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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%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Comparing to conventional Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) is considered having significant improvements, especially on de-correlation and noise resistance, in terms of ground deformation measurement. However, the applications of InSAR-based monitoring or analysis are usually carried out in a large area, at a regional scale at least; little has been explored for employing InSAR analysis in an engineering scale, such as monitoring of a bridge or highway. This study utilized PSInSAR algorithms to analyze multi-temporal C-Band and L-Band space-borne SAR images acquired during January 2007 to February 2018 in order to understand the deformations of a highway tunnel in southern Taiwan over the last decade. The tunnel runs through an active fault, which has caused significant fragmenting and deformations of the tunnel (and the ground above it). The PSInSAR analysis results of the SAR images acquired in both descending and ascending orbits clearly identified PS points along the highway and the tunnel and successfully calculated the deformations of the target during the years. The results also show that the highway has an annual deformation change rate at about 30 mm/year to 100 mm/year along the line of sight (LOS) directions of the observations. Several in-situ datasets collected from high-precision differential GPS stations along the highway and at nearby sites were used to verify and evaluate the PSInSAR analysis results by projecting the GPS measured three-dimensional movements onto the LOS directions. The analysis indicates the PSInSAR results have a high degree of agreement with the GPS-based deformation observations. The results of this study indicate that PSInSAR analysis has a great potential for the long-term monitoring of infrastructures such highways and tunnels at en engineering scale. However, detailed and sophisticated adjustments on the SAR data processing and parameters of PSInSAR analysis are necessary in order to achieve accurate and reliable assessment results. [ABSTRACT FROM AUTHOR]