Presenting and Comparing the Object Based Image Analysis and Standard Image Analysis for Change Detection of Forest Areas, Using Low-Resolution Satellite Imagery

Jovanovic, D.; Govedarica, Miro; Djordjevic, I

doi:10.5593/sgem2011/s08.103

Cited by 3 publications

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“…Change detection has been defined as a "process of identifying differences in the state of an object or phenomenon by observing it in different times" (Singh, 1989). There are two basic ways of change detec-tion: first by direct overlapping of classified vector classes from both images and then visually analyzing the changes and second by direct change detection of one image made of combined images from different epochs (Jovanović, et al, 2007, Jovanović, et al, 2011. There is also a different classification given by Shaoqing.…”

Section: Methods Of Land Cover Change Detectionmentioning

confidence: 99%

Land cover change detection by using remote sensing: A case study of Zlatibor (Serbia)

Jovanović¹,

Govedarica²,

Sabo³

et al. 2015

Geogr Pannon

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Change detection is a process of detecting differences with the objects or phenomena which are observed in the different time intervals. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1985 -2013). The area observed in this study is the region of mountain Zlatibor (Serbia) with its surroundings. The methods represented in this study are vegetation indices differencing, Supervised classification and Object based classification. These methods gave different results in term of land cover area, and it is generally concluded that supervised classification gave the most accurate results with the images of medium spatial resolution. The results of this study can be used for urban and environmental planning. All information lead to conclusion that the surface under the forests is reduced for about 4% (or about 1000 ha) while the built up area has doubled (grown about 600 ha) during the examined period. The results also highlights the importance of change detection techniques in land cover for the areas that are developing rapidly, such as Zlatibor study area.

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Section: Methods Of Land Cover Change Detectionmentioning

confidence: 99%

Land cover change detection by using remote sensing: A case study of Zlatibor (Serbia)

Jovanović¹,

Govedarica²,

Sabo³

et al. 2015

Geogr Pannon

Self Cite

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Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Singh

Kumar

2018

Journal of Landscape Ecology

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ABSTRCTDigital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to geo-registered multi-temporal remote sensing data. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time . The methods represented in this study are vegetation indices, image differencing and supervised classification. These methods gave different results in terms of land cover area. Urban expansion has brought serious losses of agriculture land, vegetation and water bodies. The present study demonstrates changes in land trajectories of Varanasi district, India using Landsat MSS (1980), TM (1990 and 2010), ETM+ (2000) and Landsat-8 OLI data (2016). The LULC classes in the study area are divided into eight categories using supervised classification method. Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are also calculated to estimate the changes in LULC classes during these time periods. Major changes are seen from 2000 to 2016 for the built-up, agriculture land, water bodies and wasteland.

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Object Oriented Image Analysis in Remote Sensing of Forest and Vineyard Areas

et al. 2015

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The study of vegetation cover, forests, orchards or vineyards and crops through satellite techniques is increasingly promoted as a result of facilities they offer. Since 2010 until today, it is launched over 50 satellite platforms, delivering images of the Earth's surface with different spectral, spatial and radiometric characteristics. New satellite images such as RapidEye, WorldView2 or WorldView3, with its high spatial and radiometric resolution, prevents the use of the standard image analysis techniques (such as supervised or unsupervised), and involves the use of modern methods in image analysis, such as object base image analysis. A number of methods and techniques for processing and analysis of satellite images are developed to increase the precision of the working, given the diversity of vegetation structure analysis and expected results. This study aimed to analyze the capabilities of object-oriented image analysis (OBIA) for recognition forest and vineyard areas. OBIA is automated process of object extraction by modelling of human visual system for image interpretation. The basis for classification process is object, which is created according to the set of characteristics. In object-oriented approach classification description is based on classification rules including spectral characteristics, size, shape, as well as content and texture information. Analysis is done on multispectral imagery of high and very high spatial resolution. Represented results show the usefulness of RapidEye and WorldView2 images as well as importance of classification based on OBIA. Object-oriented image analysis (OBIA) method based on satellite imagery has facilitated the recognition forest and vineyard areas with high accuracy.

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Presenting and Comparing the Object Based Image Analysis and Standard Image Analysis for Change Detection of Forest Areas, Using Low-Resolution Satellite Imagery

Cited by 3 publications

References 0 publications

Land cover change detection by using remote sensing: A case study of Zlatibor (Serbia)

Land cover change detection by using remote sensing: A case study of Zlatibor (Serbia)

Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Object Oriented Image Analysis in Remote Sensing of Forest and Vineyard Areas

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