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Using indigenous knowledge to link hyper-temporal land cover mapping
with land use in the Venezuelan Amazon: “The Forest Pulse”
Jesús Olivero1*, Francisco Ferri2,3, Pelayo Acevedo4, Jorge M. Lobo2, Julia E. Fa1,5,6, Miguel
Á. Farfán1, David Romero1, the Amazonian communities of Cascaradura, Niñal, Curimacare,
Chapazón, Solano and Guzmán Blanco7 & Raimundo Real1
1. Departamento de Biología Animal, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain;
jesusolivero@uma.es, jfa949@gmail.com, mafarfanaguilar@hotmail.com, davidrp@uma.es, rrgimenez@uma.es
2. Departamento de Biogeografía y Cambio Global, Museo Nacional de Ciencias Naturales CSIC & Laboratorio
Internacional en Cambio Global CSIC-PUC (LINCGlobal), Calle José Gutiérrez Abascal 2, 28006, Madrid, Spain;
francisco_ferri@mncn.csic.es
3. ‘Rui Nabeiro’ Biodiversity Chair, CIBIO, University of Évora, Largo dos Colegiais, 7000, Évora, Portugal;
francisco_ferri@mncn.csic.es
4. SaBio IREC, Instituto de Investigación en Recursos Cinegéticos (UCLM-CSIC-JCCM), Ronda de Toledo s/n, 13071,
Ciudad Real, Spain; pelayo.acevedo@gmail.com
5. Division of Biology and Conservation Ecology, School of Science and the Environment, Manchester Metropolitan
University, Manchester M1 5GD, UK
6. Center for International Forestry Research (CIFOR), Jalan Cifor, Situ Gede, Bogor 16115, Indonesia.
7. Municipios Atabapo, Río Negro and Maroa, Estado Amazonas, Venezuela, see Appendix 1; morda1968@gmail.com
* Correspondence
Received 18-XI-2015. Corrected 20-VI-2016. Accepted 21-VII-2016.
Abstract: Remote sensing and traditional ecological knowledge (TEK) can be combined to advance conserva-
tion of remote tropical regions, e.g. Amazonia, where intensive in situ surveys are often not possible. Integrating
TEK into monitoring and management of these areas allows for community participation, as well as for offering
novel insights into sustainable resource use. In this study, we developed a 250 m resolution land-cover map of
the Western Guyana Shield (Venezuela) based on remote sensing, and used TEK to validate its relevance for
indigenous livelihoods and land uses. We first employed a hyper-temporal remotely sensed vegetation index
to derive a land classification system. During a 1 300 km, eight day fluvial expedition in roadless areas in the
Amazonas State (Venezuela), we visited six indigenous communities who provided geo-referenced data on
hunting, fishing and farming activities. We overlaid these TEK data onto the land classification map, to link
land classes with indigenous use. We characterized land classes using patterns of greenness temporal change and
topo-hydrological information, and proposed 12 land-cover types, grouped into five main landscapes: 1) water
bodies; 2) open lands/forest edges; 3) evergreen forests; 4) submontane semideciduous forests, and 5) cloud
forests. Each land cover class was identified with a pulsating profile describing temporal changes in greenness,
hence we labelled our map as “The Forest Pulse”. These greenness profiles showed a slightly increasing trend,
for the period 2000 to 2009, in the land classes representing grassland and scrubland, and a slightly decreasing
trend in the classes representing forests. This finding is consistent with a gain in carbon in grassland as a conse-
quence of climate warming, and also with some loss of vegetation in the forests. Thus, our classification shows
potential to assess future effects of climate change on landscape. Several classes were significantly connected
with agriculture, fishing, overall hunting, and more specifically the hunting of primates, Mazama americana,
Dasyprocta fuliginosa, and Tayassu pecari. Our results showed that TEK-based approaches can serve as a basis
for validating the livelihood relevance of landscapes in high-value conservation areas, which can form the basis
for furthering the management of natural resources in these regions. Rev. Biol. Trop. 64 (4): 1661-1682. Epub
2016 December 01.
Key words: Amazonia, forest conservation, greenness, indigenous people, land cover, land use, remote sensing.
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Alarming deforestation rates for the entire
Amazon region imperil the future of this high-
biodiversity biome (Saatchi, Nelson, Podest,
& Holt, 2000). By the end of the 21st century,
forest cover in this region is predicted to have
declined at rates of up to 80 %, as a result of
climate warming and desiccation (Betts, Cox,
Collins, Harris, Huntingford, & Jones, 2004;
Cox et al., 2004; Intergovernmental Panel
on Climate Change, 2007; Salazar, Nobre,
& Oyama, 2007; Nepstad, Stickler, Soares-
Filho, & Merry, 2008). Range and rates of
deforestation in the Amazon have been deter-
mined using satellite imagery but mostly at
local scales (Asner, Jeller, Pereira, & Zweede,
2002; Marsik, Stevens, & Southworth, 2011).
However, effective conservation of Amazo-
nian forest regions requires land cover map-
ping (Saatchi et al., 2000) to prioritize those
expanses in greatest need of protection (Bunce,
Barr, Clarke, Howard, & Lane, 1996; Kerr &
Ostrovsky, 2003).
Fieldwork by scientists can be severely
constrained in regions where personal secu-
rity may be compromised because of armed
conflicts, or geographical remoteness. In these
situations, satellite remote sensing is a power-
ful tool for charting land use patterns and for
detecting changes over time (Iverson, Graham,
& Cook, 1989; Toivonen, Mäki, & Kalliola,
2007; Baratolo et al., 2011). Even then, ade-
quate ecological interpretation of the resulting
land units may be limited by lack of ground
data (Stehman & Czaplewski, 1998; Shao &
Wu, 2008). To overcome this limitation, local
knowledge proffered by indigenous commu-
nities living in the area of interest can assist
land-cover mapping by identifying landscapes
of value to their livelihoods (Berkes, Cold-
ing, & Folke, 2000; Herlihy & Knapp, 2003;
Robbins, 2003; Naidoo & Hill, 2006; Lauer &
Aswani, 2008).
The Venezuelan Amazon, wholly included
within the administrative State of Amazonas, is
an isolated part of the country that still remains
largely uncharted. Although the standing natu-
ral vegetation of the region has remained
relatively unchanged (Madi, Vázquez, León,
& Rodríguez, 2011), the region’s biodiversity
is known to be under severe threat (Huber,
2001). The remoteness of the region has indi-
rectly ensured its preservation. However, its
inaccessibility has also impeded adequate
monitoring of the region’s natural richness
for its conservation.
New techniques have emerged that utilize
local (or traditional) ecological knowledge
(hereafter, TEK) to monitor species distribu-
tions and population trends over time. These,
alongside the use of geospatial technologies,
can adequately defeat the constraints of assess-
ing large natural areas such as the Venezuelan
Amazonian region (Ostrom, Burger, Field,
Norgaard, & Policansky, 1999; Kerr & Ostro-
vsky, 2003). To date, a number of regional and
continental land-cover assessments have been
completed for the Amazon Basin (Saatchi et al.,
2000; Eva et al., 2004; Gond et al., 2011; Han-
sen et al., 2013; Mitchard et al., 2014; Pacheco,
Aguado, & Mollicone, 2014) that have also
included the Venezuelan portion of the Basin.
By employing a combination of remote sens-
ing techniques, supported by TEK, we generate
a 250 m resolution land-cover classification
map for the Venezuelan Amazon. First, we
develop a land-cover map from hyper-temporal
remotely sensed greenness. Land-cover classes
are individually related to pulsating patterns
of change in greenness, which help landscape
identification and have potential for character-
izing the evolution of land classes in future
time periods; these profiles form part of the
map legend (De Bie et al., 2008), and for this
reason we labelled our map as “The Forest
Pulse”. Onto this map we overlay TEK data
provided by six indigenous communities to val-
idate the relevance of our map for indigenous
livelihoods and land uses. We then characterize
land classes using additional remotely sensed
information in order to further understand
the correspondence between land classes and
landscape units.
MATERIAL AND METHODS
The study area: The study area (0º15’58”
- 6º29’20” N & 62º59’35” - 68º7’24” W)
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covered a total of 51 000 km2 (less than 5 %
of the Venezuelan territory) and is underlined
by the Guyana Shield (Fig. 1). Due to its high
topographic diversity, the state of Amazonas
includes parts of the Guyana highlands; a
region with high species diversity and ende-
mism (Bates, Hackett, & Cracraft, 1998; Silva,
Rylands, & da Fonseca, 2005; López-Osorio
& Miranda-Esquivel, 2010). In particular, the
Western portion of the Amazonas state con-
tains the world’s largest natural waterway (the
Casiquiare Canal) that links the Orinoco and the
Amazon Rivers. This waterway is a significant
natural corridor for gene flow and dispersal
of species between the two major river basins
(Winemiller, López-Fernández, Taphorn, Nico,
& Duque, 2008; Willis et al., 2010).
The area is divisible into: 1) “highlands”
along the Western border of the Amazonas
State and upper courses of the Ventuari and
Orinoco Rivers (mountain landscapes covered
by evergreen forests and scattered with table-
top mountains, tepuis), and 2) “lowlands”,
peneplains and low hills along the Casiquiare
Canal and the Guainía-Río Negro, Ventuari and
Orinoco Rivers (covered by evergreen forests,
grassland and scrubland) (Schargel, 2011).
Initial data management: A conceptual
diagram summarizing the methodological pro-
cedure used in this study is shown in Fig. 2.
We used NDVI calculations from 16 day com-
posites of MODIS Gridded Vegetation Indices
(product MOD 13) at a 250 m spatial resolu-
tion. A set of 216 images corresponding to the
period from February 2000 to February 2009
was used (Fig. 3a). These images were col-
lected by the MODIS (Moderate Resolution
Imaging Spectroradiometer) sensor onboard
the Terra (EOS AM) satellite.
Fig. 1. Study area. The Guyana Shield is delimited by a dashed line and Venezuela is shaded in grey (darker grey represents
the Amazonas State within the large rectangle). Wide dark-grey line: route of the expedition; narrow dark grey lines: rivers;
white line: Casiquiare Canal; narrow black lines: 500 m altitudinal limit; black polygons: areas with altitudes higher than
1 500 m. River names are written in black and tepuis are indicated with white abbreviations (Si: Sipapo; Gu: Guaviare
Mountains; Ma: Maigualida; Gq: Guaquinima; Ja: Jaua; Sa: Sarisaniñama; Pa: Paru; Du: Duida; Ne: Neblina). The six
indigenous communities visited are identified by stars: 1 = Cascaradura; 2 = Guzmán Blanco; 3 = Chapazón; 4 = Solano;
5 = Kurimakare; 6 = Niñal.
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In order to compensate for the negative
effect of cloud cover on the quality of the
NDVI layers, we combined these layers in bi-
monthly periods while maximizing confidence
with the support of cell “reliability” layers pro-
vided by MODIS. Values in reliability layers
range from 0 –i.e. good reliable data– to 3 –i.e.
covered with clouds (Solano, Didan, Jacob-
son, & Huete, 2010) (Fig. 3b). Following this,
we undertook two main procedures for layer
combination: in every cell, we (1) rejected all
NDVI values with quality tag >1; (2) selected
NDVI values with the lowest tag. In case of
different NDVI values with the same quality
tag, we chose the highest value.
Land classification: We used the “separa-
bility” classification approach (De Bie, Khan,
Toxopeus, Venus, & Skidmore, 2008) (Fig. 2).
The ISODATA clustering algorithm (Erdas-
Imagine software package) was run to set 19
different hypertemporal land classifications,
each containing a different number of classes:
10, 20, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
35, 36, 37, 40, 50 and 60. For every classifica-
tion, the software produced two separability
Fig. 2. Conceptual map outlining methodological steps.
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values measuring minimum and average diver-
gence between classes. We selected the classifi-
cation showing the highest separability values.
Finally, land classes appearing only in marginal
pixels were removed.
Land-class characterization based on
greenness: Average NDVI values recorded
in the cells of each land class were plotted as
NDVI profiles, representing pulsating patterns
of changes in greenness. NDVI profile com-
parisons can be used to determine the type of
vegetation cover in the landscapes represented
by each class (Arroyo-Mora et al., 2005), since
the NDVI is an indicator of photosyntheti-
cally active biomass (Sellers, 1985; Khan, de
Bie, van Keulen, Smaling, & Real, 2010) and
NDVI temporal dynamics can reflect woodi-
ness, seasonality and leaf type (DeFries et al.,
1995). Arroyo-Mora et al. (2005) proposed a
relationship between NDVI ranges and for-
ested successional stages, which provides a
basis for a preliminary interpretation of our 15
land classes. These authors interpreted NDVI
values below 0.45 as pastures; values ranging
from 0.45 to 0.58 as sparse patches of woody
vegetation, shrubs, and pastures; from 0.58 to
0.70 as intermediate forest successional stages;
Fig. 3. A sample square showing the variables used for both land classification and class characterization. A) 16
day composite of MODIS NDVI of the first half of April 2000; B) Reliability of the NDVI value for the same date (white
represents pixels covered with clouds whereas black represents reliable data); C) Natural-colour-composite raster derived
from ETM+ marked with stickers containing information from the indigenous people; D) Elevation (SRTM); E) Slope; F)
Compound topographic wetness index (CTI); G) Distance from water bodies; H) Distance from minor rivers; I) Distance
from non-perennial streams. Except for B, the values of all the variables shown by grey dashes increase with lightness.
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from 0.70 to 0.83 as evergreen mature forests.
These ranges were conceived for the analysis
of dry forests, whereas lowland humid forests
prevail in the Guyana Shield (Eva et al., 2004).
Thus, the cited correspondences between NDVI
and forest stages could be slightly undervalued
when applied to humid forests, but they can be
still used as a reference.
Serious constraints to the use of the NDVI
in late successional stages of tropical for-
est have been described, because the spectral
reflectance of dense forests reaches saturation
in the red and near-infrared bands (Saatchi et
al., 2000; Arroyo-Mora et al., 2005; Freitas,
Mello, & Cruz, 2005). In spite of this, we
distinguished different types of dense forests,
arising from peculiarities in the respective sea-
sonal oscillation profiles. Temporal dynamics
of the NDVI influence woodiness, phenology,
leaf type, plant longevity, and other vegetation
properties controlling fluxes of water, energy
and CO2 through ecosystems (DeFries et al.,
1995). Topo-hydrographic characterizations
allowed us to refine land-cover differentiations
within the saturation range of NDVI.
Although some overlap between NDVI
profiles can remain after the optimum classifi-
cation was chosen, land classes were combined
when both their profile oscillation pattern and
their average NDVI values significantly coin-
cided. This was assessed by using the follow-
ing four steps: (1) interannual average NDVI
values were calculated for every class and bi-
monthly period of the year; (2) with these aver-
ages, for each pair of classes, coincidence in
oscillation patterns was measured using Pear-
son’s correlation coefficient, and coincidence
in NDVI values quantified using Euclidean
distances; (3) land classes were then arranged
in two dendrograms using the average-linkage
(UPGMA) classification algorithm, the former
built with correlations and the latter built with
distances; (4) profile overlapping was consid-
ered to be significant when two classes were
grouped together in both dendrograms, accord-
ing to a conservative criterion for class cluster-
ing: correlations above the 95th percentile, and
Euclidean distances below the 5th percentile.
The statistical significance of resulting homo-
geneous groups was then assessed using analy-
ses of variance in NDVI between all pairs of
classes (variations attributable to inter- and
intra-annual differences were controlled).
Topo-hydrological land-class character-
ization: Elevation, and more specifically the
500 m isoline, distinguishes the two main
phytogeographical regions described in the
Guyana Shield: “Mountains” and “Peneplains
of Casiquare and Upper Orinoco” (Huber &
Alarcón, 1988; Berry, Huber, & Holst, 1995;
Schargel, 2011). In Amazonia, landscape com-
plexity needs to be characterized by a multiple-
factor approach involving slope (Mitsuda &
Ito, 2011), riverscape (Toivonen, Mäki, & Kal-
liola, 2007) and inundation (Sippel, Hamilton,
Melack, & Novo, 1998; Hamilton, Kellndorfer,
Lehner, & Tobler, 2007). Six topo-hydrologic
variables were used for land class characteriza-
tion: elevation, slope, the hydrologically-based
Compound Topographic wetness Index (CTI),
and linear distances from water bodies, minor
rivers and non-perennial streams (Fig. 3d-i).
These were derived from the 90 m Digital Ele-
vation Database v4.1, based on raw data from
the Shuttle Radar Topography Mission (SRTM)
(Jarvis, Reuter, Nelson & Guevara, 2008).
Slope was calculated from elevation using
the spatial analyst toolbox of ESRI ArcMap
software v10.0. We used the CTI as a proxy
of susceptibility to periodical flooding. The
CTI is a function of both slope and upstream
contributing area per unit width, and estimates
soil water content and surface saturation zones
(Moore, Grayson & Ladson, 1991):
CTI = ln[([FA + 1] × CA)/tan B] (1)
where FA is the water Flow Accumulation –i.e.,
the amount of upstream area draining water
into each cell–, CA is the Cell surface Area,
and B is the slope in radians (Speight, 1980).
B and FA were taken from the hydrologically
conditioned digital elevation model distributed
with HydroSHEDS (United States Geological
Survey-Science for a Changing World, n.d.).
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We used FA to distinguish between dif-
ferent river types. Cells with FA > 1 000 000
were considered to be water bodies, such as
the banks of the Orinoco and Negro Rivers,
the Southern reach of the Casiquiare canal,
and the lower reaches of some tributaries of
the Orinoco including the Ventuari, Atabapo,
Caura and Paragua Rivers. Cells with FA =
10 000 to 1 000 000 were classified as minor
rivers, and cells with FA = 0.1 to 10 000 were
considered non-perennial streams. Compared
to the DNNET coverage of the Digital Chart
of the World (Digital Chart of the World data
description, n.d.), water bodies and minor riv-
ers approximately matched “inland water body
shorelines” and “perennial streams and rivers”,
respectively. Euclidean distances to each river
type were computed using the “spatial analyst”
toolbox of ESRI ArcMap v10.0.
Average values and standard deviations
of the topo-hydrologic variables were calcu-
lated for every land class, taking into account
all cells in the study area. Additionally, we
tested whether topo-hydrology can define the
probability of cells belonging to a given class.
For this, we randomly selected 60 000 cells
throughout the study area; we then performed a
forward-backward stepwise logistic regression
per class, using the correspondence of each
cell with the class (1/0) as response variable,
and the topo-hydrologic variable values in each
cell as explanatory factors. Wald’s statistic was
used to estimate the relative importance of vari-
ables selected for entering in logistic equations.
TEK-based validation of land-classes,
according to differential use by local people:
During a 1 300 km, eight day fluvial expedi-
tion in roadless areas of the Amazonas state
(Venezuela) (Fig. 1), we visited six indig-
enous communities. Because our expedition
was prohibited from moving freely within
the study area for security reasons, we were
unable to carry out any field work in areas
more than 100 m from the main rivers. Despite
this, we were officially authorized, con-
trolled and eventually assisted by the army
to complete the planned itinerary, and made
contact with six remote indigenous settle-
ments –Cascaradura (04º00’25” N, 67º39’40”
W), Niñal (01º54’50” N, 60º36’00” W), Kuri-
makare (02º01’10” N, 66º44’15” W), Cha-
pazón (02º01’20” N, 67º05’04” W), Solano
(02º00’00” N, 66º57’05” W) and Guzmán
Blanco (02º40’10” N, 67º30’30” W)– of five
different ethnic origins (Baniva, Bare, Kur-
ripaco, Warekena and Yeral).
The collaboration between the indigenous
and scientific parties was formalized follow-
ing all the legal and cultural requirements. In
Spain, the project was evaluated and approved
by AECID –which is part of the Spanish
Ministry of Foreign Affairs– and by the Vice-
Rectorate of International Relationships of the
University of Malaga (UMA). In Venezuela,
the project had the explicit endorsement of
the Universidad Central de Venezuela and of
the Universidad Pedagógica Experimental Lib-
ertador (UPEL), and had the institutional sup-
port of the Venezuelan Ministry of Education.
Two delegates of the Venezuelan Ministry of
Education in Puerto Ayacucho (Estado Amazo-
nas), namely Antonio Largo and Luis Yakamé,
joined the expedition with instructions for
introducing the scientific team and the project
to the indigenous communities. In accordance
with the customs of the local participants, in
each community indigenous members would
celebrate a meeting to discuss our request for
them to participate in our research, in their own
languages. Finally, their collaboration in the
project was officially announced by the com-
munity leader (“el capitán”). We then accepted
the commitment to admit the indigenous com-
munities as co-authors of the products derived
from their participation. This agreement was
verbal, but all individual participants provided
written personal identification (including name,
identification card number and signature). We
then presented to a group of community mem-
bers (Appendix 1) hardcopy remote sensing
images (28.5 m resolution; 1:75 000 scale) that
approximately covered a 4 500 km2 surface
area around their settlements. These images
contained polygons outlining our land clas-
sification on a natural-colour-composite raster
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generated from Landsat 7 ETM+ data (Fig. 3c).
Indigenous participants were asked to point on
the Landsat image, as accurately as possible, to
the sites in which farming, fishing and hunting
were usually undertaken. They were also asked
to specify which species were hunted in each
hunting site. So as not to influence responses
with leading questions, the participants were
given total freedom to locate sites and their
uses, i.e. we gave no information that could
associate land classes with specific landscape
units. With only a few exceptions, participants
were able in orienting and interpreting the
maps. Participants identified sites and their cor-
responding uses by stickers markers onto the
images presented to them (Fig. 3c).
We validated the significance of our land-
cover classification for local people by testing
for non-random spatial relationships between
land classes and livelihood activities declared
by participating community members. We used
two approaches for the analysis of TEK data.
Firstly, we determined whether specific land
classes were selected for particular uses. With
this aim, we used Bonferroni confidence inter-
vals (with Z-critical values = 1.96, d.f. = 2, P =
0.05; see Byers, Steinhorst & Krausman, 1984;
Steinheim, Wegge, Fjellstad, Jnawali, & Wel-
adji, 2005) to compare observed and expected
frequencies with which every use was related
to every class (expected frequency was esti-
mated according to each land class prevalence
in the study area). Secondly, we assessed the
correspondence between uses and land classes
using logistic regressions. Both response and
explanatory variables were binary (1/0), and
described the presence/absence of a given use
in a site and the correspondence between this
site and a given land class, respectively.
RESULTS
Land classification: We chose 30 classes,
in which the highest value for average sepa-
rability coincided with intermediate values of
minimum separability (Fig. S1 in Supplemen-
tary Material 1). Ten classes were later dis-
carded because they only appeared in marginal
cells, accounting for only 0.0071 % of the study
area. The option showing the highest value for
minimum separability (within 26 classes) was
rejected because it also showed the lowest
value for average separability.
Extensive overlap between classes was
detected where NDVI > 0.8 (Fig. S2a in
Supplementary Material 1). A set of 15 classes
resulted from the combination of classes with
highly coincident NDVI values and greenness
oscillation profiles (Fig. S2b and Fig. 4).
Land-class characterization based on
greenness: Class 1 showed NDVI < 0.2;
classes 2, 3 and 5, with NDVI = 0.3 to 0.7, had
greenness profiles with a seasonal decrease
in March-April and a quick rise in May-June;
classes 9, 11, 13, 14 and 15 had NDVI > 0.8,
and showed a slight decrease in May-June fol-
lowed by a 2 months lasting recovering; class
15 had the most stable profile, at around NDVI
= 0.9; class 7 showed transitional NDVI values
and profile between classes 5 and 11; classes
10 and 12, whose NDVI values widely over-
lapped with those of classes 9, 11, 13 and 14,
had, in contrast, oscillation profiles alternating
two yearly decreases (May-June, November-
December) with two peaks (January-February,
July-August); classes 4, 6 and 8 showed the
most pronounced NDVI oscillations, class 4
having a similar pattern to that of classes 10
and 12, whereas classes 6 and 8 had a yearly
peak in January-February and a long-lasting
bottom between May and August.
Topo-hydrological land-class character-
ization: In our land classification, elevation
was the main factor conditioning the frequency
of land-class dominance (compare Figs. 1 and
4). When NDVI classes were arranged along
topo-hydrological gradients (Fig. S3 in Supple-
mentary Material 1), they showed exponen-
tial relationships with elevation (determination
coefficient, R2 = 0.95), slope (R2 = 0.95) and
susceptibility to flooding (CTI) (R2 = 0.98),
meaning gradually increasing trends. Instead,
classes showed 3rd-order polynomial trends
regarding distances to water bodies (R2 = 0.99),
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Fig. 4. Preliminary land classification of the western Guyana Shield into 15 classes. Greenness profiles identified using
the average NDVI are plotted for every class. A spatial disaggregation of the 15 classes is shown.
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to minor rivers (R2 = 0.99) and to non-perennial
streams (R2 = 0.98); this points to differences
between classes when these are either in the
close vicinity or very far from water streams.
Closeness to water bodies was the most
important topo-hydrological feature explain-
ing the presence of classes 1, 2, 3, 5 and 7
(Table 1). Class 1 is at low average elevation
(< 200 m), and is the most susceptible class to
flooding (i.e. with highest CTI) and the clos-
est one to water bodies (Fig. S3). Also at low
altitudes (< 200 m) and moderate slopes (< 5º),
there are classes 2 and 3 (Fig. S3, Table 1), the
latter being highly susceptible to flooding (CTI
> 14). Both are usually found surrounding land
class 1 (Fig. 5). Classes 5 and 7 are also prone-
to-flooding areas located at gentle slopes, and
are in the neighbourhoods of minor rivers and
non-perennial streams. In contrast, the highest
average elevations (> 1 000 m), slopes (> 15º)
and distances from rivers, and the lowest flood
risk (average CTI around 12) is shown by
classes 4 and 8, closely followed by class 6
(Fig. S3, Table 1).
Land types with the highest level of green-
ness (NDVI > 0.8) are arranged along the mid-
dle stretch of the gradient defined by elevation,
slope and susceptibility to flooding (Fig. S3).
Nevertheless, topography and hydrography still
permitted differentiation between classes even
though the NDVI could show saturation at
these values (Fig. S3, Table 1). Classes 9, 10
and 12 occupy rarely flooded highlands (in
average, CTI < 14, elevation > 400 m, slope
> 5º); class 12 is, however, farther from water
bodies than the other two. In contrast, classes
11 and 14 are found mostly in lowly sloped (<
5º) lands; class 11 is more floodable (CTI > 14)
TABLE 1
Logistic regressions of the 15 land classes initially identified in the western Guyana Shield (see Fig. 4)
on six topo-hydrological variables
Class 1 2 3
V1WB CTI S WB E NPS CTI S WB E CTI S MR
S2- + + - - + + - - - + - +
W3113 55 34 199 152 66 14 5.2 113 86 62 60 34
Class 4 5 6
V1E S MR WB WB CTI S E MR NPS E WB CTI S MR
S2+ + + - - + - + - - + - + + +
W3315 74 38 5.6 165 109 49 40 25 4.9 142 124 36 26 14
Class 7 8 9
V1WB E CTI MR NPS S E S WB NPS MR CTI E MR WB NPS S
S2- + + - - - + + - + + + + + - + +
W3378 150 111 62 21 6.8 375 99 76 75 20 5.7 160 85 72 13 11
Class 10 11 12
V1E WB CTI MR E CTI MR WB S WB E MR CTI
S2+ - - + + + - - - + + + -
W3700 39 18 7.4 147 117 82 66 34 55 41 37 11
Class 13 14 15
V1MR WB E S NPS E WB S MR NPS CTI MR S WB E NPS
S2+ - + - + - + - + + - - + + - -
W3147 99 41 20 14 809 509 290 94 18 595 392 378 274 122 120
1. V = variable ― Elevation (E), Slope (S), CompoundTopographic index (CTI), distance from Water Bodies (WB),
distance from Minor Rivers (MR), distance from Non-Perennial Streams (NPS).
2. S = sign (+/-) of the variable coefficient in the logistic equation.
3. W = Wald statistic indicating the variable contribution in the regression. All variables had significant W with P < 0.05.
Based on 60,000 randomly selected points.
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and closer to rivers than class 14, whereas this
one lays at lower altitudes than class 11. In
the middle of the elevation gradient, there are
classes 13 and 15, the latter being farther from
water bodies but closer to minor rivers and
streams. Classes 9, 13, 14 and 15 are often in
the neighbourhood of each other, and are fre-
quently limiting with class 11 (Fig. 5).
Cloudiness correction and final land-
cover classification: Despite the negative
effect of cloud cover on the quality of the
NDVI layers was compensated with the support
of information on cell reliability, some parts of
the study area still showed weaknesses in some
classes resulting from occasional or persistent
cloudiness (Fig. S4 in Supplementary Material
Fig. 1). This affected to some cells included in
classes 1, 4, 6, 9, 10, 12 and 13, and justified
class-merging decisions driving to a final num-
ber of twelve landscape types identified.
Extremely persistent cloudiness led to
weaknesses in correctly identifying class 1 in
some cells that were always surrounded by
class 2. This happened on the Duida and Jaua
tepuis, as a result of unreliably low NDVI
values (Fig. S4a), and in some “lagoon-like”
patches, showing persistent cloudiness between
March and October, in the north-west, around
the Orinoco River (Fig. S4b). These cells
Fig. 5. Sample of some characteristic patterns. Images represent natural-colour-composite raster images generated
from Landsat 7 ETM+ data (left) and the hyper-temporal land classification shown in Figure 4 (right). a) Water bodies
in the Orinoco River. b) Gradient in open vegetation cover from bareland/grassland to forest edges next to the convergence
of the Orinoco Ventuari and Guaviare rivers. c) Gallery forests along Orinoco tributaries across savannas in the north-west
of the study area. d) Duida Tepui next to the Orinoco’s upper course. e) Seasonally flooded evergreen forests around the
Casiquiare Canal. f) Patterns of terra firme evergreen forest in the highlands. g) Semideciduous forests in the eastern borders
between Venezuela and Brazil. Class colours as in Fig. 4.
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were reclassified in class 2. Also across the
savannas on the North-West, small tributaries
of the Orinoco River were frequently out-
lined by classes 6 and 8 (Fig. 5c). However,
Landsat ETM+ images for these areas hardly
differ from those in classes 5 and 7. Every
year, non-reliable NDVI values were identi-
fied there mainly in May-June (the start of the
rainy season), on cells belonging to classes 6
and 8, but rarely in cells of classes 5 and 7. So,
classes 6 and 8 occurring in the plain savannas
of the North-West were merged with classes
5 and 7, respectively.
In the forest areas, classes 9 and 13 were
visually recognized as patterns of occasional
cloudiness. Both classes were mutually associ-
ated at high frequency, and always surrounded
by class 14. Their NDVI profiles overlapped
with that of class 14, except for two sudden
decreases in June-August in both 2001 and 2007
(Fig. 4). Precisely during these months, there
is a high apparent correspondence between
cloudiness and these classes (Fig. S4c). So,
classes 9 and 13 were merged with class 14.
On the other hand, the NDVI profile of class
10 strongly overlapped with that of class 12,
except for three sharp decreases detected in
November-December of 2005, 2007 and 2008.
These drops coincided with highly apparent
cloud patterns that are easily recognizable, pre-
cisely in those dates, on class 10 (Fig. S4d). We
thus merged class 10 with class 12.
Finally, twelve landscape types (Fig. 6),
grouped into five main landscapes, were iden-
tified after greenness and topo-hydrographical
characterization: 1) Class 1 (water bodies); 2)
classes 2, 3, 5 and 7 (open lands and forest
edges, including grasslands and bare lands,
as well as open scrublands); 3) classes 11, 14
and 15 (evergreen forests, including floodable
scrublands with spare trees/narrow gallery for-
ests, floodable evergren forest edges/gallery
forests, seasonally flooded evergreen forests,
lowland evergreen forests on terra firme, and
Fig. 6. “The Forest Pulse”. Final land classification of the western Guyana Shield into 12 landscape types after a 15 class
greenness-based classification (see Fig. 4) was assessed using topo-hydrographical data. Greenness profiles as identified
using the NDVI are plotted for every type of landscape (bottom right).
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montane and submontane evergreen forests on
terra firme); 4) class 12 (submontane semi-
deciduous forests); and 5) classes 4, 6 and 8
(cloud forest, including cloud montane semide-
ciduous forests, cloud evergreen forest edges,
and cloud evergreen forests). The identification
of these landscapes is discussed below.
TEK-based validation of land-classes,
according to differential use by local peo-
ple: Traditional ecological knowledge (TEK)
obtained from the participating communities
highlighted a meaningful relationship between
livelihood activities and landscapes. Inter-
viewed community members affirmed that
farming sites –“conucos”– were always cleared
on terra firme, where flooding is not a risk
to crops. Waters near river banks and within
flooded forest were preferred to open waters
for fishing; and in flooded forests, arboreal ani-
mals like birds and primates were more hunt-
able from boats, usually shot with guns and
blowpipes. The analyses validated statistically
the existence of different preferences expressed
by the interviewees in the use made of dif-
ferent land classes (Table 2 and Table 3):
lowland evergreen forests in terra firme were
significantly preferred for agriculture, which
was an unlikely activity in seasonally flooded
evergreen forests; flooded forest were positive-
ly, and lowland evergreen forests negatively
chosen for fishing and hunting (compared to
the availability of these classes in the study
area); and submontane deciduous forests were
positively selected for hunting.
Indigenous people identified areas that
were used for hunting particular animal groups
(Table 2 and Table 3). Thus, seasonally flooded
evergreen forests were positively selected for
hunting primates; this class also recorded the
highest bird catches (“pajuí”, black curas-
sow, Crax alector; “pava”, blue-throated bip-
ing-guan, Pipile cumanensis and spix’s guan,
Penelope jacquacu; family Cracidae), but this
prevalence was not statistically significant. For
ungulates (Order Cetartiodactyla), informants
TABLE 2
Differential use of landscape types by indigenous people (see Fig. 6)
Landscape type 1 EF2OO3OF4LL5,7 UL6,7 S8
Agriculture
Lowland EGF9 on terra firme 0.5 12 0.8 0.598 1.002 +
Fishing
Seasonally flooded EGF90.3 10 0.526 0.302 0.751 +
Lowland EGF9 on terra firme 0.5 5 0.263 0.065 0.461 -
Hunting
Seasonally flooded EGF90.3 46 0.426 0.333 0.519 +
Submontane deciduous forest 0.006 6 0.055 0.012 0.099 +
Lowland EGF9 on terra firme 0.5 42 0.389 0.297 0.481 -
Hunting primates
Seasonally flooded EGF90.3 4 0.8 0.449 1.151 +
1. Only classes significantly selected (P<0.05) are shown.
2. EF = expected frequency of every class.
3. OO = number of observed occurrences of each use in every class.
4. OF = observed frequency of every class.
5. LL = lower limit of the Bonferroni confidence interval.
6. UL = upper limit of the Bonferroni confidence interval.
7. Computed from OF with Z-critical values = 1.96, d.f. = 2, P = 0.05.
8. S = significant positive or negative selection observed. The selection was positive if EF was lower than LL and negative
if EF was higher than UL.
9. EGF = Evergreen forest.
According to the method of Byers, Steinhorst & Krausman (1984).
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reported specific hunting sites for the red
brocket deer (“venado”, Mazama americana,
family Cervidae) and white-lipped peccary
(“báquiro”, Tayassu pecari, family Tayasui-
dae). Specific localities were also identified for
hunting of the Brazilian tapir (“danto”, Tapirus
terrestris, family Tapiridae, Order Perissodac-
tyla). Among the order Rodentia, two species
were mentioned: black agouty (“picures”, Das-
yprocta fuliginosa, family Dasyproctidae) and
paca (“lapa”, Cuniculus paca, family Cuniculi-
dae). The analyses validated the consideration
of floodable evergreen forest edges and gallery
forests as a favourable landscape for hunting
red brocket deer; of submontane deciduous for-
est for hunting black agouti; of lowland ever-
green forests in terra firme for hunting ground
mammals in general –i.e. the five above-men-
tioned species together–; and of montane and
submontane evergreen forests on terra firme
for hunting white-lipped peccary (Table 3).
DISCUSSION
In this paper, we developed a land-cover
map from hyper-temporal remotely sensed
greenness, whose value to the indigenous live-
lihoods in the Western Guyana Shield has been
demonstrated through TEK. Considering TEK
allowed us to get a better understanding of how
indigenous groups use different habitats, as
well as of the habitats that are favourable for
key species that are important for indigenous
peoples. However, the most important TEK
contribution here was the validation of the
significance of our land-cover classification
for the indigenous groups. This demonstrates
that the human use of land can be detected
even in wild landscapes, which indicates that
indigenous people make a logical management
of their environment according to their needs
and to the land characteristics. The informa-
tion provided by the local participants involved
different aspects of their use of environment,
mostly focused on farming, fishing and hunt-
ing; further insights on the significance of our
land classes could be achieved in the future
by analysing differential uses regarding plant
gathering with food and medicinal purposes.
Above all, our study is a public recognition of
how TEK can serve to provide purely scien-
tific outputs with social meaningfulness. TEK
is increasingly accepted by scientists as an
adequate means for understanding the natural
world (Herlihy & Knapp, 2003), already used
in a number of studies to support the develop-
ment and supervision of land-cover maps based
on remote sensing (Robbins, 2003; Lauer &
TABLE 3
Significant logistic regressions of 5 landscape types identified in the western Guyana Shield (see Fig. 6) on variables
representing land uses and/or hunted animal species by indigenous people
Landscape type Floodable EGF1 edge / Gallery forest Seasonally flooded EGF1Submontane deciduous forest
LU2RBD A BA
S3+ - +
W43.3 4.8 5.4
Landscape type Lowland EGF1 on terra firme Montane and submontane
EGF1 on terra firme
LU2A GM WLP
S3+ + +
W412.6 4.9 6.8
1. EGF = Evergreen forest.
2. LU = land use – Agriculture (A); hunting of red brocket deer (RBD), black agouti (BA), white-lipped peccary (WLP)
and ground mammals (GM).
3. S = sign (+/-) of variable coefficients in logistic equations.
4. W = Wald statistic indicating the variable contribution in the regression. All variables had significant W with P < 0.05.
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Aswani, 2008). Traditional practices are now
considered forms of ecosystem management as
well (Omotayo & Musa, 1999; Berkes, Cold-
ing, & Folke, 2000). In our work in particular,
we argue that the level of collaboration between
us was such that shared authorship was a fair
acknowledgement of contribution and deep
commitment of the Amazonian communities to
the goals of the research presented in this paper.
The land-class characterization performed
by using temporal patterns of greenness and
topo-hydrology allows us to propose hypoth-
eses about landscape units described by the
land-cover classes. However, it is not possible
to assess the accuracy of landscape identifica-
tion without ground or remote-sensing data as
reference points to supervision. For example,
major Amazonian landscapes (e.g. flooded and
semideciduous forests) are characterized by
seasonal variations that are not visible in satel-
lite images with natural-colour such as Land-
sat 7 ETM+. Nevertheless, although accuracy
assessment should be undertaken in the future,
given the available information obtained in our
study we are proposing five main landscapes
types for the Western Guyana Shield: water
bodies, open lands (i.e. bare lands, grasslands)
and forest edges, evergreen forests, semidecidu-
ous forests, and cloud forests. As outlined here-
after, the information provided by TEK showed
a strong congruence between land classes and
our interpretation of the different landscapes.
Water bodies are proposed to correspond
with class 1, which shows the lowest NDVI
values were shown by class 1. This is so
because water has extremely low reflectance
in the red and the near infra-red spectral bands
(Pope & Fry, 1997). The topo-hydrological
characterization of this class also indicated the
highest potential for receiving huge amounts
of water flow (CTI). This class appeared in
main water courses (around 750 m wide chan-
nels in the Orinoco and the mouth of the Gua-
viare River), where water was never combined
with mainland. Cells containing water and
riversides were represented, instead, by other
classes (see below).
We propose a correspondence between
open lands and forest edges with classes 2, 3, 5
and 7. According to greenness (Arroyo-Mora et
al., 2005), class 2 should be identified as pas-
ture areas, and classes 3, 5 and 7, respectively,
as early, intermediate and late forest succes-
sional stages. These classes seem to represent
a gradient in open vegetation cover from bare
land/grassland to forest, located in lowlands
and in the neighbourhood of river courses. The
oscillation patterns of the NDVI in these four
classes show a steep decrease of greenness at
the end of the dry season (March-April) and
a rapid increase after the first rains (May-
June), which is consistent with the expected
phenology of a grass cover that, after wither-
ing, sprouts as soon as water falls on it again.
Grasslands must thus have a strong presence
even in classes 5 and 7. Classes 2, 3, 5 and 7 are
generally shaped in a spatial pattern where core
class 2 patches are integrated within a matrix
of class 3 which, in turn, is surrounded by class
5. The edge of this complex area is, most of
the times, composed of thin class 7 surfaces,
which normally border class 11. Such a pattern
is closely reproduced on tepui tops.
Classes 2 and 3 seem to correspond to
grasslands including flood-prone areas. The
low greenness of class 2 suggests predomi-
nance of herbaceous plants and bare lands
–rocky outcrops; river bars–, whereas class 3
could include more stable vegetation, such as
scrubs and isolated trees combined with grass.
Different authors (Hansen, DeFries, Townsh-
end, & Sohlberg, 2000; Eva et al., 2004; Frei-
tas, Mello, & Cruz, 2005; Bontemps, Pierre, &
van Bogaert, 2010; Huber & Oliveira-Miranda,
2010) have identified mostly savannas, grass-
lands and open scrublands in the areas where
classes 2 and 3 predominate.
Classes 5 and 7 are greener than classes
2 and 3, highly prone to flooding, and asso-
ciated with minor water courses in dendritic
formations. Classes 5 and 7 surely represent
masses of floodable woody vegetation inter-
spersed with grassland patches, often arranged
as gallery forests and forest edges. The latter
fundamentally involve margins of class 11.
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The woody component is forest in class 7, and
scrubland in class 5. The identification of class
7 with gallery forests and forest edges is sup-
ported by the information provided by TEK,
which identified this class as significantly
suitable for hunting red brocket deer. This spe-
cies is typically found in mature and second-
ary forests, gallery forests, forest borders and
savannas near the forest edge (Emmons, 1997);
humid forest borders are indeed the main
habitat of this species in the Amazon (Bodmer,
1997). The indigenous people of several com-
munities used the word “savanneta” to describe
this type of landscape.
In summary, we propose identifying that
class 2 areas are grasslands and bare lands –i.e.
savannas; class 3 areas are open scrubland;
class 5 areas are floodable scrubland with
sparse trees– i.e. “savannetas” –and narrow
gallery forests; and class 7 areas are floodable
evergreen forest edges and gallery forests.
Evergreen forests are proposed to corre-
spond to classes 11, 14 and 15. The most stable
profiles were shown by seven classes that had,
throughout the year, NDVI values above 0.83,
i.e. the maximum value of the range identi-
fied by Arroyo-Mora et al. (2005) as mature
forest. These classes showed two different
types of temporal behaviours: classes 11, 14
and 15 underwent a decreasing trend of green-
ness reaching a minimum just after the start
of the rains (May-June), gradually recovering
between two to four months later. Instead, class
12 undergoes two decreases in greenness per
year. Leaf mortality in Amazonian evergreen
forest is highest at the end of the dry season,
and leaf biomass takes some months to recover
completely after the arrival of the rains (Mal-
hado, Costa, de Lima, Portilho, & Figueiredo,
2009), which is consistent with the former of
these two types of profiles. We thus identify
classes 11, 14 and 15 with evergreen forests, of
which three subtypes are distinguished: flooded
evergreen forests, evergreen forests on terra
firme, and montane and submontane evergreen
forests on terra firme.
We suggest that class 11 is identifiable
with seasonally flooded evergreen forests.
In the study area, these forests appear mostly in
the Western lands around the Casiquiare Canal,
coinciding with the areas identified by Huber
and Oliveira-Miranda (2010) as riverine bush-
land and lowland floodable forests. Besides,
they appear near to the Guaviare River and
on tepui tops. TEK supports the identification
of class 11 with floodable forests because this
class was significantly selected for fishing and
for hunting primates, which is most frequently
made from boats in flooded forests. Addition-
ally, class 11 was significantly avoided by
farmers for clearing the forest for agriculture.
Evergreen forests on terra firme, protected
from seasonal floods (Saatchi et al., 2000) and
with very steady profiles, clearly corresponded
to classes 14 and 15. The fact that class 14
–i.e., the dominant class in the Western low-
lands– was positively selected for agriculture,
and negatively for fishing, is consistent with its
identification as terra firme evergreen forests.
More strange seems to be the fact that class 14
was negatively selected for hunting, whereas it
was significantly suitable for hunting ground-
living mammals. Hunting is a frequent activity
in class 14; indeed 39 % of the hunting sites
recorded are located in this class. Its nega-
tive selection indicates, instead, that there is
a bias against the use of class 14 for hunting,
compared to the availability of this landscape
in the study area (> 50 %). The relevance of
this result is that more open landscapes are
preferred for hunting to closer terra firme for-
ests, as is the case of flooded forests (class 11,
which recorded 43 % of the hunting sites iden-
tified). Furthermore, most elevated and sloping
lands in the North and the East are covered by
class 15, which were significantly selected by
indigenous people for hunting white-lipped
peccaries; 60 % of this species’ distribution
occupies the interior of humid tropical forests
(Emmons, 1997; Sowls, 1984). We have, thus,
distinguished between lowland (class 14) and
montane/submontane (class 15) terra firme
evergreen forests. The very low susceptibility
to flooding of class 15 is likely to be the cause
of the exceptionally steady behaviour of green-
ness in these forests.
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In summary, we suggest identifying sea-
sonally flooded evergreen forests with class
11; lowland evergreen forests on terra firme
with class 14; and montane and submontane
evergreen forests on terra firme with class 15.
Submontane semideciduous forests are
proposed to correspond to class 12. As men-
tioned above, this class shows different NDVI
profiles compared with those we identify
with evergreen forests. In class 12, greenness
decreases at the start of the dry season (May-
June), and again before the rains (November-
December). In the lands where class 12 appears
with highest frequency in our study area,
around the Eastern borders between Venezu-
ela and Brazil and in the North-East, Huber
& Oliveira-Miranda (2010) described a wide
occurrence of semideciduous forests whose
dominant trees loose much of their foliage dur-
ing the dry season. Our proposal is to define
class 12 as patches of submontane semide-
ciduous forests that grow within a matrix of
evergreen forest. This is consistent with the
identification of class 10 by local people as
suitable areas for hunting black agouties, as this
species is mainly found in mature deciduous
and montane forests (Emmons, 1997).
Cloud forests are characterized by the
persistence of clouds, just like classes 4, 6
and 8. Out of the North-Western savannas,
classes 6 and 8 only appear at high altitudes
around tepuis and, in association with class
4, in the highest mountains. The distribution
of this association strongly coincides with the
distribution of cloud forests in Guyanese tepuis
(Ataroff, 2001), at altitudes between 1 500 and
2 000 m and at slopes between 20º and 35º.
Mountain areas occupied by classes 6 and 8
frequently showed non-reliable NDVI values,
caused by heavy cloud, at the start of the
rainy season (May-June). The profiles of these
classes appear like much amplified versions of
evergreen forest (classes 9, 11, 13, 14 and 15)
profiles. Maximum NDVI values of class 8 are
within the range shown by evergreen forests;
we thus suggest identifying class 8 in the high-
lands with cloud evergreen forests. Instead,
class 6 covers the tops of the rocky, vertical
cliffs of the tepuis, which form elongated
transitions between class 3 (open areas) and
class 8. Class 6 is more prone to flooding than
class 8, and surely represents cloud evergreen
forest edges.
In contrast to classes 6 and 8, heavy
cloudiness in class 4 caused unreliability of
NDVI values in the middle of the dry season
(November to February). This class’ profile is
similar to that of class 10 –which dominates in
the surroundings–, but with higher amplitude in
oscillations. We, thus, suggest identifying class
4 with cloud semideciduous forests, whose
occurrence at altitudes between 800 m and
1 500 m has also been described in the Guyana
Shield (Huber, 1995a; 1995b).
One of the main contributions of our land-
cover map is that every land class is individu-
ally related to a “time-profile-based signature”
which forms part of the map legend (see De
Bie et al., 2008). The pulsating patterns of
hyper-temporal greenness profiles, which have
defined every land-cover type, have led to us
to label our map as “The Forest Pulse”. The
expression “pulse” is taken from physics in the
sense of “a burst of some form of energy that
is sudden and strong” (Cambridge Dictionaries
Online, n.d.). With “pulse”, however, we also
want to illustrate the “firm hand” that can be
achieved when there is synergy among sci-
entific and traditional ecological knowledge,
especially where logistic and natural restric-
tions impose obstacles for scientific field-work.
In this study, greenness profiles for the
period 2000 to 2009 showed a slightly increas-
ing trend in the low-NDVI land classes repre-
senting grassland and scrubland, and a slightly
decreasing trend in the classes representing
forests. This finding is consistent with both a
gain in carbon in grassland as a consequence of
warming (Phillips et al., 1998), and also with
some loss of vegetation in the forests (Cox et
al., 2004). Nevertheless, processes other than
climate changes could explain this trend; for
example, the constant openness of forest areas
for agriculture (“conucos”) around the indig-
enous communities and the growth of second-
ary forests where farming has been abandoned
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(Freire, 2007). Using observed greenness pro-
files to characterize the evolution of land class-
es could involve circular reasoning, because
the classification of each cell in either one
class or another is itself a consequence of the
observed hyper-temporal variations of green-
ness. However, this classification still has the
potential to assess the effects of climate change
on landscape. Defining future greenness pro-
files of every type of landscape will enable the
analysis of correlations between changes in
greenness and deviations in climatic variables.
The significant relationship between land
types and hunted species adds a new dimen-
sion to the potential of our land-cover map. All
the species that were identified by the inter-
viewed people during our surveys are among
the ten most important vertebrate species for
the survival of the Amazon people (Real, 2009,
Real et al., 2009). Thus, future analyses of
how climate change or other circumstances
could impact the different landscapes can be
consistently related to their potential effects
on the natural resources of the indigenous
people. Linking the natural world and human
activities helps promote interest in preserv-
ing ecosystems and biodiversity (Real, 2009).
The participation of indigenous people in the
design of tools for conservation will improve
the level of acceptance toward management
proposals (Herlihy & Knapp, 2003). Thus,
“The Forest Pulse” could become a useful tool
for the scientific analysis of the rainforest in
the Western Guyana Shield, but also for the
management of its use by indigenous people.
It is our intention to make hard copies of “The
Forest Pulse” available to the participant com-
munities, jointly with meaningful information
about relationships found between land classes
and uses. Our results show that TEK-based
approaches can serve as a basis for validating
the livelihood relevance of a landscape classi-
fication. “The Forest Pulse” is freely available
to any user, in raster format for GIS, in Supple-
mentary Material 2.
ACKNOWLEDGMENTS
This work was supported by the Span-
ish Agency for International Cooperation and
Development (AECID) [project A/017033/08].
We thank, for their invaluable cooperation in
achieving the aims of this project, the Zona
Educativa del Estado Amazonas (Venezuelan
Ministry of Education), especially Dr. Juan
Noguera (Universidad Central de Venezuela);
Luis Yakame and Antonio Largo; Darío More-
no (Academic Division of the Secretary of
Education of the Governorship of Estado Ama-
zonas, currently coordinator of the Program for
Communication and Research within Grupo
de Trabajo Socioambiental de la Amazonía
“Wataniba); Venezuelan Ministry of Environ-
ment; Ángel R. Olivo, city councilman in the
Municipality of Atures; and Comando de la
Guarnición de Puerto Ayacucho. P. Acevedo is
currently supported by the Spanish Ministerio
de Economía y Competitividad (MINECO) and
Universidad de Castilla-La Mancha (UCLM)
through a ‘Ramón y Cajal’ contract (RYC-
2012-11970), and partly by EMIDA ERA-NET
grant Aphaea (219235 FP7 ERA-NET EMIDA;
http://www.aphaea.eu). We also thank Dr. A.L.
Márquez, Dr. A. Estrada and Dr. C. Márquez
for their support during the expedition to
Estado Amazonas.
RESUMEN
Utilización de conocimiento indígena para relacio-
nar el mapeado hiper-temporal de coberturas de suelo
con el uso del territorio en el Amazonas venezolano:
“El Pulso del Bosque”. La teledetección y el conocimien-
to ecológico tradicional (CET) se pueden combinar para
avanzar en la conservación de regiones tropicales remotas
como la Amazonía, donde la toma de datos intensiva in
situ a menudo es imposible. Integrar el CET en el segui-
miento y el manejo de estas áreas permite la participación
de la comunidad, y ofrece nuevos puntos de vista sobre el
uso sostenible de los recursos naturales. En este estudio
se desarrolla un mapa de cobertura del suelo del Escudo
Guayanés (Venezuela), con una resolución espacial de 250
m, basado en datos de teledetección, y se utiliza el CET
para validar su relevancia en relación con la subsistencia
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de los pueblos indígenas y el uso que éstos hacen del
territorio. En primer lugar se ha empleado un índice de
vegetación basado en teledetección hiper-temporal para
realizar una clasificación del territorio. Durante una expe-
dición fluvial de 8 días, a lo largo de 1 300 km por áreas
sin carreteras en el Estado Amazonas (Venezuela), se han
visitado seis comunidades que han proporcionado datos
geo-referenciados sobre sus actividades cinegéticas, pes-
queras y agrícolas. Estos datos de CET se han superpuesto
al mapa de clasificación, con el fin de relacionar las clases
de coberturas con los usos indígenas. Se han caracterizado
las clases de cobertura en función de patrones de cambio
temporal del verdor y la topo-hidrografía, y se han propues-
to 12 tipos de cobertura del suelo, agrupadas en cinco tipos
principales de paisaje: 1) masas de agua; 2) campo abierto/
márgenes del bosque; 3) bosques siempre-verdes; 4) bos-
ques semi-caducifolios submontanos; y 5) bosques nubla-
dos. Cada clase de cobertura del suelo se ha identificado
con un perfil pulsátil que describe cambios temporales en
el verdor, de ahí que el mapa haya sido titulado “El Pulso
del Bosque”. Estos perfiles de verdor han mostrado una
tendencia ligeramente ascendente, durante el periodo 2000
a 2009, en las clases que representan pastizales y zonas de
matorral, así como una tendencia ligeramente decreciente
en las clases que representan bosques. Este hallazgo es
compatible con la ganancia de carbono en los pastizales
como consecuencia del calentamiento del clima, y también
con una cierta pérdida de vegetación en los bosques. De
este modo, nuestra clasificación muestra potencial para la
evaluación de efectos futuros del cambio climático sobre
el paisaje. Algunas clases han resultado estar significati-
vamente relacionadas con la agricultura, la pesca, la caza
como práctica general, y más concretamente con la caza de
primates, de Mazama Americana, Dasyprocta fuliginosa,
y Tayassu pecari. Los resultados demuestran la utilidad
de las aproximaciones basadas en CET como base para
validar la importancia del paisaje, en áreas con alto valor
de conservación, para la supervivencia de las personas, lo
que proporciona una base para avanzar en el manejo de los
recursos naturales en estas regiones.
Palabras clave: Amazonía, conservación de los bosques,
verdor, participación indígena, cobertura del suelo, uso del
suelo, teledetección.
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APPENDIX 1
People of the indigenous communities sharing authorship
Cascaradura Community (Kurripaco ethnic background; Municipio Atabapo): Andrés Camico,
Juanita Cardozo, Cirilo Alterio Cavi, Francisco Cuiche, Vifaida Cuiche, Yuraima Cuiche, Gerónimo
Evaristo, Rosa Evaristo, Blair Ventura, Darcy Ventura, Santiago Ventura.
Niñal Community (Curripaco and Yeral ethnic backgrounds; Municipio Río Negro): José
Camico, Lucila Camico, Mauricio Camico, Mirla Camico, Roberto Camico, Tito R. M. Camico,
Nelson Dasilva C., Fabián García, Mario García, Mariluz Guaca, Alipio Guariello, Avilio López,
Lizanía López, Óscar López C., Eddi Melguero.
Kurimakare Community (Curripaco ethnic background; Municipio Río Negro): Gerónimo
Carlos, Dollas G., Eliodora García, José García, Pablo García, Yolanda García, Arcenio M., Tuna
Mutica, Mariemy Pesqueva, Carolino S., Avelino G. Sandoval, Claudia Sandoval, Clemente Sando-
val, Esther Sandoval, Leonardo Sandoval, Pedro Lino Sandoval, Plinio Sandoval, Robert Sandoval,
Sofía Sandoval, Marcino Sandoval.
Chapazón Community (Yeral ethnic background; Municipio Río Negro): Miriam Melguero,
Carlos Pereira, Egidio Pereira, Onoria Pereira.
Solano Community (Baré ethnic background; Municipio Río Negro): Manuel Francisco Gar-
cía, Simón García, Julieta Surbisana.
Guzmán Blanco Community (Baniva Curripaco and Warekena ethnic backgrounds; Municipio
Maroa): Edilto Bernabé, María Esperanza Bernabé, Paublina Bernabé, Uliezar Bernabé, Cheila Ch.
M., Milagro de Churuaidare, Hermes Churuo, Martina García, Maricela Garrido, M.R.C. José A.,
Ilda Martina, Anabel Mure, Fani María Mure, Yendri Yumileth, Karina Yuriyuri, Juana Yuriyuri.