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Wei Luo     University Educator/Researcher 
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Wei Luo published an article in September 2018.
Top co-authors
Weiming Cheng

51 shared publications

State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Xinqi Zheng

40 shared publications

School of Information Engineering, China University of Geosciences, Beijing 100083, China

Cheng-Chien Liu

37 shared publications

Global Earth Observation and Data Analysis Center, National Cheng Kung University, Tainan City 70101, Taiwan

Fahui Wang

7 shared publications

Department of Geography , Northern Illinois University , DeKalb , IL , 60115-2854 , USA

Anil Shrestha

3 shared publications

Department of Geography, Northern Illinois University, DeKalb, IL, USA

Publication Record
Distribution of Articles published per year 
(2004 - 2018)
Total number of journals
published in
Publications See all
Article 0 Reads 1 Citation Influences of the Shadow Inventory on a Landslide Susceptibility Model Cheng-Chien Liu, Wei Luo, Hsiao-Wei Chung, Hsiao-Yuan Yin, K... Published: 09 September 2018
ISPRS International Journal of Geo-Information, doi: 10.3390/ijgi7090374
DOI See at publisher website ABS Show/hide abstract
A landslide inventory serves as the basis for assessing landslide susceptibility, hazard, and risk. It is generally prepared from optical imagery acquired from spaceborne or airborne platforms, in which shadows are inevitably found in mountainous areas. The influences of shadow inventory on a landslide susceptibility model (LSM), however, have not been investigated systematically. This paper employs both the shadow and landslide inventories prepared from eleven Formosat-2 annual images from the I-Lan area in Taiwan acquired from 2005 to 2016, using a semiautomatic expert system. A standard LSM based on the geometric mean of multivariables was used to evaluate the possible errors incurred by neglecting the shadow inventory. The results show that the LSM performance was significantly improved by 49.21% for the top 1% of the most highly susceptible area and that the performance decreased gradually by 15.25% for the top 10% most highly susceptible areas and 9.71% for the top 20% most highly susceptible areas. Excluding the shadow inventory from the calculation of landslide susceptibility index reveals the real contribution of each factor. They are crucial in optimizing the coefficients of a nondeterministic geometric mean LSM, as well as in deriving the threshold of a landslide hazard early warning system.
Article 0 Reads 0 Citations Earth surface modeling for education: How effective is it? Four semesters of classroom tests with WILSIM-GC Wei Luo, Thomas J. Smith, Kyle Whalley, Andrew Darling, Caro... Published: 20 August 2018
British Journal of Educational Technology, doi: 10.1111/bjet.12653
DOI See at publisher website
Article 0 Reads 0 Citations Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio ... Anil Shrestha, Wei Luo Published: 02 June 2018
ISPRS International Journal of Geo-Information, doi: 10.3390/ijgi7060211
DOI See at publisher website ABS Show/hide abstract
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess nitrogen fertilizer leaching down into the aquifer. The percolation of nitrate depends on several hydrogeological conditions of the valley. Groundwater contamination vulnerability mapping uses hydrogeologic conditions to predict vulnerable areas. This paper presents a new Geodetector-based Frequency Ratio (GFR) method and an optimized-DRASTIC method to generate nitrate vulnerability index values for the CV. The optimized-DRASTIC method combined the individual weights and rating values for Depth to water, Recharge rate, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity. The GFR method incorporated the Frequency-Ratio (FR) method to derive rating values and the Geodetector method to derive relative Power of Determinant (PD) values as weights to generate nitrate susceptibility index map. The optimized-DRASTIC method generated very-high to high index values in the eastern part of the CV. The GFR method showed very-high index values in most part of the San Joaquin and Tulare basin. The quantitatively derived rating values and weights in the GFR method improved the vulnerability index and showed better consistency with the observed nitrate contamination pattern than optimized-DRASTIC index, suggesting that GFR is a better method for groundwater contamination vulnerability mapping in the CV aquifer.
Article 0 Reads 2 Citations Analysis of Groundwater Nitrate Contamination in the Central Valley: Comparison of the Geodetector Method, Principal Com... Anil Shrestha, Wei Luo Published: 26 September 2017
ISPRS International Journal of Geo-Information, doi: 10.3390/ijgi6100297
DOI See at publisher website ABS Show/hide abstract
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is a ubiquitous groundwater problem found in various parts of the valley. Heavy irrigation and application of fertilizer over the last several decades have caused groundwater nitrate contamination in several domestic, public and monitoring wells in the CV above EPA’s Maximum Contamination level of 10 mg/L. Source variables, aquifer susceptibility and geochemical variables could affect the contamination rate and groundwater quality in the aquifer. A comparative study was conducted using Geodetector (GED), Principal Component Analysis (PCA) and Geographically Weighted Regression (GWR) to observe which method is most effective at revealing environmental variables that control groundwater nitrate concentration. The GED method detected precipitation, fertilizer, elevation, manure and clay as statistically significant variables. Watersheds with percent of wells above 5 mg/L of nitrate were higher in San Joaquin and Tulare Basin compared to Sacramento Valley. PCA grouped cropland, fertilizer, manure and precipitation as a first principal component, suggesting similar construct of these variables and existence of data redundancy. The GWR model performed better than the OLS model, with lower corrected Akaike Information Criterion (AIC) values, and captured the spatial heterogeneity of fertilizer, precipitation and elevation for the percent of wells above 5 mg/L in the CV. Overall, the GED method was more effective than the PCA and GWR methods in determining the influence of explanatory variables on groundwater nitrate contamination.
Article 0 Reads 1 Citation An Iterative Black Top Hat Transform Algorithm for the Volume Estimation of Lunar Impact Craters Jiao Wang, Weiming Cheng, Wei Luo, Xinqi Zheng, Chenghu Zhou Published: 15 September 2017
Remote Sensing, doi: 10.3390/rs9090952
DOI See at publisher website ABS Show/hide abstract
Volume estimation is a fundamental problem in the morphometric study of impact craters. The Top Hat Transform function (TH), a gray-level image processing technique has already been applied to gray-level Digital Elevation Model (DEM) to extract peaks and pits in a nonuniform background. In this study, an updated Black Top Hat Transform function (BTH) was applied to quantify the volume of lunar impact craters on the Moon. We proposed an iterative BTH (IBTH) where the window size and slope factor were linearly increased to extract craters of different sizes, along with a novel application of automatically adjusted threshold to remove noise. Volume was calculated as the sum of the crater depth multiplied by the cell area. When tested against the simulated dataset, IBTH achieved an overall relative accuracy of 95%, in comparison with only 65% for BTH. When applied to the Chang’E DEM and LOLA DEM, IBTH not only minimized the relative error of the total volume estimates, but also revealed the detailed spatial distribution of the crater depth. Therefore, the highly automated IBTH algorithm with few input parameters is ideally suited for estimating the volume of craters on the Moon on a global scale, which is important for understanding the early processes of impact erosion.
Article 0 Reads 4 Citations Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods Wei Luo, Cheng-Chien Liu Published: 10 September 2017
Landslides, doi: 10.1007/s10346-017-0893-9
DOI See at publisher website