Landslide inventory and susceptibility modelling using geospatial tools,in Hunza-Nagar valley,northern Pakistan

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.

An international program on Silk Road Disaster Risk Reduction--a Belt and Road initiative (2016-2020)

Belt and Road Initiative(BRI) is a Chinese national strategy which calls for cooperative economic, political and cultural exchange at the global level along the ancient Silk Road. The overwhelming natural hazards located along the belt and road bring great challenges to the success of BRI. In this framework, a 5-year international program was launched to address issues related to hazards assessment and disaster risk reduction(DRR). The first workshop of this program was held in Beijing with international experts from over 15 countries. Risk conditions on Belt and Road Countries(BRCs) have been shared and science and technology advancements on DRR have been disseminated during the workshop. Under this program, six task forces have been setup to carry out collaborative research works and three prioritized study areas have been established. This workshop announced the launching of this program which involved partners from different countries including Pakistan, Nepal, Russia, Italy, United Kingdom, Sri Lanka and Tajikistan. The program adopted the objectives of Sendai Framework for Disaster Risk Reduction 2015-2030 and United Nation Sustainable Development Goals 2030 and was implemented to assess disaster risk in BRCs and to propose suitable measures for disaster control which can be appropriate both for an individual country and for specific sites. This paper deals with the outcomes of the workshop and points out opportunities for the near future international cooperation on this matter.

Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakistan

Anthropogenic activities and natural processes are continuously altering the mountainous environment through deforestation, forest degradation and other land-use changes. It is highly important to assess, monitor and forecast forest cover and other land-use changes for the protection and conservation of mountainous environment. The present study deals with the assessment of forest cover and other land-use changes in the mountain ranges of Dir Kohistan in northern Pakistan, using high resolution multi-temporal SPOT-5 satellite images. The SPOT-5 satellite images of years 2004, 2007, 2010 and 2013 were acquired and classified into land-cover units. In addition, forest cover and land-use change detection map was developed using the classified maps of 2004 and 2013. The classified maps were verified through random field samples and Google Earth imagery(Quick birds and SPOT-5). The results showed that during the period 2004 to 2013 the area of forest land decreased by 6.4%, however, area of range land and agriculture land have increased by 22.1% and 2.9%, respectively. Similarly, barren land increased by 1.1%, whereas, area of snow cover/glacier is significantly decreased by 21.3%. The findings from the study will be useful for forestry and landscape planning and can be utilized by the local, provincial and national forest departments; and REDD+ policy makers in Pakistan.

Landslide inventory and susceptibility assessment using multiple statistical approaches along the Karakoram highway,northern Pakistan

China-Pakistan Economic Corridor(CPEC)is a framework of regional connectivity,which will not only benefit China and Pakistan but will have positive impact on Iran,Afghanistan,India,Central Asian Republic,and the region.The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan.Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies.This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques.The very high-resolution(VHR)satellite images are utilized to develop a landslide inventory using the visual image classification techniques,historic records and field observations.A total of 1632 landslides are mapped in the area.Four statistical models i.e.,frequency ratio,artificial neural network,weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters,geological features,drainage and road network.The developed landslides susceptibility maps were verified using the area under curve(AUC)method.The prediction power of the model was assessed by the prediction rate curve.The success rate curves show 93%,92.8%,92.7%and 87.4%accuracy of susceptibility maps for frequency ratio,artificial neural network,weights of evidence and logistic regression,respectively.The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.

Regolith thickness modeling using a GIS approach for landslide distribution analysis, NW Himalayas

Regolith thickness is considered as a contributing factor for the occurrence of landslides.Although, mostly it is ignored because of complex nature and as it requires more time and resources for investigation. This study aimed to appraise the role of regolith thickness on landslide distribution in the Muzaffarabad and surrounding areas, NW Himalayas.For this purpose regolith thickness samples were evenly collected from all the lithological units at representative sites within different slope and elevation classes in the field. Topographic attributes(slope, aspect, drainage, Topographic Wetness Index,elevation and curvature) were derived from the Digital Elevation Model(DEM)(12.5 m resolution).Arc GIS Model Builder was used to develop the regolith thickness model. Stepwise regression technique was used to explore the spatial variation of regolith thickness using topographic attributes and lithological units. The derived model explains about 88% regolith thickness variation. The model was validated and shows good agreement(70%) between observed and predicted values. Subsequently, the derived regolith model was used to understand the relationship between regolith thickness and landslide distribution. The analysis shows that most of the landslides were located within 1-5 m regolith thickness. However, landslide concentration is highest within 5-10 m regolith thickness, which shows that regolith thickness played a significant role for the occurrence of landslide in the studied area.

Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan

Landsat-8 spectral values have been used to map the earth’s surface information for decades.However,forest types and other land-use/land-cover(LULC)in the mountain terrains exist on different altitudes and climatic conditions.Hence,spectral information alone cannot be sufficient to accurately classify the forest types and other LULC,especially in high mountain complex.In this study,the suitability of Landsat-8 spectral bands and ancillary variables to discriminate forest types,and other LULC,using random forest(RF)classification algorithm for the Hindu Kush mountain ranges of northern Pakistan,was discussed.After prior-examination(multicollinearity)of spectral bands and ancillary variables,three out of six spectral bands and five out of eight ancillary variables were selected with threshold correlation coefficients r2<0.7.The selected datasets were stepwise stacked together and six Input Datasets(ID)were created.The first ID-1 includes only the Surface Reflectance(SR)of spectral bands,and then in each ID,the extra one ancillary variable including Normalized Difference Vegetation Index(NDVI),Normalized Difference Water Index(NDWI),Normalized Difference Snow Index(NDSI),Land Surface Temperature(LST),and Digital Elevation Model(DEM)was added.We found an overall accuracy(OA)=72.8%and kappa coefficient(KC)=61.9%for the classification of forest types,and other LULC classes by using the only SR bands of Landsat-8.The OA=81.5%and KC=73.7%was improved by the addition of NDVI,NDWI,and NDSI to the spectral bands of Landsat-8.However,the addition of LST and DEM further increased the OA,and Kappa coefficient(KC)by 87.5%and 82.6%,respectively.This indicates that ancillary variables play an important role in the classification,especially in the mountain terrain,and should be adopted in addition to spectral bands.The output of the study will be useful for the protection and conservation,analysis,climate change research,and other mountains forest-related management information.

Applications of variogram modeling to electrical resistivity data for the occurrence and distribution of saline groundwater in Domail Plain,northwestern Himalayan fold and thrust belt,Pakistan

This paper studies electrical resistivity dataset acquired for a groundwater study in the Domail Plain in the northwestern Himalayan section of Pakistan. Through a combination of geostatistical analysis,geophysical inversion and visualization techniques,it is possible to re-model and visualize the single dimension resistivity data into 2D and 3D space.The variogram models are utilized to extend the interpretation of the data and to distinguish individual lithologic units and the occurrence of saline water within the subsurface. The resistivity data has been calibrated with the lithological logs taken from the available boreholes. As such the alluvial system of the Domail Plain has formed during episodes of local tectonic activity with fluvial erosion and depositionyielding coarse sediments with high electrical resistivities near to the mountain ranges and finer sediments with medium to low electrical resistivities which tend to settle in the basin center. Thus a change is depositional setting happened from basin lacustrine environment to flash flooding during the Himalayan orogeny. The occurrence of rock salt in the northern mountains has imparted a great influence on the groundwater quality of the study area. The salt is dissolved by water which infiltrates into the subsurface through the water channels. Variogram aided gridding of resistivity data helps to identify the occurrence and distribution of saline water in the subsurface.

Numerical Solution for a Similar Flow between Two Disks in the Presence of a Magnetic Field

Numerical solutions are obtained for non-steady, incompressible fluid flow between two parallel disks which at time t are separated by a distance H(1-αt)1/2 and a magnetic field proportional to B0(1-αt) -1/2 is applied perpendicular to the disks where H denotes a representative length, BO denotes a representative magnetic field and α-1 denotes a representative time. Similarity transformations are used to convert the governing partial differential equations of motion in to ordinary differential form. The resulting ordinary differential equations are solved numerically using SOR method, Richardson extrapolation and Simpson’s (1/3) Rule. Our numerical scheme is straightforward, efficient and easy to program.