Today, 3D geo visualization of flood data is perceived as a more realistic and detailed solution for making decisions regarding flood mitigation and adaptation measures. In this paper, after a multi-criteria comparative study of four virtual globes used in the visualization of geospatial flood data, it is found that CesiumJS stands out the most from the other solutions, with a score close to 100% on all criteria grouped in four categories (visualization, interaction, quality of support and experiences). Using CesiumJS and other libraries, we proposed a 3D web solution to dynamically simulate and visualize floods in urban areas of Cameroon. The main objective of this tool is to strongly involve water experts, policymakers and the general public in flood management. With-out considering a precise 3D city model, this tool, however, represents a good compromise be-tween the quality of flood management and the cost of better Flood Management by an expert.

The mapping of wetland habitats requires image data of high spatial resolution in order to establish the precise contours and space occupies a specific habitat. However, the spectral deficiency of high resolution images accentuates the problems of proximity and spectral mixing between image objects, which makes them very sensitive classification operations in such environments. The present work offers a solution based on an unsupervised approach to habitat classification of the wetland lagoon of Oualidia and its surroundings. To do this, a picture RBV (1m) covering the study area was segmented from the software GRASS, followed by extraction optimal segments as polygons from QGIS software. The partitioning algorithm K-means was used for classification of selected polygons in the respective classes, and this using three (3) discrimination criteria (color, shape, and size). The objective is to propose a solution in the discrimination of different types of wetland habitats from a poor image spectral resolution, but harboring a very high spatial resolution. As such, the algorithm permits to classify the different habitats with an accuracy of 0.88 according to the index of Kappa.

The Spanish purse seiners fishing in the area between Cape boujdour (26

Spatial accuracy is important information in the mapping of wetland habitats, hence the recourse to the use of data with very high spatial resolution such as the IKONOS satellite images. However, the mediocrity of these spectral images; the presence of mixed pixels or spectral confusion between different objects in the image, make the process of discrimination of wetlands habitats difficult. This difficulty is amplified because these areas are home to diversified habitats, and in most times have spectral similarities between them. Taking into consideration the problems mentioned above, this work proposes a hybrid classification approach to better discriminate the habitats from an IKONOS data covering the Wetland of Merja Zerga (Moroccan Wetland of International Importance). This approach combines a supervised classification (per pixel) and an object-oriented classification (unsupervised) using DBSCAN algorithm. This classification approach allows discriminating all wetland habitats of the Merja Zerga and produces a map with an accuracy of 0.86 according to the index Kappa. The results suggest that this classification approach may also provide better results by applying it on images having similar characteristics to the IKONOS image.

In this work, we present a hybrid classification technique combining an expert system and an object-oriented approach. The expert system allows the integration of a knowledge base built through a series of deductive rules, that will guide the classification whose primitives requires informations on the highest level and will be represented by semantic objects, not pixels. Instead of the original bands only, other derived data combining textural, spectral information and shapes, are included in the classification process. The result is then combined with an expert system whose rules use variables such as vegetation index (NDVI), shading of building objects and other indicators. In conclusion, this approach has allowed us to improve the accuracy of the feature extraction method by extracting objects like, roads, trees, grass, bare soil and shadow on a very high-resolution image of the city of Rabat.

This work aims at identifying and mapping using Earth Observation (EO) and Geographic Information Systems (GIS), the water-erosion risk areas. The RUSLE (Revised Universal Soil Loss Equation) model, which can be used to estimate the water-erosion risk of soil, was applied to the productive Sangu

Greenhouse effect, which contributes to the climate warning, is a mechanism that occurs in the lower atmosphere because of the presence of Greenhouse Gas (GHGs). Its reinforcement by the emissions of anthropogenic greenhouse gases has harmful consequences on the climate. Togo, a developing country, contributes more to this reinforcement by the emissions related the socio-economic activities due to the Agriculture, Forestry and Other Land Use (AFOLU) area. We carried out these inventories of Greenhouse Gas in accordance with the IPCC Guidelines for National Greenhouse Gas Inventories, version 2006, using CCNUCC software for the national inventories of GHGs. In 2004, basic year selected, based on the quality of the data, the Agriculture subsector emitted 2407.88 Gg CO₂-e of direct GHGs (CH₄, N₂O) and 252,72 Gg of GHGs precursors (NO_x, CO). In Togo, these emissions have a tendency to increase passing the aggregated emissions from 2085.89 Gg CO₂-e in 1990 to 2526.22 Gg CO₂-e in 2008. The assessment of key categories of national emissions gave the priority to the biomass of cropland remaining cropland followed by biomass of forest land converted to cropland. These estimations will enable policy makers to take right decisions in matters of mitigation and adaptation and use them as baselines for calculations of carbon credits.