JAG Volume 3 - Issue 1 - 2001 l
Mapping and modelling mass movements and gullies in mountainous areas using remote sensing and GIS techniques J Alfred Dhruba
Zinckl, Jaime L6pez2, Graciela I Metternichtj, P Shresthal and Lorenzo Wzquez-Selem4
1 ITC, Division of Soil Sciences, PO Box 6, 7500 AA [email protected]
) 2 Apartado
4 UNAM, lnstituto
characteristics. In both cases, the soil material frequently conditions the initiation and development of the erosion processes and, at the same time, is affected by them. There are also dynamic relationships between gullies and mass movements themselves, since small landslides or earth slumps often convert into gully heads, while gullies frequently expand laterally through mass wasting. Mass movements and gullies are processes of multiple origin/causes and the initiation mechanisms might take place at the terrain surface or beneath. They are chaotic phenomena, triggered by sudden alteration of the environmental equilibrium and generating catastrophic damages Common factors, such as the complexity of the processes and interactions, the catastrophic character of the events, and the difficulty in predicting their spatial and temporal occurrence, contribute to making deterministic modelling cumbersome in both cases.
hazard assessment, remote sensing,
Natural as well as human-induced severe environmental
areas is hampered
and delineate remote
But their effective
of such chaotic highlight
in combination The shortcomings
describes practical applications hazards of mass movements
used to discriminate
sensing data, is rapidly developing. modelling
have been successfully
by cloud effects and relief-con-
and gullies, using ancillary
and gullies are
hazards. Remote sensing data offer promising
possibilities for identification in mountainous
GPO Box U1987, Perth, WA 6845, Australia Ciudad Universitaria,
Aereo 5057, Santa Marta, Colombia
3 Curtin University of Technology,
Enschede, the Netherlands
However, the development of modern earth observation techniques, in particular the availability of multitemporal remote sensing data, improves the mapping and monitoring possibilities. Similarly, GIS techniques facilitate the integration of multiple data layers and spatial simulation to explore cause-effect relationships. Such issues have been addressed by a number of authors from different perspectives, focussing on mass movements and/or gullies specifically, or environmental hazards in general. In this context, research with significant input of remote sensing and/or GIS has focused on a variety of aspects, including mapping and monitoring approaches [Kienholz et al, 1983; Pike, 1988; McKean et al, 1991; PalacioPrieto & Lopez-Blanco, 1994; Verstappen, 1995; Chowdhury & Flentje, 1996; lrigaray et al, 1996; Liener et a/, 1996; Rosenbaum & Popescu, 1996; Duan & Grant, 20001, the scale at which hazard maps are prepared for regional, local and site planning purposes [Rengers et a/, 1992; van Westen, 1993; Leroi, 19961, the synergy obtained from merging different kinds of remote sensing
of remote sensing and GIS for map-
and gullies in hilly and mountainous
Mass movements and gullies are severe environmental hazards in mountainous areas. Both erosion processes, especially mass movements, cause extensive material and human losses, which are often blamed in official statistics on primary causes such as earthquakes or hurricanes. Mass movements probably constitute the single most widespread hazard on the earth’s surface. For instance in New Zealand, one of the few countries that has a countrywide landslide map, 36% of the territory shows formerly or presently active mass movements [Eyles, 19831. Although the basic processes are fundamentally different, mass movements and gullies share some common
et a/, &
et a/, 1996;
at ITC by advanced
to the efficient
gully and mass movement
of remote the
tral and temporal resolution bands
of the and
of the sensor determines
may be useful makes
area, is an important spatial
mass movement ral resolution
is determined Ideally,
1). parameters, with
such as slope gradi-
respect to sun elevation,
cause the reflectance
of the training
and induces fully
In a case study Khola
by Duan &
& Zinck data
for land degradation
solved the problem
of the training in the
and this requires
and aspect variations
assumes that the
case in areas
in the fea-
a bias towards
pixels. The effect space
& Bishop [19981.
haze and atmospheric
some kind of data transformation
such as discussed
based on available
[ 19991 recogsupport
based on statistical
by the current
His view is supported explicit
of using spatially
of a minimum
by such geometric
duces an elongation
for mass movement
values from the same surface cover type to vary. This pro-
such as in the case of radar sensors
resolution the data
to the slopes over long dis-
ent, aspect and orientation
radar and stereo
and snow effects
to the summit
but also to the
by the spatial,
of the electromagnetic
of the data.
by the sensor,
and may cross several
does not refer only to the number
of the valleys
19951. The delineation
sensing to environmental
and climatic sensing
They also cause climatic
MAPPING The application
USE OF REMOTE
use of remote
ences, scale differences,
ZOOO]. Our paper
3 - Issue 1 - 2001
likely to initiate
tion data can be used to provide
et al [ZOO01 shows that high temporal
et a/, 1990;
19981, and simulation
by the revisit-
have a tempo-
MAPPING of approaches
has been used for landslide
on-ground statistical [Duan
TABLE 1: Problem-and-solution
remote sensing data use
3 - Issue 1 - 2001
in high-mountain regions
[Buchroithner, 19951. REMOTE
High acquisition altitude Small FOV Stereo data Digital elevation models Shape from shading lnterferometry
Displacement, Reduction Displacement, Reduction Also multi-sensoral Also from remote sensing. Stereo data SAR application Shadow problem. Remains most accurate relief information
Stereo data Digital elevation GPS
Intrinsic scale changes Permits mono-plotting,
Also multi-sensoral Illumination differences Base for illumination models CPU-intensive integration Simple. Not for completely dark slopes Shadow minimisation
Multi-seasonal data Multi-directional radar Digital elevation models Digital illumination models Grey value ratioing Solar noon satellites Haze, Clouds
Multi-seasonal data (high temporal Haze correction Atmospheric correction Radar
Also multi-sensoral Simple approach Problem of data availability Cloud penetration
Multi-seasonal data (high temporal Passive microwaves Radar
Also multi-sensoral Snow type mapping Penetration to ground Snow type mapping
among others, has shown that mass movement features in a mountainous environment can be identified from remote sensing data, including the discrimination of different types of landslide (eg, rock slumps, block slides, debris lobes and slide scars) and the detection of conditioning factors such as faults and rupture lines. The use of airborne SAR data made it possible to recognise geomorphic patterns, while the combination of SAR and TM data added additional information on the vegetation cover. The comparative advantages of different kinds of remote sensing data and their integration to extract selective information on landslide characteristics (eg, distribution and classification) and factors (eg, slope, lithology, geostructure, neotectonics and land use/land cover) are also discussed by Singhroy . Overall, airphotos are judged to be the best data source for the identification and mapping of a large number of landslide-related features, while integrated satellite remote sensing data, combining optical and microwave ranges, are superior to the use of individual bands (Table 2). Using study examples from Canada, Singhroy et a/ [I9981 assessed the usefulness of integrated SAR/TM images and SAR interferometric techniques for landslide inventory and characterisation. They reached the following conclusions applicable to mountainous terrains: (1) RADARSAT SAR incidence angles varying from 40° to 59” provide the most suitable imagery to map landslides; (2) the interferometric SAR technique allows easy recognition of landslide
Feature space plots before (a) and after (b) data normalisation (units in reflectance values in eight-bit format), Likhu Khola watershed, Nepal [Shrestha & Zinck, 2001]. FIGURE 1:
TABLE 2: Remote sensing guideline for geohazard LANDSLIDES
on steep valley slopes;
SAR and TM images
P P A
P P A
E A E
and (3) a combination is appropriate
are easier to map from
was assessed using Landsat
19981. Six information to terrain
of the gullies,
traps the incoming
light and infrared,
of the other
by the gullied
(Figure 2). In the middle
by the depth
reduces the reflectance
TM and JERS-1
In the above
kinds of sur&
in the intra-mountainous
discriminating face features
(1) natural (bare
areas (mainly and
P = Poor
3 - Issue 1 - 2001
assessment [Singhroy, 19951
Distribution Classification Factors controlling slope stability Geomorphology of slopes Lithology Structure Neotectonics Landusellandcoverlinfrastructure E= Excellent;
JERS-1 SAR data, areas remained
n Badlands n Moderately
Slightly eroded areas 40
(1 to 7)
FIGURE 2: Digital numbers of selected surface components from Landsat TM bands, Sacaba valley, Bolivia [Metternicht, 19961.The X-axis represents the visible (1, 2, 3). near infrared (4), middle infrared (5, 7) and thermal (6) bands of the Landsat TM sensor.
3 - Issue 1 - 2001
intricate distribution of branching gullies generates impure pixels, whose spectral reflectances are mixtures of the reflectances of the individual components. Thus the issue is not one of spectral confusion, but one of spectral mixing of the surface components within a single pixel. To solve this problem, a linear mixture model including five pure end-members (n-l), one of them being pure gullies, was applied to a 6-band Landsat TM data set (the thermal range being excluded). The procedure is described in Metternicht & Fermont . This made it possible to generate an error map from spectral unmixing. The error between the original map and the root-mean-square image representing the best-fit output proportion map was lower than 1 percent in 36 percent of the classified area, and between 1 and 10 percent in 63 percent of the area. Only 1 percent of the classified area had an error greater than 10 percent. These results were further used to improve the image classification.
tively low, but became more variable so that the separability of the gullies from the other erosion features and surface components slightly improved (Figure 3). The effect of data fusion on spectral class separability was assessed using transformed divergence (TDij), a measure to select the best band combination for pixel labelling. Output values are usually scaled between 0 and 2000. For TDij>l600, good separability between classes i and j can be expected [Richards, 19931. In the study case [Metternicht, 19961, best between-class separability was obtained when combining JERS-1 SAR (L-band) and TM bands 1, 3, 4, 5, 6 and 7, causing all transformed divergence values to cross the threshold figure of 1600. This is particularly the case between badlands and miscellaneous land, as well as between badlands and moderately eroded areas. The improvement of class separability is also reflected in the spatial distribution of the gullies before and after data merging. With Landsat data alone, gullies appear as large undifferentiated areas. Comparatively, Landsat and JERS-1 SAR data together produce a sharper spatial segmentation of the gullies.
The efficiency of the different portions of the spectrum (ie, visible, infrared, thermal and microwave) to properly separate gullies from the other selected surface components was assessed using a procedure based on the percentage of spectral confusion, derived from the transformed divergence analysis, in relation to the total number of training samples [Metternicht, 19961. Applying this criterion to the study case, the different regions of the electromagnetic spectrum performed relatively well when separating gullies from fallow land and from slight-
However, in any case of band combination or data fusion, the class accuracy for gullies remained low (54 percent), when compared to the other surface components included in the study. This is because gullies are heterogeneous areas including variable mixtures of natural vegetation, stone pavements and eroded soils. The
n Badlands 180
w Moderatelyeroded areas H Slightlyeroded areas 1
c] Fallow land
LANDSAT TM SANDS (1 to 7’) and JEW-1
SAR (8) DATA
Digital numbers of selected surface components from Landsat TM and JERS-1SAR bands, Sacaba valley, Bolivia [Metternicht, 19961.The X-axis represents merged Landsat TM and JERS-1SAR data (1 to 7), and JERS-1SAR data alone (8). FIGURE 3:
Mapping and modelling mass movements and gullies
ly and moderately tral
Terlien, which the
19961. aims allow
mass movements different
Ideal of the
of the material
be solifluction. solid
of the soil cover,
of the soil material
must still be achieved.
and the geomorphic
353025 20 15 10 5 0
Fa: Fallow land
SI: Slightly eroded areas
Ba: Gullied badlands
Rf: Rock fragments
FIGURE4: Spectral confusion among terrain surface features, Sacaba valley, Bolivia [Metternicht,
and stone pavements
q THERMAL q MICROWAVE
Ve: Natural vegetation
the basic conceptual
can be used as a basis for designing of
able to explain
and solid consistence
exists. A simple
of the year [Zinck,
of the and/or
a mass movement
and the real moisture
liquid state, the expected Similarly,
or an earth-
a mass movement
of the soil or substratum susceptibility.
to mass wasting,
of the soil material
BOUNDARY CONDITIONS to
mines its propensity mechanical
of a meta-stable
it is the intrinsic
as well as gullies,
ena. They occur when
Volume 3 - Issue 1 - 2001
of the satellite
and stone pavements,
ible and microwave nating
cially in the visible
areas. In contrast,
before deterministic modelling gullies can be fully undertaken,
3 - Issue 1 - 2001
of mass movements and the current shortcomings
can be partially compensated by G&assisted modelling, including exploratory and predictive approaches [Zinck, 1997, 19991. EXPLORATORY
Exploratory models attempt to identify non-explicit cause-effect relationships between environmental hazard types (eg, soil erosion processes) and affected soil types (ie, soil map units) in order to predict, from these relationships, soilscape areas potentially exposed to degradation. Because it relies on relatively simple GIS operations, cartographic modelling is an exploratory mode frequently implemented to this end [Bocco et al, 19901. The overlay of information layers, usually represented by series of thematic single-attribute maps, allows highlighting of areas of coincidence between factors presumably controlling erosion processes and features resulting from these processes.
SLOPE IN % _
___________ ?hOFTOTAL GUUJED *REpI
Observed gullied areas per slope gradient units at Huasca de Ocampo, Mexico [Vazquez-Selem & Zinck, 19941.
Erosion features caused by gullies or mass movements are not randomly distributed on the landscape. They develop in response to a combination of controlling factors The simple overlay of a gully distribution map on top of maps representing environmental factors, such as geoforms, slope gradients, soils, lithologic units and land use types, highlights the degree of spatial coincidence between gully occurrence and specific factors. Such cartographic coincidence helps identify the most favourable combination of conditions and reveals underlying causeeffect relationships.
50 iis % 9 3 2 0 rp
Cartographic modelling through thematic map overlay was performed in a volcanic area located about one hundred kilometres northeast of Mexico City, in Huasca de Ocampo County [Vdzquez-Selem & Zinck, 19941. Frequency graphs were established for each forming factor to highlight the conditions most favourable to the occurrence of gullies. For example, nearly half of the total gullied area corresponds to a narrow slope gradient range of 4-7 percent (Figure 5). Thus, gully erosion does not increase proportionally to slope gradient and, in this sense, substantially deviates from the principles governing rill and sheet erosion.
DOMINANT SOIL UNITS m
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
% OF UNIT AREA
% OF TOTAL GULLIED
Ustifluvents-Fluvaquents Ustorthents (frequently Lithic) Ustorthents-Ustropepts Ustropepts (shallow: Typic and Lithic) Ustropepts (deep: Typic and Fluventic) Typic or Lithic Ustropepts-Lithic Ustorthents-Typic Haplustalfs Typic Ustropepts-Typic Haplustalfs Typic Haplustalfs Typic Paleustalfs-Typic Haplustalfs Typic Argiustolls-Typic Haplustolls Fluventic Ustropepts-Typic Argiustolls-Typic Haplustalfs
Observed gullied areas per dominant soil units at Huasca de Ocampo, Mexico [Vazquez-Selem & Zinck, 19941.
Similarly, the overlay of the gully map and the soil map shows that most gullies develop on Alfisols (Figure 6). In fact, about 90 percent of the gullied surface within the study area concentrates on deep Paleustalfs, although these soils account for only 39 percent of the total area. Alfisols in this area are composed of two main layers: a colluvial cover material, 40 to 60 cm thick, which lies on a buried Bt horizon belonging to a truncated subsoil. The surface of discontinuity between the two materials is marked by ancient human artefacts (2400 years BP). The
difference of permeability between the well-structured colluvial cover and the highly clay-dispersible subsoil favours horizontal water flow along percolines, from which gullies initiate. In contrast, the areas covered with Mollisols are virtually free of gullies, although these soils occur in conditions of slope and land use similar to those of the Alfisols.
tionships from the spatial coincidence between observed erosion features (eg, gullies, landslides) and landscape factors. Such models are not able to take into consideration, for the purpose of prediction, the role played by the activating factors (ie, exceptional rainfalls, earthquakes) in triggering the processes. The modelling rules are mainly based on the conditioning factors of the environment (eg, slope, vegetation cover, rock substratum) and a few soil properties. This type of model is able to (1) reproduce the spatial distribution of existing gullies for validation purposes and (2) predict the potential occurrence of gullies in areas with favourable conditions.
3 - Issue 1 - 2001
and 6). Using these rules, six models were established with decreasing boundary conditions (Table 3). The first model, for instance, takes into account only the thematic map units with a high percentage of observed gullied area (more than 10 percent or more than 15 percent according to the environmental factor considered). Such a combination of rules is highly selective, since only a few units satisfy the requirements. As a consequence, the gullied area estimated by model 1 is small. But, at the same time, the model is efficient because a large proportion of the calculated gullied area corresponds to existing gullies. The other models operate with less restrictive rules in decreasing order.
To illustrate the above considerations, a set of nested models was developed to confirm observed gullies and assess the hazard of potential gullies in the same Huasca de Ocampo area of central Mexico, where the exploratory models were developed [Vazquez-Selem & Zinck, 19941. The assessment rules mobilise only the area percentages covered by observed gullies in the units of the thematic maps representing environmental factors. Six environmental factors were selected on the assumption that they contribute, in one way or another, to gully formation: geoforms, lithologic units, slope gradients, slope shapes, dominant soils and land uses. Two criteria were implemented to account for the area percentages: (1) the percentage of area with observed gullies in each thematic map unit, and (2) the percentage that the observed gullied area in each thematic map unit represents in rela-
tion to the total extent of gullies (562 ha) within the whole study area (8009 ha). The first criterion was considered more diagnostic than the second one, because it is independent of the total gullied area and thus reflects better the intrinsic susceptibility of each thematic map unit to gully formation. To establish class limits, critical threshold values of area percentages were determined by iteration from the graphics showing the frequency of gullies per classes of environmental factors (eg, Figures 5
Predictive models usually implemented in GIS are based on rules and expert knowledge. Such models lack deterministic capability, because they neither simulate nor explain the mechanisms involved in gully formation or mass wasting. They are built on the results of the exploratory analysis, which identifies cause-effect rela-
The relative efficiency of the various models in corroborating observed gullies is presented in Figure 7. The total gullied surface area of 562 ha is equivalent to 7 percent of the study area. An ideal model would confirm 100 percent of the gullied area with only 7 percent of the study area. Thus, an efficient model is one which approximates this optimum performance. To assess the efficiency of the models, the gullied area calculated by each model was compared to the area percentage of existing gullies properly confirmed. Accordingly, models Zb, 3a, 3b and 4a are the best predictors.
Rule-basedspatial models for gully prediction at Huasca de Ocampo, Mexico [Nzquez-Selem & Zinck, 19941 Geopedologic map (7)
Slope gradient map
Slope shape map
> 15 %
>15% unit area
>lO% unit area
> 15 % un’it area
> 15 % unit area
>lO% unit area >5% unit area
[>lO% unit area and >lO% total gullied area] or >15% unit area
>15% unit area or >30% total gullied area
>5% unit area or >5% total gullied area
>lO% unit area or ~20% total gullied area
>5% unit area
>5% unit area and >5% total gullied area
>5% unit area or >5% total gullied area
>5% total gullied area
~5% unit area
~-3% unit area
>2% unit area
>2% unit area
~2% unit area
~2% unit area
>2% unit area
>2% unit area
SO% unit area
>O% unit area
>O% unit area
>O% unit area
>O% unit area
(1) Only geomorphic component of the geopedologic map taken into account Examples: “>I 5% unit area” means that only units in which observed gullres cover >15% of the area of the unit are considered rn the model’s calculations; “>5% total gullied area” means that only unrts that contain ~-5% of the total gullied area within the study area are considered In the model’s calculatrons.
3 - Issue 1 - 2001
% of total study area
= (total area calculated by the model/ total study area) x 100 % of total gullied area = (gullied area calculated by the model/ total gullied area) x 100
ideal model Type “a” models: Including all thematic maps except the geomorphic component of the geopedologic map (see Table 3) Type “b” models: based on the geomorphic component of the geopedologic map alone (see Table 3) Intersection model of 3-l 6% slopes and Alfisols Intersection model of 5-l 1% slopes and highly susceptible geoforms
3 20 d la
FIGURE 7: Relative efficiency of rule-based models for gully prediction at Huasca de Ocampo, Mexico [Vazquez-Selem & Zinck, 19941.
% OF TOTAL STUDY AREA
to describe ard
cordillera E z
9 3 2
of the Colombian
GULLY HAZARD UNITS m
% OF TOTAL GULLIEO
to extrapolate established
of the soil units were
on the basis of
used as vector
in a volcanic
basin the causal
to the potential
1) 2) 3) 4) 5) 6)
classes of the
opment of mass movements [L6pez & Zinck, 19911. Criteria
the structural and
hazard severity classes Very high High Moderate Low Very low No hazard
Observed gullied areas per gully hazard units at Huasca de Ocampo, Mexico [Vazquez-Selem & Zinck, 19941.
In addition also
to confirming used
decreasing Instead predictive
to the potential
the models meet
also represents initiation.
2 to 6 indicate
basis of area criteria,
Mass movement hazard zones in the upper Coello river basin, Colombia [L6pez & Zinck, 19911.
of the selected
they are difficult Remote ping
of the damages
they cause and because
sensing data substantially of landslide
use is limited
to the map-
Koopmans, B.N. & R.G. Forero. 1993. Airborne SAR and Landsat MSS as complementary Information source for geological hazard mapping. Photogrammetry & Remote Sensing 48(6): 28-37
straints Best results are obtained ity
merging applying remove
and expert tionships tion
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Des mouvements en masse et des ravinements naturels ou provoques par les hommes sont des risques environnementaux graves. Des don&es de teledetection offrent des possibilites prometteuses pour I’identification et le suivi de I’evolution. Mais leur utilisation effective dans des zones montagneuses est g@nee par des nuages et des facteurs dependants du relief, qui entrainent des distorsions geometriques et des zones d’ombres, entre autres contraintes. Neanmoins, des photos aeriennes et des images satellite (visible, infrarouge et des bandes en hyperfrequence), ou des combinaisons resultantes, ont et@ utilisees avec succes pour differencier et delinier des types de glissements de terrain et des ravinements. Une modelisation SIG de mouvements en masse et de ravinements, utilisant une information auxiliaire en combinaison avec des don&es teledetection se developpe rapidement. Les defauts de modelisation deterministe de tels phenomenes chaotiques tels que des mouvements en masse et ravinements met en valeur I’importance d’approches assistees SIG pour une modelisation exploratoire et predictive. Cet article decrit des applications pratiques de teledetection et SIG pour cartographier, suivre I’evolution, explorer les relations de cause a effet et @valuer les risques de mouvements en masse et ravinements dans des zones accidentees et montagneuses.
Vazquez-Selem, L. & J.A. Zinck, 1994. Modelling gully distribution on volcanic terrains in the Huasca area, central Mexico. ITC Journal 1994-3: 238-251.
Shrestha, D.P. & J.A. Zinck, 2001. Land use classification in a mountainous area of Nepal: integration of image processing, digital elevation data and field knowledge. JAG 2001-l : 78-85. Shroder Jr., J.F. & M.P. Bishop, 1998. Mass movement in the Himalaya: new insights and research directions. Geomorphology 26: 13-35. Singhroy, V., 1995. SAR integrated techniques for geohazard ment. Advanced Space Research lS(11): 67-78.
Singhroy, V., K.E. Mattar & A.L. Gray, 1998. Landslide characterisation in Canada using interferometric SAR and combined SAR and TM images. Advanced Space Research 21(3): 465476. van Westen, C.J., 1993. Application of geographic information systems to landslide hazard zonation. ITC Publication 15. ITC, Enschede, 245~~.
Los movimientos en masa y las cdrcavas, que Sean naturales 0 inducidos por actividades humanas, representan severos riesgos ambientales. Los datos obtenidos por teledeteccion ofrecen posibilidades prometedoras para la identification y el seguimiento.
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Pero, su uso efectivo en areas montariosas queda limitado por efectos de nubes y factores controlados por el relieve, 10s cuales causan distorsiones geometrjcas y areas de sombra, entre otras limitaciones. Sin embargo, fotografias aereas e imagenes satelitdrias (en las bandas del visible, del infrarrojo y de las microondas), o combinaciones de estas, han sido utilizadas exitosamente para diferenciar y delinear tipos de deslitamiento y carcava. La modelizaci6n de movimientos en masa y carcavas en SIG, usando information contextual en combination con datos de teledeteccion, se esta desarrollando rapidamente. Las limitaciones de la modelizacion deterministica de fenomenos caoticos coma son movimientos en masa y carcavas subrayan la importancia de 10s enfoques basados en SIG para la modelizacion exploratoria y predictiva. Este articulo describe aplicaciones practicas de teledeteccion y SIG para mapear, monitorear, explorar relaciones de causa a efecto, y evaluar riesgos de movimientos en masa y c6rcavas en areas de colinas y montaiias.
Zinck, J.A., 1986. Propiedades y estabilidad mecdnicas de 10s suelos en ambiente de selva nublada. In: 0. Huber (Ed.), La selva nublada de Ranch0 Grande, Parque National Henri Pittier. Fondo Edit. Acta Cient. Venez. y Seguros Anauco C.A., Caracas, Venezuela, pp. 91-105. Zinck, J.A., 1996. La susceptibilidad de 10s suelos a la erosibn por movimientos en masa. Con referencia especial a las montarias tropicales htimedas. In: J. Aguilar R., A. Martinez R. y A. Rota R. (Eds.), Evaluation y Manejo de Suelos. Junta de Andalucia-SECSUniversidad de Granada, Espatia, pp. 25-48. Zinck, J.A., 1997. Riesgos ambientales y suelos. Enfoques para la modelizaci6n de la erosion por carcavas y movimientos en masa. Revista de la Sociedad Espariola de la Ciencia del Suelo, Edition Especial 50 Aniversario, Granada, Espaiia, pp. 283-297. Zinck, J.A.. 1999. GIS-assisted approaches to modelling soil-induced gully and mass movement hazards. Proceedings, International Geoinformatics for Natural Resource Conference on Assessment, Monitoring and Management, 9-11 March 1999, IIRS, Dehradun. Indian Institute of Remote Sensing, National Remote Sensing Agency, Dehradun, pp. 368-376.