African Association of
Remote Sensing of the Environment


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  • 08 Oct 2012 6:30 PM | Anonymous


     Land suitability analysis is a prerequisite to achieving optimum utilization of the available land resources for sustainable agricultural production. Comprehensive, reliable and timely information on agricultural resources is very necessary for a country like Kenya, where agriculture is the mainstay of our national economy. Within Kenya, the demand for rice continues to grow as more Kenyans make changes in their eating habits, and as urban population increases but the production is very low. Lack of knowledge on best combination of factors that suit production of rice has contributed to the low production. The aim of this study was to develop a suitability map for rice crop based on physical and climatic factors of production using a Multi-Criteria Evaluation (MCE) & GIS approach. The study was carried out in Kirinyaga, Embu and Mberee counties of Central and Eastern province in Kenya. Biophysical variables of soil (soil pH, soil texture and soil drainage), climate (humidity and temperature) and topography were considered for suitability analysis. All data were stored in Arc GIS 9.3 environment and the factor maps were generated. For Multi-Criteria Evaluation (MCE), Pairwise Comparison Matrix was applied and the suitable areas for rice crop were generated and graduated. The current land use / land cover map of the area was developed from a scanned survey map of the rice growing areas in the region. According to the present land use/cover map, the rice cultivated area was 13,369 ha. Finally, we overlaid the land use/cover map with the suitability map for rice production to identify differences and similarities between the present and potential land use. However, the crop-land evaluation results of the present study identified that in the study area, 75 percent of total rice crop currently being used was under highly suitable areas and 25 percent was under moderately suitable areas. The results showed that the potential area for rice growing is 86,364 ha and out of this only 12% is under rice cultivation. This research provided information at local level that could be used by farmers to select cropping patterns and suitability.


    This post was written by Joseph Kihoro Mwangi, John Njoroge, and Hunja Murage

    (Jomo Kenyatta University of Agriculture and Technology). Contact Joseph Kihoro Mwangi at for more information.

  • 08 Oct 2012 6:00 PM | Anonymous

    Climate change became a reality. Their impacts are rampant in many parts of the world: hurricanes, persistent droughts and rising sea levels hit many people throughout the world.

    In order to investigate these disturbances, many indicators of climate change have been developed by the National Observatory on the Effects of Global Warming French (ONERC), the Environmental Protection Agency (EPA), the Organization for Economic Cooperation and Development (OECD) ... and presented by the Intergovernmental Panel on Climate Change (IPCC) and the World Meteorological Organization (WMO). An indicator represents the state of certain environmental conditions over a given area and a specified period of time. Indicators increase our understanding of the causes and effects of climate change. Environmental indicators are a key tool for evaluating existing and future programs and providing sound science for decision-making.

    Morocco is not an exception to climate change; observations have shown that all regions of the Kingdom will be affected one way or another by these changes, which will increase their vulnerability and affect the two sectors country's most important as water and agriculture. Among these regions, we chose one of Marrakesh Tensift Al Haouz because of her geographical position and limited water resources.

    So as to betray climate change in this region, a list of these indicators has been established. Between these indicators, we picked up to work on increasing the average temperature of air by the calculation of four climate indices. To do this, we relied on the maximum and minimum temperatures of the two stations study Marrakech and Essaouira using the outputs of Statistical DownScaling Model (SDSM). Subsequently, we integrated the projections of future climate of the region, given by the same model, in a Geographic Information System (GIS).

    The results of this study show an upward trend in temperatures combined with reduced rainfall, these developments are likely to increase pressure on water resources and consequently will affect agriculture and food security in the region.


    This post was written by Niama Boukachaba (Cadi Ayyad University- Marrakesh-Morocco). Contact her at for more information.

  • 08 Oct 2012 12:48 PM | Anonymous
    In the course of the severe drought at the Horn of Africa and the ongoing violent conflict in Somalia in summer 2011, more than 150,000 refugees arrived in Dadaab, Kenya, which is currently the world’s largest refugee camp complex. The enormous influx of people to the Dagahaley refugee camp, one of the three camps in Dadaab, brought the camp registration to a halt and revealed the need for a more efficient camp monitoring. Newly arrived refugees had to settle in the outskirts of the camp. The number and spatial distribution of dwellings could not be observed on the ground due to time and security constraints. In the frame of a Cooperation Agreement (Memorandum of Understanding, MoU) with Médecins Sans Frontières (MSF), the Centre for Geoinformatics at Salzburg University monitored the camp evolution using very high spatial resolution (VHSR) satellite imagery and provided in-depth information for supporting resource planning. Information on the amount and type of different dwelling structures and their spatial distribution was extracted by semi-automated analysis of WorldView-2 imagery (8 MS bands, 0.5 m GSD) from July 2011 and December 2011. Both images were partly affected by clouds and cloud shadows. Therefore, the eastern part of the December image was replaced by an additional image from January 2012.

    The semi-automated dwelling extraction relied on object-based image analysis (OBIA), which provides a methodological framework for addressing complex information classes, defined by spectral, spatial, contextual as well as hierarchical properties. Expert knowledge is represented through rulesets coded in CNL (Cognition Network Language) in eCognition 8 software, which offers a modular programming environment for (image-)object handling. Objects may be addressed individually through class modeling, a cyclic process of segmentation and classification. For the analysis of the 1st timeslot three dwelling types were distinguished: tents, huts and dwellings with corrugated iron roof. Tents and makeshift huts could mainly be observed in the newly settled areas in the western outskirts of the camp, whereas dwellings with corrugated iron roof were the predominant dwelling type in the main part of the camp. The ruleset developed for the July image could be partly transferred to the December image. However, such clearly distinctive indicators of newly settled areas nearly have disappeared at the 2nd timeslot, e.g. only very few makeshift huts were still present and many dwellings with corrugated iron roof have been covered with white plastic sheeting due to the rainy season, which made a differentiation to white tents unfeasible. Therefore only one class dwelling was extracted for the 2nd timeslot. For shaded areas in both images, even though WorldView-2 still provided appropriate information due to its high radiometric resolution, the ruleset had to be slightly adapted to extract relevant objects. Finally, minor manual refinement was performed to eliminate obvious classification errors. The analysis of the July scene revealed about 23,400 dwellings: 13,950 dwellings with corrugated iron roof, 6,650 tents and 2,800 huts. In December 21,950 dwellings were extracted. In addition to single extracted dwellings the dwelling density (dwellings/km²) was calculated using Kernel density methods to provide easy to grasp information about the spatial distribution of dwellings. Based on the dwelling density the camp extent was derived automatically (see Fig. 1). A change analysis of dwellings aggregated on hexagonal units shows a decrease of dwellings in the western outskirts of the camp from July 2011 to December 2011. On the other hand, dwelling density increased in the main part of the camp and a minor increase of single dwellings in the eastern outskirts of the camp could be observed as well (see Fig. 1). Areas which were covered by clouds in either of the two images were excluded from the change analysis. Results have been delivered as maps in PDF-format as well as Google’s kml-files.

    Figure 1: Change detection analysis based on single extracted dwellings in the Dagahaley refugee camp between July 2011 and December 2011. Blue tones indicate areas of dwelling decrease, red tones show an increase of dwellings and grey areas did not undergo a significant change. Clouds in either of the images were not taken into account for the change analysis (dashed areas). The camp extent of July 2011 is displayed in green, whereas the red outline shows the camp extent of December 2011. The WorldView-2 image in the background is a combination of the December 2011 image and the January 2012 image (eastern part) and is displayed in true colour composite.

    The study shows that relevant and up-to date information in regard to amount and spatial distribution of affected population during humanitarian crises can be provided for inaccessible areas by making use of VHSR satellite imagery. Geo-information can contribute to make humanitarian aid more efficient, timely and effective.

    This post was written by Petra Füreder, Daniel Hölbling, Dirk Tiede, Peter Zeil and Stefan Lang (Centre for Geoinformatics, University of Salzburg). Contact Petra Füreder at for more information.

  • 08 Oct 2012 12:30 PM | Anonymous


    RAKOTONDRAOMPIANA, Solofo(1),(2) ; FARAMALALA, Miadana(3) ; RAKOTONIAINA, Solofoarisoa(1),(4) ; RAZANAKA, Samuel(5)

    1. Institut & Observatoire de Géophysique d’Antananarivo (IOGA), laboratoire de géophysique de l’environnement et télédétection. Université d’Antananarivo (Madagascar)
    2. Ecole Supérieure Polytechnique d’Antananarivo, département de géologie. Université d’Antananarivo (Madagascar)
    3. Faculté des Sciences, département de biologie et écologie végétales. Université d’Antananarivo (Madagascar)
    4. Faculté des sciences, département de physique. Université d’Antananarivo (Madagascar)
    5. Centre National de Recherche sur l’Environnement (CNRE). Madagascar


    Comité National Télédétection (CNT) is a network of spatial images end-users in Madagascar. Objectives are capacity building, to share information and to encourage emergence of new projects using remote sensing data. CNT was operational since 2009 and this is our first return of experiences.

    CNT is an initiative of searchers from University of Antananarivo and it gathers about forty institutions including public administration, universities, private companies and NGOs.

    It allows us to transmit to member scientific information about satellites and remote sensing data, to get information from members about their situation so we could use this information for capacity building and negotiation with foreign partners.

    CNT has now its own website where we put all information to member and share image data to all users.

    But we also meet some problems concerning mainly the legal status of CNT.


    This post was written by Solofo Rakotondraompiana, Miadana Farmalala, Solofoarisoa Rakotoniaina, Samuel Razanaka. Contact Solofo Rakotondraompiana for more information.

  • 08 Oct 2012 12:20 PM | Anonymous
    In Morocco, establishing an Integrated Water Resource Management System is fundamental for sustainable development. With regards to its mission CRTS is carrying numerous actions through the country to demonstrate the role of the rapidly evolving earth observation programs for collecting and disseminating water related information in cost effective and sustainable ways. In this paper, parts of the results from these actions will be presented from different applications in different water contexts.

    The first case study aims at developing, in the Souss-Massa hydraulic basin, an integrated approach including the exploitation of the satellite data, the pre-existing data and the Geographical Information Systems (GIS) as sources of information and tools of analysis within the water management process. This region has continuous aquifers that are facing a severe depletion because of intensive irrigated activities. The use of multi-sensors and multi-temporal satellite images (optics and radar) enables to highlight new practices on the current situation of the land use, particularly in the irrigated areas during the last decades. The land use changes analysis shows the surface expression of the groundwater over-exploitation by generating an intensive dynamic, with regards to different aspects of land use changes, in particular in terms of irrigated zones extension. These changes are of two forms, closely related to the reduction (disappearance) or the extension (appearance) of irrigated agricultural activities. In the first case, they are essentially related to the urbanization pressure, soil degradation, and groundwater overexploitation. In the second case, considered as more dominant, these changes take form of appearance of new irrigated farms where groundwater is still more available. Although the general trend at the basin scale shows a continuous increase in irrigated surfaces, locally these irrigated zones are disappearing. The extension seems to be more generalized for the whole plain region and not simply limited to the upstream as it was expected.

    The second case study concerns Ighrem region which is facing a critical situation with regards to potable water resources sustainability. This region has a discontinuous aquifer systems and week surface water contributions because of its arid climate. This area belongs to the Anti-Atlas Mountains considered as part of the Panafrican chain with complex and intensive deformations and a large lithological variability; witch makes more difficult the characterization of its hydrogeologic context. The approach was based on the integration of multi-sensors earth observation data, the existing data and the field truth in order to contribute to the groundwater prospecting process. This allows us to produce details information from lineament and lithological mapping in order to better characterize the aquifer system. In particular, radar imagery had been used for mapping zones with high recharge potential. On the other hand, the integration of these information in a quantitative approach known as Weight of Envidence Modelling for combining evidence in support of an hypothesis. This method has been used based on key hydrogeologic predictors for groundwater prospecting: geology, geomorphology, hydrology and recharge potential. This enabled us to identify and locate the zones presenting high aquifer potentials. These zones are supposed to guide geophysical prospecting to better refine the location of productive drilling in the future.

    The third case study concerns the Sebou hydraulic basin which is receiving more than 1/3 of Moroccan surface water potentialities per year. The aim of the study is to demonstrate the use of earth observation data to better characterize the key hydrologic parameters for water balance evaluation. Actual evapotranspiration (AET) and rainfall are the elements in water balance estimation that give sound information on water availability. At this stage of the work rainfall products were taken from ground meteorological stations and the AET was estimated from remote sensing and GLDAS global meteorological data using the Surface Energy Balance System (SEBS) model. On the other hand, to better refine the calculated daily average of the evaporative fraction a detailed land use map from very high resolution imagery were used for surface parameters. Furthermore, climatological water balance estimation was done over rainfed and irrigated croplands for a specific period in 2010 and 2011.

    This post was written by A. Er-Raji and D. El Hadani (Royal centre for Remote Sensing, Morocco). Contact A. Er-Raji at for more information.
  • 08 Oct 2012 12:05 PM | Anonymous

    There are several cases of poor transport services in Uganda that are caused by the bad state of roads. Road maintenance proved ad hoc until recently when the need for preventive maintenance was recognised and plans of making it a priority put in place.

    Since roads are geographically located, the use of Geographical Information Technologies (GITs) in collecting, managing and analysing road condition is paramount. And yet, these technologies are under-utilized for road maintenance. This paper derives from research aimed at accentuating the use of GITs for Road Infrastructure Maintenance (RIM) in Uganda.

    The research addressed three objectives namely to:

    1. Access the gaps and limitations in GIT use and access for RIM,
    2. Develop a methodological framework for enhancing the use of GITs in RIM
    3. Develop a Geographical Information Systems for Transportation (GIS-T) data model for RIM in Uganda.

    In line with the 3rd objective, the paper specifically presents an object data model for RIM. This was accomplished through identification of road maintenance data requirements, review of: organisational reports, workshop proceedings, organisational terms of reference for various projects and existing data models & standards in transportation. An understanding and consideration of the Information Quality Levels (IQL) was paramount. This resulted into a conceptual and logical data model for RIM based on concepts of dynamic segmentation (Dynseg) and linear referencing. The conceptual model depicted using entity relationship diagrams identifies with 3 entities - the road’s network and the point & line events that exist on it. Besides logically documenting the various classes from the conceptual data model, the ESRI provided template for logical data modelling was used. The model separately emphasizes objects having spatial reference, objects without spatial reference and the relationships between them.

    The study concludes that a common definition and understanding of the country’s transportation network is essential to adoption of the proposed model. The choice of GIS software with the full set of dynseg tools is fundamental for implementation of the physical model. This idea of modelling data is a contribution to standardisation of geographic datasets for the sector.


    This post was written by Lydia Mazzi Kayondo - Ndandiko. Contact her at for more information.

  • 08 Oct 2012 9:00 AM | Anonymous


    The study is focused on surveying and monitoring tree cover in the argan tree sparse forest of south-west Morocco. Remote sensing data are one IKONOS image from 2003 and one GeoEye image from 2011 (obtained throughGoogle Earth); the latter image has been registered with the 10m resolution IKONOS image used as reference. An object-oriented classification approach has been used to identify tree crowns on both images. However the multi-temporal comparison of results of the two classifications appears not reliable. An alternative solution is proposed through the interactive analysis of the scattergram of 2 channels, one from IKONOS (panchromatic) and one of GeoEye image (sum of 3 bands). In the studied area, tree cover is very low (about 8%) and there are no drastic changes in tree density from 2003 to 2011, except in restricted disturbed areas.

    KEYWORDS: Morocco, argan tree, fractional vegetation cover, tree density, IKONOS, Google Earth, multitemporal analysis


    L’étude porte sur la cartographie du couvert arboré de la forêt claire d’arganiers du sud-ouest du Maroc. Les données utilisées sont une image IKONOS de 2003 et une image GeoEye de 2011, extraite de Google Earth ; cette dernière est corrigée géométriquement pour être superposable à l’image IKONOS (résolution spatiale 10m). L’approche de classification orientée objet permet de cartographier de façon assez satisfaisante les couronnes des arbres sur les deux images. Cependant la comparaison des résultats des deux classifications laisse apparaître des artefacts et ne peut servir à une analyse diachronique fiable. La solution alternative proposée repose sur l’analyse interactive de l’histogramme bi-varié de deux canaux provenant respectivement de l’image de 2003 et de celle de 2011. Pour la zone étudiée, le couvert arboré a un faible recouvrement (8% en moyenne) et apparaît stable de 2003 à 2011, avec localement une légère diminution de densité des arbres.

    MOTS-CLéS: Maroc, arganier, couvert arboré, densité d’arbres, IKONOS, Google Earth, analyse diachronique


    This post was written by Bernard Lacaze (CNRS). Contact him at for more information.

  • 08 Oct 2012 8:30 AM | Anonymous
    This paper presents a new method of texture modeling using geostatistic theory. From variographics abacus and variogram proprieties, we used fractal and exponential models to characterize Brodatz textures. In this approach, we modeled a texture by a vector called « feature vector » whose components are the parameters characterizing the experimental variogram, notably the « Slope », the « Range », the « Landing » and the « Fractal Dimension ». To estimate these parameters, we use exponential and fractal models. The parameters estimated by this approach help to promote an adequate method of quantitative analysis variogram of textural images. The new method proposed here help also to solve the problem of preferential direction selection often asked by Haralick method of co-occurrence matrix. A comparative study of the proposed method of fractal dimension evaluation and the one proposed in a literature shows that the results obtained are identical with a hundredth of precision on the Brodatz texture images. To demonstrate the applicability of our approach we use to classify a SAR image of ERS-1 from the Atlantic coast of Cameroon. Our approach is one of the great family of supervised classification. It is based on the methods of structural classification. The particularity of this approach lies in the fact that each pixel is fully characterized by its feature vector.


    This post was written by Fotsing Janvier (University of Buea). Contact him at for more information.

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