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Debris flow alluvial fans (DFAFs) are vulnerable, although they can be used as a natural resource. The relationships between different factors related to DFAF systems and between these factors and systems are important both for identifying the risks and opportunities presented by DFAFs and for tracking system status. In this regard, resilience may be used to characterize the status of a DFAF. This study aimed to explore the processes and mechanisms of interactions among the social, economic, and ecological factors related to DFAF with respect to resilience, and to discuss potential problems in a representative DFAF. Based on the site condition and characteristics of the Awang DFAF (China) in the period 1996–2017, we formed a comprehensive indicator evaluation framework by analyzing disturbance, function, and feedback. We also established a model for evaluating resilience by integrating the analytic hierarchy process (AHP) – an entropy evaluation method (EEM) and set pair analysis (SPA). The results showed that the system of the studied DFAF was dynamically stable. The domination of the ecological system was subsequently superseded by social and economic resilience. While disturbance had direct and immediate effects, coping ability was cumulative and characterized by hysteresis at a particular response time. Overall, resilience fluctuated within an acceptable range rather than linearly increasing or decreasing. This analysis illuminated the dynamic processes of DFAFs and contributed to the understanding and planning of system trade-offs and degraded-land utilization. • Focus on the dynamic change of the social-ecological compound system in the ecologically fragile area. • The disturbance and resilience of debris flow fan system are quantitatively characterized. • The evaluation indicators include the natural and man-made indicators, reflecting the hazards risk degree. • More investment is required for the maintenance of ecosystem's management and construction. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) and vegetation cover changes are two indicators of landscapes in a region. The relationship between LST anomalies, elevation, vegetation, and urban growth is significant to conservation. This study addresses this issue using night-time satellite imagery, kernel methods (points aggregation), and the trend analysis for a long-term period (2001–2017) in Iran. Variables for two seasons (summer and winter) in urban and natural land uses were derived using the Google Earth Engine (GEE) and NASA's Giovanni. Point data derived from raster maps were quantified using statistical kernel and trend analysis. As result, it was observed that LST rise in various elevations, seasons, and land uses. The LST was analyzed through kernels (point aggregation in scatter graphs), which shifted to the right. The LST anomaly in the daytime had the highest maximum value (>4 °C) and lowest minimum value (<-5 °C) in forests and mountains and metropolises with the highest population growth rate. Summer and winter seasons had positive trends in LST for forest and mountain land uses. All seasons had positive trends in EVI in the mountain, and desert land uses. This warming and increasing LST can increase vulnerability to drought, dust storms, floods, avalanches, and natural fires. The EVI is increasing over the years due to government projects in green spaces and urban parks. There is a need to protect urban and natural environments to prevent natural disasters and unplanned population growth. • Google Earth Engine (GEE) is used to analyze remote sensed data. • New workflow with open access data was developed to analyze LST and land use. • The relationship between LST and land use over a long term were significant. • Kernel points from imagery were analyzed using trend and statistical analysis. • The implication of climate change varied by land use and elevation in the study area. [ABSTRACT FROM AUTHOR]