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Agricultural and Forest Meteorology 103 (2000) 119–136

Applications of geographical information systems andremote sensing in agrometeorology

G. Maracchia,∗, V. Pérarnaudb,1, A.D. Kleschenkoc,2a Institute of Agrometeorology and Environmental Analysis for Agriculture, Research National Council,

P.le delle Cascine 18, 50144 Firenze, Italyb METEO FRANCE, SCEM/CBD, Avenue G. Coriolis 42, F-31057 Toulouse Cedex, France

c All-Russian Research Institute of Agricultural Meteorology, Lenin Street 82, 249020 Obninsk, Kaluga Region, Russia

Abstract

An eco-compatible and sustainable development requires integrated management of all available information and theextraction of analytical evaluation on the impact of human activities. Agrometeorology can be used to contribute to furtherunderstanding of the relative importance of each environmental component, organising the field activities and optimising theuse of natural resources. This is especially true today, due to the availability of new tools, as geographic information systems(GIS), which allow the management of an incredible quantity of data, as traditional digital maps, database, models etc. Theadvantages are manifold and highly important, especially for the fast cross-sector interactions and the production of syntheticand lucid information for decision-makers. Remote sensing provides the most important informative contribution to GIS,which furnishes basic informative layers in optimal time and space resolutions.

This paper describes various recent applications of GIS and remote sensing in Agrometeorology, both in developed anddeveloping countries. It illustrates the possible evolution of these systems and the expected future developments, which canbe decisive for a re-launch of the role of this science in the environmental and land resource management. © 2000 ElsevierScience B.V. All rights reserved.

Keywords:Agrometeorology; GIS; LIS; Remote sensing

1. Introduction

In the last 30 years, the role that agrometeorologycould play both in developed and in developing coun-tries has increased, because of the traditional objec-tives of agriculture, we should add the quality of prod-

∗ Corresponding author. Fax:+39-55-308910.E-mail addresses:[email protected](G. Maracchi), [email protected] (V. Perarnaud),[email protected] (A.D. Kleschenko)

1 Fax: +33-5-61078390.2 Fax: +7-7-095-2552225.

ucts and the environmental role of agriculture, also inthe context of global change. The ‘rural development’concept launched by the Agenda 2000 of the Euro-pean Union (E.U.) means an integrated managementof land where the exploitation of natural resources, in-cluding climate, plays a central role. In this context,agrometeorology can help to reduce the inputs, whilein the frame of the global change, allows the clarifica-tion of the contribution of ecosystems and agricultureto the carbon budget (Maracchi, 1991). Secondly, dueto the dramatic increase in world population, whichhas grown from 3.5 billions of people in the 1960s to6 billion in October 1999, there is a need to increase

0168-1923/00/$ – see front matter © 2000 Elsevier Science B.V. All rights reserved.PII: S0168-1923(00)00107-6

120 G. Maracchi et al. / Agricultural and Forest Meteorology 103 (2000) 119–136

the total production under the constraints of a durableenvironment.

Nevertheless, the operational application of agrom-eteorology in the last 30 years has been slowed downdue to several constraints, which can be summarisedas follows.

1.1. From a general point of view

• The minor role that agrometeorology plays in thecontext of meteorological services with few person-nel and scarce resources.

• A declining position of both national and interna-tional agricultural institutes of research, togetherwith a very weak presence in the agriculture andphysics departments.

1.2. From a technical point of view

• The need to diffuse agrometeorological informationto users very accurately in time and space, becauseagricultural activities are related to the very localsituation.With regard to the last point, many new tools are

at the disposal of agrometeorology to attain this goal.These include local area models, climatic models,satellite and radar measurements, together with theimprovements in crop models. The outputs of allthese tools, together with ancillary information on theland, should allow the downscaling up to a very de-tailed grid, consistent with the needs of the users. Inorder to combine all this information in a systematicway, the use of the GIS offers the scope to modernizeagrometeorology.

2. GISs and LISs

A geographic information system (GIS) is acomputer-assisted system for acquisition, storage,analysis and display of geographic data. GIS technol-ogy integrates common database operations such asquery and statistical analysis with the unique visu-alisation and geographic analysis benefits offered bymaps (Burrough, 1990).

Maps have traditionally been used to explore theearth and to exploit its resources. GIS technology is

an expansion of Cartographic Science, which takesadvantage of computer science technologies, enhanc-ing the efficiency and analytic power of traditionalmethodologies (Coulson et al., 1991; Ballestra et al.,1996).

Nowadays, GIS is becoming an essential tool inthe effort to understand complex processes at dif-ferent scales: local, regional and global. In GIS, theinformation coming from different disciplines andsources, such as traditional or digital maps, databasesand remote sensing, can be combined in models thatsimulate the behaviour of complex systems. Somecommon applications are relative to the control ofindustrial cycles, simulation of urban and natural sys-tems, evaluation of specific procedures and analysisof environmental impact (Mueksch, 1996; Boumanet al., 1998; Taylor and Burger, 1998).

This can be represented schematically (Fig. 1)showing the main informative sources, the componentsand the results of the GIS’s data processing. Data in-put can be obtained by means of direct digitalisation,scanning or acquisition from compatible sources.

The problem of the presentation of the geographicelements is solved in two ways: usingx, y coordi-nates (vectors) or representing the object as variationof values in a geometric array (raster). The possibil-ity to transform the data from one format to the other,allows fast interaction between different informativelayers. Typical operations include overlaying differ-ent thematic maps, contributing areas and distances,acquiring statistical information about the attributes,changing the legend, scale and projection of maps,and making three-dimensional perspective view plotsusing elevation data.

The capability to manage this diverse information,analysing and processing together the informativelayers, open new possibilities for the simulation ofcomplex systems. A GIS can be used to produce im-ages, not only maps, but the cartographic products,and drawings, animations or interactive instrumentsas well. These products allow researchers to anal-yse their data in new ways, predicting the naturalbehaviour, explaining events and planning strategies.

For the agronomic and natural components, inagrometeorology these tools have taken the name ofland information systems (LIS).

In both GIS and LIS the key components are thesame (hardware, software, data, techniques and tech-

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nicians), but in relationship to the information that isrequired, their relative importance varies.

In particular, LISs requires a series of very detailedinformation on the environmental elements, such asmeteorological parameters, vegetation, soil and water.The final product of a LIS, is often the result of acombination of numerous complex informative layers,whose precision is fundamental for the reliability ofthe whole system. An example of the main informationis schematically shown in Fig. 2.

The importance of each informative layer providesvarious examples of applications in developed coun-tries are outlined below.

2.1. The informative layers

In a GIS all the information can be linked andprocessed simultaneously, obtaining a syntactical ex-pression of the changes induced in the system by thevariation of a parameter. In this way, we have twotypes of archives: static and dynamic. This technologyallows the contemporary updating of geographicaldata and their relative attributes, producing a fast adap-tation to the real conditions and obtaining answers innear real-time. The system requires preliminary basicinformation that, in the agrometeorological sector, isoften furnished by the historical archives of differ-ent disciplines: geography, meteorology, climatology,agronomy, etc. The importation of data requires timeand attention, mainly because this information willprovide the basic knowledge of the territory and onthe individual parameters, and it is difficult to modifyin a second time. A recent important improvementis relative to the realisation of high resolution digitalelevation models (DEMs) (Moore et al., 1991), whichare three-dimensional representations of the land,realised starting from quoted contour lines (Fig. 3).A realistic reproduction of the morphology, allows aseries of considerations on many other parameters,like hydrology, sunshine duration, etc. (Bruneauet al., 1995; Mitasova et al., 1995). Nowadays, we cansay that this layer is basic to all the others, especiallyfor the agrometeorologist.

Other elements can be introduced, such as text,photographs, film, etc, to complete the informativelayer. For example, an area characterised by a typi-cal agronomic technique can be linked to an imagethat explains this technique. This is important not

only for educational purpose, but also for enlarginginformation in particular aspects.

In agrometeorological applications, data collecteddirectly in the field are the most important, becausethey present the ground truth. Meteorological sta-tions, field data collection (eco-physiological obser-vations, agronomic practices, insect attacks, diseases,soil, etc.) and direct territorial observations are fun-damental to all the possible agrometeorological GISapplications.

All these sources produce the reality of theterritory and the condition of the elements that it iscomposed of. These data are used directly or afterfurther treatment. After the preliminary considera-tions, only the availability of real time field data mayallow simulations and evaluation of the actual andfuture scenario.

When there is a lack of information, the modelscan help the users to complete the information andunderstand the real situation (Rijks et al., 1998). Forinstance, it is possible to estimate the soil water deficitof a given area in two ways: direct measurements (veryexpensive process) or using the estimated values of amodel.

Programme such as EvapoTraspirazione RealeOttimizzata (ETRO), compute daily water require-ments for crops, taking into account the differentcomponents of water balance (evapotranspiration,rainfall, irrigation and cultural coefficient) and theinitial condition of soil water condition (Battista etal., 1996). ETRO calculates daily values of differentparameters: root development, maximum water sup-ply, easily extractable water supply, effective rainfalland true EvapoTranspiration (ETr), etc. The softwareuses three different methods (Integrated model micro-meteorological, PenmanFAO and Hargreaves) for theestimation of true evapotranspiration, according tothe available measurements and the accuracy requiredby the user. Fig. 4 shows the spatialisation of thewater deficit estimated by the model with Hargreavesmethod, in the lowland of Grosseto, using the data ofthe available agrometeorological stations. The simu-lation was made for the culture of maize in the monthof June 1997. The chart in the picture shows the trendof water balance and minimal reserve, for one point,suggesting the best moment for the irrigation andthe volume of water necessary to restore the waterreserve.

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Fig. 3. Digital evaluation model (DEM) of a part of Gran Sasso National Park. Quotes are represented in false colour.

In many cases the outputs of the models are intro-duced as inputs in the GIS, but it is also possible tointroduce the models directly. In this way, all the in-formations could be used for news interactions withother informative layers.

Fig. 4. Water deficit of the Grosseto lowland (26 June 1997) as estimated by ETRO. In input the different informative layers and in outputthe cartographic representation.

2.2. The role of remote sensing

Remote sensing provides spatial coverage by mea-surement of reflected and emitted electromagnetic ra-diation, across a wide range of wavebands, from the

G. Maracchi et al. / Agricultural and Forest Meteorology 103 (2000) 119–136 125

Fig. 5. Main types of remote sensing techniques.

earth’s surface and surrounding atmosphere. Withoutentering into a detailed description of remote sensinginstruments and techniques, Fig. 5 shows the main ele-ments of these systems (Cochran, 1986).

Remote sensing provides a series of information,continually improved, which become very reliable andpractically irreplaceable (Rijks, 1995).

A modern and effective agrometeorological weatherservice, using advanced data collection methods suchas remote sensing, must be equipped with sophisti-cated devices, but above all must have efficient andtrained staff.

The improvement in technical tools of meteorolog-ical observation, during the last 20 years, has createda favourable substratum for research and monitoringin many applications of sciences of great economicrelevance, such as agriculture and forestry. Eachwaveband provides different information about theatmosphere and land surface: surface temperature,clouds, solar radiation, processes of photosynthesisand evaporation, which can affect the reflected andemitted radiation, detected by satellites. The chal-lenge research, therefore, is to develop new systemsextracting this information from remotely sensed data,giving to the final users, near-real-time information.

The platform for remote sensing can be either fixedor moving, terrestrial or operating from different alti-tudes, and be either manned or unmanned. Consider-ing the operating time, the platform can be classifiedas temporary, semipermanent or virtually permanent.These aspects are important in order to understand the

quality and quantity of the information available to theagrometeorological service. The distance of the instru-ments affects directly the resolution and the precisionof the information. The scale of observation can varyfrom a few square meters, with a scanner mounted ona vehicle, to continental scale, using a meteorologicalsatellite.

The sensors most widely used by environmentalscientists are:• cameras with quartz lenses for use with ultraviolet

film;• cameras for use in the visible and photo infrared

spectral region;• multiband cameras (VIS, NIR, SWIR);• optical mechanical scanners and radiometers for

thermal infrared and passive microwave regions;• active microwave sensors such as side-looking air-

borne radar (SLAR) and synthetic aperture radar(SAR) which are not dependent on daylight andpossess a near all-weather capability.Each instrument furnishes different information and

a station of detection is generally multipurpose, be-cause it is equipped with different sensors. The mostcommon remote sensing products and applications arereported in the Table 1 (Maracchi et al., 1998).

All informations produced by the satellite is elab-orated for the extraction of the desired information.There are many methods, algorithms and proceduresto derive fundamental data for agrometeorological ap-plication from remote sensing. Among the existingindices, the most extensively used are:


Table 1Main products of the operating satellites

Field Parameters/products Applications

SPOTLands Vegetation index maps High resolution land use analysis, biomass

Land cover and land use classification maps and vegetation hydrological conditionsDigital Elevation Model Agricultural and natural resources planningOrthophotomap and managing

Cartography

LANDSAT TMAtmosphere Cloud cover maps Meteorological forecasts, numerical models,

Albedo maps climatology, floods forecastingAerosol maps Energetic assessment, general climatology

Inland and coastal waters Water quality monitoring Pollution monitoring, environmental impactSea surface temperature maps (SST) assessment

Coastal zones monitoring, coastal streamsdynamics, sediments mapping

Lands Land surface temperature maps (LST) Temperature distribution maps for urban areas, highNormalized difference vegetation resolution land use analysis, biomass and vegetation,index maps (NDVI) hydrological conditions, fire risk maps and damageLand cover and land use classification maps assessment

Agricultural and natural resources planningand managing

AVHRR-NOAAAtmosphere Temperature and humidity profiles Meteorological forecasts, numerical models,

Precipitation maps climatology, floods forecastingCloud cover maps Energetic assessment, global climatology, supportCloud top height and temperature to air traffic control

Inland and coastal waters Oil slick maps and monitoring Pollution monitoring, environmental impactSea surface temperature maps (SST) assessment

Coastal zones monitoring, coastal streams dynamics,sediments mapping

Lands Land surface temperature maps (LST) Temperature distribution maps for urban areas, high resolu-Normalized difference vegetation tion land use analysis, biomass and vegetation hydrologicalindex maps (NDVI) conditions, fire risk maps and damages assessmentLand cover classification maps Monitoring of the temporal variations of the coverage

ERS-1 SARInland and coastal waters Oil slick maps and monitoring Monitoring and evaluation of accidental oil

Ship tracking slicks, area assessmentTemporal evolution forecasting of oil slicksand identification of coastal areas at risk incase of accidental oil slicksCoordination support for anti-pollution actionsShip identification in case of illegal polluting slicksMaritime traffic control also in fishing areas

Lands Flooded areas maps Emergency actions support in flooded areasArea and damage assessment for flooded areasStrong temporal soil moisture variation evaluation


Fig. 6. Surface temperature on 25th February 1993 in the south-west of France. The algorithm used in the calculation is a linear combinationof the satellite’s AVHRR channels (split-window technique) that enables most atmospheric effects to be eliminated.

• The sea surface temperature (SST) — index usedfor meteorological and climatological study andobservations

• The land surface temperature (LST) — good indi-cator of climatic and microclimatic conditions pre-vailing close to the surface, as well as the frost orthe moisture soil (Fig. 6).

• The normalised difference vegetation index (NDVI)— optimal index of current plant cover and its vari-ation with time.The images can be subsequently used as informa-

tive layers in the GIS, entering in a quantity of analysisor evaluation procedures. One of the simplest meth-ods is the overlapping of these images with a digitalelevation model (DEM), for a realistic representationof the observed phenomenon.

It is sufficient to remember that remote sensing out-puts are the bases for principal strategic decisions sys-tems (e.g. Early Warning System — FAO, FAS —USA, EXTEC — Russia, and meteorological archiveand retrieval system (MARS — EU)), to understandthe relevance of these contributions.

The transfer of the new techniques of processing andinterpreting remote sensing data from developed to de-veloping countries is limited by many factors, such asthe cost of receiving equipment, the restrictive or verydifficult access to the archives of satellite images anddata, the shortage of qualified staff, etc. The situationhas changed to better in recent years, thanks to theavailability of long series of satellite data; for exam-ple the data archives from NOAA (USA) and Meteor(Russia), contain information for more than 15 years.In this way, the researcher and user, have the possi-bility to apply the traditional techniques, approachesand procedures and to local studies. The access to thearchives and the transfer of information, software andso on is becoming simpler, especially with the use ofINTERNET tools.

International organisations and in particular theWMO also play an active role in coordinating ef-forts connected with receiving, processing, dissem-inating and using remote sensing data. WMO’sCommission on Basic Systems (CBS) has re-cently established a Working Group on SATellites


(WGSAT) that can be the place designed for suchactivity.

The main goal is the development of commonworking strategies and the improvement of regionaland global management capability of satellite data.For this reason, particular emphasis is placed ondata compatibility and integration between differentsources of data. WGSAT has supported a projectaimed at developing the receiving stations (both hard-ware and software) at a reasonable cost to be availablefor developing countries. The WGSAT has discusseda possible process to improve the use of satellite datafrom the present global satellite observing system,and has proposed a set of recommended actions.

Table 2 shows the requirements for agrometeoro-logical parameters, outlined by the Working Group.

The WMO strategy to improve satellite system util-isation is made up by four components:• the strategic vision;• the long-term strategic goal;• the major objectives;• the methodology to meet the objectives.

The strategic vision, to improve satellite systemutilisation, is the prospect of substantially improvedtransfer to communities around the world of the ben-efit of meteorological science and technology, viarapidly evolving global and regional communicationsnetworks. This will allow improved access to satel-lite data and services and the interactions betweendeveloped and developing countries.

The long-term strategic goalfor the next 15 yearsis to improve systematically the utilisation of satellitesystems by National Meteorological and Hydromete-orological Services with emphasis on improving util-isation in the developing countries.

The major strategic objectivesare:• to focus on the needs of the developing countries;• to improve access to satellite data through increased

effectiveness in the distribution of satellite sys-tem data and products at major hubs, in particularthose maintained by the satellite operators, WMOWMC’s, RSMCs and other entities as appropriate;

• to improve the use of satellite data through increasedcapabilities in its applications by direct involvementof existing WMO Member expertise.The methodology to improve satellite system utili-

sation is based on an iterative process to assess con-tinuously the status of the use of satellite data and ser-

vices and their impact on the various applications and,therefore, to identify limitations and deficiencies. Thenecessary steps to improve the utilisation will be de-veloped and implemented through the use of specificprojects.

The overall strategy is summarised in Table 3.

3. GISs and remote sensing applications inAgrometeorology

Nowadays, public agencies, research laboratories,academic institutions, private and public services,have established their own information system suchas GIS. Due to increasing pressure on land and wa-ter resources, land use management and forecasting(crop, weather, fire, etc.) become more essentials ev-ery day. GIS is, therefore, an irreplaceable powerfultool at the disposal of decision-makers.

As discussed earlier the term GIS is currently ap-plied to computerised storage, processing and retrievalsystems that have hardware and software specially de-signed to cope with geographically referenced spatialdata and corresponding informative attribute. Spatialdata is commonly in the form of layers that may de-pict topography or environmental elements.

In Agrometeorology, to describe a specific situation,we use all the informations available on the territory:water availability, soil types, forest and grasslands,climatic data, geology, population, land-use, adminis-trative boundaries and infrastructure (highways, rail-roads, electricity or communications systems). Eachinformative layer provides to the operator the possi-bility to consider its influence to the final result. How-ever, more than the overlap of the different themes,the relationship of the numerous layers is reproducedwith simple formulas or with complex models. The fi-nal information is extracted using graphical represen-tation or precise descriptive indexes.

Developed countries use agricultural and environ-mental GIS to plan the times and types of agriculturalpractices, territorial management activities, populationsecurity, to monitor devastating events and to evaluatedamages, etc.

More than the classical applications, such as cropyield forecasting, uses such as those of the environ-mental and human security are becoming more andmore important. For instance, an effective forest fires

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Table 3Strategy to improve satellite system utilisation

Strategic goal Major objectives Implementation plan

To improve systematically theutilisation of satellite systemswith emphasis on improv-ing utilisation in developingcountries

To focus on the needs ofdeveloping countries

Critical review process linkingmonitoring to action plans

CEOS help for better Indian Ocean/RA IIsatellite coverageLinks to WMO satellite education andtraining strategyMajor WMO project on low-cost satellite workstationExpand European METeorological SATellite(EUMETSAT) MDD system use in RA I & IIFocus on effective use of LRIT in RA II & V (withMTSAT-1)Expand US-based virtual lab network in RA III & IVSatellite utilisation hubs/networks for developingcountriesInitial funding focus on work station and networkingExpand use of DCP/DRS for agriculture and HydrologyFocus on better use of polar–orbiting data and productsStrategies for system utilisation in early/mid 2000Improved promotion of system, use at User ForumMulti agency strategy promoting satellitesystem benefits

To improve the access to satellite datathrough increased effectiveness in thedistribution of satellite system data andproducts at major hubs. In particu-lar those of satellite operators, WMOWMC’s RSMC and other entities asappropriate.

Upgrade MTN and GTS performance

Implement operational distributed database systemBetter use of Internet and systems such as VSATSpecialised WMO centres for satellite data into NWPSpecifications for improved WMC/RSMCsatellite products

To improve the use of satellite data troughincreased capabilities in their applicationsby direct involvement of existing WMOMember expertise.

Review WMO requirements for new EarthObserv. Satellite data

Better research/operational applications system transferFocus in high identified priority user require-ments for satellite applicationsFocus on improved applications for environmen-tal hazards such as volcanic ash, earthquakes,air and ocean pollution etc.Closer operational development links with Pis

prevention needs a series of information very detailedon an enormous scale. The analysis of data, such asthe vegetation coverage with different levels of in-flammability, the presence of urban agglomeration, the

presence of roads and many other aspects, allows themapping of the areas where risk is greater. The useof other informative layers, such as the position ofthe control points and resource availability (staff, cars,


Fig. 7. Informative layers for the evaluation of fire risk index.

helicopters, aeroplanes, fire fighting equipment, etc.),can help the decision-makers in the management ofthe territorial systems.

Monitoring the resources and the meteorologicalconditions, therefore, allows, the consideration ofthe dynamics of the system, with more adherence toreality.

For instance, Fig. 7 shows the informative layersused for the evaluation of fire risk in Tuscany (Italy).The final map is the result of the integration of satel-lite data with territorial data, through the use of im-plemented GIS technologies (Romanelli et al., 1998).

The input data required are:• Multitemporal satellite images (Landsat TM);• Colour Aerial photographs;• High resolution digital elevation model of region;• Vectorial map of roads;• Vectorial map of municipality boundary;• Informative technical schedule of the Tuscany

Region on fire events (period 1984–1996);• Direct measurements.

A preliminary analysis of the data has allowed therealisation of the following informative layers: land

Fig. 8. The monthly distribution of fire frequency in Tuscanyregion (1984–1996).

use; starting point of fire; distance road; elevation;slope; aspect.

The ground truth for the supervised classificationof the Landsat Tm images and auxiliaries data hasbeen done by means of direct measurements and aerialphotographs.

The classification, according to a modified maxi-mum likelihood model, has produced a thematic fuzzyrepresentation, to which has been assigned the rel-ative levels of risk. Since the risk of fire changesduring the year, the monthly distributions of the firefrequencies in the period 1984–1996 were observed(Fig. 8). The figure shows two different periods of firerisk: winter-spring with its maximum in March, andsummer–autumn with its maximum in August.

The methodology foresees the extraction of the firefrequencies for each class of land use. These valueswere normalised for the area of each class and givevalues of probability, according to the number of start-ing points of fire.

A comparative analysis of the winter map with thesummer one, shows a sharp difference in the spa-tial distribution of the risk of fire in the two seasons(Fig. 9). In summer the risk is generally higher, withextreme values in hilly zones. Instead in winter therisk is mostly definite, located mainly to the moreelevated elevations.

These maps of fire risk, constitute a valid toolfor foresters and for organisation of the public ser-vices. At the same time, this new informative layermay be used as the base for other evaluations andsimulations. Using meteorological data and satellitereal-time information, it is possible to diversify thesingle situations, advising the competent authorities

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when the situation moves to hazard risks. Modellingthe ground wind profile and taking into account themeteorological conditions, it is possible to advise theoperators of the change in the conditions that candirectly influence the fire, allowing the modificationof the intervention strategies.

In developing countries, GIS use can be promotedthrough transfer of technologies and an informationfrom the developed ones. This requires generalisa-tion of the knowledge and studies carried out else-where. Moreover, frequently in developing countries,data used for the production of the informative lay-ers are often unreliable or even lacking. Besides, themodels used in these systems are the results of studiesand projects, realised at different scales.

Implementation of a GIS requires a great effort tocollect and organise the available information on theterritory. This important activity requires a period ofvalidation for the operational use of the system.

In any case, many projects have started to imple-ment GIS in a number of different environmental andeconomical systems, mainly using information derivedfrom remote sensing to complete the direct observa-tions. The common advantage is the definition of thestate of the art and a first study of the particular prob-lems, with the suggestion of innovative specific so-lutions. At this level, the products often are alreadyused for practical applications and the operators findit sufficiently powerful and reliable.

The general philosophy of the projects, finalised toproduce GIS, is to realise more and more complexand integrated systems, to satisfy the needs of users.In developing countries, the approach has to be quitedifferent, realising in a first phase sufficiently simplesystems, which answer specific problems, arrivinggradually complete the different informative layersand to create a fully operative GIS.

An example of preliminary information system tocountry scale is given by the SISP (Integrated infor-mation system for monitoring cropping season by me-teorological and satellite data), developed to allow themonitoring of the cropping season and to provide anearly warning system with useful information aboutevolution of crop conditions (Di Chiara and Maracchi,1994).

The SISP uses:• Statistical analysis procedures on historical series of

rainfall data to produce agroclimatic classification;

• A crop (millet) simulation model to estimate milletsowing date and to evaluate the effect of the rainfalldistribution on crop growth and yield;

• NOAA-NDVI image analysis procedures in orderto monitor vegetation condition;

• Analysis procedures of Meteosat images of esti-mated rainfall for early prediction of sowing dateand risk areas.The results of SISP application shown for Niger

(Fig. 10) are charts and maps, which give indicationsto the expert of the millet conditions during the seasonin Niger, with the possibility to estimate the momentof the harvest and final production.

SISP is based on the simulation of the millet growthand it gives an index of annual productivity (range0–1) by administrative units. These values, comparedto the historical crop yields of the single administrativeunit, allow the estimation of the expected productivity.

By means of systems like this, based on modellingand remote sensing, it is possible to extract indicesrelative to the main characteristics of the agriculturalseason and conditions of natural systems. This sys-tem is less expensive, easily transferable and requiresminor informative layers, adapting it to the specificrequirements of the users.

When the information available on the territory issufficient, the passage of all this information to GISis immediate. An example in this regard is the envi-ronmental information system (EIS) realised by thePEICRE project for the department of Keita (Niger),starting from a large data set collected on this area(2.500 km2).

The data collected and the different information lay-ers are organised in a database and all the informationabout the territory is integrated in a GIS. Each layeris composed of different archives (numeric data, textand images), which were preliminary controlled andevaluated. The archives are completed with graphicalrepresentations of the main data trends and syntheticinformation, obtained by means of spreadsheet andstatistical software (Genesio and Di Vecchia, 1998).

The most important information is extracted to de-scribe the territory and then combined for understand-ing the possible relationships between the differentfactors. The representation of these data can be madefor discrete or continuous values, to obtain thematicmaps or territorial representation of the spatiallydistributed parameters. The combination of all the


Fig. 10. Examples of outputs of SISP.

informations can give a synthetic representation ofthe reality.

An analysis of Landsat TM, MSS and SPOT mul-tispectral images has been used to update the avail-able maps and to obtain territorial classifications. Theclassification procedure is based on the integrationof the different information layers (digital cartogra-phy, on-field observation, multispectral satellite im-ages, aerial photographs etc.).

In the EIS–PEICRE some models are also intro-duced, which are able to simulate the behaviour ofdifferent parameters or show possible scenarios. Inparticular, great importance is given to water and tothe soil degradation. For instance, the evaluation ofthe erosion hazard requires many different layers forthe application of the RUSLE model (Renard et al.,1994), derived from the USLE equation:

A = R K L S C P

whereA is the soil loss (t/ha/year);R represent rain-fall and runoff erosivity;K is the soil erodibility;Lrepresent slope length;S is the slope steepness;C rep-resent cover management andP denotes supportingpractices.

Fig. 11 shows the images of different layers of theRUSLE model. The erosivity (R factor) is calculatedusing rain data; theK values, given to each class of soil,are evaluated using all the available informations andindications derived from ground observations. Covermanagement and supporting practices have been intro-duced by means of combinations of different informa-tion: digital cartography, on-field observation, aerialand satellite photographs. The heart of the model is aDEM of the land, which allows the evaluation of themorphological factors (L and S of the USLE) for eachpoint of the territory and the estimation of the waterflow direction.

These new powerful tools give the possibility to in-troduce a new coefficient for the USLE, that was calledtransport capacity factor (T) and represents the soiltransport capacity of the water flow in each element(pixel).

The simple multiplication of the resulting layers,each with its own series of values for each territorialunity, allows the evaluation of the erosion hazard inthe considered time period.

In comparison between the erosion in 1984, beforethe intervention of land recovery, and 1995, after therealisation of the agronomic works by the environmen-


Fig. 11. Information layers for RUSLE model (T=L×S).

tal recovery and preservation project PIK (IntegratedProject Keita), the positive effect of the agronomicworks in the reduction of erosion has been evaluated.This effect is due to the increase of the vegetation cov-erage and to a better water management (Rapi et al.,1996).

4. Conclusions

The existence of new tools is based to reinforce theuse of agrometeorology and to increase its applicationsboth in developed and developing countries, is basedon the strengthening of this use of GIS and remotesensing in the national services, research and training.

To reach this objective there are several initiativesthat should be undertaken:

• a greater visibility of the agrometeorology at bothnational and international level;

• a larger participation to the international pro-grammes;

• better integration with meteorology and climatol-ogy.All these activities are based on the development

of new competencies as in the case of GIS utilisation.To prepare this new type of agrometeorologist we canidentify three possible ways:• the reinforcement of training in these new fields;• the promotion of specialised software;• the preparation of multimedia training tools to

make learning and the updating of the competencieseasier.The promotion of new specialised software should

make the applications of the various devices easier,


bearing in mind the possible combination of severaltypes of inputs such as data coming from standard net-works, radar and satellites, meteorological and clima-tological models, digital cartography and crop modelsbased on the scientific acquisition of the last 20 years.

In this perspective, the activity of InternationalAgencies such as the WMO and the FAO, in coop-eration with the national services and the scientificinstitutions, is the only way to guarantee the strength-ening of the role of agrometeorology for the serviceof agriculture and of environment in the world.

Acknowledgements

A special thanks to Dr. Piero Battista and to Mr.Bernardo Rapi, researchers of CeSIA-Accademia deiGeorgofili of Firenze, for their collaboration.

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