Mapping and Spatial Analysis of Socio-economic and Environmental Indicators for Sustainable Development

Many studies have been conducted the implementation of sustainable development (SD) programs by using a set of indicators in various domains such as agriculture, energy, transport, exploitation of natural resources, health, environment, etc. The analysis of those indicators is an important step in improving its relevance. Therefore, to reach an effective result, the correlation between the indicators facilitates in the decision-making process. In this context, it is plausible to refer to the document about the indicators of sustainable development of Tunisia that was edited in 2014 by the Tunisian Observatory center of Sustainable Development: (OTEDD). It contains nine challenges that deal with various socioeconomic and environmental (SEE.I) problems. The endeavor of this study, then, is to determine one of these challenges which is the citizens’ quality of life (QoL). Our study area is Tataouine. For reaching our target and prioritizing a set of indicators and geographic representation tools, we have had recourse to the analytical hierarchy process (AHP). We have used the six indicators presented in relation to this challenge: Number of inpatients per basic health center, drinking water supply rate, rate of access to adequate (non-collective) sanitation including rate of connection to the public sewerage network, monitoring of air quality in urban areas, green space areas and urban parks per inpatient and proportion of rudimentary dwellings.

For validating the feasibility and effectiveness of the WikiGIS concept that has been proposed as a solution to better meet the main dimensions and requirements of Geodesign process, we have conducted a qualitative study based on a mixed methodology: questionnaire and interview with experts in various fields such as geomatics, architecture, urban planning, Geodesign, etc. This qualitative study aims initially at ensuring the relevance of developed features, which are managing traceability contributions, navigation in the history of contributions via a time browser and cartographic interface GeoWeb 2.0; and proposed WikiGIS features, which are the parameters of the data quality, the deltification, geoprocessing and sketching tools and the multimedia hyperlinks supporting the argument. Second, it is an opportunity to better understand Geodesign. In fact, thirty (30) experts have been involved. The results show that WikiGIS features are very relevant to support the collaborative dimension of the Geodesign process.

Several studies have recently dealt with the exploitation of data obtained from transiting mobile objects. In this respect, we have formulated the hypothesis that the history of the routes taken by emergency vehicles can provide information that can be exploited in researches on the best itinerary for an intervention. Accordingly, this article proposes an approach for the prediction and evaluation of the speed of a journey based on the technique known as “plotter vehicle.” The latter is a dynamic mode with graph per interval retained from route calculation in our study.

The aim of this study is to apply an object-based approach using multi-time series of high (Spot-5 images) and medium (ETM of Landsat, OLI TIRS of Landast and MSS of Landsat images) spatial resolution to characterize Land Cover of a heterogeneous territory, called Loukkos river basin which is a part of the western area of the Rifean belt. The images segmentation tests regarding several combinations between color levels (0.1–0.9) and scales (0–255) have confirmed a strong relationship among the spectral values of images radiance, with respect to the number of objects. Indeed, the authors have concluded that this relationship, is more strongly related to the values of the Standard Deviation of the images. The “Map difference” used to assess the accuracy of mapping is made less complicated and more accurate to the classical probabilistic methods. Furthermore, the overall accuracy of the Object-Oriented Classification was 80.20%. The study has shown that farmlands have been undergone more changes than urban classes, followed by wetlands and grasslands those have been converted to agricultural lands and lastly the transformation of forests to farmland and natural vegetation.

The vegetation cover of the area of Mateur (Tunisia) is characterized by the heterogeneity of its settlements. Such a heterogeneity is caused by the interaction of anthropic, pedological, and climatic factors. In addition, these space and spectral heterogeneities limit the reliability of the conventional methods of classification related to the satellite imagery. Thus, in the present study, we propose the recourse to the methods based on the spectral similarity to chart the dominant vegetable species of the ecosystem of Mateur, that is to say the Spectral angle mapper (SAM) and the classification of maximum of probabilities (MVS). We also aim at not only comparing procedures of extraction of the “pure” spectral signatures prototypes, known as endmembers for the SAM approach but also identifying the pieces of drives for the classification MVS in terms of the cartography of the dominant vegetable species of this area. For so doing, we have used images acquired by the sensor thermal (Advanced ASTER spaceborne emission and reflection radiometer. The results obtained show that the use of the methods of SAM and MVS led to similar results in terms of distribution of the species charted, but with differences in the plan of the surfaces affected by these species. The comparison between the results obtained using MVS and those of classification by maximum of probability indicates that SAM allows to classify the dominant vegetable cover with a better precision than MVS.

Remote sensing technology has long been applied in many fields of studies, especially in structural geology and mineral exploration, where remote sensing is useful for lineaments and structural feature extraction. In this study, Landsat ETM-7 satellite data have been used and band-8 is proved to be the most suitable band in automatic delineation. The automatic lineament extraction process has been carried out with LINE module of “PCI Geomatica” based on automatic detection algorithms. The lineaments analysis is conducted through the study of geospatial information incorporating the length, density, and direction of lineaments in order to acquire knowledge about the characteristics and proprieties of the geological structures. The purpose of this work is to evaluate the result of the automatic lineament extraction method and therefore to investigate its ability to give real results in comparison with the fault map.

In this paper, mass Movements hazard (MMH) was evaluated by using weights of evidence model (WOE). The Bab region (Central Rif, Morocco) is known as one of the most areas prone to mass movements (MM). High resolution remote sensing data interpretation and a systematic field survey were performed in order to obtain an inventory of MM. A total of 247 MM of the following types: landslides (45%), rock-falls (48%) and debris flows (7%) were identified, which covered an area of 20 \({\text {km}}^{2}\) and accounting for 3% of the study area (630 \({\text {km}}^{2}\)). Then, nine conditioning parameters characterizing topographical, geological, and environmental conditions were mapped, hierarchized and integrated as the main parameters, controlling the occurrence of MM. To eliminate the causal dependency a strategy based on the establishment of three neo-predictive variables (NPV) with a geomorphological significance from the parameters responsible for the conditional independency violation was implemented. Several simulations with several combinations of parameters were performed in order to examine the influence of each variable on the predictive power of MMH. The prediction capability of each model is determined by the area under the curve value and the best combination model was selected to generate the MMH map.

Our study endeavors to evaluate some morphometric indices that are selected according to several statistical studies and applied in Quaternary lithological formations. Our study area is the Tunisian Central Atlas, which is known by a Quaternary deposit that can reach 400 m. Because of certain constraints related to remote sensing limitations for the tectonic lineaments mapping in the quaternary and the lack of terrain index in most cases in these deposits, we are interested in spatially analyzing the response of tectonics to these morphometric indices among others, Stream Length (SL), Longitudinal Profile (LP) and Drainage Anomaly (DA). Our proposed methodology consists of evaluating, on the one hand and independently, the tectonic response to morphometric indices. On the other hand, we aim at spatially and arithmetically combine the density of SL, LP, DA by an equivalent weighting for these three indices in order to locate all the zones, detected by the one of indices or the other, and which are sensitive to recent deformation or neotectonics. The calculation of these three morphometric indices (SL, LP, DA) is their representation in terms of density, which allows us to evaluate each of them. The result obtained is consistent with some field surveys in the Kasserine plain Quaternary deposit. However, the cartographic spatial analysis through the maps of the density distribution of each index, lead us to combine the three indices in order to improve the spatial analysis by an equivalent arithmetic weighting to finally generate the high-density zones to prove the existence of recent deformation or neotectonics (the Kasserine plain) and the low density medium zones according to the synthesis map of the combination of the densities of the three indices.

Water supply for the arid irrigated plains in Morocco depends largely on the upper mountainous basins where significant amounts of precipitation fall as snow. In the Oum Er-Rbia River Basin (OER), snow covers the highest elevations from November to April. Despite the importance of this component in the hydrological cycle, snowmelt contribution to streamflow is still poorly understood and no monitoring stations exist in this zone. Therefore, studying the spatiotemporal change of snow cover through satellite observations to investigate its influence on the hydrological response of this scarce region is thus required to better manage water resources. This chapter explores basic characteristics of snow cover area (SCA) in the upstream area of the OER River (Tillouguite sub-basin) using MODIS daily snow cover products (MOD10A1). Correspondence between streamflow, accumulated air temperature and SCA changes during the winter and spring periods was examined from 2001 to 2009 at a weekly time step. The result shows an inverse linear relation between the maximum SCA and the mean normalized stream flow values, and a significant relation between the relative streamflow and cumulated temperature, especially during spring melt season depending on the length of the melt period. These primary results could be used to develop simplified predictable models for spring discharge in ungauged watershed using remote sensing and accumulated air temperature.

For mapping the three-dimensional shape of the submarine bars, studying their dynamics and assessing the spatio-temporal evolution of sedimentary balances, the coastal prism of Korba (emerged and submerged beach) has benefitted from topo-bathymetric monitoring. This monitoring was carried out on an annual basis between July 2006 and July 2009. Numerical Terrain Models (N.T.M.) and transverse profiles were then analyzed. The main results show, firstly, that the shoreface of Korba is characterized by a homogeneous evolution across all profiles of its two-bar system. Moreover, the extent of changes in the sand volume of the beach suggests that the evolution takes place without significant sedimentary loss and that the beach is in a dynamic equilibrium. Accretion of the bars and migration towards the shore are found in periods of small waves (between July 2006 and July 2007). However, following a period of minor agitations, a filling of the outer trough is observed. It is proceeded by the migration of the outer bar towards the coast (between July 2007 and July 2009). These results confirm the traditional model of self-organization of a barred beach. These data and results, then, allow us to establish conceptual models and simula- tions of the evolution of the microtidal, barred beach of Korba.

Through the application of new methods of digital restitution, photogrammetry applied to the urban domain has always contributed to the improvement of the knowledge and control of urban development especially in relation to rapidly changing spaces. In this study, we seek to develop a method of extracting road network from aerial photos at a scale of 1:25000, in the area of El Marsa, a northern suburb of Tunis, Tunisia. As it is generally known, from the intensity variations on the image, the one can make the segmentation, recognize the objects and the forms, and evaluate their spatial positions as well as the relations uniting them. In this context, the roads seem to be of particular interest in urban areas. Their automatic recognition makes them useful in various tasks, including mapping, recalling multisource images, urban planning and automatic navigation. However, despite the various works carried out on this field, none of the sophisticated road detection techniques is perfect. The difficulty of road extraction is seen while establishing a statistical test to know whether a given pixel of the image participates in the road network or not. In fact, this difficulty is accentuated in the urban environment as it is characterized by a strong heterogeneity of attributes. Compared to other cartographic themes such as buildings, contour lines, and vegetation, manual road entry is fast, and the road theme appears to be the easiest one to be automatically restituted. Our major concern, throughout this study, is to elaborate a methodological approach that enables us to replace manual photogrammetric capture by automatic or a semi-automatic captures from the Orthophoto of a 30 cm resolution. In relation to the current state of art, this photogrammetric capture is advantageous in time and material. This work on automatic interpretation of aerial images thus meets the need to optimize the productivity of photogrammetric capture strings without harming the quality of the final product. The problem raised however is related to the automatic extraction of roads in 2D and its transformation into 3D with validation. Our procedure is not limited to image processing, it rather extends to:

While the first step with “eCognition” allows the transformation of the buildings into vector format and polygon type, the second step with “ArcScene” contributes to the juxtaposition of the roads in a continuous way on the Orthophoto. The last step of our procedure consists in the layering of the roads on a Digital Terrain Model (DTM) using the “LPS” software, with a resolution of 1 m in planimetry. The results obtained are evaluated by quantitative statistical approach. In this perspective, it is important to further develop an automatic chain of quality control and validation of the restituted road network.

Landscapes can quickly change in the modern world, such as roads and buildings. There is a very dynamic modern world: cities are expanding, new settlements are emerging, new roads, communication networks, and infrastructure are being built, new areas for the recovery of natural resources are being developed, forests are being cut down and the structure of land use is changing. Since the topographical maps are the basis for each thematic map, official institutions avoid long intervals of time between each revision of the topographical map. Consequently, thematic maps are always subject to updating. Mapping production and publication are linked to numerous aspects, including data availability and legislative requirements. This article examines the potential of open geospatial data for updating touristic map using open source software.

In this paper the authors try to use time series of NDVI index, interpretation of thermal bands of images acquired by the Landsat 5 and 7, and object-based classification, to reveals the geo-biophysical causes observed that explains changes in vegetation greenness and globally in land cover evolution in the study area defined between Wadi Law and the northern Mediterranean coast of the Tinjitane peninsula (North of Morocco). The study area is a vast mountainous field, with rivers and agricultural patterns, forests and artificial surfaces. The combination of multi-sources data was crucial when establishing a classification within heterogeneous data in complexes landscapes. The object-based classification helped to best categorize the land cover classes in the study area. We observed clearly a global decreasing of forest areas between 1973 and 2015, and in parallel an increasing of the surface of Agriculture and natural vegetation class. The post-classification change vector detection is an optimal method to describe the evolution of the land cover in the context of this study.

Technological advances and increasing availability of satellite sensors acquire more information about the earth and offer the potential for more accurate land cover classifications and pattern analysis. However, this type of image (satellite image) is rich and various in content, however it suffers from noise that affects the image in the acquisition. Therefore, there is a requirement of an effective and efficient method for features extraction from the noisy image. This paper presents an approach for satellite image segmentation that automatically segments image using a supervised learning algorithm into urban and nonurban area. We have applied a statistical feature including local feature computed by using the probability distribution of the phase congruency computed (El Fellah S, El haziti M, Rziza M, et Mastere M, A hybrid feature extraction for satellite image segmentation using statistical global and local feature, 2016, [1]). The results provided, demonstrate a good detection of urban area with high accuracy in absence of noise. However when noise is added to images, the classification results deteriorate. Hence, to improve these results we propose a novel features based on higher order spectra known by their robustness against noise.