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141.
Urban greenery has various beneficial effects, such as engendering peace of mind. The green view index (GVI) effectively measures the amount of greenery people can perceive and is a suitable indicator of urban greening. To date, the most common way to measure the GVI has been to photograph the street environment from eye level and use image-editing software to calculate the area occupied by vegetation. However, conventional methods are time-consuming and labor-intensive, and the calculation results may vary among individuals. In recent years, the use of Google Street View (GSV) photos and calculation of the GVI using automatic image segmentation have rapidly developed. In this study, we demonstrate the advantages of GSV and image segmentation over conventional methods, verify their accuracy, and identify the shortcomings of modern methods. We calculated the GVI in the central part of Sapporo, Japan, using the automatic image segmentation AI “DeepLab” and compared the results with those measured by Photoshop. At the exact GSV locations, we also acquired photos and again calculated the GVI using AI, subsequently comparing the results with those obtained on-site manually. Although the correlations were high, automatic image segmentation tended not to identify lawns and flowers planted in the ground as vegetation. It was impossible to determine the year when the GSV photos were taken. In addition, the distance to greenery was biased, depending on the position on the street. These points should be considered when using these modern methods. 相似文献
142.
The rapid economic development that the Hotan Oasis in Xinjiang Uygur Autonomous Region,China has undergone in recent years may face some challenges in its ecological environment.Therefore,an analysis of the spatiotemporal changes in ecological environment of the Hotan Oasis is important for its sustainable development.First,we constructed an improved remote sensing-based ecological index(RSEI)in 1990,1995,2000,2005,2010,2015 and 2020 on the Google Earth Engine(GEE)platform and implemented change detection for their spatial distribution.Second,we performed a spatial autocorrelation analysis on RSEI distribution map and used land-use and land-cover change(LUCC)data to analyze the reasons of RSEI changes.Finally,we investigated the applicability of improved RSEI to arid area.The results showed that mean of RSEI rose from 0.41 to 0.50,showing a slight upward trend.During the 30-a period,2.66% of the regions improved significantly,10.74% improved moderately and 32.21% improved slightly,respectively.The global Moran's I were 0.891,0.889,0.847 and 0.777 for 1990,2000,2010 and 2020,respectively,and the local indicators of spatial autocorrelation(LISA)distribution map showed that the high-high cluster was mainly distributed in the central part of the Hotan Oasis,and the low-low cluster was mainly distributed in the outer edge of the oasis.RSEI at the periphery of the oasis changes from low to high with time,with the fragmentation of RSEI distribution within the oasis increasing.Its distribution and changes are predominantly driven by anthropologic factors,including the expansion of artificial oasis into the desert,the replacement of desert ecosystems by farmland ecosystems,and the increase in the distribution of impervious surfaces.The improved RSEI can reflect the eco-environmental quality effectively of the oasis in arid area with relatively high applicability.The high efficiency exhibited with this approach makes it convenient for rapid,high frequency and macroscopic monitoring of eco-environmental quality in study area. 相似文献
143.
Studies on the linkages between nature exposure and physical activities often focus simply on the immediate vicinity of home locations, but path-based exercises, such as running and cycling, are continuous activities and cover a broad spatial extent. Thus, the traditional home buffer approach fails to acknowledge the settings where road running actually occurs. This study employed an activity path-based measure approach using public participation GIS (PPGIS) to investigate the associations between running satisfaction and nature exposure. The mapped routes (N=545) that included an assessment of satisfaction level were collected from 249 runners resided in the Helsinki Metropolitan Area, Finland. Logistic regression analyses revealed a positive association between running satisfaction and nature exposure, including eye-level greenness, top-down greenness and blue space density. Top-down greenness was assessed by Normalized Difference Vegetation Index (NDVI) and the eye-level greenness by Green View Index (GVI), the latter one of which uses a deep learning algorithm. Running environment was more satisfying in those routes with more public transport nodes. Other traffic-related factors breaking the momentum of runners such as traffic light density were inversely related to running satisfaction. Demographic characteristics such as education background also played a significant role in the perceived satisfaction with running routes. The positive impacts of nature exposure on running satisfaction further verify the linkages between landscape and public health. 相似文献
144.
及时、准确地获取覆膜农田的空间分布信息是防治地膜微塑料污染的基础。为准确地识别黄土高原地区的覆膜农田，本研究构建了基于Sentinel-2遥感影像和随机森林算法的适用于黄土高原覆膜农田遥感识别的特征集组合与多时相组合方案。以甘肃省临夏县、宁夏回族自治区彭阳县和山西省山阴县作为测试区，陕西省旬邑县作为验证区开展识别研究。首先，基于随机森林算法，针对3个不同的作物生育期(播期、生长旺盛期和收获期),在7种不同的特征集组合方案中优选出各时期识别精度最高的方案。然后，基于不同作物生育期的遥感影像及其对应的最优特征集组合方案，构建不同的多时相组合来进行覆膜农田识别并优选多时相组合。最后，利用旬邑县来验证构建的优选特征集组合与多时相组合识别覆膜农田的有效性，并绘制各研究区的覆膜农田空间分布图。结果表明：相比于其他遥感识别特征因子，Sentinel-2遥感影像光谱特征集中的可见光波段(B2、B3和B4)和短波红外波段(B11和B12),指数特征集中的归一化差值裸地与建筑用地指数(NDBBI)、归一化水体指数(NDWI)、裸土指数(BSI)、归一化建筑物指数(NDBI)和改进的归一化水体指数(MNDW... 相似文献