Urban heat islands (UHIs) created through urbanization can have negative impacts on the lives of people living in cities. They may also vary spatially and temporally over a city. There is, thus, a need for greater understanding of these patterns and their causes. While previous UHI studies focused on only a few cities and/or several explanatory variables, this research provides a comprehensive and comparative characterization of the diurnal and seasonal variation in surface UHI intensities (SUHIIs) across 67 major Chinese cities. The factors associated with the SUHII were assessed by considering a variety of related social, economic and natural factors using a regression tree model. Obvious seasonal variation was observed for the daytime SUHII, and the diurnal variation in SUHII varied seasonally across China. Interestingly, the SUHII varied significantly in character between northern and southern China. Southern China experienced more intense daytime SUHIIs, while the opposite was true for nighttime SUHIIs. Vegetation had the greatest effect in the day time in northern China. In southern China, annual electricity consumption and the number of public buses were found to be important. These results have important theoretical significance and may be of use to mitigate UHI effects.

High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these models’ capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent.

Decreased human activities from COVID-19 containment measures caused a brighter, earlier, and greener 2020 spring in China.

Mangroves protect coasts from extreme weather and erosion but can be destroyed by climate change and harvesting. However, there is no consistent formulation of protective capacity that integrates key factors such as area, width, height, and health. Here, we quantified and analyzed a process-based measure of mangrove coastal protection index (MCPI) incorporating cross-shore width, canopy height, and the normalized difference vegetation index (health index). Width/area generally declined at low rates but width increases in some regions reduced MCPI. Cluster exchange network analysis from 2007 to 2019 showed an 800% increase in mangrove forests with characteristically low height, width, and MCPI. Globally, this suggests a 25% decrease in MCPI from 2007 to 2019, primarily from height/biomass change, compared to a 2% decrease in area. Relatively sheltered low-latitude high mangrove strands (>20 m) of high-MCPI appear to be resilient to destruction from cyclones. In contrast, our results highlight an alarming, widespread decline in low MCPI, particularly along coasts exposed to deep water, possibly in concert with human destruction, cyclones, and intensifying oceanic boundary currents.

The exodus of people from rural areas to cities brings many detrimental environmental, social and cultural consequences. Monitoring spatiotemporal change by referencing the historical timeline or incidence has become an important way to analyze urbanization. This study has attempted to attain the cross-sectional analysis of Kathmandu valley that has been plagued by rampant urbanization over the last three decades. The research utilizes Landsat images of Kathmandu valley from 1976 to 2015 for the transition analysis of land use, land cover and urban sprawl for the last four decades. Results showed that the urban coverage of Kathmandu valley has tremendously increased from 20.19 km² in 1976 to 39.47 km² in 1989 to 78.96 km² in 2002 to 139.57 km² in 2015, at the cost of cultivated lands, with an average annual urban growth rate of 7.34%, 7.70% and 5.90% in each temporal interval, respectively. In addition, the urban expansion orientation analysis concludes the significant urban concentration in the eastern part, moderately medium in the southwest and relatively less in the western and northwest part of the valley. Urbanization was solely accountable for the exploitation of extant forests, fertile and arable lands and indigenous and cultural landscapes. Unattended fallow lands in suburban areas have compounded the problem by welcoming invasive alien species. Overlaying the highly affected geological formations within the major city centers displays that unless the trend of rapid, unplanned urbanization is discontinued, the future of Kathmandu is at the high risk. Since land use management is a fundamental part of development, we advocate for the appropriate land use planning and policies for sustainable and secure future development.

Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia.