Assessment of ecological quality in Northwest China (2000-2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality

The ecological quality of inland areas is an important aspect of the United Nations Sustainable Development Goals (UN SDGs). The ecological environment of Northwest China is vulnerable to changes in climate and land use/land cover, and the changes in ecological quality in this arid region over the last two decades are not well understood. This makes it more difficult to advance the UN SDGs and develop appropriate measures at the regional level. In this study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) products to generate remote sensing ecological index (RSEI) on the Google Earth Engine (GEE) platform to examine the relationship between ecological quality and environment in Xinjiang during the last two decades (from 2000 to 2020). We analyzed a 21-year time series of the trends and spatial characteristics of ecological quality. We further assessed the importance of different environmental factors affecting ecological quality through the random forest algorithm using data from statistical yearbooks and land use products. Our results show that the RSEI constructed using the GEE platform can accurately reflect the ecological quality information in Xinjiang because the contribution of the first principal component was higher than 90.00%. The ecological quality in Xinjiang has increased significantly over the last two decades, with the northern part of this region having a better ecological quality than the southern part. The areas with slightly improved ecological quality accounted for 31.26% of the total land area of Xinjiang, whereas only 3.55% of the land area was classified as having a slightly worsen (3.16%) or worsen (0.39%) ecological quality. The vast majority of the deterioration in ecological quality mainly occurred in the barren areas Temperature, precipitation, closed shrublands, grasslands and savannas were the top five environmental factors affecting the changes in RSEI. Environmental factors were allocated different weights for different RSEI categories. In general, the recovery of ecological quality in Xinjiang has been controlled by climate and land use/land cover during the last two decades and policy-driven ecological restoration is therefore crucial. Rapid monitoring of inland ecological quality using the GEE platform is projected to aid in the advancement of the comprehensive assessment of the UN SDGs.     

Spatiotemporal variation in vegetation net primary productivity and its relationship with meteorological factors in the Tarim River Basin of China from 2001 to 2020 based on the Google Earth Engine

Vegetation growth status is an important indicator of ecological security. The Tarim River Basin is located in the inland arid region of Northwest China and has a highly fragile ecological environment. Assessing the vegetation net primary productivity (NPP) of the Tarim River Basin can provide insights into the vegetation growth variations in the region. Therefore, based on the Google Earth Engine (GEE) cloud platform, we studied the spatiotemporal variation of vegetation NPP in the Tarim River Basin (except for the eastern Gobi and Kumutag deserts) from 2001 to 2020 and analyzed the correlations between vegetation NPP and meteorological factors (air temperature and precipitation) using the Sen slope estimation method, coefficient of variation, and rescaled range analysis method. In terms of temporal characteristics, vegetation NPP in the Tarim River Basin showed an overall fluctuating upward trend from 2001 to 2020, with the smallest value of 118.99 g C/(m²?a) in 2001 and the largest value of 155.07 g C/(m²?a) in 2017. Regarding the spatial characteristics, vegetation NPP in the Tarim River Basin showed a downward trend from northwest to southeast along the outer edge of the study area. The annual average value of vegetation NPP was 133.35 g C/(m²?a), and the area with annual average vegetation NPP values greater than 100.00 g C/(m²?a) was 82,638.75 km², accounting for 57.76% of the basin. The future trend of vegetation NPP was dominated by anti-continuity characteristic; the percentage of the area with anti-continuity characteristic was 63.57%. The area with a significant positive correlation between vegetation NPP and air temperature accounted for 53.74% of the regions that passed the significance test, while the area with a significant positive correlation between vegetation NPP and precipitation occupied 98.68% of the regions that passed the significance test. Hence, the effect of precipitation on vegetation NPP was greater than that of air temperature. The results of this study improve the understanding on the spatiotemporal variation of vegetation NPP in the Tarim River Basin and the impact of meteorological factors on vegetation NPP.     

Comparing conventional manual measurement of the green view index with modern automatic methods using google street view and semantic segmentation

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.     

Analyzing the effects of nature exposure on perceived satisfaction with running routes: An activity path-based measure approach

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.     

基于Sentinel-2遥感影像的黄土高原覆膜农田识别

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...