Detecting subtle land use change in tropical forests

Applied Geography 29 (2009) 201–211

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Detecting subtle land use change in tropical forests Anders H. Sire´n a, b,1, *, Eduardo S. Brondizio c a b c

Department of Rural and Urban Development, Swedish University of Agricultural Sciences, P.O. Box 7012, SE-750 07 Uppsala, Sweden Department of Biology, University of Turku, FIN-20014 Turku, Finland Department of Anthropology, Indiana University, Student Building 130, Bloomington, IN 40405, USA

a b s t r a c t Keywords: Tropical forest Shifting cultivation Land use change Remote sensing

This paper presents a study of land use and land cover dynamics in an indigenous community in the Amazon, in particular the methods used to deal with problems related to small size of cultivated plots, spectral similarity between land use classes, atmospheric haze and topographic shade. The main focus was on identifying and quantifying cultivated and fallow areas. Based on remote sensing alone, it was possible to identify about half of the fallows younger than 20 years of age. Combining remote sensing with field-based methods, however, it was possible to estimate the number and size of cultivated areas, the extent of fallows up to 65 years of age, as well as the rate of old-growth forest loss. Ó 2008 Elsevier Ltd. All rights reserved.

Introduction The concern for the fate of the tropical rainforests has inspired numerous studies of land use dynamics over the last couple of decades (e.g., Behrens, Baksh, & Mothes, 1994; Brondizio, Moran, Mausel, & Wu, 1994; Entwisle & Stern, 2005; Lucas, Honzak, Do Amarals, Curran, & Foody, 2002; Moran, Brondizio, Mausel, & Wu, 1994; Steininger, 2000). Methodological progress has been rapid. There are now a wide variety of methods and transformative techniques available for classifying images according to spectral properties at the pixel level as well as spatial context and texture (e.g., Adams et al., 1995; Hill, 1999; Lu, Mausel, Batistella, & Moran, 2004; Lu, Moran, & Batistella, 2003). In order to facilitate between-image comparison, methods have been developed for pre-processing of images that correct for differences in solar angle, atmospheric conditions, and sensor variation (Green, Schweik, & Hanson, 2002; Lu, Mausel, Brondizio, & Moran, 2002). Also, remotely sensed data is increasingly linked to botanical, historical and socio-economic data, in order to get a better understanding of the causes of land use change (e.g., Brondizio, Moran, Mausel, & Wu, 1996; D’Antona, Cak, & VanWey, 2008; Fox, Mishra, Rindfuss, & Walsh, 2003; Liverman, Moran, Rindfuss, & Stern, 1998). Techniques of automated or semi-automated image interpretation have allowed assessments of land use and land cover changes over large areas to a relative low cost (e.g., Asner et al., 2005). The widespread creation of parks and forest reserves managed by local farming and extractive populations, and demarcation of indigenous lands in the Amazon have created a demand for understanding the dynamics and environmental impact of small-scale land use systems upon forest resources. Several authors (e.g., Schwartzman, Moreira, & Nepstad, 2000) have argued that conservation is compatible with the interests of indigenous and rural populations, but others claim the opposite (e.g., Terborgh, 2000). The fact is that land assigned to indigenous peoples hold significant social and conservation values, and their area is much larger than the area of nature reserves (Davis & Wali, 1994; Fearnside, 2003; Peres, 1994). Nepstad et al. (2006) have shown that in the Brazilian Amazon indigenous lands were more important than nature reserves in terms of their

* Corresponding author. Department of Rural and Urban Development, Swedish University of Agricultural Sciences, P.O. Box 7012, SE-750 07 Uppsala, Sweden. E-mail addresses: anders.siren@utu.fi (A.H. Sire´n), [email protected] (E.S. Brondizio). 1 Present address (until March 2009): Herbario QCA, Pontificia Universidad Cato´lica del Ecuador, Apartado Postal 17-01-2184, Quito, Ecuador. 0143-6228/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeog.2008.08.006

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effect on inhibiting expansion of the agricultural frontier. In addition, deforestation rates in indigenous lands correlated neither with indigenous peoples’ population density nor with their contact with external populations. Indigenous land use systems are complex in structure and temporal dynamics and present significant challenges to remote sensing techniques commonly used to assess land use and cover change. Yet, there has been little in-depth investigation of land use dynamics within indigenous lands. Detecting and understanding the spatial–temporal dynamics of small-scale land use systems will contribute to addressing questions regarding the long-term interaction among population distribution and economy, as well as their land use decisions and their impact on land cover and biodiversity. Such studies have, however, been rare (but see Behrens et al., 1994; Wilkie, 1994). Instead, most studies of land use dynamics in tropical rainforests have focused on areas where immigration, socio-economic changes, or commercial timber extraction have led to rapid conversion of old-growth forest to cropland, secondary succession, or logged-over forest over large areas (e.g.,Gutman et al., 2004; Wood & Porro, 2002). This scarcity of studies of gradual land use and cover changes in shifting cultivation systems in sparsely populated tropical rainforest may partly be due to the methodological, instrumental, and logistic difficulties involved (Castro, Silva-Forsberg, Wilson, Brondizio, & Moran, 2002). Cultivated plots tend to be small, and they tend to go through a succession of different crops before gradually transforming into secondary succession (fallow), making it difficult even to clearly define land use categories (cf. Denevan & Padoch, 1987). In parts of the Amazon, depending on environmental conditions and land use history, fallow regrowth usually become spectrally undistinguishable from old-growth forest before the age of 15 years (Lucas et al., 2002; Lucas, Honzak, Foody, Curran, & Corves, 1993; Moran et al., 1994; Steininger, 1996; Steininger, 2000). Older fallows cover large extents of the Amazon, but often go undetected in remote sensing studies (Neff, Lucas, Brondizio,Santos, & Freitas, 2006). The topography is often rugged, producing marked differences in illumination on east- and west-facing slopes, in many cases limiting the level of classification detail and accuracy. Although such effects can be corrected using techniques such as digital elevation models (e.g., Colby & Keating, 1998), this is not feasible when land use units are very small in comparison to the resolution of contour lines on existing maps. In addition, many tropical rainforest areas are persistently covered with haze or clouds, affecting the availability and quality of image data. Under such conditions, it is easy to doom as impossible the task of detecting subtle land use changes using remote sensing techniques. Although alternative techniques, based on fieldwork alone, also have been used (e.g., Lawrence, Peart, & Leighton, 1998), such techniques are difficult to apply over large areas without running into difficult trade-offs between the necessity to limit the time dedicated to fieldwork and the need for a sufficient number of field plots located in space according to a non-biased strategy. This paper presents the methodological aspects of a study of land use and cover change dynamics in the lands of an indigenous community in a sparsely populated region of the Ecuadorian Amazon (Sire´n, 2007). The study area’s landscape is heavily dominated by old-growth forest, where land use is based on long-fallow cycles, and where the rate of land cover change is relatively slow. Our aim is to contribute methodological approaches to overcome the kinds of difficulties described above and to allow reasonably accurate estimates of the area of gardens, fallow, and old-growth forest, as well as their rate of change. We discuss the nature and amount of classification errors resulting from similarities between land cover classes, the spatial matching between land use systems and image spatial resolution. Study area This study was conducted in a tropical rainforest area covering 1267 km2, roughly corresponding to the area traditionally used for farming, hunting and gathering by the Kichwa indigenous people of the community of Sarayaku in the Ecuadorian Amazon (1440 S, 77 290 W). Sarayaku, centered on the banks of the Bobonaza River in Pastaza province, has no access roads. As the Bobonaza River flows through the area, the elevation decreases from 390 to 330 m a. s. l. The topography is rugged, and the highest peaks in the northwest reach 640 m a. s. l., whereas in the southeastern part they do not surpass 500 m a. s. l. The community has about a thousand inhabitants, and annual rate of population growth is estimated to 1.6% (Sire´n, 2004). The Sarayaku people practice shifting cultivation in a way that is quite typical for the indigenous people of western Amazonia (Sire´n, 2007, cf. also Brondizio, 2006; Denevan et al., 1984; Garı´, 2001; Johnson, 1983; Salick, 1989). In addition to their shifting cultivation gardens, a few people also have some pasture and a few heads of cattle, and some have small plantations of Aphandra natalia palms. Farming is concentrated primarily in the vicinity of the village itself, and secondarily along the major rivers in the area, where the people have secondary homes where they spend part of their time, in particular during school vacations. Fields are small, averaging 0.20 ha, and may be just 10 m wide when planted along the riverside. Fields typically show a succession where one stage gradually transforms into the next. Common successions are for example maize–cassava–plantain, cassava–pineapple and cassava–barbasco (Lonchocarpus nicou, an ictiotoxic). Weeding usually ceases about 2.5 years after planting, so that regrowth takes over. After a fallow period that can last from a couple of years to several decades, the land is cleared and farmed again. Fallows older than 60 years are, therefore, uncommon and, in any case, basically indistinguishable from old-growth forest. There are, however, very old fallows scattered on remote ridgetops, as remnants of a lifestyle based on dispersed settlement that gradually disappeared during the 19th and early 20th century. These old fallows are, however, difficult or impossible to distinguish from the surrounding primary forest based on their vegetation structure alone, and often the local people identify them based on the presence of pottery pieces or just on oral tradition. The study area also embraces the community of Jatun Molino, which was founded in 1983 by families who broke away from Sarayaku, and now forms an enclave inside Sarayaku lands, with a population of about a hundred inhabitants. However,

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no fieldwork took place in Jatun Molino, because of lack of interest on part of the inhabitants of this community to participate in the study. Methods Fieldwork involved georeferencing of land use plots, recording their edaphic/botanical characteristics, and interviewing their owners about their land use history. A land use plot was defined as a contiguous area of land that shared the same land use history. Using a combination of ISODATA and Maximum Likelihood algorithms and field information, we did a hybrid unsupervised–supervised image classification of a Landsat ETM þ image from August 24, 2001 to classify classes garden/ cropland, fallowed secondary vegetation and old-growth forest. We evaluated classification results based on detailed field data, and corrected the areal estimates based on this evaluation. We used Erdas Imagine 8.1 for image processing and accuracy assessment. We also classified a Landsat TM image from September 11, 1987 in the same way. Our estimation of the area of annual old-growth forest clearing was, however, based exclusively on field data alone, including interviews and field visits. We estimated the age distribution of fallows in the landscape based on field data. Integrating field and image data allowed us to estimate the proportion of fallows in the landscape that had reached an age beyond which it was impossible to distinguish them from old-growth forest. A more detailed description of the methods follows here below. Fieldwork When we started fieldwork in 1999, all relatively recent images of suitable spectral and spatial resolution of the area were of unacceptable quality because of heavy cloud cover and haze. Thus, the success of the study depended on the possibility that an image of acceptable quality would be acquired during the 3 years we had available. Fieldwork consisted primarily of georeferencing land use plots with GPS receiver (Silva Forest XL 1000) and recording their land use histories. Sampling was initially opportunistic, with a bias towards large land use units, in order to minimize problems with mixed or misplaced pixels when using these data as ground-truth for remote sensing. Later, we also selected a random sample of five families, who showed us all their currently cultivated areas. In total, we recorded field data for 92 plots. Typically, we took GPS points in 4– 20 ‘‘corners’’ of each such plot, and to improve positional accuracy we walked around them 3–4 times, and then calculated average coordinates for each corner. We wrote down a basic botanical description of the structure of land cover and recorded land use. To record the history of land use, we interviewed the owner of each plot. Usually we started by asking when the plot had been cleared for the first time, and then recorded every change in land use that the owner could recall, such as making a field, or letting a field grow back to fallow. The owners could not always tell the exact year or month of any particular land use change. However, they could usually relate each land use change to the age of their children at the time, to particular events in the history of the community, to school vacations or religious holidays. Based on this, and with the help of local field assistants, it was possible to reconstruct the history of most plots with a fair level of precision. A Landsat ETM þ image of acceptable quality was acquired on August 24, 2001. During the next few months we then visited most of the previously recorded field plots once again in order to record the state of each plot at the date of image acquisition, and consulted the owners about the dates of any recent land use changes, for example ceasing of weeding. Pre-processing Image pre-processing involved the following major steps: (1) georeferencing, (2) radiometric calibration to convert raw digital numbers to physical measures of at-sensor radiance, (3) atmospheric correction in order to convert measures of at-sensor radiance to measures of on-ground reflectance, and (4) radiometric rectification in order to eliminate or reduce any remaining differences within or between images. Ground control points (GCPs) for georeferencing the 2001 image field included river mouths, river junctions, lake ends, 1-pixel size clearings, and road forks, collected in the field. We also collected additional GCPs from 1:50,000 topographical maps. We discarded GCPs with measurement errors one by one, by discarding the GCP with the highest residual error, then re-calculating residual errors, again discarding the GCP with the highest residual error, and so on until about 30 GCPs remained and the root mean square (RMS) error was about half pixel size. The 1987 image was then registered onto the 2001 image. We did radiometric calibration according to Green et al. (2002), however, with great care not to lose valuable information when cutting off values on tails of the histograms, given that cultivated fields represented a very minor fraction of the total number of pixels on the images, and had very high reflectance, such that some of these pixels appeared far out on the barely visible tails of the histogram. We then did atmospheric correction according to Green et al. (2002), but had to use a lake outside the study area as dark area object, as all lakes within the study area were too small and shallow. Radiometric rectification based on non-vegetated dark and bright areas that were subjected to no change over time (see Green et al., 2002) was impossible because no such areas could be identified on the image. In order to correct for topographic effects, we used the Normalize function of Erdas Imagine 8.1. This function uses an ‘‘equal area normalization’’ algorithm that shifts each pixel spectrum to the same overall average brightness. A drawback of this is that it removes differences in spectral properties that consist of different total reflectance rather than different relative reflectance between the bands. However, the advantage is that it removes differences caused by different amount of light

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falling on each pixel. The net effect in this case, however, was an improvement in the separability between fallow and oldgrowth forest (Fig. 1). To correct for the haze effects, we visually assessed the haze conditions of the images, and divided them into six subsets within which haze conditions were similar (Fig. 2). A number of reference areas were selected in each subset for the purpose of within-image rectification. According to our previous knowledge of the area, and as far as could be seen by visual inspection of the original (not normalized) images, these areas were all old-growth forest on hills and not subjected to recent disturbance. We collected spectral signatures of these reference areas and calculated the average and standard deviation for each band for each subset. Using the Model Maker module of Erdas Imagine 8.1, we then recalculated the pixel values of each image subset in order to make the average and standard deviation of the primary forest reference areas become equal for all subsets:

 .   DNrectified ¼ Averagereference þ Stdevreference DNinput  Averageinput Stdevinput : Finally, we assembled the subsets back together (Fig. 2d).

Fig. 1. Spectral signatures of young fallow (broken line), old-growth forest (solid line), and garden (dotted line) of the 2001 image before (above) and after (below) equal area normalization. Error bars indicate one standard deviation, and are horizontally spaced in order to facilitate reading. Young fallow: left error bar. Old-growth forest: middle error bar. Gardens: right error bar. The equal area normalization procedure somewhat increased the overlap between old-growth forest and young fallow in the infrared bands, but substantially reduced the overlap in the visible bands, improving overall separability. The spectral signature of gardens was distinct from old-growth forest and fallow both before and after normalization.

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Fig. 2. Correction of haze effects. (a) 1987 image and (b) 2001 image: the images were divided into six subsets (yellow polygons), each one with relatively little internal variation in haze conditions. Within each subset, a number of areas of unchanged old-growth forest were selected as reference areas (black polygons). Bands 1 (blue), 3 (green) and 5 (red), histogram equalized in order to highlight the haze. See text for more details about the procedure. (c) Normalized 2001 image before correcting for haze effects, and (d) after sub-setting, radiometrically rectifying, and mosaicking the subsets back again. Bands 3 (red), 2 (green), and 1 (blue).

Classification A separability analysis using a transformed divergence algorithm indicated spectral similarities between the classes of garden, fallow, and old-growth forest. We focused our efforts on achieving accurate classification of these three classes. Supervised classification based on training areas collected in the field produced problematic results; given the dominance of old-growth forest, even a modest level of misclassification of old-growth forest into the garden and fallow classes strongly affected the total area estimates of these latter classes. Therefore, we instead ran the images through a high-dimension unsupervised classification (200 clusters), and aggregated these clusters into the classes ‘‘garden’’, ‘‘fallow’’, and ‘‘old-growth forest’’, using a combination of reference signatures and spectral separability analysis (Mausel et al., 1993). In addition, we did extensive visual analysis using criteria of shape and spatial distribution of each cluster in the landscape. Gardens were straightforward because of their distinctive shape, as well as their concentration around human settlements. Similarly, fallow could be separated from old-growth forest based on their location and context (e.g., neighboring garden plots located around settlements). If a cluster seemed to contain more than one land use class, we ran it through unsupervised classification again, in order to split it up in several smaller clusters. We took special care in order to minimize the misclassification of old-growth forest pixels. Although the classification of each images, from 1987 and 2001, respectively, seemed satisfactorily accurate in itself, they were not accurate enough for image-based change detection. Such an analysis actually showed a 5% decrease of the intervened area [gardens þ fallows] from 1987 to 2001, which was unexpected given that population was growing and to our knowledge there had been no major technological or socio-economic change that could have reduced the area needed for farming. Thus we concluded that whereas we could use remote sensing in order to estimate the current extent of different classes of land use and land cover, the changes occurring were too subtle, in terms of their spatial extent as well as in terms of the changes in spectral properties of the vegetation, to be detected by means of remote sensing. Thus, in order to assess the changes over time in land use and land cover, we needed to combine remote sensing with other methods. Evaluation of classification To evaluate the misclassification of old-growth forest, we used the same large old-growth forest reference areas as had been used for the haze correction. To evaluate other classification errors, we used spectral samples of plots georeferenced in the field, and used data from both the 1987 and the 2001 image, in order to get a sample of pixels large enough for the

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analyses. Because of the small size of the plots, however, it was necessary to first conduct unsupervised classification of small subsets of the image, containing the land use plots. Based on this unsupervised classification in combination with the GPSmeasured polygons, we selected the pixels to be used as sample for an error matrix. It could be expected that a considerable part of the errors indicated by the error matrix would be due to misplaced rather than misclassified pixels, because the size of fields, the error margin of the GPS receiver, and the spatial resolution of the image data were all in the same range of magnitude (the average size of fields was equal to a square with 45-m side and many fields were much smaller than this, the typical error margin of the GPS receiver was 15–20 m according to the manufacturer, and the pixel size of the image was 28.5 m). To distinguish errors due to misplaced pixels from errors due to misclassified pixels, we compared the percentage of misclassification of old-growth forest reference samples (i.e., our larger samples) with that of the small GPS-measured old-growth forest land use plots. These latter were often small patches of remaining old-growth forest within a landscape dominated by cultivated fields and fallows. Thus, even minor georeferencing errors could make these pixels appear as misclassified. We also explored the relation between fallow age and the percentage of pixels classified as old-growth forest. Given that, land use technology and intensity tend to be similar within the area, we assumed (based on literature data such as Moran et al., 2000) that, under such conditions of land use, fallow age tends to correspond to vegetation structure based on ratios between seedlings, saplings, and tree density and their respective height and DBH (diameter at breast height). We aggregated pixels into the age classes 0–5, 5–10, 10–15, 15–20, 20–30, 30–40, and 40–50 years, as determined based on the land use history interviews made in the field. Then, for each age class, we tabulated the percentage of pixels classified as garden, fallow, or old-growth forest. Elimination of misclassified pixels based on contextual information As the landscape was heavily dominated by old-growth forest, even a small proportion of misclassification of old-growth forest could cause a significant overestimation of the area of gardens or fallow. Therefore, we eliminated most of such misclassified pixels based on contextual information. First, we eliminated all fallow and garden pixels that did not form contiguous regions of fallow and garden that combined were at least 3 pixels large. The basis of this was that clearings for new fields usually are made adjacent to old fields or fallows, and in the relatively rare case that a new garden is cleared for in old-growth forest far from any existing other garden or fallow, this normally would affect at least 3 pixels, given the impact of big trees falling on the surrounding forest. We also manually eliminated those pixels of garden that were in unsuitable locations for gardens, such as in steep gorges or very far from settlements. Fallowed vegetation pixels that more likely represented natural secondary vegetation (e.g., vegetation knocked out by storms) were eliminated automatically by the use of buffers defining distance from village center, rivers, and garden areas. As we knew that agriculture is concentrated along the major rivers, and typically occurs relatively near gardens, we created a 1-km buffer along the major rivers, and another 1-km buffer around all garden pixels on the 2001 as well as the 1987 image. We considered that ‘‘fallow’’ pixels outside these buffers most likely represented natural secondary vegetation, and we, therefore, eliminated those pixels. Estimating age distribution of fallowed vegetation and the extent of old fallows We calculated the age distribution of fallow, based on the age of the fallow at the last clearing of each land use plot, assuming that the distribution – in terms of number – of fallow age at clearing in our sample was representative for the corresponding distribution – in terms of area – in the landscape. We also assumed a stable age distribution of the fallows. Given that the rate of agricultural expansion was slow, and thus clearing and regrowth were fairly close to a balance, this assumption probably does not introduce any major error. The proportion of fallow – in the landscape – that have an age between a and b years can be expressed as

Pða < x < bÞ ¼

Zb

PðxÞdx

a

where

Z

xmax

PðxÞdx ¼ 1:

0

The area of fallows cleared (C) at age x reduces the area of fallows of that age in the landscape with the same amount:

CðxÞ ¼





dA  dx

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and

Z Z  CðxÞdx ¼ dA thus,

AðxÞ ¼ Að0Þ 

Z

x

CðyÞdy:

0

No clearing occurs at age zero, so A(0) ¼ 0, and thus

AðxÞ ¼ 

Zx

CðxÞdx:

o

The density P of fallows that are of age x is thus

Rx CðyÞdy AðxÞ ¼ R xmax R0z PðxÞ ¼ R xmax AðzÞdz 0 0 CðyÞdy 0

dz

:

We approximated this continuous equation by a discrete equation, where the age of fallow (in years) was expressed as whole numbers:

Px 0 Ci Px ¼ Px i ¼ Pj max j¼0

i¼0

Ci

where Px is the proportion of fallows of age x years, and Ci is the number of clearings of fallow of age i. Finally, we aggregated the resulting distribution into 5-year intervals. Based on this distribution we estimated the proportion of fallow that were younger or older, respectively, than the age limit at which we had shown that fallow become spectrally indistinguishable from old-growth forest. This allowed us to quantify the area of such old fallows that we had not been able to map by means of remote sensing analysis. Estimating the rate of land use change To calculate the rate of land use change, we first tabulated the area of the land cover classes old-growth forest, fallow, and garden, after having corrected the figures for misclassification of fallow, eliminated misclassified old-growth forest pixels based on contextual information, and calculated the areal extent of fallow that were too old to be identified by means of remote sensing. To estimate the area of fallow and forest cleared annually, we divided the total cultivated area, (according to the field data of the random sample of families), with the average life span of gardens (2.5 years). To estimate the area of old-growth forest cleared annually, we multiplied the estimate of total annual clearing by 0.35, which was the proportion of all clearings made during 2000 and 2001 which had been done in old-growth forest (Sire´n, 2007). Results Out of the pixels in the old-growth forest reference areas, only 0.5% had been misclassified as fallow2, and 0,0% had been misclassified as garden. This level of classification error was quite homogenous between images and between-image subsets (Table 1). The error matrix based on ground-truthed plots, however, indicated that as much as 15% of the old-growth pixels were misclassified this way. The explanation to this difference is probably that these latter plots were largely surrounded by fallows. Thus, what appears to be misclassified pixels was probably rather pixels that were slightly misplaced due to georeferencing errors. About half of the training area pixels of cassava and maize fields were correctly classified as fields, whereas the rest were classified either as forest or young fallow. Plantain fields, barbasco, pasture, and regrowth on pasture were mostly classified as ‘‘old-growth forest’’, and the same was true for old cassava fields that were beginning to get invaded by regrowth (Table 2). For sampled fallow areas younger than 20 years, about half of the pixels were classified as such, whereas the other half got classified as ‘‘old-growth forest’’. All fallow pixels above 20 years of age got classified as old-growth forest (Fig. 3). Based on this, we multiplied by a factor 2 the amount of fallow according to the digital classification, in order to get a final estimate of the area of fallow of an age less than 20 years.

2 From the point of view of land cover classification, it may not be correct to call this misclassification, as these pixels most probably represented secondary succession grown up in treefall gaps and alike. However, for our purpose of land use classification, we wanted all land that had not been cleared and farmed by humans to be classified as old-growth forest.

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Table 1 Percent pixels within the old-growth forest reference areas shown in Fig. 2. that were classified as young secondary forest. Image

1987 2001

Subset (from west to east) 1

2

3

4

5

6

All

1.1 0.47

0.42 0.51

0.56 0.64

0.29 0.41

0.59 0.43

0.49 0.79

0.52 0.54

Thus, we reached the conclusion that fallow younger than 20 years covered 2.2% of the study area. However, the analysis of age distribution of fallows in the landscape showed that only 65% of the existing fallows were actually younger than 20 years. We estimated the area of fallow between 20 and 65 years old to cover 0.65  2.2% ¼ 1.3% of the study area, so that the total fallow area was 2.2% þ 1.3% ¼ 3.4% (rounding off to one decimal). Field data indicated that cultivated land covered 132 ha or 0.10% of the area and that 53 ha were cleared annually, out of which 18.5 ha was in old-growth forest, corresponding to an annual expansion rate of the intervened area of 0.4%, and an overall annual deforestation rate of 0.015% (Sire´n, 2007). Discussion In spite of the methodological challenges related to poor image data quality and the subtle nature of land use change, it proved possible to estimate the area of land use classes and their rate of change. Important keys to this success were the equal area normalization to reduce topographic shade effects, the sub-setting, radiometric rectification, and re-assembling procedures to minimize haze effects, the image classification strategy based hybrid classification, the dedication to extensive fieldwork. One important lesson is that field data can be integrated to aid in image analysis beyond their common role as ‘‘training areas’’ for classification. The radiometric rectification of subsets nearly eliminated the effects of varying atmospheric conditions. In addition, this also took care of the east–west DN gradient later described by Toivonen, Kalliola, Ruokolainen, and Malik (2006), which also we observed, although at the time we were unaware of the origin of this gradient, believing that all we saw was due to variation in haze and atmospheric conditions. In future studies, we recommend first correcting for the east–west DN gradient according to Toivonen et al. (2006), then to observe any remaining effects that may be due to variation in atmospheric conditions and, if needed, subset and radiometrically rectify as we did in this study. High-dimensioning cluster offered flexibility during classification decisions and allowed us to decide, based on cluster aggregation, the possible direction of the errors, an advantage over supervised classification methods. Thus, we were able to reach very low levels of misclassification of old-growth forest. Furthermore, we highlighted the importance of analyzing classification results based on contextual information. However, unsupervised classification requires some level of subjectivity, even when spectral and statistical analyses are part of the process. Therefore, it is fundamental in such situations to evaluate classification results in light of fieldwork and field data, using procedures based on independent and objective evaluation.

Table 2 The percentage of ground-truthed areas classified into different types of land cover. Ground-truthed class

Classified as 1987 Forest

Cassava/maize field Old cassava field Barbasco Plantain Fallow Pasture Regrowth on pasture Aphandra Natalia palm plantation Old-growth forest

2001 Young fallow

Field

Number of pixels

Pooled 1987 and 2001

Forest

Young fallow

Field

Number of pixels

25 31 37 34 53 10 29 0

47 15 0 7 2 6 0 0

71

0

60 62 61 73

38 26 36 9

1 12 4 18

143 155 28 11

28 54 63 59 45 84 71 100

88

12

0

102

29

Forest

Young fallow

Field

Number of pixels

85 13 19 29 157 80 70 16

52 69 68 89

46 20 31 4

2 10 1 7

300 235 98 27

7

84

16

0

109

The 1987 ‘‘ground-truth’’ data is based on interviews, not on field observation at the time of image acquisition. The 1987 ‘‘ground-truth’’ data on fields is omitted because any minor temporal error in interviewee information about date for clearing could have led to totally erroneous information regarding actual land cover at the time of image acquisition. Pastures and Aphandra natalia palm plantations were in 1987 quite recently established, which probably explains the difference in classification results as compared with the 2001 image. The difference in the classification of fallows is largely an effect of a larger proportion of old fallows in the ground-truthed pixels on the 1987 image.

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Fig. 3. Evaluation of classification of fallows according to their age. Up to 20 years of age, about half of the pixels got classified as ‘‘young fallow’’, and the other half as ‘‘forest’’. After 20 years of age, all pixels got classified as ‘‘forest’’.

As we attempted to minimize misclassification of old-growth forest, inevitably, we found higher levels of classification errors in other land cover classes. Different types of cultivated land had classification error levels of 53% or more. This was less of a problem, however, because the total area of cultivated land was small enough that these errors did not significantly affect the estimates of the total area of intervened land, i.e., the sum of the area of cultivated land and of fallows. In addition, we used a reliable estimate of the area of cultivated land, based on field measurements of cultivated plots of a random sample of families. Fallow areas younger than 20 years had a classification error level of about 50%, almost all of which was misclassified as old-growth forest. However, we did not find this to be a significant problem, as we had the option of using a correction factor (of two) to allow estimating the total area of such young fallow areas. Fallow areas older than 20 years could not be differentiated from old-growth forests on the image, but their areal extent could be calculated based on the analysis of the age distribution of fallows in the landscape. Based on previous studies (Lucas et al., 2002; Lucas et al., 1993; Moran et al., 1994; Steininger, 1996; Steininger, 2000), we did not expect to be able to distinguish fallows older than, at the most, 15 years. Thus, we made no directed effort towards sampling fallows older than this, and during fieldwork we found few fallows older than 20 years. The procedure we developed for pre-processing and classification, however, was more successful than we had expected, as we were able to distinguish fallows up to 20 years of age. The scarcity of field data from fallows older than 20 years forced us also to use land use data and image data from 1987 in order to evaluate the classification. The error introduced by this procedure was probably fairly small, given the similarity in overall classification results between the 1987 and 2001 images. Nevertheless, we highly recommend for future studies that a directed effort is made towards ground-truthing fallows in the age span of 15–40 years. This would contribute to the improvement of our ability to distinguish advanced fallow from old-growth forest. This is an important area of land use research in the Amazon and will contribute to the understanding of the dynamics of land use in forest areas, including those defined for conservation purposes. Calculating the age distribution of fallows in the landscape, based on field data from land use plots, permitted estimating the proportion of fallows that had been detected by the remote sensing analysis and, consequently, the proportion that had not been detected, and thus to do the appropriate correction in order to calculate the total area of fallows in the study area. Because of the slow pace of land use change, and the nature of the agricultural practices, relying on long-fallow cycles, in combination with problems with image data quality, it was not possible to do a change detection based on classification of a time series of images, and thus to assess past changes in land use and land cover. It was, however, possible to assess current land use and land cover change, based on analysis of field data on forest clearing, in combination with the snapshot of land use and land cover provided by the classified image from 2001. Conclusion We have shown that, in spite of a wide range of methodological difficulties that apparently have deterred scientists from studying subtle land use changes in tropical forest shifting cultivation systems, it is possible both to estimate the extent of gardens and fallows and to estimate the current rate of land use and land cover change. Assessing historical land use changes remains more difficult in cases such as this one, where the rate of change tends to be slow and spatially complex and the actual land use change becomes overshadowed even by minor errors in between-image calibration. However, as socioeconomic change may cause increased deforestation rates in the near future (see Sire´n [2007] for a more detailed discussion of current land use change in the area), studies such as this one can be important for providing a baseline for comparison and for understanding the implications of such changes.

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Furthermore, this study highlights the importance of integrating remote sensing and in-depth ethnographic and ecological fieldwork into the study of complex, small-scale land use systems. Image quality, temporal availability, spatial and spectral resolution, and processing errors often require subjective decisions during image classification, such as those concerning the allocation of distinct land use systems into spectrally similar land cover classes or vice-versa. Dedication to fieldwork provides disaggregated data to support spectral classification and accuracy assessment, as well as an understanding of social and ecological processes leading to particular changes in land use allocation and distribution over time. The availability of very high resolution imagery (e.g., Hurtt et al., 2003) contributes to, but should not substitute, efforts towards field research. In fact, beyond indigenous lands, the growing complexity of land use systems throughout the Amazon underscores the importance of more fieldwork informing and complementing remote sensing-based assessments. Increasingly, small-scale agriculture and forest management systems inter-mingle with urban areas and large-scale plantations to form distinct landscape mosaics (Brondizio, 2006). While the overwhelming majority of regional deforestation is caused by a relatively small number of large-scale clearings, most deforestation events are small in size and widespread (e.g., see regional assessments such as PRODES [ n.d.]), thus contributing to the formation of intricate forest–fallow–field landscapes over large portions of the region (Neff et al., 2006). Similarly, diverse agroforestry-based production and forest management systems, within and outside of reserves, represent a growing part of regional land use and economy and, yet, are largely invisible to most assessments of land use and land cover change due to their variability, size, and ecological particularities (Padoch & Pinedo-Vasquez, 2006). Continuing attention to fine-grain interpretation of remotely sensed data and its integration with detailed field research, besides providing information relevant to local land users and populations, will contribute to a more accurate picture of the scale, variability, and implications of regional deforestation and land use change.

Acknowledgements Thanks to Jose´ Machoa, Cesar Santi, Marcia Gualinga, Elvis Gualinga, Dorila Machoa, Reinaldo Guerra, Ena Santi, Carlos Aranda, and Isabel Quislema, for assistance with fieldwork. Thanks to all the people of Sarayaku who showed us their fields and fallows and told us their histories. Thanks to Kalle Parvinen, Jan Bengtsson and David Gibbon for comments to earlier versions of the manuscript, to Kelsey Scroggins for her valuable editing, and to Scott Hetrick for GIS support. This research was part of the PhD studies of Anders Sire´n at the Department of Rural Developent Studies at the Swedish University of Agricultural Sciences, and Eduardo Brondizio had the role of external assistant supervisor. The fieldwork of Anders Sire´n was financed by the Swedish Agency for International Development Cooperation (SIDA). The remote sensing analysis was done at the Anthropological Center for Training and Research on Global environmental Change (ACT), and the Center for the Study of Institutions, Population, and Environmental Change (CIPEC), both at Indiana University, where Anders Sire´n spent over half a year thanks to two fellowships funded by CIPEC and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT), respectively. Eduardo S. Brondizio acknowledges the support of the NASA’s LBA-Ecology program (grants NCC5-334, NCC-695, NNG06GD86A). The manuscript was finalized when Anders Sire´n was at the Department of Biology, University of Turku, with a ‘‘start-up grant for young researchers’’ from the Academy of Finland, and when he was at the Herbario QCA, Pontificia Universidad Cato´lica del Ecuador, funded by a postdoctoral stipend from SIDA.

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