high spatial resolution remote sensing data for forest ecosystem classification: an examination of...

High Spatial Resolution Remote Sensing Datafor Forest Ecosystem Classification:An Examination of Spatial Scale

Paul Treitz* and Philip Howarth†

Detailed forest ecosystem classifications have been de- (MMCD). In addition, maximum semivariance as esti-mated from the sills of the experimental variograms in-veloped for large regions of northern Ontario, Canada.creased with density of understory. Elsevier ScienceThese ecosystem classifications provide tools for ecosys-Inc., 2000tem management that constitute part of a larger goal of

integrated management of forest ecosystems for long-termsustainability. These classification systems provide de-

INTRODUCTIONtailed stand-level characterization of forest ecosystems ata local level. However, for ecological approaches to forest To achieve an integrated management system for long-management to become widely accepted by forest manag- term sustainability, detailed knowledge of the forest isers, and for these tools to be widely used, methods must required, complemented by an understanding of the re-be developed to characterize and map or model ecosys- lationships between forest structural characteristics andtem classes at landscape scales for large regions. In this the physical environment. The objective of sustainabilitystudy, the site-specific Northwestern Ontario Forest Eco- is the maintenance of ecosystem integrity and protectionsystem Classification (NWO FEC) was adapted to pro- of natural diversity and vital processes (Salwasser, 1990;vide a landscape-scale (1:20 000) forest ecosystem classi- Jensen and Everett, 1994). Ecologically sustainable de-fication for the Rinker Lake Study Area located in the velopment of forest ecosystems therefore requires spatialboreal forest north of Thunder Bay, Ontario. High spa- information about the ecological components as well astial resolution remote sensing data were collected using the timber resources in a region (Mackey et al., 1996).the Compact Airborne Spectrographic Imager (CASI) Hence, there is a need to define the forest both from anand analyzed using geostatistical techniques to obtain an ecosystem (unit) and an ecological (process) perspec-understanding of the nature of the spatial dependence of tive. A wide variety of information can be accumulatedspectral reflectance for selected forest ecosystems at high from studying these characteristics and the relationshipsspatial resolutions. Based on these analyses it was deter- among them. This information must be organized andmined that an optimal size of support for characterizing simplified in a way that facilitates enhanced decision-forest ecosystems (i.e., optimal spatial resolution), as esti- making at a variety of levels.mated by the mean ranges of a series of experimental va- The goal of this research was to determine the ex-riograms, differs based on (i) wavelength, (ii) forest eco- tent to which forest ecosystems, in terms of the North-system class, and (iii) mean maximum canopy diameter western Ontario Forest Ecosystem Classification (NWO

FEC) (Sims et al., 1989), can be discriminated using re-mote sensing data collected at high spatial resolutions.

* Department of Geography, Queen’s University, Kingston, ON, Remote sensing data may provide important forest struc-Canadatural and ecosystem information. As part of research into† Department of Geography, University of Waterloo, Waterloo,

ON, Canada the effects of spatial resolution on the spectral expressionAddress correspondence to P. Treitz, Department of Geography, of forest ecosystems, the spatial aspects of remote sens-

Faculty of Arts and Science, Queen’s University, Kingston, ON, Can- ing reflectance data for forest ecosystems at high spatialada, K7L 3N6. E-mail: [email protected] 4 September 1998; revised 15 October 1999. resolutions are examined. The objective of this analysis

REMOTE SENS. ENVIRON. 72:268–289 (2000)Elsevier Science Inc., 2000 0034-4257/00/$–see front matter655 Avenue of the Americas, New York, NY 10010 PII S0034-4257(99)00098-X

Spatial Scale and Forest Classification 269

was to determine the optimal spatial resolutions for dis- proaching the analysis of spatial structure in digital re-motely sensed images. The first of these involves thecriminating particular forest ecosystems. Variogram anal-definition of various parameters (e.g., local variance, au-yses of remote sensing reflectance data for forest standstocorrelation, one- and two-dimensional variograms) thatat high spatial resolution were performed to determinemeasure spatial structures, which are then applied to realthe optimal spatial resolutions at which contrasting forestimages. A second approach involves the definition of aecosystems may be discriminated by remote sensing.scene model, whereby the spatial structure of discreteThis analysis also provided information on appropriateobjects on a background are examined with respect towindow sizes for which textural algorithms can be ap-their effect on the spatial structure of an image taken ofplied to estimate characteristic stand structure for con-them. In this study, the first approach is used to examinetrasting ecosystems.the spatial structure of high-resolution Compact Air-borne Spectrographic Imager (CASI) reflectance data ofRemote Sensing and Forestnatural forest ecosystems that are characteristic of north-Ecosystem Characterizationwestern Ontario, Canada.Remote sensing and digital image analysis techniques of-

The reflectance values of a remotely sensed imagefer potential for assisting in the analysis of large forestare a function of spatial position during image acquisi-tracts for identification of appropriate ecosystem classestion. In the realm of geostatistics, these reflectance val-or aggregations of ecologically similar classes. However,ues are a function of spatial position and can thereforesatellite remote sensing data are acquired at predeter-be considered as values of a regionalized variable. Themined spatial resolutions, designed primarily for generaltheory of regionalized variables (Matheron, 1963) as-land-cover and land-use analysis and mapping. Althoughsumes that the spatial variation of any continuous vari-airborne systems are capable of acquiring data at a vari-able is the sum of three major components (Burrough,ety of resolutions (i.e., spatial, spectral, and temporal),1987). These components are:optimal resolutions for specific terrain analyses are gen-

i. structural (associated with a constant mean valueerally not known. This problem has been presented byor a constant trend);Woodcock and Strahler (1987) in their paper discussing

ii. random and spatially correlated; andthe scale dependence of prediction in remote sensing.iii. random and not spatially correlated (i.e., noiseRemote sensing data are generally collected at a single

and/or residual error).spatial resolution, in contrast to the many scales at whichnature’s units and processes exist. It is therefore difficult Applying regionalized variable theory to the analysisto identify a single spatial resolution of remote sensing of remotely sensed data requires adopting a stochasticdata that will provide the most suitable level of informa- view of the landscape and its spatial structure (Jupp ettion for extracting forest ecosystem characteristics. Multi- al., 1988). A stochastic surface provides a better modelresolution remote sensing data can be expected to pro- of the irregularities of spatial variability, as opposed to avide suitable information at a variety of levels for forest smooth mathematical function. This is a logical assump-ecosystem classification. tion, since underlying processes and properties of the

For remote sensing of forest ecosystems over large landscape will produce many similar scenes scatteredareas to become operational, spatial resolutions of re- across the landscape. In fact, this is intrinsic to the classifi-mote sensing data must be appropriate for the specific cation of ecosystems in the NWO FEC. It is assumed thatapplication. It is spatial resolution that determines the in- similar ecosystems will arise from similar environmentalformation content and measurement error of an image conditions and processes linked closely to landscape.(Atkinson, 1993; Atkinson et al., 1996). For instance, to The key to the theory of regionalized variables is thediscriminate forest ecosystems at a landscape scale, a variogram, a second-order spatial statistic. Remote sens-spatial resolution that best characterizes the spectral re- ing reflectance measurements may be thought of as com-flectance for a particular forest ecosystem should be opti- prising the true or underlying value of a property (i.e.,mized. However, detailed information on stand and can- information) and a component of measurement erroropy structure and dynamics, as detected through remote (Atkinson, 1993; Atkinson et al., 1996). These compo-sensing, is needed to improve our understanding of for- nents of variation are embedded in the experimental orest stands in order to develop methods for classifying and measured variogram (Fig. 1). The variogram is used tomapping forests at landscape scales. Selecting a proper describe the spatial correlation between samples in closeor optimal spatial resolution requires information on the proximity. In variogram analysis, and basic to regional-spatial characteristics of the surface under investigation. ized variable theory, two additional assumptions are re-In this study, we expected this spatial resolution to differ quired: (i) spatial stationarity, which assumes that thefor different forest ecosystem classes or aggregations of parameters of the underlying function (i.e., the regional-classes. ized variable) do not vary with spatial position; and (ii)

ergodicity, which assumes that spatial statistics takenJupp et al. (1988) describe two methods of ap-

270 Treitz and Howarth

Figure 1. The shape and descriptionof a “classic” variogram (adapted fromCurran and Atkinson, 1998).

over the area of the image as a whole are unbiased esti- ing the features embedded in the image variogram (Cur-ran, 1988; Woodcock et al., 1988a, 1988b).mates of those parameters (Jupp et al., 1988). The as-

sumption of stationarity is generally appropriate in digital Geostatistics, in particular variogram analyses, havebeen applied to (i) explore and describe spatial variabilityimage processing, at least locally or in increments (i.e.,

within the range of the variogram), where scan angle and in remotely sensed and ground data (e.g., Lacaze et al.,1994; St-Onge and Cavayas, 1995; Atkinson et al., 1996;terrain effects are minimal. The assumption of ergodicity

is generally valid for remote sensing data, since the re- Smith and Curran, 1996; Csillag et al., 1996; Collins andWoodcock, 1999); (ii) determine optimal spatial samplingflectance surface is considered stochastic (Jupp et al.,

1988). in image data (e.g., Atkinson and Danson, 1988; Marceauet al., 1994a, 1994b; Atkinson and Curran, 1997) andThe variogram has proven useful in remote sensing

because it enables researchers to relate some of the de- ground data (e.g., Webster et al., 1989; Lathrop andPierce, 1991; McGwire et al., 1993; Atkinson and Cur-scriptors of the variogram to the spatial characteristics of

the scene (Atkinson and Curran, 1997). The internal spa- ran, 1995; Bellehumeur and Legendre, 1998); and (iii)estimate appropriate window sizes for textural classifica-tial variability of information classes of interest deter-

mines how small a ground-resolution element can be be- tion (e.g., Hay et al., 1996; Carr, 1996; Wulder et al.,1998; Miranda et al., 1998). Curran and Atkinson (1998)fore it detects unnecessary within-class variability. In this

scenario, the variogram is ideal, since it defines the dis- provide a useful review of how geostatistics are appliedin remote sensing.tance above which the ground-resolution elements are

not related. Therefore, a ground-resolution element Woodcock et al. (1988b) calculated variograms fromreal digital images and found that (i) the density of cov-larger than the range will likely average the within-class

spatial variability and provide a suitable descriptor for erage of objects in the scene affects the height of thevariogram, (ii) object size affects the range of influencethat class (Woodcock and Strahler, 1987; Curran, 1988).

The range therefore provides a measure of the size of of the variogram, and (iii) the variance in the distributionof the sizes of objects affects the shape of the variogramthe elements (i.e., trees or clusters of trees) in the image

and has been suggested as a useful indicator in selecting (i.e., as variance increases, the shape of the variogramcurve becomes more rounded). Variogram analysis hasthe optimal spatial resolution, or support, for discriminat-


been used to demonstrate the potential for predicting jack pine, black spruce, balsam fir, and white spruce onhilltops and slopes, with black spruce stands dominatingconiferous forest stand structural parameters (tree size

and density) on computer-generated and high resolution lower positions (Anonymous, 1994). Pure and mixedaspen stands tend to have abundant shrub and herb com-remote sensing images (St-Onge and Cavayas, 1995).

Lathrop and Pierce (1991) used variogram analysis of ponents (e.g., Acer spicatum and Corylus cornuta) andhave average ages in the range of 80–100 years. Blackforest canopy transmittance measurements and Landsat

TM near-infrared/red ratio data to examine the scale of spruce occurs on a range of upland and lowland site con-ditions and generally in association with jack pine, bal-variation in canopy structure and determine the most ap-

propriate scale at which to sample transmittance. This sam fir, and/or white spruce, and to a lesser extent withaspen and/or white birch, tamarack, or cedar (Walsh et al.,analysis depicted the similarity between the two sets of

data with respect to spatial autocorrelation structure. The 1994). Wetland organic sites dominated by cedar, spruceand tamarack are scattered throughout the study area.range of the variogram was used to aggregate the Land-

sat TM and transmittance data sets for regression analy- Shrub species within the study area are characteris-tic of the boreal forest. Shrub vegetation in forest ecosys-sis by averaging segments of the transect (where segmenttems ranges from tall shrub-rich sites (e.g., Abies balsa-length equals variogram range). It was discovered that,mea, Acer spicatum, Alnus rugosa, Alnus crispa, Corylusby averaging within an appropriate landscape unit (e.g.,cornuta) to low shrub-poor sites (e.g., Vaccinium myrtel-hillslopes), large-scale variability of measurements (dueloides, Diervilla lonicera, Ledum groenlandicum, Linnaeato small forest gaps) was reduced. In the study reportedborealis, or Gaultheria hispidula) (Paradine, 1994). Herbhere, the within-parcel scale of study (i.e., within-standspecies are also characteristic of the boreal environment;variability) was examined to determine optimal descrip-common species include Cornus canadensis, Aster macro-tors to improve separability of forest ecosystems at a be-phyllus, Aralia nudicaulis, and Fragaria virginiana. Herbtween-parcel scale.species presence and abundance are sensitive to overstoryand shrub conditions, as well as soil characteristics. Sphag-Study Area Descriptionnum spp. and feathermoss (e.g., Pleurozium schreberi)The study area is located approximately 100 km north ofdominate the ground cover in the lowland sites, withThunder Bay, Ontario, Canada, within the Central Pla-feathermoss also occurring frequently in upland areas.teau section of the Boreal Forest Region (Rowe, 1972).

The topography is generally rolling and the terrain isData Descriptionbedrock-controlled. Trembling aspen (Populus tremu-

loides) and black spruce (Picea mariana) are dominant, Ground Reference Data Collection From NWOwith jack pine (Pinus banksiana), white spruce (Picea FEC Plotsglauca), balsam fir (Abies balsamea), white birch (Betula Ground data were collected using a methodology devisedpapyrifera), white cedar (Thuja occidentalis), and tama- by Forestry Canada for characterizing vegetation typesrack (Larix laricina) occurring in various mixtures. For- (V-types) within a forest stand (McLean and Uhlig, 1987;est-stand overstories are monospecific or mixed, and un- Sims et al., 1989). Ground samples, referred to as FECderstories range from shrub- and/or herb-rich to poor plots, are 10310-m quadrats in which V-types are deter-(Walsh et al., 1994). The forest ecosystem classification mined based on the presence and abundance of (i) can-applicable to the study area is the Northwestern Ontario opy and secondary trees; (ii) high, low, and dwarf shrubsFEC (Sims et al., 1989). The ecosystem units derived and broadleaf herbs; and (iii) mosses and lichens. FECfrom the NWO FEC for the Rinker Lake area and used plot data were collected for 71 forested sites within thein this study are summarized in Table 1. study area during 21 June to 15 July 1993 and 4 to 15

Within the study area, species mixes occur on a vari- July 1994. Data collected for each FEC plot included:ety of soil–site conditions. While even-aged jack pine differential Global Positioning System (GPS) data; (ii)stands are generally found on well-drained, coarse-tex- vegetation data (e.g., species and percent cover for tree,tured soils, black spruce stands occur on sites ranging shrub, and herb layers); (iii) mensuration data (e.g., age,from shallow mineral soils overlying bedrock to deep, height, density, and diameter breast height [dbh]); andpoorly drained organic wetlands. Many of the major tree (iv) canopy data (e.g., mean maximum crown diameterspecies, such as balsam fir, white spruce, trembling [MMCD], the average length of the two largest near-aspen, and white birch, tend to occur in mixed stands on perpendicular diameters for the average tree within asoils ranging from dry to moist and coarse-textured to plot or stand). In total, plot data were collected for 25fine (Anonymous, 1994). As a result, forest ecosystems of the 38 V-types within the NWO FEC. Of the 13cannot be modeled easily based simply on resident surfi- V-types not sampled, 6 were characteristic of the Greatcial–soil conditions. Lakes St. Lawrence forests to the south of the study

area; the rest were either too small in size to sample ef-Mature forests within the study area are mainlymixed, two-storied stands consisting of trembling aspen, fectively, or were inaccessible, or did not occur within


Table 1. Landscape-Level Forest Ecosystem Groupings for the Rinker Lake Study Area (Mapping Scale 1:20 000)

Description V-type complexes Treatment unit†

Mainly hardwoodsWhite Birch Hardwood and V4 C

Mixedwood White Birch Hardwood andMixedwood

Aspen Dominated Hardwood and V5, V6, V7, V8, V9, V10, V11 BMixedwood Aspen Hardwood and Mixedwood

Conifer mixedwoodsWhite Spruce/Balsam Fir Conifer and V14, V15, V16, V21, V24, V25 D

Mixedwood Balsam Fir–White Spruce Coniferand Mixedwood

Jack Pine Mixedwood/Shrub Rich V17, V28 GJack Pine/Shrub Rich

Jack Pine Mixedwood/Feathermoss V18, V29 FJack Pine Feather Moss

ConifersCedar Mixedwood V22 J

Black Spruce/Wet OrganicUpland Black Spruce/Jack Pine V19, V20, V31, V32, V33 E

Black Spruce–Jack Pine/Feathermoss

Lowland Black Spruce V23, V34, V35, V36, V37 JBlack Spruce/Wet Organic

Wetland Black Spruce V38 KBlack Spruce/Leatherleaf/

Sphagnum

† Treatment Units as defined by Racey et al., 1989

the study area (Kalnins et al., 1994). In 1994 and 1995, mapped information is presented at a scale of 1:20 000)(Mackey et al., 1996).a series of transects was traversed through selected forest

stands to collect additional V-type data. These V-type There are a number of V-types that aggregate to-gether based on their occurrence under similar soil mois-characterization samples were taken at fixed intervals of

50 m along predetermined transects. ture and nutrient regimes. For example, black sprucemixedwood classes (V19, V20) are often found in associa-At landscape and local scales, the partitioning of eco-

systems is based on more specific criteria with respect to tion with pure conifer (black spruce and jack pine)classes (V31, V32, V33). These classes are very similar,edaphic, topographic and vegetational features. At these

scales quantitative data on soil and vegetation parameters particularly with respect to canopy characteristics andfeathermoss ground cover. Soil moisture and nutrient re-at the community or stand level are used to classify and

characterize forest ecosystems in considerable detail gimes vary along a continuum and do not portray distinctbreaks or boundaries. For this reason, black spruce,(e.g., Sims et al., 1989).

At landscape scales, aggregation of detailed ecosys- which occurs under a range of soil moisture and nutrientconditions, has a number of V-types that grow under up-tem units is necessary for mapping, particularly in com-

plex environments (Wiken et al., 1981). In this study, to land and lowland conditions. At the landscape scale,black spruce is classed as either upland, lowland, or wet-derive forest ecosystem classes at landscape scales from

the NWO FEC, detailed V-types were grouped ac- land black spruce. As a result, V34 (Black Spruce/Labra-dor Tea/Feathermoss [Sphagnum]), which represents acording to their tendency to occur together, as observed

under field conditions. In many instances, the classes variable transition zone between the Upland BlackSpruce/Jack Pine and Lowland Black Spruce, has beencorresponded to the treatment units described by Racey

et al. (1989). Treatment units represent broader land- grouped with the Lowland Black Spruce at the landscapelevel. In addition, V23 (Tamarack [Black Spruce]/Speck-scape units than do individual V-types and soil types

(S-types) and respond similarly to certain management led Alder/Labrador Tea) will often complex with V35 andV34 (particularly when Speckled Alder is prevalent inactivities (Sims et al., 1989). Aggregation of detailed eco-

system units provides information at operational scales V34). Trembling aspen (white birch)/mountain maple(V8) were often observed in close proximity to Jack Pine(i.e., scales at which spatial information is generated that

can be incorporated into harvest schedule plans where Mixedwood/shrub-rich (V17). The landscape-level eco-


Table 2. Description of the Compact Airborne Spectrographic Imager (CASI)†

Parameter Description

Spectral coverage 418 nm to 926 nm using 288 detectors; sampling interval 1.8 nm; spectral resolution 239 nmSpectral mode 39 spectra of the full 418 nm to 926 nm range are recorded, with 2.9 nm resolution, from

39 different directions across the swath; a full-resolution image at a predeterminedwavelength is also recorded to assist in track recovery

Spatial coverage 35.58 swath, with standard lens; single camera gives 612 pixels; sampling interval 1.2 mrad;spatial resolution 1.6 mrad

Spatial mode spectral pixels are grouped to form up to 25 bands (512 pixels wide); band width and spectralpositon are under software control; the number of bands governs the integration time

† Adapted from Gower et al. 1992.

system units derived for the Rinker Lake study area are of the 512 CCD elements in the cross-track direction,summarized in Table 1. and spatial mode, where a more limited number of spec-

tral bands is recorded but complete spatial coverage forCompact Airborne Spectrographic Imager Datathe swath is provided. The CCD sensor is read out andThe CASI is a visible–near-infrared pushbroom imagingdigitized to 12 bits and recorded on 8-mm video cassettespectrograph with a reflection grating and a two-dimen-by an Exabyte recorder. The specifications of the CASIsional CCD solid-state array measuring 5123288 pixelsare outlined in Table 2.(Shepherd, 1994). This instrument is portable, in that it

The cross-track resolution across the 358 field ofcan be mounted in various lightweight aircraft and heli-view (FOV) is a function of the height above groundcopters or on a ground-based platform. The CASI lookslevel (AGL) and equates to 1.23 m ground resolution pervertically downward, imaging successive lines over the1 km AGL (Shepherd, 1994). The along-track groundterrain to build up a two-dimensional image based on theresolution is approximately equal to the product of theforward movement of the aircraft. This imaging systemintegration time and the aircraft speed and is directlycan collect data in two modes: spectral mode, where con-proportional to the number of bands being recorded intinuous spectra for ground-resolution elements are col-

lected for up to 288 spectral bands for a selected subset spatial mode or the number of look directions in spectral

Figure 2. CASI data for Lowland Black Spruce.


Figure 3. CASI data for Trembling Aspen.

mode (Shepherd, 1994). The spatial resolution character- (1992). This conversion was applied to eliminate artifactspresent in the imagery (i.e., gain and offset variations be-istics of CASI data therefore do not resemble those of

traditional remote sensing systems. That is, image pixels tween detectors) and to convert digital numbers from ar-bitrary values to physical units of radiance. The datacollected by the CASI are not necessarily square, but arewere then converted to reflectance, to eliminate atmo-often rectangular, with different dimensions in the cross-spheric effects and compensate for changes in solar illu-and along-track directions.mination during image acquisition (Shepherd, 1994).CASI data were acquired from a Piper NavajoCalibration to reflectance provides a basis for comparisonChieftain aircraft on 30 July 1993 at an altitude of 600of reflectance values between adjacent flight lines. A hy-m AGL (Figs. 2 and 3). To minimize bidirectional reflec-brid model was used to perform this calibration usingtance (BRF) and effectively cover the study area, flightPIFs and an on-board downwelling irradiance sensor (In-lines were oriented parallel to the solar azimuth (i.e.,cident Light Probe [ILP]) (Shepherd, 1994; Shepherd etaway from the sun), restricting data collection to a two-al., 1995; Gray et al., 1997). The CASI data are cali-hour time window (10:30 a.m. to 12:30 p.m. local time).brated to reflectance but stored as 16-bit integers forFlight lines were flown over the study area at a groundmore efficient storage and data processing. The relation-speed of 149 knots, heading 3008 true with the sensorship between CASI digital number (DN) and reflectancepointing at nadir. This provided an integration time of(R) is R5(CASI DN)31026.70 ms. The CASI data were collected in nine spectral

bands (Table 3) and had an average spatial resolution of0.73 m in the cross-track direction and 5.36 m in the

METHODSalong-track direction.Additional flight lines were flown over pseudoinvari- In this study, experimental variograms derived from

CASI reflectance data were used to estimate the under-ant features (PIFs) (gravel, pavement and clover) to as-sist in image calibration (Schott et al., 1988). These flight lying variogram for selected forest ecosystems. Vario-

grams generated from visible and near-infrared datalines were flown at 170 m AGL and had a spatial resolu-tion of 0.2135.36 m. The CASI data were converted to were fitted using a spherical model to estimate the range

and sill of the variogram. This analysis is a necessary pre-radiance using software developed at the Center for Re-search in Earth and Space Technologies (CRESTech), requisite to estimating optimal sizes of support for re-

mote sensing data acquisition or textural processing. Thewith input from Itres Research (the manufacturer of theCASI), using algorithms developed by Babey and Soffer variogram is used to measure the spatial dependence of


Table 3. CASI Imaging Mode Wavelengths

Channel number Center wavelength Bandwidth (nm) Band range (nm)

1 450.3 31.2 434.7–465.92 549.5 20.8 539.1–559.93 590.5 20.9 580.1–600.94 633.5 20.9 623.0–644.05 670.3 15.6 662.5–678.16 739.7 10.2 734.6–744.87 747.0 6.6 743.6–750.38 790.4 10.2 785.3–795.59 873.9 28.4 859.6–888.1

neighboring observations for any continuously varying as by calculating the indicative goodness of fit (IGF). TheIGF gives a standardized measure of how well the modelphenomenon. Hence, it is a technique that can be ap-

plied to spectral data, a variable for which position in fits the data points (Pannatier, 1996).time and space is known. In this way, spatial variation in Note that the spherical models are designed forimages can be examined in relation to ground scene and punctual variograms, or variograms derived from pointsensor parameters (Woodcock et al., 1988a). The vario- measurements. However, remotely sensed image datagram plots semivariance (c) against spatial separation are area measurements. In this case, the variogram is re-along a given relative orientation and provides a concise ferred to as regularized, an averaging of the regionalizedand unbiased depiction of the scale and pattern of spatial variable over a given length or area. Here the regulariz-variability (Curran, 1988). The semivariance c is half the ing area equates to the instantaneous field of viewexpected squared difference between values of reflec- (IFOV) of the sensor, defined by the point spread func-tance at a distance of separation or lag, h, a vector in tion and the integration period over which reflectance isboth distance and direction. Similar to the way in which recorded by the CASI sensor. Simply, the pixel dimen-sample variance estimates true variance of a variable’s sions define the extent of regularization. Hence, thepopulation, the sill represents the semivariance estimate equation defined above is used to model the regularizedof the true variance of a regionalized variable. The exper- variogram in order to estimate the range and sill values.imental variogram or c(h) is calculated as Therefore, variation at a scale finer than the regulariza-

tion cannot be detected, and variations less than two toc(h)5

12N(h)o

N(h)

i51[Z(xi)2Z(xi1h)]2 three times the scale of regularization cannot be defined

with confidence (Woodcock et al., 1988a). In fact, thegeometry of the support may be complex, due to thewhere h is the lag (or distance in pixels) over which c

(semivariance) is measured, N is the number of observa- point spread function (PSF) of the sensor. This may beparticularly significant here because of the integrationtions used in the estimate of c(h), and Z is the value of

the variable of interest at spatial position xi. The value period and resulting elongated nature of the CASI re-flectance pixel. In addition, the true support is likelyZ(xi1h) is the variable value at distance h from x. In this

study, c(h) estimates the variability of reflectance, Z, as a greater than the spatial resolution of the sensor (Atkin-son and Curran, 1995) and likely varies across the imagefunction of spatial separation. In essence, the variogram

measures the correlation between pixels at successively due to the scan angle of the sensor.For the purpose of this study, a single visible bandgreater distances and will demonstrate a peak in variance

when pixels become independent of one another. This (580–601 nm) and a near-infrared band (744–750 nm)were used for calculating semivariance and deriving vari-lag interval to the peak in variance is known as the range

of influence of the variogram (Fig. 1). The accuracy of ograms from the CASI data. Homogeneous stands (land-scape units) of sufficient size were identified on aerialthe modeling process is dependent on (i) the number of

pairs of points used in the calculation of the variogram photographs and located on the CASI images. To exam-ine the spatial variability of forest ecosystems at high spa-and (ii) the lag distance between data pairs. To estimate

the appropriate parameters for a spherical model, the tial resolutions, transects of 100 pixels in the cross-trackdirection were extracted from the CASI data for selectedpoints of the experimental variogram are first plotted

along with the variance of the data, the variance usually forest stands representing individual V-types or com-plexes of V-types. It should be noted that the range esti-being about equal to the sill (Brooker, 1991). Nugget,

range, and sill estimates are then input to the spherical mate will be affected by the relationship between the so-lar azimuth and the direction of the transect. Since themodel and interactively modified to achieve the best fit

to the measured values (Fig. 1). The best fit was judged flight lines were oriented parallel to the solar plane, theanalysis of transects in the cross-track direction mini-by viewing the model curve fit to the data points, as well


mized the effect of shadows, thereby decreasing the For example, the ASP/M stands have range values be-tween a minimum of 5 pixels (3.7 m) and a maximum ofrange of the variogram in the cross-track direction (i.e.,

relative to the along-track direction). The distance, or lag 20 pixels (14.6 m). The minimum range values for ASP/M vary between 5 and 8 pixels (3.7–5.8 m) and the maxi-(h), over which the variogram is to be measured must be

larger than the range of influence and large enough for mum ranges vary between 9 and 20 pixels (6.6–14.6 m).As with the visible data, the minimum range values doany periodicities in the data to become apparent (Wood-

cock et al., 1988a). The length of the lag h determines not vary greatly for stands of similar class; however, themaximum values display a wide range between stands ofthe number of potential lags in any particular transect.

The confidence associated with the semivariance calcula- similar class and, in particular, between stands of differ-ent classes. The discrepancy between the minimum andtion decreases with increasing lag (i.e., the number of

calculations decreases with increasing lag length). Web- maximum ranges for the various forest ecosystem typesis likely a function of gaps in the forest canopy. Largester (1985) recommends that lags should not exceed one-

fifth to one-third of the transect length. Here, a lag equal gaps due to wind damage are common in mature decidu-ous and coniferous stands. In addition, for mixed stands,to 20 pixels was selected for analysis of transects of 100

pixels for most stands. the presence of tree crowns of varying size can give riseto a large variability in range estimates for any giventransect within a stand.RESULTS The ranges derived for the near-infrared data aregreater than those derived for the visible data for eachA summary of critical variogram parameters for the CASI

data is presented in Table 4. Here, the mean ranges ob- ecosystem class (e.g., 6 pixels/4.4 m [near-infrared] ver-sus 4 pixels/2.9 m [visible] for LBS [Stand B–V36/37/served for each stand are presented, along with the mean

sill (semivariance) values. It must be emphasized that 38]). In fact, in all the stands studied, the mean rangesderived for the near-infrared data are greater than thethese values represent the mean ranges and sills deter-

mined by sampling a stand-specific number (n) of tran- corresponding ranges for the visible data (Table 4; Figs.4–6). It is important to remember that the visible andsects of 100 pixels each, within the CASI imagery, for

selected forest stands. near-infrared data were sampled from the same transects,thereby sampling the same reflectance surface. TheThe mean ranges derived from the variograms for the

visible band (580–601 nm) indicate that trembling aspen- ranges are also much more variable for the near-infrareddata than for the visible data. High mean reflectancedominated hardwood and mixedwood (ASP/M) and coni-

fer mixedwood (CONM) stands have greater ranges (>8 generally gives rise to greater variability in reflectance.This can be attributed to multiple scattering in thepixels/5.8 m) than upland black spruce (UBS) stands (>6.5

pixels/4.7 m) and lowland black spruce (LBS) stands near-infrared.The sills for the ASP/M and CONM stands are much(>4.5 pixels/3.5 m) (Table 4; Figs. 4 and 5). Range val-

ues vary significantly for transects within similar forest greater than those of the LBS stands, particularly for thenear-infrared data (Table 4; Figs. 7 and 8). For example,ecosystems. For example, ranges in the visible band for

ASP/M stands vary from 4 to 16 pixels (2.9–11.7 m). On the mean near-infrared semivariance (sill) for Stand A(ASP/M–V5/V8) is approximately 1.793107, for Stand Tthe other hand, the minimum ranges do not vary greatly

between stands of similar ecosystem class (e.g., ASP/M; (CONM–V8/V14/V15/V16/V19) it is 2.233107, but forStand F (LBS–V37) the value is 1.603106 (Table 4; Fig.4–5.6 pixels/2.9–4.1 m) but the maximum ranges can

vary substantially (e.g., ASP/M; 10–16 pixels/7.3–11.7 m), 8). Although the trend is similar in the visible band, thedifferences are not as extreme. The variability betweenparticularly between stands of differing ecosystem classi-

fication (e.g., LBS (3 pixels/2.2 m) to ASP/M (16 pixels/ minimum and maximum sill values within stands of simi-lar class is also high for ASP/M and CONM for both the11.7 m). In general, the range values for the visible band

increase with stand complexity, which corresponds loosely visible and near-infrared data (Table 4; Figs. 7 and 8).Cedar mixedwood (CM) (Stand X–V22) and Low-with the continuum from pure hardwood to pure conifer.

Hardwood stands generally occur on richer sites and rep- land Black Spruce (Stand M–V34/V35/V36) display highsemivariance in the visible band, relative to the otherresent a more complex stand structure, whereas pure co-

nifers generally occur on sites of lower productivity and stands, a characteristic not evident in the near-infrareddata (Figs. 7 and 8). Also, LBS (Stand Q–V37/38) exhib-have a simpler structure.

A similar trend is observed with respect to the range its higher semivariance than the other lowland blackspruce, particularly in the near-infrared. This stand isvalues for the near-infrared data, where ASP/M and

CONM stands have greater ranges than LBS. For exam- predominantly V38, black spruce/leatherleaf/sphagnum,and has a very open canopy.ple, range values are approximately 10 pixels/7.3 m for

ASP/M and CONM; 9 pixels/6.6 m for UBS; and 7 pix- The shape of the variograms generally resembled theclassic form with various permutations, including the clas-els/5.9 m for LBS (Table 4; Figs. 4 and 6). Again, there

is a diversity in range values within and between stands. sic–periodic and classic–multifrequency (Curran, 1988)


Table 4. Summary of Geostatistical Analysis of Forest Ecosystems†

Forest Vegetation type–complex Transects (n) Mean Mean Mean Meanstand (by stand) (@ 100 pixels) range (h) range (m) sill (3103) Cov/Cor (3103)

Visible (580 nm–601 nm)A Aspen-Dominated Hardwood–V5/V6/V8/V9 6 8.2 6.0 303 260Sw Aspen-Dominated–Hardwood–V5/V8 5 6.2 4.5 150 145D Aspen-Dominated Hardwood–V6/V8 7 7.4 5.4 140 125Y Aspen-Dominated Hardwood–V6/V8 10 8.5 6.2 273 245P Aspen Complex–V9/V10/V11 11 8.5 6.2 345 335Pse Aspen Complex–V9/V10/V11 6 8.9 6.5 364 345Pne Aspen Complex–V9/V10/V11 6 6.7 4.9 260 250Se Aspen Mixedwood–V8/V11/V19 5 7.6 5.5 216 227T Aspen/Conifer Mix–V8/V14/V15/V16/V19 10 7.4 5.4 240 232X Cedar Mixedwood–V22 8 6.4 4.7 366 304K Upland Black Spruce–V33 10 6.6 4.8 167 168M Lowland Black Spruce–V34/V35/V36 5 5.6 4.1 401 351N Lowland Black Spruce–V35 4 3.7 2.7 221 250E Lowland Black Spruce–V35/V36 10 5.6 4.1 235 207Ua Lowland Black Spruce–V35/V36/V37 5 4.0 2.9 214 198Ub Lowland Black Spruce–V35/V36/V37 5 4.8 3.5 200 177F Lowland Black Spruce–V37 4 4.0 2.9 116 107B Lowland Black Spruce–V36/V37/V38 5 2.5 1.8 128 133Q Lowland/Wetland Black Spruce–V37/V38 9 3.9 2.9 228 248

Near-infrared (743.6 nm–750.3 nm)A Aspen-Dominated Hardwood–V5/V6/V8/V9 6 8.7 6.4 17 903 18922Sw Aspen-Dominated Hardwood–V5/V8 5 7.8 5.7 10 926 9860D Aspen-Dominated Hardwood–V6/V8 7 10.7 7.8 13 243 10733Y Aspen-Dominated Hardwood–V6/V8 10 11.7 8.5 25 580 19938P Aspen Complex–V9/V10/V11 11 9.8 7.1 18 076 17555Pse Aspen Complex–V9/V10/V11 6 11.4 8.3 24 652 21776Pne Aspen Complex–V9/V10/V11 6 10.5 7.7 14 928 14492Se Aspen Mixedwood–V8/V11/V19 5 8.3 6.0 13 848 16930T Aspen/Conifer Mix–V8/V14/V15/V16/V19 10 11.4 8.3 22 267 19604X Cedar Mixedwood–V22 8 7.7 5.6 9 389 8778K Upand Black Spruce–V33 10 8.9 6.5 2 853 2781M Lowland Black Spruce–V34/V35/V36 5 8.7 6.3 6 076 6104N Lowland Black Spruce–V35 4 9.6 7.0 4 869 4565E Lowland Black Spruce-V35/V36 10 6.6 4.8 5 407 5098Ua Lowland Black Spruce–V35/V36/V37 5 6.1 4.5 3 217 2854Ub Lowland Black Spruce–V35/V36/V37 5 5.7 4.1 4 130 4034F Lowland Black Spruce–V37 4 4.6 3.4 1 634 1614B Lowland Black Spruce–V36/V37/V38 5 7.0 5.1 2 038 2045Q Lowland/Wetland Black Spruce–V37/V38 9 7.3 5.3 8 364 8136

† The parameters, calculated for CASI data, are for the cross-track direction only. Altitude5600 m AGL. Nominal Spatial Resolution50.73m35.36m(3.9 m2)

(Fig. 4). In some cases, the variogram shape appears to A summary of optimal spatial resolutions for thebe more complex for the visible than the near-infrared landscape-scale ecosystem classes, as estimated from theband. For instance, the shapes of the variograms for the mean range values of the experimental variograms, isUBS (Stand K) are of the classic variety for the near- presented in Table 5. In addition, a summary of key for-infrared data, but are classic–multifrequency for the visi- est mensurational parameters for sample plots collectedble data. The variograms for the near-infrared data ap- for the stands used in the variogram analysis is presentedpear to have a smoother shape than those derived for the in Table 6. From comparing Tables 5 and 6, a numbervisible data. The northwest slope of Stand P (Pnw) has of observations can be made:a more variable variogram in the visible band; however,

• stem density varies greatly between ecosystemthis variability is not evident in the near-infrared band.classes, particularly between ASP/M and LBS,Also, the variability is not evident in either waveband forwhich corresponds to large differences in opti-the southeastern slope of Stand P (Pse). In Stand P,mal support sizes between these ecosystemsome of the transects in the near-infrared are un-classes;bounded, indicating that a range value was not reached

• the heights of the trees between different ecosys-within the lag period used for calculating the variogram.This was not the case for the visible reflectance data. tem classes correspond not only to differences in


Fig

ure

4.V

ario

gram

sde

rive

dfr

omse

lect

edfo

rest

stan

ds–e

cosy

stem

s.


Fig

ure

5.R

ange

valu

es(5

80–6

01nm

)fo

rfo

rest

ecos

yste

mco

mpl

exes

.


Fig

ure

6.R

ange

valu

es(7

44–7

50nm

)fo

rfo

rest

ecos

yste

mco

mpl

exes

.


Fig

ure

7.Se

miv

aria

nce

(580

–601

nm)

for

fore

stec

osys

tem

com

plex

es.


Fig

ure

8.Se

miv

aria

nce

(744

–750

nm)

for

fore

stec

osys

tem

com

plex

es.


Table 5. Summary of Optimal Spatial Resolutions–Textural Window Operatorsbased on Variogram Analysis of High Spatial Resolution CASIReflectance Data†

Forest ecosystem classes Visible Near-infrared†

Aspen-Dominated Hardwood 8 pixels 10 pixelsand Mixedwood 5.8 m/31 m2 7.3 m/38 m2

White Spruce/Balsam Fir 7.5 pixels 10 pixelsConifer and Mixedwood 5.5 m/30 m2 7.3 m/39 m2

Cedar Mixedwood 6.5 pixels 7.5 pixels4.7 m/25 m2 5.5 m/30 m2

Upland Black Spruce/Jack Pine 6.5 pixels 9 pixels4.7 m/25 m2 6.6 m/35 m2

Lowland Black Spruce 4.5 pixels 7 pixels3.3 m/18 m2 5.1 m/27 m2

Lowland/Wetland 4 pixels 7 pixelsBlack Spruce 2.9 m/16 m2 5.1 m/27 m2

† The estimates are based on the analysis of rectangular pixels in the cross-track direction.Since texture is two dimensional, the assumption is made that cross-track analysis is sufficientto represent the along-tack dimension as well.

range values for corresponding classes, but also First, the spatial variation or information of interest isimportant when selecting the optimal spatial resolution.to the levels of semivariance exhibited by differ-Second, to estimate the mean of some property over aent ecosystem classes;region, the size of support determines the precision of• mean maximum canopy diameter (MMCD) of 5the estimation. When the objective is to map some prop-m for ASP/M and CONM stands correspondserty by local estimation (i.e., reflectance), the spatial vari-closely to visible range values at 600 m (5.8 mation in the sample determines the precision of the esti-and 5.5 m, respectively);mates of the spatial variation amongst them, which in• MMCD estimates are less for CM, LBS, UBS,turn determines the information displayed (Dungan etand L/WBS than the ranges derived from low-al., 1994). However, when the objective is to estimatealtitude visible and near-infrared data;the mean of some property over the region of interest,• the percent of high shrub is large for ASP/Mthe spatial variation in the sample determines the preci-and CONM; andsion of the estimate only and that information is no• the percent of low shrub is greatest for L/WBS,longer a valid criterion (Atkinson and Curran, 1995).LBS, and ASP/M.

Range and sill values are of particular interest inA number of qualitative observations can be made relat- comparing the different forest ecosystem complexes. Theing the range and semivariance estimates derived from range indicates the distance at which pixels are no longerthe remotely sensed reflectance data to forest mensura- correlated and therefore provides a measure of the sizetion parameters. For instance, ecosystems such as ASP/ of the elements embedded within the image as expressedM and CONM that have low stem density also have by the variogram. The importance here is that the rangelarger range values, whereas LBS has a high density of is often related to the size or scale of the largest ele-stems associated with small range values (Tables 5 and ments in the scene, in this case the forest stand, that6). Also, ASP/M and CONM stands contain trees of produce the correlation structure (Jupp et al., 1989). Itgreater heights than UBS, LBS, CM, or L/WBS (Table provides a measure of the distance around a point at6), a characteristic that may contribute to higher esti- which spatial interpolation or processing is valid. In themates of semivariance for ASP/M and CONM. Crown case of remote sensing data, it represents (i) the optimaldiameter, as expressed by MMCD, seems to be the pa- spatial resolution to characterize the elements embeddedrameter most closely associated with range values, partic- within the image or (ii) the optimal window size at whichularly for the visible data. High percentages of high to apply textural or contextual measures to the imageshrub are associated with ecosystems exhibiting high data. The sill provides a measure of the variability of therange and semivariance estimates (i.e., ASP/M and reflectance values for the transect across the stand andCONM) (Table 5). implies that, at these values of the lag, there is no spatial

dependence between the reflectance values. The sill hasbeen associated with the complexity of the image dataDISCUSSIONand, hence, the complexity of the target surface (i.e., for-

Atkinson and Curran (1995) identify two criteria for est canopy).In this variogram analysis, there appeared to be nochoosing the optimal size of support or spatial resolution.


Table 6. Summary of Forest Mensurational Parameters

Tree High LowForest ecosystem Dominant Density MMCD Height cover shrub shrub

classes species (#/ha) (m) (m) (%) (%) (%)

Aspen-Dominated Populus tremuloides 663 5 22 54 60 35Hardwood and Picea marianaMixedwood Abies balsamea

Pinus banksianaBetula papyrifera

White Spruce/ Populus tremuloides 1050 5 19 66 63 18Balsam Fir Conifer Picea glaucaand Mixedwood Abies balsamea

Picea marianaBetula papyrifera

Cedar Mixedwood Thuja occidentalis 1500 2 12 54 15 25Picea mariana

Upland Black Picea mariana 1350 1 10 60 13 14Spruce/Jack Pine Pinus banksiana

Lowland Black Picea mariana 1700 2 15 33 12 40Spruce Abies balsamea

Lanix laricina

Lowland/Wetland Picea mariana 900 2 10 10 0 67Black Spruce

nugget effect, representing spatially independent vari- Based on the results observed from the variogramanalysis, there are two primary trends or characteristicsance, which generally arises from measurement error

(Huijbregts, 1975). Here, measurement error would arise that require discussion. Range and semivariance esti-mates vary as a function of (i) forest ecosystem class andfrom errors in the sensor, from the analog-to-digital con-

version or from preprocessing. In the absence of spatially (ii) spectral wavelength. These trends are related to theinteractions between ground scene and sensor parame-independent variance, the variogram would normally

pass through the origin. In fact, many of the variograms ters. Forest ecosystems represent ground-scene parame-ters and vary as a function of stand–site characteristicsobserved in this analysis appeared to have a negative

rather than a positive intercept. This has been noted pre- and disturbance history. The structures and processes ofthese ecosystems differ and as a result produce variableviously and is attributed to the fact that remote sensingreflectance patterns. Reflectance also varies as a functiondata are regularized and therefore appear below or in-of wavelength, not only the intensity at that wavelengthside the punctual variogram (Woodcock et al., 1988b).but also the spatial patterns that arise from contrastingSince the area sensed by the IFOV of a remote sensinginteractions at canopy and subcanopy levels.instrument is often larger than the spatial resolution of

the sensor (based on analog-to-digital conversion/sam-Forest Ecosystem Classpling), the condition of a negative intercept should be

more common. The effect that this process has is that ad- The ASP/M and CONM stands have greater range valuesjacent measurements (pixels) should be more strongly re- than pure conifer (particularly lowland conifer, wherelated to an associated value for semivariance lower than canopy and understory are generally simple and homoge-expected. However, Atkinson (1993) did observe a nugget neous). Individual trees may be the dominant feature af-effect, albeit small compared with the underlying variation fecting the variogram, particularly for the ASP/M stands,of the variogram. The effect was particularly evident at the since the mean crown diameters of aspen treeshighest spatial resolution (1.531.5 m pixels) and was at- (MMCD55 m) (Table 6) are approximately equal to thetributed to high signal-to-noise ratios; however, the nugget ranges of influence (visible55.8 m; near-infrared57.3 m)variance decreased with increasing spatial resolution for (Table 5). This finding seems to be consistent with re-both spectral bands, implying an inverse relationship with sults reported by Cohen et al. (1990), where the rangesspatial resolution. Here, measurement errors should be for 1-m spatial resolution data were related to the meanminimal, since the CASI data were calibrated to reflec- tree canopy sizes of the stands. However, since in this

study pixels are not square and individual trees are nottance and corrected for atmospheric variations.


observable, it is possible that the range and semivariance and CONM stands, a result of contributions from a vari-result from the integration of a stable number of tree able canopy as well as sparse to dense understory. In acrowns with understory and ground-cover components lowland environment, drainage and nutrients are morefor a given ecosystem class, particularly since these are uniform, thereby giving rise to a more uniform environ-open canopy forest stands. On the other hand, the fact ment. In the case of LBS, this environment is one withthat individual tree crowns are not resolvable does not a poorer nutrient regime, deriving a more simple ecolog-preclude the influence of mean canopy diameter on the ical structure with lower diversity. The variability inrange of the variogram. Atkinson and Danson (1988) range and semivariance estimates may also be related toused variograms to measure spatial dependence in conif- location within the scan swath, since spatial resolutionerous and oak plantations. They found the range of the varies slightly from nadir to the edge of the scan. Spatialvariogram was related to stand age and species, and were resolution will also vary as a function of aircraft attitudeable to determine the optimal spatial resolutions for (i.e., pitch, roll, and yaw) during image acquisition. How-even-aged stands. Curran (1988) analyzed 232-m spatial ever, this is not expected to be a major factor, since theresolution data and found minimum range values for co- CASI is a narrow-swath sensor (i.e., 512 detector ele-niferous plantations to be 12 m and deciduous woodland ments) and has been roll-corrected. This analysis sug-26 m. This illustrates the contrasts between (i) coniferous gests that, to optimally characterize forest ecosystems, inand deciduous forest and between (ii) artificial and regu- the L-resolution case (i.e., the target being the forestlarly spaced trees versus natural and variably spaced stand as opposed to individual trees) (Strahler et al.,trees. Bowers et al. (1994) were able to measure differ- 1986), coarser resolutions would best be used to charac-ences in variogram characteristics for thinned, un- terize complex heterogeneous ecosystems and finer reso-thinned, damaged, and undamaged balsam fir stands lutions would provide optimal discrimination of bound-using SPOT panchromatic data. These spatial character- aries for simpler homogeneous ecosystems. Historically,istics were superior to spectral measures for examining variable classification accuracies by class have resulteddamage incidence and forest structure (stems/hectare). from this characteristic of single spatial resolution remote

The relationship between stand density and semiva- sensing data. Therefore, multiple spatial resolutions mayriance is also evident in this study. For example, Stand Q provide more useful information for discriminating forest(L/WBS–V37/38) has higher semivariance measures and ecosystems exhibiting differing stand structures and pro-greater range estimates than other LBS stands due to a cesses. This may be accomplished through a form oflower density (900 stems/ha versus 1700 stems/ha) and nested analysis using variable window sizes and texturaltree cover (10% versus 33%) (Table 6). Stand M (LBS), or upscaling algorithms (Wulder et al., 1996; Hay et al.,on the other hand, is a very open stand: density5300 1997; Bian and Butler, 1999). While the research pre-stems/ha; mean spacing interval (MSI) or the average sented here has provided insight into the variability ofdistance separating trees55.3 m; tree cover58%. This canopy reflectance from different types of forest ecosys-sparse stand has higher semivariance values than stands tems, the challenge remains as to how these differentof higher density. This indicates that stand density has a levels of variability can provide opportunities for ex-direct effect on the nature of the variogram, since the tracting information on different types of forest eco-character of the trees themselves (e.g., height, MMCD) systems.is similar to other LBS stands. In an open canopy, therewill be a more balanced mix between sunlit and shad-

Spectral Wavelengthowed parts of the canopy and hence a higher probabilityAs in the visible reflectance data analysis, ASP/M andthat two pixels in a pair of lag h will exhibit very differ-CONM stands have greater ranges than LBS within theent brightness levels, thereby contributing to a highernear-infrared band. This suggests that similar featuressemivariance for the stand as a whole. This is particularlyand phenomena (e.g., tree crowns and associated un-true for visible reflectance data, since in an open canopyderstory) are influencing the spectral reflectance in thethere is more potential for interaction with understorynear-infrared data for these ecosystems. The sills for theshrub components, creating greater semivariance as ob-ASP/M and CONM stands are much greater than thoseserved for Stands N (LBS) and Q (L/WBS). The majorityof the LBS stands, particularly for the near-infrared data.of LBS stands have a large number of stems/hectare ofThis indicates that the trembling aspen stands have in-uniform height and crown diameter, thereby exhibiting acreased layering, with a higher percentage of cover thanrelatively smooth surface at this scale. Cohen et al.the black spruce stand. It appears that the complexity of(1990) also observed that stands with simple canopythe stand is more prevalent with the near-infrared data,structures had lower sill values than stands with complexlikely due to the greater penetration of near-infrared en-canopies or gaps in the canopy.ergy through the canopy. Not only should spatial resolu-The LBS stands exhibit lower semivariance than thetions be optimized for specific forest ecosystem classes,ASP/M and CONM stands. The variability between vari-

ograms also indicates the complex nature of the ASP/M but also for the spectral wavelengths being collected. At


the least, the contrasting interaction of visible and near- reflectance data. In the near-infrared, the higher dy-infrared energy should be thought of as distinct, and data namic range for vegetation provides for the characteriza-acquired appropriately. Here, it has been shown that vis- tion of more subtle and local spatial variations. This char-ible and near-infrared energy are measuring slightly dif- acterization, as evidenced by the variogram analysis,ferent structures and/or processes, and to optimize dis- suggests that optimal discrimination of forest ecosystemcrimination of those structures–processes, the visible and classes at a landscape scale, rather than at a local scale,near-infrared data should be collected at different reso- requires more regularization in the near-infrared than inlutions—or at least should be processed differently. Cur- the visible portion of the spectrum. Alternatively, largerran (1988) suggested that, after analysis of variograms window sizes for textural operators should be applied tofrom different wavelengths, a minimum range should be the near-infrared data to compensate for the increasedidentified to define a minimum spatial resolution for re- spatial information and variability in this waveband.mote sensing data acquisition. However, there may bepotential to use multispatial resolution data for different

CONCLUSIONSwavebands to assist in the discrimination of features thathave defined different ranges in different spectral bands. The approach taken in this study has been to attempt to

The mean ranges derived for the near-infrared data understand the nature and causes of spatial variation inare, in all cases, greater than the corresponding ranges remotely sensed data of forest ecosystems as they relatefor the visible data. Multiple scattering occurs in the to ground scene and sensor parameters. As a result of thenear-infrared and, hence, adjacency effects are more sig- construction and analysis of variograms for forest ecosys-nificant. Different features or phenomena are therefore tems from 600 m AGL CASI reflectance data, the follow-being measured in the near-infrared as opposed to the ing conclusions can be drawn with respect to range andvisible—or, at the very least, the proportional contribu- semivariance (i.e., sills) estimates for forest ecosystems:tions of dominant features and phenomena (e.g., tree

1. Range estimates differ between various forest eco-crowns) to reflectance differ between the visible andsystem classes, and appear to be related to can-near-infrared bands. It is also likely that the near-infra-opy diameter (i.e., MMCD). It is likely that therered band contains more ground-cover information, sinceis not a clear relationship of range to MMCD,near-infrared energy has greater potential for penetrationdue to the severely elongated nature of the pixelthrough a forest canopy. Therefore, ground cover suchat this altitude. The range may more effectivelyas sphagnum moss may have an important effect onrepresent the proportion of the area covered bynear-infrared reflectance from black spruce stands withthe dominant object in the pixel or support (i.e.,little or no shrub layer. In a comparison of the visibletree crown).and near-infrared spectral bands of Landsat TM and

2. Range estimates vary as a function of wavelength;SPOT, Chavez (1992) found that the near-infrared bandi.e., contrasting estimates were derived for the visi-contains more spatial information than the visible band.ble and near-infrared wavebands. This suggestsIn general, the shapes of the variograms arethat different spatial processes are being mea-smoother and more rounded for the near-infrared datasured at these two wavelengths, or at the verythan for the visible. The near-infrared and visible dataleast, different proportions of structures–processesmay be sampling different scales of information, particu-are being measured. Since a greater percentagelarly since the visible data have a higher frequency ofof near-infrared energy is likely to penetrate thevariable sills and periodicity, with shapes more often re-canopy, the understory has a greater effect on thesembling the classic–periodic and classic–multifrequency.return signal, thereby modifying the effect of treeThe near-infrared data may be sampling a coarser scalecrown on the nature of the variogram. In theof structures–processes, since some of these minor per-near-infrared there may be lower correlation withturbations are smoothed out in the near-infrared vario-upper-canopy components than in the visible.grams. Unbounded variograms are more common for

3. Although it is a qualitative assessment, there ap-complex stands in the near-infrared than in the visible.pears to be a direct relationship between percentThis suggests that the scale of structures–processes incover of understory and semivariance (i.e., heightthe near-infrared is larger than the lag (which is 20 pix-of the variogram). We suggested that, althoughels). Periodicity and variability of sills may indicate thesemivariance is related to density of trees in natu-complexity of the canopy and correspond to clumpinessral ecosystems, it is more closely related to multi-of canopy and understory trees. However, no consistentple layers of vegetation (i.e., tree canopy density–patterns were observed within individual stands.variability and understory density–variability). InRemotely sensed data for vegetated areas have athis regard, the height of the variogram can pro-lower dynamic range and amplitude of reflectance in thevide useful information on the type of forest eco-visible portion of the spectrum than in the near-infrared.

This results in a lower spatial variability for the visible system present.


Bay, ON, Canada, August 14–18, Natural Resources CanadaRemotely sensed data often exhibit several scales ofand Ontario Ministry of Natural Resources, pp. 8–9.variation or information at a single spatial resolution.

Atkinson, P. M. (1993), The effect of spatial resolution on theThis has been exhibited in this analysis of reflectance forexperimental variogram of airborne MSS imagery. Int. J. Re-forest ecosystems in northwestern Ontario. Contrastingmote Sens. 14:1005–1011.ecosystems have different scales of variation, variation

Atkinson, P. M., and Curran, P. J. (1995), Defining an optimalthat is reflected in the contrasting range and semivari-size of support for remote sensing investigations. IEEE

ance estimates from the experimental variograms. In- Trans. Geosci. Remote Sens. 33(3):768–776.deed, it has been stated that the ability to derive infor- Atkinson, P. M., and Curran, P. J. (1997), Choosing an appro-mation about multiple scales of variation in images from priate spatial resolution for remote sensing. Photogramm.variograms may prove to be one of the more attractive Eng. Remote Sens. 63:1345–1351.features of variograms (Woodcock et al., 1988b). Al- Atkinson, P., and Danson, F. (1988), Spatial resolution for re-though the detection of multiple scales of variation has mote sensing of forest plantations. In International Geosci-

ence and Remote Sensing Symposium (IGARSS ’88): Remotebeen demonstrated, interpretation of their characteristicSensing, Moving Toward the 21st Century, 12–16 Septem-differences remains difficult.ber 1988, Edinburgh, Scotland, European Space Agency,For mapping by remote sensing it is important thatParis, France, pp. 221–223.the spatial variation or information of interest be re-

Atkinson, P. M., Dunn, R., and Harrison, A. R. (1996),solved. In mapping forest ecosystems by remote sensing,Measurement error in reflectance data and its implicationsit is the objective to estimate the mean reflectance offor regularizing the variogram. Int. J. Remote Sens. 17:different forested surfaces, under the assumption that 3735–3750.

the mean reflectances for different forest ecosystems Babey, S., and Soffer, R. (1992), Radiometric calibration of therepresent discriminable objects. This estimation requires compact airborne spectrographic imager (CASI). Can. J. Re-the appropriate regularization of reflectance arising from mote Sens. 18(4):233–242.stand structural characteristics. However, it is not ex- Bellehumeur, C., and Legendre, P. (1998), Multiscale sourcespected that optimal spatial processing of reflectance data or variation in ecological variables: modeling spatial disper-

sion, elaborating sampling designs. Landscape Ecol. 13:or collection of reflectance data at what are deemed op-15–25.timal and multiple scales will, in themselves, provide suf-

Bian, L., and Butler, R. (1999), Comparing effects of aggrega-ficient discrimination of forest ecosystems, even at land-tion methods on statistical and spatial properties of simu-scape scales. The reason for this is that optimallylated spatial data. Photogramm. Eng. Remote Sens. 65(1):regularized reflectance is not likely to represent a highly73–84.efficient surrogate for a forest ecosystem class. There-

Bowers, W. W., Franklin, S. E., Huddak, J., and McDermid,fore, suitable discrimination and classification of forestG. J. (1994), Forest structural damage analysis using image

ecosystems will require remote sensing at optimal and semivariance. Can. J. Remote Sens. 20(1):28–36.multiple scales in combination with additional terrain de- Brooker, P. I. (1991), A Geostatistical Primer, World Scien-scriptors related to ecosystem class. tific, London.

Burrough, P. A. (1987), Principles of Geographic InformationSystems for Land Resources Assessment. Monographs onFunding for this research has been provided through the North-Soil and Resources Survey No. 12, Clarendon Press, Oxford.ern Ontario Development Agreement, Northern Forestry Pro-

gram. Several agencies contributed to the success of the field Carr, J. R. (1996), Spectral and textural classification of singleprogram, including: Canadian Forest Service–Natural Resources and multiple band digital images. Comput. Geosci. 22:Canada; Northwest Region Science and Technology Unit– 849–865.Ontario Ministry of Natural Resources; Earth-Observations Chavez, P. S. Jr. (1992), Comparison of spatial variability in vis-Laboratory–Institute for Space and Terrestrial Science; and the ible and near-infrared spectral images. Photogramm. Eng.Provincial Remote Sensing Office–Ontario Ministry of Natural

Remote Sens. 58(7):957–964.Resources. Additional financial support was provided throughCohen, W., Spies, T., and Bradshaw, G. (1990), Semivario-the University of Waterloo (scholarships and teaching assistant-

grams of digital imagery for analysis of conifer canopy struc-ships), the Natural Sciences and Engineering Research Councilture. Remote Sens. Environ. 34:167–178.of Canada (NSERC)–Research Grants awarded to Philip How-

Collins, J. B., and Woodcock, C. E. (1999), Geostatistical esti-arth and Paul Treitz and the Undergraduate Student ResearchAwards program, and the Environmental Youth Corps program mation of resolution-dependent variance in remotely sensedfunded by the Ontario Ministry of Natural Resources. images. Photogramm. Eng. Remote Sens. 65(1):41–50.

Csillag, F., Kertesz, M., and Kummert, A. (1996), Samplingand mapping of heterogeneous surfaces: multi-resolution til-

REFERENCES ing adjusted to spatial variability. Int. J. Geogr. Inf. Syst.10:851–875.

Curran, P. J. (1988), The semivariogram in remote sensing: anAnonymous (1994), The physical environment of the Rinkerintroduction. Remote Sens. Environ. 24:493–507.Lake research area. In Rinker Lake Ecological Study Tour:

Curran, P. J., and Atkinson, P. M. (1998), Geostatistics and re-Field Tour Book. A workshop presented at the Global toLocal: Ecological Land Classification Conference, Thunder mote sensing. Prog. Phys. Geogr. 22(1):61–78.


Dungan, J. L., Peterson, D. L., and Curran, P. J. (1994), Alter- J.-P. (1994b) Remote sensing and the measurement of geo-graphical entities in a forested environment. 2. The optimalnative approaches for mapping vegetation quantities using

ground and image data. In Environmental Information Man- spatial resolution. Remote Sens. Environ. 49:105–117.Matheron, G. (1963), Principles of geostatistics. Econ. Geol.agement and Analysis: Ecosystem to Global Scales (W.

Michener, J. Brunt, and S. Stafford, Eds.), Taylor & Fran- 58:1246–1266.McGwire, K., Friedl, M., and Estes, J. E. (1993), Spatial struc-cis, London, pp. 237–261.

Gower, J. F. R., Borstad, G. A., Anger, C. D., and Edel, H. R. ture, sampling design and scale in remotely sensed imageryof a Californian savanna woodland. Int. J. Remote Sens.(1992), CCD-based imaging spectrometry for remote sens-

ing: the FLI and CASI programs. Can. J. Remote Sens. 14:2137–2164.McLean, N. H., and Uhlig, P. W. C. (1987), Field Methods18(4):199–208.

Gray, L., Freemantle, J., Shepherd, P., Miller, J., Harron, J., Training Manual for the Northwest Region FEC Mensura-tion Unit. Unpublished report, Ontario Ministry of Naturaland Hersom, C. (1997), Characterization and calibration of

the CASI airborne imaging spectrometer for BOREAS. Resources (OMNR), Toronto.Miranda, F. P., Fonseca, L. E. N., and Carr, J. R. (1998), Sem-Can. J. Remote Sens. 23(2):188–198.

Hay, G. J., Niemann, K. O., and McLean, G. F. (1996), An ivariogram textural classification of JERS-1 (Fuyo-1) SARdata obtained over a flooded area of the Amazon rainforest.object-specific image-texture analysis of H-resolution forest

imagery. Remote Sens. Environ. 55:108–122. Int. J. Remote Sens. 19(3):549–556.Pannatier, Y. (1996), VARIOWIN: Software for Spatial DataHay, G. J., Niemann, K. O., and Goodenough, D. G. (1997),

Spatial thresholds, image-objects, and upscaling: a multi- Analysis in 2-D. Springer-Verlag, New York, 91p.Paradine, D. (1994), Section 4.3. FEC data collection. Inscale evaluation. Remote Sens. Environ. 62:1–19.

Huijbregts, C. J. (1975), Regionalized variables and quantitative Rinker Lake Data Report: 1993–1994 (V. J. Kalnins, P. M.Treitz, and P. J. Howarth, Eds.), Technical Report ISTS-analysis of spatial data. In Display and Analysis of Spatial

Data (J.C. Davis and M.J. McCullagh, Eds.), John Wiley & EOL-TR94-002, Earth-Observations Laboratory, Waterloo,ON, pp. 4-23–4-37.Sons, London, pp. 38–53.

Jensen, M. E., and Everett, R. (1994), An overview of ecosys- Racey, G. D., Whitfield, T. S., and Sims, R. A. (1989), North-western Ontario Forest Ecosystem Interpretations. NWOF-tem management principles. In Ecosystem Management:

Principles and Applications, Eastside Forest Ecosystem TDU Technical Report No. 46, Ontario Ministry of NaturalResources, Thunder Bay, ON, Canada.Health Assessment (M.E. Jensen and P.S. Bourgeron, Eds.),

USDA Forest Service, Pacific Northwest Research Station, Rowe, J. S. (1972), Forest Regions of Canada. Publication No.1300, Canadian Forestry Service, Department of the Envi-General Technical Report PNW-GTR-318.

Jupp, D. L. B., Strahler, A. H., and Woodcock, C. E. (1988), ronment, Ottawa, ON.Salwasser, H. (1990), Conserving biological diversity: A perspec-Autocorrelation and regularization in digital images. I. Basic

theory. IEEE Trans. Geosci. Remote Sens. 26(4):463–473. tive on scope and approaches. For. Ecol. Manage. 35:79–90.Schott, J. R., Salvaggio, C., and Volchok, W. J. (1988), Radio-Jupp, D. L. B., Strahler, A. H., and Woodcock, C. E. (1989),

Autocorrelation and regularization in digital images. II. Sim- metric scene normalization using pseudoinvariant features.Remote Sens. Environ. 26:1–16.ple image models. IEEE Trans. Geosci. Remote Sens. 27(3):

247–257. Shepherd, P. R. (1994), Section 3.3. Airborne remote sensingdata. In Rinker Lake Data Report—1993–1994. (V. J. Kal-Kalnins, V. J., Treitz, P. M., and Howarth, P. J. (1994), Rinker

Lake Data Report 1993–1994. Technical Report ISTS-EOL- nins, P. M. Treitz, and P. J. Howarth, Eds.), Technical Re-port ISTS-EOL-TR94-002, Earth-Observations Laboratory,TR94-002, Earth-Observations Laboratory, Waterloo, ON,

variously paged. Waterloo, ON, pp. 3-19–3-48.Shepherd, P. R., Freemantle, J. R., McArdle, S., and Miller,Lacaze, B., Raubal, S., and Winkel, T. (1994), Identifying spa-

tial patterns of Mediterranean landscapes from geostatistical J. R. (1995), A comparison of different operational reflec-tance generation methods applied to airborne CASI imag-analysis of remotely-sensed data. Int. J. Remote Sens. 15:

2437–2450. ery. In Proceedings, 17th Canadian Symposium on RemoteSensing: Radar Remote Sensing: A Tool for Real-Time LandLathrop, R. G. J., and Pierce, L. L. (1991), Ground-based can-

opy transmittance and satellite remotely sensed measure- Cover Monitoring and GIS Integration, 13–15 June 1995,Saskatoon, SK, Canadian Remote Sensing Society, Canadianments for estimation of coniferous forest canopy structure.

Remote Sens. Environ. 36:179–188. Aeronautics and Space Institute, pp. 268–273.Sims, R. A., Towill, W. D., Baldwin, K. A., and Wickware,Mackey, B. G., Sims, R. A., Baldwin, K. A., and Moore, I. D.

(1996), Spatial analysis of boreal forest ecosystems: Results G. M. (1989), Field Guide to the Forest Ecosystem Classifi-cation for Northwestern Ontario. Ontario Ministry of Natu-from the Rinker Lake case study. In GIS and Environmen-

tal Modeling: Progress and Research Issues (M.F. Good- ral Resources, Toronto.Smith, G. M., and Curran, P. J. (1996), The signal-to-noise ra-child, L.T. Steyaert, B.O. Parks, C. Johnston, D. Maidment,

M. Crane, and S. Glendinning, Eds.), GIS World Inc., Fort tio (SNR) required for estimation of foliar biochemical con-centrations. Int. J. Remote Sens. 17:1031–1058.Collins, CO, pp. 187–190.

Marceau, D. J., Howarth, P. J., and Gratton, D. J. (1994a), Re- St-Onge, B. A., and Cavayas, F. (1995), Estimating forest standstructure from high resolution imagery using the directionalmote sensing and the measurement of geographical entities

in a forested environment. 1. The scale and spatial aggrega- variogram. Int. J. Remote Sens. 16:1999–2021.Walsh, S. A., Sims, R., and Ford, J. (1994), Section 2.1. Gen-tion problem. Remote Sens. Environ. 49:93–104.

Marceau, D. J., Gratton, D. J., Fournier, R. A., and Fortin, eral description of the Rinker Lake research area. In Rinker


Lake Data Report—1993–1994 (V. J. Kalnins, P. M. Treitz, Woodcock, C. E., and Strahler, A. H. (1987), The factor ofscale in remote sensing. Remote Sens. Environ. 21:333–339.and P. J. Howarth, Eds.), Technical Report ISTS-EOL-

TR94-002, Earth-Observations Laboratory, Waterloo, ON, Woodcock, C. E., Strahler, A. H., and Jupp, D. L. B. (1988a),The use of variograms in remote sensing: I. Scene modelspp. 2-5–2-14.

Webster, R. (1985), Quantitative spatial analysis of soil in the and simulated images. Remote Sens. Environ. 25:323–348.Woodcock, C. E., Strahler, A. H., and Jupp, D. L. B. (1988b),field. Adv. Soil Sci. 3:1–70.

Webster, R., Curran, P. J., and Munden, J. W. (1989), Spatial The use of variograms in remote sensing: II. Real images.Remote Sens. Environ. 25:349–379.correlation in reflected radiation from the ground and its

implications for sampling and mapping by ground-based ra- Wulder, M. A., Franklin, S. E., and Lavigne, M. B. (1996),High spatial resolution optical image texture for improveddiometry. Remote Sens. Environ. 29:67–78.

Wiken, E. B., Senyk, J. P., and Oswald, E. (1981), Inventorying estimation of forest stand leaf area index. Can. J. RemoteSens. 22(4):441–449.land resources through an ecologically based approach. In

In-Place Resource Inventories: Principles and Practices Wulder, M. A., LeDrew, E. F., Franklin, S. E., and Lavigne,M. B. (1998), Aerial image texture information in the esti-(T. B. Brann, L. O. House IV, and H. G. Lund, Eds.), 9–14

August 1981, University of Maine, Orono, Maine, Society of mation of northern deciduous and mixed wood forest leafarea index (LAI). Remote Sens. Environ. 64:64–76.American Foresters, Bethesda, MD, pp. 144–148.

high spatial resolution remote sensing data for forest ecosystem classification: an examination of...

Documents