isolating and characterizing woodland and savanna ... · 5/2/2008 · methods, using a remote...

ASPRS 2008 Annual Conference Portland, Oregon April 28 - May 2, 2008

ISOLATING AND CHARACTERIZING WOODLAND AND SAVANNA COMMUNITIES ON FORT LEWIS, WASHINGTON, USA

Robert A. Chastain, Research Participant

Oak Ridge Institute for Science and Education IMWE-LEW-PWE, MS17, Box 339500, Bldg 1210

Fort Lewis, WA 98433 [email protected]

ABSTRACT A vegetation map has been produced for the U.S. Army’s Fort Lewis Military Installation in western Washington through the application of feature extraction and per-pixel classification approaches, as well as spatial analytic methods, using a remote sensing data set comprising growing season and leaf-off QuickBird images and small footprint imaging LiDAR. The mapping area was stratified into discrete regions of canopy tree cover representing savanna (5-25 percent cover), woodland (25-60 percent), and forest (>60 percent) communities using raw LiDAR point data. Thus, separate analytical approaches could be tailored to these communities. An object-oriented feature extraction approach was employed within regions identified structurally (via canopy cover) as savanna or woodland to identify individual tree crowns to a species or near-species level of specificity, enabling a detailed characterization of tree species composition within these communities. A total of 11 woodland and 7 savanna community types – comprising approximately 19 percent of the installation area – were identified with this approach. Overall accuracy for savanna and woodland communities was 82.3 percent (kappa=0.8), which was similar to the overall accuracy for the comprehensive Fort Lewis vegetation community map comprised of all plant communities (85.9 percent, kappa=0.851). User’s accuracies were 20 to 100 percent for individual savanna and woodland community types. Lower user’s accuracies (< 70 percent) correspond to rare community types in localized patches with unique land-use histories (e.g., abandoned homesteads with orchard trees), community types with mixed deciduous and conifer canopies, and areas with imperfect geometric registration between the LiDAR and QuickBird image data.

INTRODUCTION

An accurate and detailed vegetation community map provides an essential and powerful tool for land managers planning and implementing natural resource conservation strategies on large military installations. An accurate portrayal of vegetation communities acts as the spatial template for meeting management goals, such as 1) supporting the military mission, 2) protecting and enhancing biological diversity, 3) producing renewable products, including recreational opportunities, and 4) integrating natural resources management plans.

The digital GIS spatial representation of vegetation communities on Fort Lewis in use prior to the creation of the current vegetation map lacked sufficient thematic detail, was not current, and contained inaccuracies that rendered it inadequate for the management of endangered species and communities-of-concern on the installation. Therefore, remote sensing data and techniques were employed to produce a more detailed and up-to-date vegetation community map for the installation. Remote sensing data is ideal for this type of application because it is a synoptic, current, and repeatable source of spatial data. Compared to relying on field survey data alone, remote sensing data is a relatively inexpensive source of information capable of characterizing the types and conditions of vegetation present over a broad spatial scale. In this paper, an approach is described wherein woodland and savanna communities were identified and mapped on Fort Lewis, Washington. This methodology identifies and classifies these communities, based on their unique structural and compositional characteristics, by fusing high-resolution passive multispectral and active LiDAR remote sensing data.

Both object-oriented and per-pixel approaches have been applied to mapping tree cover and other characteristics of savannas and woodlands, using a variety of remote sensing methods and input data sets. Application of pixel-based remote sensing mapping to open-canopy vegetation communities has often been confounded by the structure inherent in these areas. In the open tree canopies in savannas and woodlands, a mixture of tree canopy, shadow, and background vegetation produces a mixed target for moderate resolution image data, and can also lead to classification confusion when using higher resolution image data. A common misclassification error is confusion

between tree canopy and background elements. One pixel-based approach applied to mapping savanna and woodland vegetation communities when only moderate resolution remote sensing data is available is linear pixel unmixing. This approach entails estimation of the amount of different land cover elements that occur within individual pixels (Shimabukuro and Smith, 1991; Zhang and Wang, 2000; Pu et al., 2003). Distinguishing between background and canopy elements can still be problematic when employing high resolution image data in pixel-based remote sensing approaches, especially when seasonal timing of image acquisition is such that both target types are green.

With the advent of high-resolution remote sensing data, such as QuickBird and Ikonos, individual land cover elements such as tree canopies are considered high resolution (H-res) objects, because they are larger than the size of an individual pixel (Strahler et al., 1986). A pixel-based approach applied using these data entails identification of specific threshold values to separate sunlit crown, shadow, and background vegetation in order to map individual tree crowns (Wang et al., 2004; but see Yaguang et al., 2006). A ‘valley-following’ approach that uses the relative brightness of individual pixels as values of ‘elevation’ has also been applied to this problem, with varying degrees of success (Leckie et al., 2005; Chen et al., 2006). Delineation of canopy topography has also been performed by stereoplotting a forest canopy (Zagalikis et al., 2005) or by surveying ground topography, although the latter method is error-prone in areas with high topographic relief, and therefore is limited to areas with gentle terrain (Fujita et al., 2003).

One corollary of the increased availability of H-res remote sensing data has been the development of the object-oriented approach to image processing as an alternative to pixel-based approaches. This paradigm shift is based on the recognition that individual pixels are not real geographic objects, and thus should not be considered meaningful elements for image analysis (Fisher, 1997; Hay et al., 2005). Object-oriented approaches are automated methods for extracting H-res features from image data. These approaches typically combine information contained within a single pixel with contextual information (size, shape, texture, etc) derived from adjacent pixels to aggregate image objects through image segmentation, which is the division of remote sensing image data into discrete regions (or objects) that are spatially and/or spectrally homogenous (Ryherd and Woodcock, 1996). This approach has proved successful in utilizing optical image data to identify shrub vegetation in arid environments, where sufficient contrast exists between vegetation crowns and background elements (Laliberte et al., 2004; 2007).

A central research objective guiding the development of this savanna and woodland community mapping approach was to identify the most effective remote sensing data or combination of data to enable an object-oriented feature extraction of tree canopy elements. Geospatial data on types of tree canopies was subsequently used to classify the different categories of woodland and savanna communities on Fort Lewis based on tree species composition so that relevant map elements (polygons) could be created. The object-oriented method was expected to be a promising approach to identify individual tree canopy elements, because high spatial resolution remote sensing (QuickBird) data was available over Fort Lewis. Moreover, it was anticipated that elevation information from a canopy height model developed from small-footprint LiDAR data would enhance tree canopy identification in open-canopy savanna and woodland areas where sufficient contrast may not exist between tree crown elements and background vegetation present underneath the tree canopy. Study Area

Fort Lewis is a 34,874-hectare Army installation located in the Puget Lowland of Washington State (47° 10’N, 122° 48’W by 46° 54’N, 122° 22’W; Figure 1). The soils and landforms that predominate in this mapping area were formed during the Vashon Glaciation that ended 14,000 years B.P. The soils developed on glacial drift and till and are well- to excessively drained. Elevation ranges from sea level to 200 meters, and annual precipitation averages 100 cm. Major ecosystems are prairies, woodlands and savannas, wetlands, and forests. Because most of the training areas on Fort Lewis were acquired by the federal government between 1917 and the mid-1940s, development pressures have not been an issue (Figure 1b). Whereas widespread urban and exurban development has locally extirpated many natural vegetation communities in the region, Fort Lewis represents a regional island of biodiversity, and contains the best and most extensive remaining examples of certain prairie, woodland, and savanna communities in the Puget Lowland. Vegetation community mapping results indicate that there are approximately 18,000 hectares of forest, 6500 hectares of woodland and savanna, and 6700 hectares of grassland communities present on Fort Lewis.


a) b) Figure 1. Study area map showing (a) the location of the Fort Lewis Army installation and the prevalence of urbanized areas in the Puget Trough lowland ecoregion, and (b) the installation boundary shown over a mosaic of QuickBird images obtained during the summer of 2006. The latter illustration depicts the extensive development typical of the Puget Trough lowland ecoregion, which encroaches up to the installation boundary.

The savanna and woodland communities present on Fort Lewis include both conifer and deciduous (e.g., oak) tree species, both in pure and mixed stands. Among these communities are some of the largest stands of ponderosa pine (Pinus ponderosa) west of the Cascades Mountains. In addition, ecotonal oak-conifer communities between upland Douglas-fir (Pseudotsuga menziesii) forest and prairies, where Oregon white oak (Quercus garryana) is a major canopy species, are considered a Priority Habitat by the Washington Department of Fish and Wildlife. The restoration of these communities is considered critical to the recovery of the western grey squirrel (Linders and Stinson, 2007). The NatureServe (2008) conservation ranks for various associations containing Oregon white oak are G1, G2, or G3 (globally critically imperiled, imperiled, or vulnerable to extirpation or extinction).

DATA AND METHODS

Passive multispectral and active imaging LiDAR remote sensing data were fused to first identify regions that are structurally consistent with savanna and woodland communities based on canopy cover, then identify individual tree canopy elements to a near-species level of specificity so that the compositional characteristics within these regions could be calculated. A detailed cartographic depiction of these communities then proceeded based on the structural and compositional framework specified in the Fort Lewis vegetation classification scheme (Figure 2).

Classification Scheme

The classification scheme developed to categorize vegetation communities on Fort Lewis complies primarily with the group and class levels of the hierarchical National Vegetation Classification Standard (NVCS), which is the FGDC standard for vegetation mapping (Federal Geographic Data Committee, 1997). Upland forests are classified using the framework developed by Foster (2001), and oak woodland/savanna classification follows the guidelines in Chappell et al. (2000). The structural distinction between forest and woodland communities is 60 percent canopy cover, and savannas are defined using a 5 to 25 percent tree cover criterion (GBA Forestry, 2002).


Leaf-off QuickBird image data

Assign class type to savanna and woodland regions based on definitions in the Fort Lewis vegetation community classification scheme

Object-oriented feature extraction of deciduous and evergreen tree canopy

elements in woodland and savanna regions

Ponderosa pine Douglas-fir

Apply threshold to red band of

growing season QuickBird

Apply threshold to IR band of

growing season QuickBird

Isolate forest, woodland, savanna, and open areas to

produce spatial masks

Calculate canopy cover (Fusion

2.0)

< 125 > 125 (not as red) (redder)

< 800 > 800 (not as green) (greener)

Non-oak deciduous tree

Oregon white oak

Leaf-on QuickBird image data

evergreen deciduous

Calculate proportion of canopy element types present within savanna and woodland regions

Canopy height model (from lidar data)

Figure 2. Flow diagram depicting the steps involved in employing imaging LiDAR and multispectral QuickBird data to identify the structural and compositional characteristics of the savanna and woodland communities on Fort Lewis.


Remote Sensing Data An overarching objective of the Fort Lewis vegetation community mapping project was to identify effective

remote sensing data sources for discerning vegetation communities from each other. Identification of optimal data sources is necessary for the current mapping endeavor, and also informs future updates of the vegetation map. Elements of remote sensing image data that relate to its level of information content with respect to vegetation mapping, include: 1) type of sensor (active or passive), 2) timing and seasonality of image acquisition, 3) spatial resolution of image data, and 4) the amount of spectral and radiometric information contained in the image data. Often, more than one source of remote sensing image data is collected for vegetation mapping to obtain complementary information obtained during different phenological conditions.

Leaf-off (February 23, 2005) and growing season (June 27, July 20, and August 7, 2006) Quickbird data were acquired for the Fort Lewis mapping area, providing high spatial and radiometric resolution remote sensing data with two seasonal perspectives that exploit phenological differences within and between vegetation community types. QuickBird image data has 2.4 meter spatial resolution and an 11-bit radiometric resolution. The 2005 leaf-off QuickBird image data was orthorectified by the vendor to an absolute accuracy of 2.5 meters RMSE, and was used as the reference image to georeference subsequent multispectral image data sets. The 2006 growing season QuickBird data was acquired in three swaths, with the eastern portion of the mapping area acquired on June 27, the western portion on July 20, and the middle portion acquired on August 7. A total of 40 ground control points (GCPs) that were identifiable on both the 2005 leaf-off QuickBird image and the eastern portion of the 2006 growing season Quickbird images was applied to georeference the latter image, yielding a global RMSE of 0.8874, or a spatial error less than one 2.4 meter QuickBird pixel. The western portion of the 2006 QuickBird image data was similarly georeferenced with 27 GCPs, with a global RMSE of 0.7564, and the middle portion was georeferenced with 23 GCPs, with a global RMSE of 0.8594. These image portions were mosaicked to obtain seamless coverage of the Fort Lewis mapping area. A data stack was then developed from the leaf-off and growing season QuickBird image data that included the raw blue, green, red, and near-infrared (NIR) reflectance bands, the normalized difference vegetation index (NDVI), and the first three principal components of both the leaf-off and growing season image data.

Discrete-return imaging LiDAR data were also obtained over Fort Lewis during leaf-on conditions, providing supplementary information to help characterize the structural properties of vegetation communities. This active sensor transmits light energy in the NIR, permitting the collection of elevation and intensity information when this energy is reflected and recaptured by the sensor. The high resolution (average of 4.5 pulses per square meter) imaging LiDAR data were acquired in September of 2005 by Watershed Sciences, Inc (Corvallis, OR), using a fixed-wing aircraft at a height of 1100 meters above ground surface.

The imaging LiDAR data proved useful in 1) stratifying the mapping area into structurally homogeneous regions based on tree canopy cover; and 2) creating a canopy height model (Figure 2). The stratification of the mapping area into structurally homogenous zones based on canopy cover proceeded from the raw LiDAR point data. Algorithms in the FUSION software (McGaughey, 2008) were applied to produce an elevation surface from the first returns and a bare earth terrain model from the last returns of the raw LiDAR point data. These data were translated into raster format and used to calculate a 2-meter resolution canopy height model for use in further analyses. Mapping Area Stratification

Specific remote sensing challenges exist that are associated with the different vegetation community types that occur in the Fort Lewis mapping area. In open tree canopies, such as in savanna and woodland communities, there is often confusion between tree canopy and background elements if both are green during the time of image acquisition. In addition, above-canopy remote sensing cannot make a detailed discrimination of forest community types when the indicator species are partially or wholly concealed by an evergreen tree canopy, as occurs on Fort Lewis when Douglas-fir overtops Oregon white oak. Threfore, an optimal vegetation community mapping approach should begin with stratification of the mapping area based on vegetation structural attributes. Separate mapping approaches can then proceed that are tailored to the specific problems posed by these communities. Toward this end, tree canopy cover was calculated and a continuous raster representation of this attribute was created using the raw imaging LiDAR point data in an algorithm contained in the FUSION software (McGaughey, 2008; Figure 3). The resulting continuous raster representation of tree canopy cover raster was then classified into forest, woodland, savanna, and open regions based on definitions in the Fort Lewis classification scheme to generate a spatial mask of these vegetation structural types (Figure 4).


a) b)

Height cutoff

First Returns

Figure 3. Illustration of use of FUSION software to produce a spatial representation of tree canopy cover, using the first returns from the raw LiDAR data point cloud. The calculation shown in a) uses the equation (fr > H) / fr, where fr is the total number of first returns and H is the height cutoff value. This calculation was compiled over a 20x20 meter neighborhood so that a sufficient number of LiDAR first returns were available to obtain a robust estimation of tree canopy cover. The continuous raster representation of tree canopy cover shown in b) was created from this per-pixel calculation.

Figure 4. Spatial masks of forest, woodland, and savanna regions created through classification of the canopy cover estimates made from the imaging LiDAR data. Object-Oriented Feature Extraction Object-oriented feature extraction of individual tree canopies was performed using the Feature Analyst extension to ArcGIS 9.1. The effectiveness of different combinations of input variables obtained from the imaging LiDAR and leaf-off and growing season QuickBird data was assessed in a factorial manner with two separate contextual input representations through comparison of feature extraction results with field-identified tree canopies observed within two pilot areas on Fort Lewis containing savanna and woodland communities comprised of a variety of tree species.


The input variables examined were the blue, green, red, and NIR bands of the leaf-off and growing season QuickBird data, as well as NDVI and the first three principal components of the raw image band data. Also tested was elevation from the LiDAR-derived canopy height model. These input variables were tested alone and in various combinations to determine an optimal variable or combination of variables. Different input representation methods (Figure 5) were tested because previous forest mapping research has shown that feature extraction results are highly sensitive to the manner in which this Feature Analyst parameter is employed (Vanderzanden and Morrison, 2002). The results of the pilot investigations indicated that the finest distinction which could be dependably obtained from this object-oriented feature extraction method was between evergreen and deciduous canopy types. Further discernment of these canopy types was performed by applying thresholds to the radiometric values of the growing season QuickBird data for both the deciduous and evergreen types of canopy objects (Figure 2). This process permitted the identification of tree canopy elements to a near-species level of specificity (Figure 6a). Data distributions of the radiometric values of the growing season QuickBird data indicated that within the evergreen canopy type, separation was apparent between the values of Douglas-fir and ponderosa pine canopy elements in the red band. Similarly, separation was apparent between oak and non-oak deciduous tree canopy elements in the NIR band of the growing season QuickBird data. The principal non-oak tree species present within the Fort Lewis mapping area are red alder (Alnus rubra), Oregon ash (Fraxinus latifolia), bigleaf maple (Acer macrophyllum), and black cottonwood (Populus trichocarpa).

Figure 5. Input representations in the Feature Analyst software which were tested for their effectiveness in identifying individual tree canopies and extracting them as geographical features. This parameter determines the method in which contextual information from neighboring pixels is implemented during feature extraction. The user-defined representation on the left corresponds to that employed by Vanderzanden and Morrison (2002), while the bullseye representation is a software default applied to mapping small natural features such as trees and shrubs.

The relative proportions of tree canopy elements were analyzed within areas identified as being structurally consistent with savanna (5-25% tree canopy cover) and woodland (25-60% tree canopy cover) communities (Figure 6b). Individual savanna and woodland areas acted as spatial masks within which was calculated the proportion of Douglas-fir, ponderosa pine, Oregon white oak, and non-oak deciduous trees. The spatial mask regions were then classified and mapped as savanna and woodland community types based on the compositional rules which define these categories in the Fort Lewis vegetation classification scheme (Figure 6c).


a)

b)

c) Figure 6. Major steps in mapping savanna and woodland communities. Image a) depicts the results of an object-oriented feature extraction and radiometric threshold application in which tree crown elements are identified to a near-species level of detail, b) shows the spatial masks delineating areas that have vegetation structure consistent with savanna and woodland communities, and c) illustrates the results of an analysis of the relative composition of tree canopy elements within these spatial masks.


RESULTS

The factorial analysis of input variable combinations and contextual input representations indicated that the optimal object-oriented feature extraction result was obtained from a combined input variable set consisting of elevation from the LiDAR-derived canopy height model and NDVI derived from both the leaf-off and growing season QuickBird data. Additionally, the user-defined contextual input representation yielded the most robust feature extraction result. Appropriate radiometric thresholds in the growing season QuickBird data were identified by adjusting the threshold value to optimize the correspondence between the tree canopy element classification and field-observed tree canopy types. A value of 125 was chosen for the red band to best discern ponderosa pine from Douglas-fir, and 800 was identified as the best threshold value for the NIR band to separate and oaks from non-oak canopy elements (Figure 2).

A total of 11 woodland and 7 savanna vegetation community types were identified using the hybrid approach outlined above (Figure 7). The overall accuracy for these savanna and woodland community types was 82.3 (kappa = 0.8), based on 334 field validation observations. The overall accuracy estimated for the 18 savanna and woodland communities compares closely with the overall accuracy estimated using 961 field observations for the comprehensive 55-class Fort Lewis vegetation community map (85.9 percent, kappa=0.851). Summation of mapped community occurrences indicates that the areal extent of woodland vegetation communities on Fort Lewis is 4,493 hectares and the extent of savanna communities is 2,039 hectares.

User’s and producer’s accuracies for the savanna and woodland community types are shown in Table 1. The user’s accuracies for three woodland communities and one savanna community are below 70 percent, indicating a lower reliability for these classes when mapped using the present approach. However, among these four categories, only the Douglas-fir-oak woodland class amounts to more than one percent of the overall area of Fort Lewis, so the lack of reliability associated with these results has little effect on overall mapping accuracy. Both he user’s and producer’s accuracy of the Douglas-fir – non-oak woodland community type are 20 percent, indicating that this class may not be accurately identifiable using the data and methods presented here. This class is estimated to be very limited in its extent (216 ha; 0.6 percent of mapping area), and thus has a very limited effect on overall accuracy.

Table 1. User’s and Producer’s accuracies for savanna and woodland communities.

Community Type

Producer’s Accuracy

User’s Accuracy

Area (in hectares)

Douglas-fir woodland 86.7 100 2425.4 ponderosa pine woodland 100 88.9 63.4 oak woodland 79.6 92.1 255.2 deciduous (non-oak) woodland 76.9 90.9 82.3 mixed (oak/doug-fir) woodland 62.3 76.7 537.8 mixed (non-oak/doug-fir) woodland 42.9 60 339.8 Douglas-fir-oak woodland 60 40 461.7 Douglas-fir-non-oak woodland 20 20 216.2 Douglas-fir savanna 100 93.2 1473.2 ponderosa pine savanna 100 100 165.1 oak savanna 81.8 90 66.9 hardwood (non-oak) savanna 83.3 71.4 31.2 mixed (oak/doug-fir) savanna 76.9 66.7 194.8 mixed (non-oak/doug-fir) savanna 71.4 71.4 105.3 ponderosa pine/doug-fir woodland 75 100 83.9 oak/ponderosa pine/doug-fir woodland 100 100 21.6 ponderosa pine/non-oak woodland 100 100 3.4 oak/ponderosa pine/doug-fir savanna 50 100 2.7


Figure 7. Detail from the Fort Lewis vegetation community map, with woodland and savanna vegetation communities highlighted.


DISCUSSION

The hybrid method presented here is a novel approach for mapping savanna and woodland vegetation communities based on their structural and compositional characteristics; one which combines a modified object-oriented method for individual tree canopy isolation and near-species identification with areal information relating to the tree canopy structural parameters that define these communities. Although the object-oriented feature extraction method employed was successful in isolating tree canopy elements, application of this method alone was unable to identify these elements to a near-species level of classification. Subsequent identification of radiometric threshold values in the multispectral QuickBird data was needed to obtain this level of detail in the tree canopy object classification.

Fusing small-footprint imaging LiDAR and high-resolution QuickBird multispectral data produced a highly effective remote sensing data set for the structural isolation and compositional characterization of savanna and woodland communities on Fort Lewis. The raw LiDAR point data was employed in the pivotal step of isolating woodland and savanna communities based on tree canopy cover. Elevation obtained from the LiDAR-derived canopy height model proved to be a requisite input variable for the separation of tree canopy elements from background vegetation and other co-occurring ground features, which are especially problematic in savanna areas. Because NDVI represents a practical encapsulation of vegetative greenness, the contrast between the summer and winter greenness from QuickBird-derived NDVI allowed separation of deciduous and evergreen tree canopy elements.

The ability to discern tree canopy types to a near-species level using radiometric differences was successful due to the differing visible and physiognomic properties of the species present within the mapping area. Red wavelength reflectance of Douglas-fir and ponderosa pine canopies differ because the latter has reddish-brown buds throughout its canopy during the growing season as well as older yellowing needles among the younger dark green needles, while a Douglas-fir tree canopy is typically comprised of dark or blue-green needles and lacks prevalent red features. Likewise, the separation of oak from non-oak canopy elements using radiometric values of the NIR band of the QuickBird data logically follows from differences between the darker blue-green sclerophyllous leaves associated with Oregon white oak and the brighter green leaves found on red alder, Oregon ash, bigleaf maple, and black cottonwood if image acquisition occurs early in the growing season while the leaves of the latter two species are still relatively labile. The success achieved in separating oak from non-oak canopy elements and Douglas-fir from ponderosa pine through the application of thresholds to QuickBird data underscores an advantage associated with the high (11-bit) radiometric resolution of this remote sensing data source. Map classes with lower user’s accuracies (< 70 percent) were associated with savanna and woodland community types which are difficult to reliably identify due to inherent structural properties, or which are highly variable with respect to their genesis or current expression. Specifically, woodland community types with mixed deciduous and conifer canopies (i.e., Douglas-fir with only a small proportion of deciduous trees) are often characterized by a Douglas-fir canopy that has overtopped the deciduous trees, thus rendering observation using above-canopy remote sensing data relatively ineffectual in discerning the deciduous component in such stands. Additionally, some inaccurately classified community classes correspond to rare and highly variable community types with unique land-use histories (e.g., abandoned homesteads with orchard trees) or distinctive species assemblages related to landform position and hydrology (e.g., those located on an upland-wetland ecotone). Finally, some misclassified savanna and woodland areas were found through pan-ocular comparison to be characterized by imperfect geometric registration between the LiDAR and QuickBird image data, giving rise to misappropriation of the multispectral reflectance data from background vegetation to canopy tree elements and subsequent community misclassification.

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