young forest stands (> 0.5 ha) with an accuracy of 20–30% of mean ground value.     

Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment

Mean stand height is an important parameter for forest volume and biomass estimation in support of monitoring and management activities. Information on mean stand height is typically obtained through the manual interpretation of aerial photography, often supplemented by the collection of field calibration data. In remote areas where forest management practices may not be spatially exhaustive or where it is difficult to acquire aerial photography, alternate approaches for estimating stand height are required. One approach is to use very high spatial resolution (VHSR) satellite imagery (pixels sided less than 1 m) as a surrogate for air photos. In this research we demonstrate an approach for modelling mean stand height at four sites in the Yukon Territory, Canada, from QuickBird panchromatic imagery. An object-based approach was used to generate homogenous segments from the imagery (analogous to manually delineated forest stands) and an algorithm was used to automatically delineate individual tree crowns within the segments. A regression tree was used to predict mean stand height from stand-level metrics generated from the image grey-levels and within-stand objects relating individual tree crown characteristics. Heights were manually interpreted from the QuickBird imagery and divided into separate sets of calibration and validation data. The effects of calibration data set size and the input metrics used on the regression tree results were also assessed. The approach resulted in a model with a significant R² of 0.53 and an RMSE of 2.84 m. In addition, 84.6% of the stand height estimates were within the acceptable error for photo interpreted heights, as specified by the forest inventory standards of British Columbia. Furthermore, residual errors from the model were smallest for the stands that had larger mean heights (i.e., > 20 m), which aids in reducing error in subsequent estimates of biomass or volume (since stands with larger trees contribute more to overall estimates of volume or biomass). Estimated and manually interpreted heights were reclassified into 5-metre height classes (a schema frequently used for forest analysis and modelling applications) and compared; classes corresponded in 54% of stands assessed, and all stands had an estimated height class that was within ± 1 class of their actual class. This study demonstrates the capacity of VHSR panchromatic imagery (in this case QuickBird) for generating useful estimates of mean stand heights in unmonitored, remote, or inaccessible forest areas.     

Comparison of three individual tree crown detection methods

Three image processing methods for single tree crown detection in high spatial resolution aerial images are presented and compared using the same image material and reference data. The first method uses templates to find the tree crowns. The other two methods uses region growing. One of them is supported by fuzzy rules while the other uses an image produced by Brownian motion. All three methods detect around 80%, or more, of the visible sunlit trees in two pine Pinus Sylvestris L.) and two spruce stands Picea abies Karst.) in a boreal forest. For all methods, large tree crowns are easier to detect than small ones.     

Indicators for separating undesirable and well-delineated tree crowns in high spatial resolution images

Much effort has been spent on the automatic detection and delineation of individual trees from high spatial resolution images. However, delineation errors may lead to an inaccurate crown size when compared with ground measurements. Thus, it is problematic to use delineated crowns to derive information on tree variables, e.g. crown diameter, tree height, diameter at breast height (DBH), stand volume, stem volume or stand competition index. In this study, we investigated two indicators – the mean digital number (MDN) within each delineated crown and the difference between MDNs (DMDNs) for 0.6 m buffer zones outside and inside the boundary of each delineated crown – to separate poorly delineated crowns from well-delineated ones. We modelled the relationships between delineated crowns and field-based crown size, between delineated crowns and tree height, and between delineated crowns and DBH observations in a Norway spruce (Picea abies) stand, separately considering models based on all delineated results and crowns identified as being well delineated. Our results showed that the capability of the two indicators in separating poorly and well-delineated crowns varied under different thresholds. The results also indicated that models considering only well-delineated crowns were more robust and effective in estimating and predicting tree crown diameter, DBH and tree height than models that considered all delineated results.     

Forest mapping by geoinformatics for landscape fire behaviour modelling in coastal forests,Greece

This study aims at quantifying and mapping fire-related characteristics of forest structure through field inventories, statistics, remote sensing, and geographical information systems in the island of Lesvos, northeast Aegean Sea, Greece. Simulation of fire behaviour requires forest biomass inputs that describe surface fuel types/models along with canopy fuel properties, such as canopy cover, stand height, crown base height, and crown bulk density, to accurately predict surface and crown fire spread and spotting potential. Forest canopy characteristics and other vegetation attributes were sampled and derived in over 100 field plots, the majority of which were located in coastal pine forest stands. Regression models involving four dependent forest stand variables (stand height, canopy cover, crown base height, and crown bulk density) were developed using generalized additive models. The values of adjusted R ² were 0.72 for stand height, 0.68 for canopy cover, 0.51 for crown base height, and 0.33 for crown bulk density. These regression models were used to create forest fuel characteristics layers, which can be used as inputs to fire management applications and state-of-the-art landscape-scale fire behaviour models.     

Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

While forest inventories based on airborne laser scanning data (ALS) using the area based approach (ABA) have reached operational status, methods using the individual tree crown approach (ITC) have basically remained a research issue. One of the main obstacles for operational applications of ITC is biased results often experienced due to segmentation errors. In this article, we propose a new method, called “semi-ITC” that overcomes the main problems related to ITC by imputing ground truth data within crown segments from the nearest neighboring segment. This may be none, one, or several trees. The distances between segments were derived based on a set of explanatory variables using two nonparametric methods, i.e., most similar neighbor inference (MSN) and random forest (RF). RF favored the imputation of common observations in the data set which resulted in significant biases. Main conclusions are therefore based on MSN. The explanatory variables were calculated by means of small footprint ALS and multispectral data. When testing with empirical data the new method compared favorably to the well-known ABA. Another advantage of the new method over the ABA is that it allowed for the modeling of rare tree species. The results of predicting timber volume with the semi-ITC method were unbiased and the root mean squared error (RMSE) on plot level was smaller than the standard deviation of the observed response variables. The relative RMSEs after cross validation using semi-ITC for total volume and volume of the individual species pine, spruce, birch, and aspen on plot level were 17, 38, 40, 101, and 222%, respectively. Due to the unbiasedness of the estimation, this study is a showcase for how to use crown segments resulting from ITC algorithms in a forest inventory context.     

Simulation of L-band and HH microwave backscattering from coniferous forest stands A comparison with SIR-B data

Abstract

Shuttle Imaging Radar-B (SIR-B) images of coniferous forest stands dominated by Ponderosa pine in the Mt. Shasta region of northern California were used to evaluate a composite L-band HH backscattering model of coniferous forest stands. Eight forest stands were employed to describe the relative trend and distribution of backscattering coefficients. It was found that (1) both SIR-B and simulated backscattering coefficients for the eight stands have similar trends and relations to average tree height and average number of trees per pixel and (2) the dispersion and distribution of simulated backscattering coefficients from each stand broadly matched SIR-B data from the same stand. Although it is difficult to draw any strong conclusions from the comparisons because the experimental data arc limited in both quantity and quality and are also undersampled, the comparisons indicate that a stand-based L-band HH composite model seems promising for explaining backscattering features. The means of the backscattering coefficients are determined by the average tree height and average number of trees per pixel in the stands. The distributions of the backscattering coefficients are modelled through random assignment of tree numbers, heights and spatial distribution within a pixel.