Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador

Abstract

This study aims to develop an approach to characterize cropland drought conditions in El Salvador, Central America. The data were processed for 2016–2017 through three main steps: (1) reconstructing MODIS land-surface temperature (LST), (2) Landsat-MODIS data fusion and (3) drought delineation using the temperature vegetation dryness index (TVDI). The results of LST reconstruction using the random forests (RF) indicated the median RMSE value of 0.5?°C. The fusion results achieved from the STARFM compared with the reference Landsat data revealed close agreement with the correlation coefficient (r) values higher than 0.84. The TVDI results verified with that from the reference Landsat data indicated r values of 0.85 and 0.75 for 2016 and 2017, respectively. The larger very dry area was observed for the 2016 primera season due to prolonged droughts. Approximately 11.5% and 10.7% of croplands were, respectively, associated with very dry moisture condition in the 2016 and 2017 primera seasons.     

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DE–LSSVM_SLP. The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model. In this research, a GIS database with 129 historical landslide records in the Quy Hop area (Central Vietnam) has been collected to establish the hybrid model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the performance of the newly constructed model. Experimental results show that the proposed model has high performances with approximately 82% of AUCs on both training and validating datasets. The model’s results were compared with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 Decision Trees. The result comparison demonstrates that the DE–LSSVM_SLP deems best suited for the dataset at hand; therefore, the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.     

Local stress analysis in granular materials at a mesoscale

The purpose of this paper is to contribute to the change of scale techniques developed for granular materials. The proposed approach consists in considering an intermediate scale between the macroscales and microscales, called the mesoscale, using the classical homogenization scheme. In this approach, the mesoscale for 2D granular materials was defined at the level of local volumes, called mesodomains, which are local closed structures composed of particles in contact. In this paper, we focused on defining a local stress field at this scale. Two different methods are proposed, both based on the equivalent continuum mean stress but using different approximations of the mean stress tensor for each mesodomain. The two proposed methods were then compared to each other. Analyses performed on the stress field at the mesoscale show that this local field is heterogeneous and, in particular, that its heterogeneity is significantly structured at this scale. The distribution of the local mean stress (first invariant of the local stress tensor) is uniform in any mesodomain, whereas the distribution of the stress deviator (second invariant of the deviatoric part of the local stress tensor) is significantly dependent on the elongation direction and on the elongation degree of the mesodomains. The local stress ratio (ratio of the stress deviator to the mean stress) is higher within the mesodomains that are elongated in the global compression direction than that within the ones elongated in the global extension direction. Copyright © 2011 John Wiley & Sons, Ltd.     

Evidence that earthquakes have been triggered by reservoir in the Song Tranh 2 region,Vietnam

In the Song Tranh 2 (ST2) hydropower reservoir located in the Quang Nam province, Central Vietnam, earthquakes started occurring soon after impoundment of the reservoir in late 2010. Earthquakes continue to occur in the region, and two earthquakes of M 4.6 and 4.7 on October 22, 2012 and November 15, 2012, respectively, have been reported (Trieu et al. 2014; Giang et al. 2015) in the vicinity of the reservoir. In the present study, b-value has been estimated, and focal mechanism solutions have been computed for the first time using moment tensor inversion approach. Also, the influence of impoundment of reservoir on the occurrence of earthquakes has been computed for the ST2 region based on Coulomb stress. A quality data set of 595 earthquakes recorded for the period of October 2012 to April 2014 at ten stations of the seismic network operated by the Institute of Geophysics (IGP) has been used to calculate b-values for the northern and southern seismicity clusters of the region. In general, the b-values associated with reservoir-triggered seismicity (RTS) are found to be higher than the regional b-values in the frequency-magnitude relation of earthquakes. For the ST2 region, it is found that the b-values for the northern and southern clusters are 0.94 ± 0.04 and 0.90 ± 0.04, respectively. Focal mechanism solutions obtained for the two earthquakes close to the reservoir have a right-lateral strike-slip mechanism, with the preferred planes trending NW-SE. These results are concurrent with the orientation of the nearby local surface faults, which we confirm as the active faults in this region. Influence of the stresses due to reservoir water load on the local seismicity is computed based on the obtained focal mechanism by using the concept of fault stability. It is found that most of the earthquakes occur in the positive Coulomb stress region, which shows the influence of reservoir impoundment on earthquake occurrence in the vicinity. Our results suggest that the local earthquakes are triggered by the impoundment of the ST2 reservoir.     

Future high- and low-flow estimations for Central Vietnam: a hydro-meteorological modelling chain approach

We apply a complex hydro-meteorological modelling chain for investigating the impact of climate change on future hydrological extremes in Central Vietnam, a region characterized by limited data availability. The modelling chain consists of six General Circulation Models (GCMs), six Regional Climate Models (RCMs), six bias correction (BC) approaches, the fully distributed Water Flow and Balance Simulation Model (WaSiM), and extreme values analysis. Bias corrected and raw climate data are used as input for WaSiM. To derive hydrological extremes, the generalized extreme value distribution is fitted to the annual maxima/minima discharge. We identify limitations according to the fitting procedure and the BC methods, and suggest the usage of the delta change approach for hydrological decision support. Tendencies towards increased high- and decreased low flows are concluded. Our study stresses the challenges in using current GCMs/RCMs in combination with state-of-the-art BC methods and extreme value statistics for local impact studies.     

Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India

Natural Hazards - Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high...     

The Dynamics of Mangroves in the Mekong Delta (Vietnam): From Degradation to Restoration

Doklady Earth Sciences - The results of the analysis of LANDSAT satellite images were used to reconstruct the change in the area of mangrove forests of the Mekong River Delta over a 30-year-long...     

Numerical Investigation on Dynamics of the Tendon System of A TLP by Applying Absolute Nodal Coordinate Formulation

In the present study, the dynamics of the tendon system of a tension-leg platform(TLP) is investigated through the absolute nodal coordinate formulation(ANCF). Based on the energy conversion principle, the stiffness, generalized elastic force, external load and mass matrices of the element are deduced to perform the element assembling by using the finite element method. Then the motion equation of the tendon/riser is established. In this study, the TLP in the International Ship Structures Committee(ISSC) model under the first and second wave forces is considered as the case study. The simulation is performed in the MATLAB environment. Moreover, the accuracy and reliability of the programs are verified for cases of beam model with theoretical solutions. It is found that the motion response of tendons is affected by the TLP movement and environmental load, simultaneously. Then, the motion response is calculated using the SESAM software and exported as the boundary of ANCF tendons. Finally, the static and dynamic characteristics of the four tendons of ISSC TLP are analyzed systematically by the ANCF method.Performed analysis proves the effectiveness and feasibility of the ANCF method. It is concluded that the proposed method is a powerful scheme for calculating the dynamics of tendon/riser in the field of ocean engineering.     

Evaluation and comparison of LogitBoost Ensemble,Fisher’s Linear Discriminant Analysis,logistic regression and support vector machines methods for landslide susceptibility mapping

The purpose of this study was to investigate and compare the capabilities of four machine learning methods namely LogitBoost Ensemble (LBE), Fisher’s Linear Discriminate Analysis (FLDA), Logistic Regression (LR) and Support Vector Machines (SVM) to select the best method for landslide susceptibility mapping. A part of landslide prone area of Tehri Garhwal district of Uttarakhand state, India, was selected as a case study. Validation of models was carried out using statistical analysis, the chi square test and the Receiver Operating Characteristic (ROC) curve. Result analysis shows that the LBE has the highest prediction ability (AUC = 0.972) for landslide susceptibility mapping, followed by the SVM (0.945), the LR (0.873) and the FLDA (0.870), respectively. Therefore, the LBE is the best and a promising method in comparison to other three models for landslide susceptibility mapping.