African Association of
Remote Sensing of the Environment
 
 

Articles

  • 11 Oct 2012 7:00 AM | Anonymous

    Observation par les mages satellites des impacts du transfert de la gestion forestière aux communautés de base : cas de la commune de Didy, région d’Alaotra-Mangoro Madagascar

    RAKOTONIAINA S., RAKOTOMANDRINDRA P., RANAIVOARIMANANA S., RAKOTONDRAOMPIANA S.

    Laboratoire de Géophysique de l’Environnement et Télédétection, Institut et Observatoire de Géophysique d’Antananarivo (IOGA), Université d’Antananarivo, BP 3843, Antananarivo-101, Madagascar

    Dans le cadre d’un projet de gestion durable des ressources naturelles pour la conservation des régions hotspot de la biodiversité à Madagascar, le projet COGESFOR (Conservation et Gestion des Ecosystèmes Forestiers Malgaches) et l’Institut et Observatoire de Géophysique d’Antananarivo (IOGA) ont collaboré ensemble. Les objectifs principaux du projet sont de produire d’une part des cartes multidates de l’occupation du sol et d’analyser d’autre part l’évolution temporelle de cette dernière (détection des changements) au cours des dates prises comme des références afin d’évaluer l’efficacité du programme de transfert de la gestion forestière aux communautés de base. Le site d’étude est la commune de Didy, dans la région d’Alaotra-Mangoro, centre Nord de Madagascar. Les images satellitaires utilisées ont été celles acquises par le satellite Landsat TM 5 de résolution spatiale de 30 mètres à trois dates (1994, 2001 et 2009). Les descentes sur terrain ont été effectuées au cours de l’année 2011 avec pour objectif de relever les points GPS relatifs aux vérités-terrain correspondant aux différentes catégories d’occupation du sol existantes et de mener une enquête auprès de la population locale sur l’historique de l’environnement forestier dans leur zone. Les différentes catégories d’occupation du sol retenues sont les suivantes : forêt, savoka, tavy, zetra, rizière, savane, lac/rivière et sol nu. En plus de ces différentes classes, on a considéré aussi les classes nuages et ombre dans le cas où ces dernières sont présentes dans l’image. Les vérités-terrain acquises ont été divisées en 2 groupes : le premier en tant que parcelles d’apprentissage pour entraîner l’algorithme de classification et le second en tant que parcelles de contrôle pour valider les résultats de classification. Les images acquises à différentes dates ont été tout d’abord soumises à des corrections atmosphériques. Les classifications d’images qui ont été effectuées à partir des six bandes originales (à l’exception de la bande thermique) de chaque image multispectrale de Landsat ont été ensuite réalisées à l’aide de la méthode de classification non paramétrique SVM (Séparateur à Vaste Marge). Enfin, le module LCM (Land Cover Modeler) du logiciel de traitement d’images Idrisi a été appliqué à nos images classifiées pour détecter les différents changements apparus sur les occupations du sol entre les différentes dates considérées. Une approche d’aménagement concerté global a été définie, testée et mise en œuvre dans le site à travers de ce qu’on appelle organes de Transfert de Gestion (TG). Pour évaluer les actions entreprises au sein des TG au cours des trois années d’étude, trois principaux indicateurs écologiques ont été examinés : les surfaces forestées, les surfaces cultivées et les surfaces brulées. La transition des classes vers la classe ‘forêt’ indique ainsi un apport positif des organes de transfert de gestion (TG) sur la conservation des zones forestières et celle vers la classe ‘tavy’ (traduisant les surfaces brûlées) une évaluation de la dimension de l’exploitation des terrains par la population locale (défrichement). Les résultats statistiques montrent en général une nette diminution des surfaces forestières avant l’installation des TG dans la commune de Didy (2001) et une forte augmentation de ces surfaces après installation des TG (soit entre 2001 et 2009). Par ailleurs, la surface des zones classées ‘tavy’ a diminué au cours de ces années d’étude dans presque toute la zone d’étude.


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    This post was written by Solofoarisoa Rakotonianina (Université d’Antananarivo). Contact him at solofoarisoa@gmail.com for more information.



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  • 10 Oct 2012 3:00 PM | Anonymous
    KEY WORDS: Urban management system (UMS), space technology, remote sensing, geographical information system (GIS).

      ABSTRACT

      Urban evolution is a situation that concerns a very significant problem in today’s world. There is a rapid growth in urban areas especially in coastal regions due to many factors depending on the specificity of each country.  Such situation often times have both socio-economic and environmental implications.   Therefore, studies in the field of urbanization which incorporates socio-economic and environmental factors for good town planning using space technology, geographical information system (GIS) and cartography in cities are of utmost importance.  Studies of this nature are lacking in most African countries and are necessary for the improvement of the quality of life. Dakar the capital of Senegal and a major city in the West-African coastal region was used as a case study.  It has a population of 2,275,351 inhabitants representing 51 % of the national population.  Studies have revealed that Dakar has the highest urban change in Senegal due to better basic infrastructure compared to the hinterland. The aim of the paper is to demonstrate how GIS techniques, cartography and high resolution remote sensing data can be incorporated into a decision support tool for utilization in urban management system (UMS).  The urban management issues considered in solving some of the present problems posed by rapid urbanization include proper allocation of habitation zone and well defined data infrastructure.  The analysis further explains the expected results and suggests some re-orientation in the application of urban management system.


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      This post was written by Diallo Ngagne (Cadastre). Contact him at diallo.ngagne@gmail.com for more information.

    1. 10 Oct 2012 1:00 PM | Anonymous

      To be presented at the AARSE 2012:

      Manuela Hirschmugl, Roland Perko, Claudia Hörmann, Ursula Schmitt, Mathias Schardt

      1. Joanneum Research, Austria, manuela.hirschmugl@joanneum.at  
      2. Technical University of Graz, Austria

      KEY WORDS: Forest monitoring, Forest mapping, REDD, SAR and optical data integration

      ABSTRACT

      This study presents an innovative processing chain and various test results for using both SAR (PALSAR) and optical (such as AVNIR, LANDSAT) data in an integrative way for forest mapping in the Congo Basin. Three activities are described in this context: (i) geometric adjustment of SAR and optical data by automatic image matching; (ii) analysis of various pre-processing steps for SAR data and (iii) a method for efficiently classify SAR data based on an existing optical classification. In part one, the robust and fully automatic matching procedure based on the mutual information maximization principle has proven to be useful to ensure geometrical congruence between optical and SAR data sets. Results show that the RMSE is reduced from over 80 m to less than 10 m without manual interaction. The second analysis covers the wide variety of SAR pre-processing methods and options. From over hundred different options, the best processing steps are selected by using cross-correlation analysis and in addition considering the typical MMU needed for forest monitoring in the tropics. The third development concerns the so-called classification-based trainer. This method allows filling classification gaps caused by clouds or sensor failures in optical data by using SAR data without much manual effort. A first benchmarking test involving AVNIR and PALSAR shows a slight overestimation of 0.8 % of non-forest area for the resulting classification compared to the classification on optical data only. Considering the difference in data quality and properties between optical and SAR data, this is a very promising result.


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      This post was written by Roland Perko (Joanneum Research), Contact him at roland.perko@joanneum.at for more information.


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    2. 09 Oct 2012 1:41 PM | Anonymous

      A plentiful source of revenue within the aquaculture industry of Africa, mussels and oysters are ideal platforms as it concerns water quality testing.  Along the coast of South Africa the European blue mussel (mytilus galloprovinciallis) is highly prevalent.  This bivalve, which is susceptible contaminants, is ideal to serve as a testing platform to begin a Mussel Watch program within the continent of Africa.

      The Mussel Watch Program is one of the longest running contaminant monitoring programs in the coastal ocean with more than 20 years of data. Mussel Watch uses bivalves (Mussels, Oysters, and Zebra Mussels) as a means to assess water quality. The purpose of the program is geared towards assessing contaminants nationally.  Utilizing formats such as Geographic Information Systems (GIS) and Remote Sensing data assessment, this project identifies possible releasers of effluent waste into the major coastal watershed regions pertaining to ongoing research conducted within monitored Mussel Watch sites.  This project further serves as a platform for testing the mussel, a major source of income for some Coastal African nations

      The categorization of possible contaminating locations is made available through the development of a large dataset. This dataset utilizes those derived from agencies such as the United States Environmental Protection Agency (USEPA) and other federal government databases such as the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS).  Utilizing platforms such as ESRI® ArcMap™ software, spatially referenced locations, via point data, vector data, line data, and polygons depicting points and sites of interest were created using latitude and longitude information. Points and areas of interest (AOI) were verified using remote sensing imagery. As such, Polybrominated Diphenyl Ethers (PBDEs) within observable mussel watch sites were assessed by NOAA’s Center for Coastal Monitoring and Assessment (CCMA). Using this data, researchers are able to identify possible sources of contributors to the present contaminant. 

      In the attempt to identify possible contributors of the PBDEs contaminant with NOAA’s National Status & Trends Mussel Watch Program a suite of software was utilized in assessing and compiling the acquired data. ArcGIS 9.3 was used as the primary software in manipulating the dataset used in this project. Remote Sensing imagery acquired from an ESRI™ database served as the base map within the project and was used to verify Points of Interest (POI).

      Data collected from the USGS, was created and edited to show coastal watersheds reflective of Mussel Watch Program and observed coastal regions.  A national dataset was collected from the USEPA.  Wastewater treatment facilities were closely examined as contaminant release sites due to their potential to release untreated wastewater. Other contributors of contaminated water sources that were identified within this study included brownfields, superfund sites, power plants, hazardous waste sites, unidentified that comprised information regarding active National Pollutant Discharge Elimination System (NPDES) permits permitted facilities, and combined sewer overflows.  The data gathered was analyzed and checked for irregularities, corrected, and projected using World Geodetic System (WGS 1984).


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      This post was written by Patrina L. Bly (Elizabeth City State University). Contact her at patrina_bly@yahoo.com for more information.

    3. 09 Oct 2012 7:54 AM | Anonymous
      First reported at a nursery in Mpumalanga province in 1990, Fusarium circinatum is a fungal pathogen causing widespread mortality of Pinus radiata and Pinus patula seedlings. Improved methodologies for early disease detection are thus pertinent, and rely on identifying specific wavebands that correspond to specific physiological responses of the plant to stress. The objectives of this research were to i) determine the earliest possible window period, from time of infection, for disease detection, and ii) identify the specific hyperspectral wavebands that could be used for discriminating healthy and infected seedlings. To achieve these objectives, we setup an experiment with a sample of 3-month old P. radiata seedlings (n = 150) divided into three classes; healthy (n = 50), damaged (n = 50), and infected (n = 50). Reflectance measurements for all three classes were collected using an Analytical Spectral Devices Full Range spectroradiometer, weekly over a five week period. Reflectance measurements were later analysed using a random forest with a feature selection algorithm. Results of the analysis indicate that the best possible discrimination occurs at week three (KHAT = 0.81; out of bag (OOB) error = 12.67%). The results further indicate that wavelengths in the red-edge and near-infrared regions show the most promise in discriminating the healthy, damaged, and infected classes. These results could be explained by reduction in needle chlorophyll content expressed by a shift in the red-edge toward shorter wavelengths. Furthermore, lowered near-infrared reflectance has been associated with disease-induced stress. Overall, this study provides a basis for the early detection and discrimination of infected P. radiata seedlings that could be operationalized within a nursery environment.

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      Post was written by Nitesh Keshavelal Poon (Stellenbosch University). Contact him at poona@sun.ac.za for more information.

    4. 09 Oct 2012 7:00 AM | Anonymous
      We describe a method for improving Earth observation satellite image resolution, for specific areas of interest where the sensor design resolution is insufficient. Our method may be used for satellites with yaw-steering capability, such as NigeriaSat-2. We show that, according to the slanted edge modulation transfer function (MTF) plots, the effective resolution obtained by simulated yaw-steering of a satellite yielded a 138% improvement in resolution. This result equates to obtaining a 2.1 m resolution image from a sensor designed to acquire 5 m resolution images.

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      This post was written by Riaan van den Dool. Contact him at riaanvddool@gmail.com for more information.

    5. 08 Oct 2012 6:30 PM | Anonymous

      ABSTRACT

       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.


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      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 kihoromike@gmail.com for more information.

    6. 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.


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      This post was written by Niama Boukachaba (Cadi Ayyad University- Marrakesh-Morocco). Contact her at niama.boukachaba@edu.uca.ma for more information.

    7. 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.

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      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 petra.fuereder@sbg.ac.at for more information.

    8. 08 Oct 2012 12:30 PM | Anonymous

      Authors:

      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

      Abstract:

      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.


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

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