comparison of individual tree crown detection and delineation · pdf file ·...

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

COMPARISON OF INDIVIDUAL TREE CROWN DETECTION AND DELINEATION METHODS

Yinghai Ke, Graduate Student

Lindi J. Quackenbush, Assistant Professor Environmental Resources and Forest Engineering

State University of New York College of Environmental Science and Forestry Syracuse, New York 13210

[email protected] [email protected]

ABSTRACT Efficient forest management increases the demand for detailed, timely information. High spatial resolution remotely sensed imagery provides viable sources and opportunities for automated forest interpretation at an individual tree level. Recent research, which aims at providing tree-based forest inventory measurements, has considered automatic individual tree crown detection and delineation. A range of algorithms have been developed for different types of images, tested on different forest areas and evaluated using different methods of accuracy assessment. However, no research exists that compares the performance of these methods using a common dataset and the same evaluation approach. In this paper, we compared the performances of three algorithms representative of current published methods for tree crown detection and delineation. The three algorithms—marker-controlled watershed segmentation, region growing and valley-following—were tested on Emerge natural color vertical aerial image with 60 cm ground sampled distance (GSD) of a softwood study site and a hardwood study site. Overall, producer’s and user’s accuracy were applied in segmentation evaluation. While forest stand density and variation in tree crown size influenced performance, the results demonstrated that all three algorithms effectively delineate the Norway spruce tree crowns in the softwood stand, with the region growing method obtaining the best overall accuracy. However, no algorithm proved accurate for the hardwood stand. This analysis suggested that each algorithm has advantages and limitations based on stand characteristics. Future research is needed to explore adaptive algorithms that are capable of accurately delineating crowns in stands where trees vary in size and density.

INTRODUCTION

Remote sensing has been a valuable source of information for mapping and monitoring forests over the course of past few decades. Remote sensing not only provides a cost-effective tool to help forest managers better understand forest characteristics such as species and crown closure in the compartment level, but also provides opportunities for interpreting forest at an individual tree level. With the increasing availability of high spatial resolution (sub-meter) aerial and satellite images, a variety of image processing techniques were developed for automated detection and delineation of individual tree crowns. Individual tree crown identification and delineation has utility for estimating tree crown size, crown closure, and facilitates species level classification. Furthermore, such techniques enhance the derivation of parameters of interest for forest inventories such as forest stand boundaries, stand density and species composition. Other parameters, such as gap distribution and sizes, can also be easily extracted (Gougeon and Leckie, 2003).

Research into automatic tree detection and delineation from digital imagery dates back to the mid-1980s. Pinz (1991, 1998a, 1998b) reported a “Vision Expert System” designed to locate the center of a crown by searching for local brightness maxima in smoothed aerial images with 10 cm pixel size. In the mid-1990s, Gougeon (1995) presented a valley-following and rule-based algorithm to fully delineate coniferous tree crowns by following valleys of shadows between tree crowns using 36 cm digital aerial imagery. In the same time period, multiple scale analysis was applied on higher resolution imagery to estimate tree crown area (Brandtberg, 1998), and model-based template matching techniques were introduced to recognize individual trees (Pollock, 1996; Pollock, 1998). Later, other image segmentation algorithms such as region growing and watershed segmentation were introduced in tree detection and crown delineation.

The methods published in the literature are different not only in terms of the algorithms themselves, but also in that they were developed for specific site conditions, with different types of imagery, and evaluated using different


accuracy assessment methods. Therefore, it is hard to compare the performances of these algorithms based on the published literature. To obtain better understanding of the existing algorithms, it is necessary to compare them using the same images and evaluation methods. This understanding can then be used to help improve the existing algorithms or develop new algorithms.

The objective of this paper is to compare and analyze three existing tree crown delineation methods on a common study site using the same set of images. The study sites used in this project represent different forest species composition and density. The methods compared include the marker-controlled watershed segmentation (Wang et al. 2004), region growing algorithm (Culvenor, 2002), and valley-following algorithm (Gougeon, 1995).

REVIEW OF TREE CROWN DETECTION AND DELINEATION METHODS Overview

A wide variety of tree crown detection and delineation algorithms have been developed over the last decade. These algorithms can be grouped into two general categories in terms of their objective: individual tree detection and crown delineation. Tree detection is defined as those processes that deal with finding tree tops or locating trees while tree crown delineation refers to automated drawing tree crown outlines (Pouliot and King, 2002). While they are conceptually separated, these two categories often intertwined in the literature. Although there are some studies that only involve tree detection (e.g, Pouliot and King, 2005), many studies combine both tree detection and tree crown delineation in that tree detection is required prior to crown delineation (e.g., Culvenor, 2002; Sheng, et al., 2003; Wang et al., 2004). Some even took the delineation as equivalent to tree detection, i.e., individual trees were detected once the crowns were delineated (e.g., Gougeon, 1995). Since it is hard to separate detection from delineation, while the algorithms studied in this paper have the specific purpose of delineating tree crowns, they are called “detection and delineation” methods in general.

The display of a grey-scale high resolution image of a moderate density forested area as a three-dimensional surface has a spatial structure resembling a mountainous region (Wulder, et al., 2000) with high reflectance pixels as mountains and low reflectance pixels as valleys. In particular, for trees with conical structure, bright peaks in the image correspond to the tree tops because of the higher level of solar illumination. Reflectance decreases toward the crown boundaries; darker pixels surrounding the bright crown correspond to shading from neighboring tree crowns or from directional reflectance effects. This reflectance pattern is substantially used in most tree detection and delineation methods. For example, in the marker-controlled watershed segmentation described by Wang et al. (2004), local reflectance maxima were used in determining markers for segmentation; in the region growing method presented by Culvenor (2002), the local reflectance maxima were used as seeds to expand the crown region to the extent of crowns; and in the valley-following algorithm by Gougeon (1995), the shaded area between tree crowns were used to separate the adjacent crowns. These three methods represented three perspectives of image processing and are reported most frequently. Thus, we used these methods for comparison in our study.

Marker-Controlled Watershed Segmentation

Like many tree crown delineation algorithms, watershed segmentation considers an image as a topographic surface where the grey-levels represent altitude. If the image grey tone is inverted, the local maxima become local minima and vice versa. Thus, the interior regions (for example, within a crown) correspond to catchment basins and the region edges (for example, shaded area between crowns) correspond to watershed lines. Watershed segmentation aims at finding the watershed lines to isolate each region. Consider that a hole is punched in each region minimum and the region is flooded from the hole. As the water level becomes higher, a dam can be built on the watershed lines to prevent the water in the neighboring catchments from merging together. The watershed lines then define the boundary of each segment. However, this method is subjective to over-segmentation due to image noise. To avoid the algorithm’s sensitivity to noise, marker-controlled watershed segmentation was introduced (Gonzalez and Woods, 2002). This approach identifies specific points, or markers, to limit the number of segments created.

Wang et al. (2004) adopted a marker-controlled watershed segmentation method to delineate tree crowns in a mature white spruce stand using CASI image with 60cm GSD. Tree crown objects were first extracted using Laplacian of Gaussian edge detection operator. The operator separates tree crowns from the most significant background (shaded area), but trees close to each other are typically not well separated. Watershed segmentation is then used to refine the objects and separate individual tree crowns. Within each object, tree tops are determined using both local maxima in the grey level image, which represent highest reflectance, and the local maxima in a geodesic distance image, which represents the center of tree crowns. With tree tops used as markers, marker-


controlled watershed segmentation was applied to determine segment for each marker. The boundary between the segments was considered as tree crown boundary. Region Growing

Region growing is an image segmentation approach used to separate regions and recognize objects in an image. Starting at potentially arbitrary seed pixels, neighboring pixels are examined sequentially and added to the growing region if they are sufficiently similar to the seed pixel. This process is continued until a significant boundary is found and these pixels are considered to belong to the region specific to the seed pixel. In this approach, users need to provide seed points and identify the criteria for stopping growth. In Culvenor’s study (2002), local maxima were used to determine the positions of each seed, and three criteria were established to define the region of tree crown surrounding the seed. These criteria were: (1) the tree crown pixel cannot be lower than a threshold defined by the product of average brightness of local maxima and a ratio factor between 0 and 1; (2) the tree crown pixel must fall inside the local minima networks; and (3) two regions cannot overlap. It is worth noting that local maxima are detected without using window: peak reflectance is located in four directions (N-S, W-E, NE-SW, NW-SE), and a pixel was considered as local maxima once it was local peak at least n (1≤n≤4) times. Valley-Following

The valley-following algorithm was originally developed by Gougeon (1995) for automated delineation of trees in a mature coniferous forest stand in Canada using MEIS-II image with 31cm GSD. Non-forested areas were masked out in preprocessing by applying a single threshold. Within the forested area, instead of searching for local maxima as tree tops, the valley-following algorithm finds local minima as valley bottoms. Valleys are followed by searching for pixels that are in between the pixels with higher values. Gougeon (1995) then used a five-level rule-based program to complete the delineation. The lower level rules deal with following the convex shape of tree crown by following crown boundary in clockwise manner; higher levels considered some exceptions such as branches stick out so that there are some indentations in the crown boundary, or indication of separation of two crowns.

CASE STUDY Study Area and Imagery

The study site is located in the Heiberg Memorial Forest, approximately 33 km south of Syracuse in upstate New York (42.75° N, 76.08° W). Heiberg forest is a 9637 ha property owned and managed by the State University of New York College of Environmental Science and Forestry (SUNY-ESF). Vegetation at Heiberg has been managed to produce a diverse representation of forest ecosystems typical of the northeastern United States. Deciduous trees on the property consist predominantly of mixtures of red maple, sugar maple, red oak, beech, and birch. Conifer species include red and white pine, Norway spruce, hemlock, northern white cedar, and tamarack (larch) (Pugh, 2005).

In this study, two sites were selected based on general forest species (Figure 1 and Figure 2). A coniferous study site (Figure 1) covered three adjacent Norway spruce compartments that were established in 1931; trees were planted at 2×2m spacing when they were 3-year-old saplings. Three plots were selected from this site based on the thinning activities within the compartments. Plot 1 was thinned during between 1979 and 1980; in plot 2, thinning was conducted in 1980 inside the forest stand, while there was no thinning along the road; plot 3 was thinned in 1985. The deciduous study site (Figure 2) is a hardwood compartment dominated by sugar maple and white ash.

High resolution imagery was acquired by the Emerge airborne sensor on 11 October 2001. This set of imagery was 8-bit true color with 0.6 m pixel size, orthorectified and georeferenced to UTM Zone 18N, WGS84. Sub-images that covered the study sites were extracted from the full image.


Figure 1. Emerge image showing Norway spruce plots.

Figure 2. Emerge image showing hardwood plot.


Reference Data Generation To support evaluation of the algorithms, a stereo pair of 1:10,000 scale aerial photographs (with 60% overlap)

was also used in this study. The photos were acquired by a WILD 15/ 4UAG-S camera (153mm focal length) on 11 April 1998 and scanned to obtain digital images with a pixel size of 21cm. Due to the leaf-off acquisition date, only the pair that covers Norway spruce compartments was used in this study. The stereo pair was viewed in three-dimensions in ERDAS IMAGINE Stereo Analyst.

Manual delineation of tree crowns in the Emerge images was performed by three interpreters with forest engineering backgrounds. Each person visually counted individual trees on a computer screen displaying the true color Emerge image and outlined the boundary of each tree crown as polygon layers in ArcGIS 9.2. To obtain accurate estimation, the interpretation was assisted by the stereo view of the aerial photographs. While the Emerge image and aerial photos were acquired three years apart, no significant natural or human disturbance occurred in the time period, thus it appeared reasonable to assume that there was little change in the forest conditions between the two set of images.

Methods Implementation

In our study, the green band of Emerge image was selected for applying the algorithms since it shows apparent distinction between tree crowns and shaded area and provides best results after testing on each band. Parameters were chosen for each method as appropriate. For marker-controlled watershed segmentation, the window defined for looking for the marker was 3×3 in Norway spruce stand and 7×7 in the hardwood stand. For region growing, the maxima selection frequency threshold was set as 4 and the ratio factor was set as 0.5 for both Norway spruce and hardwood stands. For valley-following method, a threshold of 55 was selected to mask out non-forested area for both Norway spruce and hardwood stands. All algorithms were programmed in Matlab 7.3.

Accuracy Assessment

The output of the three delineation methods were stored as binary images where 1s represented crowns and 0s non-crown areas. The raster images were then converted as polygon vector layers in ArcGIS 9.2. The delineated crown polygons were overlapped with reference crown polygons for the evaluation. Accuracy assessment was conducted separately for each plot since they represent different forest characteristics.

In the existing literature, a wide range of evaluation methods for tree crown detection and delineation algorithms have been applied (Pouliot and King, 2005). In our study, we analyzed the commission and omission errors for each study sites by determining the ratio of delineated crown:reference crown. We also utilized the accuracy assessment approach described by Larmar et al. (2005) who introduced producer’s accuracy and user’s accuracy from pixel-based classification evaluation into segmentation assessment. Here, producer’s accuracy refers to the percentage of reference trees that were covered by one delineated trees; user’s accuracy refers to the percentage of delineated trees that were covered by one reference tree; overall accuracy is defined as proportion of correctly represented trees in the plot.

RESULTS The results of the three tree crown detection and delineation algorithms applied to the two study sites are

illustrated in Figure 3, Figure 4, and Figure 5. Red lines represent the boundaries of individual tree crowns as defined by each algorithm. In general, for all three methods, the majority of Norway spruce trees were separated from the background and delineated individually, while hardwood tree crowns were difficult to delineate due to over-segmentation. This effect is shown particularly in Figure 4 (b), where the region growing method divided most of the hardwood tree crowns into several delineated crowns, thus the sizes of identified tree crowns derived from this method were smaller than other methods. Detailed examination of the Norway spruce stand shows that tree crowns delineated by the valley-following algorithm tended to be larger than those from the other two methods. However, the valley-following algorithm was more likely to cause omission errors. For example, in the forest area along the road in Figure 5(a) where no thinning occurred, the valley-following method delineated tree clusters as an individual tree; while the other two methods gave better delineation in this area.


Figure 3. Watershed segmentation algorithm results on (a) softwood site and (b) hardwood site.

Figure 4. Region growing algorithm results on (a) softwood site and (b) hardwood site.

(a)

(b)

(a)

(b)


Figure 5. Valley-following algorithm results on (a) softwood site and (b) hardwood site.

Table 1 compares the crown counts resulting from each delineation algorithm with the reference counts in each plot. As expected by the visual interpretation, the valley-following algorithm underestimated the crown counts by 9.9% in Plot 1, by 13.4% in Plot 2 and 4.2% in Plot 3. Table 1 also shows that the marker-controlled watershed segmentation and region growing algorithms overestimated the number of tree crowns. Especially in hardwood stand, the region growing algorithm returned more than twice the actual tree counts.

Table 1. Individual tree crown counts for the four plots in Figure 1

Delineation Counts

Plot No. Tree Species Reference Count Watershed

Segmentation Region

Growing Valley Following

1 Norway Spruce 619 704 660 558 2 Norway Spruce 342 391 377 296 3 Norway Spruce 317 361 344 304 4 Sugar Maple & Ash 411 338 993 430

Further analysis of the delineation errors for each algorithm are shown in Table 2, Table 3, and Table 4. These

tables demonstrate the frequencies of various commission and omission errors from both the reference crown and delineated crown perspectives. The reference crown perspective displays the frequencies that each reference crowns was delineated as one crown (1:1 correspondence) or as more than one crown (n:1 correspondence, where n≠1) was delineated. The delineated crown perspective illustrates the frequencies that each delineated crown represented one reference crown (1:1 correspondence) or more than one reference crowns (1:n correspondence, where n≠1) represented reference crowns. For example, in Plot 1 of the 619 reference crowns, 444 reference crowns were correctly delineated (1:1 correspondence) by the marker-controlled watershed segmentation algorithm (Table 2), and there were 148 instances where the delineation process detected two trees but the interpretation showed there was only one tree crown; of the 704 delineated crowns, there were 523 instances that each delineated crown cover only one interpreted crown, and there were 130 instances where two interpreted crowns were located on a single delineated crown. It should be noted that there were cases when one reference crown were covered by multiple delineated crowns (commission error) which resulted in 1:1 correspondence in delineated crown perspective. Thus, the number of 1:1 correspondence in delineated crown perspective is greater than the number of 1:1 correspondence in reference crown perspective.

(a)

(b)


Comparison of Table 2, Table 3 and Table 4) shows that for Norway spruce study sites (Plots 1, 2, and 3), the most common commission errors were caused by two crowns being delineated where there was only one reference crown (2:1 error). The marker-controlled watershed segmentation algorithm resulted in more 2:1 commission errors compared to the other algorithms. The valley-following algorithm tended to obtain more omission errors (0:1 error). For the hardwood study site, the region growing algorithm produced large commission error, where one reference trees were frequently isolated by as many as six or more trees in delineation process. In the same plot, the valley-following algorithm had difficulty separating tree clusters since there were quite a few instances where more than three reference trees were delineated as a single tree (Table 4), which is consistent with the results shown in visually Figure 5(b).

Table 2. Delineation results for marker-controlled watershed segmentation algorithm

Delineated Crowns : Reference Crowns Reference Crowns Perspective Delineated Crowns Perspective

Plot No.

0:1 1:1 2:1 3:1 4:1 5:1 ≥6:1 Total 1:0 1:1 1:2 1:3 1:4 1:5 1:( ≥6) Total 1 5 444 148 21 1 0 0 619 43 523 130 7 1 0 0 704 2 2 245 83 8 4 0 0 342 34 274 73 9 1 0 0 391 3 5 244 60 8 0 0 0 317 40 259 58 3 1 0 0 361 4 7 264 93 40 6 1 0 411 19 157 89 41 22 6 4 338

Table 3. Delineation results for region growing algorithm

Delineated Crowns : Reference Crowns

Reference Crowns Perspective Delineated Crowns Perspective Plot No.

0:1 1:1 2:1 3:1 4:1 5:1 ≥6:1 Total 1:0 1:1 1:2 1:3 1:4 1:5 1:( ≥6) Total 1 10 522 76 8 2 1 0 619 78 472 94 14 1 1 0 660 2 7 274 55 4 2 0 0 342 48 268 48 12 1 0 0 377 3 8 269 30 7 2 1 0 317 42 251 44 4 3 0 0 344 4 3 149 97 62 60 19 21 411 149 712 113 12 7 0 0 993

Table 4. Delineation results for valley-following algorithm

Delineated Crowns : Reference Crowns

Reference Crowns Perspective Delineated Crowns Perspective Plot No.

0:1 1:1 2:1 3:1 4:1 5:1 ≥6:1 Total 1:0 1:1 1:2 1:3 1:4 1:5 1:( ≥6) Total 1 69 445 86 18 0 1 0 619 94 321 107 17 10 6 3 558 2 29 271 36 6 0 0 0 342 70 156 43 17 3 2 5 296 3 33 229 45 9 1 0 0 317 69 168 40 13 10 2 2 304 4 31 202 123 48 6 1 0 411 33 273 80 21 12 4 7 430

Table 5 summarizes the accuracies obtained by the three algorithms on each plot. The comparison between the

algorithms shows that region growing was the most accurate in delineating Norway spruce trees since this algorithm gave the highest overall accuracies. Additionally, the producer’s accuracies for the region growing algorithm are over 10% higher than the other two methods, which means it has better capability to delineate reference tree crowns. Valley-following resulted in poor user’s accuracies in the three Norway plots (less than 60%); it shows that only a small portion of delineated crowns represented reference crowns correctly. Comparison between the three Norway spruce plots demonstrates that accuracies in plot 2 were slightly lower than the other two plots for all three algorithms. None of the three algorithms obtained accurate results in delineating hardwood trees (56.2% by watershed segmentation, 61.3% by region growing, and 56.5% by valley-following algorithm).


Table 5. Delineation accuracy comparison for three algorithms

Watershed Segmentation Region Growing Valley-following Plot No.

Producer’s accuracy

(%)

User’s accuracy

(%)

Overall accuracy

(%)


(%)

User’s accuracy

(%)

Overall accuracy

(%)


(%)

User’s accuracy

(%)

Overall accuracy

(%) 1 71.7 74.3 73.1 84.3 71.5 77.7 71.9 57.5 65.08 2 71.6 70.1 70.8 80.1 71.1 75.4 79.2 52.7 66.93 3 76.8 71.8 74.2 84.9 73.0 78. 7 72.2 55.3 63.93 4 64.2 46.5 56.2 36.3 71.7 61.3 49.2 63.5 56.48

Producer’s accuracy = # 1:1 correspondence of reference crowns / Total # of reference crowns

User’s accuracy = # of 1:1 correspondence of delineated crowns / Total # of delineated crowns Overall accuracy = ( # of 1:1 correspondence of reference crowns + # of 1:1 correspondence of delineated crowns ) / (Total # of reference crowns + total # of delineated crowns) (Larmar et al., 2005)

DISCUSSION

Applied in the same study sites using the same imagery, all of the three algorithms showed the abilities to effectively delineate Norway spruce tree crowns. However, for the hardwood trees, none of them obtained high accuracy. This can be explained by the large within-crown illumination variation of the hardwood trees. Due to the non-conical shape of the tree crown, the basic assumptions for the spatial pattern of the tree crown were violated. This can also explain why limited research involves delineating hardwood trees. The other reason could be explained by that all of the algorithms were applied on a single band. In the single band of Emerge image for hardwood compartment, even the human eye has problems in isolating individual tree crowns. However, in the true color image shown in the Figure 2, the color distinction in the fall-season hardwood trees enables visual delineation of the trees. Future research may consider taking advantage of multiple spectral bands in segmentation. The results also showed that forest density influenced the performance of the algorithms. Among the three plots in the Norway spruce study site, the accuracy obtained in Plot 2 is the lowest for all the three algorithms. This is due to the fact that the forest density where no thinning occurred (along the road) is greater than that of the other plots. Trees stand close to each other such that crowns were connected and it is difficult to visually separate tree clusters into individual trees.

The three algorithms discussed in this paper represented three ways of thinking in tree crown detection and delineation in the existing methods. Valley-following took advantage of the shaded area between the adjacent tree crowns. It focused on finding local minima and followed the spectral “valleys” to form crown boundaries. Marker-controlled watershed segmentation took advantage of the local reflectance peaks of tree tops illustrated on the image. It searched for local maxima and located tree tops prior to determining tree crown regions. The region growing algorithm made use of both features in that local maxima were detected as seeds for growing and local minima were applied as restriction of crown region expansion. However, the approach for searching for local maxima was different from marker-controlled watershed segmentation. Marker-controlled watershed segmentation identified local maxima (both in spectral and spatial) within windows with fixed size; while region growing algorithm did not require user-defined search distance or window in identifying local maxima, thus it is more flexible in detecting tree crowns with variant sizes. This could be the reason that crown counts derived from region growing for our study area are closer to the interpreted tree counts, and the overall delineation accuracy is more accurate than the other methods. Similarly, unlike valley-following algorithm, local minima detected in region growing algorithm were not restricted by window size. A pixel was defined as a local minimum if it was surrounded by pixels with higher brightness value in each of the opposing direction of four searching arms (N-S, W-E, NE-WS, and NW-SE) until any of the searching arms decrease in brightness. However, this approach could be more sensitive to the reflectance variation. For example, compared to coniferous tree crowns, the within-crown brightness variation for hardwood trees tends to be greater due to the large branches and non-conical shape of the crown. The local minima identification process might detect multiple minima in the same crown. Therefore, region growing algorithm resulted in greatest commission errors in hardwood tree crown delineation (Table 3.).

Analysis of the algorithms and their results showed that different algorithms could be employed in different applications. Marker-controlled watershed segmentation detected tree tops using the intersection of both spectral local maxima and spatial local centers, which was based on the assumption that tree tops have highest reflectance


and are located at or near the center of the crown. Thus, it could be used in delineating trees with circular shape. However, in off-nadir images where tree crowns do not display as circular shape, the boundaries delineated by the algorithm might not be consistent with the actual crown boundaries. Furthermore, since the method applied a fixed-size window to detect local maxima, it could be more suitable to detect trees with similar crown size. The region growing method might be flexible in detecting trees with different crown sizes, but the fixed ratio factor (ratio between the local maximum pixel value and the boundary pixel values) still posed a limitation in that this global parameter may not consider the local variation in the tree crown reflectance. The valley-following algorithm was applicable in forest stands where there are well-defined gaps between neighboring trees. In this case, crown boundaries obtained with this algorithm were more accurate since it effectively followed the boundary pixels with shaded area on one side and the crown area on the other. However, as illustrated in the results, the valley-following algorithm has weakness in separating individual trees from within tree clusters due to the lack of defined valleys between adjacent trees. Developing a more robust algorithm with broad applicability would require taking advantage of the characteristics of multiple approaches.

CONCLUSION Automated detection and delineation of individual tree crowns from high resolution remotely sensed imagery

has now drawn intensive attention of researchers. Numerous methods have been developed and have had various applications. In this paper, we compared the performances of three tree crown detection and delineation algorithms in delineating coniferous and hardwood trees on the same study sites using a uniform dataset. The results indicated that all of the three methods provided useful results in delineating coniferous trees, but had limited ability to delineate hardwood trees. Among the three algorithms, region growing by Culvenor (2002) provided highest accuracies in coniferous stand, while large commission errors were caused in hardwood stand due to the sensitivity to the variation of the tree crown reflectance. The valley-following algorithm has the advantage in locating crown boundary pixels, while it had problems in separating trees within clusters.

Analysis of the current algorithms indicates that each algorithm has advantages and limitations. This suggests that an appropriate avenue for future research includes developing adaptive algorithms capable of delineating tree crowns with various sizes and different densities. Accurate hardwood tree delineation methods also need be studied.

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