Depletion of Forested Area: Geidam Perspective (Published)
Land cover maps provide best understanding of current landscape change over time. One can evaluate past land cover maps for several different years for management decisions as well as gain insight into the possible effects on decisions making. One of the key monitoring areas is how the environment keeps degrading resulting from increased anthropogenic activities such as the removal of the forest covers. This study monitors the pattern changes of the Geidam Yobe state Nigeria, using Landsat images of two different periods from Enhanced Thematic Mapper (ETM+) image of data of 1988 and 2018. The images were geometrically and atmospherically pre-processed then classified, using maximum likelihood (MLC) algorithm to produce land cover maps of the Geidam. The accuracy of the classification was assessed with confusion matrices giving results morethan the minimum 85% required. The results revealed that the built-up and tree area increase by (+30.97%), water body reduced by (-5.06%) and forest reduce by (-23.48%) within the study period. This shows a rapid decrease in the forest, which is partly attributed to deforestation activities and partly to climate change impact.
Keywords: Change Detection, Classification, Landsat data, Remote Sensing
Depletion of Forested Area: Geidam Perspective (Published)
Land cover maps provide best understanding of current landscape change over time. One can evaluate past land cover maps for several different years for management decisions as well as gain insight into the possible effects on decisions making. One of the key monitoring areas is how the environment keeps degrading resulting from increased anthropogenic activities such as the removal of the forest covers. This study monitors the pattern changes of the Geidam Yobe state Nigeria, using Landsat images of two different periods from Enhanced Thematic Mapper (ETM+) image of data of 1988 and 2018. The images were geometrically and atmospherically pre-processed then classified, using maximum likelihood (MLC) algorithm to produce land cover maps of the Geidam. The accuracy of the classification was assessed with confusion matrices giving results morethan the minimum 85% required. The results revealed that the built-up and tree area increase by (+30.97%), water body reduced by (-5.06%) and forest reduce by (-23.48%) within the study period. This shows a rapid decrease in the forest, which is partly attributed to deforestation activities and partly to climate change impact.
Citation: Alhaji, Mustapha Isa; Ayuba, Abubakar Fusami, and Danboyi, Joseph Amusu (2022) Depletion of Forested Area: Geidam Perspective, British Journal of Earth Sciences Research , Vol.10, No.4, pp.1-,6
Keywords: Change Detection, Classification, Landsat data, Remote Sensing
Effect of Urbanization on Land Use Land Cover in Gombe Metropolis (Published)
This study examined the integration of Remote Sensing and Geographic Information System (RS/GIS) for analyzing land use and land cover dynamics in Gombe Metropolitan, the Gombe State capital for the period 1976 to 2016. Land sat (TM) images of 1976, 1996and 2016 were used. The study employed supervised digital image classification method using Erdas Imagine 9.2 and Arc GIS 10.5 software and classified the land use into undisturbed vegetation, sparse vegetation, Settlements, Farmlands, Rock outcrops, Bare surfaces. The images were analyzed via georeferencing, image enhancement, image resampling and classification. The results obtained showan increasing settlements (from 0.36% – 4.01%) and farmlands (from 24.8% – 51.2%), over a decreasing of other LULC classes (bare surfaces, undisturbed and sparse vegetation, and rocky outcrops) for the time period of 1976 to 2016. These results could help city planners and policy makers to attain and sustain future urban development. It is therefore recommended that encouragement should be given to people to build towards the outskirts, like New mile 3 and Tumfure,etc through the provision of incentives and forces of attraction that is available at the city center in these areas to avoid the problem of overcrowdings.
Keywords: : GIS, Change Detection, Remote Sensing, Urbanization, gombe, land use
Remote Sensing Application in Forest Monitoring: An Object Based Approach (Published)
Object-based methods for image analysis have the advantage of incorporating spatial context and mutual relationships between objects. Few studies have explored the application of object-based approaches to forest classification. This paper introduced an object based method to SPOT5 image to map the land cover in Yen Nhan commune in 2015. This approach applied multi-resolution segmentation algorithm of eCognition Developer and an object based classification framework. In addition, forest maps from 2000 to 2015 were used to analyze the change in forest cover in each 5 years period. The object based method clearly discriminated the different land cover classes in Yen Nhan. The overall kappa value was 0.73 was achieved. The estimation of forest area was 89.05 % of forest cover in 2015. By overlaying achieve forest maps of 2000, 2005, 2010 and the classified map of 2015 shows vegetation changed during 2000-2015 remarkably.
Keywords: : GIS, Change Detection, Forest Classification, Remote Sensing, SPOT 5 Image