Mapping of land cover and estimation of their emissivity values for gas flaring sites in the Niger Delta (Published)
This study examines the changes in land cover (LC) types at 6 gas flaring sites in Rivers State, Niger Delta region of Nigeria; and to estimate their emissivity (Ɛ) values. 15 Landsat scenes (3 Landsat 5 Thematic Mapper (TM) and 12 Landsat 7 Enhanced Thematic Mapper Plus (ETM+)) from 17 January 1986 to 08 March 2013 with < 30 % cloud contamination were used. All the sites are located within a single Landsat scene (Path 188, Row 057). Radiometric calibration of the multispectral bands of the data, and atmospheric correction for multispectral bands using dark object subtraction (DOS) method was carried out. The first unsupervised cluster analysis of the atmospherically corrected reflectance (bands 1-4) using the K-mean function of the MATLAB tool was carried out. The results obtained give 3 classes of LC type and cloud as the 4th class. The second cluster analysis was performed with the cloud-masked reflectance (bands 1-4) to give vegetation, soil, built up area and water LC types for all flaring sites. This was confirmed through the fieldwork observation for ground validation of Landsat 5 TM and Landsat 7 ETM+ in the Niger Delta that LC types obtained from satellite data are the same with those observed during the fieldwork. The method used to estimate Ɛ value for LC types at these sites is based on the Ɛ of 4 LC types present at each site. The changes in LC differ throughout the period for the 6 sites due to different human activities within each site. The Ɛ values estimated for the 4 LC types for the sites are not stable but changing from 1986 to 2013 due to changes in LC types. The results of LC classification show that K-mean method can distinguish up to 4 LC types very well in the Niger Delta.
Keywords: Estimation, Gas-flaring, Land Cover, Mapping, Niger-Delta, emissivity
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