ELSEVIER
Identifying Terrestrial Carbon Sinks: Classification of Successional Stages in Regenerating Tropical Forest from Landsat TM Data Giles M. Foody,* Gintautas Palubinskas,* Richard M. Lucas,* Paul J. Curran,* and Miroslav Honzak** R e m o t e ,s'ensin~z has ;zenerally been used to stud!l the role of tropical fi,wsts as a source o[ atmospheric carbon, primaril!t th rou' of Southampton. Ilighlivid, Sm~thampton, United Kingdmn *~ I)rpt. of (;ro~¢raphy, Univcrsitv of k'Va]cs Swansca, S'¢,ausea, United Kin~d(ml. Address rorr('sp(md('nre to (;ilcs 3.1. Food3, l)cpi, of (;eo~raph?., { : n i x . . f Salfi)rd, Saltord, M5 4WT, U,K. l{eccir('d I¢i June 199,5, recised 17 Au~us't 1995. ttEM(JTI,~ SENS. EN'~ Ill(iN. 55:205-216 (199(';) :Cq':lst'\i('r S('i('n('t' In('., 199(~ (~55 \\tqm~' ~1' ~hc Americas, Nrw York, NY I(l(}l()
cation accuraey, with a weighted kappa coejficient of 0.9315 observed for ¢xll 11 class' elas.s'ifieation. A range of tropical fi~rest classes that t~ar{t in strenczth as a carbon sink eouhl ther@~re be identified aceuratel!t fi'om Landsat TM data. Although the blvader generality of the results requires filrther im:estit~ation, this indicates the potential to use image classifications to scale-up point measuremerits of the carbon flux between regeneratintz ,finest elasse.s" and the atmosphere, ocer large areas.
INTRODUCTION Carbon dioxide in tile atmosphere absorbs some c,f the long wavelength radiation emitted by the Earth. The increasing eonee,m'ation of carbon dioxide in tile a t . m sphere, as well as other ~,'eenhouse ~ases has therefore become an issue of considerable concern, especial]} in the context of global warming (Warrick and Farmer, 1990; Roberts, 1994). Althoug{h atmospheric earbot~ dioxide concentration and global mean temperature are rising, there is considerab]e uncertainty over the exact relationship between climate and atmospheric cart)on dioxide concentration. While there is much debate oxer the exact figures and tile spatial and temporal variations, it is known that the atmospheric carbon dioxide loadin R has increased by around 25% fl'om its preindustrial, mid-18th century, concentration and is rising at a rate of approximate]y 0.4-0,5% per a m m m (ttoughton et al., ! 990; Warriek and Farmer, 1990). The precise effect of this and futm'e increases is unknown, })ut predietions abound of an atmospheric carbon loading that is dout)le that of prein(h,stria] times 1)\ the year 2030, with a rise (10:34-4257 / 96 / $15.00 ,%S1)! 0(1:34--t257(95)¢)¢119(~-4
206 Foody et al.
in global temperature of some 1-3°C.
The consequences of this may be dramatic and range from significant rises in sea level to increased frequency and intensity of extreme eliinatic events (IGBP, 1990; Carter et al., 1991; Lonergan and Kavanagh, 1991; HendersonSellers, 1994; Rosenzweig and Parry, 1994). The predictions of the effect of enhanced atmospheric carbon dioxide loadings on climate and proposals for combatring its rising concentration, however, vary markedly (Warriek and Farmer, 1990; Idso, 1991; Fajer and Bazzaz, 1992), This variability arises partly as a result of the complexity of the environment but also due to the paucity and poor quality of our knowledge and data sets relevant to understanding environmental processes at a range of scales. Our understanding of the carbon cycle at a range of spatial and temporal seales is poor (Houghton and Skole, 1990; Smith et al., 1993). This uncertainty is highlighted by our inability to balance the global carbon budget, with the sourees of atmospheric carbon larger than the marine and terrestrial sinks (Tans et al., 1990; Sundquist, 1993) and consequently some carbon "'missing" (Houghton and Skole, 1990; Smith et al., 1993). To improve our knowledge and understanding of the carbon cycle, more information on the sources and sinks of atmospheric carbon dioxide is required. Since much of the uncertainty in the carbon cycle is associated with tropical regions and terrestrial ecosystems, which are dominated by forests, considerable attention has focused on the role of tropical forests. This has mostly addressed the contribution of tropical forests as a source of atmospheric carbon by measuring de[brestation and hiomass burning. This article will, however, focus on the role of tropical forests as a sink of atmospheric carbon.
Forests and the Carbon Cycle The relatively recent increases in atmospheric carbon dioxide concentration are a reflection of an increase in the strength of sources of carbon over that of sinks. There are a number of major sources of carbon dioxide, notably the combustion of fossil fuels, respiration, landuse change, and biomass burning (Houghton and Skole, 1990; Dale et al., I991; Goreau, 1992). Of these sources, only the flux to the atmosphere from the combustion of fossil fuels is known accurately (Tans et al., I990; Goreau, i990). Greater uncertainty is associated with the sinks of atmospheric carbon, especially in the tropics (Goreau, 1990). Much uncertainty in the carbon budget is associated with the role of vegetation in the carbon cycle (Jarvis and Dewar, 1993; Sampson et al., 1993). While the vegetation carbon pool is much smaller than that of the oceans, its flux to the atmosphere is of the same order of magnitude (Houghton and Skole, 1990). Globally, terrestrial vegetation withdraw some 100Pg of carbon
from the atmosphere each year in the production of organic matter through photosynthesis, with about half of the photosynthesis occurring in the tropics. However, the size of the carbon pool and the strength, and sometimes the direction, of the carbon fluxes associated with terrestrial ecosystems are not known accurately. A better understanding of processes controlling carbon storage and release in space and time from terrestrial ecosystems is thus needed to reduce uncertainties in the carbon cycle (Wisniewski and Sampson, 1993). Vegetation will be both a source of carbon, due, for instance, to respiration, burning, and decay, and a sink of atmospheric carbon due to photosynthesis and plant growth. There is, however, considerable variation in the balance between vegetal sources and sinks of carbon at a range of spatial and temporal scales. At the global scale, for instance, there is uncertainty over whether terrestrial vegetation may be considered on an annual basis to be a carbon source or sink (Roughgarden eta]., 1991; Smith et al., 1993; Sampson et al., 1993). As considerable uncertainty over the role of vegetation in the carbon cycle is associated with forest ecosystems, which contain some 75-90% of the carbon stored in the vegetation pool and cover some 30% of the Earth's land surface, these have become a focus of attention (Dale et al., 1991; Sedjo, 1993; Solomon et al., 1993). The destruction of highly productive forest ecosystems, particularly through the process of deforestation in which tracts oftbrested land may be completely cleared of trees, is a major source of atmospheric carbon dioxide. The annual loss of forest of some 15-17x 10 ~ ha is estimated to contribute in the order of 1.2-2,2Pg of carbon to the atmosphere (Smith et al., 1993). The magnitude of this carbon source is, however, uncertain since the rate of deforestation (Houghton et al., 1985; Sader et al., 1990; Rotmans and Swart, 1991) and the amount of hiomass harvested are not known accurately (Smith et al., 1993; Sampson et al., 1993). The restoration of productive vegetation upon previously cleared areas, however, results in a considerable increase in the conversion of atmospheric carbon dioxide into biomass and so may be a significant sink of atmospheric carbon dioxide (Harmon et al., 1990). The role of the various major forest ecosystems in the carbon cycle, particularly as carbon sinks which may be carefully managed to reduce the atmospheric carbon loading, is therefore being assessed (Tans et al., 1990; Brown et al., 1993; Dixon et al., 1993; 1994; Wofsy et al., 199a), Here attention is focused on tropical forests, which cover some 7% of the Earth's land surface, which play a major hnt poorly understood role in the cycling of carbon. Mature tropical forests have generally been considered to be approximately carbon neutral, absorbing as much carbon in the day as is released during the day and night. Processes such as deforestation, however, result in the conversion of large tracts of land from this
Identifying Terrestrial Carbon Sinks 207
carbon neutral status into a major source of atmospheric carbon. Not only is tile carbon stored in the vegetation released to the atmosphere but so too may carbon from other pools, such as the soil (Gorean and de Mello, 19881. Drastic land cover change such as deforestation is, however, relatively easy to detect remotely and consequently remotely sensed data have been used to measure deforestation (Woodwell, 1984; Grainger, 1993; Sknle and Tucker, 19931 and thereby quantify this source ()f carbon for input to global climate models (}/otmans and Swart, 1991; Henderson-Seflers, 19931. These models should, however, account for more than just the change in fi)rest extent. For instance, although mature torests have conventionally been thonght to be in ca,'l)on balance, recent research has shown that a carbon-emiched atmosphere may, in the absence of other growth-limiting variables, have a fertilization effect which encourages growth by increasing photosynthesis and water-use efficiency and st) transforms these forests into a carbon sink (Grace et al., 1995), ahbongh some studies indicate that this effect may be short-lived with the t'orest reverting }lack to the previous conditions within a ).ear (Smith et al., 19931 and increased tree turnover could resuh in the fi)rmation of a forest that was a carbon source (Phillips and Gentry, 19941. Furthermore, the effect of" land-use change o n the strength of the somce or sink of carbon depends on a range of forest properties. For example, many forests have been degraded through selective logging, fi]elwood harvesting, and grazing, and therefore, relative to untouched fi)rest, have a lower biomass (Houghton et al., 1985; Houghton, 19911. This degradation process may result in a fhlx o f carbon to the atmosphere which may account for levels of atmospheric carbon dioxide above that expected as a consequence of detbrestation alone (Houghton, 1991). However, deforestation or clearance of a degraded fi)rest will contribute less carbon to the atmosphere than tile clearance of a similar area of" previously untouched forest as a resuh of the biomass difference (Brown et al., 1991; Hall and Uhlig, 19911. Some estimates of the carbon source arising from deforestation may therefore be erroneous as a result of inaccurate estimates of forest biomass, or cart)on pool size, and the extent of deforestation (Smith et a]., 19931. Models should also account for changes in the carbon fluxes between the land and the atmosphere after the conversion from forest cover has occurred. While the clearance ()f"the forest for agriculture is a source of atmospheric carl)on, tile use of the land afterwards can result in the formation of a carbon sink. Much of the land cleared for agricuhure is cultivated for only some 1-3 years and then abandoned, often for 10 years or more. In the period of al)andonment a secondary forest may establish that has a higher net primary production than mature forest and rapidly sequesters carbon from the atmosphere, converting it to biomass. This reduces
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the effect of the carbon source arising from the initial clearance (Detwiler and Hall, 1988) and increases the size of the vegetation carbon pool (Dale et al., 1993). Ahhough, in the year of clearance, the strength of the carbon sink due to forest regeneration will be much less than the source clue to deforestation, the effect of regeneration is longer lasting, extending many years after the act of deforestation (Houghton et el., 1990). The strength of this carbon sink and size of the carbon pool formed depends on a range of factors such as species composition (Uhl et el., 1988; Scatena et al., 1993), but notably on the age of the regenerating forest (Fig. 1). In estimating the carbon flux fbr a region of tropical forest, it is therefore important to know the forest age, as this determines the strength of the carbon sink as well as the size of the carbon pool and so potential carbon source upon clearance. The effect of this regeneration together with the lower biomass of regenerating forest may therefore result in a downward adjustment of some estimates of the source of carbon resnhing from deforestation (Detwiler and Hall, 1988). Knowledge of forest age and historical land-nse may therefore significantly help to increase the accuracy of estimates of carbon fluxes and pools (Wofsy et al., 1993), which are currently major limits to our understanding of the carbon cycle (Smith et al., 19931. Data on the carbon flux between regenerating secondary forests and of mature forests with the atmosphere together with accurate estimates of their areal extent are required to reduce the uncertainties associated with the role of tropical forests in the carbon cycle (Curran et al., 19951. Since the rate of carbon fixation of a regenerating tbrest is age-dependent, a number of age classes must be identified. Within these age classes, however, there may be forests of markedly different character that vary in their growth rate and so strength as a carbon sink (Uhl et al., 1988). While regenerating toward mature tbrest status, these forests may be following different successional pathways, as a result of factors sneh as the method of clearance or location. Although
208 Foody et al.
more difficult to detect remotely than deforestation, forest regeneration and succession may be observed (e.g., Sader et al., 1989; Foody and Curran, 1994). In general, recent forest clearances are spectrally distinct as pasture and bare soil have higher reflectance than mature forest in visible, near- and middle-infrared wavebands used by satellite sensors. Forest regeneration, however, results in a decrease in visible reflectance due to the increase in vegetation density and associated rise in chlorophyll absorption, an initial increase of near-infrared reflectance due to increased scattering followed by a decline due to shadowing effects associated with canopy structural development, and a decrease in middle-infrared reflectance due to increased water absorption and shadowing (Mausel et al., 1993; Boyd et al., 1995). This article aims to assess the separability of successional tropical forest classes that may be important sinks of atmospheric carbon dioxide.
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DATA A N D M E T H O D S
The research focused on an area of Amazonian tropical forest. Significant deforestation has only occurred within Amazonia over the last three decades, and consequently many of the secondary h)rests are relatively young (Fearnside, 1990). Furthermore, the land use and management practices often leave a landscape comprising a patchwork of mature and regenerating forests of different ages, Within relatively small areas, therefore, a wide range of forest types may be h>und. Although detailed records of land clearance and use are not usually available, these forests may be aged from an analysis of multitemporal remotely sensed data. Relatively fine spatial resolution data have been available for over 20 years from the Landsat sensors and as deforested areas may be clearly defined in such data a time sequence of imagery may be used to derive a map of forest age classes. Landsat TM data acquired in August 1991 for an area north of Manaus in the Brazilian Amazon (2°20'S, 60°00'W) were used for the classifications (Fig. 2), corresponding approximately to the area used by Adams et al. (1995). To reduce the effects of atmospheric attenuation, the data acquired in the shortest wavelengths were excluded from the analyses. Furthermore, since Landsat TM data are generally three-dimensional in character, with the dimensions relating to reflectance in visible, near- and middle-infrared wavelengths (Townshend et al., 1988), three such wavebands only were used. These were TM Bands 3, 4, and 5. Although the region was originally covered by primary forest, large areas were cleared for cattle pastures and plantations in the late 1970s and 1980s. By 1991 many of these clearances had been abandoned allowing secondary forest vegetation to establish. These regenerating forests were identified and aged using postclassifi-
Figure 2. Location of the test site (dashed box) in Brazil. The extent of re~,enerating forest at the test site fi~rest is indicated tLx the dark tone.
cation comparison of a time series of Landsat MSS and TM data from 1976, from which a map depicting forest regenerative age classes was produced. The oldest image was acquired by the Landsat MSS in December 1976 and formed the base for the multitemporal analysis. To mi,dmize seasonality effects, all the remaining data sets, remotely sensed and ground, were acquired in the months of July and August only. The five other Landsat sensor images were acquired in 1977, 1985, 1988, 1989, and 1991, and each was classified separatelx. An age class map of the site was produced from a postclassificatinn analysis; filrther details on the age class map are gixen in Lucas et al. (1993). This map {Fig. 3) was refined and verified with the aid of'fieldwork performed in 1993, and used as the ground data for this investigation. In addition to a general extensive survev of land cover at the site, the field campaign concentrated on the collection of data on forest tree species and bioph.vsical properties such as leaf area index (Honzak et al., 1996: Lncas et al., 1996). These data were acquired from 100 m x 10 m sample pluts located in regions of tmiform forest age and land-use history, determined from analysis of the multitemporal Landsat sensor data set. A total of 1,5 plots, which spanned the fidl range of forest classes, were studied. From detailed inventories of all trees with a diameter of > 3 em in these plots, tw() successional pathways were identified, each differing markedly in species composition (Lucas et al., 1996). Forest Type I was dominated by trees behmging to the family C e c r o p i a c e a e and occurred mainly on land that
Identifying Terrestrial Carbon Sinks ;209
where X is an ohject c()mprising N pixels. A range of approaches may be identified that use both spectral and spatial (class conditional correlation) characteristics in tile classification (e.g., Paluhinskas, 1988). t t e r e attention fl)cused on one approach based on the assumption that an image object is a Markov random field (Kalayeh and Landgrebe, 1987) which is represented b \ a thirdorder causal autoregressive model. The object was defined as a 3 x 3 pixel square box and classified on the basis of
v(xl,,',)
= fi v(x~l.r~,. -r,~. _r,~. ,'~). i
~3~rt~ "e
Figm-e 3. Extract of the fin'est age class niap. The productiow of this map from multitemporal Landsat sensor data is disc,ssed ill lmcas ctal. (1993). was abandoned within a year of forest clearance. Forest Type II was dominated by species of the families Clusiacoat, Flacourtiaceae, and Melastomataceae, with some Ceeropiaccae species, and was (btlnd t}])ically on sites that had been cleared initiall~ by fire and used for pasture for several years before abandomnent. The location of the training and testing sites fi)r the elassifications was t)ased (m both the forest age class map and the site,s visited in the field. Blocks of pixels were thert'fi)re extracted |or training and testing the elassifications from the regions containing the 1.5 plots smveved in the field and fiom one other site visited in tit(, field but not surveyed in detail due to logistical constraints. From each region up to {'(mr blocks generally containing 20-150 pixels were extracted. These data were lhen divided into independent training and testin~ sets. The 1,andsat TM data were classified with maximum likelihood classifiers. Two approaches were investigated. First, a corn entional per-pixel classification of the data, This allocated the pixel x to the class i fi.om m possible (,lasses when
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where p ix the class-conditional density functioll for class w~. Each pixel was thereIbre allocated to the class with which it had the highest probability of membership, and this was determined on the basis of the spectral properties of the image pixels. Seeond, an object-oriented approach to classification was used. With such approaches the image pixels may 1)e grouped into objects, bl)icall~ cross-shaped or square blocks of pixels, and each pixel ()f an object or the whole object classified. For this approach the classification decision rule was
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!
where xj~, xj2, and xj:~are respectively the right horizontal, right vertical, and right down nearest neighbors of x i. This approach to classification has been flmnd in some imestigations to cIassit\' data more accm'ate]v than the conventional per-pixel maximum likelihood classifier (Mardia, 1984; Palubinskas, 1988). Both classifiers were performed using the IMAX softxvare, fm'ther details on which and the algorithms used may be f}mnd in Palubinskas (1988; 1993). The accuracy of the classifications was assessed and expressed as the percentage correct allocation a.nd the kappa coelficient ot" agreement (Rosenfield and Fitzpatrick-Lins, 1986) which are widely used in the assessment of image classifications. Since some of the classes may be considered to lie along an ordered settle, fiom recently deforested through regenerating forests to mature t})rest, these measures may not provide an idea] index of classification pertormance. For ordinal level ('lasses the distribution of error between classes is important as, for instance, the misallocation of a recently cleared area to the mature forest class ix more erroneous than the misallocation of a young regeneratin~ torest to an o l d e r regenerating forest class. For such a classificatiom the use of the percentage correct allocation or kappa coefficient ix inappropriate as these indices were derived fi)r application to nominal level classifications in which all classification errors are of equal ma~znittrde ((:ohen, 1968; Jolayemi, 1990: Foody, 1994). For the assessment of the accuracy of an ordinal level classification, the index of accuracy used should account for the ~ariations in the de~ree of error that may I)e associated xxith a set of class allocations reached by the classifieation (Jolayemi, 1990). ()he approach to compensate ft," the effects of variable degrees of classification error is to use the weighted kappa coefficient (Cohen, 196S). This index of accuracv ix an extension of the kappa coefficient in which a predefined error weighting is associated with all possible class allocations (i.e., an error weight ix associated with each element of the classification conthsi¢m matrix). Ahhough determining the weights m a ) t ) e ditSficult, it does enable the variations in the degree of classification error that may t)e tbund in m~ ordinal le,'el classification to be accounted for in the assessment of
210
eoody et al.
Table 1. Confusion Matrix from the Per-Pixel Classification of the Six Regenerating Forest Age Classes" Actual Class Pasture < 2 years 2-3 years 3 -6 years 6-14 years > 14 years ~,, User's accuracy" (%)
Predicted Class, Forest Age Class (years) 2-3 3-6 6-14
Pasture
<2
298 0 6 0 1 0 305 97.70
1 97 5 17 3 0 123 78.86
1 44 49 1 3 0 98 50.00
0 99 0 119 75 7 300 39.67
0 39 0 80 218 0 337 64.69
> 14
z~
0 1 0 6 0 293 301 97.34
300 280 60 224 300 300 1464
Producer's Accuracy (%) 99.33 34.64 81.67 53.13 72,67 97.67
" The overall percentage correct allocation was 73.36%, the kappa coefficient was 0.6743, and the weighted kappa coefficient was 0.7966.
classification accuracy. Furthermore, the weightings may be defined in a fashion that enables the assessment of the accuracy of a classification in which only some of the classes lie on an ordered scale. The weighted kappa coefficient K,,. may be derived from K,,. = 1 - ~ vijf,~i/ ~ v~jf.,i,
where v,j is the weight associated with the error in the confusion matrix element/j, f,0 the observed frequency in element /j, and f.~ the frequency that cotdd be expected to occur in element/j by chance (Cohen, 1968). To indicate the quality of the classifications tbr individual classes, the percentage correct allocation fi)r each class from the user's and producer's perspectives (Story and Congalton, 1986) were also derived. These indices of classification accuracy are supplemented bv the classification confusion matrices to allow more detailed study on classification accuracy. RESULTS AND DISCUSSION
Initial attention ibcused on the classification of the six classes that were identified trom the time series of Landsat sensor data. These classes may be considered to lie along a forest age and carbon continuum, from pasture (recently deforested / potential forest) to mature forest. The classes are identified and the results of these classifications summarized in Tables 1 and 2. Both classifications show a reasonable degree of class separa-
bility, with, as expected, a higher accuracy (79.49%) derived from the object-based classification. In both classifications the end points of the continuum, the pasture and mature forest classes, were classified accurately (an accuracy of > 97%) with much of the confusion limited to between the regenerating forest classes. A large proportion of the error that was observed arose from misallocations between neighboring classes. For instance, approximately 53% and 83% of the observed errors in the per-pixel and object-based classifications, respectively, were between neighboring classes. Although still incorrect class allocations, the majority of the errors observed were therefore of the least severity. Consequently, the weighted kappa coefficient would be expected to provide a more realistic measure of classification accuracy than the percentage correct allocation or the kappa coetklcient. The weighted kappa coefficient was calculated for both classifications with the weights for the errors determined such that an error weight of 1 was associated with a misallocation to a "neighboring" class, a weight of 2 associated with a misallocation to the class adjacent to a neighboring class and so on, with the highest weight, 5, associated with misallocation between the fi)rest and pasture classes. With these weightings the weighted kappa coefficient was calculated as 0.7966 and 0.8569 fi)r the per-pixel and object based classifications respectively.. Closer inspection of the contusion matrices (Tables l and 2), however, reveals that much of the error that
Table 2. C o n f u s i o n M a t r i x from t h e O b j e c t - B a s e d C l a s s i f i c a t i o n of t h e Six R e g e n e r a t i n g F o r e s t Age (:lasses"
Actual Class Pasture < 2 years 2-3 years 3 -6 years 6-14 years > 14 years User's accuracy (%)
Pasture
<2
192 0 1 0 0 0 193 99.4~
0 75 0 1 0 0 76 98.68
Predicted Class, Forest Age Class (.qears') 2-3 .3-6 6-14 0 49 31 0 0 0 80 38.75
0 13 0 5,5 [2 0 811 68.7.5
0 37 0 74 180 0 291 (i 1.86
> 14
~
0 0 0 0 0 192 192 100.00
192 174 32 130 192 192 912
f'roducer~s' Accuracy (%) 100.00 .t3.10 96.88 42.31 93.75 100.00
" The overall percentage correct allocation was 79.49%, the kappa coefficient was 0.7476, and the weighted kappa coefficient was 0.8569,
hlentif~tingTerrestrialCarbonSinks 211
was not between neighboring classes was associated with sites that belonged to the youngest forest regenerative age class. For instance, with the object-based classificatinn (l'able 2) there were only 52 misalloeations that were not between neighboring classes and of these ,50 were cases of the youngest regenerating forest age class. Cases of this class were confi~sed with all the other tbrest classes. This indicated that the youngest age class may be more variable in its composition and spectral resl)onse than the older forest classes. This could be a fimction of a range of successional pathways being tblh)wed: with tree density ranging from 1290 to 5280 trees per plot (tlnnzak et al., 1996), it was unlikely that factors such as soil backgronnd had an effect on the spectral responses. W h e r e more than one successional pathway is present, the greatest wtriability between classes will be apparent in the yotmg classes rather than in the nkh'r classes, as each pathway ends ultimately at a mature forest. At this site two distinct regenerative pathways \~ere present. On sites which were cleared and used In'ieflv for pasture Forest Type l, dominated by Cecropiaceaespecies, was found. Whereas on sites in which forest clearance involved burning prior to use fin" pasture |'or several years, Forest Type II which was dominated l)v Clusiaceae, Hacourtiaceae, and Melastomataceae species was found. Sites following these
Cecropiaceae Clusiaceae Flacourtiaceae Malpighiaceae Verbenaceae
different regenerative pathways differed markedly in species composition in the early years of regeneration, but beeame less distinct with time (Fig. 4). Furthermore, as different species associated with these successional pathways differ in their rate of carbon accumulation, perhaps by a factor of 2 (Uhl et al., 1988), an ability to discriminate the dominant species within a regenerating forest would not only be beneficial in terms of increasing classification accuracy but also in refining carbon accounting models. On the basis of field identification, the regenerating forest classes were split into two groups depending on the successional pathway followed. Three age classes nf Forest T x1~e I and two of Forest THoe lI were defined in this way. Together with sites of six other classes also identified in the fiend the classifications were repeated; the classes are identified in Table 3. The results showed a high degree of interclass separability with accuracies of 78.61% and 86.65% derived from the per-pixel and object-based maximum likelihond classifications (Tables 3 and 4). These aeeuracies w e r e higher than those derived from the six class classifications, indicating that subdividing the regenerating forest classes into the two successional pathways helped raise overall interelass separability. While the 11 classes did not lie ahmg a simple
. . . . . ~,
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Generalized species composition of plots belon~cin~ to each of the re/generating fin'est classes and a mature forest plot. For simplicity only the data fin the five most important families at each plot are strewn. The inlf)ortanc'e value was ca.]culatvd from 1/3 (sum of the relative density, relative basal area. and relative fiequency for a fiunil.x) and expressed as a percellta~e. Further details on the K|'Olllld data |i'om which this in|'ormation was derived are ~ix'en in lA]cas el al. (1996).
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212 Foody et al.
Table 3. Confusion Matrix from the Per-Pixel Maximum Likelihood Classification" Predicted Class
Actual Class
BP
BP P RV H FIIa FIIb FIa Fib FIc MF Pl ~,,
1 0 0 2 0 0 0 0 0 0 0 3
P 161 75 0 3 0 0 0 0 0 0 0 239
RV 0 0 25 0 0 5 0 0 1 1 0 32
H 0 0 0 170 0 0 16 0 0 0 () 186
FIIa 0 0 0 0 7 8 0 2 1 0 0 18
Fllb 0 0 0 0 9 166 0 2 2 0 l 180
Fla 0 () () 7 0 0 50 77 0 0 8 142
Fib 0 0 0 0 0 3 2 68 3 () 9 85
FIc
MF
0 0 0 0 0 9 () 1 68 0 0 78
0 0 0 0 0 0 0 0 0 552 0 552
87.18
100.00
Pl 4 0 0 11 0 1 4 7 0 0 141 168
z~, 166 75 25 193 16 192 72 157 75 553 159 1683
Producer's Accuracy (%) 0.60 i00.00 100.00 88.08 43.75 86.46 69.44 43.31 90.66 99.82 88.68
User's
accuracy (%)
33.33
31.38
78.13
91.39
38.88
92.22
:35.21 80.00
83.93
"The overall percentage correct allocation was 78.61%, the kappa coefficient 0.7455, and the weighted kappa coefficient was 0.8711. The classes are: burnt pasture (BP), pasture (P), riverine vegetation (RV), fallow (herbaceous vegetation) (H), Forest Type II < 3 years (Flla), Forest Type II 4-10 years (FIIb), Forest Type 1 4-6 years (Fla), Forest Type I 7-10 years (Fib), Forest Type 1 > 14 years (Fie), mature forest (MF), and plantation (PI).
continuum
as
in
the
previous
classifications,
some
e f f i c i e n t w a s c a l c u l a t e d as 0 . 8 7 1 1 a n d 0 . 9 3 1 5 , r e s p e c -
c l a s s e s c o u l d b e c o n s i d e r e d r e l a t e d in t h e s e n s e t h a t m i s a l l o c a t i o n o f c a s e s b e t w e e n t h e m w a s less e r r o n e o u s t h a n m i s a l l o c a t i o n to o t h e r classes. T h u s e r r o r o b s e r v e d between the forest regenerative classes or the pasture c l a s s e s m a y b e j u d g e d less s e v e r e t h a n o t h e r m i s a l l o c a t i o n s . It w a s a p p a r e n t f r o m T a b l e s 3 a n d 4 t h a t m o s t o f
tively. T h e s e r e s u l t s i n d i c a t e t h a t t h e s u c c e s s i o n a l p a t h ways may be identified to a high accuracy. Moreover, t h e d y n a m i c s o f t h e f o r e s t s u c c e s s i o n w a s to s o m e e x t e n t m a n i f e s t in t h e r e m o t e l y s e n s e d d a t a as t h e p a t h w a y s f r o m f o r e s t to p a s t u r e a n d l a t e r r e g e n e r a t i o n , a l o n g o n e o f t w o r o u t e s , w e r e e v i d e n t in f e a t u r e s p a c e (Fig. 5).
the classification error observed was between these r e l a t e d c l a s s e s . N o t e t h a t w i t h t h e o b j e c t - b a s e d classific a t i o n o n l y 1 . 8 % o f t h e o b s e r v e d e r r o r w a s to a n o n r e -
A l t h o u g h t h e c l a s s i f i c a t i o n a c c u r a c i e s m a y b e artificially h i g h d u e to t h e l i m i t e d r a n g e o f l o c a t i o n s f r o m which training and testing sites were drawn the results i n d i c a t e t h a t r e m o t e s e n s i n g m a y b e u s e d t o classify r e g e n e r a t i n g t r o p i c a l f o r e s t classes a c c u r a t e l y and, w h e r e appropriate, identify successional pathways. Conseq u e n t l y , it m a y b e p o s s i b l e to u s e s u c h c l a s s i f i c a t i o n s to s c a l e - u p p o i n t m e a s u r e m e n t s o f t h e c a r b o n flux asso-
l a t e d class. T o m o r e a p p r o p r i a t e l y assess t h e a c c u r a c y of the classifications, the weighted kappa coefficient was c a l c u l a t e d u s i n g w e i g h t s t h a t a t t e m p t e d to r e f l e c t t h e v a r i a t i o n s in e r r o r s e v e r i t y ( T a b l e 5). F o r t h e p e r - p i x e l a n d o b j e c t - b a s e d c l a s s i f i c a t i o n s , t h e w e i g h t e d k a p p a co-
Table 4. Confusion Matrix from the Object-Based Maximum Likelihood Classification" Predicted Class
Actual Class
BP
BP P RV
9 0 0
1t
FIIa Fllb FIa Fib FIc MF PI
P
RV
H
Flla
Fllb
81 25 0
0 0 9
0 0 0
() 0 0
0 () 0
O
0
0
81
0
0 0 0 0 0 0 0 9
0 0 0 0 0 () 0 106
0 0 0 0 0 0 0 9
0 0 0 0 0 0 0 81
1 0 0 0 0 0 0 1
Fla
z~
Producer's' Accuracy (%)
Fib
Flc
MF
Pl
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
90 25 9
10.00 100.00 100.00
0
0
0
0
0
18
99
81.82
3 124 0 0 0 0 0 127
I) 0 42 29 0 0 0 71
0 0 0 46 0 () 0 46
0 () 0 0 27 0 0 27
0 0 0 0 0 397 0 397
0 0 0 0 0 0 89 107
4 124 42 75 27 397 89 981
25.00 100.00 100.00 61.33 100,00 100.00 100.00
User's
accuracy (%)
100.00
23.58
100.00
100.00
100.00
97.64
59.15
100.00
100.00
100.00
83.18
" The overall percentage correct allocation was 86.65 %, the kappa coefficient was 0.8311, and the weighted kappa coefficient was 0.9315. The class abbreviations are the same as in Table 3.
Identifying Terrestrial Carbon Sinks 213
7"able 5. Matrix of Error Weights Used in the Calculation of the Weighted Kappa Coefficient for the 11 Class Classifications Illustrated in Tables 3 and 4
BP P RV It FIIa Fllb FIa Fib File MF PI
BP
P
RV
H
Flla
Fllb
FIa
Fib
Flc
MF
PI
0 1 4 4 4 4 4 4 4 4 4
1 0 4 4 4 4 4 4 4 4 4
4 4 0 4 4 4 4 4 4 4 4
4 4 4 0 4 4 4 4 4 4 4
4 4 4 4 0 1 3 3 3 3 4
4 4 4 4 1 0 2 2 2 :3 4
4 4 4 4 2 2 0 l 2 3 4
4 4 4 4 ,3 2 l I) 1 2 4
4 4 4 4 4 3 2 1 0 1 4
4 4 4 4 4 4 3 2 1 0 4
4 4 4 4 4 4 4 4 4 4 0
ciated with each class over a large area. Before the methods and conclusions may be applied at regionalgh)bal scales, however, their validity to a wider range of tropical tbrest types and environments must be evaluated; unlike' other regions oftrupical tbrest, the Brazilian Amazon is fairly flat, contained within one life-zone and possesses a fairly simple vegetation mosaic. Some of these issues will be addressed in current researeh, which aims to derive a torest regeneration stage classification of the whole of the Legal Amazon and parts of West Africa for input to a cart)on accounting model in a bid to help reduce some of the uncertainty currently associated with the role of tropical forests in the carbon /'vcle.
CONCLUSIONS
Rising concentrations of atmospheric carbon dioxide have become a focus of environmental concern. While it is apparent that the strength ufsourees of atmospheric carbon dioxide exceeds that of sinks, our knowledge of the carbon cycle is poor. Particular uncertainty is associated x~ith the rule of terrestrial vegetation and tropical ti>rests in particular. While remote sensing has been used widely to assess the contribution of deforestation and hiomass [)urning to the concentration of carbon dioxide in the atmosphere, it has been less used in studies aimed at determining the role of tropical ft)rests as a sink of atmospheric carhon. With the latter the main ft)cus of attention is the secondary fi)rests which regenerate on previously deft)rested regions. These young fi)rests rapidly sequester carbon fi'()m the atm()sphere and so act to reduce the strength of the carbon sonree arising from defnrestation and biomass hurning. tiowever, to understand the role ()f these regenerating ft)rests as carbon sinks, infbrmatiun on their age, species composition, location, and extent is required, and the only fieasible way to derive this information is through remote sensing. This information together with data on the car|)on flux with the atmosphere associated with particular forest classes will enable an accounting model
to be developed which may reduce some of tile uncertainty associated with the role of tropical forests in the carbon cycle. This article has focused on the use of remote sensing to identify regenerating tropical ft)rest classes that may differ in strength as sinks of atmospheric carbon. Ahhough the generality of tire results to a broader range of tropical forest and environmental conditions to those found at the test site needs to he established before the application may be considered operational, three main conclusions may be drawn from the research presented in this article: 1. Six age classes of regenerating tropical forest exhibited a high degree of separability; the highest accuracy observed was a weighted kappa coeflicient of 0.8569. 2. Of the errors that were observed in tire six (_'lass classification most were associated with the youngest forest age class. Since there were two successional pathways to regeneration in tire region, arising mainly ft'om differences in the method-uf initial clearance and period of cultivation, differences between ft~rests following the different pathways would be most evident with the youngest torests, and this could be the cause of the classification errors associated with the youngest forest age class. Forests of the same age class but foflowing the different successional pathways were found to have markedly different species composition and so likely to differ in the rate of biomass accumulation and strength as a carbon sink; research bv Uhl et al. (1988) indicates that these fi)rest classes may differ l)v a factor of 2 in terms uf carbon sink strength. These two successional pathways were, however, evident in the remotely sensed data. The forest age classes could therefore he divided by successional pathway and the classes defined were highly separable in Landsat TM data, with a weighted kappa coefficient of up to 0.9315 observed for an I 1-class classification.
214
Foody et al.
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1.0-
'T E "1
'7. E
u3 I--
-ff 0~
IVI 1-'
0.2 4.0
9.0 Near infrared (TM4), W m 2 sr -1 pro-1
Figure 5. Location of the torest regenerative classes in a middle infrared-near-infrared feature space plot. Note that the two successional pathways which may be followed as the forest regenerates appear spectrally separable; the ellipses plotted represent 1 standard deviation from the mean value. Arrows have been plotted by eye to illustrate a potential to observe the dynamics of land cover change from forest to pasture and the succession, along one of two routes, back to forest. For comparison with other studies, the Landsat TM data were calibrated radiometrically to radiance nsing prelaunch coefficients and adjusted for the effects of a standard tropical atmosphere.
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