Vineyards Mapping Using Object Based Analysis

Selver Senturk Satellite Comm. and Remote Sensing

Istanbul Technical University Istanbul, Turkey

selver .senturk@gmail.com

Elif Sertel Geomatics Engineering

Istanbul Technical University Istanbul, Turkey

sertele@itu.edu.tr

Sinasi Kaya Geomatics Engineering

Istanbul Technical University Istanbul, Turkey

kayasina@itu.edu.tr

Abstract-Precise vineyards digital plotting for grape­

growing regions can produce thematic maps, which subsequently

could be employed within Geographical Information Systems

(GIS) as a part of the formation of national Vineyard

Information System (VIS) in Turkey. This study proposed

vineyards parcels delineation approach using sub-meter

Worlview-2 (WV2) VHR satellite images. The WV2 satellite data

files incorporate featured 8 (eight) multi-spectral and one

panchromatic bands. They were used to differentiate and label

the spatial distribution of viticulture practices in Tekirdag

Province, Eastern Thrace region, which is one of the areas with

highest efficiency for grape-growing practices. The applied

classification process employed object-based image analysis

method (OBIA, using eCognition). It is a technique that

aggregates neighboring pixels into groups acknowledged as

image objects. These object primitives convey similar values for

several variables according to predefined spatial likelihood and

homogeneity measures. Spectral, textural, customized vegetation

indices, various band ratios, and other object features were

integrated into the object-based image analysis with aim to

produce consistent classification results. WV2 pan-sharpened

images of O.5m spatial resolution were taken as the input data.

The validation of the created land cover mapping was assessed

using formerly produced labeled maps from field work. The

plantations with linear, straight row vineyards planting were

almost completely mapped, while the allocation accuracies for the

other planting types were comparatively lower. Nonetheless,

these accuracy results for the dissimilar vineyards planting

routines put forward that OBIA can support further in vineyards

mapping for wine producers and classification of viticulture

practices in general.

Keywords- vineyards mapping; WorldView-2; ORIA; precision farming;

I. INTRODUCTION

Mapping the human activities and the physical state of Earth's surface plays a fundamental role in agriculture management. The rudimentary way of mapping is the one called as in-situ i.e. in-field mapping. However, on account of its extremely labor, time demanding and irregular nature the agricultural land use management activities turn out to be exceptionally costly as well as not all the time satisfactory and precise. Remote sensing data and technology for automatic analysis help to create an accurate geospatial data useful for producing and updating geographical databases for land management. In that sense, vineyards are perfectly suited to spatial information system applications as they demonstrate qualities associated with spatial and temporal variables.

T.R. Ministry of Food, Agriculture and Livestock and I.T.U. T ARBIL Agro-infonnatics Research Center

Undeniably, over the recent decades satellite remote sensing has exhibited noteworthy advances in acquisition of data. Nonetheless, at vineyards scale, data processing, classification, acquired information interpretation and eventually precise mapping of the vine parcels is not at a fulfilling level. The evidence of this is the substantial amount of work and studies done on these subjects.

In this preliminary study just a few studies reflecting the challenge of vineyards mapping are mentioned. Without using any mUltispectral information and employing merely the HSR (High Spatial Resolution) Ikonos panchromatic image data, T.A. Warner et a1. performed analysis of autocorreleograms using ORCHARD software program in attempt to classify orchards and vineyards spatially in Granger area, Washington, USA [1]. On aerial photographs, inspired by the regular plantation pattern of the vineyards, Rabatel et a1. implemented frequency analysis for vine parcels detection using Gabor filters. Although the quite satisfactory 84% automatic detection performance of the method it has the main application constraint of being relevant only to linearly planted vineyards [2]. C. Dalene et al. in a comparative study reviewed the textural and frequency approaches and concluded that the frequency one owns the superiority over the textural one [3]. In their study as a texture analysis reference is given the work of Da Costa et al. [4], which involves sampling window that characterizes each vine block. Another weakness of this method is the absence of numerical or comparative efficiency measure of it. Some further recent works on vineyards delineation were carried out by A. Le Bris [5] using unsupervised frequency analysis executed on aerial images semi-variograms data in addition to a supervised method using texture indices derived from SIFT descriptors, and Mathews et al. [6] using terrestrial LiDAR scanning, trying to benefit from the height peculiarity of the vine plants as a worthy feature.

In our specific case and study region, owing to their inexpensive and easy to employ routines the distributed and the grid-wise planting patterns are the two most frequently used practices. The linear or also known as contour vineyard growing practice is still limited, as it is comparatively more expensive and used only by the well-off grape growing farmers and winemakers. Another peculiar grape-growing routine for the region is the olive-yard cultivating type. It is a shared parcel where next to the olive-tree rows there are cultivated vine plants, too. This reality is also based on cost-effective concerns. The fonnerly mentioned frequency approaches would not perform well for our study region since very

dissimilar planting routines are present and these approaches were already found to rely highly on crop patterns periodicity. Grape cultivation on trellises makes the linear vinegrowing pattern the most undemanding planting routine for classification, while the sporadic and heterogeneous nature of the other vine-growing planting practices makes their delineation complicated to some degree.

A. TheArea

II. STUDY AREA & DATA

The two test sites were selected from Sarkoy, one of the districts of Tekirdag province, Turkey. They were chosen in a particular way so in a single scene they could reflect different vineyards planting techniques from the region. Refer to Figure 1 (a). Located in northwestern part of Turkey, Sarkoy is lying on the north coast of Marmara Sea with a shoreline facing south. The total area of the district is 481sq.km and in terms of elevation above the sea level the altitude is of around 225m [7].

Leading winemaking regions share a few things in common; the necessity for lots of sunlight and for the right soil conditions. The local climate of Sarkoy's region is quite alike with the one of Bordeaux in France [8]. With a total agricultural area of 153sq.km Sarkoy holds the prevalent portion of grape production in Tekirdag province. There are 40 vine-producing enterprises with an annual vineyards yield of 37,285tonnes. The annual wine production for 2008 was 14,760 tons, which makes 96.4% from the total production in Tekirdag [9]. Whether used for wine or consumed as a table grape, fresh or dried, assorted grape types are cultivated in the region. Some of the well-known sorts for the region are Alphonse Lavallee, Cinsault, Gamay, Merlot, Sauvignon Blanc, Semi lion, Riesling, Cardinal, and Shiraz [8].

B. The Data

The WorldView-2 (WV2) image data used in this study were obtained on July 30th, 2011 offering eight (8) spectral and one panchromatic bands. The geometric calibration and the projection to UTM, Zone 35 with WGS-84 spheroid and datum were performed using ERDAS Imagine 2011 program. The geographical calibration was done through resampling to 5m spatial resolution of DEM (Digital Elevation Model) data, produced from 60(sixty) 1125,000 scaled topographic maps. In aim to aid the [mal ortho accuracy, the WV2 data was delivered with RPC (Rational Polynomial Coefficients) files, which have the ability to be utilized in no presence of sufficient GCPs (Ground Control Points).

The WV2 sensor resolutions for panchromatic images are 0.46m GSD at nadir and 0.52m GSD at 20 degrees off-nadir, and for multispectral images is 1.84M GSD at nadir ,and 2.08m GSD at 20 degrees off-nadir. The dynamic range of radiometric resolution is II-bits per pixel. The four (4) from the eight (8) spectral bands are standard colors from the visible-red, green, blue- and NIR, and four (4) are classified as new bands -namely -coastal, yellow, red edge, and near-infrared2 (NIR2). [10] The advent of the last four new peculiar bands promotes the identification and analysis of vegetation biomass, and appears also quite promising on identification and type classification of different crops and individual plants.

Beforehand the WV2 satellite images were pansharpened by applying the Hyperspherical Color Space (HCS) algorithm, which is particularly designed for sharpening WV2 images.

(a)

(b)

(c)

Fig.1. (a) The location of the two test sites, (b) The upper left comer subset image from (a), (c) The lower right image from (a) with the distribution of the randomly created accuracy assessment points.

.,. ... ,._ ... _ Distributed VY

Fig. 2. Classification result of the image subset given in Figure I (b)

TABLE I: ACCURACY ASSESSMENT FOR THE IMAGE SUBSET GIVEN IN FIGURE l(B)

Classes

Paved Roads

Reference Classified Number Producers Users Totals Totals Correct Accuracy Accuracy

57 64 57 100.00% 89.06% :

.... Oii����;d VY····· -65········64····· -62····· -95-.38%···· 96�88%··-: . . ····�efrlbb�� ········································· ......... .

Lands Woods/Shrubb

83 64 64 77.11% 100.00%

ery 61 64 56 91.80% 87.50%

•••••• ��!l��� •••••••• �1 •••••••• �� •••••• 6.4. •••••• 1.O.

O.QQ!,� ••• ! �9;.o.�� •. • • : Linear VY 59 64 59 100,00% 92,19% : ·····tJt�etagr� ········································ ........... .

parcels

Total

61

562

64

564

43

506

III. METHODOLOGY

70.49% 67.19%

In the attempt to identify and classify vineyard parcels in this work Object-Based Image Analysis (OBIA) approach was employed using eCognition software provided by Trimble Germany GmbH. Visually assessed objects are easier to be interpreted than pixels as they deliver additional information based on their geometry, texture, relation to the other objects within a given scene and many other properties [11]. The availability of the wide range of functions embedded within the program favors OBIA over pixel-based in many ways. A hierarchical image segmentation technique was adapted to separate grape plant plots from the remaining objects within the two test image subsets. The segmentation used in our process tree algorithm starts with image objects with a size of a single pixel merged iteratively until the predefmed homogeneity threshold was not exceeded locally. Two main scale parameters of ..1.=80 (shape=0.35 and compactness=0.5) and ..1.=200 (shape=0.6 and compactness=0.5) were used. Scale terms the

Impervious

Linear VY Oliveyard VY Other agricultural and arid area.>

Paved Roads

Ploughed/Bare/scarce vegetation land, unpaved roads n footways

Scarce vegetation

Shadow

U RS

Vegetation

Water Bodies

Woods/shrubbery

maximum color divergence on a given layer [11]. The first scale parameter was set in an attempt to identify the individual structures and small-sized objects, while the second one is used in parcels delineation and identification. In the course of the process tree algorithm development for the image subsets given in Figure 1, a fusion of vegetation, soil and water indices was applied to. In discrimination of different agricultural blocks the most familiar vegetation index NDVI (Normalized Difference Vegetation Index), IRPVI (Infrared Percentage Vegetation Index), SAVI (Soil Adjusted Vegetation Index), NDBSI (Normalized Difference Bare Soil Index (1)) [12], plus various combinations of the new bands, notably the coastal and red edge, were frequently utilized under the customized object features menu.

NDBSI = Mean Blue-Mean Coastal

Mean Blue+Mean Coastal (1)

The first and the second statistical moments, namely the mean and the standard deviations of the band layers value features were used as WV2 bands combination offers fairly rich spectral domain. Among the geometry features the area (number of pixels) and the length to width ratios were the two mostly used elements. The eight satellite image layers were weighted depending on their standard deviation values. The higher standard deviation value, the more important and significant a band is for the segmentation results. Nine (9) classes for image subset given in Figure 1 (b) and eleven 11 classes for the image subset given in Figure (c) were defined. The results from the classification are given in Figure 2&3 and Tables 1&2 respectively.

IV. RESULTS AND DISCUSSIONS

Considering the vineyards peculiar texture and planting routines visually they are easily recognized in VHRS images. However, when the mapping problem is tried to be resolved through an automatic, with no human interaction approach the results are not always flawless. In our limited to object-based image analysis specific case, where the user collaboration demand was high, the attained results for overall classification accuracy for Figure l(b) with nine (9) classes was equal to 89.72% with Kappa statistics (K) equal to 0.8843 and in that order for Figure l(c) with eleven (11) specified classes it was equal to 85.79% and 0.8427. It is observed that with the increase of indicated class number the classification accuracy decreases. This supposedly is based on the entropy intensification. Every new class contributes to the total with new level of uncertainty. For instance, when we downsized the classes to just two as vineyards and not-vineyards the overall accuracy was 94.66% with kappa K equal to 0.8932.

Positively with superior hardware and system performance, with implementation of textural (e.g. Haralick textures), hierarchy, or thematic features will vastly contribute to the accuracy improvement. The textures defmitely are good point to start from as texture feature is one of the most characterizing marks not only for the vineyards, but for the majority of permanent crops.

Fig. 3. Classification result of the image subset given in Figure I (c)

TABLE 2: ACCURACY ASSESSMENT FOR THE IMAGE SUBSET GIVEN IN FIGURE ICc)

Classes Reference Classified Number Producers

Users Accuracy Totals Totals Correct Accuracy

Paved 14 12 12 85.71% 100.00%

Roads

Disc. URS 5 5 5 100.00% 100.00%

Bare/Ploug 21 20 20 95.24% 100.00%

h. Lands Annual

20 20 20 100.00% 100.00% •• ��Qn� ••••••••••••••••••••••••••••••••••••••••••••••••••••••••• (Linear VY 19 20 19 100.00% 95.00%

••

: Grid n : Distributed 22 20 18 81.82% 90.00% : VY : Oliveyard •

• VY 15 20 15 100.00% 75.00% .: ................................................................

Orchards

Group of or individual

trees Unpaved

roads, footpaths

Natural Soil

Total

18 20 18 100,00%

17 20 14 82.35%

I I 20 9 81.82%

34 20 19 55.88%

196 197 169

Land

�·O Arable land

� .. ·o Annual Crops associated with temp crops

1 1 .... ·0 Bare or ploughed arable land

1 L.O Crops

� .... Permanent Crops

8.. Orchards

I .... ·• Group of trees or individual trees

1 .... ·0 orchard - big trees

L .. ·O orchard- small trees

� . .• Vineyards

1 . . . . 0 Grid n Distributed VY

L .... LinearVY

Oliveyard VY

• Natural grassland or shrubs

Natural soil

90.00%

70.00%

45.00%

95.00%

: .... . Unpaved, secondary roads and footpaths or interrows

� . URS

I .... ·• Discontinuous Urban Fabric

L .. ·O Paved roads n impervious surfaces

V. CONCLUSION

In this study, we analyzed WorldView-2 satellite images ability in delineation vineyards parcels using Object-Based Image Analysis (OBIA) approach. Although within the process tree algoritiun as a result of limited hardware capability many features incorporating texture analysis were excluded, integrating just different band ratios and customized object features aided to map the image subsets with overall accuracy higher than 89% for the fIrst image and 85% for the second one. As expected in vineyards delineation the highest accuracy of 92.19% and 95% respectively for the fIrst and second subset images, was found for the linear type planting pattern, and the lowest for oliveyard and distributed patterns with accuracies falling down to 75%. As it was realized so far, equally as many other agricultural crops, vineyards are well suited to the application of spatial information systems. Further study could be employed on more capable hardware, incorporating texture and computationally demanding object and class related features.

ACKNOWLEDGMENT

We thankfully acknowledge the fInancial support given by The Ministry of Food, Agriculture Livestock of Turkish Republic, and TARBIL Agro-informatics Research Center at Istanbul Technical University. Furthennore, the provision of WorldView-2 satellite images by The ScientifIc and Technological Research Council of Turkey, TUBITAK, is highly appreciated, too.

REFERENCES

[I] T.A Warner, and K. Steinmaus, "Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imgery ", Photogrammetric Engineering and Remote Sensing, Vol. 71, No. 2, pp. 179-187,2005.

[2] G. Rabatel, C. Delenne, and M. Deshayes, "A non-supervised approach using Gabor filters for vine-plot detection in aerial images," in Computers and Electronics in Agriculture, vol. 62, Issue 2, pp. 159-168, 2008.

[3] C. Delenne, S. Durrieu, G. Rabatel, M. Deshayes, J. S. Bailly, C. Lelong, and P.Couteron, 'Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data," International Journal of Remote Sensing, Vo1.29, No.4, pp.1I53-1167,2008.

[4] J.P. Da Costa, F. Michelet, C. Germain, O. Lavialle, and C. Genier, "Delineation of vine parcels by segmentaion of high resolution remote sensed images," Precision Agriculture, Vo1.8, No.1, pp.95-110, 2007.

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[9] Tekirdag Valiligi, Glda, Tarim ve Hayvanclhk Mudurlugu; (ing), Available at http://www.tekirdagtarim.gov.tr/index.php#. Last accessed on 08 June 2013.

[10] Digital Globe, http://www.digitalglobe.com/about-us/content­collection#worldview-2, Last accessed on 08 June, 2013.

[II] eCognition® Developer 8.7.2, User Guide, Trimble Documentation, Miinchen, Germany 2012.

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