Quantification of landscape change from satellite remote sensing1

by S.E. Franklin2, E.E. Dickson2, D.R. Farr3, M.J. an sen^ and L.M. Moska14

Satellite remote sensing data and methods can be used to develop maps of large areas at different times in order to assess changes in forest ecosystem patterns and processes. Such maps are useful in understanding wildlife populations and habitat, forest biodiver- sity, and forest productivity. They may be important in ecological monitoring programs at multiple spatial and temporal scales, and could include assessment of structural aspects of the landscape, such as forest or habitat fragmentation. Quantification and measure- ment of landscape structure depend on the definition of landscape classes or patches, defined on the basis of more or less homoge- neous elements, which differ in some measurable way from neighbouring patches. In this paper, we review some of the issues, and provide examples using satellite remote sensing data, in the quantification of landscape structure in two Canadian forests. The link between landscape structure and biodiversity is provided through the emergence of ecological understanding of species richness, species-habi- tat or niches, and metapopulation dynamics.

Key words: forest disturbance, landscape metrics, satellite remote sensing, forest fragmentation, monitoring, biodiversity, change detection

Les donnCes et les mCthodes issues de la tClCdCtection par satellite peuvent &tre utilisCes pour Claborer des cartes couvrant de grandes superficies ii diffkrentes pCriodes de f a ~ o n B Cvaluer les changements dans les patrons et les processus des Bcosystttmes forestiers. De telles cartes permettent de mieux saisir les habitats et les populations fauniques, la biodiversitk forestittre, et la productivitC des for& Elles peuvent ttre importantes dans le cas des programmes de surveillance Ccologique effectuke h BiffCrentes Cchelles spatiales et tempo- relles, et pourraient comprendre 1'Cvaluation des aspects structuraux du paysage, comme la fragmentation des f o r b ou des habitats. La quantification et la mesure des structures du paysage dCpendent de la dkfinition des classes du paysage ouparcelles, qui sont dCfinies selon plus ou moins dlCICments homogttnes, qui different d'une quelconque f a ~ o n mesurable des parcelles adjacentes. Dans cet arti- cle, nous rivisons certaines de ces questions, et nous apportons des exemples tires des donnCes de tClCdCtection par satellite sur la quan- tification de la structure du paysage dans le cas de deux for& canadiennes. Le lien entre la structure du paysage et la biodiversitC est Ctabli par 1'Cmergence de la reconnaissance de la richesse en terme d'espttces, de l'habitat pour chaque espttce ou des niches, et des dynamiques des metapopulations.

Mots-clks : perturbation forestihre, mesures du paysage, tClBdCtection par satellite, fragmentation forestikre, surveillance, bio- diversit6, dCtection des changements

Introduction Natural and human disturbances, ecological succession, and

recovery from previous disturbances are all forces that mod- ify ecosystem pattern and processes within the landscape. For- man (1995) described five disturbance processes that change a landscape, influence habitat loss, and can occur simultane- ously. The types of alterations to the landscape produced by these processes are distinctive, providing target patterns to detect and monitor over time. Pe$oration results in the creation of holes in the patch or landscape. Dissection cuts a landscape area or matrix into equally wide, linear features, such as roads and pipelines. Fragmentation breaks and separates the matrix into smaller, non-contiguous segments or patches. Shrinkage causes the sizes of patches to decrease. Attrition results in patch disappearance. Together these concepts underlie current understanding of landscape structure and patch diver- sity - or the way in which patches are embedded in the back- ground matrix of the landscape - and provide suggested pat- terns for management (Baskent 1999), and potential monitoring

'Presentation to the Canadian Institute of Forestry, Rocky Mountain Section, Spring Technical Session on "Applications of Remote Sensing to Forestry: Current and Future," March 26, 1999, Edmonton, Alberta. 2Department of Geography, University of Calgary, Calgary, Alberta T2N 1N4. 3~oothills Model Forest, Box 6330, Hinton, Alberta T7V 1x6. 4Department of Geography, University of Kansas, Lawrence, Kansas.

programs (Bricker and Ruggerio 1998, Noss 1999). Compo- sition, structure, and boundary conditions are considered essential to functions of ecological systems (Urban et al. 1987, Turner 1989), and consequently, are critical for under- standing and monitoring processes important in wildlife pop- ulation and habitat analysis, forest productivity, and biodiversity or species richness assessment (Davis and Dozier 1990, Stoms 1992, Stoms and Estes 1993, Farr et al. 1999).

Dissection and fragmentation usually decrease the connectivity over an area in uninterrupted corridors or mahix (Forman 1995). Movement of species between habitat patches occurs when the distance between patches is not too great, and when corridors connect the patches. Isolation of habitat patches is likely to result in a decline or extinction of existing populations, especially for those species with large ranges and little tolerance for cross- ing less than optimal habitat. Elk (Cewus elaphus), for exam- ple, require extensive sections of adjoining habitat (Silbaugh and Betters 1997). Dispersal among subpopulations pro- motes gene flow and helps to decrease the probability of population extinction and fluctuations in population size (Anderson and Danielson 1997). The size and shapes of habitat patches are important because area-related edge effects may influence reproduction. Organisms sensitive to patch size may be particularly sensitive to habitat fragmentation and shrink- age. For example, the varied thrush (Ixoreus naevius) is a for- est interior species that avoids forest edges (Hansen et al. 1991).

NOVEMBERIDECEMBER 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE 877

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

If a large percentage of this bird's forest habitat were fragmented, its ability to survive would markedly diminish.

There may be a three-way relationship between spatial heterogeneity, the productivity of large-scale environments, and species richness of an area (Walker et al. 1992, Wickham et al. 1997). Areas of high net primary productivity (NPP) have more resources to partition among competing species, and can thus support a greater number of species and larger popula- tions than areas with low net productivity. The relationship will depend on the level of productivity, the particular species, and the balance between available soil nutrients and light (Rosenzweig and Abramsky 1993, Tilman and Pacala 1993). A positive relationship between avian species diversity and annu- al vegetative biomass was found using data obtained from NOAA AVHRR satellite data in Senegal (Jorgensen and Nohr 1996); a similar result with species richness was reported in California (Walker et al. 1992). Species richness in a community or land- scape may be a function of the number of niches, which may be predicted based on indicator species (Debinski and Brussard 1992), or habitat requirements (Avery and Haines- Young 1990, Stoms 1992, Aspinall and Veitch 1993, Lauver and Whistler 1993). Species richness of islands is a function of island area and colonization rate (MacArthur and Wilson 1967). At equilibrium, the local extinction rate is inversely relat- ed to area; that is, higher rates of extinction occur in smaller areas. Rates of immigration to islands decline with distance from the mainland colony. Therefore, larger islands closer to the mainland will have greater species richness than smaller, more distant, ones. Forest landscapes have been described as being oceans with islands (patches) of habitat; as forests become fragmented by disturbance, patches become smaller and more distance from one another (Harris 1984).

The monitoring of forest fragmentation and other structural changes is complex. For example, there is no single, simple measure of fragmentation that is insensitive to scale and patch input (Davidson 1998, Brown et al. 2000, Jaeger 2000); instead, fragmentation is an interpretation of several individual landscape metrics. Considered together, decreased mean patch size, mean patch perimeter, and dominance, and increased fractal dimension and total edge, were interpreted to be consistent with increased fragmentation by new roads and harvesting patterns in Wyoming (Reed et al. 1996). These patterns were described for a 35 350-ha study area based on aerial photography from 1950 to 1993 (Reed et al.. 1996). Sachs et al. (1998) and Hansen et al. (2000) suggest that even larger study areas must be considered to gain an appreciation of the regional context and broad-scale patterns (Tinker et al. 1998). Such area studies must be based on data that are consistent (no spatially correlated error) and complete (not sam- pled). Only satellite remote sensing data have these charac- teristics - the only synoptic, repeatable, and digital informa- tion source on large-area ecosystem patterns and processes. Although the technological learning curve is sometimes considered formidable (Wickland 1991), it appears likely that remote sensing technology will be used increasingly in large-area, multitemporal, ecological, and biodiversity mon- itoring programs. The US GAP program (Scott et al. 1996) is probably the best example; entire states or even larger regions are classified and mapped, with as many as 40 different cover type classes, using satellite remote sensing (e.g., Homer et al. 1997, Gonzalez-Rebeles et al. 1998).

Objectives In this paper, we briefly review the literature and present

our own examples of the use of satellite remote sensing clas- sification for landscape structure quantification for the ultimate purpose of monitoring biodiversity. Remote sensing data are an obvious choice in attempting to study and monitor the rela- tionships between biodiversity, productivity, and spatial pat- terns; consequently, an important challenge for the forestry user community is to understand and better utilize satellite remote sensing technology (Cohen et al. 1996, Olson and Weber 2000). Careful consideration is needed of a large number of issues, ranging from image spatial resolution, radiometric and geometric correction, image processing algorithms, stan- dardized output format, and accuracy assessment or validation procedures (e.g., Lillesand 1996, Congalton and Brennan 1998, Bergen et al. 2000, Lachowski et al. 2000, Wynne et al. 2000). To illustrate the process, we used satellite image classification maps over time in British Columbia and Alber- ta as input to a landscape analysis, which enabled the comparison and interpretation of landscape stnxcture in relation to disturbance features, such as harvesting and wildfires. In this paper, class and landscape level analyses are considered in light of our cur- rent ecological understanding of the link between landscape structure and biodiversity elements, such as forest fragmen- tation, and landscape and class patch dynamics.

Measuring Landscape Structure - A Review Landscape patterns, as they relate to landscape patches with-

in the background matrix, can be quantified from measures of: 1) composition, 2) structure, and 3) boundary conditions (Table 1). The variety and relative abundance of patch types are measures of composition; included are patch richness, patch diversity, and diversity indices. The size distribution of patch- es, the dispersion of patch types throughout the landscape, the contrast among patches, the patch-shape complexity, the contagion or clumping of patch types, and the corridors between patches are structural components of the landscape that can be quantified (McGarigal and Marks 1995, Haines- Young and Chopping 1996, Elkie et al. 1999). Structure

Table 1. Description of Measures of Landscape Pattern

Composition

Richness Diversity

Structure Size distribution

Dispersion

Contrast Shape complexity

Connectedness Boundary Boundary Richness Boundary Convergence

Boundary Diversity Index

General Description

The variety and relative abundance of patch types The number of different patch types The relative abundance of different patch types The configuration of the landscape The relative abundance or frequency of patches in different size classes The distribution of patches with respect to each other, regularly dispersed vs. clumped The relative difference among patch types The relative amount of edge per unit area; the fractal dimension The tendency of a patch type to occur next to another patch type The functional joinings between patches The transition zone between patches The number of different boundary types The proportion of of points where more than three patches converge The number and the area evenness of boundarv tvues

878 NOVEMBREDECEMBRE 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

relates to patterns of landscape disturbance, and is comprised of measures that quantify patch size, interpatch distance, spatial association of patches, edge to area ratios, corridors and connectivity.

The transition zones between patches and their intersections are important features to describe and quankfy. The patch bound- aries perform important e c o ~ o ~ i d a l functions, allowing passage of biotic and inorganic factors between patches, and contacts between core and edge dwelling species (Metzger and Muller 1996). Points where the boundaries of three or more landscape elements congregate may be important centres of resources and corridors for wildlife (Forman and Godron 1986). Boundaries resulting from anthropogenic activities (roads, fields, clearcuts) are generally sharper and less com- plex than those generated by natural processes, i.e., transitions between meadow and forest. or conifer and mixed conifer. Many phenomena are not naturally divided into discrete spatial units; instead, boundaries must be chosen from variables that gradually change across space - boundaries are oftenfuuy (Foody 1996,1999). For example, the boundaries between dif- ferent forest stands may be blurred when one stand structure smoothly intergrades into the next. For mapping forest stands, the line between a mixed conifer and pure conifer stand could be drawn in several places, as to determine from the ground as from the air (Lowell et al. 1996).

A confusing and overwhelming number of metrics has been developed to quantify landscape composition, struc- ture, and boundary conditions (Table 2). Modelled on ecological theory, these metrics attempt to quantify aspects of land- scape pattern thought to reflect or influence underlying eco- logical processes, i.e., patch area, shape, edge, core area, nearest neighbour, diversity, richness, evenness, contagion, inter- spersion, juxtaposition, contiguration, connectivity and circuity. Certain metrics require information concerning habitat require- ments for the species of interest (e.g., core area calls for the habitat area requirements of targeted species as input). How- ever, other measures, such as the mean shape average and land- scape diversity indices, are independent of underlying ecological process or habitat requirements, relying strictly on the geometric and spatial relationships of patches. Deciding on a set suitable for a particular study has been problematic; clearly, the met- rics chosen should offer unique information (O'Neill et al. 1988), and have ecological relevance. Interpreting the ecological rel- evance of landscape composition, structure, and boundaries - as expressed in these and other multidimensional land- scape metrics - has been problematic as well, remaining a fer- tile area for further research (Legendre and Fortin 1989, McGarigal and McComb 1995, Imhoff et al. 1997, Jorge and Garcia 1997, Frohn 1998, Nichols et al. 1998).

Many landscape metrics share fundamental measures of patch size, shape, perimeter-area ratio and inter-patch distance, and are therefore related to one another (Li et al. 1993, Cain et al. 1997, Hargis et al. 1998). Five indices, based on mul- tivariate factor analysis of 26 metrics calculated for 85 land use and land cover maps, were suggested by Riitters et al. (1995): 1) average patch per perimeter-area ratio, 2) Shannon conta- gion (texture), 3) average patch area normalized to the area of a square with the same perimeter, 4) patch perimeter-area scal- ing, and 5) the number of attribute classes. Certain metrics may be sensitive to map scale, number of classes, size and shape of patches, spatial distribution of patches, and other remote sens-

ing factors, such as image resolution (or minimum mapping unit in aerial photography), pixel size, and pixel or raster orientation (Frohn 1998). Benson and MacKenzie (1995) compared landscape parameters calculated from water and land classes using SPOT HRV, Landsat TM and NOAA AVHRR images of northern Wisconsin. For each image classified, different patch areas, shapes, and locations resulted. For example, small bodies of water that were detected at medium spatial resolutions were not recorded at low spatial resolution; the percent water and number of lakes estimated lowered as the image resolution decreased. Estimations of homogeneity and entropy were relatively invariant across the images, but the contrast, the number of patches, the average lake area, perime- ter, and fractal dimension increased.

Patch size and shape can significantly affect measures of edge density, contagion, mean nearest neighbour distance (for thinly distributed patches), proximity index, perimeter- area fractal dimension (for abutting patches), and mass frac- tal dimension (for enlarging patches with increased disturbance) (Hargis et al. 1998). Raster orientation changes the proportions of pixel adjacency (Fmhn 1998). By shifting an image or a raster- based classification map 45 degrees, the straight edges of a rec- tilinear patch become serrated as the comers of edge pixels jut into the adjacent patch; this shift effectively increases the pro- portion of adjacent pixels. Contagion is determined by calculating pixel adjacencies; therefore, raster orientation will affect val- ues of contagion. Understanding the ways in which image res- olution, pixel size, number of patch classes, patch size, patch shape, and raster orientation affect measures of landscape pat- tern may be critical when analyzing the types and quantities of change in the landscape over time. Whenever possible, images should be compared that share the same image resolution, pixel size, and raster orientation. When comparing images using met- rics affected by issues that can not be held constant, i.e., patch size and shape, other unaffected metrics should be included in the study as controls.

Example 1: Areas of Forest Change near Hinton, Alberta (1996-1998)

The first example is located just south of Hinton, Alberta at the eastern edge of the Rocky Mountains. The analysis of this area using satellite imagery acquired at two different times shows that differences in landscape structure can be detect- ed and quantified by class and over the entire landscape (Dickson et al. 2000). We classified 1996 and 1998 Landsat satellite images and compared two subareas within this region that differed in their harvesting patterns over the two-year time period; the first subarea (Fig. la) showed little change. Only a few new roads, cutblocks, and oil and gas well sites appeared in the time between the two images. The second subarea showed more change (Fig. lb); clearcuts were recognizable (rhomboidal shapes), and mad development was relatively exten- sive. Four classes were identified by a modified unsupervised classification approach: Vegetation 1 (forest); Vegetation 2 (re-growth in older clearcuts, shrub, and grassland); Vegeta- tion 3 (rapidly growing vegetation including recent clearcuts); and Disturbance (roads, gravel, mining). These classes were used as the patch and matrix components for analysis using Patch Analyst software (Elkie et al. 1999).

Landscape metrics calculated for two subareas in Fig. l a and lb suggest that the Low Activity (Fig. la) subarea was less frag-

NOVEMBERlDECEMBER 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

Table 2. A Selection of Landscape Indices

Index type Index descriptionldefinition

Area Metrics Total Landscape Area Largest Patch Index (%) Percentage of area accounted for by the largest patch Number of Patches Patch Density Number of patches per unit area Number of Classes Mean Patch Size Patch Size Standard Deviation Absolute measure of patch size variability Standard Deviation of Mean Patch Size Percentage variation (relative) Dominance The degree to which proportions of each patch type on the landscape predominates Permeability Area of unsuitable patches (for transmission) divided by the total area

Edge Metrics Total edge Total length of all patch edges Edge density Length of patch edge per area Contrast-weighted edge Length of patch edge per area, weighted by edge contrast Total edge contrast index The degree of contrast between a patch and its immediate neighborhood Mean edge contrast index The average contrast for patches of a particular class Area-weighted MECI Patches are weighted by their size Isolation % edge adjoining similar patch types

Shape Metrics Landscape shape index Measures of landscape compared to a standard Mean shape index Average patch shape (perimctertarea) for a patch clas Area-weighted mean shape index Patches are weighted by their size, then mean shape calculated for class and landscape 2 x log fractal dimension Departure of landscape mosaic from Euclidean geometry Index type Index description/definition Fractal Dimension The complexity of patch shape on a landscape Mass fractal dimension The total complexity of the map matrix Mean patch fractal dimension Based on the fractal dimension of each patch Area-weighted mean patch fractal dimension Patches are weighted by their size, then fractal dimension calculated for class and landscape Elongation Diagonal of smallest enclosing box divided by the average main skeleton width Square Pixel (SqP) The shape complexity of patches on a landscape

Core Area Metrics Core area Area of interior habitat defined by specified edge buffer width Number of core areas Core area density Number of core areas per unit area Mean core per patch Core area standard deviation Absolute measure of core area variability Disjunct core Within a patch, 2 or more disjunct core areas Total core area index The percentage of a patch comprised of the core area

Nearest Neighbor Metrics Mean nearest-neighbour distance For a class or for the landscape as a whole Nearest-neighbour distance The distance of a patch to the nearest neighbouring patch of the same type based on edge to

edge distance Proximity index The size and proximity distance of all patches whose edges are within a specified radius

of the focal patch Nearest-neighbour distance standard deviation A measure of patch dispersion Spatial autocorrelation Patch type spatial correlation; patch type distribution Mean proximity index For a class or for the landscape as a whole Interpatch Distance

Diversity, Richness and Evenness Metrics Shannon's diversity index A single number that captures both abundance and variety. The amount of information per patch Simpson's diversity index A single number that captures both abundance and variety. The probability that any types

selected at random would be different types. Patch richness Number of different patch types Patch richness density Patch richness standardized to per area Relative richness density Richness as a percentage of the maximum potential richness Shannon's evenness Relative abundance of different patch types Simpson's evenness Relative abundance of different patch types

Interspersion/Juxtaposition, Contagion and Configuration Metrics Contagion The tendency of land covers to clump within a landscape Dispersion Degree of fragmentationlcomplexity of patch boundaries Association Concentration of spatially distributed attribute variables Interspersion The number of pixels in a 3x3 square that are of a different habitat than the central pixel Juxtaposition Habitat edges are weighted by their habitat quality for each organism and those

surrounding the central pixel in a moving window are summed Fragmentation The tendency of land covers to break into small pieces within a landscape Patch Per Unit area (PPU) The degree of fragmentation of patches on a landscape

Connectivity and Circuitry Connectivity Number of links in a class network divided by the maximum number of links Circuitry Number of circuits in a class network divided bv the maximum number of circuits

NOVEMBREIDECEMBRE 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

I. LowAa~v~suberra~minadungesintheFormofnmcutbbdudo~rd~~~s~butmnynmcodsinche~lpcnoftht~.

D . c r W r C u m- rn-

b. High Acrhtiry subam. Note prrcnce of many new cutblocks d n d i v e road development

j Ei- W =- .rrru .- I - Fig. 1. Satellite imagery showing changes in two subareas in 1996 and 1998 imagery of the Hinton, Alberta region.

mented (larger, more complex patches, mare forest) than the High Activity (Fig. lb) subarea. By using the 1996 landscape metrics as a baseline for comparison, two obvious differ- ences between the subareas were cbxted: 1) the number of patches and 2) the mean patch size (Table 3). The Lav A&- ity subarea had significantly fewer (87 versus 140) and larg- er (179 ha versus 11 1 ha) gsrt~hes than the More Change sub- ~ I n a d d i ~ t h e p l r b c h s i a e ~ o f ~ m ~ ~ for t o w Activity (462) than for Mow Change (765). This sug- gests that in 1996 the largerjmtches of the Lav Activity laud- scape were more uniform in size than the patches of the High Activity landscape. The lan- megics ean be compiled not only to compare

t h e t w o ~ t s ~ g ~ ~ , ~ ~ m ~ Wrences in each of the classes used in the analysis. In 1998, for example, the iLbw Activity subarea was composed of larger areas afforest (Vegetation1 = 10 8 10 ha) than the High Aetivity subarea (Vegetation1 = a608 ha). Within the 15 625 ha subsueas, nearly 8% less forest was nmeasured in the High A c l i v i t y t h a u m t h e l a o ~ ~ ~ , i n t f i e ~ S l V d y ~ ~ v e ~ o n b ~ ~ T h e r s fore, the percentage of the area in forest is a rough indjcator ofthe amount of dis-, consequently, one i n w o n is that the High Activity area shown in Fig. lb was the more distorbed landscape of the two.

The total mount of edge is an indication of the spatial het- erogeneity of the landscape. Again, the differences in metrics

NOVENIBERIDECEMBER 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

Table 3. Selected landscape metries for the areas of forest change in the Hinton, Alberta subareas shown in Fig. 1

High Activitv (Fia. lb)

Metric Area Number of Patches Mean Patch Size (ha) Median Patch Size (ha) Patch Size Coefficient of Variance Patch Size Standard Deviation Total Edge (m) Edge Density Mean Patch Edge Mean Shape Index Area Weighted Mean Shape Index Mean Perimeter-Area Ratio Mean Patch Fractal Dimension Area Weighted Mean Patch Fractal Dimension Shannon's Diversity Index Shannon's Evenness Index

Metric Area Number of Patches Mean Patch Size (ha) Median Patch Size (ha) Patch Size Coefficient of Variance Patch Size Standard Deviation Total Edge (m) Edge Density Mean Patch Edge Mean Shape Index Area Weighted Mean Shape Index Mean Perimeter-Area Ratio Mean Patch Fractal Dimension Area Weighted Mean Patch Fractal Dimension Shannon's Diversity Index Shannon's Evenness Index

1996 1998 Change % Change 15 625 15 625 ' 0 - 0

140 197 ' 57 2 * 40.71 111.61 79.3 1 -32.29 ' -28.93 10.38 10.25 . -0.13 p;? -1.20

765.63 819.96 54.33 fl-t 7.10 854.50 650.35 -204.15 , A? -23.89

1 423 150 1453 550 ' ' 30 400 +* 2.14 91.08 93.03 , 1.95 . 2.14

10 165.36 . 7 378.43 , , -2786.93 ,-,*te - -27.42 2.41 2.11 -0.29 -12.14

11.93 10.70 -1.23 -10.28 348.97 452.90 103.93 29.78

1.13 1.11 -0.02 -1.55 1.27 1.24 -0.02 -1.82 1 .OO 1.07 0.07 7.19 0.72 0.77 0.05 7.19

Low Activity (Fig. la) 1996 1998 Change r2"sd$ %Change

15 625 15 625 0 0 87 90 3 3.45

179.60 173.61 . -5.99 -3.33 12.56 11.63 -0.94 -7.46

462.00 467.57 - ' ,I.;* ,; 5.56 - 1.20 829.75 Z. . 81 1.75 d?';' ' ' r ..&b.r:218.00 -2.17

' 1 455 250 1464400 ; 11: 9150 1 0.63 93.14 93.72 Y 0.59 . - - 0.63

16 727.01 16 271.11 -455.90 -2.73 2.91 2.87 -0.04 . - . -1.46 -

10.56 10.59 0.03 0.31 . 337.69 352.56 14.87 4.40

1.14 1.14 0.00 -0.19 1.26 1.26 0.00 -0.01 0.78 0.79 0.01 1.56 0.56 0.57 0.01 1.56

clearcuts). Of the 57 new patches in the High Activity subarea, 28 new patches appeared as Vegetation3 (the predominately new clearcut class), with the others distributed among the other three classes (e.g., 21 new patches appeared as fas t ) . The har- vesting that occurred between 1996 and 1998 dissected for- merly larger patches of forest, but new, forest patches appeared as well.

Other interpretations based on the class-by-class change in landscape metrics were suggested, for example, that the sub- area with the larger amount of change had patches that by 1998 were becoming more regular in size; that the largest increase in the uniformity of patch size occurred for the cutblocks; that larger patches were increasingly dissected by harvesting into smaller more uniform patches; that patch size of the remain- ing forest class was due to larger blocks being cut between 1996 and 1998 than prior to 1996; that total edge was increased (up to 75%) in the clearcut class and in the forest class (19%); that the mean shape index for clearcuts decreased by more than 50% where there were many clearcuts, but decreased by only 5% in areas of little change (Dickson et al.. 2000).

Example 2: Old-growth Forest Fragmentation near Mount Revelstoke National Park (1975-1997)

The study area for the second example of quantification and measurement of landscape structure is located to the north of

Mount Revelstoke and Glacier National near Bevelstoke, . British Columbia. The influence of extensive timber har- vesting and natural dis-ces (such as wildfb and avalanch- es) in the region has resulted in the replacement of large areas of the natural forest cover with an extensive pattan of clearcuts, burns, and avalanche paths. Of note is the dispro- portionate removal of old-growth cedar-hemlock stands by harvesting operations. The structural attributes of this vege- tation component provide important habitat requirements for old growth-dependent species, such as woodland caribou (Rangifer tarandus caribou), and must be managed in such a way as to maintain regional habitat connectivity. We gener- ated land cover maps from Landsat imagery acquired in 1975 and 1997. Using a combination of GIs, satellite imagery, and DEM data and methods (described fully by Deuling 1999, and Hansen et al. 2000), all cedar-hemlock stands classified with- in the cedar-hedock habitat units greater than 180 years of age were extracted for the 1975 and 1997 time per id and sub- jected to landscape metric analysis using Patch Analyst (EIkie et al. 1999).

In the maps shown in Fig. 2, the cedar-hemlock old-growth habitat areas were coded with a value of 1 (black) and all other features a value of 0 (white). The black patches uf old-pwth forest habitat appear embedded in the white matrix of the com- plete background landscape. As disturbance incretlEied between

NOVEMBREID~CEMBRE 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

1975 Time Period

N S4wdy area barndary

81113 Natmal Park

swle 0 5 l 0 h

5-

1997 %me Mod

M W - k r u n d a r y

Nafional Park

Scab 0 5 l O h

5- - ~-

1975 and 1997, the &--st habitat was evidedy eroded or perforated with background; i.e., areas of old growth, which cow& the patch, are inmingly cmwert- ed to the background matrix comprised of non-forested, mature forested habitat, m disturbance classes. Tbe old- growth forest class decmaml in size from 17 285 ha to 15 485 ha, a more than 10% change in the 22-year penad (Table 4). The number ofpatches increased frPJHl2592 to 2750, a 6% change. Mean paeh size decreased over 15%, and patch density i n c r d as would be e e with mare patches in the same total afea

The mean shape index m Table 4 showed am increase between time periods, indicating that & - M o ~ k old- growth f m t patches were becoming mom gxmetrlcw c o m p l e x o v e r t i m e , d ~ & e a x l k o r e n t d ~ i n t h i s ~ wasnotlarge(1.44%).Themeansb.lape~mayalso~ t o q u a n t i f y t t r e l d o n m ~ c 5 n ~ ~ ~ ~ ~ in old-growth patches. For example, the placement of a clearcut in the central portion of a habitat patoh, or perfom- t i o a o f t b e o l d - g r o w t h p t & , m a y ~ ~ ~ i n ~ l & tion to the patch ma. Conseque;mly, the g e d ~ wm- plexity and shape indez d the patch. would inemas. The edge density for the cxdm-hemlock old-growth forest

class from Id42 to 17.14 (4.39%). ?%is is tohaveoccnnedasa--ofthein- patches and the pdo~4tti~aaf the cedar-hemlock f m t area by the cutove~ class. lCllean am aim dmmsd nwre than 35%, and in 1997 was below 10 ha (based on a 100-m buffer d d f ~ t e d g e ~ ) . T h i s n z e t r i ~ i s l i n k e d b t h e ~ r m e a n ~ - imity index, which also sbQWpd a large pefcentagedecline from 1975 to 1997 (37.35%). These meamm appear to co&m the fragmentation of the Ce-darMock old-gmvth fmmt class: more edges, less core area, and a wider, m o ~ dispersed patch configuration. However, tJE thebution of class areas would appear to conform more to a perfomtion pz@m rather than a fragmentation pattan (Ebmm 1393, wi$l the netrv clkQ&aw appearing as new "hale%" and "incmsions9% in wntiguous fabric of the old-pwth forest habitat. The mean area is the metric in the old This suggests a stage cursor to, or an earlier stags, of th~ lhgmmted landscap in which the forest habitat or matrix is broken into &mete pieces.

Conclnsian Measuring and qntPntifying landwaye tmmpmition, Saruc-

ture, and boundary d t i o n s over time and ray l a r p ~ canbe-* d a n d a s u i t e o f ~ d ~ ~ W a r i s e ~ ~ ing the optimal remote sensing cladication product, and interpreting the ecological and statistical r e h o e o f detect- ed patterns. Difficulties include deciding on the appropiate set of landscape mtdcs and andblishing a link Iretween the data and the comp1ex ~ c o s y s ~ d ~ oal. the ground, such as increased forest fragmeatation and dec& Witat con- nectivity. Only now are tbe appmpriat~ m l o @ d intape-

I tations emerging, such that the changing, landscape pWms Fig. 2 Areas of old-growth cedar hemlock forest c h p from 1975 Can be amlied and Unh'St~od em a largw ~ o l o % i d to 1997 as detected by ~ s o l d s a t i m g q ~ mrn~melstake ~rdianal context. An emerging ec~logical mdemtanding of the link Park between landscape structure and ecosystem fundmhg, pw-

NOVEMBERIDECEMBER 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

Table 4. Comparison of Patch Metrics for old-growth cedar-hemlock forest stands at analysed on Lendsat imagery acgmIred in 1975 and 1997 (after Deuling 1999. Hansen et aL 2000) - ,

Metric 1975 1997 Change % Change

Class Area (Ha) 17 285.44 15 484.94 -1800.50 -10.42 Number of Patches 2592 2750 158 . 6.10 Mean Patch Size (Ha) 6.67 5.63 -1.04 ;, -15.59 Patch Density 1.48 1.57 0.09 6.10 Mean Shape Index 1.39 1.41 0.02 1.44 Edge Density 16.42 17.14 0.72 4.39 Mean Proximity Index 1 925.48 1 206.23 -719.25 -37.35 Mean Core Area (Ha) 14.50 9.17 -5.330 -36.76

ductivity, and biodiversity is partly based on an inmasing appre- ciation and use of satellite remote sensing data and methods in the user community. In this paper, we illustrate the current technology with two examples in the Hinton, Alberta and Rev- elstoke, British Columbia.

Fist, in Hinton, we used Landsat 1996 and 1998 imagery and a modilied unsupervised classification of two separate areas selected because of the different amounts of change that were visible in satellite imagery. The metrics indicated that the area of greater change (visible in the form of cutblocks and an extensive road network) had more, smaller, less complex, and more regularly shaped patches than did the area interpreted to contain fewer changes in the 1996 to 1998 time period. Class- by-class comparisons of metrics revealed that the area with the greater amount of change had most of the changes associat- ed with the Vegetation3 class that was comprised of cutblocks embedded in the matrix of undisturbed forest; such patches, by 1998 were becoming more regular and uniform in size. Larger patches of forest were increasingly dissected by harvesting into smaller more uniform patches; total edge was increased up to 75% in the clearcut class, and in the for- est class up to almost 20%; the mean shape index for the cut- blocks decreased by more than 53%. These patterns lead naturally, though tentatively, to the conclusion that the area that was most heavily impacted by harvesting and road devel- opment displayed a pattern of change that was consistent with increased fragmentation and decreased connectivity of the forest matrix.

Second, in Revelstoke, we used Landsat 1975 and 1997 imagery and a GIs-based decision-tree classifier to examine the overall pattern of change caused by harvesting and wild- fire in the old-growth cedar-hemlock forest. The old-growth forest class decreased in size by more than 10% change in the 22-year period; the number of patches increased by 6% change; mean patch size decreased over 15%; mean core area decreased more than 35%, and in 1997 was below 10 ha (based on a 100 m buffer around all forest edges). This met- ric when interpreted together with the mean proximity index (declined by 37.35%) appeared to confirm the fragmentation, or rather perforation, of the cedar-hemlock old-growth forest class; more edges, less core area, and a wider, more dis- persed patch configuration.

Acknowledgements The authors are grateful to Dr. Ron Hall (Canadian Forest

Service - Northern), Dr. Michael Wulder (Canadian Forest Ser- vice -Pacific), the Alberta Forest Biodiversity Monitoring Tech- nical Committee, and Murray Peterson of Parks Canada, for comments on earlier draft reports, which have since formed

the basis of this submission. Funding for this work was pro- vided by the Alberta Forest Biodiversity Monitoring Sheering Committee, the Canadian Forest Service, Parks Canada, and the Naanal Science and Enginmiq Research Council of Cana- da. The anonymous reviewers made many valuable sugges- tions and comments.

References Anderson, GS. and B J. D a n i e h 1997. The effects of landscape composition and physiognomy on metapopulation size: the role of corridors. Landscape Ecology 12: 261-271. Aspinall, R. and N. Veitch. 1993. Habitat mapping from satellite imagery and wildlife survey data using a Bayesian modeling pmdure in a GIS. Photogrammetric Engineering and Remote Sensing 59: 537-543. 1

Avery, M.I. and R.H. Haines-Young. 1990. Population estimates for the dunlin Calidris alpina derivedtkom remotely sensed satellite imagery of the Flow Country of m ~ k m Scotland Natwe 344 860-862. Baskent, E.Z. 1999. Controlling spatial structure of fmsted land- scapes: a case study towards landscape management. Landscape Ecology 14: 83-97. Bemn,B J. and M.D. MacKenzia 1995. Effects of sensor spatial resolution on landscape structure parameters. Landscape Ecology 10: 113-120. Bergen, g, J. Colwen and F. Sapia rnRemote sensing and fomtry: collaborative implementation for a new century of forest informa- tion solutions. Journal of Forestry 98: 5-9. Bricker, O.P. and MA, Ruggerio. 1998. Toward a national program for monitoring environmental resources. Ecological Applications 8: 326-329 Brown, D.G, J.D. Dub and S.A. Dmyzgt. 2000. Estimating error in an analysis of forest fragmentation change using North American Landscape Characterization (NALC) data. Remote Sensing of Envi- ronment 71: 106-1 17. Cain, D.H., ILK Riitters and K Orvis. 1997. A multi-scale anal- ysis of landscape statistics. Landscape Ecology 12: 199-212. Cohen, W.B., J.D. Kushla, WJ. Ripple aud S.L. Garman. 1996. An intmhction to digital methods in remote sensing of fmested ecosys- tems: focus on the Pacific Nolthwest, USA En-tal Management 20: 421-435. Congalton, RG. and M. Bmnnan. 1998. Change detection accu- racy assessment: pitfalls and consideratiom. In Proaxdngs, ASPRS Resource Technology Institute Annual Meeting, Tampa Bay, FL. On CD-ROM: 919-932. Davidson, C. 1998. Issues in measuring landscape fragmentation. Wildlife Society Bulletin 26: 32-37. Davis, F.W. and J. Dozier. 1990. Information analysis of a spatial database for ecological land classification. Photogrammetric Engi- neering and Remote Sensing 56: 605-613. Debinski, D.M. and P.F. Brussard. 1992. Biological diversity assessment in Glacier National Park, Montana: I. Sampling Design. In D.H. McKenzie, D.E. Hyatt and V.J. McDonald (eds.). Ecolog- ical Indicators. pp. 393407. Hartnoll's Ltd., Great Britain.

NOVEMBRE/D~~CEMBRE 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

Denling, M.J. 1999. Quantification and assessment of caribou habi- tat fragmentation. Unpublished MSc Thesis, Department of Geog- raphy, University of Calgary. Dickson, E.E., S.E. Franklin and L.M. Moskal. 2000. Monitoring of forest biodiversity using remote sensing: Regional landscape (medium and low spatial resolution) protocol and examples. Alberta Forest Biodiversity Monitoring Program Technical Report 4, Part 1. Elkie, P.C., R.S. Rempel and A.P. Carr. 1999. Patch Analyst User's Manual: A Tool for Quantifying Landscape Structure. NWST Technical Manual TM-002. Minstry of Natural Resources, Northwest Science and Technology, Thunder Bay, Ontario. Farr, D.R., S.E. Franklin, E.E. Dickson, G. Scrimgeour, S. Kendall, P. Lee, S. Hanus, N.N. Winchester and C.C. Shank. 1999. Monitoring forest biodiversity in Alberta program framework. Alberta Forest Biodiversity Monitoring Program Technical Report No. 3, Review Draft, March 1999. Foody, G.M. 1996. Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. Inter- national Journal of Remote Sensing 17: 13 17-1 340. Foody, G.M. 1999. The continuum of classification fuzziness in the- matic mapping. Photogrammetric Engineering and Remote Sensing 65: 443-45 1. Forman, RT.T. 1995. Some general principles of landscape and region- al ecology. Landscape Ecology 10: 133-142. Forman, R.T.T. and M. Gordon. 1986. Landscape ecology. Wiley and Sons, New York. Frohn, R.C. 1998. Remote Sensing for Landscape Ecology: New Met- ric Indicators for Monitoring, Modeling, and Assessment of Ecosys- tems. CRC Press LLC, Boca Raton, Florida. Gonzalez-Rebeles, C., VJ. Burke, M.D. Jennings, G. Cebanos and N.C. Parker. 1998. Transnational gap analysis of the Rio BravoRio Grande region. Photogrammetric Engineering and Remote Sensing 64: 1115-1118. Haines-Young, R. and M. Chopping. 1996. Quantifying land- scape structure: a review of landscape indices and their application to forested landscape. Progress in Physical Geography 20: 41 8 4 5 . Hansen, MJ., S.E. Franklin and C. Woudsma 2000. Caribou habi- tat classification and fragmentation analysis of old-growth cedarhem- lock forests in British Columbia using Landsat TM and GIs data Remote Sensing of Environment. In press. Hansen, AJ., T.A. Spies, FJ. Swanson and J.L. Ohmann. 1991. Conserving biodiversity in managed forests. BioScience 41 : 382-392. Hargis, C.D., J.A. Bissonette and J.L. David. 1998. The behavior of lan* metrics commonly used in the study of habitat fragmentation. Landscape Ecology 13: 167-186. Harris, L.D. 1984. The fragmented forest: Island biogeography the- ory and the preservation of biotic diversity. The University of Chicago Press, Chicago. Homer, C.G., R.D. Ramsey, T.C. Edwards, Jr. and A. Falconer. 1997. Landscape cover-type modeling using a multi-scene The- matic Mapper mosaic. Photogrammetric Engineering and Remote Sens- ing 63: 59-67. Imhoff, M.L., T.D. Sisk, A. Milne, G. Morgan and T. Orr. 1997. Remotely sensed indicators of habitat heterogeneity: Use of synthetic aperture radar in mapping vegetation structure and bird habitat. Remote Sensing of Environment 60: 217-227. Jaeger, J.A.G. 2000. Landscape division, splitting index, and effec- tive mesh size: new measures of landscape fragmentation. Landscape Ecology 15: 115-130. Jorge, L.A.B. and G.J. Garcia. 1997. A study of habitat frag- mentation in Southeastem Brazil using remote sensing and geographic information systems (GIs). Forest Ecology and Management: 35-47. Jorgensen, A.F. and H. Nohr. 1996. The use of satellite images for mapping landscape and biological diversity in the Sahel. International Journal of Remote Sensing 17: 91-109. Lachowski, H., P. Maus and N. Roller. 2000. From pixels to decisions: digital remote sensing technologies for public land man- agers. Journal of Forestry 98: 13-15.

Lauver, C.L. and J.L. Whistler. 1993. A hierarchical classification of Landsat TM imagery to identify natural grassland areas and rare species habitat. Photogrammetric Engineering and Remote Sensing 59: 627-634. Legendre, P. and M.J. Fortin. 1989. Spatial pattern and ecologi- cal analysis. Vegetatio 80: 107-1 38. Li, H., J.F. Franklin, F.J. Swanson and T.A. Spies. 1993. Devel- oping alternative forest cutting patterns: a simulation approach. Landscape Ecology 8: 63-75. Lillesand, T.M. 1996. A protocol for satellite-based land cover classification in the upper Midwest. In J.M. Scott, T.H. Tear and F.W. Davis (eds.). Gap Analysis: A Landscape Approach to Biodiversi- ty Planning. pp. 103-1 18. American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland. Lowell, K.E., G. Edwards and G. Kucera. 1996. Modelling the het- erogeneity and change of natural forests. Geomatica 50: 425-440. MacArthur, R.H. and E.O. Wilson. 1967. The Theory of Island Bio- geography, Princeton University Press, Princeton, New Jersey. McGarigal, K. and B.J. Marks. 1995. FRAGSTATS: Spatial pat- tern analysis program for quantifying landscape structure. USDA For- est Service General Technical Report PNW-GTR-351. Corvallis, Oregon. McGarigal, K. and W.C. McComb. 1995. Relationship between landscape pattern and breeding birds in the Oregon Coast Range. Eco- logical Monographs 65: 235-260. Metzger, J.P. and E. Muller. 1996. Characterizing the complexi- ty of landscape boundaries by remote sensing. Landscape Ecology 1 1 : 65-77. Nichols, W.F., K.T. Killingbeck and P.V. August. 1998. The influence of geomorphological heterogeneity on biodiversity: 11. A landscape perspective. Conservation Biology 12: 371-379. Noss, R.F. 1999. Assessing and monitoring forest biodiversity: a sug- gested framework and indicators, Forest Ecology and Management, 115: 135-146. Olson, C.E., Jr. and F.P. Weber. 2000. Foresters' roles in remote sensing. Journal of Forestry 98: 11-12. O'Neill, R.V., J.R Krummel, RH. Gardner, G. Sugihara, B. Jack- son, D.L. DeAngelis, B.T. Milne, M.G. Turner, B. Zygmunt, S.W. Christensen, V.H. Dale and R.L. Graham. 1988. Indices of landscape pattern. Landscape Ecology 1: 153-162. Reed, R.A., J. Johnson-Barnard and W.L. Baker. 1996. Frag- mentation of a forested Rocky Mountain landscape, 1950-1993. Bio- logical Conservation 75: 267-277. Riitters, K.H., R.V. O'Neill, C.T. Hunsaker, J.D. Wickham, D.H. Yankee, S.P. Timmins, K.B. Jones and B.L. Jackson. 1995. A factor analysis of landscape pattern and structure metrics. Land- scape Ecology 10: 23-39. Rosenzweig, M.L. and Z. Abramsky. 1993. How are diversity and productivity related? In R.E. Ricklefs and D. Schluter (eds.). Species diversity in ecological communities: historical and geographical perspectives. -- pp. 52-65 The University of Chicago Press, Chicago, IL. Sachs, D.L., P. Sollins and W.B. Cohen. 1998. Detecting landscape changes in the interior of British Columbia from 1975 to 1992 using satellite imagery. Canadian Journal of Forest Research 28: 23-36. Scott, J.M., T.H. Tear and F.W. Davis (eds.). 1996. Gap Analy- sis: A Landscape Approach to Biodiversity Planning. American Society for Photogrammetry and Remote Sensing, Bethesda, Mary- land. Silbaugh, J.M. and D.R. Betters. 1997. Biodiversity values and mea- sures applied to forest management, In O.T. Bouman and D.G. Brand (eds.). Sustainable forests: Global challenges and local solu- tions. pp 235-245. Food Products Press, an imprint of The Haworth Press, Inc., New York. Stoms, D.M. 1992. Effects of habitat map generalization in biodi- versity assessment. Photogramrnetric Engineering and Remote Sensing 58: 1587-1591.

NOVEMBERIDECEMBER 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.

Stoms, D.M. and J.E. Estes. 1993. A remote sensing research agenda for mapping and monitoring biodiversity. International Jour- nal of Remote Sensing 14: 1839-1860. Tilman, D. and S. Pacala. 1993. The maintenance of species rich- ness in plant communities. In R.E. Ricklefs and D. Schluter (eds.). Species diversity in ecological communities: historical and geographical perspectives. pp. 13-25. The University of Chicago Press, Chicago, IL. T i e r , D.B., C.A. C. Resor, G.P. Beauvais, K.F. Kipfmuller, C.I. Fernandes and W.L. Baker. 1998. Watershed analysis of forest frag- mentation by clearcuts and roads in a Wyoming forest. Landscape Ecology 13: 149-165. Turner, M.G. 1989. Landscape ecology: the effect of pattern on pro- cess. Annual Review of Ecology and Systematics 20: 171-197. Turner, M.G. 1990. Landscape changes in nine rural counties in Geor- gia. Photogrammetric Engineering and Remote Sensing 56: 379-386.

Urban, D.L., R.V. O'Neill and H.H. Shugart, Jr. 1987. Landscape Ecology: A hierarchical perspective can help scientists understand spatial patterns. BioScience 37: 119-127. Walker, R.E., D.E. Stoms, J.E. Estes and K.D. Cayocca. 1992. Rela- tionships between biological diversity and multi-temporal vegetation index data in California. pp. 562-571. Technical Papers, ASPRSIACSM Annual Convention, Albuquerque, New Mexico. Wickham, J.D., J. Wu and D.F. Bradford. 1997. A conceptual %e- work for selecting and analyzing stressor data to study species rich- ness at large spatial scales. Environmental Management 21: 247-257. Wickland, D. 1991. Mission to Planet Earth: the ecological perspective. Ecology 72: 1923-1933. Wynne, RH., RG. Oderwald, G.A. Reams and J.A. Scrivani. 2000. Optical remote sensing for forest area estimation. Journal of Forestry 98: 31-36.

NOVEMBREDECEMBRE 2000, VOL. 76, NO. 6, THE FORESTRY CHRONICLE

The

For

estr

y C

hron

icle

Dow

nloa

ded

from

pub

s.ci

f-if

c.or

g by

Uni

vers

ity o

f W

ashi

ngto

n on

12/

01/1

7Fo

r pe

rson

al u

se o

nly.