Land Cover Classification of Kei Kecil Island in 2019 Based on Multispectral Image Analysis

Land coverage of an island can be determined based on a multispectral image analysis. This research was carried out in Kei Kecil Island, Southeast Maluku Regency. The research aimed to determine land cover based on multispectral analysis of Landsat 8 (OLI) record on 27 November 2019. This research was carried out through several stages, namely pre-processing of image data (radiometric correction, correction geometric and image cutting), digital analysis of Landsat Image (Image Processing) and Accuracy Test. The classification method used was the Maximum Likelihood (MCL) by considering the prior probability factor, namely the chance of a pixel to be explained into a certain class. The results of Landsat 8 (OLI) image classification showed that there were 7 classes of land cover, with the coverage area of each land cover: settlements 34.73%, secondary forests 10.54%, water bodies 0.05%, shrubs 34.77%, mixed gardens 14.57%, open land 1.91%, and cloud 3.43%. The land cover of the multispectral image of 543 was dominated by shrubs, which was 34.8%, and the smallest was water body, which was 0.1%. In the multispectral image of 654, settlements dominated the land cover of the research area, which was 31.5% and the narrowest was open land, which was 0.9%. The accuracy was shown with an overall accuracy value of 88% and a Kappa score of 0.85%. This showed that the level of accuracy of classification results obtained through Landsat 8 multispectral image analysis (OLI) in 2019 had a very high level of accuracy (very good). These results met the requirements applied by USGS (United States Geological Survey).  &nbsp

Advanced Digital Image Processing Techniques for Natural Resource Assessment at Stephen F. Austin State University

Graduate course work concentrating on land cover classification and digital image processing within the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University is presented

ACCURACY ASSESSMENT OF PIXEL-BASED IMAGE CLASSIFICATION OF KWALI COUNCIL AREA, ABUJA, NIGERIA

In this study, Kwali Council Area located on the western part of the Federal Capital Territory, Abuja was selected as a study area covering approximately 1,206 km² for comparing the two major pixel-based image classification algorithms (Supervised and Unsupervised classification). For this purpose, land use and land cover classification of the study area was conducted by supervised classification particularly maximum likelihood classification (MLC) and Iso-cluster unsupervised classification procedures and the results were compared with one another using 2011 Landsat-7 ETM+ satellite. However, the result of classification accuracy illustrates that light vegetation shrubs records dominance value of 27.54%, savannah grasses 23.04%, cultivated areas 20.12%, wetland flood plain 13.78%, sand open surfaces 11.01% and water body 4.52%. Overall, supervised pixel-based classification methods are found to be more reliable, accurate and outperformed unsupervised pixel-based classification methods in this study. The higher accuracy was attributed to the fact that supervised classification took advantage of spectral information of land cover, based on the spectral signature defined in the training set and digital image classification software that determines each class on what it resembles most in the training set in the remotely sensed imagery. This study is a good example of some of the limitations of unsupervised pixel-based image classification techniques, whereby the unsupervised image classification technique is commonly used when no sample sites exist. These improvements are likely to have significant benefits for land-cover mapping and change detection applications. It is recommended that, the two approach can be used together to provide a standard, accurate and finest result for specific applications by users in different parts of the world.   Keywords: Accuracy, assessment, pixel-based image classification algorithms, iso-cluster unsupervised, ML

A GIS based Land Capability Classification of Guang Watershed, Highlands of Ethiopia

The main objective of this study was to spatially classify lands of Guang watershed, Ethiopia based on their capability for sustainable use by USDA criteria in 2014. Land use land cover was determined from LANDSAT satellite image by applying supervised classification method in ENVI 5.0 software. “Spatial Analyst Tool Extract by Mask” in GIS environment was used to obtain soil depth and soil texture map of the watershed from Amhara Regional digital soil map. Digital Elevation Model (DEM) data of 30 m resolution was used to derive slope. Intersect overlay analysis method was applied to obtain the spatial and attribute information of all the input parameters using Geographical Information System (GIS) 10.1 soft ware. The study demonstrates that GIS provide advantage to analyze multi-layer of data spatially and classify land based on its capability. The study revealed that 1,540 ha (61.6%), 442.25 ha (17.69%) and 518 ha (20.52%) of the watershed was categorized in the range of land classes I to IV, V to VII and VIII, respectively. It was observed that present land use land cover was not as per the capability of the land. Keywords:  land capability classification, GIS, Guang watershe