Individual tree detection based on variable and fixed window size local maxima filtering applied to IKONOS imagery for even-aged Eucalyptus plantation forests

Detection of individual trees remains a challenge for forest inventory efforts especially in homogeneous, even-aged plantation scenarios. Airborne imagery has mainly been used for detection of individual trees using local maxima filtering, as point spread function and signal-to-noise ratio are smaller than with satellite-borne imagery. This led to the development of a novel approach to local maxima filtering for tree detection in plantation forests in KwaZulu-Natal, South Africa, using satellite remote sensing imagery. Our approach is based on Gaussian smoothing for noise elimination and image classification, that is, natural break classification to determine the threshold for removing pixels of extremely bright and dark areas in the imagery. These pixels are assumed to belong to the background and hinder the search for tree peaks. A semivariogram technique was applied to determine variable window sizes for local maxima filtering within a plantation stand. A fixed window size for local maxima filtering was also applied using pre-determined tree spacing. Evaluation of the various approaches was based on aggregated assessment methods. The overall accuracy using a variable window size was 85%, root mean square error (RMSE)?=?189 trees, whereas a fixed window size resulted in an accuracy of 80%, RMSE?=?258 trees. The approach worked remarkably well in mature forest stands as compared to young forest stands. These results are encouraging for temperate–warm climate plantation forest companies, who deal with even-aged, broadleaf plantations and forest inventory practices that require assessment 1 year before harvesting.     

Airborne laser data for stand delineation and information extraction

A literature review of new publications in the field of 3D data for forest applications shows that the application of airborne laser scanner data (ALS) is in the focus of research today due to its great potential for practical applications. While there is a lot of research carried out to derive forest management parameters based on laser metrics deduced from a single tree assessment or a statistical area based assessment, the delineation of stand or sub‐stand units derived from laser metrics itself is a rather new approach. In order to describe stand characteristics statistical grid cell approaches or single tree approaches have been developed. The LIDAR based segmentation of stand or sub‐stand units is rarely documented. This article provides information on enhanced processes to delineate stand or sub‐stand units and to extract different forest information based on airborne laser derived parameters. For the stand delineation an automatic process was developed which provides a stand or sub‐stand unit delineation which is according to the first results sufficiently uniform within stands and sufficiently different in species, age class, height class, structure and composition between stands in order to be distinguishable from adjacent areas. With a combined method the stand boundaries as they are established by the mapping units today, as well as sub‐stand units which have in common physical characteristics indicating the same management disposition, were assessed. Finally a first validation of the forest stand unit delineation is provided, indicating the high potential of ALS data for separating stand units.     

Classification of Australian forest communities using aerial photography, CASI and HyMap data

Within Australia, the discrimination and mapping of forest communities has traditionally been undertaken at the stand scale using stereo aerial photography. Focusing on mixed species forests in central south-east Queensland, this paper outlines an approach for the generation of tree species maps at the tree crown/cluster level using 1 m spatial resolution Compact Airborne Spectrographic Imager (CASI; 445.8 nm–837.7 nm wavelength) and the use of these to generate stand-level assessments of community composition. Following automated delineation of tree crowns/crown clusters, spectral reflectance from pixels representing maxima or mean-lit averages of channel reflectance or band ratios were extracted for a range of species including Acacia, Angophora, Callitris and Eucalyptus. Based on stepwise discriminant analysis, classification accuracies of dominant species were greatest (87% and 76% for training and testing datasets; n = 398) when the mean-lit spectra associated with a ratio of the reflectance (ρ) at 742 nm (ρ₇₄₂) and 714 nm (ρ₇₁₄) were used. The integration of 2.6 m HyMap (446.1 nm–2477.8 nm) spectra increased the accuracy of classification for some species, largely because of the inclusion of shortwave infrared wavebands. Similar increases in accuracy were achieved when classifications of field spectra resampled to CASI and HyMap wavebands were compared. The discriminant functions were applied subsequently to classify crowns within each image and produce maps of tree species distributions which were equivalent or better than those generated through aerial photograph interpretation. The research provides a new approach to tree species mapping, although some a priori knowledge of the occurrence of broad species groups is required. The tree maps have application to biodiversity assessment in Australian forests.     

Automated tree recognition in old growth conifer stands with high resolution digital imagery

Automated individual tree isolation and species determination with high resolution multispectral imagery is becoming a viable forest survey tool. Application to old growth conifer forests offer unique technical issues including high variability in tree size and dominance, strong tree shading and obscuration, and varying ages and states of health. The capabilities of individual tree analysis are examined with two acquisitions of 70-cm resolution CASI imagery over a hemlock, amabilis fir, and cedar dominated old growth site on the west coast of Canada. Trees were delineated using the valley following approach of the Individual Tree Crown (ITC) software suite, classified according to species (hemlock, amabilis fir, and cedar) using object-based spectral classification and tested on a tree-for-tree basis against data derived from ground plots.Tree-for-tree isolation and species classification accuracy assessment, although often sobering, is important for portraying the overall effectiveness of species composition mapping using single tree approaches. This accuracy considers not only how well each tree is classified, but how well each automated isolation represents a true tree and its species. Omissions and commissions need to be included in overall species accuracy assessment. A structure of rules for defining isolation accuracy is developed and used. An example is given of a new approach to accuracy analysis incorporating both isolation and classification results (automated tree recognition) and the issues this presents.The automated tree isolation performed well on those trees that could be visually identified on the imagery using ground measured stem maps (approximately 50-60% of trees had a good match between manual and automated delineations). There were few omissions. Commission errors, i.e., automated isolations not associated with a delineated ground reference tree, were a problem (25%) usually associated with spurious higher intensity areas within shaded regions, which get confused in the process of trying to isolate shaded trees. Difficulty in classifying species was caused by: variability of the spectral signatures of the old growth trees within the same species, tree health, and trees partly or fully shaded by other trees. To accommodate this variability, several signatures were used to represent each species including shaded trees. Species could not be determined for the shaded cases or for the unhealthy trees and therefore two combined classes, a shaded class and unhealthy class with all species included, were used for further analysis. Species classification accuracy of the trees for which there was a good automated isolation match was 72%, 60%, and 40% for the non-shaded healthy hemlock, balsam, and cedar trees for the 1996 data. Equivalent accuracy for the 1998 imagery was 59% for hemlock, 80% for balsam, with only a few cedar trees being well isolated. If all other matches were considered an error in classification, species classification was poor (approximately 45% for balsam and hemlock, 25% for cedar). However, species classification accuracies incorporating the good isolation matches and trees for which there was a match of an isolations and reference tree but the match was not considered good were moderate (60%, 57%, and 38% for hemlock, balsam, and cedar from the 1996 data; 62%, 61%, and 89%, respectively, for the 1998 imagery).Automated tree isolation and species classification of old growth forests is difficult, but nevertheless in this example useful results were obtained.     

Evaluating the potential of multispectral imagery to map multiple stages of tree mortality

Insect outbreaks are major forest disturbances, causing tree mortality across millions of ha in North America. Resultant spatial and temporal patterns of tree mortality can profoundly affect ecosystem structure and function. In this study, we evaluated the classification accuracy of multispectral imagery at different spatial resolutions. We used four-band digital aerial imagery (30-cm spatial resolution and aggregated to coarser resolutions) acquired over lodgepole pine-dominated stands in central Colorado recently attacked by mountain pine beetle. Classes of interest included green trees and multiple stages of post-insect attack tree mortality, including dead trees with red needles (“red-attack”), dead trees without needles (“gray-attack”), and non-forest. The 30-cm resolution image facilitated delineation of trees located in the field, which were used in image classification. We employed a maximum likelihood classifier using the green band, Red-Green Index (RGI), and Normalized Difference Vegetation Index (NDVI). Pixel-level classification accuracies using this imagery were good (overall accuracy of 87%, kappa = 0.84), although misclassification occurred between a) sunlit crowns of live (green) trees and herbaceous vegetation, and b) sunlit crowns of gray- and red-attack trees and bare soil. We explored the capability of coarser resolution imagery, aggregated from the 30-cm resolution to 1.2, 2.4, and 4.2 m, to improve classification accuracy. We found the highest accuracy at the 2.4-m resolution, where reduction in omission and commission errors and increases in overall accuracy (90%) and kappa (0.88) were achieved, and visual inspection indicated improved mapping. Pixels at this resolution included more shadow in forested regions than pixels in finer resolution imagery, thereby reducing forest canopy reflectance and allowing improved separation between forest and non-forest classes, yet were fine enough to resolve individual tree crowns better than the 4.2-m imagery. Our results illustrate that a classification of an image with a spatial resolution similar to the area of a tree crown outperforms that of finer and coarser resolution imagery for mapping tree mortality and non-forest classes. We also demonstrate that multispectral imagery can be used to separate multiple postoutbreak attack stages (i.e., red-attack and gray-attack) from other classes in the image.     

Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions

In this article, six individual tree crown (ITC) detection/delineation algorithms are evaluated, using an image data set containing six diverse forest types at different geographical locations in three European countries. The algorithms use fundamentally different techniques, including local maxima detection, valley following (VF), region-growing (RG), template matching (TM), scale-space (SS) theory and techniques based on stochastic frameworks. The structurally complexity of the forests in the aerial images used ranges from a homogeneous plantation and an area with isolated tree crowns to an extremely dense deciduous forest type. None of the algorithms alone could successfully analyse all different cases. The study shows that it is important to partition the imagery into homogeneous forest stands prior to the application of individual tree detection algorithms. It furthermore suggests a need for a common, publicly available suite of test images and common test procedures for evaluation of individual tree detection/delineation algorithms. Finally, it highlights that, for complex forest types, monoscopic images are insufficient for consistent tree crown detection, even by human interpreters.     

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.     

The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data

In mixed-species forests of complex structure, the delineation of tree crowns is problematic because of their varying dimensions and reflectance characteristics, the existence of several layers of canopy (including understorey), and shadowing within and between crowns. To overcome this problem, an algorithm for delineating tree crowns has been developed using eCognition Expert and hyperspectral Compact Airborne Spectrographic Imager (CASI-2) data acquired over a forested landscape near Injune, central east Queensland, Australia. The algorithm has six components: 1) the differentiation of forest, non-forest and understorey; 2) initial segmentation of the forest area and allocation of segments (objects) to larger objects associated with forest spectral types (FSTs); 3) initial identification of object maxima as seeds within these larger objects and their expansion to the edges of crowns or clusters of crowns; 4) subsequent classification-based separation of the resulting objects into crown or cluster classes; 5) further iterative splitting of the cluster classes to delineate more crowns; and 6) identification and subsequent merging of oversplit objects into crowns or clusters. In forests with a high density of individuals (e.g., regrowth), objects associated with tree clusters rather than crowns are delineated and local maxima counted to approximate density. With reference to field data, the delineation process provided accuracies > ∼70% (range 48-88%) for individuals or clusters of trees of the same species with diameter at breast height (DBH) exceeding 10 cm (senescent and dead trees excluded), with lower accuracies associated with dense stands containing several canopy layers, as many trees were obscured from the view of the CASI sensor. Although developed using 1-m spatial resolution CASI data acquired over Australian forests, the algorithm has application elsewhere and is currently being considered for integration into the Definiens product portfolio for use by the wider community.     

Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests

Although open forests represent approximately 30% of the world's forest resources, there is a clear lack of reliable inventory data to allow sustainable management of this valuable resource from semi‐arid areas. This paper demonstrates that the low ground cover of open forest offers a unique opportunity for deriving single tree attributes from high‐resolution satellite imagery, allowing reliable biomass estimation. More particularly, this study investigates the relationship between field‐measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan‐sharpened Quickbird imagery with a 0.61 m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south‐western Turkey. First tree shadows and crowns were identified and delineated as individual polygons. Both visual delineation and computer‐aided automatic classification methods were tested. After delineation, stem volume as a function of these image‐measured attributes was modelled using linear regression. The statistical analyses indicated that stem volume was correlated with both shadow area and crown area. The best model for stem volume using shadow area resulted in an adjusted R ² = 0.67, with a root mean square error (RMSE) of 12.5%. The model for stem volume using crown area resulted in an adjusted R ² = 0.51, with a RMSE of 15.2%. The results showed that pan‐sharpened Quickbird imagery is suitable for estimating stem volume and may be useful in reducing the time required for obtaining inventory data in open Crimean juniper forests and other similar open forests.     

Delineation of individual deciduous trees in plantations with low-density LiDAR data

Delineation of individual deciduous trees with Light Detection and Ranging (LiDAR) data has long been sought for accurate forest inventory in temperate forests. Previous attempts mainly focused on high-density LiDAR data to obtain reliable delineation results, which may have limited applications due to the high cost and low availability of such data. Here, the feasibility of individual deciduous tree delineation with low-density LiDAR data was examined using a point-density-based algorithm. First a high-resolution point density model (PDM) was developed from low-density LiDAR point cloud to locate individual trees through the horizontal spatial distribution of LiDAR points. Then, individual tree crowns and associated attributes were delineated with a 2D marker-controlled watershed segmentation. Additionally, the PDM-based approach was compared with a conventional canopy height model (CHM) based delineation. The results demonstrated that the PDM-based approach produced an 89% detection accuracy to identify deciduous trees in our study area. The tree attributes derived from the PDM-based algorithm explained 81% and 83% of tree height and crown width variations of forest stands, respectively. The conventional CHM-based tree attributes, on the other hand, could explain only 71% and 66% of tree height and crown width, respectively. Our results suggest that the application of the PDM-based individual tree identification in deciduous forests with low-density LiDAR data is feasible and has relatively high accuracy to predict tree height and crown width, which are highly desired in large-scale forest inventory and analysis.     

The Comparison of Urban Tree Crown Extraction based on Airborne LiDARElevation Difference and High Resolution Imagery

With the support of airborne Light Detection and Ranging (LiDAR) data and high spatial resolution aerial imagery,this paper presents an individual tree extraction method that takes the region of urban as the study area.The elevation difference model originated from LiDAR data was used to extract regions of interest including trees. Then,masking was applied to the high spatial resolution aerial imagery to get the same regions. Besides,image segmentations,based on the marked watershed algorithm,were processed on the high spatial resolution aerial imagery and the elevation difference model separately to extract individual tree crowns. Finally,we took a visual interpretation to delineate tree crowns manually and this result was regarded as the reference crowns map. The extraction accuracies were assessed by comparing the spatial relationships of the reference crowns and the automated delineated tree crowns based on the elevation difference model and the high resolution imagery. The results show that the LiDAR data is developed to improve the efficiency of obtaining forest region that the canopy height model include 85.25% forest information. In addition,the tree crowns extraction accuracy based on the high resolution aerial imagery is 57.14%,while another extraction accuracy based on the elevation difference model is 42.47%,which indicated that the marked watershed algorithm proposed in this paper is effective and the high resolution imagery is better than the elevation difference model to extract tree crowns.     

Differentiation of Acacia koa forest stands across an elevation gradient in Hawai‘i using fine-resolution remotely sensed imagery

Koa (Acacia koa) forests are found across broad environmental gradients in the Hawaiian Islands. Previous studies have identified important environmental factors controlling stand structure and productivity at the plot level, but these have not been applied at the landscape level because of small-scale spatial variability. The goal of this study is to compare the differentiation of koa forest types across an elevation/temperature gradient ranging from 1200 to 2050 m asl (17–13°C mean annual temperature (MAT)) through the analysis of field measurements of forest structure and fine-resolution remotely sensed imagery. Several vegetation indices (VIs) (atmospherically resistant vegetation index (ARVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), simple ratio (SR) and modified simple ratio (MSR)) are calculated from IKONOS satellite imagery of these stands and analysed using supervised classification techniques. This procedure allows a clear differentiation of koa stands from areas dominated by grasses, shrubs and bare lava. Across the elevation gradient, VIs allow differentiation of three koa forest stand classes at upper, intermediate and lower elevations. In agreement with the image classification, analysis of variance (ANOVA) of tree height and leaf phosphorus (P) suggests that there are also three significantly different groups of koa stands at those elevations. A landscape-scale map of land cover and koa stand classes demonstrates both the general trend with elevation and the small-scale heterogeneity that exists across the elevation gradient. Application of these classification techniques with fine spatial resolution imagery can improve the characterization of different koa stand types across the islands of Hawai‘i, which should aid both the conservation and utilization of this ecologically important species.     

Non-linear matrix modeling of forest growth with permanent plot data: The case of uneven-aged Douglas-fir stands

Uneven‐aged forest management is acquiring increasing importance throughout the world as an alternative to clear cutting. Simple stage‐structured matrix models can be used to predict the growth of uneven‐aged stands, and they are easily incorporated in optimization analysis. Parameters of these models can be estimated from readily available permanent plot data. Such a growth and yield model is presented for mixed, uneven‐aged Douglas‐fir and hardwood stands. The model was calibrated with data from 66 permanent plots in Oregon and Washington. The density‐dependent matrix model predicts the number of trees by diameter class and species type, softwood or hardwood. The parameters are based on individual tree growth equations, individual tree mortality equations, and stand ingrowth equations. The individual tree equations are a function of tree diameter, stand density, species, and site index. The stand‐level ingrowth equations are a function of stand density. The model was validated in the short term by comparing the number of trees in each diameter and species class at the time of the second inventory with the number predicted by the model, given conditions at the first inventory. A long‐term validation of the model was also done by comparing the steady‐state stand structure and stand volume with those observed for old‐growth stands. The model was applied to show the effects of different cutting cycles on productivity, diversity of tree species and size, and financial returns. The results suggest that uneven‐aged management of the Douglas‐fir forest type in the Pacific Northwest can be as productive as even‐aged management, with the added benefits of continuous cover forestry.     

A comparison of three methods for automatic tree crown detection and delineation from high spatial resolution imagery

This article compares the performance of three algorithms representative of published methods for tree crown detection and delineation from high spatial resolution imagery, and demonstrates a standardized accuracy assessment framework. The algorithms – watershed segmentation, region growing and valley-following – were tested on softwood and hardwood sites using Emerge natural colour vertical aerial imagery with 60 cm ground sampled distance and QuickBird panchromatic imagery with an 11? look angle. The evaluation considered both plot-level and individual tree crown detection and delineation results. The study shows that while all three methods reasonably delineate crowns in the softwood stand on the Emerge image, region growing provided the highest accuracies, with producer's and user's accuracy for tree detection reaching 70% and root mean square error for crown diameter estimation of 15%. Crown detection accuracies were lower on the QuickBird image. No algorithm proved accurate for the hardwood stand on either image set (both producer's and user's accuracies < 30%).     

A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery

Based on very high resolution satellite images, object-based classification methods can be used to produce large scale maps for forest management. These new products require a method to derive quantitative information about the accuracy and precision of delineated boundaries. This assessment would complement any measure of thematic accuracy derived from the confusion matrix. This study aims to assess the positional quality of the boundaries between different landscape units produced by automated segmentation of IKONOS and SPOT-5 satellite images over temperate forests. A robust method was developed to assess the accuracy and the precision of the forest boundaries, respectively measured by the bias and the standard deviation. The two main sources of positional error, namely residual parallax and automatic segmentation, were independently assessed. Positional errors caused by the residual parallax were quantified using a 3D model. Forest stand boundaries generated by automatic segmentation were compared to corresponding visual delineations. The results showed that residual parallax was the major source of positive bias (area overestimation) along forest/non-forest boundaries and depended on the interactions between forest stand patterns and sensor viewing angles. Due mainly to tree shade, the automatic segmentation also produced a positive bias on forest areas, which remained under 1 m for both IKONOS-2 and SPOT-5 images. Standard deviation did not increase linearly with pixel size and was influenced by the nature of the boundary. Production of 1:20,000 scale forest maps from very high resolution satellite data clearly requires acquisition of near nadir imagery or knowledge of landscape object height for true orthorectification. In these cases, IKONOS-2 segmentation outputs were found to correspond with 1:20,000 scale map specification, and SPOT-5 imagery with 1:30,000 scale.     

An automated approach for segmenting and classifying a large sample of multi-date Landsat imagery for pan-tropical forest monitoring

The TREES-3 project of the Joint Research Centre aims at assessing tropical forest cover changes for the periods 1990-2000 and 2000-2010 using a sample-based approach. This paper refers to the 1990-2000 assessment. Extracts of Landsat satellite imagery (20 km × 20 km) are analyzed for these reference dates for more than 4000 sample sites distributed systematically across the tropical belt. For the processing and analysis of such a large amount of satellite imagery a robust methodological approach for forest mapping and change detection has been developed. This approach comprises two automated steps of multi-date image segmentation and object-based land cover classification (based on a supervised spectral library), followed by an intense phase of visual control and expert refinement. Image segmentation is done at two spatial scales, introducing the concept of a minimum mapping unit via the automated selection of a site-specific scale parameter. The automated segmentation of land cover polygons and the pre-classification of land cover types mainly aim at avoiding manual delineation and at reducing the efforts of visual interpretation of land cover to a reasonable level, making the analysis of 4000 sample sites feasible. The importance of visual control and correction can be perceived when comparing to the initial automatic classification result: about 20% of the polygon labels were changed through expert knowledge by visual interpretation. The component of visual refinement of the mapping results had also a notable impact on forest area and change estimates: for a set of sample sites in Southeast Asia (~ 90% of all sites of SE-Asia) the rate of change in tree cover (deforestation) was assessed at 0.9% and 1.6% before and after visual control, respectively.     

A method for calibrated maximum likelihood classification of forest types

A modification to the maximum likelihood algorithm was developed for classification of forest types in Sweden's part of the CORINE land cover mapping project. The new method, called the “calibrated maximum likelihood classification” involves an automated and iterative adjustment of prior weights until class frequency in the output corresponds to class frequency as calculated from objective (field-inventoried) estimates. This modification compensates for the maximum likelihood algorithm's tendency to over-represent dominant classes and under-represent less frequent ones. National forest inventory plot data measured from a five-year period are used to estimate relative frequency of class occurrence and to derive spectral signatures for each forest class. The classification method was implemented operationally within an automated production system which allowed rapid production of a country-wide forest type map from Landsat TM/ETM+ satellite data. The production system automated the retrieval and updating of forest inventory plots, a plot-to-image matching routine, illumination and haze correction of satellite imagery, and classification into forest classes using the calibrated maximum likelihood classification. This paper describes the details of the method and demonstrates the result of using an iterative adjustment of prior weights versus unadjusted prior weights. It shows that the calibrated maximum likelihood algorithm adjusts for the overclassification of classes that are well represented in the training data as well as for other classes, resulting in an output where class proportions are close to those as expected based on forest inventory data.     

Incorporating texture into classification of forest species composition from airborne multispectral images

Although research with digital airborne remote sensing data has been undertaken in different ecoregions to classify forested areas, the potential role of such imagery in deriving information to assist forest management has not yet been fully defined. The objective of this study was to determine the extent that the addition of texture could improve spectral classification of high spatial resolution images (pixel size 1m). These images represented pure and mixed wood forest stands from ecoregions in Alberta and New Brunswick, Canada. This study employed a judicious, selective application of texture to stands within a hierarchical classification framework. In Alberta, the addition of texture made a modest improvement in classification accuracy from 60% to 65%. In New Brunswick, the application of texture to selected land cover types resulted in an overall 12% improvement in classification accuracy. The addition of image texture increased classification accuracy for high spatial detail imagery relative to low spatial detail imagery. Incorporating texture into classification also improved classification accuracies for hardwood stands more so than for softwood stands, but greater attention to stand structure and composition will be needed in future work. Classification accuracies on the order of 60-65% were achieved with simple texture derivatives, maximum likelihood decision rules and conventional classification methods.     

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.