.Geomorphology 39 2001 211219www.elsevier.nlrlocatergeomorph

Remote sensing and GIS-based regional geomorphologicalmappinga tool for land use planning in developing countries

G. Boccoa,), M. Mendozaa, A. Velazquezba Instituto de Ecologa, Uniersidad Nacional Autonoma de Mexico, AP 27-3, 58089 Xangari, Michoacan, Mexico

b Instituto de Geografa, Uniersidad Nacional Autonoma de Mexico, Mexico

Received 9 September 1999; received in revised form 11 December 2000; accepted 20 December 2000

Abstract

Land use planning and necessary supporting data are crucial to developing countries that are usually under severeenvironmental and demographic strains. Approaches and methods to map the variability of natural resources are importanttools to properly guide spatial planning. In this paper, we describe a method to quickly map terrain at reconnaissance . .1:250,000 and semi-detailed 1:50,000 levels. This method can be utilized as a basis for further land evaluation and landuse planning in large territories. The approach was tested in the state of Michoacan, central-western Mexico, currentlyundergoing rapid deforestation and subsequent land degradation.

Results at the reconnaissance level describe the geographic distribution of major landforms and dominant land cover, andprovide a synoptic inventory of natural resources. Results at the semi-detailed level indicate how to nest individuallandforms to major units and how they can be used to run procedures for land evaluation. If combined with appropriatesocioeconomic data, governmental guidelines for land use planning can be formulated on the basis of reconnaissance andsemi-detailed terrain analysis.q2001 Elsevier Science B.V. All rights reserved.

Keywords: Geomorphological mapping; Land use planning; Remote sensing; GIS; Mexico

1. Introduction

Land use planning results from a reasonable com-promise between the environmental potential mea-

sured in terms of the availability of natural re-. sources and the social demand measured in terms

of the requirements of goods and services by specific.human communities . Land use planning and neces-

sary supporting data are crucial to developing coun-

) Corresponding author. Tel.:q52-43-244537; fax:q52-43-244537.

.E-mail address: gbocco@oikos.unam.mx G. Bocco .

tries that are usually under severe environmental anddemographic strains see, e.g. Food and Agriculture.Organization, 1995 . Third World countries have

difficulty in meeting the high costs of controllingnatural hazards through major engineering works and

.rational land use planning Guzzetti et al., 1999 .In Mexico, for instance, a substantial amount of

the population lives in poverty conditions, especiallyin rural communities. This has important environ-mental implications because 80% of the remaining

.Mexican forested areas temperate and tropical aremanaged by indigenous people in rural communities .Thoms and Betters, 1998 . Usually, however, data

0169-555Xr01r$ - see front matterq2001 Elsevier Science B.V. All rights reserved. .PII: S0169-555X 01 00027-7

( )G. Bocco et al.rGeomorphology 39 2001 211219212

on natural resources are either incomplete or non-up- .dated Brodnig and Mayer, 2000 . In Mexico and in

many Latin American countries, basic geographic .data topographic and thematic exist at different .scales see, e.g. Lugo and Cordova, 1996 . Monitor-

ing and analysis of natural resources at coarse scales,however, is often lacking.

Feasible methods to map variability of naturalresources and natural hazards, and to assess land

capabilities see Christian, 1957; Mabbut and Stew-art, 1963; Wright, 1972; Cooke and Doornkamp,1974; Steiner et al., 1994; Panizza, 1996; Rivas et

.al., 1997; Pasuto and Soldati, 1999 are importanttools to properly guide spatial planning and may bevery useful in developing countries.

Geomorphological mapping still holds as a valu-able research tool see the case of fluvial geomor-

.phology, for instance, in Castiglioni et al., 1999 . Forapplied purposes, however, a rather pragmatic ap-proach is recommendable, especially when surveysencompass large areas and results must be availablequickly. In this paper, we describe a method toquickly map terrain in relatively large territories .thousands of square kilometers and show how itcan be used as a basis for further land evaluation andland use planning in the event that relevant resourcedata are either scarce, non-updated, or unavailable.This is the case in many developing countries, mostlocated in inter-tropical regions under fragile envi-ronmental conditions.

2. The study area

We tested the approach in the state of Michoacan, . 2Mexico Fig. 1 ca. 60,000 km and 4 million.inhabitants . The region has undergone severe land

use change: deforestation rates are the highest in thecountry, per capita income is half the national aver-age, and indigenous groups living in marginal condi-tions impact resource use. Climates in the regionvary from tropical dry at the coast, to temperate andsemiarid inland, depending on elevation. Altitudesrange from sea level to ca. 3900 m asl. Majorphysiographic units include Quaternary VolcanicTemperate Sierras, Geologically Complex Temperateand Tropical Sierras, Fluvio-Tectonic Tropical De-

Fig. 1. Location map of study area.

pressions, and Temperate Highlands Commission.for Environmental Cooperation, 1997 .

3. The approach: landform and landscape classifi-cation

This approach uses landform mapping, at differ-ent resolutions, as the major entry to landscapeclassification. In this sense, we partially followed theland system and terrain analysis mapping schemesdeveloped in the 1950s and 1960s, especially in

Europe and Australia for a review, see Verstappen,.1983; van Zuidam and van Zuidam, 1985 . Invento-

ries of natural resources were completed relativelyquickly using those frameworks.

Major technological advances, primarily duringthe last two decades, involve the following.

.i The use of digital remote sensing and geo- .graphic information systems GIS techniques in re-

source surveying e.g. Lopez-Blanco and Villers,1995; Pickup and Chewings, 1996; Garca-Melendez

.et al., 1998; Novak and Soulakellis, 2000 . An op-portunity now exists to gain fresh insights into bio-physical systems through the spatial, temporal, spec-tral, and radiometric resolutions of remote sensing

( )G. Bocco et al.rGeomorphology 39 2001 211219 213

systems and through the analytical and data integra- .tion capability of GIS Walsh et al., 1998 .

.ii Developments in digital elevation modeling atdifferent resolutions and operational in personal

computers Daymond et al., 1995; Giles and Franklin,.1998 . This technique allows full data extraction

from topographic maps, and the automation of slopegradient and aspect calculations and display, includ-ing the pseudo three-dimensional views.

.iii The development of automated frameworksfor land evaluation e.g. Rossiter, 1990; Food and

.Agriculture Organization, 1995 . Land capability as-sessments were eased by automating analyses of soilproperties and the relationship between land formand land quality.

All the above-mentioned advances were consid-ered in this research. In addition, geomorphologicmapping for the exercise followed a slightly differentapproach. Landforms are discrete units that can read-ily be defined and verified at different scales byproven techniques. Vegetation and soils tend to varypredictably within a landform unit and are affectedby altitude and slope aspect and gradient. Relation-ships between landforms and soil, vegetation and

.land use the latter embodied here as land cover canbe described using different analytical techniques .such as map overlaying in automated databases ofa GIS. In other words, landforms are acceptableintegrated classifiers of the landscape, and can beused to divide it into discrete segments.

Another relevant issue in this approach is the useof a hierarchic classification of landforms, fromwhich nested legends can be derived at different

.scales Zinck, 1988 . We formulated a legend andmapped the entire state at 1:250,000 reconnaissance

.level and zoomed in on one area at 1:50,000 toshow how nesting could be accomplished at a

.semi-detailed level . For each scale, we focused ondifferent geomorphic and landscape criteria. Weaimed at developing mapping schemes that could, inthe future, be used by land use planners and conser-vationists.

Throughout the entire analysis, we extensively .used i interpretation of topographic maps and digi-

.tal terrain models for relief; ii interpretation of .lithologic maps for bedrock, iii interpretation of

aerial photographs and Landsat imagery for both .landforms and land cover, iv selective field verifi-

.cation, and vi automated data management andanalysis in a GIS. We applied map-overlaying tech-niques coupled to statistical analyses to describe thequantitative relationships between landscape compo-nents: landforms, soils and vegetation.

For this exercise, we used the Integrated Land andWatershed Management Information System ILWIS,

.2000 , a powerful, albeit user-friendly PC-based GISwith vector including aerial photograph rectifica-

.tion , raster and relational capabilities, and modelingtools such as terrain modeling, geostatistics, mapcalculation and Boolean algebra. For the carto-

.graphic output, we used Arc View version 3.2 .

4. Method and materials

.The region that was mapped Fig. 1 is carto-graphically represented in five 1:250,000 base maps,each constituted by 24 1:50,000 maps. All mapswere produced and edited by INEGI, the Mexicannational mapping agency. For the regional analysis,we interpreted the topographic expression of reliefand lithology, respectively, on the topographic androck type maps at 1:50,000 for the entire state andexpressed results on 1:250,000 topographic maps. At

this scale, we basically used morphometry reliefamplitude and slope gradient, derived from digital

.terrain models and morpholithology as discriminat-ing criteria. We specifically excluded morphogenesisat this coarse approximation; rather, we emphasizeda more physiognomic approach that eased mapping,despite using quantitative criteria. The idea behindthis could be described asAyou map what you seeB;we thought that the scheme could be comprehensiveand useful to other specialists involved in planning.A goal was to be clear and descriptive without losinggeomorphic quality.

Table 1Major landforms with prominent relief expression

Unit name Relief amplitude Slope Dominant .m steepness lithology

Very low hills -250 388 volcanicLow hills 250500 6208 volcanicHigh hills 5001000 20458 variousSierras 10004000 )308 various

( )G. Bocco et al.rGeomorphology 39 2001 211219214

Table 2Major landforms without prominent relief expression

Unit name Relief amplitude Slope Dominant .m steepness lithology

Valleys -100 -38 alluvialPlains -100 -38 alluvialPlateaus -100 -68 volcanicPiedmonts 100500 -108 alluvio-colluvial

The entire area was divided into two broad groupsof major landforms, with and without important re-lief expression. For the first group, we differentiatedfour geomorphic regions: very low hills, low hills,high hills, and sierras. The second group was formedby four other regions: valleys, plains, highplains, andpiedmonts. The thresholds for discriminating criteria .relief amplitude and slope steepness are given inTables 1 and 2, respectively, for both groups oflandforms; in this way, the method can be replicatedin similar environmental conditions.

.Dominant vegetation and land use land coverwas visually interpreted from improved color com-positions of Landsat images, geometrically correctand printed at 1:250,000 scale by INEGI. Mapping

categories were tropical dry forest, temperate forest,shrubsgrasslands, crops, and human-induced fea-tures. Spectral criteria depicted on the imagery werecoupled to ancillary data layers: altitude and slopecharacteristics from the DEM, climate, rock type andrelief. The resulting information was manually digi-tized to GIS databases where cartographic overlayingoperations provided quantitative relationships be-tween landforms and land cover. Field verificationconsisted of transects following roads that inter-sected major environmental units. At this scale, webasically verified land cover and ambiguous geomor-phic contacts.

For the semi-detailed analysis, we focused on avolcanic area near Morelia, the capital city of Mi-choacan. We interpreted 1:50,000 and 1:80,000panchromatic black-and-white, up-to-date aerial pho-tography for landform and land cover delineation .van Zuidam and van Zuidam, 1985 . Within eachregional unit, landforms were discriminated primar-ily according to morphogenesis. Because of scaleconstraints of the regional mapping, same landformunits may be located within more than one regional

.unit Table 3 . Vegetation delineation differentiatedsome of the categories defined above.

Table 3Geomorphic regions and landforms, characterized by lithology and dominant soil and land cover

Geomorphic region Landform

. .A Plains 1 Alluvial plain with vertisols and crops .2 Mesa on basic lava with feozems and crops

. .B Piedmonts 1 Alluvial plain with vertisols and crops .3 Scoria cones with andisols, crops, and shrubs .4 Concave upper footslopes on basic volcanic rocks with a pyroclastic cover,luvisols, crops, and grasslands .5 Convex upper footslopes on basic volcanic rocks without a pyroclastic cover,luvisols, grasslands, and oak forest .6 Lower footslopes on volcanic colluvium with clayey soils and crops .7 Basaltic lava flows with leptosols and andisols, shrubs, and crops

. .C Very low hills 1 Alluvial plain with vertisols and crops .2 Mesa on basic lava with feozems and crops .7 Basaltic lava flows with litosols and andisols, shrubs, and crops .8 Gentle slopes on basic volcanic rocks, with andisols, crops and shrubs .9 Undifferentiated footslopes, on basic rocks with acrisols and crops

. .D Low hills 9 Undifferentiated footslopes on basic rocks with acrisols and crops .10 Steep slopes on basic rocks with andisols, and pines, oaks, and mixed forests

. .E High hills 8 Gentle slopes on basic volcanic rocks with andisols, crops, and shrubs .10 Steep slopes on basic rocks with andisols, and pines, oaks, and mixed forests .11 Summit surface on basic volcanic rocks, with andisols and crops

Notice that the same landform may be recognized in more than one region.

( )G. Bocco et al.rGeomorphology 39 2001 211219 215

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Fig. 3. Semi-detailed analysis of landforms, dominant soils and land cover. See description of mapping in Table 3.

( )G. Bocco et al.rGeomorphology 39 2001 211219 217

Interpretations were manually digitized directlyfrom photographs and geometrically corrected using

the monorestitution capability of the GIS Mc-.Cullough and Moore, 1995 . This method allows the

rectification of aerial photographs through groundcontrol points and digital elevation data. Soil infor-mation was digitized from INEGI maps at 1:50,000.

We verified landforms and cover interpretationsin the field along a transect from plain to high hills.The accuracy of the database was inspected follow-

.ing Bocco and Riemann 1997 . This method teststhe efficiency during labeling of digitized polygonsand allows for error correction.

5. Results and discussion

The results of the mapping are presented in a .generalized manner Figs. 2 and 3 . Quantitative data

are summarized in Table 4. Results at the reconnais-sance level quantitatively describe the geographicdistribution of major landforms and dominant landcover. This shows a synoptic inventory of forestresources that can guide planning efforts at the statelevel. In the case of Michoacan, comparison of landcover to landforms indicates that severe deforestation

.is occurring in steep terrain hills and sierras thatshould be devoted to forest because of its unsuitabil-

.ity for other uses Bocco et al., 1998 . Areas ofinappropriate or potentially conflictive land use arethus easily detected at this coarse scale and permit

Table 4Quantitative distribution of major landforms and dominant cover

Geomorphic Percentage Dominant coverregion of total area

Valleys 5.3 crops, dry forestPlains 7.5 cropsPlateaus 1.0 dry and temperate

forestsPiedmonts 8.0 cropsVery low hills 17.9 crops, dry forestLow hills 15.0 dry forest, cropsHigh hills 16.6 dry and temperate

forests, grassshrubsSierras 27.1 dry and temperate

forests, grassshrubs

The difference to 100% is occupied by water and man-madefeatures. Dominant cover represents more than 60% of unit areas.

the narrowing-down of future research and policyconcern.

At the semi-detailed level, the results of nestingindividual landforms were discriminated using mor-

phogenetic criteria grouped into major units Table.3 . The approach at 1:50,000 can be used to run land

evaluation procedures Rossiter, 1990; Steiner et al.,.1994 whose results can be further combined with

appropriate socioeconomic data to formulate guide-lines for land use planning. In Mexico, 1:50,000 is asuitable scale for environmental planning of mostmunicipalities.

This mapping effort is currently used by theMinistry of the Environment to assess the change of

.land cover at a regional scale Bocco et al., 1998 .The statistics obtained indicate severe trends of de-

.forestation in temperate 1% annual rate and dry .forests 2% annual rate , as well as a strong increase

of the areas under shrubs and grasses followingcattle grazing in scarcely populated areas. In turn,deforested areas for cattle are abandoned and othernon-productive uses may prevail. In many remote

.areas, illegal crops such as cannabis are found.Because land cover data can be easily updated in theautomated GIS created, sequential analysis of thechange in cover is feasible. Landforms remain, how-ever, as the basic analytical spatial unit.

The entire survey took 12 personrmonths. Be-cause the investigation was carried out in an aca-demic institution, costs of human resources wereminimized, and hands-on training of assistants wasachieved. The total cost, including maps, images,scholarships and fieldwork, was around US-$0.50rkm2.

The method avoids the use of specialized termi-nology as much as possible without becoming vague.This insures the use of data by non-geomorpholo-gists, such as social scientists, involved in planning.In Mexico, regional ecological mapping, based ongeomorphology, is used by the National Institute of

.Ecology Ministry of the Environment for land useplanning at the national and local scales. In Michoa-can, the regional geomorphologic mapping describedin this paper is the basis for further mapping andplanning efforts by the local planning authority inthe Cuitzeo basin, the second largest lake in Mexico.This basin is severely degraded; off-site effects ofsoil erosion are dramatic on the water body.

( )G. Bocco et al.rGeomorphology 39 2001 211219218

6. Conclusions

In the 21st century, scientists will be judged onhow well they generate new knowledge, and also forhow well they help solve local and global problems.Scientists in every nation must take action to ensurethat policy makers and the public make their deci-

sions based on the best available information Al-.berts, 2000 .

The approach and results discussed in this paperare in line with the idea of geomorphologists influ-

encing societal decision-making Gupta and Ahmad,.1999 . This also holds for other scientific communi-

ties that are concerned with the outreach of scientific .results at large Ludwig et al., 1993 . Especially in

Third World countries searching for sustainable de-velopment strategies, the gap between science andpolicy can be bridged through multidisciplinary ef-forts.

.Two possible linked paths are i matching theknowledge base to user needs and transforming input

to decision making into publishable research Snow,. .1998 ; and ii strengthening capabilities in rural

communities for resource management through par- .ticipatory research approaches Gobin et al., 2000 .

Acknowledgements

We thank Lorenzo Vazquez, Alan Woods andGlenn Griffith for critically reading an early versionof the paper. Mauro Soldatti and Mario Panizzakindly reviewed the manuscript. Two anonymousreferees critically contributed to improving the finalversion of this paper. Research on which the article

is based was funded by CONACYT SIMORELOS.Programme 1996: Land Use Change in Michoacan .

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