Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt

Integrating Sponge City Concept and Neural Network into Land Suitability Assessment: Evidence from a Satellite Town of Shenzhen Metropolitan Area

Land suitability assessment is fundamental in space control planning and land development because of its effects on land use and urban layout. Rainstorms and waterlogging have become one of the most common natural disasters in the coastal areas of China. As a result, the concept of an ecological sponge city was incorporated into the construction of cities in the future. Taking Shenzhen–Shantou special cooperation zone (SSCZ), we constructed a storm flooding model based on the SCS flow generation model and GIS to explore the spatial distribution characteristics of the flooding risk in a rainstorm of 100-year lasting 1 h. Combined with population and economic indicators, a radial basis function (RBF) network was utilized to evaluate the environmental risk, the vulnerability of disaster-bearing bodies, and the rain–flood resilience of sponge cities. The self-organizing feature mapping (SOFM) model was used for cluster analysis. Spatial differences were found in the construction suitability of the study area. A suitable construction area (73.59% of the entire area) was located downtown. The construction of the artificial spongy body in the highest vulnerable area (3.25%) needs to be strengthened. The control construction area (3.3%) is located along the banks of the river, with relatively high risk and low resilience of flood control engineering. Ecological construction (19.85%) serves as the sponge body of ecological buffer. The factors of waterlogging, ecology, population, and economy could be integrated comprehensively by applying neural network methods for urban planning and construction.

Estimation of surface runoff using NRCS curve number in some areas in northwest coast, Egypt

The sustainable agricultural development in the northwest coast of Egypt suffers constantly from the effects of surface runoff. Moreover, there is an urgent need by decision makers to know the effects of runoff. So the aim of this work is to integrate remote sensing and field data and the natural resource conservation service curve number model (NRCS-CN).using geographic information systems (GIS) for spatial evaluation of surface runoff .CN approach to assessment the effect of patio-temporal variations of different soil types as well as potential climate change impact on surface runoff. DEM was used to describe the effects of slope variables on water retention and surface runoff volumes. In addition the results reflects that the magnitude of surface runoff is associated with CN values using NRCS-CN model . The average of water retention ranging between 2.5 to 3.9m the results illustrated that the highest value of runoff is distinguished around the urban area and its surrounding where it ranged between 138 - 199 mm. The results show an increase in the amount of surface runoff to 199 mm when rainfall increases 200 mm / year. The north of the area may be exposed to erosion hazards more than the south and a change in the soil quality may occur in addition to the environmental imbalance in the region.

Monitoring the Extent of Reclamation of Small Scale Mining Areas Using Artificial Neural Networks

Small scale mining is mainly widespread in developing and underdeveloped countries. Although it is a source of livelihood for several people, it causes environmental degradation. Reclamation is needed to restore mined areas to an acceptable condition. This study uses ANN to monitor reclamation activities in small scale mining area. Landsat satellite images of study area (2007, 2011 and 2016), ground truth data and ESRI shapefile of the study area were used for the analyses. Two ANN classification methods, Unsupervised Self – Organized Mapping (SOM) and Supervised Multilayer Perceptron (MLP), were used for the classification of the satellite images. Normalized Difference Vegetation Index (NDVI) change maps were generated in order to help confirm where actual change had occurred and to what extent it had occurred. The results show disturbance and revegetation in the study area between 2007 and 2016. The Barelands/mined areas class increased by 60.4% and a decrease in the vegetation class by 18.7% from 2007 to 2011. There was revegetation from 2011 to 2016 with the Barelands/Mined Area decreasing by 51.7% and the vegetation increasing by 3.9%. The study shows an increase in the settlement class by 87.3%. The research concludes that the application of ANN be strongly encouraged for image classification and mine reclamation monitoring in the country due to the size and quality of training data, network architecture, and training parameters as well as the ability to improve the accuracy and fine tune information obtained from individual classes as compared to other classification methods.

Monitoring and assessment of seasonal land cover changes using remote sensing: a 30-year (1987-2016) case study of Hamoun Wetland, Iran

The availability of Landsat data allows improving the monitoring and assessment of large-scale areas with land cover changes in rapid developing regions. Thus, we pretend to show a combined methodology to assess land cover changes (LCCs) in the Hamoun Wetland region (Iran) over a period of 30-year (1987-2016) and to quantify seasonal and decadal landscape and land use variabilities. Using the pixel-based change detection (PBCD) and the post-classification comparison (PCC), four land cover classes were compared among spring, summer, and fall seasons. Our findings showed for the water class a higher correlation between spring and summer (R² = 0.94) than fall and spring (R² = 0.58) seasons. Before 2000, ~ 50% of the total area was covered by bare soil and 40% by water. However, after 2000, more than 70% of wetland was transformed into bare soils. The results of the long-term monitoring period showed that fall season was the most representative time to show the inter-annual variability of LCCs monitoring and the least affected by seasonal-scale climatic variations. In the Hamoun Wetland region, land cover was highly controlled by changes in surface water, which in turn responded to both climatic and anthropogenic impacts. We were able to divide the water budget monitoring into three different ecological regimes: (1) a period of high water level, which sustained healthy extensive plant life, and approximately 40% of the total surface water was retained until the end of the hydrological year; (2) a period of drought during high evaporation rates was observed, and a mean wetland surface of about 85% was characterized by bare land; and (3) a recovery period in which water levels were overall rising, but they are not maintained from year to year. After a spring flood, in 2006 and 2013, grassland reached the highest extensions, covering till more than 20% of the region, and the dynamics of the ecosystem were affected by the differences in moisture. The Hamoun wetland region served as an important example and demonstration of the feedbacks between land cover and land uses, particularly as pertaining to water resources available to a rapidly expanding population.