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Jhe-Syuan Lai
Department of Civil Engineering, Feng Chia University, Taichung 40724, Taiwan

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Journal article
Published: 06 January 2021 in ISPRS International Journal of Geo-Information
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The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in conducting field surveys and updating the LULC map regularly, thus significantly affecting the feasibility of multi-temporal analysis of soil erosion. To address this issue, this study uses data mining to build a random forest (RF) model between eight geospatial factors and the C-factor for the Shihmen Reservoir watershed in northern Taiwan for multi-temporal estimation of soil loss. The eight geospatial factors were collected or derived from remotely sensed images taken in 2004, a digital elevation model, and related digital maps. Due to the memory size limitation of the R software, only 4% of the total data points (population dataset) in each C-factor class were selected as the sample dataset (input dataset) for analysis using the stratified random sampling method. Seventy percent of the input dataset was used to train the RF model, and the other 30% was used to test the model. The results show that the RF model could capture the trend of vegetation recovery and soil loss reduction after the destructive event of Typhoon Aere in 2004 for multi-temporal analysis. Although the RF model was biased by the majority class’s large sample size (C = 0.01 class), the estimated soil erosion rate was close to the measurement obtained by the erosion pins installed in the watershed (90.6 t/ha-year). After the model’s completion, we furthered our aim to address the input dataset’s imbalanced data problem to improve the model’s classification performance. An ad-hoc down-sampling of the majority class technique was used to reduce the majority class’s sampling rate to 2%, 1%, and 0.5% while keeping the other minority classes at a 4% sample rate. The results show an improvement of the Kappa coefficient from 0.574 to 0.732, the AUC from 0.780 to 0.891, and the true positive rate of all minority classes combined from 0.43 to 0.70. However, the overall accuracy decreases from 0.952 to 0.846, and the true positive rate of the majority class declines from 0.99 to 0.94. The best average C-factor was achieved when the sampling rate of the majority class was 1%. On the other hand, the best soil erosion estimate was obtained when the sampling rate was 2%.

ACS Style

Fuan Tsai; Jhe-Syuan Lai; Kieu Anh Nguyen; Walter Chen. Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds. ISPRS International Journal of Geo-Information 2021, 10, 19 .

AMA Style

Fuan Tsai, Jhe-Syuan Lai, Kieu Anh Nguyen, Walter Chen. Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds. ISPRS International Journal of Geo-Information. 2021; 10 (1):19.

Chicago/Turabian Style

Fuan Tsai; Jhe-Syuan Lai; Kieu Anh Nguyen; Walter Chen. 2021. "Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds." ISPRS International Journal of Geo-Information 10, no. 1: 19.

Journal article
Published: 19 November 2020 in ISPRS International Journal of Geo-Information
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The present researchers took multistation-based panoramic images and imported the processed images into a virtual tour platform to create webpages and a virtual reality environment. The integrated multimedia platform aims to assist students in a surveying practice course. A questionnaire survey was conducted to evaluate the platform’s usefulness to students, and its design was modified according to respondents’ feedback. Panoramic photos were taken using a full-frame digital single-lens reflex camera with an ultra-wide-angle zoom lens mounted on a panoramic instrument. The camera took photos at various angles, generating a visual field with horizontal and vertical viewing angles close to 360°. Multiple overlapping images were stitched to form a complete panoramic image for each capturing station. Image stitching entails extracting feature points to verify the correspondence between the same feature point in different images (i.e., tie points). By calculating the root mean square error of a stitched image, we determined the stitching quality and modified the tie point location when necessary. The root mean square errors of nearly all panoramas were lower than 5 pixels, meeting the recommended stitching standard. Additionally, 92% of the respondents (n = 62) considered the platform helpful for their surveying practice course. We also discussed and provided suggestions for the improvement of panoramic image quality, camera parameter settings, and panoramic image processing.

ACS Style

Jhe-Syuan Lai; Yu-Chi Peng; Min-Jhen Chang; Jun-Yi Huang. Panoramic Mapping with Information Technologies for Supporting Engineering Education: A Preliminary Exploration. ISPRS International Journal of Geo-Information 2020, 9, 689 .

AMA Style

Jhe-Syuan Lai, Yu-Chi Peng, Min-Jhen Chang, Jun-Yi Huang. Panoramic Mapping with Information Technologies for Supporting Engineering Education: A Preliminary Exploration. ISPRS International Journal of Geo-Information. 2020; 9 (11):689.

Chicago/Turabian Style

Jhe-Syuan Lai; Yu-Chi Peng; Min-Jhen Chang; Jun-Yi Huang. 2020. "Panoramic Mapping with Information Technologies for Supporting Engineering Education: A Preliminary Exploration." ISPRS International Journal of Geo-Information 9, no. 11: 689.

Journal article
Published: 23 September 2020 in Applied Sciences
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Landslide sources and runout features of typical natural terrain landslides can be observed from a geotechnical perspective. Landslide sources are the major area of occurrences, whereas runout signatures reveal the subsequent phenomena caused by unstable gravity. Remotely sensed landslide detection generally includes runout areas, unless these results have been excluded manually through detailed comparison with stereo aerial photos and other auxiliary data. Areas detected using remotely sensed landslide detection can be referred to as “landslide-affected” areas. The runout areas should be separated from landslide-affected areas when upgrading landslide detections into a landslide inventory to avoid unreliable results caused by impure samples. A supervised data mining procedure was developed to separate landslide sources and runout areas based on four topographic attributes derived from a 10–m digital elevation model with a random forest algorithm and cost-sensitive analysis. This approach was compared with commonly used methods, namely support vector machine (SVM) and logistic regression (LR). The Typhoon Morakot event in the Laonong River watershed, southern Taiwan, was modeled. The developed models constructed using the limited training data sets could separate landslide source and runout signatures verified using the polygon and area constraint-based datasets. Furthermore, the performance of developed models outperformed SVM and LR algorithms, achieving over 80% overall accuracy, area under the curve of the receiver operating characteristic, user’s accuracy, and producer’s accuracy in most cases. The agreement of quantitative evaluations between the area sizes of inventory polygons for training and the predicted targets was also observed when applying the supervised modeling strategy.

ACS Style

Jhe-Syuan Lai. Separating Landslide Source and Runout Signatures with Topographic Attributes and Data Mining to Increase the Quality of Landslide Inventory. Applied Sciences 2020, 10, 6652 .

AMA Style

Jhe-Syuan Lai. Separating Landslide Source and Runout Signatures with Topographic Attributes and Data Mining to Increase the Quality of Landslide Inventory. Applied Sciences. 2020; 10 (19):6652.

Chicago/Turabian Style

Jhe-Syuan Lai. 2020. "Separating Landslide Source and Runout Signatures with Topographic Attributes and Data Mining to Increase the Quality of Landslide Inventory." Applied Sciences 10, no. 19: 6652.

Journal article
Published: 05 September 2019 in ISPRS International Journal of Geo-Information
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This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with diverse topographic conditions. The second issue is the mixing problem of landslide inventory that the detection of landslide areas from remotely-sensed data generally includes source and run-out features unless the run-out portion can be removed manually with auxiliary data. To this end, different statistical sampling strategies and the run-out influence on random forests (RF)-based landslide susceptibility modeling are explored for Typhoon Morakot in 2009 in southern Taiwan. To address the construction of models with an extremely high false alarm error or missing error, this study integrated cost-sensitive analysis with RF to adjust the decision boundary to achieve improvements. Experimental results indicate that, compared with a logistic regression model, RF with the hybrid sample strategy generally performs better, achieving over 80% and 0.7 for the overall accuracy and kappa coefficient, respectively, and higher accuracies can be obtained when the run-out is treated as an independent class or combined with a non-landslide class. Cost-sensitive analysis significantly improved the prediction accuracy from 5% to 10%. Therefore, run-out should be separated from the landslide source and labeled as an individual class when preparing a landslide inventory.

ACS Style

Jhe-Syuan Lai; Shou-Hao Chiang; Fuan Tsai. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling. ISPRS International Journal of Geo-Information 2019, 8, 397 .

AMA Style

Jhe-Syuan Lai, Shou-Hao Chiang, Fuan Tsai. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling. ISPRS International Journal of Geo-Information. 2019; 8 (9):397.

Chicago/Turabian Style

Jhe-Syuan Lai; Shou-Hao Chiang; Fuan Tsai. 2019. "Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling." ISPRS International Journal of Geo-Information 8, no. 9: 397.

Journal article
Published: 27 August 2019 in Sensors
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This study developed a systematic approach with machine learning (ML) to apply the satellite remote sensing images, geographic information system (GIS) datasets, and spatial analysis for multi-temporal and event-based landslide susceptibility assessments at a regional scale. Random forests (RF) algorithm, one of the ML-based methods, was selected to construct the landslide susceptibility models. Different ratios of landslide and non-landslide samples were considered in the experiments. This study also employed a cost-sensitive analysis to adjust the decision boundary of the developed RF models with unbalanced sample ratios to improve the prediction results. Two strategies were investigated for model verification, namely space- and time-robustness. The space-robustness verification was designed for separating samples into training and examining data based on a single event or the same dataset. The time-robustness verification was designed for predicting subsequent landslide events by constructing a landslide susceptibility model based on a specific event or period. A total of 14 GIS-based landslide-related factors were used and derived from the spatial analyses. The developed landslide susceptibility models were tested in a watershed region in northern Taiwan with a landslide inventory of changes detected through multi-temporal satellite images and verified through field investigation. To further examine the developed models, the landslide susceptibility distributions of true occurrence samples and the generated landslide susceptibility maps were compared. The experiments demonstrated that the proposed method can provide more reasonable results, and the accuracies were found to be higher than 93% and 75% in most cases for space- and time-robustness verifications, respectively. In addition, the mapping results revealed that the multi-temporal models did not seem to be affected by the sample ratios included in the analyses.

ACS Style

Jhe-Syuan Lai; Fuan Tsai. Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning. Sensors 2019, 19, 3717 .

AMA Style

Jhe-Syuan Lai, Fuan Tsai. Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning. Sensors. 2019; 19 (17):3717.

Chicago/Turabian Style

Jhe-Syuan Lai; Fuan Tsai. 2019. "Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning." Sensors 19, no. 17: 3717.

Conference paper
Published: 18 October 2016 in Earth Resources and Environmental Remote Sensing/GIS Applications VII
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ACS Style

Fuan Tsai; Jhe-Syuan Lai; Yi-Hsiu Cheng. Waveform fitting and geometry analysis for full-waveform lidar feature extraction. Earth Resources and Environmental Remote Sensing/GIS Applications VII 2016, 1000504 -1000504-7.

AMA Style

Fuan Tsai, Jhe-Syuan Lai, Yi-Hsiu Cheng. Waveform fitting and geometry analysis for full-waveform lidar feature extraction. Earth Resources and Environmental Remote Sensing/GIS Applications VII. 2016; ():1000504-1000504-7.

Chicago/Turabian Style

Fuan Tsai; Jhe-Syuan Lai; Yi-Hsiu Cheng. 2016. "Waveform fitting and geometry analysis for full-waveform lidar feature extraction." Earth Resources and Environmental Remote Sensing/GIS Applications VII , no. : 1000504-1000504-7.

Journal article
Published: 22 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.

ACS Style

Jhe-Syuan Lai; F. Tsai; S.-H. Chiang. INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B8, 89 -93.

AMA Style

Jhe-Syuan Lai, F. Tsai, S.-H. Chiang. INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B8 ():89-93.

Chicago/Turabian Style

Jhe-Syuan Lai; F. Tsai; S.-H. Chiang. 2016. "INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8, no. : 89-93.

Journal article
Published: 01 January 2016 in Terrestrial, Atmospheric and Oceanic Sciences
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Treat full-waveform LiDAR point clouds as volumetric data sets 3D texture measures were used for LiDAR feature extraction 3D texture features improve...

ACS Style

Fuan Tsai; Jhe-Syuan Lai; Yu-Hua Lu. Full-Waveform LiDAR Point Cloud Land Cover Classification with Volumetric Texture Measures. Terrestrial, Atmospheric and Oceanic Sciences 2016, 27, 549 .

AMA Style

Fuan Tsai, Jhe-Syuan Lai, Yu-Hua Lu. Full-Waveform LiDAR Point Cloud Land Cover Classification with Volumetric Texture Measures. Terrestrial, Atmospheric and Oceanic Sciences. 2016; 27 (4):549.

Chicago/Turabian Style

Fuan Tsai; Jhe-Syuan Lai; Yu-Hua Lu. 2016. "Full-Waveform LiDAR Point Cloud Land Cover Classification with Volumetric Texture Measures." Terrestrial, Atmospheric and Oceanic Sciences 27, no. 4: 549.

Journal article
Published: 01 December 2013 in Ecological Engineering
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ACS Style

Fuan Tsai; Jhe-Syuan Lai; Walter W. Chen; Tang-Huang Lin. Analysis of topographic and vegetative factors with data mining for landslide verification. Ecological Engineering 2013, 61, 669 -677.

AMA Style

Fuan Tsai, Jhe-Syuan Lai, Walter W. Chen, Tang-Huang Lin. Analysis of topographic and vegetative factors with data mining for landslide verification. Ecological Engineering. 2013; 61 ():669-677.

Chicago/Turabian Style

Fuan Tsai; Jhe-Syuan Lai; Walter W. Chen; Tang-Huang Lin. 2013. "Analysis of topographic and vegetative factors with data mining for landslide verification." Ecological Engineering 61, no. : 669-677.

Journal article
Published: 11 January 2013 in IEEE Transactions on Geoscience and Remote Sensing
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This paper presents a novel approach for the feature extraction of hyperspectral image cubes. In this paper, hyperspectral image cubes are treated as volumetric data sets. Features that are most helpful in separating different targets are effectively extracted from the hyperspectral image cubes using a newly developed high-order texture analysis method. The traditional texture measure of the gray-level cooccurrence matrix is extended to a 3-D tensor field to explore the complicated volumetric data more effectively and to extract discriminant features for better classification. As the kernel size is one of the most important parameters in statistics-based texture analysis, a semivariance analysis and a spectral separability measure are used to determine the most appropriate kernel size in the spatial and spectral domains, respectively, for computing 3-D gray-level cooccurrence. In addition, a few statistical indexes are also extended to third-order forms in order to calculate quantitative texture properties of the generated cooccurrence tensor field. An airborne hyperspectral data set and an EO-1 Hyperion image are used to test the performance of the developed algorithms. Experimental results indicate that the developed 3-D texture analysis outperforms conventional second-order texture descriptors and the support vector machine-based classifier in supervised classifications of both hyperspectral data sets.

ACS Style

Fuan Tsai; Jhe-Syuan Lai. Feature Extraction of Hyperspectral Image Cubes Using Three-Dimensional Gray-Level Cooccurrence. IEEE Transactions on Geoscience and Remote Sensing 2013, 51, 3504 -3513.

AMA Style

Fuan Tsai, Jhe-Syuan Lai. Feature Extraction of Hyperspectral Image Cubes Using Three-Dimensional Gray-Level Cooccurrence. IEEE Transactions on Geoscience and Remote Sensing. 2013; 51 (6):3504-3513.

Chicago/Turabian Style

Fuan Tsai; Jhe-Syuan Lai. 2013. "Feature Extraction of Hyperspectral Image Cubes Using Three-Dimensional Gray-Level Cooccurrence." IEEE Transactions on Geoscience and Remote Sensing 51, no. 6: 3504-3513.

Journal article
Published: 25 July 2012 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Spatial information technologies and data can be used effectively to investigate and monitor natural disasters contiguously and to support policy- and decision-making for hazard prevention, mitigation and reconstruction. However, in addition to the vastly growing data volume, various spatial data usually come from different sources and with different formats and characteristics. Therefore, it is necessary to find useful and valuable information that may not be obvious in the original data sets from numerous collections. This paper presents the preliminary results of a research in the validation and risk assessment of landslide events induced by heavy torrential rains in the Shimen reservoir watershed of Taiwan using spatial analysis and data mining algorithms. In this study, eleven factors were considered, including elevation (Digital Elevation Model, DEM), slope, aspect, curvature, NDVI (Normalized Difference Vegetation Index), fault, geology, soil, land use, river and road. The experimental results indicate that overall accuracy and kappa coefficient in verification can reach 98.1% and 0.8829, respectively. However, the DT model after training is too over-fitting to carry prediction. To address this issue, a mechanism was developed to filter uncertain data by standard deviation of data distribution. Experimental results demonstrated that after filtering the uncertain data, the kappa coefficient in prediction substantially increased 29.5%.The results indicate that spatial analysis and data mining algorithm combining the mechanism developed in this study can produce more reliable results for verification and forecast of landslides in the study site.

ACS Style

J. S. Lai; F. Tsai. VERIFICATION AND RISK ASSESSMENT FOR LANDSLIDES IN THE SHIMEN RESERVOIR WATERSHED OF TAIWAN USING SPATIAL ANALYSIS AND DATA MINING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2012, XXXIX-B2, 67 -70.

AMA Style

J. S. Lai, F. Tsai. VERIFICATION AND RISK ASSESSMENT FOR LANDSLIDES IN THE SHIMEN RESERVOIR WATERSHED OF TAIWAN USING SPATIAL ANALYSIS AND DATA MINING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2012; XXXIX-B2 ():67-70.

Chicago/Turabian Style

J. S. Lai; F. Tsai. 2012. "VERIFICATION AND RISK ASSESSMENT FOR LANDSLIDES IN THE SHIMEN RESERVOIR WATERSHED OF TAIWAN USING SPATIAL ANALYSIS AND DATA MINING." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B2, no. : 67-70.