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image provided by TRFIC had the highest geometric
accuracy as determined using Global Positioning System (GPS) coordinates collected in the field
and resulting in a root mean square error (RMSE) of less than one pixel. Therefore, we coregistered
the 1986 and 1994 images to the “base” 1999 ETM
The geometrically corrected 1999 ETM
+
image using recognizable fixed objects (such
as road intersections) in ERDAS Imagine 8.4. We used nine “fixed” locations, known as ground
control points (GCPs), to register both images. For the 1986 and 1994 MSS images, the RMSEs
were 0.54 and 0.47 pixels, respectively.
Additional image processing included the derivation of tasseled-cap indices for each image.
Tasseled-cap transformed spectral bands 1, 2, and 3 (indices of brightness, greenness, and wetness,
respectively) were calculated for the TM images using Landsat-5 coefficients published by Crist
et al. (1986). Although Huang et al. (2002) recommended using a reflectance-based tasseled-cap
transformation for Landsat 7 (ETM
+
) based on at-satellite reflectance, those recommended tasseled-
cap coefficients for Landsat 7 were not published at the time of this study. Tasseled-cap bands 1
and 2 (brightness and greenness) were calculated for the MSS image using coefficients published
by Kauth and Thomas (1976). These investigators have shown tasseled-cap indices to be useful in
differentiating vegetation types on the landscape, and the tasseled-cap indices were therefore
included in this analysis of mapping LC. Image stacks of the raw spectral bands and tasseled-cap
bands were created in ERDAS Imagine 8.4. This resulted in one 6-band image for 1986 (MSS
spectral bands 1, 2, 3, 4, and tasseled-cap bands 1 and 2), a 10-band image for 1994 (TM spectral
bands 1-7 and tasseled-cap bands 1, 2, and 3), and an 11-band image for 1999 (ETM
+
spectral
bands 1-8 and tasseled-cap bands 1-3). The 15-m panchromatic band in the 1999 ETM
+
image
+
was not used in this analysis.
6.2.2
Reference Data Collection
As in many remote areas in developing countries, data sources for producing and assessing
accuracy of LC maps for our study area were limited. Upon project initiation (2000) no suitable
LC reference data were available. Historical aerial photographs were not available for discriminating
between LC types for our study area. In this context, satellite imagery was the only spatially
referenced data source for producing reliable LC maps for the area.
Because we wanted to document LC change from the early stages of human settlement and
development (beginning in 1985), when major forest conversion projects were established, our
objective was to compile retrospective data to develop and validate a time series of LC maps. The
challenge of compiling retrospective data became an opportunity to engage community stakeholders
in the mapping process and “bring farmers into the map.” We decided to enlist the help of farmers,
who are very knowledgeable about land occupation practices and the major forces of land use
dynamics, to be our source for contemporary and retrospective data collection. Also, by engaging
the locals early in the process, we could examine the advantages and limitations of this strategy
for future resource inventory projects in the region conducted by researchers and local stakeholders.
We utilized a seven-category LC classification scheme as defined in Table 6.1. The level of
detail of this classification scheme is similar to that of others used in the region and should permit
some level of comparative analysis with collaborators and stakeholders (Rignot et al., 1997; de
Moraes et al., 1998).
In August 2000, with the assistance of members of nine small-scale farmer associations in the
study area, we collected field data to assist in the development of spectral models of each cover
type for image classification and to validate the resulting LC maps. All associations that we
contacted participated in the mapping project. Initially, we met with the leadership of each asso-
ciation and presented our research goals and objectives, answered questions, and invited members
of each association to participate in the study. After developing mutual trust and actively engaging
the association, data collection groups were formed averaging 12 individuals per association (total
over 100 individuals). Special effort was made to include individuals in each group who were long-
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