كليدواژه :
فرآيند تصويري شي ء گرا , سنجش از دور , روش عيني , سبزينگي شهري قابل رويت , شاخص سبز
چكيده لاتين :
Introduction
In the mid-20th century, the rapid growth of urban population followed by high construction density have ever increasingly attracted the urban planners and designers toward considering the subject matter of urban vegetation. This has been seen as a tool to not only control adverse environmental phenomena, but also to enhance the psycho-environmental qualities for the citizens by providing the urban landscapes, with adequate levels of greenness at the same time. In this respect, numerous methods and techniques have been developed to assess and estimate the quantity and function of green spaces. During the past decades, urban scientists have tried to formulate different green indices in different situations to understand the impact of vegetation on the functions of the urban landscapes, with each index following a particular objective and methodology. The present paper is aimed at introducing different green indices and their development paths and assessment methods. Moreover, the pros and cons of each index in terms of its effect on the urban landscape are is further discussed. Finally, it is concluded that three-dimensional objective methods are superior to mental methods as well as two-dimensional objective methods.
Methodology:
Once finished with assessing the green indices based on ground surveys, such as the “green coverage ratio”, development of remote sensing technology could smooth the way toward assessing numerous green indices via different approaches by the researchers. The “Normalized Difference Vegetation Index (NDVI)” was used as a binary indicator of green and non-green areas (in terms of vegetation) by combining the near-infrared and visible light bands; providing a reference index for the assessment of other indices. Assessment of other indices, such as the “Green Index”, “Proximity to Green Space Index”, and “Urban Neighborhood Green Index”, became possible by taking a 2D view with the help of NDVI and image processing of satellite images via object-based, rather than pixel-based methods; this change of procedure led to more accurate evaluation of the studied scenes. However, particular indices such as “Green View Index” and “Google Street View”, that were intended to sense urban visual greenness recognized the previous indices as not being consistent with the citizens’ views on the ground, and hence, used images taken by visible light-sensing digital cameras combined with special software (e.g. Adobe Photoshop) to measure the greenness level. Next, considering the inevitable vertical growth of the cities, some researchers started to formulate indices for measuring the visible greenness from the floors of high-rise urban buildings. For instance, the “Floor Green View Index” makes use of the remote sensing technology and multi-spectra images as well as digital 3D models of the surface and buildings to obtain the topography and 3D morphology of the study area, which respectively leads in determining the viewpoint of each floor. The “Building Visual Green Index” uses the remote sensing technology, multispectral images, and an analytic model of the viewshed, the blind zone in the ArcGIS, and eCognition software, to evaluate the visible green space from each floor compared to that of other floors. Finally, all of the above-mentioned indices were evaluated in an objective approach, some with 2D view provided by the satellite images, while the others were based on the 3D view of the citizens.
Results and discussion:
Although the use of NDVI compare to the ground surveys for the assessment of vegetation greenness has led major change in the accuracy of measurement, but its inherent 2D view to the greenness and ignorance of the distribution of green spaces across urban areas of different heights and construction densities made the index serve as no more than a reference for assessing other indices in the urban landscape studies. This situation further ended up incorporating the green indices focused on the assessment of visible greenness to the citizens into computer-assisted software tools (e.g. ArcGIS) and image processing algorithms. It can be stipulated that, combination of the high-resolution satellite images with software tools and digital models provides the urban researchers with brilliant opportunities for the assessment of green indices.
Conclusion:
The most important and fundamental factor contributing to the revolution of green index assessment during the studied period has been the replacement of ground surveys by the remote sensing technology and high-resolution images, imposing significant influences on the accuracy of the obtained green indices. Respecting the importance of the accuracy of the green indices, the objective methods with 3D views seem to present larger potentials thanks to the absence of common problems in the subjective methods (observer’s judgment effect and possible perception issues); while assessing the greenness based on the citizens’ views rather than a solely 2D aerial view from the top, could producing produce closer estimations to the actual greenness. In the meantime, the accuracy and precision of various 3D objective methods depend on the researcher’s viewpoint, and the success of such methods in achieving their set targets is affected by particular limitations. Accordingly, complementary studies are required to address the existing limitations, and hence, achieve even more accurate greenness indices that can be assessed for various applications, including urban planning, urban design, landscape design, etc.
