Analysis of green space in Chongqing and Nanjing, cities of China with ASTER images using object‐oriented image classification and landscape metric analysis

Fung, Tung; So, Lkh; Chen, Y.; Shi, Peijun; Wang, J.

doi:10.1080/01431160802199868

Cited by 18 publications

(8 citation statements)

References 26 publications

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“…Various investigators have also underlined as a key advantage of the object-based over pixel-based classification, the fact that, in addition to the spectral information, additional information on image data (e.g. object size, object complexity, texture and spectral difference to neighboring objects) is available, allowing more accurate mapping and achieving higher classification accuracy (Benz et al 2004, Fung et al 2008). …”

Section: Hyperion Object-based Classificationmentioning

confidence: 98%

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Elatawneh

Kalaitzidis

Petropoulos

et al. 2012

International Journal of Digital Earth

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Information on Earth's land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors. In this study, we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery. For this purpose, the spectral angle mapper (SAM), the object-based and the non-linear spectral unmixing based on artificial neural networks (ANNs) techniques were applied. A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification, namely of the pixel purity index (PPI) and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites. Object-based classification outperformed the other techniques with an overall accuracy of 83%. Sub-pixel classification based on the ANN showed an overall accuracy of 52%, very close to that of SAM (48%). SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%. Yet, all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery, which affected the spectral separation among the land use/cover classes.Peer reviewe

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Section: Hyperion Object-based Classificationmentioning

confidence: 98%

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Elatawneh

Kalaitzidis

Petropoulos

et al. 2012

International Journal of Digital Earth

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“…The urban environment represents the Earth's surface area with the highest human activity and the highest land use intensity. Urban green space is an important part of the urban ecosystem and plays a key role in its development (Fung et al 2008;Onishi et al 2010). Urban green space is a comprehensive concept referring to areas covered by vegetation.…”

Section: Introductionmentioning

confidence: 99%

An effective Building Neighborhood Green Index model for measuring urban green space

Liu

Meng

Zhang

et al. 2015

International Journal of Digital Earth

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Urban green space forms an integral part of urban ecosystems. Quantifying urban green space is of substantial importance for urban planning and development. Considering the drawbacks of previous urban green space index models, which established either through a grid method or green distribution, and the difficulty of the validation process of earlier urban green space index models, this study exploits the advantages of multisource high-resolution remote sensing data to establish a Building Neighborhood Green Index (BNGI) model. The model which analyzes the spatial configuration of built-up areas and the vegetation is based on the building-oriented method and considers four parameters -Green Index (GI), proximity to green, building sparsity, and building height. Comparing BNGI with GI in different types of characteristic building regions, it was found that BNGI evaluates urban green space more sensitively. It was also found that high-rise low-sparsity area has a lower mean value of BNGI (0.56) as compared with that of low-rise low-sparsity neighborhood (0.62), whereas mean GI (0.24) is equal for both neighborhoods. Taking characteristics of urban building and green types into consideration, BNGI model can be effectively used in many fields such as land suitability analysis and urban planning.

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“…Since the portion of the Red River running through Hanoi is small, we manually digitized a layer of the Red River as a masking layer, and the raster math was then used to merge the original land cover map with the masked layer. We followed the SVM classification with a 4 × 4 majority filter kernel to remove the 'salt and pepper' effect common in Landsat classifications [46].…”

Section: Remote Sensing Of Built-up Areasmentioning

confidence: 99%

Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data

Nong

Fox

Miura

et al. 2015

Land

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Abstract:In 1986, the Government of Vietnam implemented free market reforms known as Doi Moi (renovation) that provided private ownership of farms and companies, and encouraged deregulation and foreign investment. Since then, the economy of Vietnam has achieved rapid growth in agricultural and industrial production, construction and housing, and exports and foreign investments, each of which have resulted in momentous landscape transformations. One of the most evident changes is urbanization and an accompanying loss of agricultural lands and open spaces. These rapid changes pose enormous challenges for local populations as well as planning authorities. Accurate and timely data on changes in built-up urban environments are essential for supporting sound urban development. In this study, we applied the Support Vector Machine classification (SVM) to multi-temporal stacks of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 1993 to 2010 to quantify changes in built-up areas. The SVM classification algorithm produced a highly accurate map of land cover change with an overall accuracy of 95%. The study showed that most urban expansion occurred in the periods 2001-2006 and 2006-2010. The analysis was strengthened by the incorporation of population and other socio-economic data. This study provides state authorities a means to examine correlations between urban growth, spatial expansion, and other socio-economic factors in order to not only assess patterns of urban growth but also become aware of potential environmental, social, and

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Analysis of green space in Chongqing and Nanjing, cities of China with ASTER images using object‐oriented image classification and landscape metric analysis

Cited by 18 publications

References 26 publications

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

An effective Building Neighborhood Green Index model for measuring urban green space

Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data

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