Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images. Issue 4 (16th February 2019)

• Record Type: Journal Article
• Authors: Abdu, Haruna Ayuba
• Is Part Of: International journal of remote sensing. Volume 40:Issue 4(2019)
• Page Start: 1275
• Page End: 1300
• Publication Date: 2019-02-16
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1524587
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: The paper evaluated the accuracy of classifying Land Cover-Land Use (LCLU) types and assessed the trends of their changes from Principal Components (PC) of Land satellite (Landsat) images. The accuracy of the image classification of LCLU was evaluated using the confusion matrices and assessed with cross-referencing of samples of LCLU types interpreted and classified from System Pour l'Observation de la Terre (SPOT) images and topographical map. LCLU changes were detected, quantified, and statistically analysed. The interpretation error of the composite image of Landsat Enhanced Thematic Mapper Plus (Landsat ETM + ) (2006) was high compared with that from the PC image of Landsat ETM + (2006). From 1986 – 2006 the area covered by settlements increased by 0.8% (230, 380.00 km 2 ), agricultural land decreased by 7.5% (1009.40 km 2 ), vegetation cover decreased by 0.9% (114.00 km 2 ) while waterbody increased by 0.2% (25.91 km 2 ). Also, from 1986 – 2006 the average annual rates of change in the area of settlements was 6.7%. Agricultural land and bare land showed fluctuations of change rates from 6.7% and 5.0% annually in 1986 and 2006 respectively. The quantitative evidences of LCLU changes revealed the growth of settlements. The conversions of land from agriculture to urban land represent the most significant land cover changes. The rate of change was as high as 4.8% for settlements while agricultural lands were converted at 5.0% per year. The Principal Component Analysis (PCA) of the Landsat images and supervised classification method used made it possible to classify and determine the area of LCLU classes from the set of Landsat images without prior depiction and delimitation of individual LCLU type. It permitted the measurement of area of each LCLU class at a high accuracy level and kept the level of error relatively constant. The PCA analysis in this study affirms the previous research findings. Future research works should focus on the use of remotely sensed images with high temporal and spatial resolutions such as Quick Bird and SPOT 6 to develop effective and accurate LCLU change mapping and monitoring at the local scale. The PCA technique has been used quite widely to study changes in land cover and land use in many 'developed' countries but much still needs to be done in developing and undeveloped countries where land cover and land use change is poorly mapped and knowledge of such changes is very important for planning development of the country.