Classification and quantification of green in the expanding urban and semi-urban complex: Application of detailed field data and IKONOS-imagery

Green in the expanding urban and semi-urban complex: application of detailed field data and IKONOS imagery

Urban Tree Canopy (UTC) is the layer of leaves, branches and stems of trees that cover the ground when viewed from above. Remotely sensed data have played an important role in detecting urban morphologies effectively. Remote sensing datasets contain more information of earth surface as compared with usual urban maps hence used in urban planning and management. Very high resolution (VHR) satellite imageries provide resolution less than 1m. These imageries have enhanced the applications of remote sensing. Timely and accurate information of urban land cover and biophysical parameters is crucial. Earth observations which are being useable play an important role in detecting, management and solving environmental problems such as climate changes, deforestation, disasters, land use, water resource and carbon cycle. With the help of high resolution satellite imageries it is possible to get the details on earth surface. A different approach is used to get the efficient result called Object-based image analysis (OBIA) in which the image is divided into homogeneous regions prior to classification instead of classifying individual pixels. These are called segments, or image objects. OBIA have gain popularity as a method bridging the gap between the increasing amount of detailed geospatial data and the inefficient results of conventional pixel base classifiers.

Urban tree canopy detection using object-based image analysis for very high resolution satellite images: A literature review

Lexical meaning of ecology is “the study of the interrelations of organisms and their surroundings” (Odum and Barrett 2008) Every individual in the society must be aware of and know his surroundings, plants in his surroundings, other living beings and all the factors that affect the life in the nature in order to continue his life Because of various opportunities provided by the success in technology for our lives, many of us are not aware of the fact that we depend on nature for air, water, and food, though indirectly It is among the biggest misconceptions of developed societies of the present time that individuals perceive opportunities offered by technology as direct needs in addition to the basic needs provided by nature for us in daily life Tangible value of services provided by nature for us both as a human being and as a society is very low In this sense, we tend not to understand and comprehend the value of these services unless there is a problem (Odum and Odum 2000)

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Sustainability in Urban Ecosystems and Detecting Urban Vegetation from Different Images Using an Object-Based Approach

In the context of high-quality urban development and the increasingly important role of urban green infrastructure (UGI) in public life, landscape activity (LA) has gradually become a dominant indicator for improving UGI quality and efficiency, as well as optimizing its environmental friendliness and meeting the recreational needs of the public. Relevant studies have shown that the ecological index (EI) and the public vitality index (PVI) can characterize LA from the perspectives of greening quality and public activities, respectively, and their simultaneous analysis can provide professional judgment and quantitative technical approaches for the LA analysis of UGI. At the same time, with the support of remote sensing, big data, GIS, and other spatial information data, the LA model coupling EI and PVI of UGI needs to be developed. First, this article established a research framework for UGI landscape activity, and by combining environmental remote sensing and location-based services (LBS) technology, a technical LA measurement strategy suitable for the coupled analysis of EI and PVI was formed. Then, based on the MATLAB platform and the entropy-weighted TOPSIS model, this research developed a fusion analysis algorithm of EI and PVI to establish the LA model, taking the central urban area of Zhongshan as a case study. Finally, four-quadrant classification and quantitative grading of LA were developed based on the ArcGIS platform. Empirical research showed that the UGI area of the study area was about 176.43 km2, and 160 UGI units were identified. The minimum LA value is 0.06, and the maximum is 0.85. The LA of UGI in the study area can be divided into three grades: low (0–0.24), medium (0.24–0.46), and high (0.46–0.85). Among them, the top 5% of UGI units mainly correspond to urban parks and waterfront greenways, and the bottom 5% mainly correspond to islands and farmland. The quantitative distribution of UGI in the four quadrants of LA in the study area is relatively balanced: among them, the number of high-quality developing types is the largest, accounting for 29.4%, and that of high-quality mature types is the least, accounting for 20.0%. This article forms a concise model and technical process for the LA of UGI, which can be used for its quantitative analysis and evaluation. It is expected that the research result will be significant for the high-quality construction of UGI and the sustainable development of the urban landscape in terms of research and exploration.

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On the Landscape Activity Measure Coupling Ecological Index and Public Vitality Index of UGI: The Case Study of Zhongshan, China

Urban vegetation plays an important role for sustainable development policies, environmental conservation and urban planning process of a city. It is necessary to detect the amount of green areas and their distribution to form the ecosystem model of the urban environment. It is quite important to use satellite imagery having different ground sampling distance (GSD) in the economic and accurate detection of urban green areas. Especially object-based image analysis has been frequently used today for object extraction processes. In object-oriented image analysis, not only pixel gray values but also spectral and contextual data that help to distinguish the segments consisting of interrelated pixels on the image are used. For this reason, more positive results are obtained in comparison with pixel-based approaches. In this study, city center of Bartin, in which there is a rich amount of green areas and its vicinity was chosen as the test area. As the satellite image data, LANDSAT 7 ETM+ (28.5 m GSD), SPOT 4 Level 2 A (20 m GSD) and IKONOS (1 m GSD) were used. Test area was divided into segments involving lots of different classes on each image. Urban vegetation class was formed by determining the suitable functions for the objects which will be involved in the urban vegetation class. eCognition v4.06 software was used for object-based classification analysis. Classification results were transformed into vector data and visual and digital analyses were made using GIS.

M.A. Marangoz

Papers

Detecting Urban Vegetation from Different Images Using an Object-Based Approach in Bartin, Turkey