Kano State is one of the frontline states in northern Nigeria that have suffered land degradation. This degradation results from urbanization and anthropogenic influence. In Danbatta Local Government Area (LGA), persistent degradation of the land has adversely affected the environment and the economy and there is lack of studies on land degradation in the area. Therefore, this study assessed land degradation in Dambatta Local Government Area (LGA) of Kano State paying special attention to the causes and effects of the reduction in the lands' actual or potential uses. The images used for the research analysis were obtained from National Space Research and Development Agency (NASRDA), Abuja. The data sets were captured by Landsat Multispectral Scanner/Thematic Mapper (MSS/TM) 1997, Landsat Enhanced Thematic Mapper Plus (ETM+) 2007 and Operational Land Imager (OLI) 2017. Post classification change detection technique was conducted in ILWIS 5.2, and later converted to shape files where it was imported to Arc Map 10.2 GIS software. The results showed Normalized Difference Vegetation Index (NDVI) ranges from −0.056 to 0.18 in 1997, −0.07 to 0.11 in 2007 and −0.128 to 0.217 in 2017. This depicts that there has been a progressive loss in vegetation cover in DambattaLGA over a period of 20 years with corresponding acceleration in bare lands and developed areas. The Land Surface Temperature (LST) results generally show a continuous and constant increase in surface temperature from the developed and urban areas to the undeveloped and rural areas. The LST results also show that no area under consideration in the study area experienced an extreme temperature (≥44˚C) during the period of study. In 2017, a large part of the study area fell within the higher temperature zones (≥40˚C) and other areas fell into mid-temperature zones (35˚C -40˚C). This high surface temperature resulted from increase in bare land, high insolation,

This research analyzed desertification and land degradation in the Dambatta Local Government Area (LGA) of Kano State with the view to delineating hotspot areas that require intervention. The imageries used for the research analysis were obtained from the National Space Research and Development Agency (NASRDA), Abuja. The data sets were captured by Landsat Multispectral Scanner/Thematic Mapper (MSS/TM) 1997, Landsat Enhanced Thematic Mapper Plus (ETM+) 2007 and Operational Land Imager (OLI) 2017. The Maximum Likelihood Classifier (MLC) algorithm was used for classification. Post classification change detection technique was conducted using ILWIS 5.2 and later converted to shapefiles where it was imported to ArcMap 10.2 GIS software. The result of the classification was presented in tables, which were subsequently compared using the Post Classification Comparison (PCC) technique to estimate and compute temporal and spatial changes as well as the rate and area extent of changes between the four images. The result shows that desert encroachment has occurred in the study area at the rate of 5.65km2/yr over the 20 years. A composite Land Cover map and NDVI map of 2017 was created and superimposed with the localities within Dambatta LGA, where the settlements requiring intervention were then drawn out. It revealed that almost all parts of the LGA require intervention. . However, some areas have more serious land degradation issues than others. This has resulted from anthropogenic activities, environmental factors and erosion with negative effects on farmers, rural development, forest reserves and policymakers. Hence intervention in the form of afforestation is recommended to prevent further expansion of bare lands in the area. Cette recherche a analysé la désertification et la dégradation des terres dans la zone d’administration locale de Dambatta (LGA) de l’État de Kano en vue de délimiter les zones de hotspot qui nécessitent une intervention. Les images utilisées pour l’analyse de la recherche ont été obtenues auprès de l’Agence nationale de recherche et de développement spatial (NASRDA), Abuja. Les ensembles de données ont été capturés par Landsat Multispectral Scanner/Thematic Mapper (MSS/TM) 1997, Landsat Enhanced Thematic Mapper Plus (ETM+) 2007 et Operational Land Imager (OLI) 2017. L’algorithme MLC (Maximum Likelihood Classifier) a été utilisé pour la classification. La technique de détection des modifications post-classification a été réalisée à l’aide d’ILWIS 5.2 et convertie ultérieurement en fichiers de forme où elle a été importée dans le logiciel SIG ArcMap 10.2. Le résultat de la classification a été présenté dans des tableaux, qui ont ensuite été comparés à l’aide de la technique de comparaison post-classification (PCC) pour estimer et calculer les changements temporels et spatiaux ainsi que le taux et l’étendue des changements entre les quatre images. Le résultat montre que l’empiètement du désert s’est produit dans la zone d’étude au rythme de 5,65 km2 / an au cours des 20 années. Une carte composite de la couverture terrestre et une carte NDVI de 2017 ont été créées et superposées aux localités de la LGA de Dambatta, où les colonies nécessitant une intervention ont ensuite été dessinées. Il a révélé que presque toutes les parties de la LGA nécessitent une intervention. Cependant, certaines régions ont des problèmes de dégrdation des terres plus graves que d’autres. Cela résulte des activités anthropiques, des facteurs environnementaux et de l’érosion qui ont des effets négatifs sur les agriculteurs, le développement rural, les réserves forestières et les décideurs. Par conséquent, une intervention sous forme de boisement est recommandée pour empêcher une nouvelle expansion des terres nues dans la région.

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Abuja is witnessing an upsurge of victims from Road Traffic Crash (RTC) which is mostly due to the attendant rapid increase in the volume of vehicles, traffic jams, bad driving, over speeding, insufficient road signs and bad conditions of vehicles that ply the roads. The problem is compounded by a lack of early emergency response. Geographic Information System (GIS) based travel time model was applied in the street network analysis to identify RTC black spots that are outside the close reach of Federal Road Safety Commission (FRSC) rescue points/health facilities in Federal Capital City (FCC). Five minutes, Ten minutes and Fifteen minutes travel times were used as the impedance factor. Remote Sensing and GIS techniques were used to carry out network analysis. This was achieved by conducting the closest facility operation in the ArcGIS network analyst extension using the time of travel from each FRSC zebra point location to the RTC black spot zones/health facility. The results were presented on road network maps and bar graphs. The areas where quick response and medical facilities are insufficient were identified. It was concluded that the available health centres can sufficiently service RTC black spots in FCC, but the FRSC zebra points are insufficient which renders rescue operations inefficient and thereby exposes RTC victims to more danger. In order to ensure that there is sufficient coverage for response times, it was suggested that additional zebra points be created.