Dhaka city water logging hazards: area identification and vulnerability assessment through GIS-remote sensing techniques.

Water logging is one of the most detrimental phenomena continuing to burden Dhaka dwellers. This study aims to spatio-temporarily identify the water logging hazard zones within Dhaka Metropolitan area and assess the extent of their water logging susceptibility based on informal settlements, built-up areas, and demographical characteristics. The study utilizes integrated geographic information system (GIS)-remote sensing (RS) methods, using the Normalized Difference Vegetation Water and Moisture Index, distance buffer zone from drainage streams, and built-up distributions to identify waterlogged zones with a temporal extent, incorporating social and infrastructural attributes to evaluate water logging effects. These indicators were integrated into an overlay GIS method to measure the vulnerability level across Dhaka city areas. The findings reveal that south and south-western parts of Dhaka were more susceptible to water logging hazards. Almost 35% of Dhaka belongs to the high/very highly vulnerable zone. Greater number of slum households were found within high to very high water logging vulnerable zones and approximately 70% of them are poorly structured. The built-up areas were observed to be increased toward the northern part of Dhaka and were exposed to severe water logging issues. The overall findings reveal the spatio-temporal distribution of the water logging vulnerabilities across the city as well as its impact on the social indicators. An integrated approach is necessary for future development plans to mitigate the risk of water logging.

Spatial soil loss prediction impacted by long-term land use/land cover change: a case study of Swat District.

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha-1/year-1, 120 t/ha-1/year-1, and 96 t/ha-1/year-1, respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography.

Temporal modelling of Lymnaea natalensis (Krauss, 1848) in tropical aquatic habitats.

Lymnaea natalensis is the only snail intermediate host of Fasciola gigantica, the causative agent of fascioliasis, in Nigeria. The species also serves as intermediate host for many other African trematode species of medical and veterinary importance, and it is found throughout the country. However, there is no detailed information on the factors that influence its distribution and seasonal abundance in the tropical aquatic habitats in Nigeria. This study used the geographic information system and remotely sensed data to develop models for predicting the distribution of L. natalensis in South-Western Nigeria. Both land surface temperature (LST) and normalised difference vegetation index (NDVI) were extracted from Landsat satellite imagery; other variables (slope and elevation) were extracted from a digital elevation model (DEM) while rainfall data were retrieved from the European Meteorology Research Programme (EMRP). These environmental variables were integrated into a geographic information system (GIS) to predict suitable habitats of L. natalensis using exploratory regression. A total of 1410 L. natalensis snails were collected vis-à-vis 22 sampling sites. Built-up areas recorded more L. natalensis compared with farmlands. There was no significant difference in the abundance of snails with season (p  0.05). The regression models showed that rainfall, NDVI, and slope were predictors of L. natalensis distribution. The habitats suitable for L. natalensis were central areas, while areas to the north and south were not suitable for L. natalensis.Contribution: The predictive risk models of L. natalensis in the study will be useful in mapping other areas where the snail sampling could not be conducted.

Sanitary municipal landfill site selection by integration of GIS and multi-criteria techniques for environmental sustainability in Safita area, Tartous governorate, Syria.

Urban waste disposal is a problem that poses a major challenge to city planners as a result of rapid population growth and urbanization. Finding suitable sites for solid waste is one of the most important solutions developed globally to manage this problem. In this regard, a set of physical, socio-economic and technological criteria must be considered to tackle the problem. Safita area (Tartous governorate) witnessed a rapid population growth during the decade of the war in Syria due to the onrush of internal refugees, which resulted in several environmental problems, including random waste dumps. After perusing the previous literature and considering expert opinions, a map of the spatial suitability of sustainable waste sites in the Safita area was developed by integrating the multi-criteria decision- making methodology (analytic hierarchy process) with the geographic information system. Thirteen criteria, including elevation, slope, permeability, distance to faults, distance to settlement, land use/land cover, distance to drainage, distance to water supplies, distance to lakes, distance to road, distance from tourist centers, distance from archaeological centers, and distance from religious centers, were used to achieve the goal of this study. The layer maps for these criteria were developed based on various data sources, including conventional and remote sensing data. Potential landfill sites were identified and divided into five categories: unsuitable (83.28%), less suitable (8.49%), moderately suitable (4.49%), highly suitable (2.57%), and very highly suitable (0.72%). The results of this study provide reliable spatial outputs that will help in suggesting new landfill sites that maintain environmental and socio-economic sustainability in the post-war phase. Moreover, the application of the methodology of this study can be generalized to the rest of the regions in Syria within the framework of the integrated management of the problem of random landfills.

Models for predicting bulinids species habitats in southwestern Nigeria.

Background: Schistosomiasis prevalence is high in southwestern Nigeria and planorbids of the genus Bulinus had been implicated in the transmission of the disease in the area. The knowledge of species distribution in relation to environmental variables will be auspicious in planning control strategies. Methods: Satellite imagery and geographic information system (GIS) were used to develop models for predicting the habitats suitable for bulinid species. Monthly snail sample collection was done in twenty-three randomly selected water contact sites using the standard method for a period of two years. Remotely sensed variables such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) were extracted from Landsat TM, ETM+; Slope and Elevation were obtained from digital elevation model (DEM) while Rainfall was retrieved from European Meteorology Research Program. These environmental factors and snail species were integrated into QGIS to predict the potential habitats of different bulinid species using an exploratory regression model. Results: The following environmental variables: flat-moderate slope (0.01-15.83), LST (21.1 °C-23.4 °C), NDVI (0.19-0.52), rainfall (> 1569.34 mm) and elevation (1-278 m) contributed to the model used in predicting habitat suitable for bulinids snail intermediate hosts. Exploratory regression models showed that LST, NDVI and slope were predictors of Bulinus globosus and Bulinus jousseaumei; elevation, LST, rainfall and slope were predictors of Bulinus camerunensis; rainfall, NDVI and slope were predictors of B. senegalensis while NDVI and slope were predictors of Bulinus forskalii in the area. Bulinids in the forskalii group showed clustering in middle belt and south. The predictive risk map of B. jousseaumei was similar to the pattern described for B. globosus, but with a high R-square value of 81%. Conclusion: The predictive risk models of bulinid species in this study provided a robust output for the study area which could be used as base-line for other areas in that ecological zone. It will be useful in appropriate allocation of scarces resources in the control of schistosomiasis in that environment.