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Remote Sensing And Image Interpretation

All chapters of the previous edition [see HbA 37, 2103] have been completely rewritten to cover the rapid increase in research in this area. The number of literature citations have been reduced by referring to recent review articles and the book aims at a comprehensive and critical rather than encyclopaedic summary of the data of environmental chemistry. The 1st 4 chapters deal with air, water, rocks and soils; the cycling of C, N, H, O2 and S in the biosphere are covered in 1 chapter and the elemental Other CABI sites 

Environmental Chemistry of the Elements

The recognition of the importance of Integrated Water Resource Management (IWRM) is largely in response to addressing the ever-increasing limited water quality and quantity problems that the world faces Whereas officially, the United Nations through the Global Water Partnership (GWP) has endorsed IWRM as the best means to water resources management, lessons learnt from implementation to date, suggest the need to highlight the planning process more than the plan itself The methodological synthesis of the study incorporates ideas from broad theoretical areas covering ecological, economics, community and change management settings to inform the design of what is described as the appreciative systems planning (ASP) methodology used in this book The major contribution of this book is the design of a framework to overcome the dilemma of facilitating stakeholder involvement in IWRM planning processes Of particular importance is the explicit focus on the connection between social, economic and environmental dimensions in decision-making that the water resources issues represent

Integrated Water Resources Management

Global Isotope Hydrogeology―Review

The study of land use/land cover (LULC) has become an increasingly important stage in the development of forest ecosystems strategies. Hence, the main goal of this study was to describe the vegetation change of Azrou Forest in the Middle Atlas, Morocco, between 1987 and 2017. To achieve this, a set of Landsat images, including one Multispectral Scanner (MSS) scene from 1987; one Enhanced Thematic Mapper Plus (ETM+) scene from 2000; two Thematic Mapper (TM) scenes from 1995 and 2011; and one Landsat 8 Operational Land Imager (OLI) scene from 2017; were acquired and processed. Ground-based survey data and the normalized difference vegetation index (NDVI) were used to identify and to improve the discrimination between LULC categories. Then, the maximum likelihood (ML) classification method was applied was applied, in order to produce land cover maps for each year. Three classes were considered by the classification of NDVI value: low-density vegetation; moderate-density vegetation, and high-density vegetation. Our study achieved classification accuracies of 66.8% (1987), 99.9% (1995), 99.8% (2000), 99.9% (2011), and 99.9% (2017). The results from the Landsat-based image analysis show that the area of low-density vegetation was decreased from 27.4% to 2.1% over the past 30 years. While, in 2017, the class of high-density vegetation was increased to 64.6% of the total area of study area. The results of this study show that the total forest cover remained stable. The present study highlights the importance of the image classification algorithms combined with NDVI index for better understanding the changes that have occurred in this forest. Therefore, the findings of this study could assist planners and decision-makers to guide, in a good manner, the sustainable land development of areas with similar backgrounds.

Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco

Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area

Groundwater is a most important resource in arid and semi-arid regions and is required for drinking, irrigation and industrialization. Assessing the potential zone of groundwater recharge is extremely crucial for the protection of water quality and the management of groundwater systems. To identify the groundwater potential zone in the study area, thematic layers of lithology, slope, karst degrees, land cover, lineament and drainage density were generated using topographic maps, thematic maps, field data and satellite image, and were prepared, classified, weighted and integrated in a geographic information system (GIS) environment by the means of fuzzy logic. The fuzzy membership values have been assigned to different thematic layers according to their classification on respect for their contribution and their occurrence in groundwater. Based on the generated groundwater potential map, it was found that about 8% of the investigation area was categorized as very high potential for groundwater recha...

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2016.1181676

Groundwater potential of Middle Atlas plateaus, Morocco, using fuzzy logic approach, GIS and remote sensing

The studies of forest ecosystems from remotely-sensed data are of great interest to researchers because of ecosystem services provided by this ecosystem, including protection of soils and vegetation, climate stabilization, and regulation of the hydrological cycle. In this context, our study focused on the use of a spectral angle mapper (SAM) classification method for mapping species in the Azrou Forest, Central Middle Atlas, Morocco. A Sentinel-2A image combined with ground reference data were used in this research. Four classes (holm oak, cedar forest, bare soil, and others-unclassified) were selected; they represent, respectively, 27, 11, 24, and 38% of the study area. The overall accuracy of classification was estimated to be around 99.72%. This work explored the potential of the SAM classification combined with Sentinel-2A data for mapping land cover in the Azrou Forest ecosystem.

https://www.mdpi.com/2220-9964/6/9/275/pdf

Ali Essahlaoui

Papers

Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco

Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area

Groundwater potential of Middle Atlas plateaus, Morocco, using fuzzy logic approach, GIS and remote sensing

Mapping Forest Species in the Central Middle Atlas of Morocco (Azrou Forest) through Remote Sensing Techniques

Modélisation de l’érosion hydrique via les SIG et l’équation universelle des pertes en sol au niveau du bassin versant de l’Oum Er-Rbia