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首頁(yè)> 外文會(huì)議>Joint annual meeting of the International Society of Exposure Science and the International Society for Environmental Epidemiology >Quantifying Residential Access and Exposure to Greenspace Using High-Resolution Remotely Sensed Data: A Case Study of Metro Vancouver, British Columbia
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Quantifying Residential Access and Exposure to Greenspace Using High-Resolution Remotely Sensed Data: A Case Study of Metro Vancouver, British Columbia

機(jī)譯：使用高分辨率遙感數(shù)據(jù)量化居民對(duì)綠色空間的接觸和暴露：以不列顛哥倫比亞省大溫哥華地區(qū)為例

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Despite growing evidence demonstrating the health benefits of greenspaces, the potential differences in effect depending on type of greenspace exposure remain relatively unexplored. The present research aims to develop a methodology to (1) identify greenspace using remotely sensed data and (2) model residential access and exposure to greenspace and the relative difference in vegetation type. Methods: 2014 RapidEye satellite imagery and LiDAR datasets from 2008-2015 were combined in eCognition for object-based segmentation, and then classified using Random Forest in R to produce a high-resolution (5m) land cover map of Metro Vancouver, Canada. The land cover map includes 14 classes - covering, for example, coniferous, deciduous, shrub, and grass-herb vegetation. Using the land cover map, greenspace access was calculated as the presence of a public park, recreation area or reserve (≥ 1 hectare) within 300m of residential postal codes and greenspace exposure was calculated as the proportion of greenspace and each land cover type, within several buffer zones of residential postal codes. Results: The land cover map has an overall accuracy of 89% with a kappa of 0.88. Compared to traditional greenness metrics, such as NDVI, the land cover map provides a more detailed model of the variety and distribution of greenspace. Initial analyses suggest that more urbanized areas have greater access to public greenspace and higher exposure to built-up classes and broadleaf vegetation, with grass-herb vegetation increasingly dominant in rural and agricultural areas. Conclusions: This research presents a method of identifying and quantifying greenspace that can be applied widely. Differentiating greenspace access and exposure metrics, including relative distribution of land cover type, will help define which aspects and qualities of greenspaces may provide the most benefits. Such information will provide important guidance for prioritization in urban planning and public health policy.
機(jī)譯：盡管有越來(lái)越多的證據(jù)證明了綠色空間對(duì)健康的益處，但根據(jù)綠色空間暴露類(lèi)型的不同，潛在的效果差異仍未得到充分挖掘。本研究旨在開(kāi)發(fā)一種方法，以（1）使用遙感數(shù)據(jù)識(shí)別綠地，以及（2）對(duì)居民進(jìn)入和接觸綠地以及植被類(lèi)型的相對(duì)差異進(jìn)行建模。方法：將2014年RapidEye衛(wèi)星圖像和2008年至2015年的LiDAR數(shù)據(jù)集結(jié)合在一起進(jìn)行eCognition進(jìn)行基于對(duì)象的分割，然后使用R中的Random Forest進(jìn)行分類(lèi)，以生成加拿大大溫哥華地區(qū)的高分辨率（5m）土地覆蓋圖。土地覆蓋圖包括14個(gè)類(lèi)別-包括針葉樹(shù)，落葉樹(shù)，灌木和草草本植物。使用土地覆蓋圖，將綠地通行權(quán)計(jì)算為住宅郵政編碼中300m以?xún)?nèi)的公園，休閑區(qū)或保護(hù)區(qū)（≥1公頃）的存在，并計(jì)算綠地暴露度，作為綠地和每種土地覆蓋類(lèi)型的比例。住宅郵政編碼的幾個(gè)緩沖區(qū)。結(jié)果：土地覆蓋圖的總體準(zhǔn)確度為89％，kappa為0.88。與傳統(tǒng)的綠色度指標(biāo)（例如NDVI）相比，土地覆蓋圖提供了更詳細(xì)的綠色空間分布模型。初步分析表明，更多的城市化地區(qū)擁有更多的公共綠地通道，并且更多地接觸建筑工人和闊葉植被，而草草植被在農(nóng)村和農(nóng)業(yè)地區(qū)越來(lái)越占主導(dǎo)地位。結(jié)論：這項(xiàng)研究提出了一種識(shí)別和量化綠地的方法，可以廣泛應(yīng)用。區(qū)分綠地的獲取和暴露指標(biāo)，包括土地覆蓋類(lèi)型的相對(duì)分布，將有助于定義哪些方面和質(zhì)量的綠地可能會(huì)提供最大的收益。這些信息將為優(yōu)先考慮城市規(guī)劃和公共衛(wèi)生政策提供重要指導(dǎo)。

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