003962

ISBN 978-92-9060-330-6Natural ResourcesManagement DivisionWorking PaperNo. 2007-5

Wo

rkin

g P

aper

200

7- 5

Utilization of high-resolutionsatellite images to improve statisticsfor the sweetpotato cultivated area ofKumi district, Uganda

P. Zorogastúa, International Potato Center (CIP), Lima, PeruR. Quiroz, International Potato Center (CIP), Lima, PeruM. Potts, International Potato Center (CIP), SSA1

S. Namanda, International Potato Center (CIP), SSAV. Mares, International Potato Center (CIP), Lima, PeruL. Claessens, International Potato Center (CIP), SSA

1 Now at Catholic Relief Services (CRS), Nairobi.

iii

Table of Contents Utilization of high-resolution satellite images to improve statistics for the sweetpotato cultivated area of Kumi district, Uganda 1 INTRODUCTION 1 Agricultural statistics and remote sensing 2 Physical basis of the discrimination between crops 3 MATERIALS AND METHODS 6 Material and equipment 7 Image Processing 7 RESULTS AND DISCUSSION 9 CONCLUSIONS 13 REFERENCES 14

Preface This working paper has been written by scientists from different research divisions of the

International Potato Center (CIP). It describes a methodology based on the use of high-resolution

satellite images and spectroradiometry to gather data to construct reliable statistics on

sweetpotato cropping areas. The pilot study was conducted in a region located in eastern

Uganda, where sweetpotato is cultivated in two cropping seasons. The results suggest that the

tested method substantially improves estimation of cropping areas compared to traditional

statistics based on field surveys and censuses.

v

Acknowledgements The authors appreciate the technical support of fellow members of the NRM Division.

U T I L I Z A T I O N O F H I G H R E S O L U T I O N S A T E L L I T E I M A G E S 1

Utilization of high-resolution satellite images to improve statistics for the sweetpotato cultivated area of Kumi district, Uganda

INTRODUCTION

Crop statistics information is important for the implementation of research and development

projects. Agricultural statistics are utilized for decision making with regard to planning

development activities to benefit the populations occupying a target region. FAO (1999, 2003)

points out that in order to plan agricultural research and rural development strategies, it is

absolutely necessary to have an information system that provides reliable data on the total area

cultivated under a specific crop, the size of production units, the agroecological areas where the

crops are found, soil and climate characteristics, the socioeconomic condition of the producers

and the cropping schedule, among others.

In recent years, CIP has introduced several orange-fleshed sweetpotato clones in Africa as a

strategy to solve the severe vitamin A deficiency affecting the human population. This

introduction requires precise information on the current area of sweetpotato cultivation to assess

potential dissemination of new germplasm and its beneficiaries on both spatial and time scales.

This knowledge is necessary for estimating production, marketable volumes, per capita

consumption, required inputs and to orient research. There are reasons to believe that public

statistics on cultivated extensions in Africa do not bear much relation to the actual cultivated

area. This reasonable doubt is based on the fact that crop statistics related to small producers are

obtained through field sampling and survey techniques that have some limitations. For this

reason, CIP’s Natural Resources Management Division is developing and validating a

methodology based on spectroradiometry and the use of high-resolution remote sensing images

that provides reliable, accurate, and dynamic information for estimating cropping areas. This

methodology has been tested in Uganda where CIP has recently introduced orange-fleshed

sweetpotato clones and where sweetpotato is perceived as a replacement for cassava as one of

the principal staple crops. According to recent data, sweetpotato is the fifth most important crop

in Uganda, with respect to cultivated area, after banana, beans, corn, and millet and is third in

terms of fresh weight produced (Odongo et al., 2004). In eastern Uganda, sweetpotato is

cultivated in two cropping seasons, the first between March and June, and the second in August

through November. These cropping seasons are determined by the occurrence of precipitation

C I P • N A T U R A L R E S O U R C E S M A N A G E M E N T W O R K I N G P A P E R 2 0 0 7 - 5

2 U T I L I Z A T I O N O F H I G H R E S O L U T I O N S A T E L L I T E I M A G E S

and the availability of moisture in the soil. Most of the sweetpotato is produced by poor farmers,

in plots of less than 0.1 ha.

Agricultural statistics and remote sensing

The National Agricultural Statistics Service (NASS) of the United States Department of Agriculture

(USDA) has been involved since 1972 in applying information-gathering techniques for statistical

purposes and agricultural monitoring, having utilized LANDSAT scenes since 1976 to establish a

sampling frame as primary auxiliary variable. However, in this case the images were available only

at the end of the growing season (Hanuschak and Delince, 2004).

One of the largest projects to obtain crop statistics from images of LANDSAT satellite, agricultural

and climate data and advanced data processing, was the Large Area Crop Inventory Experiment –

LACIE, that was carried out between 1974 and 1978 by three US agencies, in order to predict in

real time the production of wheat of the Soviet Union (MacDonald and Hall, 1980).

Estimation of the extent of crop areas based on the integration of field surveys and satellite

images of high spatial resolution has been carried out operationally in the United States and

Europe since 1980 (Allen et al., 2002). However, due to limitations imposed by the spatial

resolution of satellite products, until the end of the 1990s many institutions utilized the images

only as a sampling frame. In the European Union, progress was made by the expansion of the

project Monitoring Agriculture through Remote Sensing (MARS) to more countries (Hanuschak

and Delince, 2004). An example of evolution in the utilization of remote sensing techniques for

the acquisition of data on agriculture is the case of Hungary, where changes in the economic

model brought about changes in land tenure, size of farms and number of farmers, as well as in

agricultural technology and investment. This created the need for an efficient information

system, which led to the establishment in 1980 of the Hungarian Agricultural Remote Sensing

Program – HARSP (Csornai et al., 2006), whose objective was to implement a monitoring system

for the gathering of reliable, accurate information about major crops, including the extent of

cropping areas, their growth and problems (particularly the evaluation of drought risks), as well

as to predict yields.

A key step in producing agricultural statistics is the stratification of the target region by dividing it

into sub-units (Roller and Colwell, 1986), which have less variability than the region as a whole,

allowing for more precise target crops assessment. In some cases the administrative limits of a

region are used for stratification, such as the instance described by Fang (1998) concerning the

estimation of rice yields through remote sensing data.


U T I L I Z A T I O N O F H I G H R E S O L U T I O N S A T E L L I T E I M A G E S

3

In April 1986 the program SPOT Image led by France and other European countries was launched.

CNESS SPOT has put in orbit several satellites for observing terrestrial resources. Currently, the

SPOT 5 satellite is operational. The characteristics of this satellite are presented in Table 1.

Source: http://www.spotimage.fr/automne_modules_files/standard/public/p229_3a1cd2cb59b76fc75e20286a6abb7efesatSpot_E.pdf

Physical basis of the discrimination between crops

When radiant energy strikes any material an interaction occurs. Thus, the crops interact with

solar radiation. A fraction of incoming radiation is absorbed by the plant pigments

chlorophyll, carotenoids, xanthophylls and others (Figure 1), another fraction is transmitted

toward the bottom of the canopy and the soil, and a further fraction is reflected back to

space (Colwell, 1983: 2310).

The property that is utilized to obtain information on the interaction between radiant energy and

objects on the earth’s surface is reflectance, which is the ratio of the energy reflected in a given

wavelength to the incoming energy (Lillesand and Kiefer, 1994). Vegetation, soil, and water

bodies have distinct spectral patterns of reflectance (Figure 2), which makes it possible to

differentiate them. Furthermore, different kinds of vegetation coverage have distinct spectral

Table 1. Characteristics of the products generated by the SPOT Satellite.



patterns or spectral signatures (Jensen, 1996: 316; Richards, 1993) that are utilized to distinguish

them from other plant coverage and land uses, making it possible to quantify their respective

areas. This differentiation between plant covers is based on the radiometric differentiation of the

classes being considered (Lemoine and Kidd, 1998).

Source: http://generalhorticulture.tamu.edu/lectsupl/Physiol/physiol.html

To accommodate the diversity of plot sizes, crop species, varieties, clones and cropping systems

found in a region, satellite products with the appropriate level of spatial, spectral and time

resolution are needed. Spatial resolution is the property that makes it possible to distinguish

between two objects when they are very close. Spectral resolution, with a given number of bands

that covers not only the visible region of the electromagnetic spectrum, but also the near-

infrared, makes it possible to discriminate different spectral signatures. In turn, time resolution

permits registry of information from the same place with a given time sequence.

The spectral bands that the SPOT 5 satellite registers are presented in the Table 2

Figure 1. Spectra of

absorption ofchlorophyll and

carotenoids.



5

Source: http://landsat.usgs.gov/resources/remote_sensing/remote_sensing_applications.php

Source: www.spotimage.com

Sensors Electromagnetic Spectrum Pixels Size Spectral Bands

SPOT 5

Panchromatic B1 : green B2 : red B3 : near-infra-red B4 : short-wave infrared (SWIR)

2.5 m OR 5 m 10 m 10 m 10 m 20 m

0.48 – 0.71 µm 0.50 – 0.59 µm 0.61 – 0.68 µm 0.78 – 0.89 µm 1.58 – 1.75 µm

Figure 2.Spectral patterns of vegetation, soils and water.

Table 2. Characteristics of SPOT 5 satellite sensors and resolution.



MATERIALS AND METHODS

Kumi district (Figure 3), located in northeast Uganda at latitude 1º 29’15 N and longitude 33º 55’

58 E, was chosen as a pilot site for the presence of sweetpotato cropping fields. This district has

an area of 2,848 km2, a population of 388,015 inhabitants and a population density of 136.24

inhab/km2, which is higher than the national average. Most of the population (97.8%) lives in the

rural area. Two areas were stratified as a function of annual precipitation. In the dry area there is

limited cultivated land, mostly used for drought tolerant crops such as millet and sorghum and

some sweetpotato. The moist area has much more cultivated land with crops such as

sweetpotato, peanut, corn, manioc, Chinese beans, and other species.

Source: Google Maps

Figure 3. Kumi

district–Uganda.



7

Material and equipment

Cartographic material.

Multispectral (XS) and panchromatic (P) images from the SPOT satellite with a spatial

resolution of 10 and 5 meters, respectively, captured on May 6, 2006 (Figure 4) and on

October 15, 2006. The initial spatial resolution in the multispectral scene was 10 m and

the panchromatic band with a 5 m resolution was used for resampling the scene to this

resolution. Since the photogrammetric chart for the geometric correction was not

available, the requested scenes were corrected with the parameters of navigation of the

SPOT 5 satellite.

Handheld ASD FieldSpec pectroradiometer.

Magellan Platinum Global Positioning System.

Laptop Dell Inspiron 600 m.

Canon Digital Camera.

Environment for Visualizing Images (ENVI) v. 4.1 Software.

Image Processing

The methodology utilized for sweetpotato cropping area data acquisition is based on the use of

high-resolution images captured by the SPOT satellite, complemented by radiometric evaluation of

the cultivated fields. Transects from Mbale to Soroti were marked by the access roads, following the

distribution of the areas with the highest density of sweetpotato fields, where radiometric

measurements were made with a handheld ASD FieldSpec spectroradiometer. The coordinates of

the cultivated fields were recorded in order to locate them in the SPOT satellite scenes.

ENVI software was used for processing the digital satellite data. An unsupervised classification

was initially carried out, using clustering algorithms for ordering the high variability of land use

and plant coverage. A supervised classification was subsequently carried out based on the

spectral patterns recorded in the field, which was used for obtaining the spatial distribution of

the sweetpotato plots, as well as the cultivated area through the maximum likelihood classifier.

The digital process consisted of the sampling of the scene areas covered with sweetpotato,

verified in the field in May and November of 2006, which were then graphically shown in the

corresponding scene in order to carry out a supervised classification.



Mango tree

House roof

Grass

Road

Mango tree

House roof

Grass

Road

Source: SPOT Image

Subsequently, the geographical area of the district was delimited by means of a mask (image

bitmap), which defined the cropping areas of sweetpotato and of other crops (Figure 5). In order

to establish the percentage of success of the selected classes, the contingency matrix was

calculated. It showed that the misclassification error for sweetpotato was 6%, which is

satisfactorily low.

Figure 4. Multispectral

Spot Image of the study

area.



9

Source: SPOT Image

RESULTS AND DISCUSSION

Figure 6 shows the different spectral patterns of the main crops and land uses in Kumi.

Sweetpotato can be differentiated both in the range of the visible spectrum, where it shows low

reflectance attributed to the foliage pigmentation and total soil coverage, and in the near-

infrared where the reflectance is very high. This high reflectance of sweetpotato fields in the near-

infrared spectrum has been instrumental in distinguishing it from other crops.

Figure 7 is the supervised classification carried out with the May 2006 image. It shows that most

of the sweetpotato fields are located in moist areas while the drier areas are primarily covered by

grasses. For ease of reading, the legend of Figure 7 is represented in Table 3. The table shows that

sweetpotato covered 24,556 ha on the date the satellite image was captured. This area,

representing 8.6% of the area of the district, corresponds to the sweetpotato crop in the first

growing season only. On the other hand, the satellite image captured in October 2006 (Figure 8)

processed and ground truthed with the same methods as the other image, shows that

sweetpotato covers 20,064 ha, or 7% of the area of the district. It appears that this decreased

Figure 5. SPOT Image of the Kumi district.

House roof Dry grassTreeRoadHouse roof Dry grassTreeRoad



cropping area observed in the second season was due to abnormally delayed rains. Adding up

the sweetpotato cropping areas observed in the images from the two cropping seasons, the

yearly total for 2006 amounts to 44,620 ha. However, the more recent official data from the

Department of Statistics of Uganda (Figure 9), point out that in the whole year of 2003 the area

cultivated with sweetpotato in Kumi was 28,000 ha. Assuming that this estimation remains

approximately constant for the next years, including 2006, our remote sensing data suggests that

the official statistics record just the 63% of the sweetpotato cropping area in Kumi district. In

years with normal precipitation, the shortage of the official data would be even greater. If the

proportion holds true for other areas, the total area of sweetpotato in Uganda might well exceed

1 M ha.

Figure 6.Spectral

patterns ofthe main

crops andland uses in

Kumi.

CROPS REFLECTANCES

00.10.20.30.40.50.60.70.80.9

1

300 400 500 600 700 800 900 1000 1100 1200

Wavelength

Ref

lect

ance

Maize Bare Soil Millet Sweet potato Cassava Banana



11

Source: CIP, NRM Geospatial Analysis Laboratory, based on a SPOT scene of May 6, 2006

Figure 7.Classified SPOT Image of Kumi district, May 2006.



Source: CIP, NRM Geospatial Analysis Laboratory, based on a SPOT scene of Oct. 15, 2006

Ha % Ha %Water bodies 39310 13.8 37823 13.3Wetland 23858 8.4 22932 8.1Grassland/other crops 118324 41.5 104218 36.6Sweetpotato 24556 8.6 20064 7.0Forest/Mangoes 17710 6.2 17698 6.2Urban areas 11 0.0 11 0.0Fallow land 30840 10.8 70031 24.6Main/Secondary road .--/ 242 0.1 242 0.1Clouds 4872 1.7 0 0.0No data 25078 8.8 11782 4.1Total 284800 100.0 284800 100.0

LEGENDMay-06 Oct-06

Category

Figure 8.Classified

SPOT Image ofKumi district,

October 2006.

Table 3. Area covered by

different land use and plant cover

categories, May and October 2006.



13

CONCLUSIONS

Radiometric evaluation and processing of SPOT scenes of Kumi district, Uganda, have made it

possible to determine that sweetpotato foliage has a distinct spectral pattern defined by low

reflectance in the visible range of the spectrum and high reflectance in the near-infrared range.

This spectral pattern makes it possible to identify a sweetpotato crop with a high degree of

certainty, which allows defining the cultivated area and spatial distribution of the crop with

precision through the utilization of high-resolution SPOT images. The results suggest that

traditional statistics underestimate the total annual sweetpotato cropping area in Kumi. Using

the tested method substantially improves estimation of cropping areas.

Figure 9. Sweetpotato cropping area in Kumi district according to official statistics of Uganda.

Area planted to Sweetpotato in Kumi

0 5,000

10,000 15,000

20,000 25,000 30,000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2,003

Year

Ha.

Source: Uganda Bureau of Statistics (UBOS)



REFERENCES

Allen, R., G. Hanuschak, and M. Craig. 2002. Future vision for the utilization of remotely sensed

data and Geographic Information Systems (GIS) in the National Agricultural Statistics Service.

From: http://www.usda.gov/nass/nassinfo/remotevision.htm

Colwell, R.N., ed. 1983. Manual of Remote Sensing. 2d Ed. 2 vols. Falls Church, Virginia: American

Society of Photogrammetry.

Csornai, G., Cs. Wirnhardt, Zs. Suba, P. Somogyi, G. Nádor, L. Martinovich, L. Tikász, A.

Kocsis, B. Tarcsai, Gy. Zelei. 2006. Remote Sensing based crop monitoring in Hungary.

From: http://www.fomi.hu/Internet/magyar/Projektek/remsensmonit.htm and abstract

at: http://www.igik.edu.pl/earsel2006/abstracts//

Fang, H. 1998. Rice crop area estimation of an administrative division in China using remote

sensing data. International Journal of Remote Sensing, Vol. 19(17): 3411–3419.

FAO. 1999. Crop Information Systems. From: http://www.fao.org/sd/EIdirect/EIre0078.htm

FAO. 2003. Remote Sensing for Decision-makers. From: http://www.fao.org/sd/EIdirect/EIre0072.htm

Hanuschak, G., and J. Delince. 2004. Utilization of data captured through remote sensing and of

geographic information systems for the livestock statistics in the United States and the European

Union. Third International Conference on Agricultural Statistics 2–4 November, 2004, Cancun,

Mexico.

Jensen, J. R. 1996. Introductory Digital Image Processing. 2d ed. Saddle River NJ: Prentice Hall.

Lemoine, G., R. Kidd. 1998. An alternative sampling technique for deriving crop area statistics

from hybrid remote sensing data. In: Proceedings: Retrieval of Bio- and Geo-Physical Parameters

from SAR Data for Land Applications Workshop. ESTEC, 21–23 October.

Lillesand, T.M., and R.W. Kiefer. 1994. Remote Sensing and Image Interpretation. 3d ed. New

York: John Wiley & Sons.

MacDonald, R. B., and F. G. Hall. 1980. Global Crop Forecasting. Science, Vol. 208 (4445):

670–679.

Odongo, B., R.O.M. Mwanga, and C. Niringiye, 2004. Promoting improved sweetpotato

varieties and processing technologies in Lira, Pallisa and Soroti Districts. Final Technical Report.

Namulonge Agricultural and Animal Production Research Institute (NAARI), Kampala, Uganda.

Richards, J.A. 1993. Remote Sensing Digital Image Analysis: An Introduction. 2d ed. N.Y.: Springer

Verlag.

Roller, N. E. G., and J. E. Colwell. 1986. Coarse-resolution satellite data for ecological surveys.

Bioscience Vol. 36(7): 468–75.

International Potato CenterApartado 1558 Lima 12, Perú • Tel 51 1 349 6017 • Fax 51 1 349 5326 • email [email protected]

CIP’s MissionThe International Potato Center (CIP) seeks to reduce poverty and achieve food security ona sustained basis in developing countries through scientific research and related activities onpotato, sweetpotato, and other root and tuber crops, and on the improved management ofnatural resources in potato and sweetpotato-based systems.

The CIP VisionThe International Potato Center (CIP) will contribute to reducing poverty and hunger; improvinghuman health; developing resilient, sustainable rural and urban livelihood systems; andimproving access to the benefits of new and appropriate knowledge and technologies. CIPwill address these challenges by convening and conducting research and supportingpartnerships on root and tuber crops and on natural resources management in mountainsystems and other less-favored areas where CIP can contribute to the achievement of healthyand sustainable human development.www.cipotato.org

CIP is supported by a group of governments, private foundations, and international andregional organizations known as the Consultative Group on International AgriculturalResearch (CGIAR).www.cgiar.org

Documents

003962