Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region

The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the changes in surface runoff in addition to forecasting the future surface runoff based on LULC and precipitation factors. On one hand, six different LULC classes from Spot-5 satellite image were extracted by object-based Support Vector Machine (SVM) classifier. Conjointly, Land Transformation Model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020. On the other hand, ARIMA model was applied to the analysis and forecasting the rainfall trends. The parameters of ARIMA time series model were calibrated and fitted statistically to minimize the prediction uncertainty by latest Taguchi method. Rainfall and streamflow data recorded in eight nearby gauging stations were engaged to train, forecast, and calibrate the climate hydrological models. Then, distributed-GIS-based SCS-CN model was applied to simulate the maximum probable surface runoff for 2000, 2010, and 2020. The comparison results showed that first, deforestation and urbanization have occurred upon the given time and it is anticipated to increase as well. Second, the amount of rainfall has been nonstationary declined till 2015 and this trend is estimated to continue till 2020. Third, due to the damaging changes in LULC and climate, the surface runoff has also increased till 2010 and it is forecasted to gradually exceed

A new semiautomated detection mapping of flood extent from TerraSAR-X satellite image using rule-based classification and taguchi optimization techniques

Floods are among the most destructive natural disasters worldwide. In flood disaster management programs, flood mapping is an initial step. This research proposes an efficient methodology to recognize and map flooded areas by using TerraSAR-X imagery. First, a TerraSAR-X satellite image was captured during a flood event in Kuala Terengganu, Malaysia, to map the inundated areas. Multispectral Landsat imagery was then used to detect water bodies prior to the flooding. In synthetic aperture radar (SAR) imagery, the water bodies and flood locations appear in black; thus, both objects were classified as one. To overcome this drawback, the class of the water bodies was extracted from the Landsat image and then subtracted from that extracted from the TerraSAR-X image. The remaining water bodies represented the flooded locations. Object-oriented classification and Taguchi method were implemented for both images. The Landsat images were categorized into three classes, namely, urban, vegetation, and water bodies. By contrast, only water bodies were extracted from the TerraSAR-X image. The classification results were then evaluated using a confusion matrix. To examine the efficiency of the proposed method, iterative self-organizing data analysis technique (ISODATA) classification method was applied on TerraSAR-X after employing the segmentation process during object-oriented-rule-based method, and the results were compared. The overall accuracy values of the classified maps derived from TerraSAR-X using the rule-based method and Landsat imagery were 86.18 and 93.04, respectively. Consequently, the flooded locations were recognized and mapped by subtracting the two classes of water bodies from these images. The acquired overall accuracy for TerraSAR-X using ISODATA was considerably low at only 57.98. The current research combined the methods and the optimization technique used as an innovative flood detection application. The successful production of a reliable and accurate flood inventory map confirmed the efficiency of the methodology. Therefore, the proposed method can assist researchers and planners in implementing and expediting flood inventory mapping

Spatial Monitoring of Desertification Extent in Western Iraq using Landsat Images and GIS

Copyright © 2017 John Wiley & Sons, Ltd. Desertification refers to land degradation in arid, semi-arid, and dry sub-humid areas caused by various factors, including climatic variations and human activities. In recent decades, sandstorms have increased significantly in Western Iraq, which primarily increased desert lands. Proper management is required to control and to monitor the phenomena, as well as to calculate the desertified areas caused by desertification. The study area covered 50,861.854 km2 in Western Iraq. Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI data for 1990, 2002, and 2014 were used. Maximum likelihood algorithm was used to classify the images. Change detection results were discussed in two terms: short-term (1990–2002) and (2002–2014) and long-term (1990–2014) analysis. Change detection analysis from 1990 to 2014 showed that desert area increased to 2286.7308 km2, becoming a new source of dust storms. Hazard occurrence probability was studied on September and October 2014. The desertification amount decreased from 1990 to 2002 and increased significantly from 2002 to 2014. Sandstorms have recently been considered a hazardous phenomenon affecting the human population, the vegetation, and the ecosystem in Iraq. Copyright © 2017 John Wiley & Sons, Ltd

Data fusion technique using wavelet transform and taguchi methods for automatic landslide detection from airborne laser scanning data and quickbird satellite imagery

Landslide mapping is indispensable for efficient land use management and planning. Landslide inventory maps must be produced for various purposes, such as to record the landslide magnitude in an area and to examine the distribution, types, and forms of slope failures. The use of this information enables the study of landslide susceptibility, hazard, and risk, as well as of the evolution of landscapes affected by landslides. In tropical countries, precipitation during the monsoon season triggers hundreds of landslides in mountainous regions. The preparation of a landslide inventory in such regions is a challenging task because of rapid vegetation growth. Thus, enhancing the proficiency of landslide mapping using remote sensing skills is a vital task. Various techniques have been examined by researchers. This study uses a robust data fusion technique that integrates high-resolution airborne laser scanning data (LiDAR) with high-resolution QuickBird satellite imagery (2.6-m spatial resolution) to identify landslide locations in Bukit Antarabangsa, Ulu Klang, Malaysia. This idea is applied for the first time to identify landslide locations in an urban environment in tropical areas. A wavelet transform technique was employed to achieve data fusion between LiDAR and QuickBird imagery. An object-oriented classification method was used to differentiate the landslide locations from other land use/covers. The Taguchi technique was employed to optimize the segmentation parameters, whereas the rule-based technique was used for object-based classification. In addition, to assess the impact of fusion in classification and landslide analysis, the rule-based classification method was also applied on original QuickBird data which have not been fused. Landslide locations were detected, and the confusion matrix was used to examine the proficiency and reliability of the results. The achieved overall accuracy and kappa coefficient were 90.06% and 0.84, respectively, for fused data. Moreover, the acquired producer and user accuracies for landslide class were 95.86% and 95.32%, respectively. Results of the accuracy assessment for QuickBird data before fusion showed 65.65% and 0.59 for overall accuracy and kappa coefficient, respectively. It revealed that fusion made a significant improvement in classification results. The direction of mass movement was recognized by overlaying the final landslide classification map with LiDAR-derived slope and aspect factors. Results from the tested site in a hilly area showed that the proposed method is easy to implement, accurate, and appropriate for landslide mapping in a tropical country, such as Malaysia

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

Landslide is considered as one of the most devastating and most costly natural hazards in highlands, which is triggered mainly by rainfalls or earthquakes. In comparison with other methods, landslide mapping and monitoring via remote sensing data products are considered as the least expensive method of data collection. The current research attempts to detect landslides which occurred due to a 6.9 magnitude earthquake in Sikkim Himalaya, India, on 18 September 2011 and also to establish the spatial relationship between landslides and the slope of the terrain. To detect the landslides, decision tree method was applied on two Indian remote sensing satellites linear imaging self-scanning sensor (LISS III) images acquired from 2007 and 2011 which were taken before and after the earthquake. As the study area was relatively huge for identifying the landslides, the region was separated into two parts: “tested study area” and “real study area”. The overall accuracy of landslide detection was 76%, and 75% for tested and real study area, respectively. Then, the spatial relationship between the landslides and the slope of the terrain was conducted using the digital elevation model. The results revealed that most of the landslides occurred between the slope of 25° and 45° covering 2.3 km2 and no landslide recorded in the slope of 65°–90° in the real study area. The results obtained in this study may be useful for decision-making and policy support towards reconstruction effort after the landslide occurrence. In addition, the information can be useful for reducing the risk of potential damages to substructures and properties by developing new and efficient strategies

Assessment of multi-scenario rockfall hazard based on mechanical parameters using high-resolution airborne laser scanning data and GIS in a tropical area

Rockfall hazard is a main threat for mountainous and hilly areas that can cause loss of life, damage to infrastructures, and traffic interruption. Rockfall frequency and magnitude vary both spatially and temporally; therefore, multi-scenarios related to rockfall characteristics (trajectories, frequency and kinetic energy) can provide early warnings by identifying the areas at risk for mitigation purposes. The aim of this study is to predict the areas at risk from future rockfall incidents and suggest suitable mitigation measures to prevent them. The most significant elements in rockfall analysis are slope topography interpretation or the digital elevation model (DEM) and the rockfall modeling approach. Light detection and ranging (LiDAR) techniques have been widely used in rockfall studies because of their capability to provide high-resolution information regarding slope surfaces. In the current study, airborne laser scanning (ALS) is used to obtain a high-density point cloud (4 pts./m2) of the study area for the construction of an accurate DEM via a geographic information system. Rockfall source areas were identified based on multi-criteria method including DEM derivatives (e.g., slope, aspect, curvature and topographic contrast) in addition to terrain type and aerial photos. A 3D rockfall model has been established to determine rockfall multi-scenarios based on their characteristics according to a range of restitution coefficient (normal and tangential) and friction angle values; these parameters are particularly crucial in rockfall simulation to delineate the spatial prediction of rockfall hazard areas along the Jelapang corridor of the North–South Expressway in Malaysia. In addition, a barrier location was suggested based on limited rockfall height and kinetic energy to mitigate rockfall hazards. Results show that rockfall trajectories (stopping distance) and, subsequently, their frequency and energy are increased; moreover, barrier efficiency is reduced when the values of the mechanical parameters (Rn, Rt, and friction angle) are increased. Nonetheless, the suggested barrier location is an efficient and mitigative measure to eliminate the rockfall effect