Donald G. Leckie

Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests

Experiences from Nordic countries and Canada have shown that the retrieval of the stem volume and mean tree height of a tree or at stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. This paper reviews the methods of small‐footprint airborne laser scanning for extracting forest inventory data, mainly in the boreal forest zone. The methods are divided into the following categories: extraction of terrain and canopy height model; feature extraction approaches (canopy height distribution and individual‐tree‐based techniques, techniques based on the synergetic use of aerial images and lidar, and other new approaches); tree species classification and forest growth using laser scanner; and the use of intensity and waveform data in forest information extraction. Despite this, the focus is on methods, some review of quality obtained, especially in the boreal forest area, is included. Several recommendations for future research are given to foster the methodology development.

Stand delineation and composition estimation using semi-automated individual tree crown analysis

Abstract Stand delineation and species composition estimation are cornerstones of forest inventory mapping and key elements to forest management decision making. Improved mapping techniques are constantly being sought in terms of speed, consistency, accuracy, level of detail, and overall effectiveness. Semi-automated analysis of high-resolution imagery at the individual tree crown level may offer such benefits. Methods, however, need to be developed and tested under a variety of forest conditions. High-resolution (60 cm) multispectral airborne imagery was acquired over a predominantly young conifer forest and plantation test area on the west coast of Canada. Automated tree isolation algorithms were applied to the data in order to delineate tree crowns or clusters of crowns. An object-oriented single tree classification was conducted using a maximum likelihood classifier. Stands of similar species composition, closure, and stem density were defined through a sequence that first generated images of these parameters from the automated delineation and classification, used these as input to an unsupervised classification, and then filtered and smoothed the resulting classification clusters. Because of the dense nature of the stands and small crowns on the site, the isolation process often delineated clusters of several trees. Species classification accuracy was determined by comparing the average stand composition from the automated technique to that derived from ground transects or plots. Species classification was good, with average composition error (difference between field measured and automated composition) over all 16 test stands being 7.25%. Most errors for individual species in stands were below 20%, but a few were up to 30%. The automatically generated stand boundaries mimicked well those of known plantation and interpreted inventory boundaries. The automated technique created a few larger stands and some additional small stands in areas of complex forest structure. Overall, for the young fairly uniform stands of the site, both stand delineation and species composition estimation were of a quality suitable for operational use in inventory and forest management. Further development and testing is needed to extend results to situations covering large areas, multiple flight lines, varied topography, and different forest conditions.

Recent rates of forest harvest and conversion in North America

Incorporating ecological disturbance into biogeochemical models is critical for estimating current and future carbon stocks and fluxes. In particular, anthropogenic disturbances, such as forest conversion and wood harvest, strongly affect forest carbon dynamics within North America. This paper summarizes recent (2000-2008) rates of extraction, including both conversion and harvest, derived from national forest inventories for North America (the United States, Canada, and Mexico). During the 2000s, 6.1 million ha/yr were affected by harvest, another 1.0 million ha/yr were converted to other land uses through gross deforestation, and 0.4 million ha/yr were degraded. Thus about 1.0% of North Americas forests experienced some form of anthropogenic disturbance each year. However, due to harvest recovery, afforestation, and reforestation, the total forest area on the continent has been roughly stable during the decade. On average, about 110 m3 of roundwood volume was extracted per hectare harvested across the continent. Patterns of extraction vary among the three countries, with U.S. and Canadian activity dominated by partial and clear-cut harvest, respectively, and activity in Mexico dominated by conversion (deforestation) for agriculture. Temporal trends in harvest and clearing may be affected by economic variables, technology, and forest policy decisions. While overall rates of extraction appear fairly stable in all three countries since the 1980s, harvest within the United States has shifted toward the southern United States and away from the Pacific Northwest.

The Individual Tree Crown Approach Applied to Ikonos Images of a Coniferous Plantation Area

In forestry, the availability of high spatial resolution (� 1 m/pixel) imagery from new earth observation satellites like Ikonos favours a shift in the image analysis paradigm from a pixel-based approach towards one dealing directly with the essential structuring element of such images: the individual tree crown (ITC). This paper gives an initial assessment of the effects of 1 m and 4 m/pixel spatial resolutions (panchromatic and multispectral bands, respectively) on the detection, delineation, and classification of the individual tree crowns seen in Ikonos images. Winter and summer Ikonos images of the Hudson plantation of the Petawawa Research Forest, Ontario, Canada were analyzed. The panchromatic images were resampled to 0.5 m/pixel and then smoothed using a 3 � 3 kernel mean filter. A valleyfollowing algorithm and rule-based isolation module were applied to delineate the individual tree crowns. Local maxima within a moving 3 � 3 window (i.e., Tree Tops) were also extracted from the smoothed images for comparison. Crown delineation and detection results were summarised and compared with field tree counts. Overall, the ITC delineation and the local maxima approaches led to tree counts that were on average 15 percent off for both seasons. Visual inspection reveals delineation of clusters of two or three crowns as a common source of error. Crown-based species spectral signatures were generated for six classes representing conifer species, plus a hardwood class and a shrub class. After the ITC-based classification, classification accuracy was ascertained using separate test areas of known species. The overall accuracy was 59 percent. Important confusion exists between red and white spruces, and mature versus immature white pines, but post-classification regroupings into single spruce and white pine classes led to an overall accuracy of 67 percent.

Issues in species classification of trees in old growth conifer stands

Old growth temperate conifer forest canopies are composed of assemblages of tree crowns that vary by species, height, size, and intercrown distance. The challenge this complexity presents to species classification is formidable. In this paper we describe the exploration of spectral properties of old growth tree crowns as captured on two independent acquisitions of 0.7 m ground resolution compact airborne spectrographic imager (CASI) airborne multispectral imagery. Underlying spectral separability is examined, and classifications of manually delineated crowns are compared against field-surveyed ground truth. Technical issues and solutions addressing individual tree species classification of old growth conifer stands are discussed. The study site is a western hemlock, amabilis fir, and red cedar dominated old growth area on Vancouver Island on the west coast of Canada. Within-species spectral variability is large because of illumination and view-angle conditions, openness of trees, natural variability, shadowing effects, and a range of crown health. As well, spectral differences between species are not large. An object-oriented illumination and view-angle correction was effective at reducing the effect of view angle on spectral variability. Radiometric normalization between imagery from flight lines of the same site and time period was successful, but normalization with data from other sites and days was not. The use of spectral samples from sunlit areas of the tree crowns (mean-lit signature) produced the best spectral separability and species classification. Because of the wide within-species variability and spectral overlap among species, it was also found useful to create internal subclasses within a species (e.g., normal and bright crowns). It was not feasible to consistently classify species of shaded crowns or stressed trees, and it was necessary to create overall shaded tree and unhealthy classes to prevent these trees from corrupting the final species classification. Classification results also depend on the visibility of trees in the imagery. This was demonstrated by different visibility and shade conditions of trees between the two image dates. This effect is particularly strong in old growth stands because of variations in stem density and spacing and the range of tree heights and sizes. Old growth stands will have shaded, unhealthy, and visually or spectrally unusual trees. Excluding these and considering species classification of manually delineated trees of the normal and bright spectral classes, modest success was achieved, in the order of 78% accuracy. Hemlock and balsam were confused, and cedar classification was mostly confused by the presence of unhealthy trees. If speciation of shaded and unhealthy trees was required, overall species classification was weak. It was shown, however, that shaded and unhealthy trees could be identified well using a classification with shaded and unhealthy classes. Species classification of the remaining trees, including the unusual trees, was 67% for the 1996 imagery and 77% for the 1998 imagery. Despite the difficulties in classifying species in an old growth environment, practical solutions to issues are available and viable spectral classifications are possible. Fully automated species isolation and classification add further complications, however, and new approaches beyond simple spectral techniques need to be explored.

Automated Mapping of Stream Features with High-Resolution Multispectral Imagery: An Example of the Capabilities

The capabilities of high-resolution, multispectral remote sensing imagery to map important stream features is investigated. Eighty centimeter spatial resolution CASI imagery was acquired in eight spectral bands over Tofino Creek on the west coast of Vancouver Island, British Columbia. A spectral angle mapping algorithm was used to classify stream habitat including hydraulic habitat, substrate material, and woody debris. Subclasses were attempted in terms of streambed material and water depth, but results were not reliable. A classification of deep water, moderate depth water, shallow water, sand, gravel and cobble, and woody debris in sunlit conditions, however, proved accurate (80 percent on average). Individual logs and piles of woody debris were consistently detected. Silty substrate in a tidal flats zone was also classified, but results indicated that different substrate material beneath the water may require separate classes and can result in problematic water depth classification. Patterns of general classes were reasonably represented within shadowed areas cast by isolated trees or groups of trees. However, problems do arise within lengthy shadowed stretches. Some boundaries of stream features with surrounding forest and between some zones of sand, gravel, and cobble were also misclassified. High-resolution, multispectral imagery in four or more bands combined with good geometric correction, image mosaicking, and appropriate automatic classification techniques offer a viable tool for stream mapping to meet a variety of issues and applications. In the future, a powerful suite of stream information may be compiled from multispectral classification combined with high-resolution thermal and lidar data.

Polarimetric classification of Boreal forest using nonparametric feature selection and multiple classifiers

Abstract Polarimetric SAR data contains a large amount of potential information that may be used to characterize forested scenes. However, the large number of PolSAR parameters and discriminators cannot all be used in most classification problems. Some form of feature selection will improve classification results and improve the efficiency of the system. In addition, classification of PolSAR data may be improved with an ensemble of classifiers, each tuned to a different class. Our research is in the Petawawa experimental forest, in the boreal forest northwest of Ottawa, Ontario, Canada. We employ Radarsat-2 fine-quad image data acquired in August (leaf-on) and November (leaf-off) of 2009. We present two system designs in this paper. The first system consists of a feature selector based on a non-parametric evaluation function and a support vector machine for classification. We demonstrate that the feature selection step improves classification accuracy significantly over a baseline classifier. We then present a system consisting of an ensemble of SVM classifiers, each with its own feature selection component and trained on an individual class. The classifier likelihoods are combined in a final step. We demonstrate that this system improves classification accuracy significantly over a single-classifier system. Finally, we demonstrate that classification accuracies are significantly higher when leaf-on and leaf-off images are combined over a single season image.

Earth Observation for Sustainable Development of Forests (EOSD): project overview

Canada requires a next generation forest measuring and monitoring system that responds to key policy drivers related to climate change and to report upon sustainable forest development of Canadas forest both nationally and internationally. The Canadian Forest Service, in partnership with the Canadian Space Agency, is using space-based Earth observation (EO) technologies to create products for forest inventory, forest carbon accounting, monitoring sustainable development, and landscape management. The Earth Observation for Sustainable Development of Forests (EOSD) initiative will work in partnership with the Provinces and Territories and develop a land cover map of the forested area of Canada. Research programs are also a component of EOSD to develop techniques for change monitoring, biomass estimates and automated processing to aid in production. Inputs from EOSD will be an important data source in the National Forest Carbon Accounting Framework and will also be used to enhance Canadas new plot-based National Forest Inventory. Initially EOSD, working with the provinces, territories, universities and industry, will work to develop a national map of the forested land cover of Canada with the long term goal of producing not only land cover maps, but maps of forest change over time, and biomass. The National Forest Information System will be used to integrate and synthesize applicable data and products and make them accessible to a wide range of users through the web.

Radarsat-2 Polarimetric SAR Data for Boreal Forest Classification Using SVM and a Wrapper Feature Selector

The main objective is to propose a wrapper feature selection algorithm for analyzing the Radarsat-2 polarimetric SAR data for the classification of boreal forest. The method is based on the concept of feature selection and classifier ensemble. The support vector machine (SVM) algorithm is used as the classifier. The limitation of SVM as the evaluation function for feature selection is its time-consuming optimization. To accelerate the SVM training process, a training sample reduction strategy based on the notion of support vectors is proposed. Two fine quad-polarized Radarsat-2 images, which were acquired in leaf-on and leaf-off seasons, were chosen for this study. A wide range of SAR parameters were derived from each PolSAR image. A combined dataset was also considered. The classification results compared to the baseline methods demonstrate the effectiveness of the proposed wrapper scheme for forest classification.

Deforestation estimation for Canada under the Kyoto Protocol: A design study

Deforestation is a persistently important issue locally, nationally, and internationally. It is of interest to the public, foresters, environmental organizations, and governments, yet it is difficult to obtain reliable estimates of its extent and nature. Climate change and the role of forests has given a large impetus for formalizing reporting on deforestation. Under the proposed Kyoto Protocol, industrialized nations are required to report on the carbon consequences of deforestation and include them in their greenhouse gas emissions accounting. Canada must develop measurement systems to report on the area of deforestation and the carbon stock loss. Possible data sources include the new plot-based National Forest Inventory (NFI), land use records, and satellite remote sensing. The NFI is a network of 2 × 2 km plots at a 20 km spacing for which land cover and stand attributes are interpreted from medium-scale aerial photography. In this study, medium-resolution satellite imagery, such as Landsat Thematic Mapper (TM), was explored as a potential tool for deforestation estimation and a survey of available land use records was conducted. Factors affecting the utility of each data source and various system design options were examined. An integrated system is suggested that utilizes the NFI as a base, augmented by satellite remote sensing plots and supported by local records as appropriate.