Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

Mapping and identifying basal stem rot disease in oilpalms in North Sumatra with QuickBird imagery

Heri Santoso • Totok Gunawan • Retnadi Heru Jatmiko •

Witjaksana Darmosarkoro • Budiman Minasny

Published online: 8 May 2010� Springer Science+Business Media, LLC 2010

Abstract The application of remote sensing technology and precision agriculture in the

oil palm industry is in development. This study investigated the potential of high resolution

QuickBird satellite imagery, which has a synoptic overview, for detecting oil palms

infected by basal stem rot disease and for mapping the disease. Basal stem rot disease

poses a major threat to the oil palm industry, especially in Indonesia. It is caused by

Ganoderma boninense and the symptoms can be seen on the leaf and basal stem. At present

there is no effective control for this disease and early detection of the infection is essential.

A detailed, accurate and rapid method of monitoring the disease is needed urgently. This

study used QuickBird imagery to detect the disease and its spatial pattern. Initially, oil

palm and non oil palm object segmentation based on the red band was used to map the

spatial pattern of the disease. Secondly, six vegetation indices derived from visible and

near infrared bands (NIR) were used for to identify palms infected by the disease. Finally,

ground truth from field sampling in four fields with different ages of plant and degrees of

infection was used to assess the accuracy of the remote sensing approach. The results show

that image segmentation effectively delineated areas infected by the disease with a map-

ping accuracy of 84%. The resulting maps showed two patterns of the disease; a sporadic

pattern in fields with older palms and a dendritic pattern in younger palms with medium to

low infection. Ground truth data showed that oil palms infected by basal stem rot had a

higher reflectance in the visible bands and a lower reflectance in the near infrared band.

Different vegetation indices performed differently in each field. The atmospheric resistant

vegetation index and green blue normalized difference vegetation index identified the

H. Santoso (&) � W. DarmosarkoroIndonesian Oil Palm Research Institute (IOPRI), Jalan Brigjen. Katamso 51, Kampung Baru, 20158Medan, North Sumatera, Indonesiae-mail: [email protected]

T. Gunawan � R. H. JatmikoFaculty of Geography, Gadjah Mada University, Yogyakarta, Indonesiae-mail: [email protected]

B. MinasnyAustralian Centre for Precision Agriculture, The University of Sydney, Sydney, NSW 2006, Australiae-mail: [email protected]

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Precision Agric (2011) 12:233–248DOI 10.1007/s11119-010-9172-7

disease with an accuracy of 67% in a field with 21 year old palms and high infection rates.

In the field of 10 year old palms with medium rates of infection, the simple ratio (NIR/red)

was effective with an accuracy of 62% for identifying the disease. The green blue nor-

malized difference vegetation index was effective in the field of 10 years old palms with

low infection rates with an accuracy of 59%. In the field of 15 and 18 years old palms with

low infection rates, all the indices showed low levels of accuracy for identifying the

disease. This study suggests that high resolution QuickBird imagery offers a quick, detailed

and accurate way of estimating the location and extent of basal stem rot disease infections

in oil palm plantations.

Keywords Remote sensing � GIS � QuickBird imagery � Oil palm (Elaies guineensis) �Basal stem rot disease � Ganoderma boninense � Disease pattern � Image segmentation �Indonesia

Introduction

There have been several discussions on the possible adoption of precision agriculture in the

oil palm industry (Robert 2002; Wahid et al. 2004; McBratney et al. 2005). Similarly,

although remote sensing techniques are promising and have been discussed for a long time

by oil palm research groups, only a few projects to research them have been undertaken

(Corley and Tinker 2004). This study investigated the use of high resolution QuickBird

satellite imagery to detect oil palms infected by basal stem rot disease and map the pattern

of the disease.

Basal stem rot disease is a major threat to the plantations of oil palm (Elaies guineensis)

in South-East Asia, especially in Indonesia and Malaysia (Darmono 2000). It is caused by

the fungus Ganoderm boninense and is the most damaging disease for oil palms; it can

cause up to 50% loss of the palms in a plantation (Darmono 2000; Corley and Tinker

2004). An area with considerable infection by this disease is at Dolok Ilir plantation, part of

the national plantation PT1 Perkebunan Nusantara IV in Simalungun, North Sumatra,

Indonesia. This plantation of 7000 ha has suffered large reductions in yield caused by the

death of palms and the smaller yield of oil from infected palms. Basal stem rot can be

identified readily in the field because infected oil palms show the following symptoms: the

failure of young leaves to open, which indicates that the stem is already extensively

damaged and water uptake is restricted (Corley and Tinker 2004), leaves turn yellow, the

fruiting body of Ganoderma sp. appears, and the base of the stem rots and blackens. In

severe infections the whole crown of the palm may fall off or the trunk may collapse

(Corley et al. 1976; Turner 1981; Corley and Tinker 2004). Currently identification of the

symptoms is based on manual observations, which are labour intensive and time con-

suming. Mapping of the disease involved sampling the palms by blocks and the disease

status of those in each block wase recorded manually (Abdullah 2000; Azahar et al. 2008).

Mapping of the whole plantation was impractical on this basis.

Various actions have been proposed to control the disease (Susanto et al. 2005); the

common control practice is a fungicide injection into the stem combined with mounding

soil at the base of diseased palms. These actions, however, only slow down the develop-

ment of the disease in infected palms and prolong their life rather than preventing the

spread of infection. Thus early detection of basal stem rot symptoms and information on

1 PT is Peseroran Terbatas, or company limited.

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the spatial spread of the disease are important for rapid and accurate preventive action

because the plants can die within 12 months of the first symptoms being observed (Ab-

dullah 2000). A rapid and accurate monitoring scheme for large areas is required urgently

for disease control and plantation management.

The synoptic overview provided by remote sensing technology is promising for mon-

itoring this oil palm disease. Remote sensing can provide data with high spatial, spectral

and temporal resolutions. Specifically, QuickBird satellite imagery, which provides pan-

chromatic imagery at 0.6 m resolution and multispectral imagery at 2.5 m resolution, can

distinguish individual oil palms with fronds of 6–8 m. Remote sensing has been used

successfully to identify nutrient deficiencies and to assess the health status of various plants

(Goodwin et al. 2008). Dutta (2006) used a texture analysis to determine the health status

of a tea plantation. Pozdnyakova et al. (2002) examined the correlation between soil and

spectral characteristic of plants infected by Phytophthora in cranberries. Danielsen and

Munk (2003) showed the successful use of spectral reflectance to measure the severity of

downy mildew in quinoa. Coops et al. (2006) demonstrated the use of QuickBird imagery

to detect red attack damage caused by infestation of the mountain pine beetle in British

Columbia, Canada. Sharp et al. (1985) used several vegetation indices obtained from a

handheld multispectral radiometer to identify rust infection in wheat. Yang (2010) ana-

lysed hyperspectral canopy reflectance spectra to assess the severity of bacterial leaf blight

in rice, and Devadas et al. (2004) evaluated various vegetation indices obtained from a

hyperspectral sensor to identify rust infection in individual wheat leaves.

There have been a few research projects on the application of remote sensing tech-

nology in oil palm production. Naert et al. (1990) and Nguyen et al. (1995) found rela-

tionships between near infrared reflectance of a SPOT image and the nutrient contents of

palm leaves. McMorrow (2001) discussed the possibility of using Landsat imagery to

determine the age of oil palms. Thenkabail et al. (2004) calculated carbon stock levels of

the West African oil palms using multi-date wet and dry season IKONOS images. Azahar

et al. (2008, 2009) used geostatistical methods to map disease patterns from manual census

data. Shafri and Hamdan (2009) used various vegetation indices and red edge techniques

obtained from airborne hyperspectral imagery to detect and map oil palm trees that were

infected by basal stem rot in Peninsular Malaysia.

This study used high resolution QuickBird satellite imagery to map the pattern of basal

stem rot disease and to identify oil palms that were infected by the disease. An image

segmentation procedure was used to identify the spatial pattern of the disease. Various

vegetation indices were also investigated to detect symptoms of infection in oil palms. The

spatial pattern of the disease will help us to understand further the mechanism of the spread

of the disease. This research should be useful for providing site-specific information to

plantation managers to assess the damage caused by the basal stem rot, to develop a

strategic plan for infection control over the whole plantation, to identify areas that need to

be controlled by showing the precise locations and the treatment of individual palms.

Methods

The research location is at Dolok Ilir plantation, which is part of the national plantation (PT.

Perkebunan Nusantara IV) in the administrative district of Simalungun and Deli Serdang,

North Sumatra province, Indonesia (Fig. 1). The geographical location is in the range of

3�1.580–3�13.920 North Latitude and 99�5.490–99�13.440 East Longitude. It has an area of

8184 ha with 7087 ha used for plantation. The plantation has a relatively uniform

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topography from flat to undulating with oil palms established of various age. This plantation

has a heterogeneous cover due the high infection rate by basal stem rot disease. According

to a disease census conducted in September 2008, 18% of the total population (896 743

trees) were infected and of these 16% were dead and 2.4% were ailing (PTPN 2008).

This study used QuickBird imagery acquired on 4 August 2008 with the following

bands: visible red (630–690 nm), green (520–600 nm) and blue (450–520 nm), NIR (near

infrared, 760–900 nm) and panchromatic (450–900 nm). The imagery is Ortho Ready

Standard Imagery with radiometric data and geometric correction. As the topography in the

study area is relatively flat, topographic correction for the imagery was not required.

This study used two image processing procedures to identify and map basal stem rot

infection. Firstly, an image segmentation procedure was used to delineate areas where the

palms had died because of infection from the disease. This resulted in a map showing the

spatial pattern of the disease. Secondly, six vegetation indices were investigated for their

ability to detect living palms that were infected by the disease. Finally, a field survey was

carried out to assess the accuracy of the imagery and image processing procedures.

Image processing

Image processing was performed with Envi 4.3 and ArcGIS 9.2. At the first stage a

segmentation procedure was used to delineate areas with and without oil palm. A healthy

plantation will have a uniform cover where the palms were planted in a regular triangular

pattern. The final stage of the infection is the collapse of the oil palm trees, which creates a

space in the canopy that can be identified readily by image processing. Subsequently, a

map of the extent of the disease can be created. Tamaluddin and Kamaruzaman (1999)

Fig. 1 The location of Dolok Ilir Plantation, North Sumatra, Indonesia

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observed that oil palms have low reflectances in the red band. This band is absorbed by leaf

chlorophyll, which is involved in photosynthesis (Jensen 2004). The segmentation to

separate oil palm from non oil palm areas was done with the density slicing tool in Envi,

which visually enhanced the variation based on image brightness.

Various vegetation indices can be calculated from the visible and NIR bands of the

QuickBird imagery. Healthy plants usually have low reflectances in the visible region and

high reflectances in the NIR regions. Infected oil palms show stress in their leaves that can

be detected by the vegetation indices. We applied the following vegetation indices to four

fields in the plantation that have different ages of palm and population density. The

vegetation indices used were based on previous studies with QuickBird and other imagery

(Huete 1988; Dutta 2006; Wu et al. 2006; Wang et al. 2007):

• Atmospherically resistant vegetation index

ARVI ¼ NIR� 2R� Bð Þ½ �= NIRþ 2R� Bð Þ½ �; ð1Þ

• Green normalized difference vegetation index

GNDVI ¼ NIR� Gð Þ= NIRþ Gð Þ; ð2Þ

• Green blue normalized difference vegetation index

GBNDVI ¼ NIR� Gþ Rð Þ½ �= NIRþ Gþ R½ �; ð3Þ

• Normalized difference vegetation index

NDVI ¼ NIR� Rð Þ= NIRþ Rð Þ; ð4Þ

• Soil adjusted vegetation index

SAVI ¼ NIR � Rð Þ= NIR þ R þ Lð Þ½ � � 1þ Lð Þ; ð5Þ

• Simple ratio

SR ¼ NIR=R; ð6Þ

where R is the red band (630–690 nm), G is the green band (520–600 nm), B is the blue

band (450–520 nm), NIR is the near infrared band (760–900 nm) and L is the soil

background adjustment factor, 0.5 is commonly used for medium density vegetation.

Image interpretation

Each of the vegetation indices was grouped into classes based on its histogram of digital

numbers that were stretched to 8 bits. This grouping or classification based on the quan-

tiles, was used to identify healthy and infected palms in the image. The largest values were

interpreted as healthy palms, large values were interpreted as palms infected by the basal

stem rot disease, medium values were interpreted as weeds, small values were interpreted

as grass and very small values were interpreted as bare soil. This classification was also

based on manual observations in the oil palm plantations (Sudharto et al. 2005). Image

interpretation was done before ground truthing. Figure 2 shows an example of the clas-

sification using ARVI in field 1.

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Field sampling

A field survey was performed to assess the accuracy of interpretation of the image. The

survey was conducted from 7th to 15th January 2009 based on random sampling of four

fields with different ages of palm and severity of infection or population densities. The first

field had palms of 21 years age (mature palms) with high rates of infection and low palm

densities (83 trees ha-1). The second field had 16-year old plants (adult palms) with

medium density (113 trees ha-1). The third field had palms of 15 and 18 years old (adult

palm) with low infection rates and high palm densities (116 trees ha-1). The fourth field

had 10-year old palms (adolescent palm) with low plant densities (95 tress ha-1). This

variation in palm density was due to disease that killed many of the palms.

The number of samples from each field was based on the proportion of infected area

identified from the imagery. More samples were taken in the areas with greater rates of

infection. There were 53 samples in the first field, 53 in the second, 55 samples in the third

and 45 in the fourth field. A global positioning system (GPS) was used to navigate to the

sampling locations and the health status of each tree was observed manually and assessed

(Fig. 3).

Assessment of accuracy

Results of image interpretation from the QuickBird imagery were compared with those

from the ground survey. The field samples were compared with the corresponding

QuickBird pixels within a 50-m radius (3 9 3 pixels) of the GPS coordinates. Assessment

of the accuracy is based on an error or confusion matrix. The results from the imagery were

reinterpreted based on those of the ground survey.

Results and discussion

Zonation of areas infected by basal stem rot disease

The red band (630–690 nm) of the QuickBird imagery was used to zone oil palm areas

infected by the disease. The image segmentation results from the first field (Fig. 4) show

Fig. 2 The distribution of ARVI values in field 1 showing the classification of healthy and infected palms

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that 53 ha of the total area of 130 ha were infected by Ganoderma boninense. Assuming an

initial planting density of 130 palms ha-1, 6500 palms were estimated to have died

between 1987 and August 2008. In the second field 24 ha out of 150 ha were infected with

the disease. Assuming a planting density of 130 palms ha-1, it was estimated that 3107

palms had died. In the third field 18 ha of the total 131 ha were infected, resulting in the

death of 2340 palms. The fourth field had 38 ha of the total 129 ha infected, resulting in the

death of 4931 palms.

Identifying oil palms infected by the basal stem rot disease

The segmentation results were used to mask the area not covered by oil palms. Six

vegetation indices were then applied to identify palms infected by the disease. The

vegetation indices applied to the high resolution QuickBird imagery were effective

because of the regular planting pattern of oil palms in a triangular grid (9 m 9

9 m 9 9 m) with a planting direction from North to South. The canopy width was

12–16 m for adult palms, which gave a structured pattern that separated the palms from

other objects in the field such as roads, harvest tracks, rivers, drainage lines, weeds, fences

and piles of old fronds.

The six vegetation indices, classified according to their quantiles (Fig. 3), were used to

identify palms infected by the disease. In the field different symptoms or combination of

symptoms can occur that can complicate interpretation of the image and subsequently the

classification of infected palms. Figure 5 shows an example of the identification of oil

palms infected by the basal stem rot disease based on classification by ARVI in field 1.

Assessment of accuracy

The accuracy of the image classification was assessed using ground truth data. The seg-

mentation procedure for the zonation of oil palm and non oil palm areas gave a high overall

classification accuracy of 96%. Table 1 gives the average accuracy assessed for the four

fields.

Fig. 3 Ground survey of the plantation

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Fig. 4 Segmentation result of areas infected by the basal stem rot disease in field 1: a QuickBird imagerywith false colour composite and b segmentation result

240 Precision Agric (2011) 12:233–248

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The suitability of various vegetation indices for detecting palms infected by the disease

was assessed with data from the ground survey. In field 1 with mature oil palm and a large

infected area, ARVI and GBNDVI are the most accurate (Table 2) with an interpretation

accuracy of 85% and a mapping accuracy for infected palms of 67%.

For the second field with 16 year old palms and medium infection, SR is the most

appropriate VI for disease identification. The interpretation accuracy is 85% with a

mapping accuracy for infected palms of 62% (Table 3). The ARVI is also reasonably

accurate (60%) for identifying infected palms.

Fig. 5 Identification of oil palms infected by basal stem rot disease based on classification byatmospherically resistant vegetation index (ARVI) for field 1

Table 1 The accuracy assessment matrix for zonation of oil palm and non oil palm areas in the four fields

Reference orground truth data

Predicted object (interpretation) Row total Errors ofomissiona

Errors ofcommissionb

Mappingaccuracy

Oil palm Non oil palm

Oil palm 155 2 157 1.27 3.82 95.09

Non oil palm 6 43 49 12.24 4.08 84.31

Column total 161 45 206

Overall accuracy of classification 96.12

a Errors of omission = pixels incorrectly excluded from a particular classb Errors of commission = pixels incorrectly assigned to a particular class that actually belong in otherclasses

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For field 3 with 15 and 18 year old palms and lower infection rates than fields 1 and 2,

GBNDVI gives the best results with an interpretation accuracy of 73% and a mapping

accuracy of 35% (Table 4).

Table 5 gives the accuracy assessment for field 4 with 10-year old palms and high

infection rates. The GBNDVI and GNDVI both appear to provide good results with an

interpretation accuracy of 84% and a mapping accuracy of 59%.

The results from this study show that the vegetation indices provide a good overall

accuracy of around 80%, which is similar to the results of Shafri and Hamdan (2009). In

general, the GBNDVI and ARVI provided good results for identifying infected palms in

areas with high infection rates with an accuracy of around 60–65%. The performance of

vegetation indices in fields with younger palms and lower infection rates was poor

(accuracy of 35%). This might be due to the low incidence of the disease and the fact that

the classification procedure was not optimal. As this was a preliminary study to assess the

value of the QuickBird imagery, the capability of the vegetation indices to classify infected

palms was not optimized. There was also a time lag of 5 months between the image

acquisition and field survey. This may have caused additional errors in the accuracy

assessment because the disease can spread rapidly. Therefore, the field survey should be as

close as possible to the time of image acquisition.

Table 2 Evaluation and accuracy of mapping for six vegetation indices in identifying disease infected oilpalms in field 1

Vegetation indices Interpretation accuracy (%) Mapping accuracy (%)

Healthy oil palms Infected oil palms

ARVI 84.91 74.07 66.67

GBNDVI 84.91 74.07 66.67

GNDVI 81.13 67.86 57.89

NDVI 81.13 66.67 60.00

SAVI 81.13 66.67 60.00

SR 81.13 66.67 60.00

ARVI atmospherically resistant vegetation index, GBNDVI green blue normalized difference vegetationindex, GNDVI green normalized difference vegetation index, NDVI normalized difference vegetation index,SAVI soil adjusted vegetation index, SR simple ratio


Vegetation indices Interpretation accuracy (%) Mapping accuracy (%)

Healthy oil palm Infected oil palm

ARVI 84.91 74.19 60.00

GBNDVI 79.25 60.71 57.69

GNDVI 75.47 58.06 50.00

NDVI 83.02 70.00 59.09

SAVI 83.02 70.00 59.09

SR 84.91 73.33 61.90

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Analyses of variance (ANOVA) were conducted on the ground survey samples to

investigate the differences in spectral reflectance between healthy and infected oil palms

(Fig. 6). The results show that the reflectances at all visible bands (blue, green and red) of

the infected palms are statistically higher than for healthy plants (p \ 0.05). The reflec-

tances in the NIR region for the infected palms are statistically lower than for healthy

palms (p \ 0.001). These results confirm our initial hypothesis that the contrast between


Vegetation index Interpretation accuracy (%) Mapping accuracy (%)


ARVI 69.09 51.52 32.00

GBNDVI 72.73 57.58 34.78

GNDVI 72.73 58.82 31.82

NDVI 69.09 51.52 32.00

SAVI 69.09 51.52 32.00

SR 70.91 52.94 34.78


Vegetation index Interpretation accuracy (%) Mapping accuracy (%)


ARVI 80.00 66.67 53.63

GBNDVI 84.44 75.00 58.82

GNDVI 84.44 75.00 58.82

NDVI 80.00 66.67 52.63

SAVI 80.00 66.67 52.63

SR 80.00 66.67 52.63

Fig. 6 Box plots of the distribution of red and NIR values for healthy and infected palms. The ends of thebox are the 25th and 75th quantiles. The line across the middle of the box is the median value

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the reflectance of palm leaves at visible and NIR bands can characterize the health status of

the palms.

Basal stem rot disease pattern

The pattern of basal stem rot disease can be inferred from the image segmentation result.

Assuming that the non oil palm areas are due to dead palms, the spatial pattern of the

disease can then be mapped and studied. The extent of the disease is shown in Figs. 7, 8, 9,

10 where white represents areas of dead palms caused by the basal stem rot infection.

The pattern of disease for field 1 is sporadic, but there is a primary direction across the

field from SE to NW (Fig. 7). A similar pattern is observed in field 2, but there is a primary

Fig. 7 The pattern of basal stem rot disease for field 1 (white represents areas infected by the disease)


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direction scross the field from SSW to NNE (Fig. 8). The pattern in field 3 follows a

dendritic drainage pattern (Fig. 9), and that in field 4 is like a tree branch with the stem in

the mid-bottom of the field and extending to the upper left and right corners (Fig. 10).

Some studies on spatial patterns of the disease in oil palms have shown that diseased

palms in some fields tend to be in clumps, which suggests that the spread of infection could

be through root contact. Root contact with an inoculum source such as old oil palm trunks,

would result in early infection (Rees et al. 2009). However, in other areas the disease

appears to be more sporadic and randomly distributed, which might be expected if the

infection was spread by spores (Corley and Tinker 2004). This is most likely for older palms

of 15–20 years. Azahar et al. (2008) showed that the occurrences of basal stem rot disease

over four years in three locations in Malaysia did not have a spatial pattern or were random

based on a geostatistical analysis. The variogram analysis of the disease incidence was pure

nugget. However, this could arise from inadequate sampling for variogram analysis.



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The pattern of disease in Dolok Ilir appears to be more sporadic rather than concen-

trated. In fields 1 and 2 with 16 and 21 year old palms the disease outbreak is most likely to

be caused by aerial spores. For fields 3 and 4 with palms of 15 and 10 years old, the pattern

of disease follows the lines of drainage and its spread follows some environmental factors

such as microclimate, soil and drainage. In this instance, it was hypothesized that the

infection is through root contact with sources in the soil (Rees et al. 2009). The exact cause

needs to be investigated further.

This study shows that high resolution spatial data, such as QuickBird imagery, is very

suitable for site-specific crop management in oil palm. The remotely sensed image pro-

vided the spatial data necessary for delineation and management of homogenous classes

within a field (Taylor et al. 2007). The principles of precision agriculture can be readily

applied to monitor and control the spread of the disease through the creation of manage-

ment zones. Furthermore, there is the potential to use these management zones to optimize

fertilizer application.

Imagery taken at various times could also provide additional data that may enhance

further understanding of the dynamics of the palm’s health and nutrition. Other high

resolution imagery sources that can record information over large areas at a reasonable cost

are also useful (such as SPOT and Ikonos). A new remote sensing product, RapidEye, is

now available with 5-m spatial resolution and a red-edge band. The red-edge band, which

is the region of rapid change in reflectance of chlorophyll in the near infrared range, can

improve the accuracy for mapping disease.

Conclusions

• High resolution QuickBird imagery can be used effectively to detect basal stem rot

disease infection in oil palms. Vegetation indices derived from the imagery can dis-

criminate between healthy and infected oil palms successfully.

• Infected palms have higher reflectances in the visible bands and lower ones in the NIR

band.

• The red band of QuickBird imagery (630–690 nm) can be used to delineate areas

infected by basal stem rot by using an approximation based on non oil palm areas or

dead oil palms. It provided a mapping accuracy of 96% and was 84% accurate in

identifying dead palms. The segmentation procedure enabled the pattern of disease to

be mapped. Two patterns emerged in the research area: a sporadic pattern that

suggested the infection was from aerial spores and a dendritic pattern, suggesting the

infection followed environmental factors, e.g. soil.

• Vegetation indices performed differently in different fields depending on the palm age

and the severity of infection. In areas with low infection rates and with 15 and 18 year

old palms, the accuracy was only 35%. Further work needs to be done to identify the

spectral characteristics of healthy and infected palms. This will further improve the

accuracy of the classification method.

• This work needs to be expanded to take into consideration the heterogeneity of the

palms, their age and the bias that could be caused by different disease treatments. A

multi-temporal analysis of the imagery (Goodwin et al. 2008) could provide a further

understanding of the disease dynamics and the pattern of spread over time.

• QuickBird imagery is suitable for providing high resolution spatial information for site-

specific crop management in oil palm. The principles of precision agriculture can then

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be applied to monitor yield, to control the spread of the disease and to optimize

fertilizer application.

Acknowledgements The researchers would like to acknowledge the Director of Indonesian Oil PalmResearch Institute (IOPRI) in providing the grant for this research. Mr. Agus Susanto, A. Fadillah Lubis,Tumin and Sanip who provided great help in the field and Mr. Michael Short who corrected the paper. Theauthors thank Dr. Margaret Oliver and two anonymous reviewers for their useful comments.

References

Abdullah, F. (2000). Mapping of the incidence of basal stem rot of oil palms (Elaeis guineensis) on a formercoconut (Cocos nucifera) plantation. In J. Flood, P. D. Bridge, & M. Holderness (Eds.), Ganodermadiseases of perennial crops (pp. 183–194). Wallingford: CABI Publishing.

Azahar, T. M., Boursier, P., & Seman, I. A. (2008). Spatial analysis of basal stem rot disease usinggeographical information system. In: Map Asia 2008, 18–20 August 2008, Kuala Lumpur, Malaysia.http://www.gisdevelopment.net/proceedings/mapasia/2008/.

Azahar, T. M., Seman, I. A., & Boursier, P. (2009). Spatial and hotspots analysis of basal stem rot disease inoil palm plantations: An analysis on peat soil. In: Map Asia 2009, 18–20 August 2008, Singapore.http://mapasia.org/2009/proceeding/proceeding.htm.

Coops, N. C., Johnson, M., Wulder, M. A., & White, J. C. (2006). Assessment of QuickBird high spatialresolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sensingof Environment, 103, 67–80.

Corley, R. H. V., Hardon, J. J., & Wood, B. J. (Eds.). (1976). Oil palm research. Amsterdam: Elsevier.Corley, R. H. V., & Tinker, P. B. (2004). The oil palm (Elaeis guineensis Jacq.) (4th edition ed.). Oxford:

Wiley-Blackwell.Danielsen, S., & Munk, L. (2003). Evaluation of disease assessment methods in quinoa for their ability to

predict yield loss caused by downy mildew. Crop Protection, 23, 219–228.Darmono, T. W. (2000). Ganoderma in Oil Palm in Indonesia: Current status and prospective use of

antibodies for the detection of infection. In J. Flood, P. D. Bridge, & M. Holderness (Eds.), Ganodermadiseases of perennial crops (pp. 249–266). CABI Publishing: Wallingford.

Devadas, R., Lamb, D. W., Simpfendorfer, S., & Backhouse, D. (2004). Evaluating ten spectral vegetationindices for identifying rust infection in individual wheat leaves. Precision Agriculture, 10, 459–470.

Dutta, R. (2006). Assessment of tea bush health and yield using geospatial techniques. MSc Thesis,International Institute for Geo-information Science and Earth Observation. Eschende: ITC & IIRS.http://www.gisdevelopment.net/thesis/thesis7/.

Goodwin, N. R., Coops, N. C., Wulder, M. A., Gillanders, S., Schroeder, T. A., & Nelson, T. (2008).Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensingof Environment, 112, 3680–3689.

Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.

Jensen, J. R. (2004). Introductory digital image processing (3rd ed.). New Jersey: Prentice Hall.McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture.

Precision Agriculture, 6, 7–23.McMorrow, J. (2001). Linear regression modelling for the estimation of oil palm age from Landsat TM.

International Journal of Remote Sensing, 22, 2243–2264.Naert, B., Gal, R., Lubis, A. U., Suwandi, & Olivin, J. (1990). Preliminary evaluation of the possibilities of

using spatial remote sensing to study developments on an oil palm plantation in North Sumatra.Oleagineux, 45, 201–214.

Nguyen, H. V., Lukman, F., Caliman, J. P., & Flori, A. (1995). SPOT image as a visual tool to assesssanitary, nutrient and general status of estate oil palm plantation. In A. Ibrahim & M. J. Ahmad (Eds.),Proceedings of the 1993 PORIM international palm oil congress (pp. 548–554). Kuala Lumpur: PalmOil Research Institute Malaysia.

Perry, E. M., & Davenport, J. R. (2007). Spectral and spatial difference in response of vegetation indices tonitrogen treatments on apple. Computers and Electronics in Agriculture, 59, 56–65.

Pozdnyakova, L., Oudemans, P. V., Hughes, M. G., & Gimenez, D. (2002). Estimation of spatial andspectral properties of phytophthora root rot and its effects on cranberry yield. Computers and Elec-tronics in Agriculture, 37, 57–70.

Precision Agric (2011) 12:233–248 247

123

http://www.gisdevelopment.net/proceedings/mapasia/2008/

http://mapasia.org/2009/proceeding/proceeding.htm

http://www.gisdevelopment.net/thesis/thesis7/

PTPN IV. (2008). Plant performances and basal stem rot disease monitoring in Dolok Ilir Plantation, a partof PT, September 2008. Simalungun, North Sumatera: Perkebunan Nusantara IV.

Rees, R. W., Flood, J., Hasan, Y., Potter, U., & Cooper, R. M. (2009). Basal stem rot of oil palm (Elaeisguineensis): Mode of root infection and lower stem invasion by Ganoderma boninense. PlantPathology, 58, 982–989.

Robert, P. C. (2002). Precision agriculture: A challenge for crop nutrition management. Plant and Soil, 247,143–149.

Shafri, H. Z. M., & Hamdan, N. (2009). Hyperspectral imagery for mapping disease infection in oil palmplantation using vegetation indices and red edge techniques. American Journal of Applied Sciences, 6,1031–1035.

Sharp, C. R., Perry, A. L., Scharen, G. O., Boatwright, D. C., Sands, L. F., Lautenschiager, et al. (1985).Monitoring cereal rust development with a spectral radiometer. Phytopathology, 75, 936–939.

Sudharto, P., Syamsuddin, E., Darmosarkoro, W., & Purba, A. (2005). The land cover crop and weeds in theoil palm plantation. Books I and II. Medan: Indonesian Oil Palm Research Institute.

Susanto, A., Sudharto, P. S., & Purba, R. Y. (2005). Enhancing biological control of basal stem rot disease(Ganoderma boninense) in oil palm plantations. Mycopathologia, 159, 153–157.

Tamaluddin, S., & Kamaruzaman, J. (1999). Remote sensing (RS) and geographic information system (GIS)technology for field implementation in Malaysian agriculture. Seminar on repositioning agricultureindustry in the next millennium, 13–14 July 1999. Serdang: Universiti Putra Malaysia.

Taylor, J. A., McBratney, A. B., & Whelan, B. M. (2007). Establishing management classes for broadacreagricultural production. Agronomy Journal, 99, 1366–1376.

Thenkabail, P. S., Stucky, N., Griscom, B. W., Ashton, M. S., Diels, J., Van Der Meer, B., et al. (2004).Biomass estimations and carbon stock calculations in the oil palm plantations of African derivedsavannas using IKONOS data. International Journal of Remote Sensing, 25, 5447–5472.

Turner, P. D. (1981). Oil palm diseases and disorders. Oxford: Oxford University Press.Wahid, M. B., Abdullah, S. N. K., & Henson, I. E. (2004). Oil palm––achievements and potential. In T.

Fischer (Ed.), 4th international crop science congress, September 2004, Brisbane, Australia. Gosford:The Regional Institute Ltd.

Wang, F. M., Huang, J. F., Tang, Y. L., & Wang, X. Z. (2007). New vegetation index and its application inestimating leaf area index of rice. Rice Science, 14, 195–203.

Wu, J., Wang, D., Rosen, C. J., & Bauer, M. E. (2006). Comparison of petiole nitrate concentrations, spadchlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies.Field Crops Research, 101, 96–103.

Yang, C. M. (2010). Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectralreflectance. Precision Agriculture, 11, 61–81.

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Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

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