Remote Sensing and Geographic Information System for Agriculture

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					               Remote Sensing and Geographic Information System
                         for Agriculture Advancement

                        N.N.Mahmood, K.F.Loh , Marimni.H and Saiful.B
                            Malaysian Centre For Remote Sensing (MACRES)
                   Fax : 2645646, Tel: 2645640 and E.Mail : Director@

Abstract                                                 multiple goal analysis. The potentials of these
                                                         tools are being verified in Malaysia and some
This paper highlights the potentials of three            results are illustrated in this paper.
revolutionary spatial information tools to further
improve agricultural management – microwave
remote sensing, precision farming and                    2.0 Microwave Technology – Monitoring
interactive multiple goal       analysis.     The            Rice performance
potentials of these tools for rice monitoring,
precision farming and agricultural land use              To ensure sustainability of a targeted level of
planning are being verified respectively in              rice production, regular monitoring of the rice
Malaysia and some results are illustrated in this        crop is necessary. Accurate yield prediction
paper.                                                   early in each growing season enable better
                                                         management of rice production and timely
                                                         implementation of measures to overcome
1.0 Introduction                                         shortage of rice supply in case of crop failure.

Remote sensing and other related spatial                 The conventional method of monitoring rice
technologies have been widely used in the                yield is the statistical compilation of yield
developed world for agricultural inventorying,           records through ground surveys over selected
yield prediction, plantation crop management,            areas. The information is then extrapolated to
early warning for crop failure due to drought,           generate yield forecasts on a regional basis. This
disease and pest. Such technologies are making           method is time consuming and expensive.
inroads into the developing world in recent
years. India has used remote sensing and                 Remote sensing offers the alternative for more
Geographic Information System (GIS) for                  accurate rice yield prediction because of its large
agriculture Land Use Planning, China for flood           coverage and near real-time data acquisition.
and erosion monitoring and Thailand (                    Satellites carrying optical payloads such as
H.Herman et al, 1994) for rice yield prediction.         Landsat TM and SPOT are unsuitable for rice
                                                         yield prediction in tropical areas because of
With the forthcoming availability of high                extensive cloud coverage - Rice growing
resolution satellite data both in the optical and        seasons in Malaysia coincided with the rainy
microwave modes coupled with more advanced               seasons during which there is normally
and accurate satellite data processing techniques        excessive cloud coverage.
and that most GIS packages have both image
processing and spatial modeling capabilities,            Radar satellites with its all weather capability
agricultural land use management using these             has potential in rice yield prediction. Tests sites
tools will become more reliable and cost                 in Thailand (1) , Malaysia ( ) and Indonesia ( )
effective.                                               have demonstrated this potential using ERS-1
                                                         and 2 Synthetic Aperture Radar (SAR) data. The
This paper highlights three revolutionary tools          peculiar interactions between the radar beams
that have potential to further         improve           and the rice crop in the presence of underlying
agricultural management – microwave remote               water during the various stages of the growth
sensing, precision agriculture and interactive           cycle give rise to a characteristically temporal

variation in backscatter response ( A. Rosenqvist          demands for a specified yield target, and
et al, 1994, K.F.Loh et al, 1995). This allows             modulation of input spreading with in–field
easy delineation of different stages of growth in          accuracy according to the plant needs real–time.
the rice field as shown in the temporal radarsat
composite over the Muda Plain, Kedah in Figure             Precision farming uses GPS and remote sensing
1. Using a specially developed change detection            to measure in–field variability and define site
ratio technique , one can delineate between rice           specific actions for application of inputs with
and non rice areas and between rice in the                 good spatial accuracy. The field measurements
vegetative and reproductive stages (Figure 2).             include soil fertility, bio-mass production, water
Thus there is the potential of SAR images to               availability and crop yields as inputs to the crop
predict the periodic production of rice on a               growth simulator. The model will produce
regional basis.                                            output projected yield map in a GIS
                                                           environment. Figure 3 shows in a nutshell the
                                                           basic ingredients of precision farming.
3.0 Precision Farming – The Management of
    In-Field Variability                                   MACRES is currently implementing this tool in
                                                           the rice growing area of Selangor in
Precision farming takes into account in-field              collaboration with the North West Selangor
variability in crop performance due to factors             Integrated Agriculture Development Project of
influencing crop growth – soil fertility, pest and         the Ministry of Agriculture.
disease occurrence and planting density. This
idea surfaced as early as 1902 when in-field
variability of soil acidity was considered for             4.0 Interactive Multiple Goal Analysis For
different lime treatments. The modern                          Agricultural Land Use Planning
manifestation of this concept is the result of
environmental awareness and the availability of            This is a tool to facilitate decision making on the
economical viable spatial tools which enables              desired land use plan by comparing the
precision application of inputs such as fertilizers,       consequences of alternate land uses plans over
lime, herbicide, seeding and pesticide ( E.Lynn            an area based on both bio-physical, political and
Usery et al, 1995, R.Earl et al, 1996). The                social economic factors (H.Herman et al, 1996,
utilization of these tools incorporating remote            C,W.Dane et al, 1997) . Three main steps are
sensing, Geographic Information System (GIS)               involved – Agro-Ecological Zoning (AEZ),
and Global Positioning System (GPS) enables                Agro-Suitability Zoning (ASZ) and Interactive
maximization of farm income through                        Multiple Goal Linear programming (IMGLP).
appropriate reduction in input cost and
simultaneously safeguarding the environment                The AEZ is a method to zone land units of
against farm chemical pollution.                           similar bio-physical       characteristics (Soil,
                                                           terrain, drainage, agro-climate and landform).
The main output is the production of yield maps,           The AEZ is then evaluated for ASZ by matching
which are created by continuously recording the            land utilization types (LUTs) requirements with
harvests using a combine harvester at different            bio-physical characteristics. Evaluation of land
sites. The decision to the type and quantity of            at the AST level is insufficient for agricultural
input treatments at each site is based upon the            land use planning. As for example, A LUT can
previous yield map and other supporting                    be rated highly suitable in the AST but can be
changes in bio-physical characteristics. An input          actually marginal or even not suitable when
application plan is designed and transmitted to            social economic, economic and political factors
the tractor equipped with a GPS and a variable             are taken into consideration. When the goals of
rate spreader.                                             politicians, planners and land users conflict,
                                                           IMGLP offers comprise solutions. IMGLP for
Four important steps of precision farming are -            land use planning provides alternate land use
training farmers in the use of relevant tools, in–         options goals (food production, employment ,
field variability data collection, usage of crop           income, investment costs) optimized at
model simulator to link farm practices and plant           acceptable levels after taking into account the

constraints ( land size, labor, capital resources       Development Project area as given in Tables 1
and marketability). The method is illustrated by        and 2, and Figure 4. Remote Sensing , GIS and
an example of land use planning over the North          PCProg. Ver.2 tools were adopted.
West      Selangor     Integrated     Agriculture

Table 1 : Matrix Showing Gross Margin (RM) / ha /year for 5 LUTs and 12 Land Units

 Land Units                                   Land Utilization Types

                      Paddy          Cocoa              Coconut         Oil Palm          Rubber

     1                  0                0                0                0                 0
     2                  0                0                0               1745               0
     3                 5310              0               1181             1745               0
     4                 5310             420              1181             1745              1645
     5                 5310              0                0                0                 0
     6                 2950             420              1181             2586              1645
     7                  0               420              1181             2586              1645
     8                  0               420              1181             1745              1645
     9                  0               420               0               1745              1645
     10                2950             420              1181             1745              1645
     11                 0               420              1750             2586              2437
     12                 0               420               0               1745              2437

Table 2 : Optimization of Goal Values

              Goals                     With Limiting Input Cost (RM)            Desired Goal Values

Cultivated Area (Ha)                 116129                                  -
Income /Household/Yr (RM)            10346                                   >6000
Total Income (RM)                    210749000                               117,000,000
Input Costs (RM)                     75000000                                <75000000
Food (Paddy- tons / yr)              176769                                  >176769

5.0 Conclusion                                          private sectors , to support MACRES efforts in
                                                        promoting these technologies at al large scale in
Remote Sensing and GIS have vast potential in           the agricultural sector.
the advancement of agriculture management and
productivity. The use of tools such as
microwave remote sensing, precision agriculture         References :
and interactive multiple goal land use planning
have been used successfully in advanced                 A.    Rosenqvist     and   H.Ogama,   1994.
countries like USA, U.K., the Netherlands and           Phenological Characteristics of Cultivated
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operational use of these tools in Malaysia              Synthetic Aperture Radar Data – Preliminary
requires the participation and commitment of all        Results. International Seminar on Vegetation
concerned , both in the public as well as the

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University, Japan.

C.W.Dane, Nancy C.Meador and John B.White,
1977. Goal Programming in Land Use Planning.
Journal of Forestry N0.325, June 1997.

E.Lynn Usery, Stuart.P, and Broughton.B, 1995.
Precision Farming Data Management Using
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H.Herman and B.Kees, 1994. Interactive
Multiple Goal Analysis For Land use Planning.
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K.F.Loh, L.Nordin and K.M.N.Ku Ramli
(1995). Complementary Nature of ERS-1 SAR
and Optical Data for Land Cover Mapping in
Johore, Malaysia. International Seminar on the
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R.Earl, P.N.Wheeler, B.S.Blackmore and
R.J.Godwin, 1996. Precision Farming – The
Management of Variability. Landwards vol.51,
No.4 pp18 – 23 Dec., 1996

 Figure 1: Multi-date Radarsat Composite Image of Muda Plain, Kedah      Figure 2: Change Detection Image Using Ratioing Technique Shoeing
(Red-12/05/97, Green-05/06/97, Blue-29/06/97) Showing Different Stages   Both Vegetative and Reproductive Stages of Rice Growth, and non Rice
                           of Rice Growth.                                                              Areas
Figure 3: Precision Farming
Figure 4: Desired Land Used Map of North-West Selangor Integrated Agriculture Development Area

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