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HYPERSPECTRAL ANALYSIS OF JAPANESE OAK WILT TO DETERMINE NORMALIZED WILT INDEX Kuniaki Uto, Yuji Takabayashi and Yukio Kosugi Toshinari Ogata Tokyo Institute of Technology Yamagata Prefectural Government email@example.com 1. INTRODUCTION Signiﬁcant Japanese Oak Wilt has been wide-spreading in the Japanese Islands since the 1980’s . The mass mortality is caused by the mass attacks of Platypus quercivorus beetles, i.e. vectors of Raffaelea quercivora, into the Japanese oak trees. The prevention and extermination based on early detection of attacked trees is the only measure to keep the mortality out at the borders. The principal schemes are 1) early detection of attacked trees based on frequent inspection, 2) exterminate P. quercivorus before the emergence to keep the mortality out at the borders, 3) prevention by prohibiting taking out the attacked woods from the attacked area. Although, at present, the wilt distribution are acquired by mapping manually inspected data in a ﬁeld survey, the efﬁciency of the prevention and extermination is restricted by the complication in cost and precision, i.e. 1) difﬁculty in covering the whole area by manual inspection, 2) difﬁculty in distinguishing the wilt from autumnal tints by manual inspection in autumn. We propose an automatic detection method based on remotely sensed hyper- or multi-spectral data to overcome the problems. In this paper, we ﬁrstly introduce a normalized wilt index (NWI) in consideration of spectral characteristics of withered leaves, and, then, we verify the method by applying the index to hyperspectral remote sensing data and a multispectral satellite image. 2. METHODOLOGY Normalized difference indices, e.g. NDVI , are utilized widely for remote sensing data analyses. Because of the normalized form which is not affected by the illumination condition much, the normalized difference indices are the most successful ones to be applicable to a variety of data. In this section, we propose a normalized wilt index (NWI) based on two normalized difference indices, i.e. NDVI and NDGI . Spectral change of healthy Japanese Oak Tree leaves, according to the evolution of autumn coloring, is shown in Fig.1(a). The spectral data are observed by an imaging spectrograph of visible and near infrared (400-1000nm) with 5nm spectral resolution, 121 channels, 484 spatial pixels and 10bit/pixel. The reﬂectance is estimated based on the radiance of the standard white reference. The numbers in the ﬁgure correspond to ’greenness’ based on manual decision as shown in Fig.1(b): #1 to #6 are collected before leaf drop, #7 is the fallen dead leaf. The absorption of chlorophyll around red band, i.e. 670nm, decreases in accordance with the progress of autumnal tints (#1-#6), and the spectral proﬁle results in smooth monotone increasing curve in 500-1000nm (#7). We pick out two characteristics to differentiate the dead leaves from 1 #1 #2 0.9 #3 #4 0.8 #5 #6 0.7 #7 reflectance 0.6 #1 #2 #3 0.5 #4 #5 0.4 #6 #7 0.3 0.2 0.1 0 400 500 600 700 800 900 1000 wavelength [nm] (a) Spectral proﬁles of leaves (b) Image of leaves Fig. 1. Spectral change of healthy Japanese Oak Tree leaves. 0.2 0.18 0.16 0.14 (a) Color image of hyperspectral data 0.12 0.1 0.08 0.06 0.05 0.045 0.04 0.035 0.04 0.03 0.025 0.02 0.02 0.015 0.01 0.005 0 0 (b) NWI distribution of hyperspectral data (c) Pseudo color image of ASTER (d) NWI distribution of ASTER Fig. 2. NWI distribution of hyperspectral and ASTER data. the green and yellow leaves: 1)The ﬁrst derivative between green (550nm) and red (670nm) is positive, 2)The second derivative at red channel is positive. This tendency was prominent in the case of oak wilt due to P. quercivorus. We propose a NWI, a product of the ﬁrst and second derivatives, which is expected to indicate a higher value in dead leaves than in fresh green leaves. dR(λ) d2 R(λ) NWI = · = −N DGI · (N DV I + N DGI) (1) dλ λ=λR dλ2 λ=λR Where, λR is wavelength at red (670nm), and R(λ) is reﬂectance at wavelength λ. 3. EXPERIMENTAL RESULTS At ﬁrst, we applied the NWI to hyperspectral data which were observed by the imaging spectrograph in Mogami district, Yam- agata in Japan on September 3, 2007. The data contains normal trees and dead oak trees by the mass attacks of P. quercivorus (red regions in Fig.2(a)). The NWI distribution is shown in Fig.2(b) in which the dead trees are extracted appropriately from a variety of trees. Secondly, the NWI is applied to ASTER VNIR data with 15m spatial resolution which were observed in Shonai district, Yamagata in Japan on August 15, 2007. The reﬂectance is calculated based on the ground truth data of airport apron and the surface of the sea. Fig.2(c) is a part of pseudo-color ASTER data around Mogami district, and Fig.2(d) is the corresponding NWI distribution. In Fig.2(b),(d), we masked out all NDVI values less than 0.2 to exclude non-vegetation re- gions. We conﬁrmed that the result is consistent with the wilt map by manual inspection. The more detailed data are under ﬁeld investigation. 4. CONCLUSION We proposed a normalized wilt index for the purpose of the automatic detection of Japanese Oak Wilt distribution based on multispectral remote sensing data. We veriﬁed that the NWI is an effective index when applyed to the hyperspectral remote sensing data and multispectral satellite images. Since the NWI is an index which evaluates quite simple characteristics, i.e. the spectral proﬁles of dead leaves are smooth monotone increasing curves, more detailed veriﬁcation based on various ﬁled survey data is indispensable. 5. 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