Earthquakes are unpredictable and potentially destructive natural disasters that take a long time to recover from. Monitoring post-earthquake human activity (HA) is of great significance to recovery and reconstruction work. There is a strong correlation between night-time light (NTL) and HA, which aid in the study of spatiotemporal changes in post-earthquake human activities. However, seasonal and noise impact from National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data greatly limits their application. To tackle these issues, random noise and seasonal fluctuation of NPP/VIIRS from January 2014 to December 2018 is removed by adopting the seasonal-trend decomposition procedure based on loess (STL). Based on the theory of post-earthquake recovery model, a post-earthquake night-time light piecewise (PNLP) pattern is explored by employing the National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) monthly data. PNLP indicators, including pre-earthquake development rate (k_p), recovery rate (k_r1), reconstruction rate (k_r2), development rate (k_d), relative reconstruction rate (k_rp) and loss (S), are defined to describe the PNLP pattern. Furthermore, the 2015 Nepal earthquake is chosen as a case study and the spatiotemporal changes in different areas are analyzed. The results reveal that: (1) STL is an effective algorithm for obtaining HA trend from the time series of denoising NTL; (2) the PNLP pattern, divided into four phases, namely the emergency phase (EP), recovery phase (RP-1), reconstruction phase (RP-2), and development phase (DP), aptly describes the variation in post-earthquake HA; (3) PNLP indicators are capable of evaluating the recovery differences across regions. The main socio-economic factors affecting the PNLP pattern and PNLP indicators are energy source for lighting, type of building, agricultural economy, and human poverty index. Based on the NPP/VIIRS data, the PNLP pattern can reflect the periodical changes of HA after earthquakes and provide an effective means for the analysis and evaluation of post-earthquake recovery and reconstruction.

Urban heat island (UHI), referring to higher temperatures in urban extents than its surrounding rural regions, is widely reported in terms of negative effects to both the ecological environment and human health. To propose effective mitigation measurements, spatiotemporal variations and control machines of surface UHI (SUHI) have been widely investigated, in particular based on the indicator of SUHI intensity (SUHII). However, studies on SUHI frequency (SUHIF), an important temporal indicator, are challenged by a large number of missing data in daily land surface temperature (LST). Whether there is any city with strong SUHII and low SUHIF remains unclear. Thanks to the publication of daily seamless all-weather LST, this paper is proposed to investigate spatiotemporal variations of SUHIF, to compare SUHII and SUHIF, to conduct a pattern classification, and to further explore their driving factors across 305 Chinese cities. Four main findings are summarized below: (1) SUHIF is found to be higher in the south during the day, while it is higher in the north at night. Cities within the latitude from 20° N and 40° N indicate strong intensity and high frequency at day. Climate zone-based variations of SUHII and SUHIF are different, in particular at nighttime. (2) SUHIF are observed in great diurnal and seasonal variations. Summer daytime with 3.01 K of SUHII and 80 of SUHIF, possibly coupling with heat waves, increases the risk of heat-related diseases. (3) K-means clustering is employed to conduct pattern classification of the selected cities. SUHIF is found possibly to be consistent to its SUHII in the same city, while they provide quantitative and temporal characters respectively. (4) Controls for SUHIF and SUHII are found in significant variations among temporal scales and different patterns. This paper first conducts a comparison between SUHII and SUHIF, and provides pattern classification for further research and practice on mitigation measurements.

Opencast iron mine and mine solid waste (OIM-MSW) are closely associated with a wide range of environmental issues, such as destroying soil structure, affecting air quality, and damaging biodiversity. Therefore, accurately grasping the distribution of OIM-MSW is of great significance to monitoring and managing environmental conditions. In order to reduce the impact of background features on the identification of OIM-MSW, we constructed an index based on medium spatial resolution satellite data, and proposed a semiautomatic method to determine the threshold value of the index. Experimental results indicated that: 1) The proposed index (OIM-MSWI) can effectively identify the OIM-MSW of which the OA (overall accuracy) and K (kappa coefficient) value are 0.98 and 0.75, respectively. 2) The index owns satisfying performance in identifying OIM-MSW in different months, and its recognition ability in plant growing season is better than that in the nonplant growing season. 3) Both Landsat 8 images and Sentinel 2 images have the potential to identify OIM-MSW via the proposed index, but the recognition accuracy of Landsat 8 images (OA = 0.98, K = 0.75) is slightly better than Sentinel 2 images (OA = 0.96, K = 0.64). (4) OIM-MSWI is effective in different climatic conditions, with OA and K of 0.98, 0.75 and 0.97, 0.63 in the cases of semiarid and humid zones, respectively. In conclusion, OIM-MSWI is a simple and effective index for identifying OIM-MSW, and can provide support for OIM-MSW monitoring and management.