واکاوی برچگونگی توسعه‌ی روش‌های ارزیابی شاخص سبزینگی در مناظر شهری

در اواسط قرن بیستم میلادی، رشد سریع جمعیت شهرنشین و به‌دنبال آن، تراکم بالای ساختمانی، بیش از هر زمان دیگری توجه ویژه‌ی برنامه‌ریزان و طراحان شهری را به مقوله‌ی پوشش گیاهی شهری جلب کرد تا از این طریق بتوانند با کنترل عوامل مخرب زیست‌محیطی، همچون آلودگی هوا، تاثیرات ناشی از جزایر گرمایی شهری و دیگر آلودگی‌های منبعث از مناطق شهری، زمینه را برای ارتقاء کیفیت‌های روان‌شناختی محیطی شهرنشینان با درنظر گرفتن میزان مناسب سبزینگی در چشم‌اندازهای شهریشان به‌طور هم‌زمان فراهم کنند. در این راستا، روش‌ها و تکنیک‌های متفاوتی برای ارزیابی و تخمین کمیت فضاهای سبز و عملکرد آن‌ها گسترش یافته است. به‌دست آوردن شاخص‌های سبزینگی متعدد در موقعیت‌های گوناگون درراستای درک میزان تاثیرگذاری سبزینگی بر عملکردهای مناظر شهری ازجمله تلاش‌های متخصصان عرصه‌ی شهری در چند دهه‌ی اخیر بوده است که هریک با هدف و روشی متمایز ارائه شده‌اند. در تحقیق حاضر، با معرفی شاخص‌های گوناگون سبزینگی، سلسله‌مراتب شکل‌گیری، روش‌های ارزیابی و بیان نقاط ضعف و قوت هریک درخصوص اثرگذاری موثرشان در مناظر شهری، این نتیجه حاصل شده است که روش‌های عینی با نگاه سه‌بُعدی مزیت بیشتری درمقایسه با روش‌های ذهنی و همچنین روش‌های عینی با نگاه دوبُعدی دارند؛ زیرا به واقعیت موجود نزدیک‌تر و عاری از مشکلات روش‌های ذهنی هستند.

An Investigation on the Development of Green Index Assessment Methods for Urban Landscapesdscapes

IntroductionIn the mid20th 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 psychoenvironmental 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 threedimensional objective methods are superior to mental methods as well as twodimensional objective methods.Methodology:Once finished with assessing the green indices based on ground surveys, such as the ldquo;green coverage ratio rdquo;, development of remote sensing technology could smooth the way toward assessing numerous green indices via different approaches by the researchers. The ldquo;Normalized Difference Vegetation Index (NDVI) rdquo; was used as a binary indicator of green and nongreen areas (in terms of vegetation) by combining the nearinfrared and visible light bands; providing a reference index for the assessment of other indices. Assessment of other indices, such as the ldquo;Green Index rdquo;, ldquo;Proximity to Green Space Index rdquo;, and ldquo;Urban Neighborhood Green Index rdquo;, became possible by taking a 2D view with the help of NDVI and image processing of satellite images via objectbased, rather than pixelbased methods; this change of procedure led to more accurate evaluation of the studied scenes. However, particular indices such as ldquo;Green View Index rdquo; and ldquo;Google Street View rdquo;, that were intended to sense urban visual greenness recognized the previous indices as not being consistent with the citizens rsquo; views on the ground, and hence, used images taken by visible lightsensing 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 highrise urban buildings. For instance, the ldquo;Floor Green View Index rdquo; makes use of the remote sensing technology and multispectra 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 ldquo;Building Visual Green Index rdquo; 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 abovementioned 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 computerassisted software tools (e.g. ArcGIS) and image processing algorithms. It can be stipulated that, combination of the highresolution 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 highresolution 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 rsquo;s judgment effect and possible perception issues); while assessing the greenness based on the citizens rsquo; 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 rsquo;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.