Land use land cover analysis with pixel-based classification approach

Abstract

<p>Rapid development in certain urban area will affect its natural features. Therefore, it is important to identify and determine the changes occur for further analysis and future development planning. This process will influence several factors such as area development, environmental issues and human social activities. The selection of remote sensing data and method will derive the accurate land use land cover maps. This research study accessed the classification accuracy of different classifier approach for land use land cover classification in urban area. The objective of this paper is to compare the accuracy of the classification for each technique used. The study was conducted in a highly urbanized area in Kuala Lumpur, Malaysia. The dataset used for this study is the multi temporal LANDSAT satellite imageries for the year of 2001,2006,2011 and 2016. The pre-processing and analysis of the dataset has been done using software ENVI 5.3. Five land use classes (Urban/built up area, Forest, Agriculture, Water Body and fallow land) were identify for classification process. The classification approach for this study is the supervised classification with two algorithms namely Maximum Likelihood (ML) and Support Vector Machine (SVM). The overall accuracy and kappa statistic of the classification indicate that support vector machine algorithm was more accurate than maximum likelihood algorithm for five different time intervals.Therefore, this classification approach is acceptable and highly recommended for mapping the changes of land cover.</p>

Decadal Land Use and Land Cover Transformation in Shaheed Bhagat Singh Nagar of Punjab Based on Geospatial Technology

“…Producer's accuracy was maximum (100 %) in riverine sand and algae bloom classes, while it was minimum (62.50 %) in fallow land /barren land/wasteland class. Table 4.12 also shows that the overall accuracy was 86.6 % with a kappa coefficient value of 0.83 A related interpretation of the outcome was made by Hashim et al (2019).…”

Remote sensing and geographic information systems technics for spatial-based development planning and policy

“…Then for the LULC classification, he used the maximum likelihood classification (MLC) algorithm. In contrast, several studies recommend machine learning methods such as support vector machine (SVM) and random forest (RF) for LULC classification than the MLC algorithm [64], [65]. Furthermore, the inverse distance weighted (IDW) method generates the elevation model.…”

Land use and land cover change analysis using satellite images in Gua Musang, Kelantan

“…This technology can simplify and facilitate the forest information using multiple sources of spatial data due to it ability in the acquisition of remotely sensed data about an object, area or phenomenon or area under investigation [8]. Remote sensing can provide benefits in quantifying forest structure, mapping aboveground biomass (AGB), monitoring temporal changes, estimating timber volume and planning purposes [9], [10]. Besides, it can overcome the issue in monitoring any forest disturbances or to be specific, logging operations that occurred deep in the forest by using conventional method is found to be difficult, such as transportation being blocked by fallen timber and cannot proceed the journey [11] also for land use land cover classification and mapping is time consuming and high cost to process [12].…”