Determination of Tree Species using Worldview-2 data

Zulkiflee Abd Latif & Izzati Zamri Centre of Studies Surveying Science and Geomatics

Faculty of Architecture, Planning and Surveying, UiTM Shah Alam, Malaysia

zulki721@salam.uitm.edu.my, izza_ixora@yahoo.com

Hamdan Omar Geoinformation Programme, Environment & Forestry Division, Forest Research Institute Malaysia (FRIM),

Kepong, Malaysia hamdanomar@frim.gov.my

Abstract—Determining tree species has involved a lot of studies in the past decades. This is because of the availability of certain tree species to produce a high market value type of wood or that it can be use for meditational purposes. In the old days, conventional method of survey needs to be conducted to identify tree species. This type of method is rather hard to inaccessible area and time consuming. With the advancement of the satellite imaging technology, inaccessible area can be visualize from an satellite image .Worldview-2 image data is one of the proofs of the remote sensing evolving technology that can be used in a lot and varieties of studies. The available of the near infra-red band makes it very useful in vegetation analysis. Based on this study that has been conducted to eight sample leaves taken from the Bukit Nanas Forest Reserve, Kuala Lumpur, spectral signature extraction was successfully conducted on the near infrared bandwidth due to its sensitivity towards vegetation. The different of about 5% between the signature extracted from the Spectra Vista GER 1500 and Worldview-2 data indicates that this image can be use successfully for vegetation analysis. However, it is very questionable whether high resolution can successfully delineate tree crown of a tree in a natural forest due to the complexity of the characteristics of the crown which is overlapping each other. This complexity is also a challenge for this study as to relate the spectral signature extracted with the accuracy of the segmentation and classification result. Furthermore it also will give some ideas to researchers as to figure what type of improvements can be made towards this study especially for natural forest.

Keywords— Worldview-2, Infra-red, natural forest, Spectra Vista GER 1500 Spectroradiometer, Spectral reflectance

I. INTRODUCTION In recent years, high spatial resolution remote sensing has

become an operational tool for producing forest inventory data over large areas combining with conventional field sample plots. In Malaysia, most new forest inventories are based on high spatial remote sensing data [6]. Currently, there are several satellite sensors can provide high spatial resolution images and various methods have been developed to delineate individual tree canopy [7-8].

Worldview-2 was used in this study due to its availability of the two modes; multispectral and panchromatic bands [1]. The availability of infrared band is very useful in terms of vegetation related studies due to its sensitivity towards vegetation’s chemistry and physical composition. The greenest of the chlorophyll varies for different types of species thus, make them useful to be differentiated and identified. Multispectral band mode give 2m resolution and operates with 8 bands consisting of visible and infrared band while panchromatic band mode has 0.5m resolution act as a very valuable product especially to conduct feature extraction and tree counting.

The conversion of digital numbers to Top of Atmosphere Spectral Reflectance makes the Worldview-2 data more flexible to be used in a lot of studies involving vegetation, building and so many others [1]. The conversion to spectral reflectance is needed because vegetation has a unique spectral graph and it varies between different types of vegetation especially in terms of the infrared bands [6].

Spectra Vista GER 1500 spectroradiometer is an instrument that can be used for the purpose to evaluate spectral reflectance readings of different sample species. The spectral data can be displayed in a single graph and can be converted to ASCII file to make it possible for other interpolation of the data. Spectral measurement can be conducted in an open area with the visibility of sunlight to ensure that the measurement imitates as exactly as possible with the scene the satellite capture the data[6]. Some limitations must be taken into consideration to minimize the error propagate from the measurement. Hence, the objective of this study was to assess the potential of the Worldview-2 data in determining tree species.

II. STUDY AREA AND DATA COLLECTION

The study area chosen was at Bukit Nanas Forest Reserve, Kuala Lumpur. The forest reserve is next to the Kuala Lumpur Tower, the fifth highest telecommunication tower in the world built on the hills. This forest has been the habitat for various types of flora and fauna. The dominant tree species are Hopean Odorata Roxb, Shorea Leprosula, Neobalanocarpus Heimii, Gymnacranthera Bancana (Ihiq) Sinclair, Rusty Sterculia,

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Palaqium Rostratum, Eugenia Oleina and Dyera Costulata. It is geographically located at 3° 9' 6" latitude and 101° 42' 7" longitude and the average temperature is 32 degree Celsius. The Worldview-2 imagery was acquired on 23 January, 2010 and provided by the Forest Research Institute of Malaysia (FRIM).

III. METHODOLOGY In this research, spectral reflectance of the Worldview-2

imagery was extracted and compared with the spectra derived using Spectra Vista GER 1500 Spectroradiometer.

Figure 1. Methodology flowchart

A. Fieldwork

Fieldwork is done to collect the sample leaves. There were 8 sample leaves that were taken from Bukit Nanas Forest Reserve, KL for this study.

TABLE 1. TREES LOCATION OF THE SAMPLE LEAVES

B. Acquiring data from spectroradiometer

GER 1500 is able to read wavelength from 350 nm to 1050 nm which come in very handy as it agree with the wavelength of the 8 bands from Worldview-2 data [1]. These data will be use to in comparison with the reflectance data from the Worldview2 image. Hence, such condition such as sun illumination must be taken into consideration so that the reflectance is synch with the reflectance from the image. It is recommended to do so because the spectra vista GER 1500 do not has its own light which mean sun illumination can affect the result.

Figure 2. Spectral signature from Spectra Vista GER 1500 Spectroradiometer

C. Conversion of digital numbers to top of atmosphere spectral reflectance

The conversion was carried out referring to the papers from

[2], [3] and [4].

(1)

where;

Tree species Tree location (RSO,m)

Scientific name

Local Name

Easting (E)

Northing (N)

Hopean Odorata Roxb

Merawan Siput Jantan

349592 411958

Shorea Leprosula Meranti Tembaga

348593 411592

Neobalanocarpus Heimii

Cengal 348609 411597

Gymnacranthera Bancana(Ihiq) Sinclair

Penarahan 348619 411938

Rusty Sterculia Kelumpang

348652 411993

Palaqium Rostratum

Nyatoh Putih 348600 411928

Eugenia Oleina Kelat

348612 411916

Dyera Costulata Jelutong

348615 411928

LλPixel,

Band =top of atmosphere spectral radiance image

pixels [Wm¯²sr¯¹μm¯¹],

KBand =the absolute radiometric calibration factor [Wm¯²sr¯¹count¯¹] for a given band,

qPixel,Band = radiometrically corrected image pixels [counts]

ΔλBand = the effective bandwidth [μm] for a given band.

WorldView-2 Image

Panchromatic

Species classification and number of trees

Inventory data Coordinate species

Classification validation and

accuracy assessment

In situ measurement Tree

position and species

Tree spectral Spectral signature

extraction

Tree counting Image classification

Canopy

Multispectral

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(2)

where;

D. Spectral signature extraction

Spectral signature extraction was conducted based on the position of the tree.

Figure 3. Spectral reflectance graph of Worldview-2 image

IV. RESULTS AND DISCUSSION

The classification on the forest trees can be a bit low in accuracy due to the nature of natural forest in which the trees are located close to each other making it hard to be differentiated. Trees in natural forest do not stand in a group in accordance to their own species. There will be different species grouped in an area.

A. Comparison and validation for Worldview-2 spectral reflectance data.

The reading of Spectral reflectance for both Spectra vista

GER 1500 Spectroradiometer and Worldview-2 image varied around 5%. This indicates that the variations are very minimal and accurate (see Figure 4 and Figure 5).

Figure 4. Spectral reflectance derived from Spectra Vista GER 1500 Spectroradiometer

WV2- Spectral Reflectance data for each sample species

Wavelength (nm)

Coastal blue Blue Green Yellow Red Red edge NIR 1 NIR 2

Ref

lect

ance

(%)

0

10

20

30

40

50

60

70

80

90

100

Hopea Odorata RoxbShorea LeprosulaNeobalanocarpus HeimiiGymnacranthera Bancana (Ihiq.) SinclairRusty SterculiaPalaqium RostratumEugenia OleinaDyera Costulata

Figure 5. Spectral reflectance derived from Worldview-2 data.

LλPixel, Band = top of atmosphere band averaged spectral radiance

dES2 π = the earth sun distance [AU] during the

image acquisition EsuλBand = the band averaged solar spectral irradiance

[Wmˉ²μmˉ¹ ] normal to the surface being illuminated

θs = the solar zenith angle (degrees) during the image acquisition Spectral Reflectance of Each Sample Species

(Spectra Vista GER 1500 Spectroradiometer)

Wavelength (nm)

Coastal Blue Blue Green Yellow Red Red edge NIR1 NIR2

Ref

lect

ance

(%)

0

10

20

30

40

50

60

70

80

90

100

Hopea Odorata RoxbShorea LeprosulaNeobalanocarpus HeimiiGymnacranthera bancana (Ihiq.) SinclairRusty SterculiaPalaqium RostratumEugenia OleinaDyera Costulata

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With this result it can be concluded that Worldview-2 data is capable of detecting different type of vegetation. The spectral reflectance of the Worldview-2 data agree with the spectral reflectance of the Spectra Vista GER 1500 Spectroradiometer which is set as the constant for this study. To check whether it is true that Infra-red band in Worldview-2 image can actually detect vegetation the best, the difference between the Spectra Vista and Worldview-2 data is compared. This is because, data from Spectroradiometer is assumed to be correct as it was collected in a very control environment of the sunlight and temperature

B. Tree Classification Minimum distance algorithm was selected to perform

classification on the image because it can produce most reasonable classification among other supervised classification algorithms [5].

Figure 6. Final map of classified trees based on minimum distance algorithm

The classification on the forest trees can be a bit low in accuracy because of the nature of the natural forest in which the trees are located to close to each other making it hard to be differentiated. Trees in natural forest do not stand in a group in accordance to their own species. There will be different species grouped in an area. One other reason is that the species sampled only on the eight species, the other species which are not sampled may be taken into classification. Therefore species which is not belonging to the study may include together, and considered as the study species

TABLE 2. ACCURACY ASSESSMENT OF TREE SPECIES CLASSIFICATION

C. Tree Count Overlay of minimum distance, spectral angle mapper and

segmentation. Overlay is very important to derive accuracy assessment in which tree counting will be carried out based on the classification and segmentation made. This application can be considered as value-added product that can be benefited from high resolution characteristic of WorldView-2 panchromatic band that has higher spatial resolution [6]. Tree counting was conducted based on the feature extraction of the image overlaid with the classification conducted on the image. This procedure needs a lot of image interpretation and concentration. Segmentation can help give accurate representation of each tree crown. It is to make sure that the classified region is actually represent one tree.

Figure 7. Overlay of classification and segmentation

Tree

Species (Local name)

Accuracy Assessment

Commission (%)

Omission (%)

Producer Accuracy

(%)

User Accuracy

(%) Cengal 66.7 50 50 33.3 Jelutong 80.0 91.7 8.3 20 Kelat 66.7 83.3 16.7 33.3 Kelumpang 88.9 66.7 33.3 11.1 Meranti Tembaga 94.7 66.7 33.3 5.3 Merawan Siput Jantan 0 33.3 66.7 100 Nyatoh Putih 100 100 0 0 Penarahan 57.1 33.3 66.7 42.9

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Segmentation can also be low in accuracy because it cannot segment the tree crown as it is hard to delineate crowns of trees which are located together and are at the same range of height. One segment probably consist more than one tree.

Figure 8. Tree count using remote sensing versus measured in-situ

The accuracy for classification and segmentation for Hopea Odorata Roxb (Merawan Siput Jantan) is 27.3%, Shorea Leprosula (Meranti Tembaga) is 50%, Neobalanocarpus Heimii (Cengal) is 0%, Gymnacranthera Bancana (Ihiq) Sinclair (Penarahan) is 60%, Rusty Sterculia (Kelumpang) is 75%, Palaqium Rostratum (Nyatoh Putih) is 87.2%, Eugenia Oleina (Kelat) is 33.3% and Dyera Costulata (Jelutong) is 57.9% (Figure 7). There was strong relationship between predicted and measured number of trees (R2 = 0.95; Figure 8). This result is in agreement with previous study by [1] which applied Worldview-2 data as a best estimator for natural forest reserve.

V. CONCLUSION

This study has demonstrated that it is possible to identify tree species from Worldview-2 data. The use of remotely sensed data greatly reduced the time and effort required and can provide comprehensive tree information that is difficult to obtain using traditional methods. In this case of natural forest, classification of individual trees was a bit complex due to the scattered pattern of arrangements of tree species. Mixed species that are grouped in one area plus the same range of heights of different tree species makes it harder to interpret the segmentation. This leads to other evaluation whether one segment actually represent one tree crown or more. This research contributed for further studies on tree species determination in natural rainforest. This studies also shows that some other field data is needed in order to get a higher accuracy result that can be use to better the application of forest management.

ACKNOWLEDGMENTS

The author would like to thank the Forest Research Institute of Malaysia (FRIM) for providing the Worldview-2 data as long as academic advice, Forestry Office of Bukit Nanas Forest Reserve for giving access to the rainforest and the Centre of Studies Surveying Science and Geomatics, FSPU, UiTM for supporting this research.

REFERENCES

[1] Omar, H. (2010). Commercial Timber Tree Species Identification Using

Multispectral Worldview-2 Data. In D. Globe (Ed.): Geoinformation Program, Environment and Forestry Division Forest Research Institute Malaysia (FRIM).

[2] Updike, T., & Comp. C. (2010). Radiometric Use of WorldView-2 Imagery. 1601 Dry Creek Drive Suite 260 Longmont, Colorado, USA, 80503: Digital Globe.

[3] Abdul Razzak, T. Z., Zubaidy, R. Z. A., & Mahmoud, S. A. K. (2010). Remote Sensing Model for Monitoring Trophic State of Al Huweizah Marsh. Eng. & Tech. Journal, 28(16).

[4] Sridharan, H. (2010). Multi - Level Urban Forest Classification Using the Worldview-2 8-Band Hyperspatial Imagery. 8-Band Research Challenge. DigitalGlobe.

[5] Jusoff, K. (2009). Airborne Hyperspectral Sensor for Individual Species Counting and Mapping of Karas (Aquilaria malaccensis) in Bukit Nanas Forest Reserve, Malaysia. World Applied Sciences Journal, 7(10), 1246-1251.

[6] Omar, H. & Hamzah, K.A. (2010) High Spatial Resolution WorldView-1 Image For Tropical Forest Individual Canopy Counting. In Proc. MRSS 6th Int. Remote Sensing & GIS conf and Exhibition. 28-29 April 2010, PWTC, Kuala Lumpur. 11p

[7] Bai, Y., Walsworth, N., Roddan, B., Hill, D. A., Broersma, K., & Thompson, D. (2005), Quantifying treecover in the forest–grassland ecotone of British Columbia using crown delineation and pattern detection. Forest Ecology and Management, 212, 92• 100.

[8] Abd Latif, Z., Aman, S.N.A and Ghazali, R., (2011). Delineation of tree crown and canopy height using LiDAR and aerial photo. 2011 7th International Colloquium on Signal Processing & Its Applications (CSPA 2011). 4 – 6 March 2011, Penang, Malaysia.

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