Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®

This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS^® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS^® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP.     

Avalanche hazard mitigation in east Karakoram mountains

Natural Hazards - Karakoram mountains range in north-western part of Himalayas is about 500&nbsp;km in length and hosts some of the world’s highest peaks and longest glaciers. It is...     

Analysis of dry and wet climate characteristics at Uttarakhand (India) using effective drought index

Natural Hazards - Drought is a complex natural disaster that adversely affects human life and the ecosystem. A variety of drought indexes are available for monitoring meteorological drought events....     

Evaluation of spatial probability of landslides using bivariate and multivariate approaches in the Goriganga valley,Kumaun Himalaya,India

Natural Hazards - In this study, new hybrid artificial neural network (ANN) models were used for predicting the groundwater resource index. The salp swarm algorithm (SSA), particle swarm...     

Correction to: Effect of time and space partitioning strategies of samples on regional landslide susceptibility modelling

Landslides - A Correction to this paper has been published: https://doi.org/10.1007/s10346-021-01646-0.     

Effect of time and space partitioning strategies of samples on regional landslide susceptibility modelling

Landslides - Assessment of the spatial probability of future landslide occurrences for disaster risk reduction is done through landslide susceptibility modelling. In this study, we investigated the...     

Geospatial landslide inventory of India—an insight into occurrence and exposure on a national scale

Landslides - India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India’s landslide...     

Extenuating the parameters using HEC-HMS hydrological model for ungauged catchment in the central Omo-Gibe Basin of Ethiopia

Characteristics of ungauged catchments can be studied from the hydrological model parameters of gauged catchments. In this research, discharge prediction was carried out in ungauged catchments using HEC-HMS in the central Omo-Gibe basin. Linear regression, spatial proximity, area ratio, and sub-basin mean were amalgamated for regionalization. The regional model parameters of the gauged catchment and physical characteristics of ungauged catchments were collated together to develop the equations to predict discharge from ungauged catchments. From the sensitivity analysis, crop coefficient (CC), storage coefficient (R), constant rate (CR), and time of concentration (TC) are found to be more sensitive than others. The model efficiency was evaluated using Nash–Sutcliffe Efficiency (N_SE) which was greater than 0.75, varying between ?10% and +10% and the coefficient of determination (R²) was approximated to be 0.8 during the calibration and validation period. The model parameters in ungauged catchments were determined using the regional model (linear regression), sub-basin mean, area ratio, and spatial proximity methods, and the discharge was simulated using the HEC-HMS model. Linear regression was used in the prediction where p-value ≤ 0.1, determination coefficient (R²) = 0.91 for crop coefficient (CC) and 0.99 for maximum deficit (MD). Constant rate (CR), maximum storage (MS), initial storage (IS), storage coefficient (R), and time of concentration (TC) were obtained. The result is that an average of 30 m³/s and 15 m³/s as the maximum monthly simulated flow for ungauged sub-catchments, i.e. Denchiya and Mansa of the main river basin .     

Econometric analysis of the ship demolition market

The topic of ship recycling has obtained considerable attention during the last two decades for a variety of reasons with the likelihood of the adoption of a new international convention under the auspices of the International Maritime Organization (IMO). This study applies econometric modeling to a unique data set to provide insight into the dynamics of the ship recycling market. The data set contains information on 51,112 ships over 100 gt and includes 748,621 events over a period of 29 years. The analysis confirms a negative relationship of earnings and a positive relationship of scrap prices for all locations while Bangladesh seems to be more sensitive to changes in earnings than the other locations and more likely demolishes larger and older vessels. The results for flag and ownership vary across scrapping locations with Malta and Cyprus indicating potential importance from a registry perspective. The overall safety profile of a vessel seems to be less important towards the probability of a ship being scrapped. Possible implementation of the convention at EU level will mostly likely affect Turkey while non-ratification of one of the major flags will most likely affect China or Bangladesh.