Can. J. Remote Sensing, Vol. 30, No. 6, pp. 855–866, 2004
Comparison of forest attributes extracted from fine spatial resolution multispectral and lidar data Nicholas C. Coops, Michael A. Wulder, Darius S. Culvenor, and Benoît St-Onge Abstract. Fine spatial resolution multispectral imagery and light detection and ranging (lidar) data capture differing, yet complementary characteristics of forest structure. Using a dataset consisting of fine spatial resolution multispectral imagery, discrete-return lidar data, and detailed ground-based measurements of individual tree attributes, we applied an automatic tree delineation routine (tree identification and delineation algorithm) to compare and contrast remotely sensed predictions with field observations. The results indicate the automatically extracted crowns derived from lidar data matched tree crown area (coefficient of determination r2 = 0.46, n = 36) and height (r2 = 0.88, n = 36) better than spatial clusters defined in the multispectral imagery (crown area r2 = 0.26, n = 36) for individual trees that were identifiable in both the lidar and multispectral imagery. Differences between crown delineation characteristics were related to the information content of the lidar and multispectral fine spatial resolution data. Investigation of the spectral characteristics of objects defined in the multispectral imagery revealed strong relationships between the vertical positions derived from the lidar data and the apparent multispectral reflectance, with low-reflectance spatial clusters occurring lower in the forest canopy. The application of lidar and multispectral datasets together, in the context of tree crown delineation, provides information not available from either data source independently. Résumé. Les images multispectrales à résolution spatiale fine et les données lidar (« light detection and ranging ») enregistrent des caractéristiques à la fois différentes mais complémentaires de la structure de la forêt. À l’aide d’un ensemble de données comportant des images multispectrales à résolution fine, des données d’impulsions discrètes lidar et des mesures détaillées de terrain des attributs d’arbres individuels, nous avons appliqué une routine automatique de délimitation d’arbres (« tree identification and delineation algorithm ») pour comparer et relativiser les prédictions réalisées par télédétection par rapport aux observations de terrain. Les résultats montrent que les couronnes extraites automatiquement et dérivées des données lidar correspondaient mieux à la surface des couronnes (r2 = 0.46, n = 36) et à la hauteur (r2 = 0.88, n = 36) que les agrégats spatiaux définis sur les images multispectrales (surface de la couronne r2 = 0.26, n = 36) pour les arbres individuels qui étaient identifiables sur les images lidar et les images multispectrales. Les différences entre les caractéristiques de délimitation des couronnes étaient liées au contenu en information des données lidar et des données multispectrales à résolution spatiale fine. L’investigation des caractéristiques spectrales des objets définis sur les images multispectrales a mis en valeur des relations fortes entre les positions verticales dérivées des données lidar et la réflectance multispectrale apparente, avec des agrégats de faible réflectance spatiale se manifestant plus bas dans le couvert forestier. L’application conjointe des ensembles de données lidar et multispectrales dans le contexte de la délimitation des couronnes d’arbres fournit une information non disponible à partir de l’une ou de l’autre source de données individuellement. [Traduit par la Rédaction]
Introduction
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Two active areas of remote sensing research are the collection, processing, and application of digital fine spatial resolution multispectral imagery and the use of light detection and ranging (lidar) to measure a range of structural forest attributes. Being relatively recent technological advancements, however, there has been limited integration of these data into a combined, multisensor dataset, and few comparisons have been undertaken to assess the benefit of a combined dataset as opposed to each technology in isolation. The last decade has seen a significant drive to develop forest mapping and inventory tools that explicitly recognise the inherent variation in forest structure, as demonstrated by the shift from traditional coarseresolution mapping of forest resources to assessments at much
© 2004 CASI
finer spatial resolutions (e.g., from hectares to tenths of a hectare and, ultimately, individual trees). The derivation of Received 8 July 2003. Accepted 9 June 2004. N.C. Coops1 and D.S. Culvenor. CSIRO Forestry and Forest Products, Private Bag 10, Clayton South 3169, Victoria, Australia. M.A. Wulder. Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada. B. St-Onge. Department of Geography, Université du Québec à Montréal, Case postale 8888, succursale Centre-ville, Montréal, QC H3C 3P8, Canada. 1
Corresponding author. Present address: Department of Forest Resource Management, Forest Sciences Centre, 2424 Main Hall, University of British Columbia, Vancouver, BC V6T 1Z4, Canada (e-mail:
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individual crown and stem attributes, rather than averaged stand attributes from forest-type maps, may allow for more appropriate measurement and management of the forest resource (Czaplewski, 1999). Fine spatial resolution multispectral data are available from a variety of airborne and satellite platforms. Fine spatial resolution satellite imagery is currently available in panchromatic mode at submetre resolution (e.g., the QuickBird 0.61 m panchromatic channel) and between 2 and 4 m resolution in multispectral wavelengths (such as collected by the QuickBird and IKONOS sensors). Airborne platforms with digital camera and digital videography can obtain multispectral and panchromatic imagery at extremely fine spatial resolutions (e.g., 10 cm) (Haddow et al., 2000). The interpretation of fine spatial resolution data has required the development of new image processing techniques (Dikshit, 1996; see Culvenor (2003) for a review of individual tree isolation approaches). At the same time, the application of both discrete-return and waveform-sampling lidar has increased over the past decade. Waveform-sampling lidar systems compensate for a coarse spatial resolution (10–100 m) with a finer and fully digitized vertical resolution, providing full submetre vertical profiles, whereas discrete-return lidar systems (with a footprint size of 0.1–2.0 m) typically record only one to five returns per laser footprint (Lim et al., 2002) and are often used to provide submetre estimates of terrain and surface heights (Blair et al., 1999; Schenk et al., 2001). Studies have demonstrated that the lidar measurement error for individual tree height (of a given species) is less than 1.0 m (Persson et al., 2002) and less than 0.5 m for plot-based estimates of maximum and mean canopy height with full canopy closure (Næsset, 1997; Magnussen and Boudewyn, 1998; Magnussen et al., 1999; Næsset and Økland, 2002; Næsset, 2002). Several studies have identified the potential of fine spatial resolution imagery and lidar to predict a range of forest inventory attributes. For example, digital fine spatial resolution imagery acquired from spacecraft and airborne platforms has been analysed with individual tree detection routines to predict stem numbers (Gougeon, 1995; Larsen and Rudemo, 1998; Wulder et al., 2000) and to stratify imagery for canopy health assessment (Coops et al., 2002). Waveform-sampling lidar has been applied to the prediction of leaf-area index (LAI) and biomass in temperate (Lefsky et al., 1999) and tropical forest (Drake et al., 2002). Forest attributes estimated from waveform-sampling lidar have also been found to be more accurate than those estimated from a suite of imaging remote sensing instruments (Lefsky et al., 2001). Discrete-return lidar has been used to assess individual tree heights (Næsset and Økland, 2002; Persson et al., 2002) and crown and stem diameter (Hyyppa et al., 2001). With accessible lidar technology being relatively recent, however, there has been limited fusion of multispectral and lidar data into combined multisensor datasets. Additional exploratory research studies are required to compare and contrast combined datasets for forest inventory and resource assessment. These studies will aid in the determination of whether the data produced are an improvement over those from other remote sensing technologies, 856
or how to combine the data to allow for improved attribute estimation over one sensor type alone. In this paper, a co-located dataset that includes fine spatial resolution multispectral imagery, discrete-return lidar data, and detailed ground-based measurement of individual trees is utilized. The analysis is undertaken using an automated tree crown delineation algorithm by applying it separately to the multispectral imagery and a gridded canopy height model derived from the lidar data. Using attributes extracted from the tree delineation process on the differing data types, comparisons between the generated tree objects are afforded. The tree objects are compared and contrasted with the field-based estimates of individual tree characteristics (including crown area and radius and tree height). Factors impacting the generation of tree objects from the differing data types are also presented and discussed. Once the capacity of the technologydependent data types is demonstrated at the individual tree scale, the data are generalized to 25 m grid cell aggregates to provide information on the spatial nature of the agreement– disagreement between the data-type-dependent objects generated and to illustrate the implications at a scale typically characterized for forest inventory and management (0.1 ha).
Methods Study area The Abitibi region of Quebec, Canada (48.5°N, 79.3° W), contains hardwood, softwood, and mixed forest stands growing on a part of the Canadian Shield with trees ranging from 50 to more than 230 years in age (St-Onge, 1999). Common species include trembling aspen (Populus tremuloides); white spruce (Picea glauca); white birch (Betula paperifera); balsam fir (Abies balsamea), which was severely attacked by spruce budworm; jack pine (Pinus banksiana); eastern white cedar (Thuja occidentalis); and black spruce (Picea mariana). In addition, gaps in the canopy opened from balsam fir mortality have been exploited by mountain maple (Acer spicatum). The present study focuses largely on the locally dominant species of trembling aspen and white spruce. Field data A field dataset of 36 trees was developed. This dataset includes trees that are dominant in the stand, representative of the species and growth stage, and identifiable on the lidar and multispectral imagery in the study area (Table 1). The heights of individual trees were measured from the ground using a standard clinometer and distance tape method. Two measures were taken from different vantage points separated by at least 90° to ensure independence between the two measures. Trees for which the two height measures differed by more than 3 m or by more than 15% were discarded to ensure confidence in the dataset when assessing lidar height accuracy. These two heights, for all reliably measured trees, were later used to assess the inherent operator error when measuring tree height from the ground. Other ground measures include diameter at breast © 2004 CASI
Canadian Journal of Remote Sensing / Journal canadien de télédétection
height (DBH), crown radius measured in the four cardinal directions, species, and location measured with differential global positioning system (GPS) to an approximate accuracy of 5 m. A stem map was then produced, which is critical to allow tree locations to be assessed relative to each other, ensuring individual trees could be identified on the remotely sensed data. Computed from these data were the total crown area and crown perimeter, assuming an elliptical crown shape. Table 1 shows the mean individual tree attributes measured at the site. Video data Video image data were acquired using a video camera functioning in zoom mode operating from a fixed-wing platform on 27 September 1997. The video data were recorded on a Super VHS tape and converted to green (520–600 nm), red (630–690 nm), and near infrared (NIR; 760–900 nm) data using a frame grabber producing 0.5 m spatial resolution imagery. The imagery was collected at 1100 to 1300 hours local time, resulting in a sun elevation ranging from 37° to 39° (St-Onge, 1999). Lidar data Scanning laser data were acquired on 28 June 1998 using the Optech ALTM 1020 instrument on a fixed-wing platform flying at an altitude of 700 m. As the instrument is a single-return lidar system, separate passes were flown for vegetation (two passes) and terrain description (single pass) to provide sufficient hit density. Based on the pulse frequency, lowest sustainable flight speed, and altitude, hit densities of 1 hit/m2 (vegetation) and 1 hit/2.5 m2 (ground) were achieved (St-Onge, 1999). The spot size was 19 cm. Tree growth between the end of September 1997 (multispectral data) and the end of June 1998 (lidar data) is minimal at these latitudes (48.5°N), allowing for direct comparison of the tree crown geometry (size, shape) between the two dates. Separation of vegetation and terrain was carried out by the data provider using Optech REALM™ software, which classifies the lidar data into four classes, namely ground last returns, ground first returns, vegetation last returns, and vegetation first returns (Lim et al., 2001). Two surfacegeneration software packages were used to convert the discrete lidar point data into a continuous three-dimensional gridded surface. First, the ground-derived lidar hits were gridded using geographic information system (GIS) based digital elevation model (DEM) generation software (Arc/Info, triangulated irregular network (TIN)). All operator ground hits were assumed to be correctly classified, and the DEM was developed Table 1. Average individual tree attributes summarized from field data (n = 36).
Mean Min. Max.
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DBH (cm)
Height (m)
Crown radius (m)
Crown area (m2)
33.1 9.5 53.8
15.6 7.0 25.9
3.0 1.3 5.4
21.7 2.5 51.5
using all available ground-classified lidar hits at a grid size of 0.5 m. Surface-generation software (QuickGrid; Coulthard, 2002) was used to convert the vegetation discrete lidar point data into a continuous three-dimensional gridded surface. Key parameters required by the algorithm are the desired grid dimension, scan bandwidth cutoff (referring to minimum contribution of a point to the evaluation of a grid intersection before the point is ignored), and distance cutoff (specifying the maximum distance of points from a grid intersection as a function of average point density for inclusion in the intersection calculation). The software was parameterised to produce a grid size of 0.5 m. Points were included in the evaluation of grid intersections if they contributed more than 1.0% to the value of the intersection and were no more than 10 times the average point distance from the intersection. The terrain grid was then subtracted from the canopy surface grid to produce a canopy elevation grid for further analysis. Individual crown delineation The tree identification and delineation algorithm (TIDA) (Culvenor, 2000) automatically delineates tree crowns in fine spatial resolution remotely sensed digital imagery. The algorithm is described in Culvenor (2000; 2002). TIDA uses a “top-down” approach to tree delineation, which initially involves the identification of radiometric maxima within a local neighbourhood of pixels. Local maxima are then used to calculate the position of “seeds” around which the rest of the crown is clustered. The clustering process proceeds until a given threshold brightness value is reached, or a local radiometric minima boundary is encountered. Once this process is complete, each “cluster” is individually numbered and a series of crown-based statistics are computed (Culvenor, 2002). It is possible that each delineated cluster has a direct 1:1 correspondence to a tree crown; in large open crowns multiple TIDA clusters may define a single tree crown; or alternatively in the case of a number of closely located small crowns, multiple crowns may lie within a single TIDA-delineated cluster. When applied to multispectral data, the assumption that the centre of a tree crown is brighter than the edge of the crown provides a basis for inferring crown geometry from crown radiometric characteristics. The validity of this assumption, however, may be strongly influenced by the remote sensing environment at the time of image acquisition, such as solar illumination angle and view zenith angle (Culvenor, 2000). Clearly, some assumptions regarding geometric crown shape are also required when applying TIDA to lidar data. In this case, it is assumed that the approximate centre of the crown is taller than the edges of the crown. Although shadow plays an important role in the delineation of the tree boundaries when applying TIDA to multispectral imagery, this distinction is not as clear when using lidar data, as individual crown boundaries may be overlaying vegetation at a slightly lower height.
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Application of TIDA The multispectral imagery was rectified to the lidar data using commonly locatable ground objects (such as identifiable gaps between trees and road verges) on both images using a nearest neighbour resampling technique. The estimated root mean square (RMS) error of the rectification was 0.65 m. TIDA was applied to the multispectral channels individually and the gridded canopy height model derived from the lidar data. An optimization of overall image spatial resolution was undertaken using incremental Gaussian smoothing as described in Culvenor (2000). This process exploits TIDA sensitivity to changes in spatial resolution by identifying, from a series of incrementally Gaussian-smoothed images, which image coincides with the maximum rate-of-change in total number of clusters identified. Once an overall scene optimal resolution was identified for each of the multispectral bands and the gridded lidar data, a range of cluster statistics were derived from each image including cluster size, minimum and maximum crown diameter, and spectral cluster attributes (such as the maximum and minimum cluster spectral brightness). Stratification using lidar In addition to its use as a tree-delineation dataset, the lidar data were also applied as a stratification tool to classify the multispectral imagery according to its vertical position in the canopy. To do this, the canopy was divided into four arbitrary vertical height layers representing a top or overstory (≥10.0 m), mid-story (2.0 to
