Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera)

The fundamental difficulty in mapping urban areas, especially in semi-arid and arid environments, is the separation of built-up areas from bare lands, owing to their similar spectral characteristics. Accordingly, this study aims to identify the suitable spectral index that can provide high differentiation, between urban areas and bare lands, in semi-arid areas of three cities of the province of Djelfa, namely, Djelfa, Messaad, and Ain Oussera (Algerian central highlands), through a selection of four spectral indices including Urban Index (BUI), Band ratio for built-up area (BRBA), Normalized Difference Tillage Index (NDTI) and Dry Bare-soil Index (DBSI). In order to increase the mapping accuracy of the built-up in studied areas, a multi-index approach has been applied focusing on identifying an adequate combination of spectral indices of remote sensing that provides the highest performance compared to the images of sentinel 2A. The multi-index approach was developed using three spectral indices combinations and was created using a layer stack process. For forming bare land layer stacking data, both NDTI and DBSI indices were used, while the built-up area layer stacking data was made with both BUI and BRBA indices. The main process was carried out on the Cloud Computing Platform based on geospatial data of Google Earth Engine (GEE) and using machine learning classification by the Support Vector Machine (SVM) algorithm, based on imagery from sentinel 2A acquired during the dry season. The results indicated that the thresholds of the built-up areas are difficult to delineate and distinguish from bare land efficiently with a single index. The obtained results also revealed that the use of multi-index including BUI index provided the best results as they showed the highest effects with NDTI index and DBSI index compared to BRBA index, where the overall accuracies of the multi-index (DBSI/ NDTI/ BUI) were 98.7% in Djelfa, 96.5% in Messaad, and 97.87 % in Ain Oussera, and the kappa coefficients were 97.3%, 85.4%, and 95.3% respectively. These results show that this multi-index is effective and reliable and can be considered for use in other areas with similar characteristics.

Delineation of Built-Up Areas from Very High-Resolution Satellite Imagery Using Multi-Scale Textures and Spatial Dependence

Very high spatial resolution (VHR) satellite images possess several advantages in terms of describing the details of ground targets. Extracting built-up areas from VHR images has received increasing attention in practical applications, such as land use planning, urbanization monitoring, geographic information database update. In this study, a novel method is proposed for built-up area detection and delineation on VHR satellite images, using multi-resolution space-frequency analysis, spatial dependence modelling and cross-scale feature fusion. First, the image is decomposed by multi-resolution wavelet transformation, and then the high-frequency information at different levels is employed to represent the multi-scale texture and structural characteristics of built-up areas. Subsequently, the local Getis-Ord statistic is introduced to model the spatial patterns of built-up area textures and structures by measuring the spatial dependence among frequency responses at different spatial positions. Finally, the saliency map of built-up areas is produced using a cross-scale feature fusion algorithm, followed by adaptive threshold segmentation to obtain the detection results. The experiments on ZY-3 and Quickbird datasets demonstrate the effectiveness and superiority of the proposed method through comparisons with existing algorithms

Anthropogenic and climatic controls on surface water across the contiguous United States

Anthropogenic activities and climatic processes heavily influence surface water resources by causing their progressive depletion, which in turn affects both societies and the environment. Therefore, there is an urgent need to understand the contribution of human and climatic dynamics on the variation of surface water availability. Here, this investigation is performed on the contiguous United States (CONUS) using remotely-sensed data. Three anthropogenic (i.e., urban area, population, and irrigation) and two climatic factors (i.e., precipitation and temperature) were selected as potential drivers of changes in surface water extent and the overlap between the increase or decrease in these drivers and the variation of surface water was examined. Most of the river basins experienced a surface water gain due to precipitation increase (eastern CONUS), and a reduction of irrigated land (western CONUS). River basins of the arid southwestern region and some river basins of the northeastern area encountered a surface water loss, essentially induced by population growth, along with a precipitation deficit and a general expansion of irrigated land. To further inspect the role of population growth and urbanization on surface water loss, the spatial interaction between human settlements and surface water depletion was examined by evaluating the frequency of surface water loss as a function of distance from urban areas. The decline of the observed frequency was successfully reproduced with an exponential distance-decay model, proving that surface water losses are more concentrated in the proximity of cities. Climatic conditions influenced this pattern, with more widely distributed losses in arid regions compared to temperate and continental areas. The results presented in this Thesis provide an improved understanding of the effects of anthropogenic and climatic dynamics on surface water availability, which could be integrated in the definition of sustainable strategies for urbanization, water management, and surface water restoration