研究者データベース

王 秀峰(オウ シユウホウ)
国際連携機構
学術研究員

基本情報

所属

  • 国際連携機構

職名

  • 学術研究員

学位

  • 博士(農学)(北海道大学)
  • 農学修士(北海道大学)

ホームページURL

科研費研究者番号

  • 30301873

J-Global ID

研究キーワード

  • 農業気象   環境情報学   環境リモートセンシング   Agricultural meteorology   Environmental information   Remote sensing   

研究分野

  • 環境・農学 / 農業環境工学、農業情報工学
  • 社会基盤(土木・建築・防災) / 防災工学
  • 環境・農学 / 環境動態解析
  • 環境・農学 / 環境政策、環境配慮型社会
  • 環境・農学 / 環境影響評価

職歴

  • 2021年04月 - 現在 北海道大学 国際連携機構 客員准教授
  • 2012年04月 - 2020年03月 北海道大学 大学院農学研究院 准教授
  • 2002年04月 - 2012年03月 北海道大学 大学院農学研究院 講師
  • 1998年04月 - 2002年03月 北海道大学(大学院農学研究科農地環境情報学分野) 助手
  • 1998年04月 - 2002年03月 北海道大学 大学院農学研究院 助手
  • 1997年04月 - 1998年03月 財団法人北海道農業近代化コンサルタント研究部 技師
  • 1997年 - 1998年 Technical Staff
  • 1994年04月 - 1997年03月 北海道大学農学部農業工学科 ポスドク
  • 1994年04月 - 1997年03月 Postdoctoral, Hokkaido University
  • 1982年02月 - 1988年11月 中国黒竜江省農業科学院 研究員
  • 1982年02月 - 1988年11月 Researcher,Heilongjiang Academy of Agricultural Sciences, China

学歴

  •         - 1994年   北海道大学   農学研究科   農業工学専攻
  •         - 1994年   北海道大学
  •         - 1991年   北海道大学   農学研究科   農業工学専攻
  •         - 1991年   北海道大学
  •         - 1982年   東北農業大学( Northeast Agricultural University)   農学部(農学系)   農学科
  •         - 1982年   Northeast Agricultural University, China

所属学協会

  • 日本農業気象学会北海道支部   日本写真測量学会北海道支部   農業農村工学会   環境情報科学センター   日本写真測量学会   日本農業気象学会   日本リモートセンシング学会   Japan Wetlands Society   The Japanese Association for Arid Land Studies   Drainage and Reclamation Engineering   Journal of the Japanese Society of Irrigation   Journal of the Japan Society of Photogrammetry and Remote Sensing   The Japanese Society of Irrigation Drainage and Reclamation Engineering   Center for Environmental Information Science   The Society of Agricultural Meteorology of Japan   Japan Society of Photogrammetry and Remote sensing   Remote sensing Society of Japan   

研究活動情報

論文

  • Shuai Yin, Meng Guo, Xiufeng Wang, Haruhiko Yamamoto, Wei Ou
    Environmental Pollution 268 115849 - 115849 2021年01月 [査読有り]
  • Zhongyi Sun, Xiufeng Wang, Haruhiko Yamamoto, Hiroshi Tani, Tangzhe Nie
    Climatic Change 163 2 913 - 930 2020年11月 [査読有り]
  • The abnormal change of air quality and air pollutants induced by the forest fire in Sumatra and Borneo in 2015
    Shuai Yin, Xiufeng Wang, Meng Guo, Heri Santoso and Hongyou Guan
    Atmospheric Research 243 1 1 - 20 2020年10月 [査読有り][通常論文]
  • Influence of biomass burning on local air pollution in mainland Southeast Asia from 2001 to 2016
    Shuai Yin, Xiufeng Wang, Xirui Zhang Meng Guo, Moe Miura and Yi Xiao
    Environmental Pollution 254 A 2019年11月01日 [査読有り][通常論文]
  • Combination of SAR Polarimetric Parameters for Estimating Tropical Forest Aboveground Biomass
    TRUONG THI CAT TUONG, HIROSHI TANI, XIUFENG WANG, NGUYEN QUANG THANG AND HA MANH BUI
    Polish Journal of Environmental Studies 2019年11月01日 [査読有り][通常論文]
  • SEMI-SUPERVISED CLASSIFICATION AND LANDSCAPE METRICS FOR MAPPING AND SPATIAL PATTERN CHANGE ANALYSIS OF TROPICAL FOREST TYPES IN THUA THIEN HUE PROVINCE, VIETNAM
    TRUONG THI TUONG, HIROSHI TANI, XIUFENG WANG AND NGUANG QUANG THANG
    FORESTS 10 8 1 - 24 2019年08月01日 [査読有り][通常論文]
  • Heri Santoso, Hiroshi Tani, Xuifeng Wang, Agus Eko Prasetyo, Rei Sonobe
    International Journal of Remote Sensing 40 19 7624 - 7646 2019年05月 [査読有り][通常論文]
  • Exploring the effects of crop residue burning on local haze pollution in Northeast China using ground and satellite data
    Shuai Yin, Xiufeng Wang, Xirui Zhang, Yi Xiao, Hiroshi Tani, Zhongyi Sun
    Atmospheric Environment 199 189 - 201 2018年11月 [査読有り][通常論文]
  • Classifying the severity of basal stem rot disease in oil palm plantations using WorldView-3 imagery and machine learning algorithmsdom
    Heri Santoso, Hiroshi Tani, Xuifeng Wang, Agus Eko Prasetyo, Rei Sonobe
    International Journal of Remote Sensing 38 16 4683 - 4699 2018年11月 [査読有り][通常論文]
  • Predicting oil palm leaf nutrient contents in kalimantan, indonesia by measuring reflectance with a spectroradiometer
    Heri Santoso, Hiroshi Tani, Xiufeng Wang, Hendrik Segah
    International Journal of Remote Sensing 2018年09月 [査読有り][通常論文]
  • Analyzing temporo-spatial changes and the distribution of the CO2 concentration in Australia from 2009 to 2016 by greenhouse gas monitoring satellites
    Shuai Yin, Xiufeng Wang, Hiroshi Tani, Xirui Zhang, Guosheng Zhong, Zhongyi Sun, Anthony R. Chittenden
    Atmospheric Environment 192 1 - 12 2018年08月 [査読有り][通常論文]
  • Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982-2015
    Zhongyi Sun, Xiufeng Wang, Haruhiko Yamamoto, Hiroshi Tani, Guosheng Zhong, Shuai Yin, Enliang Guo
    Ecological Informatics 46 156 - 165 2018年06月 [査読有り][通常論文]
  • XバンドおよびCバンドSARデータを併用した機械学習アルゴリズムによる圃場の作物分類
    山谷 祐貴, 谷 宏, 王 秀峰, 薗部 礼, 小林 伸行, 望月 貫一郎, 野田 萌
    写真測量とリモートセンシング 57 2 78 - 83 2018年05月 [査読有り][通常論文]
  • Rei Sonobe, Yuki Yamaya, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Kan-Ichiro Mochizuki
    Journal of Applied Remote Sensing 12 2 1 - 17 2018年04月01日 [査読有り][通常論文]
     
    The identification and mapping of crops are important for estimating potential harvest as well as for agricultural field management. Optical remote sensing is one of the most attractive options because it offers vegetation indices and some data have been distributed free of charge. Especially, Sentinel-2A, which is equipped with a multispectral sensor (MSI) with blue, green, red, and near-infrared-1 bands at 10 m red edge 1 to 3, near-infrared-2, and shortwave infrared 1 and 2 at 20 m and 3 atmospheric bands (band 1, band 9, and band 10) at 60 m, offer some vegetation indices calculated to assess vegetation status. However, sufficient consideration has not been given to the potential of vegetation indices calculated from MSI data. Thus, 82 published indices were calculated and their importance were evaluated for classifying crop types. The two most common classification algorithms, random forests (RF) and support vector machine (SVM), were applied to conduct cropland classification from MSI data. Additionally, super learning was applied for more improvement, achieving overall accuracies of 90.2% to 92.2%. Of the two algorithms applied (RF and SVM), the accuracy of SVM was superior and 89.3% to 92.0% of overall accuracies were confirmed. Furthermore, stacking contributed to higher overall accuracies (90.2% to 92.2%), and significant differences were confirmed with the results of SVM and RF. Our results showed that vegetation indices had the greatest contributions in identifying specific crop types.
  • Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover
    Rei Sonobe, Yuki Yamaya, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Kan-ichiro Mochizuki
    Geocarto International 1 - 17 2018年01月 [査読有り][通常論文]
  • Zhongyi Sun, Xiufeng Wang, Haruhiko Yamamoto, Hiroshi Tani, Guosheng Zhong, Shuai Yin
    Ecological Indicators 84 218 - 234 2018年01月01日 [査読有り][通常論文]
     
    Quantitative estimations of the GPP (gross primary production) and its variations at spatial scales are important issues with future significance due to the increasing atmospheric CO2 levels. However, the effects of the spatiotemporal variability in the atmospheric CO2 concentrations on GPP estimations are challenging with respect to the terrestrial ecosystem due to land cover component characteristics and difficulties associated with measuring CO2 concentrations over large spatial areas. The development of remote sensing offer a means to routinely monitor CO2 concentrations both spatially and temporally from space. To introduce continuous spatial CO2 data as an indicator for the estimation of the terrestrial biosphere GPP, we used the decoupling coefficients to evaluate the canopy CO2 concentrations, photosynthetic biochemical models to calculate the photosynthetic rate, and Big-leaf model to scale up to a global scale. The GPPs estimated by this method are relatively consistent with the GPP derived from Flux tower sites. Thus, the method proposed in this study utilizing continuous spatial CO2 data to estimate the GPP is practicable and feasible. Finally, we compared the GPPs under different atmospheric CO2 concentrations conditions between 2000 and 2014 and analyzed the effects of the spatiotemporal variability in the atmospheric CO2 concentrations on the GPP estimates. The results show that, in general, the terrestrial GPP increases as atmospheric CO2 concentrations increase, and the increases in the lower latitudes are more significant than those in the middle and high latitudes by comparing the annual GPP estimates in 2000 with those in 2014, it was observed that the increases in forest GPP is greater than that of other functional types. The effects of the variations in the spatial distribution of atmospheric CO2 concentrations on the terrestrial GPP distribution vary based on time and location. Regarding the annual GPP estimates, without considering the CO2 spatial distribution, the estimates overestimate the GPP in the lower latitudes and underestimate those in the middle and high latitudes. Regarding the monthly GPP estimates, using the annual averages caused the GPP estimates of the Northern Hemisphere to be overestimated during the first half of the year, while those during the second half of the year were underestimated the GPP estimates for the Southern Hemisphere were underestimated each month. However, using monthly averages caused the GPP estimates for the Northern Hemisphere to be overestimated in summer and underestimated in spring and autumn, which are opposite to the estimates for the Southern Hemisphere.
  • CバンドSARデータを利用した機械学習アルゴリズムによる圃場の作物分類
    責任著者, 山谷 祐貴, 共著者]谷, 宏, 王 秀峰, 薗部 礼, 小林 伸行, 望月貫一郎, 野田 萌
    写真測量とリモートセンシング 56 4 143 - 148 2017年09月 [査読有り][通常論文]
  • Analyzing CO2 concentration changes and their influencing factors in Indonesia by OCO-2 and other multi-sensor remote-sensing data
    Shuai Yin, Xiufeng Wang, Heri Santoso, Hiroshi Tani, Guosheng Zhong, Zhongyi Sun
    International Journal of Digital Earth 1 - 20 2017年08月 [査読有り][通常論文]
  • Meng Guo, Jing Li, Jiawei Xu, Xiufeng Wang, Hongshi He, Li Wu
    ENVIRONMENTAL POLLUTION 226 60 - 68 2017年07月 [査読有り][通常論文]
     
    In the summer of 2010, more than 6 hundred wildfires broke out in western Russia because of an unprecedented intense heat wave that resulted from strong atmospheric blocking. The present study evaluated the CO2 emissions using GOSAT (Greenhouse gases Observing SATellite) data from July 23 to August 18, 2010 for western Russia. The results demonstrated that the GOSAT CAI (Cloud and Aerosol Imager) was well-suited for the identification of smoke plumes and that the GOSAT FTS (Fourier Transform Spectrometer) TIR (Thermal InfraRed) could be used to calculate the height of the plumes at approximately 800 hPa (1.58 km). Using GOSAT data, we estimated that the 2010 fires in western Russia emitted 255.76 Tg CO2. We also calculated the CO2 emissions by employing the Biomass Burning Model (BBM) for the same study site and obtained a similar result of 261.82-302.48 Tg CO2. The present study proposes a new method for the evaluation of CO2 emissions from a wildfire using remote sensing data, which could be used to improve the knowledge of the burning of biomass at a regional or a continental scale, to reduce the uncertainties in modeling greenhouse gases emissions, and to further understand how wildfires impact the atmospheric carbon cycle and global warming. (C) 2017 Elsevier Ltd. All rights reserved.
  • A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land
    Guosheng Zhong, Xiufeng Wang, Meng Guo, Hiroshi Tani, Anthony R. Chittenden, Shuai Yin, Zhongyi Sun, Shinji Matsumura
    Remote Sensing 9 6 1 - 25 2017年05月 [査読有り][通常論文]
  • Heri Santoso, Hiroshi Tani, Xiufeng Wang
    INTERNATIONAL JOURNAL OF REMOTE SENSING 38 16 4683 - 4699 2017年 [査読有り][通常論文]
     
    Ganoderma boninense is a fungus that causes basal stem rot (BSR) disease in oil palm plantations. BSR is a major disease in oil palm plantations in both Indonesia and Malaysia. There is no effective treatment for curing BSR; current treatments only prolong the life of oil palms. One strategy to control BSR is early detection of G. boninense infection. Based on the infection symptoms, many researchers have applied remote-sensing techniques for early detection and mapping of BSR disease in oil palms. The main objectives of this article were to evaluate the potential of machine-learning models for predicting BSR disease in oil palm plantations and to produce maps of the distribution of BSR disease. QuickBird imagery archived on 4 August 2008 was applied in three classifier models: Support Vector Machine, Random Forest (RF), and classification and regression tree models The RF model was best at predicting, classifying, and mapping oil palm BSR in terms of overall accuracy (OA), producer accuracy, user accuracy, and kappa value. Using 75% of the data for training and 25% for testing, the RF classifier model achieved 91% OA. In addition, this model separated the healthy and unhealthy oil palms in the study sites into 37,617 (75%) and 12,320 (25%) individuals, respectively.
  • Rei Sonobe, Yuki Yamaya, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Kan-ichiro Mochizuki
    INTERNATIONAL JOURNAL OF REMOTE SENSING 38 15 4348 - 4361 2017年 [査読有り][通常論文]
     
    Crop classification maps are useful for estimating amounts of crops harvested, which could help address challenges in food security. Remote-sensing techniques are useful tools for generating crop maps. Optical remote sensing is one of the most attractive options because it offers vegetation indices (VIs) with frequent revisits and has adequate spatial and spectral resolution and some data has been distributed free of charge. However, sufficient consideration has not been given to the potential of VIs calculated from Landsat 8 Operational Land Imager (OLI) data. This article describes the use of Landsat 8 OLI data for the classification of crops in Hokkaido, Japan. In addition to reflectance, VIs calculated from simple formulas that consisted of combinations of two or more reflectance wavebands were evaluated, as well as the six components of the Kauth-Thomas transform. The VIs based on shortwave infrared bands (bands 6 or 7) improved classification accuracy, and using a combination of all derived data from Landsat 8 OLI data resulted in an overall accuracy of 94.5% (allocation disagreement = 4.492 and quantity disagreement = 1.017).
  • Shuai Yin, Xiufeng Wang, Yi Xiao, Hiroshi Tani, Guosheng Zhong, Zhongyi Sun
    ENVIRONMENTAL POLLUTION 220 A 204 - 221 2017年01月 [査読有り][通常論文]
     
    With China as the study area, MODIS MOD14A1 and MCD12Q1 products were used to derive daily crop residue burning spots from 2014 to 2015. After vectorization of crop residue burning pixels and with the use of fishnet, burning density distribution maps were eventually completed. Meanwhile, the daily air quality data from 150 cities in 2014 and 285 cities in 2015 were used to obtain daily and monthly PM2.5 distribution maps with the Kriging interpolation. The results indicate that crop residue burning occurs in a seasonal pattern, and its spatial distribution is closely related to farming activities. The annual PM2.5 in China decreased 11.81% from 2014 to 2015, and the distribution of PM2.5 in China's east and north is always higher than in China's west and south. Furthermore, the changes in PM2.5 exhibit a hysteresis after crop residue burning in summer and autumn-winter. Regarding summer crop residue burning in China's middle east, the r between crop residue burning spots and PM2.5 is 0.6921 (P < 0.01) in 2014 and 0.5620 (P < 0.01) in 2015, while the correlation coefficient of autumn-winter crop residue burning in China's northeast is slightly lower with an r of 0.5670 (P < 0.01) in 2014 and 0.6213 (P < 0.01) in 2015. In autumn-winter, crop residue burning can induce evident PM2.5 increase in China's northeast, and that is more obvious than summer crop residue burning in China's middle east. Furthermore, when data of summer and autumn-winter crop residue burning from 2014 to 2015 are compared, we can see that the change in number of crop residue burning spots significant changes PM2.5 in these regions. Both the summer and autumn-winter crop residue burning areas presented spatial consistency with high PM2.5. By contrast, the results from many aspects indicated that the crop residue burning in spring did not cause a notable change of PM2.5. (C) 2016 Elsevier Ltd. All rights reserved.
  • Zhongyi Sun, Xiufeng Wang, Haruhiko Yamamoto, Jiquan Zhang, Hiroshi Tani, Guosheng Zhong, Shuai Yin
    PADDY AND WATER ENVIRONMENT 15 1 181 - 191 2017年01月 [査読有り][通常論文]
     
    Rice is the second largest staple crop in the world and therefore plays an important role in food security. As a thermophilic crop, rice is sensitive to temperature changes. Thus, research on the chilling damage of rice is essential. The Sanjiang Plain is an emerging rice production area and is located at high latitudes in China, the world's largest rice-producing country. Landsat data were used to extract rice-planting area from 1985 to 2015. MODIS 13Q1, which was uniformly distributed during the growing period of rice, was used to obtain NDVI values of paddies during 2002-2015. Dynamic Identification Index of sterile-type chilling damage and monitoring standard of delayed-type chilling damage were the proposed methods used in this paper, which were used to judge the chilling damage of rice. The results show that in the study region, the rice-planting area in 2015 is nearly 12 times larger than that in 1985. Delayed-type chilling damage occurred in 2002 and 2009, while sterile-type chilling damage occurred in 2005, 2006, 2009, 2010, 2014, and 2015. Comparing with the prevalent meteorological standards, the results indicate that the index and standards proposed in this paper are precise, applicable, and more sensitive than them. The method is a macroscopic and accurate method to identify chilling damage in rice and can also provide a scientific basis to ensuring the stability of rice yield.
  • Rei Sonobe, Yuki Yamaya, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Kan-ichiro Mochizuki
    GISCIENCE & REMOTE SENSING 54 6 918 - 938 2017年 [査読有り][通常論文]
     
    Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.
  • Guosheng Zhong, Xiufeng Wang, Hiroshi Tani, Meng Guo, Anthony R. Chittenden, Shuai Yin, Zhongyi Sun, Shinji Matsumura
    REMOTE SENSING 8 12 1 - 22 2016年12月 [査読有り][通常論文]
     
    In this paper, we introduced a new algorithm for retrieving aerosol optical depth (AOD) over land, from the Cloud and Aerosol Imager (CAI), which is one of the instruments on the Greenhouse Gases Observing Satellite (GOSAT) for detecting and correcting cloud and aerosol interference. We used the GOSAT and AErosol RObotic NETwork (AERONET) collocated data from different regions over the globe to analyze the relationship between the top-of-atmosphere (TOA) reflectance in the shortwave infrared (1.6 m) band and the surface reflectance in the red (0.67 m) band. Our results confirmed that the relationships between the surface reflectance at 0.67 m and TOA reflectance at 1.6 m are not constant for different surface conditions. Under low AOD conditions (AOD at 0.55 m < 0.1), a Normalized Difference Vegetation Index (NDVI) based regression function for estimating the surface reflectance of 0.67 m band from the 1.6 m band was summarized, and it achieved good performance, proving that the reflectance relations of the 0.67 m and 1.6 m bands are typically vegetation dependent. Since the NDVI itself is easily affected by aerosols, we combined the advantages of the Aerosol Free Vegetation Index (AFRI), which is aerosol resistant and highly correlated with regular NDVI, with our regression function, which can preserve the various correlations of 0.67 m and 1.6 m bands for different surface types, and developed a new surface reflectance and aerosol-free NDVI estimation algorithm, which we named the Modified AFRI(1.6) algorithm. This algorithm was applied to AOD retrieval, and the validation results for our algorithm show that the retrieved AOD has a consistent relationship with AERONET measurements, with a correlation coefficient of 0.912, and approximately 67.7% of the AOD retrieved data were within the expected error range (+/- 0.1 +/- 0.15AOD((AERONET))).
  • Heri Santoso, Hiroshi Tani, Xiufeng Wang
    INTERNATIONAL JOURNAL OF REMOTE SENSING 37 21 5122 - 5134 2016年11月 [査読有り][通常論文]
     
    In the past, oil palm density has been determined by manually counting trees every year in oil palm plantations. The measurement of density provides important data related to palm productivity, fertilizer needed, weed control costs in a circle around each tree, labourers needed, and needs for other activities. Manual counting requires many workers and has potential problems related to accuracy. Remote sensing provides a potential approach for counting oil palm trees. The main objective of this study is to build a robust and user-friendly method that will allow oil palm managers to count oil palm trees using a remote sensing technique. The oil palm trees analysed in this study have different ages and densities. QuickBird imagery was applied with the six pansharpening methods and was compared with panchromatic QuickBird imagery. The black and white imagery from a false colour composite of pansharpening imagery was processed in three ways: (1) oil palm tree detection, (2) delineation of the oil palm area using the red band, and (3) counting oil palm trees and accuracy assessment. For oil palm detection, we used several filters that contained a Sobel edge detector; texture analysis co-occurrence; and dilate, erode, high-pass, and opening filters. The results of this study improved upon the accuracy of several previous research studies that had an accuracy of about 90-95%. The results in this study show (1) modified intensity-hue-saturation (IHS) resolution merge is suitable for 16-year-old oil palm trees and have rather high density with 100% accuracy; (2) colour normalized (Brovey) is suitable for 21-year-old oil palm trees and have low density with 99.5% accuracy; (3) subtractive resolution merge is suitable for 15- and 18-year-old oil palm trees and have a rather high density with 99.8% accuracy; (4) PC spectral sharpening with 99.3% accuracy is suitable for 10-year-old oil palm trees and have low density; and (5) for all study object conditions, colour normalized (Brovey) and wavelet resolution merge are two pansharpening methods that are suitable for oil palm tree extraction and counting with 98.9% and 98.4% accuracy, respectively.
  • Zhongyi Sun, Xiufeng Wang, Hiroshi Tani, Guosheng Zhong, Shuai Yin
    American Journal of Climate Change 5 77 - 87 2016年03月 [査読有り][通常論文]
  • CO2 Concentration Spatial Distribution of Different ENSO Episodes in South America Using GOSAT Data
    Zhongyi Sun, Xiufeng Wang, Hiroshi Tani, Guosheng Zhong, Shuai Yin
    American Journal of Climate Change 5 1 77 - 87 2016年03月 [査読有り][通常論文]
  • An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data
    Rei Sonobe, Hiroshi Tani, Xiufeng Wang
    Geocarto International 1 - 11 2016年01月 [査読有り][通常論文]
  • 北海道岩見沢市におけるTerraSAR-X データを活用した作付状況のクラスタリング
    小林 伸行, 薗部 礼, 谷 宏, 王 秀峰
    環境情報科学学術研究論文集 29 67 - 70 2015年12月 [査読有り][通常論文]
  • 北海道岩見沢市におけるTerraSAR-X 2 重偏波データによるイネの生育モニタリング
    野田 萌, 薗部 礼, 谷 宏, 王 秀峰, 小林 伸行
    環境情報科学学術研究論文集 29 61 - 66 2015年12月 [査読有り][通常論文]
  • Rei Sonobe, Hiroshi Tani, Xiufeng Wang, Yasuhito Kojima, Nobuyuki Kobayashi
    JARQ-JAPAN AGRICULTURAL RESEARCH QUARTERLY 49 4 377 - 381 2015年10月 [査読有り][通常論文]
     
    Classification maps are required for agricultural management and the estimation of agricultural disaster compensation. The extreme learning machine (ELM), a newly developed single hidden layer neural network is used as a supervised classifier for remote sensing classifications. In this study, the ELM was evaluated to examine its potential for multi-temporal ALOS/PALSAR images for the classification of crop type. In addition, the k-nearest neighbor algorithm (k-NN), one of the traditional classification methods, was also applied for comparison with the ELM. In the study area, beans, beets, grasses, maize, potato, and winter wheat were cultivated; and these crop types in each field were identified using a data set acquired in 2010. The result of ELM classification was superior to that of k-NN; and overall accuracy was 79.3%. This study highlights the advantages of ALOS/PALSAR images for agricultural field monitoring and indicates the usefulness of regular monitoring using the ALOS-2/PALSAR-2 system.
  • 薗部 礼, 谷 宏, 王 秀峰, 小島康人, 小林伸行
    農業農村工学会論文集 83 4 117 - 122 2015年08月 [査読有り][通常論文]
  • 薗部 礼, 谷 宏, 望月貫一郎, 王 秀峰
    写真測量とリモートセンシング 54 2 95 - 100 2015年05月 [査読有り][通常論文]
  • Assessment of Bamboo Forest Damage Using Temporal Sequence of Landsat Images
    Guosheng ZHONG, Xiufeng WANG, Hiroshi TANI, Shinji MATSUMURA
    Journal of Environmental Information Science 43 5 51 - 58 2015年03月 [査読有り][通常論文]
  • Rei Sonobe, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Hideki Shimamura
    PHYSICS AND CHEMISTRY OF THE EARTH 83-84 2 - 13 2015年 [査読有り][通常論文]
     
    Although classification maps are required for management and for the estimation of agricultural disaster compensation, those techniques have yet to be established. This paper describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X (including TanDEM-X) dual-polarimetric data. In the study area, beans, beets, grasslands, maize, potatoes and winter wheat were cultivated. In this study, classification using TerraSAR-X-derived information was performed. Coherence values, polarimetric parameters and gamma nought values were also obtained and evaluated regarding their usefulness in crop classification. Accurate classification may be possible with currently existing supervised learning models. A comparison between the classification and regression tree (CART), support vector machine (SVM) and random forests (RF) algorithms was performed. Even though J-M distances were lower than 1.0 on all TerraSAR-X acquisition days, good results were achieved (e.g., separability between winter wheat and grass) due to the characteristics of the machine learning algorithm. It was found that SVM performed best, achieving an overall accuracy of 95.0% based on the polarimetric parameters and gamma nought values for HH and VV polarizations. The misclassified fields were less than 100 a in area and 79.5-96.3% were less than 200 a with the exception of grassland. When some feature such as a road or windbreak forest is present in the TerraSAR-X data, the ratio of its extent to that of the field is relatively higher for the smaller fields, which leads to misclassifications. (C) 2014 Elsevier Ltd. All rights reserved.
  • Meng Guo, Jiawei Xu, Xiufeng Wang, Hongshi He, Jing Li, Li Wu
    INTERNATIONAL JOURNAL OF REMOTE SENSING 36 17 4363 - 4383 2015年 [査読有り][通常論文]
     
    Because of the limited number of observation stations and the short time series of orbiting carbon satellite data, it is difficult to monitor CO2 concentrations (XCO2) at broad spatial scales for long time spans. Therefore, we are limited in accurately forecasting change in XCO2. Studies based on the approach of using satellite sensor-derived data as independent variables to model CO2 exchange show promising results for closed forest stands. There is a need to extend this approach to other land-cover types to monitor XCO2 at large spatial scales. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived indices were used to model XCO2. Greenhouse Gases Observing Satellite (GOSAT) data and MODIS-derived indices in 2010 and 2011 were selected to construct XCO2 models during the growing season (May-October). We selected three ground stations to assess the accuracy of the modelled XCO2 for each month from 2011 to 2013. The accuracy of the results indicates that the average bias was 2.25, 4.53, and 4.43 ppm at the three ground stations, respectively, although the largest bias was 10.03 ppm (at Shangdianzi Station in June 2013). We also used GOSAT Thermal and Near Infrared Sensor for Carbon Observation (TANSO) point data in 2012 and 2013 as the observed data to assess the accuracy of the XCO2 models, and achieved a slightly favourable result for each month, except June. The overall conclusion of this study is that the proposed new approach to obtaining XCO2 at the regional scale needs to be perfected in the future.
  • Rei Sonobe, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Hideki Shimamura
    INTERNATIONAL JOURNAL OF REMOTE SENSING 35 23 7898 - 7909 2014年12月 [査読有り][通常論文]
     
    This article describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X data. In the study area, beans, beets, grasslands, maize, potatoes, and winter wheat were cultivated. Although classification maps are required for both management and estimation of agricultural disaster compensation, those techniques have yet to be established. Some supervised learning models may allow accurate classification. Therefore, comparisons among the classification and regression tree (CART), the support vector machine (SVM), and random forests (RF) were performed. SVM was the optimum algorithm in this study, achieving an overall accuracy of 89.1% for the same-year classification, which is the classification using the training data in 2009 to classify the test data in 2009, and 78.0% for the cross-year classification, which is the classification using the training data in 2009 to classify the data in 2012.
  • Rei Sonobe, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Hideki Shimamura
    JARQ-JAPAN AGRICULTURAL RESEARCH QUARTERLY 48 4 471 - 476 2014年10月 [査読有り][通常論文]
     
    This paper describes a method for monitoring winter wheat growth using multi-temporal TerraSAR-X dual-polarimetric data. Six TerraSAR-X HH/VV images were collected in Hokkaido, and the temporal responses to the winter wheat fields were analyzed. The height, moisture content and dry matter of the crops were measured at nearly the same time as TerraSAR-X data was acquired, and the relationships between these parameters and SAR data, including sigma naught and coherence, were studied. Quadratic relationships between the crop height and sigma naught were observed for HH polarization. The determination coefficient was 0.73 and the model had an RMS error of 0.17 dB for the validation data. Coherence is expressed as a regression equation with two explanatory variables: crop height and elongation. Next, the determination coefficient of 0.69 was observed for HH, while the RMS error of coherence was 0.01 for the validation data. The possibility of using the co-polarization ratio of TerraSAR-X to estimate the vegetation's water content was also analyzed and a determination coefficient of 0.70 was obtained. The results confirm that X-band SAR data possess great potential for the development of an operational system for monitoring wheat growth.
  • Rei Sonobe, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Atsushi Kimura, Hideki Shimamura
    JARQ-JAPAN AGRICULTURAL RESEARCH QUARTERLY 48 4 465 - 470 2014年10月 [査読有り][通常論文]
     
    Winter wheat is an important crop for many countries, and monitoring of its planted area is considered important. Optical sensors have been used to monitor agricultural land, and have shown good classification and monitoring capabilities. However, observations using optical sensors sometimes suffer from interference due to cloud cover or rain. In contrast, synthetic aperture radars (SAR) can be used for Earth observation even under rainy, cloudy or dark conditions, hence SAR is expected to be effective in monitoring agricultural fields and identifying winter wheat fields. The objective of this study is to analyze the potential of TerraSAR-X dual images, in the StripMap mode, for mapping winter wheat planted areas. Using the separability statistic (D), it emerged that the sigma naught acquired in mid-July possesses great potential. The method applied in this study has an overall accuracy exceeding 96% for HH and VV polarization data for identifying winter wheat fields.
  • 薗部礼, 谷宏, 王秀峰, 小林伸行, 島村秀樹
    農業農村工学会論文集 82 3 141 - 146 2014年06月 [査読有り][通常論文]
  • Rei Sonobe, Hiroshi Tani, Xiufeng Wang
    FOREST SYSTEMS 23 1 178 - 182 2014年04月 [査読有り][通常論文]
     
    Aim of study: The ambrosia beetle, Platypus quercivorus, is a vector of Japanese oak wilt, which causes massive mortality of oak trees in Japan. ALOS/AVNIR-2 true color images can be used to help detect areas of oak wilt, although such detection by inventory surveys is not realistic. Applying pan-sharpening techniques, a higher spatial resolution multispectral image can be generated from lower-resolution multispectral images and higher-resolution panchromatic images. In this study, some pan-sharpening algorithms were considered and evaluated for the detection of damage points. Area of study: The oak forests in Kanazawa prefecture, Japan. Material and methods: The ALOS/AVNIR-2 and ALOS/PRISM sensors were used. The pan-sharpening algorithms adopted were: Brovey transformation, Modified IHS transformation, Wavelet transformation, Ehlers fusion and High Pass Filter Resolution Merge. Four types of quantitative spectral analyses and visual detection were conducted to evaluate these algorithms. Main results: The Brovey transformation was the most useful algorithm to detect damage points, although it had an issue with the preservation of spectral characteristics. Research highlights: The detection rate of damage points was improved in 50% by applying the Brovey algorithm to a 10 m panchromatic image and 62.5 m multispectral image.
  • Rei Sonobe, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Hideki Shimamura
    REMOTE SENSING LETTERS 5 2 157 - 164 2014年02月 [査読有り][通常論文]
     
    The classification maps are required for the management and the estimation of agricultural disaster compensation; however, those techniques have yet to be established. Some supervised learning models may allow accurate classification. In this study, the Random Forest (RF) classifier and the classification and regression tree (CART) were applied to evaluate the potential of multi-temporal TerraSAR-X dual-polarimetric data, on the StripMap mode, for the classification of crop type. Furthermore, comparisons of the two algorithms and polarizations were carried out. In the study area, beans, beet, grasslands, maize, potato and winter wheat were cultivated, and these crop types were classified using the data set acquired in 2009. The classification results of RF were superior to those of CART, and the overall accuracies were 0.91-0.93.
  • Kunpeng Yi, Hiroshi Tani, Qiang Li, Jiquan Zhang, Meng Guo, Yulong Bao, Xiufeng Wang, Jing Li
    SENSORS 14 2 3207 - 3226 2014年02月 [査読有り][通常論文]
     
    In this paper, an Urban Light Index (ULI) is constructed to facilitate analysis and quantitative evaluation of the process of urbanization and expansion rate by using DMSP/OLS Nighttime Light Data during the years from 1992 to 2010. A unit circle urbanization evaluation model is established to perform a comprehensive analysis of the urbanization process of 34 prefecture-level cities in Northeast China. Furthermore, the concept of urban light space is put forward. In this study, urban light space is divided into four types: the core urban area, the transition zone between urban and suburban areas, suburban area and fluorescent space. Proceeding from the temporal and spatial variation of the four types of light space, the pattern of morphologic change and space-time evolution of the four principal cities in Northeast China (Harbin, Changchun, Shenyang, Dalian) is analyzed and given particular attention. Through a correlation analysis between ULI and the traditional urbanization indexes (urban population, proportion of the secondary and tertiary industries in the regional GDP and the built-up area), the advantages and disadvantages as well as the feasibility of using the ULI in the study of urbanization are evaluated. The research results show that ULI has a strong correlation with urban built-up area (R-2 = 0.8277). The morphologic change and history of the evolving urban light space can truly reflect the characteristics of urban sprawl. The results also indicate that DMSP/OLS Nighttime Light Data is applicable for extracting urban space information and has strong potential to urbanization research.
  • 薗部礼, 谷宏, 王秀峰, 小林伸行, 島村秀樹
    農業農村工学会論文集 82 1 57 - 58 2014年02月 [査読有り][通常論文]
  • 薗部礼, 谷宏, 王秀峰, 小林伸行
    農業農村工学会論文集 82 1 19 - 24 2014年02月 [査読有り][通常論文]
  • 谷宏, 薗部礼, 王秀峰
    環境情報科学論文集 28 293 - 296 2014年 [査読有り][通常論文]
  • 大木隼人, 薗部礼, 谷宏, 王秀峰, 小林伸行
    環境情報科学論文集 28 275 - 280 2014年 [査読有り][通常論文]
  • 薗部礼, 谷宏, 王秀峰, 小林伸行
    環境情報科学論文集 28 269 - 274 2014年 [査読有り][通常論文]
  • 小林伸行, 薗部礼, 谷宏, 王秀峰, 大木隼人
    環境情報科学論文集 28 263 - 268 2014年 [査読有り][通常論文]
  • Kunpeng Yi, Hiroshi Tani, Jiquan Zhang, Meng Guo, Xiufeng Wang, Guosheng Zhong
    REMOTE SENSING 5 12 6938 - 6957 2013年12月 [査読有り][通常論文]
     
    This paper describes the long-term effects on vegetation following the catastrophic fire in 1987 on the northern Great Xing'an Mountain by analyzing the AVHRR GIMMS 15-day composite normalized difference vegetation index (NDVI) dataset. Both temporal and spatial characteristics were analyzed for natural regeneration and tree planting scenarios from 1984 to 2006. Regressing post-fire NDVI values on the pre-fire values helped identify the NDVI for burnt pixels in vegetation stands. Stand differences in fire damage were classified into five levels: Very High (VH), High (H), Moderate (M), Low (L) and Slight (S). Furthermore, intra-annual and inter-annual post-fire vegetation recovery trajectories were analyzed by deriving a time series of NDVI and relative regrowth index (RRI) values for the entire burned area. Finally, spatial pattern and trend analyses were conducted using the pixel-based post-fire annual stands regrowth index (SRI) with a nonparametric Mann-Kendall (MK) statistics method. The results show that October was a better period compared to other months for distinguishing the post-and pre-fire vegetation conditions using the NDVI signals in boreal forests of China because colored leaves on grasses and shrubs fall down, while the leaves on healthy trees remain green in October. The MK statistics method is robustly capable of detecting vegetation trends in a relatively long time series. Because tree planting primarily occurred in the severely burned area (approximately equal to the Medium, High and Very High fire damage areas) following the Daxing'anling fire in 1987, the severely burned area exhibited a better recovery trend than the lightly burned regions. Reasonable tree planting can substantially quicken the recovery and shorten the restoration time of the target species. More detailed satellite analyses and field data will be required in the future for a more convincing validation of the results.
  • Meng Guo, Xiufeng Wang, Jing Li, Hongmei Wang, Hiroshi Tani
    INTERNATIONAL JOURNAL OF REMOTE SENSING 34 12 4281 - 4303 2013年06月 [査読有り][通常論文]
     
    Measurements of land-cover changes suggest that such shifts may alter atmospheric concentrations of greenhouse gases (GHGs). However, owing to the lack of large-scale GHG data, a quantitative description of the relationships between land-cover changes and GHG concentrations does not exist on a regional scale. The Greenhouse Gases Observing Satellite (GOSAT) launched by Japan on 23 January 2009 can be of use in investigating this issue. In this study, we first calculated the monthly average GHG concentrations in East Asia from April 2009 to October 2011 and found that CO2 concentration displays a seasonal cycle, but that the CH4 seasonal trend is unclear. To understand the relationship between land cover and GHG concentrations, we used GHG data from GOSAT, normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and land-cover data from EAS-GlobCover (2009) to analyse the correlation coefficients between land cover and GHG concentrations. We observed that vegetation may generally be considered as a source of, but not a sink for, CO2 and CH4, either on a yearly scale or during the growing season. With respect to the relationships between land-cover types and GHG concentrations, we conclude that on a yearly scale, land-cover types are not closely correlated with GHG concentrations. During the growing season, croplands and scrublands are negatively correlated with XCO2 (the ratio of the total number of CO2 molecules to that of dry air molecules), and forest, grasslands and bare areas are positively correlated with XCO2. Forest and croplands can be viewed as CH4 sources, while scrublands and grasslands can be thought of as CH4 sinks.
  • M. Guo, X. -F. Wang, J. Li, K. -P. Yi, G. -S. Zhong, H. -M. Wang, H. Tani
    JOURNAL OF ARID ENVIRONMENTS 91 119 - 128 2013年04月 [査読有り][通常論文]
     
    Land degradation and global warming are currently highly active research topics. Land degradation can both change land cover and surface climate and significantly influence atmospheric circulation. Researches have verified that carbon dioxide (CO2) and methane (CH4) are major greenhouse gases (GHG) in the atmosphere and are directly affected by human activity. However, to date, there is no research on the spatial distribution of GHG concentrations and also no research on how land degradations affect GHG concentrations in arid and semi-arid regions. In this study, we used GHG data from the ENVIronment SATellite (ENVISAT) and the Greenhouse gases Observing Satellite (GOSAT), the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data from the MODerate resolution Imaging Spectroradiometer (MODIS) and precipitation data from ground stations to analyze the way land degradation affects GHG concentrations in northern China and Mongolia, which exhibit the most serious land degradation process in East Asia. Our research revealed that the CO2 and CH4 concentrations (XCO2 and XCH4) increased from 2003 to 2009 and then decreased into 2011. We used geostatistics to predict and simulate the spatial distribution of XCO2 and XCH4 and found that the distribution of XCO2 displays a seasonal trend and is primarily affected by plant photosynthesis, soil respiration and precipitation. As the distribution of XCH4 is mainly affected by the sources' distribution, microbial processes, LST and submarine hydrate, the CH4 concentration presents no obvious seasonal changes and the high XCH4 values are primarily found in northeast and southeast China. Land degradation increases the concentration of GHG: the correlation coefficient between NDVI and XCO2 is R-2 = 0.76 (P < 0.01) and the value between NDVI and XCH4 is R-2 = 0.75 (P < 0.01). Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.
  • Meng Guo, Xiufeng Wang, Jing Li, Kunpeng Yi, Guosheng Zhong, Hiroshi Tani
    SENSORS 12 12 16368 - 16389 2012年12月 [査読有り][通常論文]
     
    Carbon dioxide (CO2) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO2 concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO2 concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO2 concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO2 concentrations on a global scale. We assumed that CO2 concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson's correlation coefficient (R-2) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R-2 was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO2 concentration (XCO2), we found that the accuracy throughout the World is between -2.56 similar to 3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.
  • Desertification assessment using multivariate data and overlay analysis in Kerqin sandy land, China
    Guo, M, Wang, X, Matsuoka, N, Tani, H, Matsumura, S
    Journal of Environmental Information Science 44 5 11 - 22 2012年03月 [査読有り][通常論文]
  • Improvement of knowledge classifier to enhance classification in arid semi-arid area
    Guo, M, Wang, X, Liu, Y, Matsuoka, N, Tani, H, Matsumura, S
    Journal of Arid Land Studies 21 4 155 - 165 2012年03月 [査読有り][通常論文]
  • Liu, Y, Wang, X, Guo, M, Tani, H
    International Journal of Remote Sensing 33 10 3004 - 3025 2012年 [査読有り][通常論文]
     
    Developed by Japan, the Greenhouse Gases Observing Satellite (GOSAT), also known as IBUKI, was successfully launched on 23 January 2009 to monitor greenhouse gases on the Earth's surface. Observations started in April 2009, and data on Levels 1, 2 and 3 products became available to general users in November 2009, February 2010 and October 2010, respectively. For this article, the Kriging method was proposed to generate the spatial distribution of the daily GOSAT XCO2 and XCH4 data within the region of East Asia from June 2009 to May 2010. The relationship between the distance and difference of daily data for each month were represented by variogram models. The concentration distributions of XCO2 and XCH4 in East Asia can be intuitively seen on a Kriging interpolation map. Seasonal changes were observed. The concentration of XCO2 was high in winter and spring, which might be due to smoke and dust from coal burning. The concentration of XCH4 changed significantly with latitude in autumn and winter, mainly according to temperature changes. In addition, by comparison, the Level 2 Kriging interpolation values were lower than ground observed data and consistent with the higher tendency of Level 3 data.
  • Meng Guo, Xiufeng Wang, Yang Liu, Jing Li, Hongmei Wang, Nobuhiro Matsuoka, Hiroshi Tani
    INTERNATIONAL JOURNAL OF REMOTE SENSING 33 21 6838 - 6853 2012年 [査読有り][通常論文]
     
    In Asia, sand dust storms (SDSs) occur nearly every year, especially in northern China. However, there is little research about the relationship between SDSs and greenhouse gases (GHGs). In this article, we selected four SDSs that occurred in Asia in the spring of 2009 and 2010. We monitored the areas covered by these SDSs using Moderate Resolution Imaging Spectroradiometer (MODIS) data, then we used Greenhouse Gases Observing Satellite (GOSAT) data to check how the SDSs affected the concentrations of CO2 and CH4. We then compared the concentrations of CO2 and CH4 on SDS days with the monthly mean values of the months in which SDSs occurred. We also compared the concentrations of CO2 and CH4 on SDS days with the values before and after the SDSs. After analysis, we found that SDSs had increased the concentrations of CO2 and CH4 in the atmosphere. When the SDSs occurred, the concentrations of CO2 and CH4 increased and reached peak values on the last or penultimate days of the storms and then decreased to their normal values. Atmospheric flow is the main reason for the increase in concentration of CO2, and the lack of the free radical (OH) during SDSs and the presence of CH4 sources in southeast China are the main reasons for the increase in concentration of CH4. We also found that in arid and semi-arid areas, SDSs had little effect on the concentration of these two GHGs.
  • Kunpeng Yi, Hiroshi Tani, Xiufeng Wang, Meng Guo, Guosheng Zhong
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) 6423 - 6426 2012年 [査読有り][通常論文]
     
    Post-fire vegetation can be monitored and analyzed over large areas in a time- and cost-effective manner by using satellite sensor imagery in combination with spatial analysis as provided by Geographical Information Systems (GIS). In this study, spatio-temporal distribution dynamics of burned area in the Northeast of China were analyzed by using a time series of MODIS Burned Area Product (MCD45) data from 2000 to 2010. The forest area damage caused by a large fire which occurred in the northeast of china, in May 1987 was also analyzed as a case study using Landsat TM/ETM+ images. Digital image processing methods, such as spectral profile analysis, vegetation indices and burn severity classification, were applied to the satellite images acquired before and after the forest fire. The inter-annual vegetation dynamic before, after fire events shows that vegetation recovery is a slow process, even after 23 years, some low-severely burned areas still exist in the maps.
  • Yang Liu, Xiufeng Wang, Meng Guo, Hiroshi Tani, Nobuhiro Matsuoka, Shinji Matsumura
    GISCIENCE & REMOTE SENSING 48 3 371 - 393 2011年07月 [査読有り][通常論文]
     
    This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.
  • Ji-Quan Zhang, Kun-Peng Yi, Tani Hiroshi, Xiu-Feng Wang, Zhi-Jun Tong, Xing-Peng Liu
    Chinese Journal of Applied Ecology 22 1 189 - 195 2011年01月 [査読有り][通常論文]
     
    This paper explored the main driving forces and stresses contributing to the eco-environmental changes of Baishan City in Jilin Province, through the analysis of the ecological security problems in the City. The framework of DPSIR was applied to establish an ecological security assessment index system, and further, to create an ecological security assessment model suitable for mountain areas. By using the 1989, 1999, and 2006 TM images, and in combining with the DEM data and field survey data, the interpretation of the land cover in Baishan City was conducted, and the landscape classification was carried out. With the support of Fragstats, the important ecological indicators were extracted. Then, the situations of ecological security in various districts and counties of Baishan City were assessed. The results indicated that there was an obvious regional difference in the ecological security of Baishan City, with a deteriorating trend of the overall ecological security situation. Human activities had deeper influence on the land cover pattern and species habitat distribution, and even, became the main driving force of the pattern changes in ecological security.
  • Kebiao Mao, Sanmei Li, Daolong Wang, Lixin Zhang, Xiufeng Wang, Huajun Tang, Zhao-Liang Li
    INTERNATIONAL JOURNAL OF REMOTE SENSING 32 19 5413 - 5423 2011年 [査読有り][通常論文]
     
    The accuracy of a radiance transfer model neural network (RM-NN) for separating land surface temperature (LST) and emissivity from AST09 (the Advanced Spaceborne and Thermal Emission and Reflection Radiometer (ASTER) Standard Data Product, surface leaving radiance) is very high, but it is limited by the accuracy of the atmospheric correction. This article uses a neural network and radiance transfer model (MODTRAN4) to directly retrieve the LST and emissivity from ASTER1B data, which overcomes the difficulty of atmospheric correction in previous methods. The retrieval average accuracy of LST is about 1.1 K, and the average accuracy of emissivity in bands 11-14 is under 0.016 for simulated data when the input nodes are a combination of brightness temperature in bands 11-14. The average accuracy of LST is under 0.8 K when the input nodes are a combination of water vapour content and brightness temperature in bands 11-14. Finally, the comparison of retrieval results with ground measurement data indicates that the RM-NN can be used to accurately retrieve LST and emissivity from ASTER1B data.
  • A Study on Estimation and Changing of Net Primary Productivity in the Yellow River Basin using Satellite Data and Climate Data
    Journal of Environmental Information Science 37 5 15 - 20 2009年 [査読無し][通常論文]
  • 農業情報学会誌 17 4 171 - 177 2008年 [査読無し][通常論文]
  • 環境情報科学論文集 22 553 - 558 2008年 [査読有り][通常論文]
  • K. B. Mao, H. J. Tang, X. F. Wang, Q. B. Zhou, D. L. Wang
    INTERNATIONAL JOURNAL OF REMOTE SENSING 29 20 6021 - 6028 2008年 [査読無し][通常論文]
     
    An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near-surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0K. The mean and the standard deviation of estimation error are about 2.0K and 2.3K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM-NN can be used to estimate near surface air temperature from ASTER data.
  • Kebiao Mao, Jiancheng Shi, Huajun Tang, Zhao-Liang Li, Xlufeng Wang, Kun-Shan Chen
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 46 1 200 - 208 2008年01月 [査読無し][通常論文]
     
    Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11, 12,13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations can be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11, 12, 13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.005 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11, 12, 13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated.
  • PALSARおよびAVNIR-2を用いたサロベツ湿原と周辺における土壌水分の推定
    環境情報論文集 21 477 - 482 2007年 [査読有り][通常論文]
  • 航空機レーザスキャナを用いた土壌水分の推定に関する事例研究
    環境情報論文集 21 463 - 466 2007年 [査読有り][通常論文]
  • A study on desertification in the Yellow River Basin: Investigation of actual status using satellite and climatic data, and their relationship
    Journal of Remote Sensing 11 5 742 - 747 2007年 [査読無し][通常論文]
  • 環境情報科学論文集 20 361 - 366 2006年 [査読無し][通常論文]
  • Kebiao Mao, Jiancheng Shi, Zhaoliang Li, Zhihao Qin, Xiufeng Wang, Lingmei Jiang
    2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8 1358 - + 2006年 [査読有り][通常論文]
     
    We intend to propose a multiple-band algorithm which can simultaneously retrieve land surface temperature and emissivity from ASTER data. We build four radiance transfer equations for ASTER band 11, 12, 13, 14, which involve six unknown parameters (average atmosphere temperature, land surface temperature and four bands emissivity). We also analyze the emissivity characteristics of common objects about 160 kinds provided by JPL spectral database between thermal band 11, 12, 13, 14 and find that there is approximate linear relationship between them. For common 80 kinds terrors, the average emissivities error of band Hand 14 are all under 0.01, the max emissivity error is under 0.0097 for band 11 and 14. So we can obtain six equations and six unknown parameters. In order to improve the accuracy, we can make some classification before retrieving land surface temperature. We can use three methods to resolve the equations. The first is that we make classification for image and get different equation, then resolve the equation. The second is Least-squares. The third is that, we can simulate database according to the characteristics of objects and utilize the neural network to resolve equations. The analysis indicates that the neural network can improve the practical and accuracy of algorithm.
  • The relationship between vegetation changes and cut-offs in the lower Yellow River based on satellite and ground data
    Journal of natural Disaster Science 27 1 1 - 7 2005年 [査読有り][通常論文]
  • Estimation of soil moisture for bare fields using a soil line derived from satellite data
    Journal of Environmental Information Scinece 33 5 1 - 12 2005年 [査読有り][通常論文]
  • 農業遊休地の検出と分類のための農業土地利用マッピング
    農村計画学会誌 第24巻別冊 103 - 108 2005年 [査読有り][通常論文]
  • Land-use and climate change in Sanjiang Plain, China, using satellite data
    Journal of Agricultural Meteorology 60 5 997 - 1000 2005年 [査読有り][通常論文]
  • PAPERS ON ENVIRONMENTAL INFORMATION SCIENCE 18 143 - 148 2004年 [査読有り][通常論文]
  • 環境情報科学論文集 17 89 - 94 2003年 [査読有り][通常論文]
  • 環境情報科学論文集 17 65 - 70 2003年 [査読有り][通常論文]
  • 王 秀峰, 武田 知己
    農業気象 59 2 131 - 140 2003年 [査読有り][通常論文]
     
    Basic studies on the estimation of normals for decade air temperature at unit cell were performed using daily mean air temperature derived from satellite IR data. Analyses were taken with the assumption that "the difference between a daily mean air temperature and normals for decade air temperature is approximate among areas sharing similar climates". Items and procedures for analyses are as follows. (1) Characteristic for mean decade air temperature and frequency for decade air temperature. (2) Classification for AMeDAS observation sites in Hokkaido to make some groups of areas sharing similar climates. (3) Characteristic of difference between a daily mean air temperature and normals for decade air temperature, and proof of the assumption. (4) Estimation of daily mean air temperature derived from satellite IR data. (5) Estimation of quasi-normals for decade air temperature using satellite IR data. The results for analyses are as follows. (1) The assumption that the difference between a daily mean air temperature and normals for decade air temperature is approximate among areas sharing similar climates was proved. Therefore, it is possible to estimate quasi-normals for decade air temperature using daily mean air temperature derived from satellite IR data. (2) There was a significance level of 1% between daily mean air temperature of AMeDAS data and surface temperature for 960m cell derived from Landsat TM data on July 8, 1993 in Ishikari Plain. And quasi-normals for decade air temperature in early July could be estimated to 0.1 °C accuracy, except for data from Eniwa-Shimamatsu AMeDAS observation site. © 2003, The Society of Agricultural Meteorology of Japan. All rights reserved.
  • 地形因子と衛星データを用いたメッシュ単位の風速推定に関する研究
    北大農邦紀要 24 1 1 - 14 2001年 [査読有り][通常論文]
  • 中国寧夏におけるツヤハダゴマダラカミキリの生態と気象に関する基礎的研究
    北大農邦紀要 23 4 309 - 317 2001年 [査読有り][通常論文]
  • 王 秀峰, 川角 妙子, 谷 宏
    農業気象 56 4 283 - 294 2000年09月01日 [査読有り][通常論文]
     
    Supplemental analyses using Landsat data were performed to study the possibility of estimating daily mean air temperature using surface temperature derived from satellite IR data. It is recognized that daily mean air temperature can be estimated relatively accurately from surface temperature. Also, studies of the basic relation between daily mean air temperature andsurface temperature measured on the ground (over forestand soybean fields) were performed. The results are as follows: (1) When there is comparatively small variation in vegetation density (e.g. forest), the correlation coefficients between daily mean airtemperature and the temperature of surfaces receiving sunshine are large. (2) The correlation tends to be better in the morning and evening, and worse at noon. (3) When there is comparatively large variation in vegetation density (e.g. soybean fields), the correlation coefficient between daily mean airtemperature and surface temperature for clear days is comparatively large, but the correlations are small for fine days. (4) RMSE over forest or soybean fields for both clear days and fine days are large in spite of a large correlation coefficient. (5) The correlation coefficients and RMSE can be improved by multi-regression analysis between two-hour surface temperature and daily mean air temperature. (6) The mean time at which surface temperature was closest to daily mean air temperaturewas 8-9 a.m. in the morning and 4-6 p.m. in the evening for May to October over a forest. © 2000, The Society of Agricultural Meteorology of Japan. All rights reserved.
  • 衛星データによる中国遼寧省の気温分布と気温区分の推定
    北大農邦紀要 22 1 51 - 61 1999年 [査読有り][通常論文]
  • Relationships between recent land-use change and legal land-use classification in the area of greater Sapporo
    J. Fac. Agr. Hokkaido Univ. 69 1 31 - 45 1999年 [査読有り][通常論文]
  • 衛星データによる土地利用状態の変化検出のためのパラメータに関する研究
    北大農邦紀要 21 2 197 - 208 1998年 [査読有り][通常論文]
  • 衛星データによる排水不良地,地下水位の推定に関する基礎研究
    北大農邦紀要 21 2 171 - 183 1998年 [査読有り][通常論文]
  • The relationship between remotely sensed canopy surface temperature and canopy structure
    J. Fac. Agr. Hokkaido Univ. 68 1 45 - 60 1998年 [査読有り][通常論文]
  • 王 秀峰, 堀口 郁夫, 町村 尚
    農業気象 50 3/4 177 - 183 1994年 [査読有り][通常論文]
     
    Infrared thermometers have been adopted for measurements of surface temperature, and are used routinely to measure the temperature of the earth's surface from satellites. To obtain accurate measurements of surface temperature by infrared thermometry, it is necessary to know the surface emissivity in the atmospheric window region. In this study, emissivities of plant leaves and canopies were measured by the box method using an infrared thermometer. Results are summarized as follows: (1) Soil emissivity increases with an increase in soil water. (2) The emissivity of potato leaves was lowest among the six varieties of plants measured, and that of gramineous leaves was the highest. (3) Inclination angles of leaves less than 60° do not affect leaf emissivity. (4) Higher emissivities of leaves lead to higher values for plant canopy emissivities. (5) The water content of soil has an effect on the emissivities of plant canopies. (6) When vegetation cover is heavy, the emissivities of plant canopies are close to, or greater than, those of leaves. Above and the reported results confirmed that plant leaf temperature is measured with considerable accuracy, if a value of 0.97 or 0.98 is used for the emissivity setting of the infrared thermometer. A value of -0.04∼+0.01 must be added to plant leaf emissivity for measurements of canopy temperature. © 1994, The Society of Agricultural Meteorology of Japan. All rights reserved.
  • 赤外放射温度計による畑地および 森林の群落表面温度の研究
    北大農邦紀要 18 4 379 - 387 1993年 [査読有り][通常論文]
  • 王 秀峰, 堀口 郁夫, 町村 尚
    農業気象 49 3 149 - 158 1993年 [査読有り][通常論文]
     
    Infrared thermometers to measure surface temperature have been increasingly adopted in recent years. The characteristics of the IR thermometer, however, are not well known. IR thermometers manufactured in Japan systematically adjust for ambient radiation based on the internal temperature of the thermometer. If, therefore, there is a large difference between the internal temperature of the IR thermometer and the apparent temperature associated with the surrounding radiation, a large error will be induced into the measured surface temperature. The purpose of our research was to determine the characteristics and measurement errors of IR thermometers. Experiments were performed with regard to the following items:(1) Measurement errors related to the internal temperature of the IR thermometer. (2) Linearity of the output signal of the IR thermometer.(3)Response of the output signal to changes in the emissivity setting. (4) Effect of sky radiant emittance on the measured surface temperature. (5) Calibration method for the terrestrial surface. The following is a summary of the results: Measurement error is affected by the internal temperature of the IR thermometer. Measurement accuracy is improved with a controlled internal temperature of 20-30. The measurement error becomes larger at emissivity settings under 0.7. The measurement error outdoors was not proportional to the downward longwave radiation, but to the sky radiant temperature measured by the IR thermometer. Calibration for sky radiant emittance was improved by using the difference between sky radiant temperature and air temperature. When the surface temperature measured by the infrared thermometer is plotted against the surface temperature measured by thermocouple, the sky radiant emittance error is obtained from the Y intercept. Additionally, the difference between true temperature and output of the IR thermometer for a reference plate was compared to that obtained for vegetation, and the RMS obtained was about 1.0. © 1993, The Society of Agricultural Meteorology of Japan. All rights reserved.
  • 衛星熱紅外遥感信息在泥炭地等土地分類分析中的応用
    国土資源遥感 11 1 34 - 39 1992年 [査読有り][通常論文]
  • 衛星データによるサロベツ原野の地表面温度の解析
    北大農邦紀要 17 4 505 - 516 1991年 [査読有り][通常論文]
  • 陸稲田熱平衡要素的観測
    中国農業気象 10 2 25 - 30 1989年 [査読有り][通常論文]

書籍

  • 中国山岳地帯の森林環境と伝統社会
    北海道大学図書出版会 2006年
  • 衛星からわかる気象-マルチチャンネルデータの利用-
    日本気象学会 2006年
  • 局地気象学
    堀口郁夫, 小林哲夫, 塚本 修, 大槻恭一 (担当:共著)
    2004年11月

その他活動・業績

  • Yang Liu, Xiufeng Wang, Meng Guo, Hiroshi Tani, Nobuhiro Matsuoka, Shinji Matsumura GISCIENCE & REMOTE SENSING 48 (3) 371 -393 2011年07月 [査読無し][通常論文]
     
    This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.
  • Analysis of Relationship between NDVI and GHG in DAXING’AN Mountain Region, China
    Proceedings for ISPRS workshop on Dynamic and Multi-dimensional GIS 40 -43 2011年 [査読無し][通常論文]
  • Comparison of GOSAT CAI and SPOT VGT NDVI Data with Different Season and Land Cover in EAST ASIA
    ISPRS Workshop on Geospatial Data Infrastructure: from data acquisition and updating to smarter services 94 -97 2011年 [査読無し][通常論文]
  • Lu Q. Iao, Li-Xin Chen, Wen-Biao Duan, Rui-Qing Song, Xiu-Feng Wang 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings IEEE Xplore digital library, 2011./IEEE Xplore digital library, 2011.,8124-8127 8124 -8127 2011年 [査読無し][通常論文]
     
    The paper investigated the feasibility of Hyper spectra to determine the concentration of soil organic matter (SOM) in Harbin. The 95 soil samples were collected to a depth from 0 to 20cm. Reflectance measurements from 350nm to 2500nm were collected in a controlled laboratory environment. Three multivariate techniques (stepwise multiple linear regression(SMLR), artificial neural network(ANN), partial least-squares regression(PLSR)) and pre-processing transform nations of spectral data were compared with the aim of identifying the best combination to predict soil organic matter. The coefficient of determination (R2), the root mean square error (RMSE) were used to evaluate the models. compared three multivariate methods of inferential modeling, based on R2 and RMSE, partial least-squares regression performed best (the highest averageR2= 0.826, the lowest average RMSE = 0.161). © 2011 IEEE.
  • Analysis the Impact of Drought on NDVI in Drought Periods Combined with Climate factors and Land Cover in Southwest China
    34nd International Symposium on Remote Sensing of Environment TS-62-6 1 -4 2011年 [査読無し][通常論文]
  • Analysis of desertification and wood land distribution: A case study on the Balinyou Banner of Inner Mongolia, China
    34nd International Symposium on Remote Sensing of Environment TS-85-2 1 -4 2011年 [査読無し][通常論文]
  • Analysis of Relationship between NDVI and GHG in DAXING'AN Mountain Region, China
    Proceedings for ISPRS workshop on Dynamic and Multi-dimensional GIS 40 -43 2011年 [査読無し][通常論文]
  • Kebiao Mao, Sanmei Li, Daolong Wang, Lixin Zhang, Xiufeng Wang, Huajun Tang, Zhao-Liang Li INTERNATIONAL JOURNAL OF REMOTE SENSING 32 (19) 5413 -5423 2011年 [査読無し][通常論文]
     
    The accuracy of a radiance transfer model neural network (RM-NN) for separating land surface temperature (LST) and emissivity from AST09 (the Advanced Spaceborne and Thermal Emission and Reflection Radiometer (ASTER) Standard Data Product, surface leaving radiance) is very high, but it is limited by the accuracy of the atmospheric correction. This article uses a neural network and radiance transfer model (MODTRAN4) to directly retrieve the LST and emissivity from ASTER1B data, which overcomes the difficulty of atmospheric correction in previous methods. The retrieval average accuracy of LST is about 1.1 K, and the average accuracy of emissivity in bands 11-14 is under 0.016 for simulated data when the input nodes are a combination of brightness temperature in bands 11-14. The average accuracy of LST is under 0.8 K when the input nodes are a combination of water vapour content and brightness temperature in bands 11-14. Finally, the comparison of retrieval results with ground measurement data indicates that the RM-NN can be used to accurately retrieve LST and emissivity from ASTER1B data.
  • Yang Liu, Xiufeng Wang, Meng Guo, Hiroshi Tani GEOSPATIAL DATA INFRASTRUCTURE: FROM DATA ACQUISITION AND UPDATING TO SMARTER SERVICES 38-4 (W25) 94 -97 2011年 [査読無し][通常論文]
     
    The Normalized Difference Vegetation Index (NDVI) has become one of the most widely used indices in remote sensing applications in a variety of fields. Many studies have compared the NDVI values for different satellite sensors. Nowadays, the Greenhouse Gases Observing Satellite (GOSAT) was successfully launched on January 23, 2009. It is used to monitor greenhouse gases on the Earth's surface and also has a sensor, the Cloud Aerosol Imager (CAI), that senses red and near infrared spectrums. It can also process NDVI data. Therefore, we are first compare GOSAT CAI and SPOT VGT NDVI data in different seasonal and land cover in East Asian, to explore the relationship between the two types of datasets, and to discuss the possibility of extending SPOT VGT data using GOSAT CAI NDVI data for the same area. We used GOSAT CAI Level 3 data to derive 10-day composite NDVI values for the East Asia region for November 2009 and January, April and July 2010 using the maximum value composite (MVC) method. We compared these values with 10-day composite SPOT VGT NDVI data for the same period. The results show that the correlation coefficients of regression analysis generally revealed a strong correlation between NDVI from the two sensors in November 2009 and January, April and July 2010 (0.88, 0.85, 0.77 and 0.74, respectively). The differences place may be affected by cloud cover. From the combined analysis of seasonal changes and land cover, we found that the correlations between the SPOT VGT and the GOSAT CAI NDVI data are less affected by seasonal change and the SPOT VGT data is more sensitive to high vegetation coverage than the GOSAT CAI data. In the future, through continued monitoring and processing by cloud removal technology, the accuracy of GOSAT CAI NDVI data will be further improved and thus be more widely used.
  • Lu Q. Iao, Li-Xin Chen, Wen-Biao Duan, Rui-Qing Song, Xiu-Feng Wang 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings IEEE Xplore digital library, 2011./IEEE Xplore digital library, 2011.,8124-8127 8124 -8127 2011年 [査読無し][通常論文]
     
    The paper investigated the feasibility of Hyper spectra to determine the concentration of soil organic matter (SOM) in Harbin. The 95 soil samples were collected to a depth from 0 to 20cm. Reflectance measurements from 350nm to 2500nm were collected in a controlled laboratory environment. Three multivariate techniques (stepwise multiple linear regression(SMLR), artificial neural network(ANN), partial least-squares regression(PLSR)) and pre-processing transform nations of spectral data were compared with the aim of identifying the best combination to predict soil organic matter. The coefficient of determination (R2), the root mean square error (RMSE) were used to evaluate the models. compared three multivariate methods of inferential modeling, based on R2 and RMSE, partial least-squares regression performed best (the highest averageR2= 0.826, the lowest average RMSE = 0.161). © 2011 IEEE.
  • Analysis the Impact of Drought on NDVI in Drought Periods Combined with Climate factors and Land Cover in Southwest China
    34nd International Symposium on Remote Sensing of Environment TS-62-6 1 -4 2011年 [査読無し][通常論文]
  • Analysis of desertification and wood land distribution: A case study on the Balinyou Banner of Inner Mongolia, China
    34nd International Symposium on Remote Sensing of Environment TS-85-2 1 -4 2011年 [査読無し][通常論文]
  • Measurement and Estimation of Farmland in Rural Residential Spots of Bayan County based on 3S
    China Land Science 24 (11) 69 -74 2010年 [査読無し][通常論文]
  • A neural network method for retrieval of land surface temperature and emissivity from ASTER1B Data
    Agricultural Land Use and its Effect in APEC Member Economies 47 -57 2010年 [査読無し][通常論文]
  • Measurement and Estimation of Farmland in Rural Residential Spots of Bayan County based on 3S
    China Land Science 24 (11) 69 -74 2010年 [査読無し][通常論文]
  • A neural network method for retrieval of land surface temperature and emissivity from ASTER1B Data
    Agricultural Land Use and its Effect in APEC Member Economies 47 -57 2010年 [査読無し][通常論文]
  • Land cover change of two typical cities in northeastern Asia using satellite data
    33nd International Symposium on Remote Sensing of Environment(CD-ROM) 1 -4 2009年 [査読無し][通常論文]
  • Land cover change of two typical cities in northeastern Asia using satellite data
    33nd International Symposium on Remote Sensing of Environment(CD-ROM) 1 -4 2009年 [査読無し][通常論文]
  • Influence of Topographic Factors on Soil Water and Bulk Density in A Typical Slope Land in the Black Soil Area of Northeast China
    Bulletin of Soil and Water Conservation 28 (6) 16 -20 2008年 [査読無し][通常論文]
  • Analysis on Wetland Reclamation and Environment Effect Based on RS and GIS-Taking Fujin and Tongjiang City in Sanjiang Plain as Examples
    Research of Agricultural Modernization 29 (5) 603 -606 2008年 [査読無し][通常論文]
  • Profit and Loss Analysis on Ecological Value Under Land Use Changes in Reclamation Area of Heilongjiang - Taking Jiansanjiang for Example
    Research of Agricultural Modernization 29 (2) 201 -203 2008年 [査読無し][通常論文]
  • Rei Sonobe, Hiroshi Tani, Xiufeng Wang, Masami Fukuda Agricultural Information Research 17 (4) 171 -177 2008年 [査読無し][通常論文]
  • Influence of Topographic Factors on Soil Water and Bulk Density in A Typical Slope Land in the Black Soil Area of Northeast China
    Bulletin of Soil and Water Conservation 28 (6) 16 -20 2008年 [査読無し][通常論文]
  • Estimation of soil moisture for bare soil fields using ALOS/PALSAR data
    Papers on Environmental Information Science 22 553 -558 2008年 [査読無し][通常論文]
  • Analysis on Wetland Reclamation and Environment Effect Based on RS and GIS-Taking Fujin and Tongjiang City in Sanjiang Plain as Examples
    Research of Agricultural Modernization 29 (5) 603 -606 2008年 [査読無し][通常論文]
  • Profit and Loss Analysis on Ecological Value Under Land Use Changes in Reclamation Area of Heilongjiang - Taking Jiansanjiang for Example
    Research of Agricultural Modernization 29 (2) 201 -203 2008年 [査読無し][通常論文]
  • K. B. Mao, H. J. Tang, X. F. Wang, Q. B. Zhou, D. L. Wang INTERNATIONAL JOURNAL OF REMOTE SENSING 29 (20) 6021 -6028 2008年 [査読無し][通常論文]
     
    An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near-surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0K. The mean and the standard deviation of estimation error are about 2.0K and 2.3K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM-NN can be used to estimate near surface air temperature from ASTER data.
  • Kebiao Mao, Jiancheng Shi, Huajun Tang, Zhao-Liang Li, Xlufeng Wang, Kun-Shan Chen IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 46 (1) 200 -208 2008年01月 [査読無し][通常論文]
     
    Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11, 12,13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations can be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11, 12, 13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.005 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11, 12, 13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated.
  • Evaluation of Desert Control by Landsat Data Analysis in Yulin Prefecture, Shanxi Province of China
    32nd International Symposium on Remote Sensing of Environment, CD-ROM 1 -4 2007年 [査読無し][通常論文]
  • Soil moisture estimation in and around Sarobetsu mire using PALSAR and AVNIR-2 imagery
    Paoers on Environmental Information Science 21 477 -482 2007年 [査読無し][通常論文]
  • Study on estimation of soil moisture by airborne laser scanner data
    Papers on Environmental Information Science 21 463 -466 2007年 [査読無し][通常論文]
  • A study on desertification in the Yellow River Basin: Investigation of actual status using satellite and climatic data, and their relationship
    Journal of Remote Sensing 11 (5) 742 -747 2007年 [査読無し][通常論文]
  • XF Wang, Zhang, SR PROCEEDINGS OF THE SECOND IASTED INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGY IN THE ENVIRONMENTAL FIELD 189 -+ 2006年 [査読無し][通常論文]
     
    ASTER satellite data were used to study spectral features and classifications of land cover in the Queer Mountains of western Sichuan Province, China. Ground investigation data allowed the evaluation of quantitative precision and accuracy of spatial distribution. Results indicated that some band combinations of ASTER data are effective in the land cover classification. Classification accuracy of the 14-band combination (Case 1) after geometric correction was more than 83.9%. Comparison of results showed that accuracy of the combinations of Bands 1, 2, 3, 4, 6, 9, 10 and 13 (Case 2), and those of Bands 1, 3, 6, 8, 12 and 14 (Case 3), were 76.6-92.6% and 85.6-97.5% respectively. That of Bands 1, 2, 3, 4, 6 and 9 (Case 4) were not effective on the land cover classification in the mountains. We infer that TIR bands are necessary to classify land cover on mountains with more than 2000 m relative altitude. Spatial distributions of land cover types, roads, residential land, cropland, and rock were easily misinterpreted; rock was also misinterpreted as residential areas or cropland.
  • Relationship between changing vegetation in lower reaches of Yellow River and absence of river flow
    30th International Symposium on Remote Sensing of Environment, CD-ROM 1 -4 2003年 [査読無し][通常論文]
  • Detailed Analysis of Land Cover and Vegetation Status in Dahua District, Guangxi,China -By Using Landsat Data and Very High-Resolution Satellite Ikonos Data
    ",International Conference on Computer Graphics and Spatial Information System 348 -354 2002年 [査読無し][通常論文]
  • DISTRIBUTION OF BARREN LANDS IN URBAN AND URBANIZING AREAS USING SATELLITE DATA AND GIS-CASE STUDY OF GREATER SAPPORO AREA-
    ,Proceedings of International Workshop on LUCC Contribution to Asian Environment Problems Hyderabad,CD-ROM 1 -4 2002年 [査読無し][通常論文]
  • 北海道における冷害対策技術に関する調査研究
    自然災害科学 18 (2) 191 -198 1999年 [査読無し][通常論文]
  • Study on the annual output and the carbon and nitrogen contents of organic waste in each municipality of Hokkaido
    International Symposium of Bio-Recycling/Composting in Sapporo 63 -65 1999年 [査読無し][通常論文]
  • 北海道の農林災害について
    自然災害科学 17 (3) 206 -213 1998年 [査読無し][通常論文]
  • The relationship between canopy surface temperature by IR-thermometer and canopy structure
    International Archives of Photogrammetry and Remote Sensing XXXI B7 (VII) 756 -762 1996年 [査読無し][通常論文]
  • Information of regeneration for burnt forests using vegetation index and surface temperature derived from NOAA/AVHRR data
    GIS in ASIA (selected papers of the GIS/LIS AM/FM and Spatial Analysis Conference 293 -308 1996年 [査読無し][通常論文]
  • T MACHIMURA, HORIGUCHI, I, T SASAKI, WANG, X PROCEEDINGS OF THE FIRST INTERNATIONAL AIRBORNE REMOTE SENSING CONFERENCE AND EXHIBITION: APPLICATIONS, TECHNOLOGY, AND SCIENCE, VOL I 237 -244 1994年 [査読無し][通常論文]
  • Analysis of relationship between NDVI and GHG in Daxing’an Mountain region, China
    Liu, Y, Wang, X, Tani, H, Guo, M [査読無し][通常論文]

受賞

  • 2021年03月 日本農業気象学会 永年功労者
     
    受賞者: 王 秀峰
  • 2017年03月 日本農業気象学会 フェロー
     
    受賞者: 王 秀峰
  • 2005年09月 日本農業気象学会学術賞
  • 2005年 Scientific Award the Society of Agricultural Meteorology of Japan

共同研究・競争的資金等の研究課題

  • 中国東北部の冷帯稲作地帯における気象・水資源の100年変動に基づく生産リスク予測
    科研:基盤研究(B),No.15H05256
    研究期間 : 2015年04月 -2018年03月 
    代表者 : 山本晴彦
  • 中国福建省竹林害虫大発生が生物多様性消失によるとする仮説の衛星画像を用いた検証
    科研:基盤(B),No. 23405005
    研究期間 : 2011年04月 -2014年03月 
    代表者 : 王 秀峰
  • 人工衛星計測による中国大興安嶺における森林衰退のモニタリング
    三井物産:環境基金
    研究期間 : 2010年04月 -2013年03月 
    代表者 : 谷 宏
  • 中国西北部乾燥地を対象としたダストストームの抑止技術の検証と危険地域への普及
    科研:基盤(B),No. 21404007
    研究期間 : 2009年04月 -2012年03月 
    代表者 : 松岡延浩
  • 黄河流域の農業・牧畜業地域に対する砂漠化ハザードマップ作成
    科研:基盤(B),No. 16404005
    研究期間 : 2004年04月 -2007年03月 
    代表者 : 松岡延浩
  • 広域農業生産システムモデルの構築に関する研究
    科研:基盤(B),No. 15380178
    研究期間 : 2003年04月 -2005年03月 
    代表者 : 谷 宏
  • 黄河流域の農業地帯に近年頻発する干ばつの発生機構の解明と対策のための現地調査
    科研:基盤(B),No.13574015
    研究期間 : 2001年04月 -2004年03月 
    代表者 : 松岡延浩
  • 人口衛星データを用いたシミュレーションによる作物生育モニタリングに関する研究
    科研:基盤(C),No. 13660259
    研究期間 : 2001年04月 -2003年03月 
    代表者 : 谷 宏
  • 中国西南部における生態系の再構築と持続的生物生産性の総合的開発
    科研:複合領域,No. 98I00601
    研究期間 : 1998年04月 -2003年03月 
    代表者 : 出村克彦
  • 中国チベット高原における作物の光合成と物質生産の実態とCO2増加の影響の解明
    科研:基盤(B),No.12575018
    研究期間 : 2000年04月 -2002年03月 
    代表者 : 岩間和人
  • フィールド・オートメーションのための環境資源データベースに関する研究
    科研:地域連携推進研究費,No. 12792017
    研究期間 : 2000年04月 -2002年03月 
    代表者 : 岸浪建史
  • 水稲群落の低水温ストレスに対する生育・収量反応の解明とそのモデル化
    科研:基盤(B),No. 11460006
    研究期間 : 1999年04月 -2002年03月 
    代表者 : 長谷川利弘
  • 地上データと衛星データによる地域気象の解析手法の確立
    科研:基盤(B),No. 08456130
    研究期間 : 1996年04月 -1999年03月 
    代表者 : 堀口郁夫
  • Studies on Regional Environment Information by Remote Sensing.

大学運営

委員歴

  • 2006年 - 2020年03月   日本農業気象学会北海道支部   評議員   日本農業気象学会北海道支部
  • 2017年01月 - 2019年01月   日本農業気象学会   評議員
  • 2010年 - 2013年   日本農業気象学会   永年功労会員表彰審査委員   日本農業気象学会
  • 2004年 - 2006年   日本農業気象学会北海道支部   幹事   日本農業気象学会北海道支部
  • 2002年 - 2004年   日本農業気象学会北海道支部   幹事   日本農業気象学会北海道支部


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