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Towards automated segmentation of forest inventory polygonson high spatial resolution satellite imagery

by Michael A. Wulder1,2, Joanne C. White1, Geoffrey J. Hay3 and Guillermo Castilla3

ABSTRACTHigh spatial resolution satellite imagery, with pixel sizes of one metre or less, are increasingly available. These data pro-vide an accessible and flexible source of information for forest inventory purposes. In addition, the digital nature of thesedata provides an opportunity for automated and computer-assisted approaches for forest stand delineation to be consid-ered. Specifically, automation has the potential to realize cost savings by minimizing the time required for manual delin-eation of forest stands; however, inappropriate automation could result in increased costs due to time-consuming revi-sions of automated delineations. The aim of this research is to present, through example, investigations of an automatedsegmentation approach for delineating homogeneous forest stands on high spatial resolution satellite imagery. An evalu-ation of the suitability of IKONOS 1-m panchromatic data for this application is also presented, along with several keyissues that must be considered regarding automated segmentation approaches.

Key words: forest inventory, segmentation, automation, IKONOS, high spatial resolution satellite, photo interpretation,stand delineation, object

RÉSUMÉLes images par satellite à haute résolution spatiale, caractérisées par des espacements des pixels d’un mètre ou moins, sontde plus en plus disponibles. Ces données procurent une source de renseignements accessibles et souples aux fins d’inven-taire forestier. De plus, la nature numérique de ces données offre la possibilité d’étudier diverses approches automatiséeset assistées par ordinateur pour effectuer la délimitation des peuplements forestiers. L’automatisation a, plus particulière-ment, le potentiel de permettre de réaliser des économies en minimisant le temps nécessaire pour procéder à une délim-itation à la main des peuplements forestiers; cependant, une automatisation inadéquate pourrait se solder par une aug-mentation des coûts en raison de la quantité de temps considérable nécessaire pour effectuer les révisions desdélimitations automatisées. L’objectif de cette recherche est de présenter, au moyen d’un exemple, les enquêtes réalisées surune approche de segmentation automatisée en vue de la délimitation des peuplements forestiers homogènes sur desimages par satellite à haute résolution spatiale. Ce document présente également une évaluation du caractère adéquat desdonnées panchromatiques produites par IKONOS 1-m pour cette application, de même que plusieurs questions clés quidoivent être prises en considération relativement aux approches de segmentation automatisée.

Mots clés : inventaire forestier, segmentation, automatisation, IKONOS, satellite à haute résolution spatiale, interprétationde photos, délimitation des peuplements, objet

1Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, Victoria, British Columbia.2Corresponding author. 506 West Burnside Rd., Victoria, British Columbia V8Z 1M5. E-mail: mike.wulder@nrcan.gc.ca3Department of Geography, University of Calgary, Calgary, Alberta.

Michael A. Wulder Joanne C. White Geoffrey J. Hay Guillermo Castilla

IntroductionTypically, a forest inventory is a detailed accounting of the loca-tion, spatial extent, and characteristics of forest stands within aforested area. Forest inventories may be represented by manyforms; however, in the context of this paper, we refer primarilyto forest inventories known as “reconnaissance” or “strategic”inventories, commonly collected at a scale of 1:20 000 (Köhl etal. 2006). These inventories are designed to provide an approx-imation of forest characteristics, location, and spatial extentthrough the use of aerial photography and field sampling sup-port. These types of inventories are generally completed in twophases: the first is the acquisition, delineation, and interpreta-tion of the air photos, while the second phase involves fieldsampling based on strata identified in the initial phase of theinventory (Gillis and Leckie 1993). In this first phase, polygonsrepresenting homogenous areas of specific forest attributes(such as height, species, age, and density) are delineated on thephotos, followed by the interpretation of attributes such asspecies composition, age, height, and crown closure. In the sec-ond phase of the inventory, these polygons are then stratified bya specific combination of attributes, and a sub-sample isselected for field sampling (Gillis and Leckie 1993). The fieldsampling verifies the estimates of the photo interpreter, andfacilitates collection of additional data. These field data are thenused to adjust systematic errors in photo interpreter estimates,or to generate new attributes that cannot be calculated based onphoto interpretation alone (Kangas and Maltamo 2006). A for-est inventory is often the best available characterization of for-est resources on the ground; however, it is not without error(Thompson et al. 2007).

Aerial photographs are the most frequently used source ofinformation for forest inventory and mapping (Hall 2003),although more recently, high spatial resolution satellite imageryhas emerged as a potential data source for forest inventory(Culvenor 2003). Advances in digital camera technology havealso made digital aerial photography more widely available forforest inventory applications (McRoberts and Tomppo 2007).Despite these advances, however, there currently exists no dig-ital system that can match the spatial resolution, data storage,and hardcopy output capabilities of analog photo systems com-bined with film scanners (Hall 2003). Furthermore, image pro-cessing methodologies commonly applied to digital remotelysensed data are not directly transferable to digital air photos(Tuominen and Pekkarinen 2004, 2005).

Imagery is used to stratify forest land into homogenousstands or sampling units that later serve to spatially distributethe estimates derived from inventory plots. Delineation ofhomogenous units on air photos has conventionally been com-pleted by photo interpreters, who manually draw the bound-aries between units based on the visual differences resultingfrom variations in tree species, crown closures, and tree heights.However, this process can be slow and costly. Furthermore,skilled and experienced photo interpreters are increasingly rarein the workforce. Recent advances in digital image processingmay facilitate delineation by providing photo interpreters withan automatically generated set of stand boundaries, therebypotentially reducing the time required for manual interpreta-tion and digitization (Leckie et al. 1998, 2003).

Image segmentationAutomated image segmentation is the partitioning of a digitalimage into a set of jointly exhaustive; mutually disjointregions that are more uniform within themselves than when

compared to adjacent regions. Uniformity is typically evalu-ated by a dissimilarity measure(s) upon which the partitionitself is constructed (Pal and Pal 1993). Automated segmenta-tion exploits the spatial information inherent in remotelysensed imagery in addition to the spectral information(Cohen et al. 1996). There are hundreds of image segmenta-tion algorithms that have been developed not only for remotesensing applications, but also for computer vision and bio-medical imaging. The techniques are so varied that there is noup-to-date review in the literature, with the last comprehen-sive survey undertaken more than a decade ago (Pal and Pal1993). However, in the context of forest inventory, only a fewmethods have been applied, including aggregation of individ-ually segmented tree crowns (Leckie et al. 2003), wavelet-based segmentation (Van Coillie et al. 2006), and the morecommonly used region merging (e.g., Hagner 1990,Woodcock and Harward 1992, Baatz and Schape 2000,Pekkarinen 2002). The latter typically consists of the sequen-tial aggregation of adjacent regions according to their similar-ity until a stop criterion is reached, which can be based oneither a similarity or a size threshold. To the best of ourknowledge, only one of these methods, the multi-resolutionsegmentation of Baatz and Schape (2000), has been imple-mented into commercial software (Definiens 2005).

ObjectivesResearch has increasingly focussed on linking satelliteremotely sensed data with forest inventory (Franklin et al.2001; Reese et al. 2003; Wulder et al. 2004, 2008; Remmel etal. 2005; Chubey et al. 2006; Donoghue and Watt 2006). Theobjective of this paper is to explore and communicate possi-ble opportunities and limits to the combined use of high spa-tial resolution satellite remotely sensed imagery and auto-mated segmentation software to generate homogenous forestunits, which could subsequently be used to support manualdelineation and/or photo interpretation. The intention is notto suggest that human interpreters should be replaced byautomated procedures. Rather, this approach is seen as a wayof augmenting existing methods and products; thereby,increasing the speed, consistency, and efficiency with whichthe first phase of a forest inventory could be completed.Additionally, this research is not a trial of a given segmenta-tion software tool, rather, the software is used as an exampleof the information developed through segmentation. Ourgoals for this communication are threefold: (i) that segmenta-tion software developers can learn more of the specific infor-mation needs of the forest inventory community; (ii) that theforest inventory community can learn more regarding thepotential and limits to the use of segmentation for standdelineation, and (iii) that communication between thesegroups can be enhanced and facilitated, resulting in improvedtools and methods to meet increasingly sophisticated inven-tory demands.

Study AreaThe study area selected for this trial is a 2-km by 2-kmCanadian National Forest Inventory (Gillis 2001) photo plot,located approximately 220 kilometres northwest of PrinceGeorge in central British Columbia (Fig. 1). The forests in thisarea are dominated by lodgepole pine (Pinus contorta) thatare approximately 60 to 80 years of age. Other species includewhite spruce (Picea glauca), black spruce (Picea mariana) andaspen (Populous tremuloides) (Table 1). Topography in the

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Fig. 1. Location of study area and sample of IKONOS imagery overlaid with forest inventory polygons.

study area is undulating, with elevations in this area rangingfrom 740 to 900 metres.

Data The IKONOS satellite, launched in 1999, simultaneously col-lects 1-m panchromatic and 4-m multispectral images withan 11-bit radiometric resolution. The advantages of theIKONOS instrument include its global coverage, consistentacquisition schedule, and its capability to acquire imagerywith near nadir viewing angles. The spatial resolution of thesensor is suitable for high accuracy photogrammetric pro-cessing and mapping applications (Tao et al. 2004). With therobust geometric accuracy of the IKONOS sensor, 1:10 000scale mapping can be produced without ground control and1:2400 scale mapping with ground control (Dial et al. 2003).Costs for IKONOS imagery vary: to purchase archivedimagery costs approximately $0.07 to $0.12 C$/ha for eitherthe panchromatic or the multispectral, or from $0.10 to 0.17C$/ha for both. Tasking the satellite to acquire data in a spe-cific area of interest costs approximately $0.18 to $0.20 C$/hafor either the panchromatic or multispectral, or $0.25 to $0.28for both4. Variation in pricing reflects the level of geometricprocessing, with fully orthorectified products being the mostexpensive. These costs for IKONOS imagery are only the datacosts and do not include costs for processing, classification, orcreation of the final deliverables.

The 1-m panchromatic IKONOS image used for this studywas acquired on August 6, 2003 with a sensor view angle of17°, and was delivered georeferenced with a positional accu-racy of approximately 15 metres (Dial et al. 2003) (Fig. 1).This data set was selected for its similarity in spatial resolutionto panchromatic aerial photography that is commonly used inBritish Columbia for forest inventory applications. Radouxand Defourny (2007) used IKONOS imagery to generatestand delineations that corresponded to 1:20 000 scale mapspecifications and recommended an optimal spatial resolu-tion of 2 m to 3 m for delineation in temperate forest condi-tions. QuickBird data, with a spatial resolution of 0.67 m(panchromatic) and 2.7 m (multispectral), is another com-mercially available remotely sensed data source with demon-strated utility for forestry applications (Coops et al. 2006,Hyde et al. 2006, LeBoeuf et al. 2007).

MethodsManual delineationThe manual delineation and interpretation of the IKONOSpanchromatic imagery was undertaken by a certified5 and expe-rienced photo-interpreter who had local knowledge of the studyarea. Delineation was conducted as per standard operationalinventory procedures (Natural Resources Canada 2004).

Automated delineationThe automated delineation tool used in this study, the Size-Constrained Region Merging (SCRM) algorithm (Castilla2003, Castilla et al. [In press]) has previously been applied tothe segmentation of forest scenes (Hay et al. 2005). SCRMautomatically segments an orthorectified aerial or satelliteimage (single or multi-channel) into a series of homogenous

segments. The automated delineation resembles the work of ahuman interpreter. This delineation may then be used as aninitial template in the task of the interpreter, whom simplyneeds to aggregate (and sometimes correct) pre-delineatedregions by software supported drag-and-click operations.

SCRM starts the region merging sequence from an initialpartition derived from a morphological process. This parti-tion consists of blobs, i.e., tiny homogeneous regions in theimage, darker, brighter or of different hue than its surround-ings. Blobs correspond to the area of influence of local min-ima in a gradient magnitude image (i.e., an edge image) com-puted from a filtered version of the original image. Filtering isconducted with an edge-preserving smoothing algorithm.These initial regions are then aggregated sequentially byincreasing dissimilarity until they all exceed the size of theminimum mapping unit (MMU). The dissimilarity measurebetween a pair of adjacent regions is simply their absolute dif-ference in mean brightness value. In each iteration, the twoadjacent regions that merge are those best fitting (i.e., thosehaving the least dissimilarity distance). After a merge, thebrightness value of the new region is the weighted (by size)mean of the signatures of the two merged regions. Finally, thelabelled image containing the final partition is converted intoa vector layer in ESRI shapefile format.

To run the SCRM algorithm, the user must define at leastone of the following four parameters: the desired mean size ofoutput polygons (in hectares); the minimum size required forpolygons, or MMU (in hectares); the maximum size allowedfor mergers (in hectares); and finally, the minimum distancebetween vertices in the vector layer, or minimum vertex inter-val (in metres). Bights and ledges (convolutions in thelinework) smaller than the minimum vertex interval areremoved and generalized into a smooth cartographic line.

Comparison and evaluation of manual and automated delineationsThe photo interpreter who conducted the manual delineationalso evaluated the automated delineation produced by theSCRM algorithm and where possible assigned attributes tothe automatically delineated polygons. The photo inter-preters’ comparison of the two delineations was largely quali-tative, as per Leckie et al. (2003). A recent study by Radouxand Defourny (2007) identified a quantitative method forcomparing two delineations; however, the authors use a man-ual delineation as truth, and all measures of error are maderelative to the manual delineation. A shortcoming of such anapproach is that manual delineations are based on subjectivecriteria and as a result may vary substantially from one inter-preter to the next (Kadmon and Harari-Kremer 1999,Culvenor 2002), undermining user confidence in the quanti-

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4Estimated costs as of December 2, 2007.5http://www.for.gov.bc.ca/hts/vri/contractinfo/rpt_all_certified.pdf

Table 1. Properties of forest species in the study area

Leading Proportion of Average Average species study area (%) height (m) age (years)

AT 1.14 9.6 22PL 76.00 23.6 59SB 1.47 22 2.3SW 11.30 29.4 145Non-Forest 10.09 N/A N/A

AT = aspen; PL = lodgepole pine; SB = black spruce; SW = white spruce.

tative assessments. Communication of the nature of the vali-dation data and the accuracy assessment methodology is crit-ical to assisting the user in interpreting the validation find-ings. For the exploratory purposes of this study, the primarilyqualitative comparisons made are sufficient to satisfy thestated objectives.

Results and DiscussionThe segmentation results from SCRM were generated usingonly the radiometric distance between region centroids (i.e.,mean value of inner pixels), whereas a human interpreter usesa number of criteria (i.e., shape, size, tone, texture, pattern,location and associations of objects), in addition to externalinformation sources, such as personal experience, knowledgeof the ecological relations of the vegetation in the specificregion being studied, and other ancillary data. Differencesbetween automated and manual delineations will likely begreatest in those locations where the interpreter uses delin-eation criteria that have no radiometric context, such as prox-imity, or where boundaries are delineated that artificiallydemarcate transitions. In these situations, automated delin-eations would require manual adjustment of stand bound-aries to capture the necessary criteria. Depending on the levelof adjustment required, the automated process may still pro-vide time savings and improve the consistency of delineation.Alternatively, a mixture of manual and automated delin-eations may be selected for a project that covers large areas ofhomogenous forest in addition to more complex, heteroge-neous forests. In this scenario, automated segmentation maybe deployed over the homogenous forest, while the efforts ofthe photo interpreters are focused on the more complex for-est areas. To provide a more synoptic perspective, the relativeadvantages and disadvantages of manual and automateddelineation are presented in Table 2.

The results of the manual delineation and automated seg-mentation procedures applied to a sample of the IKONOSstudy area are shown in Fig. 2. Upon visual inspection, severaldifferences in the linework are evident. Of particular note arethe amount of detail and the convoluted nature of thelinework from the automated segmentation lines relative tothe more generalized manual delineation. This difference isreflected in the mean perimeter of the polygons generatedfrom the two processes: the SCRM output has a mean poly-gon perimeter that is approximately 14% greater than the

perimeter of the manually delineated polygons, while themean polygon size for the SCRM polygons is approximately20% less than the manual polygons (Table 3). The manualpolygons have a much greater range in polygon sizes, with astandard deviation that is more than double that of the auto-matically delineated polygons. When the photo interpretertyped the polygons delineated with SCRM, only those poly-gons that the photo interpreter deemed reasonable (i.e., notrequiring any modification) were assigned an attribute. Table 3indicates that 40% of the SCRM polygons, representingapproximately 50% of the total NFI photo plot area, weredeemed suitable and assigned attributes by the photo inter-preter. The remaining 60% of segments required either minormodifications to the linework (17%), or were not appropriatefor reasons that we discuss in detail in the following section.

The photo interpreter inspected and assessed the quality ofthe linework generated from the SCRM algorithm, with theobjective of characterizing the SCRM linework in three con-texts: (i) locations where the SCRM segmentation performedwell, (ii) locations where the SCRM segmentation did notperform well, and (iii) locations where the SCRM lineworkcould be easily and quickly modified to produce a logicalpolygon. Examples of the first context, where the SCRM algo-rithm produced polygons that were ready for typing withoutany manual alteration, are provided in Fig. 3. These polygonsrepresent locations with homogenous texture and tone, whichhave relatively uniform species composition, age, andheight—demonstrating the ability of automated segmenta-tion routines to perform well in uniform stand conditions.

Conversely, Fig. 4 illustrates examples of where the auto-mated segmentation did not perform well. In polygon A, theroad has been included within a larger vegetated polygon.The rules for including roads within the bounds of typedpolygons are subjective and interpreters often make the deci-sion on whether or not to include a road based on the contextof the surrounding forest conditions and the width of theroad. Typically, if a road (or other linear feature such as apipeline or a power line right of way) is greater than 30 metreswide, it will be delineated as a separate polygon. In the man-ually delineated polygons in Fig. 2, there are examples wherestands having similar attributes are bisected by a road that isless than 30 metres wide. In these examples, the interpreterhas delineated these as a single polygon, with the portion ofroad separating the two stands included within the polygon.

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Table 2. Relative advantages and disadvantages of manual delineation and automated segmentation.

Method Advantages Disadvantages

Manual delineation • Established standard • Slow• Delineation and attribution • Costly

can be achieved in a single step • Scarcity of skilled interpreters• Prone to show inconsistencies between different interpreters

or even the same interpreter at different times• Highly subjective and hence not well suited for monitoring

Automated segmentation • Faster • Likely to require manual correction of some linework/polygons• Cheaper • Efficiency rapidly decreasing with the amount of manual• More consistent tweaking required• Less subjective and more repeatable • Undesired results in areas with low contrast or where

and hence better for monitoring. different appearance does not imply different meaning

In the second example depicted in Fig. 4, polygon B is a com-plex polygon that combines vegetated and non-vegetatedareas, and the segmentation has partitioned non-vegetatedareas that appear to have tone and textural characteristics.The examples in Fig. 4 indicate that the SCRM automatedsegmentation did not perform well in complex areas wherethere was an amalgamation of both forest and non-forestareas. In these examples, the polygons would require a largeamount of manual editing before they could be interpreted.Lastly, the photo interpreter identified examples where theautomatically generated polygons required only minor edit-ing to facilitate interpretation. In Fig. 5 two examples are illus-trated where the addition of a single line was effective at sep-arating the polygons into interpretable units.

Table 4 provides a summary of the distribution of forestspecies in the NFI photo plot as represented by each methodof delineation. The bottom portion of Table 4 provides a morereasonable comparison between the two delineations, since it

represents the coincident spatial area where both delineationversions have attributes assigned to the polygons. Table 5 pro-vides a spatially explicit comparison of this coincident area inthe form of a cross-tabulation matrix. From this we can seethat although the total area of lodgepole pine differs by 12%,the manual delineation resulted in pine as leading species inonly 84% of the area that was attributed as pine from the auto-mated delineation. The discrepancies identified in Table 5result primarily from the differences in the delineation ofpolygon boundaries.

The photo interpreter noted several trends in the SCRMpolygons: scattered treed areas that appeared to be wet orflooded were often merged with upland areas of grass, shrub,and treed areas; coniferous stands of pine and mixed conifer-ous stands of pine and spruce were not distinguished satisfac-torily; and stands of aspen were frequently (and erroneously)merged with non-treed cut block areas. In general, thelinework generated by the SCRM was considered by thephoto interpreter to be too complex, compared to the typicalgeneralized linework produced by manual delineation.However, we note two items: (i) the complexity of the bound-aries produced through segmentation can be regulated, eitheras an output option (in the case of SCRM) or through post-processing in GIS software; (ii) the perceived complexity ofboundaries can also be seen as a beneficial feature, due to thefit between source imagery and resultant polygons, withenhanced management opportunities emerging.

The results of this trial suggest that the automated seg-mentation approach, while promising, would require someimprovements before it could be used in an operational con-text. In some portions of the study area, the automated seg-mentation performed very well and produced polygons thatthe photo interpreter considered suitable for interpretation,without any modification. However, for other stands, theautomated delineation required some modification by the

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Fig. 2. Comparison of manual (left) and automated (SCRM) (right) polygon delineation.

Table 3. Properties of delineated polygons.

Attribute Manual Automated

Total # of polygons 66 82Attributed polygonsa 66 34Mean polygon size (ha) 6.06 4.87Minimum polygon size (ha) 0.26 0.10Maximum polygon size (ha) 77.55 24.57Standard deviation polygon size (ha) 11.30 5.10Mean polygon perimeter (m) 1418.31 1614.45Minimum polygon perimeter (m) 249.82 134.59Maximum polygon perimeter (m) 11 317.00 5669.40Standard deviation polygon perimeter (m) 1553.76 1181.79

aOnly those polygons which had “reasonable” linework in the opinion of the photointerpreter were assigned attributes.

photo interpreter. This underscores the unparalleled ability ofhuman interpreters to consider a wide range of structural andcontextual cues to produce a delineation that has utility forforest management, although it should not be expected thatan automated approach can integrate as readily the samebroad range of experiences and management intentions as a

experienced interpreter where many interpretation clues arecontext-, not image-, based. Further trials of the SCRM algo-rithm under a range of different forest conditions (particu-larly in areas with a heterogeneous species composition)would be required to make conclusive statements regardingthe algorithm’s performance for automated stand delineation.

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Fig. 3. Examples of where automated segmentation performed well, resulting in polygons that required no manual tweaking prior tointerpretation. Polygon A is a homogenous stand with consistent texture and tone throughout. Polygon B has consistent species compo-sition, age, and height throughout the stand. The finger of polygon B that extends to the south could be separated and grouped into aseparate polygon.

Fig. 4. Examples where the automated segmentation did not perform well. Polygon A is a combination of non-vegetated road and vegeta-tion south of the road. Polygon B is a complex polygon that fails in three areas: the treed area in the southernmost extent of the poly-gon was not separated from non-treed area; areas with similar tone/texture were separated unnecessarily; and roads were includedwithin vegetated polygons.

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Table 4. Distribution of leading forest species over the total NFI photo plot area, and then only over the spatially coincident areawhere both the manual and automated delineations were attributed.

Manual AutomatedDifference

Tree Species ha ha ha %

Total area of the NFI photo plot

Aspen 72.58 7.91 64.67 10.89Common paper birch 7.97 0.00 7.97 100.00Lodgepole pine 239.49 163.32 76.17 31.81Black spruce 1.66 0.00 1.66 100.00White spruce 71.59 25.72 45.87 64.07

Area where both the manual and automated polygons have attributes

Aspen 9.59 7.91 1.68 17.51Common paper birch 4.51 0.00 4.51 100.00Lodgepole pine 145.89 163.32 -17.43 -11.94Black spruce 0.00 0.00 0 0.00White spruce 36.95 25.72 11.23 30.39

Table 5. Matrix showing the spatial correspondence (by ha) in the distribution of leading species, as interpreted using the manualand automated delineations.

Automated Segmentation

Leading Tree Species AT EP PL SW Total Area (ha)

AT 3.46 0.00 4.76 1.37 9.59EP 4.00 0.00 0.39 0.13 4.51

Manual Delineation PL 0.12 0.00 136.71 9.08 145.90SW 0.34 0.00 21.47 15.15 36.95Total Area (ha) 7.91 0.00 163.32 25.72 196.96

AT = aspen; EP = common paper birch; PL = lodgepole pine; SB = black spruce; SW white spruce.

Fig. 5. Examples where the automated segmentation could be altered with a few additional line segments. An additional line in polygon Aseparates the deciduous and coniferous stands. In polygon B, a single line segment separates the approximately 100-year-old lodgepolepine leading stand to the east from the 185-year-old white spruce leading stand to the west

The future of automated segmentation for forest inventorydelineation may be linked to the incorporation of other spa-tial and or multispectral data layers into the segmentationprocess. For example, Radoux and Defourny (2007) foundthat the impact of shadow was greater on the IKONOS 1-mpanchromatic data and that the incorporation of IKONOS 4-m multispectral data may help to alleviate the impact of shad-ows on the segmentation algorithm. Additional spatial datamay be used to stratify areas in order to reduce confusionassociated with linear features such as roads and rivers. GIStechnology readily facilitates this type of data integration andmay increase the contextual intelligence of segmentationalgorithms. Although not evaluated in this study, IKONOSimagery may also be viewed in stereo, increasing the inter-preter’s ability to assess landscape position, discern species,and estimate tree heights.

Segmentation algorithms that consider and incorporateknowledge of forest inventory needs, the spatial structure offorest environments, and traditional forest inventory cues aremore likely to produce useful results; however, these charac-teristics are not trivial to implement in code. While alreadyincluding controls for scale and polygon sizes, ongoing devel-opment of the SCRM algorithm is addressing the inclusion ofnew components in the dissimilarity metric, including aboundary saliency measure, which precludes the merging ofsimilar regions separated by a strong edge (like a narrow roadseparating two stands of similar characteristics), as well asincorporating measures of the internal texture of regions,possibly at different scales, into the segmentation. Further-more, a reduction in line complexity can be facilitated byselecting a minimum vertex interval more appropriate to thelevel of generalization required by a photo interpreter, andbased on results in Table 3, polygon size parameters (as inputsto SCRM) could also be selected to better match those of aphoto interpreter.

ConclusionsThis trial demonstrated the utility of IKONOS panchromaticimagery for delineation and subsequent typing of homoge-nous forest units. The photo interpreter was able to apply allof the standard photo interpretation principles to the highspatial resolution satellite imagery, and was comfortable dis-criminating coniferous and deciduous species in both mixedand pure stands. For programs like Canada’s National ForestInventory, the utility of high spatial resolution satelliteimagery may be greatest in inaccessible areas such as in thenorth of Canada, where there is a paucity of pre-existing for-est inventory information and where logistical barriers to airphoto acquisition exist. Further encouraging the use of seg-mentation and satellite data in these remote locations is thelack of confounding elements such as harvested areas, land-ings, and roads.

It is anticipated that suitable potential has been demon-strated for automated segmentation to warrant further devel-opment and investigation with a goal of increasingly aug-menting the manual delineation and interpretation processesand facilitating the production of timely and consistent mapproducts. With this in mind, automated approaches must beinexpensive and simple to apply, and must not substantiallyalter the mapping workflow, nor involve inordinate fine-tun-ing by the interpreter (Leckie et al. 1998). From the lessons

learned from this research, it is clear that segmentation soft-ware developers need to consider the information needs ofthe forest inventory community and focus development on arobust delineation that can help to facilitate the work ofhuman photo interpreters, rather than attempting to developalgorithms that replicate and/or supplant human delineation.

At this time, forest managers should consider automatedsegmentation as an emerging tool for forest inventory, partic-ularly where there are large areas of relatively homogenousforest conditions to be delineated. In addition, high spatialresolution panchromatic satellite imagery (≤ 1 m) should beconsidered as a potential data source for manual delineationand interpretation of forest inventory attributes and should bethe subject of further research.

AcknowledgementsMark Hemstock (FDI Forest Dimensions Inc.) is thanked forhis efforts and professionalism in delineation and interpreta-tion of the IKONOS imagery. Sarah McDonald, of theCanadian Forest Service, is thanked for providing GIS sup-port. Financial support to Dr. Hay from the University ofCalgary, Alberta Ingenuity, and the Natural Sciences andEngineering Research Council of Canada, is acknowledged.

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