Canada's national parks system includes 43 terrestrial parks covering 3% (276,275 km2) of the country'slandmass and representing its full range of natural regions. Considering the vast and often remote areasunder protection, Parks Canada Agency envisions Earth Observation technology to be the basis for a ParkEcological Integrity Observing System (Park-EIOS), and integral component of a larger national parks eco-logical integrity (EI) monitoring program. Park-EIOS is planned for operational use beginning in 2008 andincludes coarse filter EI indicators corresponding to landscape pattern, succession and retrogression, netprimary productivity, and focal species distributions within parks and their surrounding greater park eco-systems. A primary input to produce all four indicators is a time series of land cover information derived frommedium (~30 m) resolution, Landsat-class sensors. This paper describes a generic, end-to-end changedetection framework developed for Park-EIOS, labelled Automated Multi-temporal Updating through Sig-nature Extension (AMUSE). AMUSE involves radiometric normalization steps, production of a baseline landcover, change vector analysis to identify changed pixels, and a new constrained signature extension approachto update the land cover of changed areas. We present the method and results applied to six pilot parks usingtime series of Landsat TM/ETM+ imagery from 1985–2005.
Satellite-based Earth observation (EO) provides a source of syn-optic environmental information that is increasingly being exploitedfor ecological and biodiversity conservation applications (Turner et al.,2003; Cohen & Goward, 2004; Kerr & Ostrovsky, 2003). Examples ofecological variables measurable using EO include vegetation type andland cover, phenology, vegetation structure, and plant biophysicalattributes (Turner et al., 2003). These variables can provide spatiallydistributed inputs for ecological process based models (Liu et al.,2002), species niche models (Scott et al., 1996; Zimmerman et al.,2007), and for studying patterns of biological diversity (Turner et al.,2003; Luoto et al., 2004). This information also holds great promise forcost-effective monitoring of habitat amount and configuration withinparks and other protected areas (Parmenter et al., 2003)—a basicrequirement for characterizing ecological integrity (EI) at landscapescales.
A Panel on the Ecological Integrity of Canada's National Parksconcluded in 2000 that the EI of virtually all of Canada's national parksis threatened from a variety of internal and external stressors (ParksCanada Agency, 2000). One response to this report was a Parliamen-tary amendment to the Canada National Parks Act to state that “themaintenance or restoration of ecological integrity, through the pro-tection of natural resources and natural processes, shall be the first
1 613 947 1385.raser).
09 Published by Elsevier Inc. All rig
priority of the Minister when considering all aspects of the manage-ment of parks”. This Act defines EI, with respect to a park, as acondition characteristic of its natural region and being likely to persist.Parks Canada Agency (PCA), who is entrusted with the responsibilityto protect national parks, will help meet this obligation through areporting framework that includes preparing a State of the ParkReport (SOP) for each park every five years, and a national-scale Stateof Protected Heritage Areas report every two years (Parks CanadaAgency, 2001). The PCA Ecological Integrity Monitoring Program isbeing developed across all national parks to provide data for reportingon the EI of parks and their Greater Park Ecosystems (GPEs), and istargeted to be operational by 2008 (Parks Canada Agency, 2006). GPEsare delineated by PCA according to the zone of ecological influencesurrounding a park boundary that is most likely to cause impacts tothe park.
Given the large area encompassed by the existing 43 national parksas well as their wide and often remote distribution across Canada'slandmass (Fig. 1), effective and efficient monitoring presents a con-siderable challenge. Satellite-based EO techniques are therefore seenas an integral component of the EI Monitoring Program. To intro-duce this technology rapidly and effectively, PCA entered into col-laboration with the Canada Centre for Remote Sensing of NaturalResources Canada, the University of Ottawa, and the Canadian SpaceAgency beginning in 2004. The project has addressed those majorcomponents of the PCA monitoring framework to which EO couldlikely make strong contribution at the present: stressors, ecosystemprocesses, and biodiversity (Parks Canada Agency, 2006). In particular,four EO-based EI indicators have been proposed under PCA's
Fig. 1. Distribution of protected areas within Canada's National Parks System (shown in gray). Pilot parks used to develop the AMUSE change method are circled and numbered 1–6,corresponding to the descriptions provided in Table 2.
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framework related to habitat fragmentation, succession and retro-gression, productivity, and species richness (Table 1). Together, theseEI measures will form a Park Ecological Integrity Observing System(Park-EIOS). A common feature of the Park-EIOS indicators is thattemporal land cover information, currently unavailable formost parks,
Table 1Proposed Park-EIOS remote sensing based indicators for which land cover time-seriesinformation is required as input.
General EIcomponent
EO-based EIindicator
Description of indicator Land cover requirement
Stressors Habitatfragmentation
Change in habitat amountand connectivity based onecologically-scaled landscapeindices (Fragcube developedby Parks Canada)
Land cover convertedto binary habitat/matrix landscape(e.g. forest/non-forest)
Ecosystemprocesses
Succession andretrogression
Changes in the frequency ofdisturbance, vegetation ageclass, and unique habitats
Land cover types andnew disturbances
Ecosystemprocesses
Productivity Change in average Net PrimaryProductivity estimated usingthe physically based EALCOmodel (Wang, 2005)
Land cover and derivedleaf area index (LAI)
Biodiversity Speciesrichness
Changes in focal speciesdistributions based on nichemodeling techniques, such asmaximum entropy
Land cover as a proxyfor habitat types,vegetation indices
serves as a primary input (Table 1). An additional constraint for theintegration of remote sensing to Park EI monitoringwas to exploit wellestablished methods that enable operational application and wheresoftware, processing requirements, and processing protocols could beeasily transferred.
This paper presents an end-to-end change detection and land coverupdating framework developed to create such temporal land covertime series—named Automated Multi-temporal Updating throughSignature Extension (AMUSE). The major processing steps are firstdescribed using examples, and involve radiometric normalization,change vector analysis to identify changed pixels, and constrainedsignature extension to update land cover of changed areas. Key resultsand their implications for park EI are then presented, followed by asummary and conclusions. Six pilot parks covering a range of Bio-regions and ecological conditions were selected for development andapplication of the methods. These are highlighted in Fig. 1 and des-cribed in Table 2.
2. AMUSE change detection method
The development of satellite-based change detection methods hasbeen an area of active investigation for more than 25 years. A widerange of change algorithms are now available for application (Singh,1989; Coppin et al., 2004; Lu et al., 2004), which may be broadlygrouped as either spectral or classification approaches. Spectral
Table 2Description of Pilot parks examined for the development and demonstration of the AMUSE change detection framework.
Park Bioregion Park significance Major EI stressors on park and GPE Landsat data and epochs
1. Kejimkujik Atlantic/Quebec Inland with historic canoe routesand portages
Habitat fragmentation in GPE due toforest harvesting
WRS-2 frames 8/12 and 9/29; four epochs(circa '85, '90, '95, '00)
2. La Mauricie Atlantic/Quebec Transition zone between northernhardwood and southern boreal forests
Habitat fragmentation in GPE due toforestry
WRS-2 frames 14/27 and 14/28; five epochs(circa '85, '90, '95, '00, '05)
3. Nahanni Northern UNESCO World Heritage Site, representativeof the Mackenzie Mountains Natural Region
Mining, timber production, hydrocarbonresource exploration/extraction, andglacier retreat due to climate change
WRS-2 frames 52/17, 53/17, 54/17, 55/16,55/17, 56/16; two epochs (circa '90 and '00)
4. Pacific Rim Pacific Coastal temperate rainforest, cultural legacyof Nuu-chah-nulth First Nations
Loss of old-growth forest habitat in GPEdue to forest harvesting
WRS-2 frames 48/26 and 49/26; four epochs(circa '90, '00, '05, '07)
5. Prince Albert Interior Plains Transition zone between parkland andnorthern boreal forest, free ranging plains bison
Managing wildfire as a natural processand forest habitat fragmentation
WRS-2 frames 38/22 and 38/23 and fourepochs (circa '85, '90, '95, '00)
6. St LawrenceIslands
Great Lakes High biodiversity including many species at risk Visitor pressures, habitat fragmentationand isolation, exotic species
WRS-2 frame 16/29; four epochs(circa '90, '95, '00, '05)
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approaches quantify the magnitude of reflectance changes betweendates, which relate to a land surface change if sources of image “noise”are adequately controlled. Examples of spectral algorithms includeimage differencing and ratioing, regression, and change vector analysis(Fraser et al., 2000; Johnson&Kasischke,1998; Prakash&Gupta,1998).One advantage is the potential to fine-tune change detection sen-sitivity, while a limitation is the inability to provide information onthe nature of change (i.e. class label). By contrast, classification ap-proaches, such as post-classification comparison and two-date image
Fig. 2.Method used to create a seamless baseline mosaic. The baseline image is first normalizThe corrected LANDSAT mosaic on the right shows a mosaic with each square a different yeconsistency. The centre shows a blow up enlargement of the area.
clustering, identify both the occurrence of changed pixels and the typeof change by directly labelling land cover at two time periods (Yuanet al., 2005). However, they are susceptible to generating high levelsof commission error due to the multiplication of individual errors.Hybrid change procedures have also been proposed that exploitthe advantages of each approach, while attempting to minimize theirlimitations. These typically involve combining a spectral approachfor identifying changes with post-classification comparison to assignchange labels (Luque, 2000; Petit et al., 2001; Silapaswan et al., 2001).
ed to coarse resolution (1 km) SPOT VEGETATION (VGT) image using robust regression.ar of the normalized imagery using the same enhancement illustrating the radiometric
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Selection of the most appropriate change method for a givenapplication can be difficult, and requires consideration of the changetype of interest (e.g. among major land cover classes or more subtlewithin class changes), availability of ground truth, capacity foraccurate radiometric normalization, and desired level of productomission and commission error. Themajor requirements identified forchange detection under Park-EIOS were: (1) low occurrence ofcommission error, or falsely detected change; (2) detection only ofchanges sufficiently intense to alter land cover class; (3) thematiclabelling of from–to land cover transitions; (4) a five-year time stepcorresponding to a given Park's SOP reporting schedule; and (5) semi-automation to provide an efficient and repeatable method.
To best deliver on these requirements, a hybrid change methodis proposed called Automated Multi-temporal Updating through Sig-nature Extension (AMUSE). In this approach, a mask of potentialchanged pixels is first created by thresholding a two-date changevector analysis (CVA) product. Land cover class is then updated forchanged areas only by spectral signature extension, whereby changedpixels are matched to the most similar labelled cluster from a baselineland cover map. A unique feature of signature extension presented inthis paper is the application of expert rules that constrain changetrajectories to those that are physically and ecologically realistic. Forexample, deciduous regeneration may have the same spectral sig-nature as mature deciduous forest within only a few years of beingdisturbed. Therefore, if a changed pixel is assigned to mature deci-duous forest following a recent stand-replacing disturbance, it can bere-labelled more accurately as a regeneration class.
The processing steps performed for the AMUSE change procedureare described in more detail below.
1. Radiometricallynormalizebaseline (Master) imagery to1-km imagery.The majority of national parks and their surrounding GPEs requiremore thanoneLandsatWorldwideReference System-2 (WRS-2) frameto provide complete coverage (e.g. Table 1). Therefore, changedetection must be performed either separately on individual framesthen the products combined, or on a mosaic consisting of severalframes. For simplicity, the latter was chosen for AMUSE so that eachmosaic could be treated as a single virtual scene and processed onlyonce.Mosaicing of Landsat scenes is commonly accomplished by normal-izing to a Master scene, followed by further normalization to anormalized scene. One drawback to this approach is that errors canaccumulate as successive scenes are added to the mosaic (Guindon,
Table 3Summary of reference data and results obtained for pilot parks.
Park Training data forcluster labelling
Validation or comparison data Baselin
1. Kejimkujik Landsat classificationcreated and validatedin 1999
Photos from field visits to 225 sites Overall
2. La Mauricie Field visits to 43 sites 2000 land cover tabulated beneath2001 provincial forest inventory(Ecoforestiere) polygons
Overallused fo14 no-c
3. Nahanni Based on previoustwo-week field campaignalong Dempster Hwytransect (150 sites)
Compare to overlapping land covermap from NWT govt (1440 points)
83% ag(88% if
4. Pacific Rim Field visits to 100 sites All ground data used for training Visual
5. Prince Albert Google Earth imageryand overlapping BOREASland cover map having90% accuracy
1985 land cover tabulated beneath1968 Forest Resource Inventorypolygons
GeneraInvento
6. St LawrenceIslands
Field visits to 150 sites Independent field samples for 105sites and interpretation of 105 sitesusing IKONOS image mosaic
Overall85%/808 redurespect
1997). Another method, introduced in Olthof et al. (2005a,b),provides a solution to this problem by normalizing Landsat scenessimultaneously to coarse resolution composited imagery thatcontains approximately the same spectral bands. This approachwas used here to create seamless, baseline Landsat mosaics for eachpilot park by resampling each channel to 1-km resolution andregressing to the corresponding channel from a 1-km SPOTVEGETATION (VGT) composite of surface reflectance (Fig. 2). Inputbaseline Landsat scenes were obtained from the Landsat-7 Orthor-ectified Imagery over Canada data set, which were corrected withtriangulation data or the most accurate National Topographic DataBase (Natural Resources Canada, 2003). These data provide growingseason coverage for circa 2000. The SPOT VGTcompositewas derivedfrom mean July 1–August 31, 2000 reflectance. Additional details ofthe method are presented in Olthof et al. (2005a).
2. Produce Master baseline land cover classification.The change detection procedure requires a baseline land coverclassification fromwhich changes are detected at nominal five-yearintervals. The baseline land cover is produced using an unsuper-vised clustering approach that combines features of the Enhance-ment Classification (ECM) (Beaubien et al., 1999) and Classificationby Progressive Generalization (CPG) (Cihlar et al., 1998) methods.Landsat mosaics are first displayed with channels 4 (near infrared;NIR), 5 (shortwave infrared; SWIR) and 3 (Red) as R, G, B andinteractive enhancements are applied to maximize the separationamong land cover classes by compressing the low and high ends ofthe image histograms representing water and cloud or bare soil,while enhancing the portions of the histograms representing landcover. Once an optimal enhancement is determined for the threespectral channels, it is applied to the original imagery using Red,NIR, and SWIR look-up tables.The enhanced imagery is clustered to a large number of spectralclusters, typically 150 or more, and a pseudo-colour table isgenerated from the enhanced imagery and applied to the clusterimage. Visual quality checking is an important part of this and eachsubsequent generalization step, and is performed by comparing theprevious generalization with the current one to ensure that nosignificant land cover information is lost. Generalization proceedsby progressively merging spectrally similar and spatially adjacentclusters to generate approximately 60 clusters. Final clustermerging and labelling to a land cover classification is based onexpert image interpretation and available reference data, whichincluded field visits to four of the seven pilot parks (Table 3). Land
e land cover accuracy assessment AMUSE change detection key result(s)
accuracy of 67% Monotonic 15-year trend of decreasingmature forest types in park GPE
accuracy of 72% for 43 field sitesr training (27 change andhange sites)
Little change within park, but losses ofmature conifer forest within GPE due toforest harvesting
reement between mapsweighted by cover proportions)
Generally stable land cover within parkand GPE
assessment by park ecologists 15% decline in old-growth forest habitatin GPE due to forest harvesting
l agreement with Forest Resourcery maps (Fig. 6)
Reduction in conifer forest from 19% to 14%,spruce budworm infestations, and in waterbodies from 10% to 8% over 16 years
/Kappa accuracies of 75%/73%,%, and 92%/87% for 21 FGDC classes,ced FGDC classes, and forest/non-forest,ively
Little variation in 1990–2005 land coverproportions. Limited urban developmentwithin the city of Kingston
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cover classes are based on the Federal Geographic Data Committee,National Vegetation Classification Standard (FGDC-NVCS) legendmodified for use across Canada, and describing 45 classes(Appendix A). Note that, while the combined ECM-CPG methodwas determined to be optimal for use in the change procedure,other unsupervised approaches could be substituted, such asISODATA clustering and labelling. Furthermore, an existing Land-sat-based land cover product could serve as the baseline, as long asa channel containing the original clusters is available for signatureextension (step 6 below).
3. Remove haze and topographic effects.Spatially variable effects from atmospheric haze and topography cancause pixels to shift in brightness to a different spectral cluster andthus be incorrectly labelled. For example, in forested environments,pixelsmay be assigned to a denser conifer class on north aspect slopeshaving lower illumination. All Landsat images are therefore subject tohaze removal and topographic normalization procedures, whennecessary, before being radiometrically normalized to the baselineVGT or Landsat imagery.Haze effects due to atmospheric aerosols are removed using The HazeOptimized Transform (HOT) developed by Zhang et al. (2002). TheHOTalgorithmexploits the strong, land cover independent correlationin reflectance between the blue and red channels under clear-skyconditions. This relationship was demonstrated using spectra from 4-m resolution PROBE-1 measurements taken over conifer forest,deciduous forest, lakes, urban areas, bare soil, grassland, cropland,and snow. Migration of pixels from this “clear-sky line” in anorthogonal direction is proportional to the amount of haze, whichcan be represented using a channel-specific HOT function. The HOTfunction is thresholded to create amask of contaminated pixels in thered channel, towhich a histogram shift correction is applied. Note thatopaque haze and cloud cannot be compensated for using thismethod,and longer wavelengths in the NIR and SWIR are not corrected due tothe fact that the atmosphere is relatively transparent to aerosols inthese wavelengths and are also affected differently.Manynational parks, especially those innorthern andwesternCanada,contain areas of significant topographic relief. This leads to brightnessvariability for a given land cover class depending on the influence ofterrain steepness and orientation on solar illumination angle. Thiscreated an especially large impact on north facing slopes in PacificRim's GPE, where conifer forest classes appear darker and may be er-roneously classified as a denser conifer class (Fig. 3). The C-correction,introduced in Teillet et al. (1982), is a simple and effective method fornormalizing topographic illuminationeffects (Meyeret al.,1993;Rianoet al., 2003) and is used here. The method introduces an empirically
Fig. 3. Impact of C-correction topographic normalization on image reflectance (RGB=4, 5, 3facing slopes (S) appear lighter by comparison to the same forest type on flat terrain (F). T
derived parameter (c) designed to account for overcorrection typicallyproduced by the simpler cosine method in areas of low illumination(Eq. (1)).
LH = LTcos szð Þ + ccos ið Þ + c
ð1Þ
where:
LH radiance observed from a horizontal surface (correctedimage value),
LT radiance observed over sloped terrain (uncorrected imagevalue),
sz solar zenith angle,i solar incident angle in relation to the surface normal
direction, andc C-correction coefficient derived by regressing LT against cos
(i) and taking the quotient of the intercept and slope.
The C-correction coefficient (c)wasderived byextracting LTand cos(i)values from digitized polygons containing conifer forest over a rangeof slopes and aspects. The correctionwas therefore optimized for thismost commonly occurring cover type and would be expected toperform less well for other types (Thomson & Jones, 1990). Results ofapplying the C-correction in Pacific Rim are shown in Fig. 3. Aftercorrection, the brightness variation of conifer forests having similarcrown closure is minimized, resulting in their placement into a singleland cover class and also avoiding potential confusion with waterbodies. Note that topographic correctionmay perform poorly in parkshaving steeper mountainous terrain, such as those in the CanadianRockies, where slopes may lie in cast-shadows rather than havingreduced, direct illumination (Meyer et al., 1993).
4. Radiometrically normalize other dates to Master.After a careful image-to-image spatial registration of the five-yearLandsat imagery to the Master Landsat mosaic developed in step 1,the images must be radiometrically normalized to the Master.Accurate normalization is essential for the combined CVA andsignature extension change detection approach, since both meth-ods assume that a pixel's reflectance is stable through time unless aland cover change occurs. Other images from the change intervalsare radiometrically normalized to the Master based on an approachsimilar to that used for normalizing the Master to 1-km VGTimagery. In this case, images are first resampled to a coarserresolution using a 7×7 average filter to minimize misregistrationeffects between dates. The Thiel-Sen regression robust regression
). North facing forested slopes (N) in the uncorrected image appear darker, while southhese areas have a more uniform appearance in the C-corrected image.
Fig. 4. Example change detection based on change vector analysis. In the change image, red and yellow areas are regeneration, blue are disturbance (cutting). Compiling change fromeach period (e.g. 85–90, 90–95, etc.) provides a disturbance age layer used on the application of signature extension rules.
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approach is then used to generate coefficients to normalize allother dates to the Master (Fernandes & Leblanc, 2005). Asignificant advantage of Thiel-Sen is its insensitivity to up to 29%of outliers, which may represent changed or cloudy pixels.Normalization coefficients are therefore automatically generatedbased on the trend of stable pixels, eliminating the need to pre-select invariant pixels required by other approaches (Du et al.,2002; Elvidge et al., 1995).The normalization is quality checked by visually examiningimagery from both dates side-by-side and using the same channelstretch to ensure that they appear similar and seamless except inchanged areas. Because this step normalizes imagery to the Master,which is already in units of apparent surface reflectance, these datacan used with algorithms that convert surface reflectance todifferent biophysical parameters, such as leaf area index.
5. Identify changed pixels using change vector analysis.Change vector analysis (CVA) is a robust method for detectingradiometric change in multispectral imagery (Johnson & Kasischke,1998). CVA calculates both the overall intensity and direction ofspectral change between dates in two or more channels. While CVArequires careful radiometric normalization between dates bycomparison to other approaches (e.g. vegetation index differen-cing), it has the advantage of fully exploiting the multispectralimage information.CVA is used to flag the occurrence of changed pixels in the first of twochange detection steps in the hybrid AMUSE method. A changemagnitude image is produced between each consecutive pair ofimage dates by calculating the multispectral Euclidean Distance forchannels 3, 4, and 5 on a per-pixel basis (Fig. 4). An optimal thresholdto create a binary mask of change pixels is then determinedinteractively by examining known land cover changes in both datesagainst change masks created over a range of thresholds. In caseswhere sufficient change/no-change reference information is avail-able, thresholds can be optimized tominimize an error criterion (e.g.overall error) using accuracy assessment curves (Morisette &Khorram, 2000). This CVA step is the most subjective in the AMUSEprocedure, as the resulting change mask can be sensitive to smallchanges in threshold. Note, however, that this sensitivity isdiminished somewhat by the following signature extension step inwhich pixels from the change mask may be reassigned to same landcover class if spectral change is not sufficient to produce a re-labellingof land cover type.
6. Update land cover using constrained signature extension.As described previously, change detection under Park-EIOS mustidentify thepre- andpost-change land cover class, as this informationis required to produce the higher-level EI indicators described inTable 1. Change labelling is accomplished by iteratively updating landcover starting from the baseline classification for only those pixelsidentified as changed in theCVAchangemask. Updating is performed
using a new constrained signature extension approach, while landcover for non-change pixels is simply carried over from the previousdate.Signature extension involves comparing the new multispectralsignature of each pixel under the change mask to the originalsignatures of the 150 clusters from the baseline land cover. A new landcover class is identified by assigning a pixel to themost similar clusterand matching this to the FGDC land cover contained in the look-uptable thatwasdeveloped for cluster labelling. Similarity is basedon theminimum Euclidean distance to the three-channel means of clusters.The major advantages of using such a signature extension approachare that it exploits the rich knowledge embedded in the baseline landcover and also automates the change labelling process. As previouslynoted, however, this relies on accurate normalization of image pairsperformed using robust regression (Olthof et al., 2005c). The methodalso allows for an entire cluster in a scene to migrate to a new landcover class, for example in the case of a regenerating forest. Thisimplies a second requirement, that the new land cover class ofchanged pixels are represented in the baseline land cover product.A second step required for labelling the new land cover class ofchanged pixels is to constrain land cover changes to those that arephysically or ecological feasible (Olthof & Pouliot, 2005). This involvesdefining a series of expert rules that limit and/or adjust the range ofallowable change types between the change intervals. Such rules maybe based on transitions occurring over more than two intervals incaseswhere a longer time series is available, such as for thepilot parks.Ruleswill alsovarydependingon theuniquevegetationandecologicalconditions occurring with a given park or group of parks containedwithin the same Bioregion. Below, we provide examples of expertrules or constraints that were applied to the pilot parks.
i. Followa logical vegetation regeneration trajectory. A separate rasterchannel is used to track stand regeneration age at each changeinterval following stand-replacing disturbances, such as clear-cutharvesting or fire. An age rule is used to reset mature deciduousclasses to regeneration classes, which are prone to being spectrallyconfused. For example, boreal forest regeneration less than 6 years(i.e. one change interval) can be required to assume FGDC class 15(low regenerating to young mixed cover; Appendix A), while 6–15 year regenerationmust followclasses 2, 5,12 or 14, dependingonevergreen, deciduous, or mixed forest type. An example of applyingthis rule is shown at the bottom of Fig. 5.
ii. The signature from wetlands can be highly variable, and they canbe misclassified as open water depending on hydrologic condi-tions. Therefore, if a pixel is classified aswetlands in themajority ofa time series, all dates are converted to the wetland class.
iii. Areas containing high biomass crops (e.g. St Lawrence Islands, LaMauricie, Kejimkujik) can be spectrally confused with deciduousforest and shrub classes. To help reduce this confusion, rules areapplied to the time series that check for isolated occurrences of
Fig. 5. Spectral signatures generated for the baseline land cover classification are used to classify the image data for the update. Signature extension provides a first estimate of thenew land cover type, but in some cases this can be confused for classes that are spectrally similar. To reduce signature extension error, constraints are applied so that land covertemporal trajectories are ecologically realistic. The top panel shows an overview for La Mauricie, while in the bottom (A) shows a blow up of signature extensionwithout constraintsapplied where harvested forest reverts to a forest class within five years of disturbance and (B) shows the same areawith update rules applied, producing amore realistic transition toa regeneration class, then mature forest class. Refer legend in Fig. 11.
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forest or non-forest as follows with the land cover reduced to forestand non-forest classes:
if t0 = forest and t1 = non-forest and t2 = forest then t1 = forestif t0 = non-forest and t1 = forest and t2 = non-forest then t1 =non-forest.As the change interval used in the time series is too short for eitherforest or non-forest to occur in isolation it is considered to be an errorand reclassified to the appropriate class at the given thematic legend.This rule cannot be applied at the start and end of a time series.Currently, the majority class from the first three starting or ending
values is used. To correct errors a grid based search is used to visuallyidentify any areas that have changed for the starting and endingpositions in the time series. A more effective approach is to use off-peak of growing season imagery to generate a forest/non-forestmask (Latifovic & Pouliot, 2008), but this requires the additional costand processing of imagery.
7. Validate baseline land cover and changes.Field validation of EO-based land cover classification and changeproducts is normally a preferred method of assessing productaccuracy. In the case of Canada's national parks, extensivefield visits
Fig. 7. Accuracy assessment for 2005 baseline land cover of St Lawrence Islands and GPE.Overall accuracy increases from 75% for the full legend containing 21 FGDC classes to92.5% for a two-class forest/non-forest legend.
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are often prohibited by the expense and logistical difficulty inaccessing remote areas, which are often serviced by few roads.Nevertheless, limited reference data were collected during visits tofour of the six pilot parks (Kejimkujik, La Mauricie, Pacific Rim, andSt Lawrence Islands). Field data were also collected at 133 sitesalong a 1000 km north–south transect adjacent to the DempsterHighway in Yukon and Northwest Territories, and at 97 sites nearYellowknife, Northwest Territories. These additional sites covered adiverse range of vegetation and land cover types in northernCanada, and served as reference data for the northern pilot park(Nahanni). Given the limited number (43–150) of field sites avail-able for each park, these served exclusively as training data forcluster labelling and development of the baseline land cover. Arigorous statistical validation of the baseline land cover productsusing field data was not possible due to the reasons stated above.Instead, the products were either compared to other sources of landcover to determine their general agreement, or a limited accuracyassessment was performed using interpretation of site photos orhigh resolution satellite imagery (Table 3).Provincial Forest Resource Inventory (FRI) maps were available forPrince Albert and La Mauricie (called Ecoforestiere in Quebec),which provide GIS polygons delineating homogenous forest stands.These were gridded at the Landsat spatial resolution (30 m) andprojection, and the FRI classes simplified to a legend consisting ofdeciduous, mixed deciduous, mixed, mixed conifer, and conifer forcomparison to the FGDC land cover. We summarized the land coverdistribution beneath each of the simplified FRI classes to conduct acomparison over undisturbed areas. This can not be viewed as arigorous validation considering that it compares only forest landcover classes at different scales and dates, and forest inventorymaps are themselves subject to error (Dussault et al., 2001).Fig. 6 shows a summary of 1985 FGDC land cover types containedwithin the 1968 FRI forest types covering Prince Albert NationalPark. The evergreen classes are dominated by FRI conifer species(~70%) with a small contribution from FRI mixed deciduous andmixed. The FGDC mixed class is composed of all four FRI classes,with nearly equal contributions from conifer, mixed deciduous,and deciduous. The FGDC mixed deciduous and deciduous areboth dominated by FRI deciduous, but the former also includessome FRI mixed. Overall, this comparison (and also that for LaMauricie) indicates a general level of agreement between the 1985classification generated from signatures extended from a 1996baseline land cover, and FRI data produced in 1968.A per-pixel comparison of land cover was conducted for Nahannibased on the Northwest Territories Land Cover Classification(NWTLCC) that covers 85% of the park's GPE, and which was alsogenerated using Landsat imagery. A set of 2000 randompointswas
Fig. 6. FGDC class distributions contained within polygons of four Forest ResourceInventory (FRI) forest types.
selected fromwithin the GPE, which were reduced to 1440 pointsafter removing highly inconsistent classes, such as clouds, snow,ice, and shadows. A modal filter was applied to both maps toreduce the effect of geo-referencing error before extracting landcover class from both products for all 1440 points. As the differentlegends could not be easily cross-walked, a many-to-one relation-ship was created to convert the FGDC legend to the NWTLCClegend. This comparison yielded an 83% overall agreement, or 88%agreement if classes were weighted by their proportions.A more detailed validation of a baseline land cover product wasperformed for St Lawrence Islands using 105 field samples and byvisually interpreting FGDC land cover at an additional 105 sitesfrom an IKONOS imagemosaic providing 4-mmultispectral and 1-m panchromatic resolution. Legends at three levels of general-ization were evaluated: a full legend of 21 FGDC classes, anaggregated 11-class legend, and forest/non-forest. A graphshowing accuracy for each legend is shown in Fig. 7. Thisdemonstrates that accuracy increases as the number of landcovers decreases, and provides insight into the trade-off betweenclassification information content and accuracy. This also suggeststhat, in parks where limited training and validation data areavailable, a greater level of class aggregation would be warrantedto produce a reliable land cover product. Table 4 shows theconfusion matrix for the reduced 11-class legend. In general,agriculture environments are difficult to classify accuratelybecause they are heterogeneous and fragmented, resulting inmixed pixels containing spectral properties from several landcover types. Confusion occurs between mixed forest classes andother forest classes, presumably due to the fuzzy nature of forestclass boundaries. Wetlands and lakes are confused, as aquatic
Table 4Confusion matrix for the 8-class FGDC legend over St Lawrence Islands.
Percentage of correctly classified samples are shown in bold.Overall accuracy=84.76%.Kappa coefficient=0.80101.Standard deviation=0.03186.
Fig. 8. FGDC land cover proportions for the entire Pacific Rim National Park and GPE for 1990, 2000, and 2005. Refer to Appendix A for labels corresponding to FGDC Class numbers.
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vegetation in lakes can cause them to appear as wetlands. Finally,barren, high biomass crops, and low vegetation (crops, pasture)could not be separated, as field data was collected one year afterthe imagewas acquired thus these classes could not be determinedin the field.
The above discussion addressed assessing the accuracy of baselineland cover products. Validation of changes occurring during the 10–20 year land cover time series is a more challenging task due to thepaucity of temporal land cover information and the requirement thatthis informationmatch the spatial and temporal scales examined in thepilot parks. Therefore, besides visually checking change products forconsistency, we performed only a limited change/no-change assess-ment for two time periods (1990–2000) in the Pacific Rim land covertime series. This involved linking imagery fromboth dates andmanuallydigitizing points representing 100 changed sites and 100 non-changedsites. Changed sites were represented by clear land cover changes ofinterest for EI monitoring (forest clearcuts, regenerating forest, devel-opment),whilenon-changed sites includedareaspossibly susceptible toproducing false change, such as vegetation on poorly illuminated slopesand areas of apparent differences in vegetationphenology and conditionbetween dates.
The results of this assessment indicated that 92/100 changed sitesand 93/100 non-changed sites were correctly identified, representingan 8% omission error rate and 7% commission error rate. Of the sevennon-changed sites that were incorrectly mapped as changed, six hadbeen reclassified to a similar FGDC category (e.g. to a different den-sity evergreen class or from herb–shrub bare to herb–shrub). This
suggests that a more accurate land cover change product would beobtained if FDGC classes were first merged to a legend having fewerclasses.
3. Change results for pilot parks and implications for EI
Table 3 summarizes some key results obtained using the AMUSEchange detection procedure the seven pilot parks. Available space doesnot permit a full presentation of change results, so instead we focusmore closely on two parks: Pacific Rim (Pacific Bioregion) and PrinceAlbert (Interior Plains Bioregion). Change results are discussed in termsof those occurring within the park boundary and those within the GPE(excluding the park), and their potential implications for park EI.
Pacific Rim National Park is located on the west coast of VancouverIsland and contains much of the island's intact coastal temperaterainforest. Summary statistics of land cover through time for the parkand GPE are shown in Fig. 8. Within the park boundary, land cover ishighly stable. Some of the observed changes are due to either aninaccurate park boundary definition with logging right up to the parkboundary, or possible encroachment of logging across the park border.Note that Pacific Rim is almost entirely bounded by active provincialcrown timberlands under lease to forest companies. One class thatappears to have decreased considerably in 2005 is bare disturbed area(class 40), which is caused by roads being visible in 1990 and 2000,while tree crowns obstructed roads from above in 2005. Other minorsources of variation are within the data itself.
Land cover in Pacific Rim's GPE is more dynamic. An overall trend thatcan by seen in both the images (Fig. 9) and in the summary statistics is a
Fig. 9. Land cover time series for Pacific Rim's Long Beach Division (top) and West Coast Trail Division (bottom). The park boundary is shown in yellow. Refer to legend in Fig. 11.
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decrease in high-density mature conifer forest (class 1) and replacementwith young to regenerating conifer (class 2), deciduous (class 12) andmixed (class 15) forest. Over 17% of dark, densemature conifer forest wasremoved from the GPE between 1990 and 2005 due to logging and
Fig. 10. Reduction in water bodies (appearing black) within the largely a
replaced with brighter bare ground or mixed to deciduous regeneration,leading to an overall brightening of the region. These GPE land coverchanges could have an impact on park EI due to loss of habitat for old-growth dependent species in the region. One such species is the Marblet
gricultural portion of Prince Albert's GPE. Refer to legend in Fig. 11.
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Murrelet (Brachyramphus marmoratus), a small seabird that nests up to75 km inland on large, moss-covered limbs of conifer trees that aretypically more than 250 years old (Burger, 2002).
Prince Albert National Park is located in central Saskatchewanwithinthe transition zone between the prairie region to the south and the borealregion to the north. Mixed forest predominates in the south, beinggradually replaced by coniferous dominated forest in the north. Based onland coverproportions (not shownhere), relatively little changeoccurredin forest cover proportions between 1985 and 2001 within the park. Asmall increase in the forest regeneration classes was observable in 2001due to threewildfires that occurred in 1996 and 1998.We also noted thatan outbreak of spruce budworm during the 1990s in the northern half ofthe park wasmapped by provincial aerial defoliation surveys, yet did notproduce a spectral change sufficiently intense to cause a shift in forestcover class. Severe defoliation, resulting in nearly complete leaf loss andtree death, could effectively alter land cover class to one having either alower canopy cover or that is representative of the forest understory(Fraser & Latifovic, 2005).
Land cover changes were more frequent in the GPE of Prince Albert.Conifer forest has decreased steadily from 1975–2000 by ~7% due tofires and harvesting. Open water bodies showed a trend of decreasingarea, as shown in Fig. 10 for the Park, its surrounding GPE, the forestedarea in the Canadian Shield, and the prairies to the south. The decrease isespecially pronounced for the prairie region area south of the Park, but
Fig. 11. Close-up of FGDC land cover products produced using AMUSE for
exists across the whole study area including the Park and GPE. ThesetrendshavebeendocumentedbyvanderKampetal. (2003) andare alsosupported by observations fromDroughtWatch, a section of Agricultureand Agri-Food Canada that provides information of the impacts ofclimatic variability on water supply and agriculture. Since the 1930s,they have identified nine major drought years, five of which haveoccurred since 1985. Drought events are significant to wetland ecology,causing wetland birds and mammals to cease using these areas duringdry periods. While animals and aquatic plants avoid or endure drywetlands, for many wetland plants drought is the only time they canbecome established because their seeds cannot germinate under water.Consequently, emergent and wet meadow species, which are ofteneliminated during wet years when water levels become too deep forthese species to survive, can only become re-established during drought(Van Der Valk, 2003). Persistent or frequent drought may cause lastingecological impacts in this region.
Forest harvesting and wildfire were a considerable source of dis-turbance in the northern portion of the GPE. Fig. 11 shows an area withespecially dynamic land cover 35 km north of park. In 1990, the area ispredominantly mature conifer forest, with areas of recent forestharvesting and regeneration. Between 1990 and 1995 the area wassubject to further harvesting, while in 1995, a wildfire occurred in thenortheast portion that burned almost 500 km2 ofmature forest. In 1998,fires burned 9610 km of forest in northern Saskatchewan, an area more
an area 10-km across in the northern portion of Prince Albert GPE.
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than twice the size of the park (Canadian Council of Forestry Ministers,2004). Between 1995 and 2000, both the harvested and recently burnedforest had regenerated to early successional forest classes. Land coverclass in the southwest corner remained highly stable for all three dates.
4. Summary and conclusions
A framework for land cover change detection was developedto meet the requirements of an EO-based Park Ecological IntegrityObserving System (Park-EIOS), which will form one componentof Parks Canada's Ecological Integrity Monitoring Program. Park-EIOS will use land cover generated at five-year intervals to derivelandscape-scale EI indicators corresponding to habitat fragmentation,succession and retrogression, productivity, and species richness. TheAMUSE change procedure employs a hybrid approach that combinesCVA for flagging the occurrence of changes, followed by signatureextension to assign labels to changed pixels. Signature extension isconstrained using expert rules created for each Bioregion that requireland cover to followa trajectory that is both physically and ecologicallyrealistic. Robust image pre-processing and normalization steps arecritical components of the overall methodology and essential forproducing reliable results. While the AMUSE provides a set of genericprocedures and tools for change detection, its successful applicationrequires an analyst experienced in land cover interpretation andimage processing. In particular, the baseline land cover labelling,assessing results from the image correction methods, determining aCVA change threshold, and development of signature extension rules,are subjective and will determine the final accuracy of the land coverchange products.
AMUSE was developed and demonstrated using six nationalparks covering a range of geographic and ecological conditions, andsubject to a variety of change agents including forest harvesting,wildfire, land use development, and climate/weather. The methodwas effective in capturing land surface changes where reflectancechanged sufficiently to alter land cover class. More subtle changes tovegetation, such as those caused by insect defoliation and drought,were generally not detectable due to the design requirement that themethod produce low levels of commission error. Such changes couldalso be detected in special cases by applying a more liberal CVAthreshold, but at the expense of increased noise. Other methods suchas trend analysis using calibrated time-series data of sufficientlength and temporal frequency can be used to detect more subtlechanges such as northern greening due to climate warming (e.g.Myneni et al., 1997).
The change methods will be applied operationally to select parksin the PCA system beginning with Pacific Rim in 2008. Informationrelated to changes in the amount of old-growth forests will bederived from the 1990–2005 land cover time series and serve as oneinput into the 2008 State of the Park Report. For the duration of thisproject, 30 m resolution Landsat TM and ETM+ sensors served as acost-effective source of imagery and provided a reasonable balanceof spatial coverage and resolution. Similar alternative sources ofimagery, including SPOT HRVIR and ResourceSat-1, are beingevaluated for filling any Landsat data gap that could occur priorto the availability of the Landsat replacement scheduled for launchno earlier than 2011 (Wulder et al., 2008). Landsat 7 ETM+ dataacquired after the 2003 Scan Line Corrector (SLC) failure shouldalso be considered if gap-filling can be performed using SLC-offimage pairs acquired within a short interval. A follow-on projectwith PCA called ParkSPACE is addressing the specific EO require-ments for reporting on the ecological integrity of Arctic parks,where climate change is a major stressor. This will includeexamining the use of fractional land cover mapping to detectgradual and more subtle changes in vegetation and the potentialrole of medium and coarse resolution sensors (250–1000 m) forannual EI monitoring.
Acknowledgments
We thank research assistants Andrea Clouston, Mélanie Carrière,Jonathan Orazietti, and Guillaume Girouard for their help in imageprocessing and generating the change products. Jean Poitevin, DonaldMcLennan, and numerous other staff from Parks Canada Agencyprovided valuable suggestions and insight during the development ofthemethodology. The Government Related Initiatives Program (GRIP) ofthe Canadian SpaceAgencyprovided themajor source of funding for thisproject.
Appendix A. Modified FGDC land cover legend
Tree dominated (tree crown density N25%)
1 Evergreen forest (N75% cover) — old 2 Evergreen forest (N75% cover) — young 3 Deciduous forest (N75% cover) 4 Mixed coniferous (50–75% coniferous) — old 5 Mixed coniferous (50–75% coniferous) — young 6 Mixed deciduous (25–50% coniferous) 7 Evergreen open canopy (40–60% cover) — moss–shrub understory 8 Evergreen open canopy (40–60% cover) — lichen–shrub understory 9 Evergreen open canopy (25–40% cover) — shrub–moss understory 10 Evergreen open canopy (25–40% cover) — lichen (rock) understory 11 Deciduous open canopy (25–60% cover) 12 Deciduous open canopy — low regenerating to young broadleaf cover 13 Mixed evergreen-deciduous open canopy (25–60% cover) 14 Mixed deciduous (25–50% coniferous trees; 25–60% cover) 15 Low regenerating to young mixed cover
Shrub dominated
16 Deciduous shrubland (N75% cover)
Herb dominated
17 Grassland, prairie region 18 Herb–shrub — bare cover, mostly after perturbations 19 Shrubs–herb–lichen — bare 20 Wetlands 21 Sparse coniferous (density 10–25%), shrub–herb–lichens cover 22 Sparse coniferous (density 10–25%), herb–shrub cover 23 Herb–shrub 24 Shrub–herb–lichen — bare 25 Shrub–herb–lichen — water bodies 26 Lichen–shrubs–herb, bare soil or rock outcrop 27 Lichen–shrubs–herb, bare soil/rock outcrop, water bodies 28 Low vegetation cover (bare soil, rock outcrop) 29 Low vegetation cover, with snow 30 Woodland–cropland 31 Cropland–woodland 32 Annual row-crop forbs and grasses — high biomass 33 Annual row-crop forbs and grasses — medium biomass 34 Annual row-crop forbs and grasses — low biomass
38 Rock outcrop, low vegetation cover 39 Recent burns 40 Mostly bare disturbed areas (e.g. cutovers) 41 Low vegetation cover 42 Urban and built-up 43 Water bodies 44 Mixes of water and land 45 Snow/ice 46 Clouds
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