Tag Archives: Change Detection

The Effect of Diminishing Urban Green Spaces on Environmental Quality in Kisumu City (Published)

Green spaces contribute to aesthetics and environmental quality of life in urban areas. Kisumu City, the study area, has been experiencing demographic, environmental, economic, socio-spatial and institutional challenges leading to loss of green spaces. The main problem addressed by the study was dysfunctional use of green spaces leading to their loss, aesthetic value and low environmental quality. The study objective was to determine the effect of spatial change of urban green spaces on environmental quality. Data were both qualitative and quantitative and were collected through observation, interviews, questionnaires, photography, remote sensing and Geographic Positioning System (GPS). Qualitative research focused on site-specific analysis of urban and peri-urban neighbourhoods in Milimani and Nyalenda, respectively, which were purposively sampled. Results showed that area under green in 2005 was 44.8% while in 2004 it was 24.87% showing  a decrease of 55.5%. However, in 2010, there was a temporary increase of green space of 51.82% due to demolitions to pave way for road expansion leading to decrease in carbon sink, resulting in increase in carbon footprint. This has led to low environmental quality. The study projects that by the year 2030, without proper planning interventions, the city will lose all its urban green cover. The research recommends the use of remote sensing for creating land-use inventory and monitoring systems. Citizen involvement in planning and management of urban green spaces is recommended because this will transform ecotourism in Kisumu City.

Keywords: Change Detection, Green Planning, Open Space, Quality of life, 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

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

Keywords: Change Detection, Geographic information System (GIS), IDRISI 32 software, Land Cover, Landuse, Mapping, Remote Sensing, Satellite image