Mapping Of Palm Trees In Urban And Agriculture Areas Of Kuwait Using Satellite Data

ABSTRACTQuickbird panfused data with 60 cm resolution is used to map the locations of date palm trees in the arid land of Kuwait. In this study, Laplacian maxima fi ltering was applied to classify date palm trees using high-resolution satellite imagery. The processing was done in two steps: the fi rst step involved smoothing of the data using non-linear diffusion and the second was extracting local spatial maxima of Laplacian blob used for palm tree identifi cation. The results are promising and the classifi cation accuracy in the two test areas is 96% and 98%, which is higher than maximum likelihood classifi cation for the same dataset. The results show that this meth-odology can be adopted for the mapping of palm trees in arid Middle Eastern countries. Keywords: arid land, blob, image classifi cation, laplacian fi ltering, quickbird.1 INTRODUCTIONPalm trees are very common in Middle Eastern countries. They are of signifi cant environmental and commercial importance [1]. In recent decades, the Middle Eastern region has witnessed an extensive planting of Date Palm trees, both in urban and agricultural areas. Millions of trees are estimated to have been planted in these arid deserts [2, 3]. Extensive plantation in urban areas rarely gives a clue that these are arid and hyper arid countries. Among these species, most common are date palm trees, which are seen planted along the roads, in front of houses, in parks, and organized plantation in agricultural areas. However, there is limited knowledge of actual tree counts and their exact spatial locations, which is a requirement for any agricultural census.Remote sensing data have been used for the identifi cation of urban treed areas, but with limited classifi cation accuracies. These lower classifi cation accuracies are attributed to a variety of spectral and textural properties [4]. Medium resolution satellites including LANDSAT, SPOT, and ASTER have been used in urban treed classifi cation, but their spatial resolution permits only larger patches of treed areas to be classifi ed [4–6]. With the advancement in satellite technology and availability of high spatial resolution images, it is now feasible to achieve higher classifi cation accuracies in urban and agricultural areas. The present study is an attempt to map the date palm trees in urban and agri-cultural areas of Kuwait. An accuracy assessment is also made to compare results from maximum likelihood and the Laplacian blob classifi cations of date palm trees within the test areas.Similar studies for classifying and quantifying olive trees were taken up by European Union Countries. The European Economic Committee realized the need to quantify the olive plantation in 1997 and launched the OLISTAT project in September 1997 to estimate the number of olive trees in France, Italy, Spain, Portugal, and Greece [7]. The counting of trees is a classic example of remote sensing applications in forestry. However, crown counting of trees is not an easy or straightforward task as there are limitations of satellite data resolution as well as problems related to the subjective nature of interpretation. Howard [8] indicated that the capacity to distinguish different objects is governed by the size of the object relative to pixel.The multispectral classifi cation methods have provided reasonably good results, but there is still room for further improvement in classifi cation accuracy if textural parameters are taken into account. It was believed that with the availability of higher resolution satellite data, classifi cation accuracies