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GIS-based logistic regression method for landslide susceptibility mapping in regional scale

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第12期7卷 2007-2017页

作者： ZHU Lei huang jing-feng Department of Natural Science. Zhejiang University Hangzhou 310029 China Institute of Agricultural Remote Sensing and Information Technology Application Zhejiang University Hangzhou 310029 China Shandong Agriculture Administrators College Jinan 250100 China

Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and divides these methods into 3 types. The logistic linear regression approach is further elaborated on by crosstabs method, which is used to analyze the relationship between the categorical or binary response variable and one or more continuous or categorical or binary explanatory variables derived from samples. It is an objective assignment of coefficients serving as weights of various factors under considerations while expert opinions make great difference in heuristic approaches. Different from deterministic approach, it is very applicable to regional scale. In this study, double logistic regression is applied in the study area. The entire study area is first analyzed. The logistic regression equation showed that elevation, proximity to road, river and residential area are main factors triggering land- slide occurrence in this area. The prediction accuracy of the first landslide susceptibility map was showed to be 80%. Along the road and residential area, almost all areas are in high landslide susceptibility zone. Some non-landslide areas are incorrectly divided into high and medium landslide susceptibility zone. In order to improve the status, a second logistic regression was done in high landslide susceptibility zone using landslide cells and non-landslide sample cells in this area. In the second logistic regression analysis, only engineering and geological conditions are important in these areas and are entered in the new logistic regression equation indicating that only areas with unstable engineering and geological conditions are prone to landslide during large scale engineering activity. Taking these two logistic regression results into account yields a new landslide susceptibility map. Double logistic regression analysis improved the non-landslid

关键词： Landslide Susceptibility Logistic regression GIS Spatial analysis

Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

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Pedosphere 2014年第4期24卷 450-460页

作者： ZHANG feng ZHANG Li-Wen SHI jing-jing huang jing-feng Institute of Agricultural Remote Sensing and Information Technology Zhejiang University

Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.

关键词： enhanced vegetation index modified soil-adjusted vegetation index normalized difference vegetation index temperature vegetation dryness indices

Comprehensive Suitability Evaluation of Tea Crops Using GIS and a Modified Land Ecological Suitability Evaluation Model

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Pedosphere 2012年第1期22卷 122-130页

作者： LI Bo ZHANG feng ZHANG Li-Wen huang jing-feng JIN Zhi-feng D.K.GUPTA College of Public Administration Zhejiang UniversityHangzhou 310058(China) Institute of Agricultural Remote Sensing and Information Technology Zhejiang UniversityHangzhou 310058(China) Zhejiang Climatic Centre Zhejiang Meteorological BureauHangzhou 310017(China) Departamet of Biochemistry Molecular Cell Biology of Plant Experimental InstitutionGranada(Spain)

Tea （Camellia sinensis） is one of the most valuable cash crops in southern China; however, the planting distribution of tea crops is not optimal and the production and cultivation regions of tea crops are restricted by law and custom. In order to evaluate the suitability of tea crops in Zhejiang Province, the annual mean temperature, the annual accumulated temperature above 10 ℃, the frequency of extremely low temperature below -13 ℃, the mean humidity from April to October, slope, aspect, altitude, soil type, and soil texture were selected from climate, topography, and soil factors as factors for land ecological evaluation by the Delphi method based on the ecological characteristics of tea crops. These nine factors were quantitatively analyzed using a geographic information system （GIS）. The grey relational analysis （GRA） was combined with the analytic hierarchy process （AHP） to address the uncertainties during the process of evaluating the traditional land ecological suitability, and a modified land ecological suitability evaluation （LESE） model was built. Based on the land-use map of Zhejiang Province, the regions that were completely unsuitable for tea cultivation in the province were eliminated and then the spatial distribution of the ecological suitability of tea crops was generated using the modified LESE model and GIS. The results demonstrated that the highly, moderately, and non-suitable regions for the cultivation of tea crops in Zhejiang Province were 27552.66, 42 724.64, and 26507.97 km2, and accounted for 28.47%, 44.14%, and 27.39% of the total evaluation area, respectively. Validation of the method showed a high degree of coincidence with the current planting distribution of tea crops in Zhejiang Province. The modified LESE model combined with GIS could be useful in quickly and accurately evaluating the land ecological suitability of tea crops, providing a scientific basis for the rational distribution of tea crops and acting as a reference to land pol

关键词： analytic hierarchy process (AHP) grey relational analysis (GRA) soil temperature topography

Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第6期8卷 883-895页

作者： YANG Xiao-hua huang jing-feng WANG Jian-wen WANG Xiu-zhen LIU Zhan-yu Institute of Agricultural Remote Sensing ＆ Information Application Zhejiang University Hangzhou 310029 China Key Laboratory of Agricultural Remote Sensing ＆ Information System Hangzhou 310029 China Communication Training Base of General Staff Headquarters Beijing 102400 China Zhejiang Meteorological Institute Hangzhou 310004 China

Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.

关键词： Artificial neural network (ANN) Radial basis function (RBF) Remote sensing Rice Vegetation index (VI)

Monitoring the Sequential Cropping Index of Arable Land in Zhejiang Province of China Using MODIS-NDVI

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Agricultural Sciences in China 2007年第2期6卷 208-213页

作者： PENG Dai-liang huang jing-feng JIN Hui-min Institute of Remote Sensing ＆ Information System Placation Zhejiang University Hangzhou 310029 P.R.China

The sequential cropping index of arable land is important agricultural information. The aim of this article is to monitor and analyze the parameter, and offer reference for agricultural production. The cropping index of arable land in Zhejiang Province, China from 2001 to 2004 was calculated using the second order difference based MODIS （moderate resolution imagine spectroradimeter） vegetation data from NASA （National Aeronautic and Space Administration） in America and the land use map with a scale of 1：25 000. It was found that the peak of the time series of the NDVI curve indicated that the ground biomass of crops reached the maximum, and fluctuated with the crops growing processes such as sowing, seeding, heading, ripeness, and harvesting within one year. Thus, the sequential cropping index was defined as the number of peaks of the time series of the NDVI curve. The sequential cropping index of all cities in Zhejiang Province, China was worked out. It is seen from the spatial distribution that the cropping index in the southwest Zhejiang Province is larger than that in the northeast. As for the temporal distribution, the sequential cropping index decreased from 2001 to 2003, whereas it increased slightly from 2003 to 2004. However, the index of arable land was relatively low, as far as the geographic position and climatic resource were concerned, and the potential of the sequential cropping index was great.

关键词： sequential cropping index MODIS-NDVI monitoring

Empirical Line Method Using Spectrally Stable Targets to Calibrate IKONOS Imagery

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Pedosphere 2008年第1期18卷 124-130页

作者： XU Jun-feng Institute of Agricultural Remote Sensing and Information Technology Zhejiang University Hangzhou 310029 （China）.

By using speetrally stable targets, the empirical line （EL） method was tested to correct the multispectral IKONOS imagery acquired over Putuo Mountain, Zhejiang, China. A series of calibration targets, which were spectrally stable over time, were selected to establish the linear predicted equation. Subsequently, a series of spectrally stable validation targets were selected to assess the accuracy of the equations. And, validation targets, which were speetrally unstable over time, were used to test the feasibility of using the EL method to calibrate the archival remotely sensed data. Ground reflectance measurements for each target were made using an ASD FieldSpec spectroradiometer. A Trimble GeoXTTM GPS unit with sub-meter accuracy was used to estimate the target position accurately. Linear regression equations for four tKONOS bands were derived. The coefficients of determination for the blue, green, and red bands were all greater than 0.9800 and it was 0.9697 for the near infrared band. It was concluded that reasonable results could be obtained by using speetrally stable targets.

关键词： calibration empirical line method, IKONOS spectrally stable targets

Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

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Rice science 2008年第3期15卷 232-242页

作者： LIU Zhan-yu huang jing-feng TAO Rong-xiang Institute of Agricultural Remote Sensing ＆ Information System Application Zhejiang University Hangzhou 310029 China Institute of Plant Protection and Microbiology Zhejiang Academy of Agricultural Sciences Hangzhou 310021 China

Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

关键词： derivative spectrum hyperspectral reflectance ratio of spectral reflectance rice brown spot disease severity Bipolaris oryzae ( Helminthosporium oryzae) sensitivity analysis remote sensing

SOX7 is involved in aspirin-mediated growth inhibition of human colorectal cancer cells

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World Journal of Gastroenterology 2011年第44期17卷 4922-4927页

作者： Xin Zhou Shu-Yan huang jing-Xin feng Yan-Yan Gao Li Zhao Jun Lu Bai-Qu huang Yu Zhang The Institute of Genetics and Cytology School of Life Science Northeast Normal University Changchun 130024 Jilin Province China

AIM:To confirm the role of sex-determining region Y-box 7(Sox7) in aspirin-mediated growth inhibition of COX-independent human colorectal cancer ***:The cell survival percentage was examined by MTT(Moto-nuclear cell direc cytotoxicity) ***7 expression was assessed by using reverse transcription-polymerase chain reaction and Western ***203580 was used to inhibit the p38MAPK signal ***7 promoter activity was detected by Luciferase reporter ***:SOX7 was upregulated by aspirin and was involved in aspirin-mediated growth inhibition of SW480 human colorectal cancer *** p38MAPK pathway played a role in aspirin-induced SOX7 expression,during which the AP1 transcription factors c-Jun and c-Fos upregulated SOX7 promoter ***:SOX7 is upregulated by aspirin and is involved in aspirin-mediated growth inhibition of human colorectal cancer SW480 cells.

关键词： SOX7 Aspirin p38MAPK Colorectal cancer SB203580

A GIS-based approach for estimating spatial distribution of seasonal temperature in Zhejiang Province, China

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第4期7卷 647-656页

作者： LI Jun huang jing-feng WANG Xiu-zhen Department of Natural Resource Science Zhejiang University Hangzhou 310029. China Institute of Agricultural Remote Sensing ＆ Information Application Zhejiang University Hangzhou 310029 China Zhejiang Meteorological Institute Hangzhou 310004 China

This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, using a regression-based approach. Variables used for modelling include: longitude, latitude, elevation, distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal temperature data, for the observation period 1971 to 2000, were obtained from 59 meteorological stations. Temperature data from 52 meteorological stations were used to construct the regression model. Data from the other 7 stations were retained for model validation. Seasonal temperature surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted surface. Latitude, elevation and distance from the sea are found to be the most important predictors of local seasonal temperature. Validation determined that regression plus kriging predicts seasonal temperature with a coefficient of determination (R2), between the estimated and observed values, of 0.757 (autumn) and 0.935 (winter). A simple regression model without kriging yields less accurate results in all seasons except for the autumn temperature.

关键词： GIS Multiple regression analysis Interpolation Seasonal temperature Spatial distribution