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.
Assessment of Anthropogenic Activities and Their Impact on Ngong Hills Forest in Kajiado County, Kenya: A Remote Sensing Approach (Published)
Human beings are dependent on forests for various livelihood needs. Forests offer a variety of benefits, including ecological, social as well as economic benefits. As such, the development and conservation of forests around the world is vital. Monitoring of the forest ecosystem is mandatory in order to detect any changes in the ecosystem. Forest cover change detection gives an opportunity to track the productivity, health and the forest cover as well over the years so as to enable proper management, promote conservation and enhance functionality. Optical and radar remote sensors make it possible to monitor changes by use of various analytical techniques that include visual interpretations. The study investigated how remote sensing can be applied to detect change in forest ecosystem and to assess the rate of change of Ngong Hills Forest in Kenya. The project sought to determine whether anthropogenic activities are the major cause of the change in Ngong Hills Forest. Data from satellite images was analysed from 1984 to 2019 to identify the changes that have occurred on the ecosystem. Landsat and Rapid-Eye images were used to inform on change detection. In this case, rapid eye data was found to be better than Landsat data in informing on change detection because of its high resolution thus high precision and better results. The changes depicted by the remotely sensed data were mapped for ease of analysis and visualization. The research depicted a massive decrease in the forest cover despite the afforestation efforts by the Kenya Forest Service (KFS) in the 1990s. The forest has been depreciating massively from 1995 depicting greater deforestation rates between the years 2010 and 2019. This depreciation has been acknowledged by the KFS as it is said to be occurring due to the anthropogenic activities mainly settlement and logging. The means of detecting change by use of remote sensing is thus able to identify the exact areas that change has occurred and thus provide insight for the Kenya Forest Service and other ecosystem protection bodies on the most affected areas and the extent of change. Once the study area is mapped, it is possible to calculate the areas that have decreased in vegetation quantity, areas where increase has occurred as well as the areas that have remained unchanged. The findings of the study make it possible for management agencies to enforce conservation because of the presence of reliable data.
Dealing with large amount of data and running analytics on those data is becoming challenging with rapidly increase in various types of data. Big data is the technology which deals with such large amount of data analytics. It covers wide range of application areas from managing data of social networking sites to the large amount of data on ecommerce portals for decision making. In this paper an attempt is made to present a review of State of Art technology in Big Data, its importance, major benefits and challenging in this domain.
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.
Change Detection in Landuse / Landcover Mapping in Asaba, Niger Delta B/W 1996 And 2015. A Remote Sensing and GIS Approach (Published)
Remote sensing is used in this research work for the development and acquisition of Landuse/land cover data, pattern and its attendant effects in Asaba, Delta State Nigeria. Remote sensing images and digital data verified by ground trothing (field work) satellite data are used to assess the rate of change in Landuse / Land cover between 1996 and 2015. It also examines the extent to which images and GIS softwares effectively contribute to mapping landuse/cover in the Niger Delta region. Remote sensing and geographic Information System (GIS) help integrate natural, cultural, social and economic information to create spatial information system on the available terrain resources. Sets of NARSDA images were acquired corresponding with the years, field checked to ascertain the data captured on the terrain.. The digital satellite data are incorporated as input data into IDRISI 32 GIS environmental to separately map out the landuse/land cover units and their magnitude determine. Five distinct units were identified in classification of landuse/landed cover pattern categories as follows: Farmland, Build up land, Waste land, Forest land and Water bodies. Land consumption rate indicate a progressive spatial expansion of the city was high in 1996/2006 and higher between 2006 and 2015. Also, land absorption coefficient being a measure of consumption of new urban land by increased urban population, was high between 1996 and 2006 and between 2006 and 2015. Ground trothing was carried out to ascertain the accuracy of data and there are major changes in the landuse/land cover. It was discovered that there is rapid inbuilt-up areas evidently explained in buildings projects that resulted in decrease in forest land, agricultural land and open space. This is attributed to the anthropogenic activities of farming, bush burning, grazing, etc. However, the area occupied by water remained unchanged over the years. This study demonstrates that remotely sensed data and GIS based approach is found to be timely and cost effective than the conventional method of analysis, classification of land use pattern effective for planning and management