Comprehensively analyzing optical and polarimetric SAR features for land-use/land-cover classification and urban vegetation extraction in highly-dense urban area

Abstract. Recently the sensing data for urban mapping used is in high demand together with the accessible of very high resolution (VHR) satellite data such as Worldview and Pleiades. This article presents the use of very high resolution (VHR) remote sensing data for urban vegetation mapping. The research objectives were to assess the use of Pleiades imagery to extricate the data of urban vegetation in urban area of Kuala Lumpur. Normalized Difference Vegetation Index (NDVI) were employs with VHR data to find Vegetation Index for classification process of vegetation and non-vegetation classes. Land use classes are easily determined by computing their Normalized Difference Vegetation Index for Land use land cover classification. Maximum likelihood was conducted for the classification phase. NDVI were extracted from the imagery to assist the process of classification. NDVI method is use by referring to its features such as vegetation at different NDVI threshold values. The result showed three classes of land cover that consist of low vegetation, high vegetation and non-vegetation area. The accuracy assessment gained was then being implemented using the visual interpretation and overall accuracy achieved was 70.740% with kappa coefficient of 0.5. This study gained the proposed threshold method using NDVI value able to identify and classify urban vegetation with the use of VHR Pleiades imagery and need further improvement when apply to different area of interest and different land use land cover characteristics. The information achieved from the result able to help planners for future planning for conservation of vegetation in urban area.

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Land Use Land Cover of an Urban Area using Remote Sensing (Texture Analysis Applications) and GIS - A Case Study of Central Region of Almadinah Almunawarah, Saudi Arabia

International Journal of Advanced Remote Sensing and GIS ◽

10.23953/cloud.ijarsg.256 ◽

2017 ◽

Vol 6 (1) ◽

pp. 2114-2123

Author(s):

Saleh Daqamseh ◽

Keyword(s):

Remote Sensing ◽

Land Use ◽

Saudi Arabia ◽

Land Cover ◽

Texture Analysis ◽

Urban Area ◽

Central Region ◽

Land Use Land Cover

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A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

Sustainability ◽

10.3390/su13063070 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3070

Author(s):

Patrycja Szarek-Iwaniuk

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Classification ◽

Classification Systems ◽

Anthropogenic Pressure ◽

Land Use Land Cover ◽

Land Use Classification ◽

Land Cover Data ◽

Spatial Changes ◽

Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

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