BackgroundGetting a random household sample during a survey can be expensive and very difficult especially in urban area and non-specialist. This study aimed to test an alternative method using freely available aerial imagery.MethodsA gridded map and random selection method was used to select households for interviews. A hundred numbered of points were put along the edges of an updated map of Maroua. Then two numbers were randomly draw at a time and a line was drawn between those two numbers. A lot of different kinds of shapes of different sizes obtained were numbered. Ten shapes were randomly draw and the one selected were considered as ‘neighbourhoods’. A grid of 30 m × 30 m was drawn over each and then numbered. 202 grids considered here as households were randomly selected from the ten neighbourhoods for interviews.ResultsOut of 202 households visited, only 4 were found to be something other than a house. In addition, 30 sampled households (14.85%) were abandoned or the occupants had relocated elsewhere. This method resulted in an accuracy level of 72%, its advantage is the ability to generate efficient random sample at relatively low cost as well the time required.ConclusionsThe method proposed in this study was efficient and cost-effective when compared to the infield generation of a household inventory or Global Positioning System (GPS) tracking of households. It can then be used by researchers in low-incomes countries where funding for research is a challenge. However, this method needs to train the investigators on how to use the GPS.
Cette étude porte sur la dynamique des prairies inondables avec un accent sur sa dimension spatio-temporelle et son impact sur le pastoralisme dans la plaine d'inondation du Logone. Pour parvenir aux objectifs escomptés, la méthodologie a consisté à une collecte des données de terrain aux moyens des enquêtes, entretiens, levés GPS, relevés phytosociologiques et à un traitement de ces données au moyen des SIG. Les images satellites de 2011 à 2015 (6 ans) ont permis d'évaluer la dynamique (spatio-temporelle) saisonnière et interannuelle des prairies inondables grâce à une classification supervisée. Il apparait que l'étendue des prairies varie en fonction des saisons (sèche/pluvieuse) et de l'étendue des inondations. Sur le long terme, il en ressort une variation interannuelle des prairies suivant des tendances à la baisse avec en moyenne des extrêmes allant de 9,1% à 26,3% respectivement pour les années 2012 et 2013. Cette oscillation de l'étendue des prairies suit la dynamique des précipitations et de l'hydrologie de la plaine. La dynamique des pairies dans la plaine est due aux effets conjugués des caprices du climat et des facteurs anthropiques. Cette dynamique des prairies, qui constituent la principale ressource fourragère, impacte de manière négative le pastoralisme et pousse les éleveurs à élaborer des stratégies d'adaptation.
AbstractThis study focuses on the dynamics of floodplains with an emphasis on its spatio-temporal dimension and its impact on pastoralism in the Logone floodplain. In order to achieve the expected objectives, the methodology consisted of collecting field data by means of surveys, maintenance, GPS surveys, phytosociological surveys and processing of these data using GIS. The satellite images from 2011 to 2015 (6 years) were used to assess the seasonal and inter-annual dynamics (spatio-temporal) of floodplains thanks to a supervised classification. It appears that the extent of grassland varies according to the seasons (dry / rainy) and the extent of the floods. In the long term, an inter-annual variation in grassland is observed following downward trends, with extreme ranging from 9.1 % to 26.3 % for 2012 and 2013. This oscillation in the extent of Meadows follows the dynamics of the precipitation and hydrology of the plain. The dynamics of peerage in the plain is due to the combined effects of climate degradation and anthropogenic factors. This dynamics of grasslands, which constitute the main forage resource, negatively impacts pastoralism and encourages livestock farmers to develop adaptation strategies.
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