韧性（resilience）是指个人面对生活逆境、创伤、悲剧、威胁及其他生活重大压力的良好适应，也是个人应激（stress）和应对（coping）的正面结果。在过去的数十年，护理学等健康相关领域的学者们一直在关注为什么一些人在逆境、压力、疾病等高危（high risk）经历下也能较好地维持健康，而一些人则被逆境打败，身心健康也随之处于不良状态。近年来，应激心理学、健康心理学、精神医学、教育学、护理学等各学科对韧性概念越来越重视，尤其是临床领域的研究，从过去强调人的缺点和脆弱性的病理模式转变为关注个人的优势和强韧性的健康模式。

韧性（Resilience）的概念分析

The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

https://www.mdpi.com/2072-4292/11/1/48/pdf

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Ecosystem services response to urbanization in metropolitan areas: Thresholds identification

Fusing satellite observations and station measurements to estimate ground-level PM2.5 is promising for monitoring PM2.5 pollution. A geo-intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM2.5 in a deep belief network (denoted as Geoi-DBN). Geoi-DBN can capture the essential features associated with PM2.5 from latent factors. It was trained and tested with data from China in 2015. The results show that Geoi-DBN performs significantly better than the traditional neural network. The out-of-sample cross-validation R2 increases from 0.42 to 0.88, and RMSE decreases from 29.96 to 13.03 μg/m3. On the basis of the derived PM2.5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM2.5 of greater than 35 μg/m3. This study provides a new perspective for air pollution monitoring in large geographic regions.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2017GL075710

Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach

https://par.nsf.gov/servlets/purl/10054862

Yanxu Liu

Papers

Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region

Ecosystem services response to urbanization in metropolitan areas: Thresholds identification

Linking ecosystem services and circuit theory to identify ecological security patterns

Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas

Linking ecological degradation risk to identify ecological security patterns in a rapidly urbanizing landscape