The impacts of landscape patterns spatio-temporal changes on land surface temperature from a multi-scale perspective: A case study of the Yangtze River Delta.

Impacts of climatic zones on urban heat island: spatiotemporal variations, trends, and drivers in China from 2001–2020

Anthropogenic heat is a dominant component in the urban surface energy system and a key to understanding urban thermal environments. The top-down method was widely used to estimate anthropogenic heat flux (AHF) using statistical energy consumption data and proxies. However, there are several limitations. First, the coarse resolutions of current proxies cannot capture the heterogeneous AHF. Besides, the temporal resolution is generally low (annual) in most AHF studies using the top-down method. This study estimated AHFs from three sectors and their monthly and hourly patterns in Beijing, China by developing a new framework. We first used a new proxy of building volume to obtain the AHF from buildings. Then, we estimated the AHF from vehicles and human metabolism using road density and population density, respectively. Finally, the monthly and hourly (workday and non-workday) AHFs were derived using temporal downscaling methods. The results show that the historic buildings in the urban center have a relatively low AHF. Areas with high AHF mainly distribute in the region between the 2nd and 4th ring-road and industrial zones outside the 5th ring-road. The magnitude of AHF varies among months, with the maximum monthly AHF at the district level reaching 68.1 W/m2 in January. AHF in January workdays is significantly higher than that in January non-workdays during 7:00 h to 20:00 h. The estimated AHF in this study can better capture multi-temporal AHF through the top-down method and temporal downscaling methods. The improved AHF data help policymakers design various strategies to improve urban thermal environments under sustainable development goals.

Estimating multi-temporal anthropogenic heat flux based on the top-down method and temporal downscaling methods in Beijing, China

Method of calculating land surface temperatures based on the low-altitude UAV thermal infrared remote sensing data and the near-ground meteorological data

• Long-term vegetation degradation is assessed by means of time-series Landsat NDVI. • Mean annual NDVI and rainfall anomaly index (RAI) were compared. • Anthropogenic stressors are primarily responsible for vegetation degradation. • Vegetation degradation leads to declining land productivity and ecosystem services. Land degradation is a serious problem affecting the livelihoods of people leaving in marginal lands. Its assessment has been made easy by a plethora of remote sensing techniques. This study seeks to establish the spatiotemporal trends in vegetation degradation and its response to climate change and anthropogenic stressors in Southern Bui Plateau, Cameroon. The study used mean annual Landsat-derived NDVI of cloud-free months to model vegetation degradation dynamics over 37 years (1984–2021) based on Ordinary Least Square (OLS) Regression and Pearson's Product Correlation of NDVI with Rainfall Anomaly Index (RAI) on a pixel-by-pixel basis. Areas undergoing significant degradation are estimated at 10.81% (1469.08 km 2 ) and slight degradation, 23.57% (3202.39 km 2 ). Total degraded lands accounted for 34.38% (either 4671.47 km 2 ). Areas with slight improvement in vegetation cover accounted for 24.88%, while 9.69% area showed significant improvement. NDVI-Rainfall relationship revealed that areas significantly impacted by human activities and pressures ( r ≤ -0.50) driving vegetation changes covered 24.67% (either 3352.03 km 2 ), while those under climatic variability and change influence ( r ≥ 0.50 ≥ 0.90) accounted for 55.84% (either 7587.26 km 2 ), under both climatic and human stressors ( r ≥ 0.50 < 0.70), 13.09% (either 1779.01 km 2 ) and areas not significantly impacted, i.e., somewhat stable nature over the years accounted for 6.40% (869.04 km 2 ). Areas under human vegetation pressures occupy the heavily grazed landscapes, the transhumance paths, and forested areas of the Kilum-Ijim forest. The practice of sustainable land management and landscape restoration initiatives are key to achieving land degradation neutrality at watershed scale. 

Landsat NDVI-based vegetation degradation dynamics and its response to rainfall variability and anthropogenic stressors in Southern Bui Plateau, Cameroon

Data mining for adverse drug reaction signals of daptomycin based on real-world data: a disproportionality analysis of the US Food and Drug Administration adverse event reporting system

Quantifying Climatic and Anthropogenic Effects on Vegetation Cover Change in China by Considering Time-Lag and Time-Accumulation Effects of Climate

The impacts of climate change on vegetation cover in different regions in China are not entirely clear because of the interference of non-climatic factors, such as human activity. This study aims to analyze the spatiotemporal differences in climate impacts qualitatively and quantitatively by applying trend, correlation, and multiple linear regression (MLR) analyses to the data of Normalized Difference Vegetation Index (NDVI) and two climatic factors (air temperature and precipitation) during 1982–2015 in China. The MLR equation linking two climatic variables with NDVI was used to identify the NDVI trend caused by climate change. We demonstrated that the central and eastern regions of China, dominated by deciduous and evergreen broadleaf forests, experienced a rapid increase in NDVI from 1982 to 2015. The response of NDVI to variations in temperature and precipitation exhibited large spatiotemporal differences across China, which was closely related to climatic conditions and vegetation types. Overall, warming, particularly the sharp rise in spring, was the main climatic driving force behind China’s NDVI increase, and precipitation also influenced the NDVI increase in temperate grassland and desert regions due to the relatively arid climate, particularly in summer. The contributions of climate change to the total NDVI trend (CC) showed a large spatiotemporal heterogeneity across China. Overall, only 45% of the pixels (with a resolution of 8 km) in the study area showed that the MLR equations between NDVI and two climatic factors were significant at the 0.05 significance level during the growing season (April–October), and the average CC of these pixels was 38%. Among the eight vegetation sub-regions of China, the temperate desert and Qinghai-Tibet Plateau alpine meadow regions generally exhibited relatively larger CCs than other vegetation sub-regions in different seasons. At a national scale, the regional average CC reached 64% during the growing season. These results at multiple scales can help to deeply understand the mechanisms of regional environmental variation and sustainability.

Spatiotemporal differences in climate change impacts on vegetation cover in China from 1982 to 2015

Assessing the spatiotemporal variation in anthropogenic heat and its impact on the surface thermal environment over global land areas

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Papers

Data mining for adverse drug reaction signals of daptomycin based on real-world data: a disproportionality analysis of the US Food and Drug Administration adverse event reporting system

Quantifying Climatic and Anthropogenic Effects on Vegetation Cover Change in China by Considering Time-Lag and Time-Accumulation Effects of Climate

Spatiotemporal differences in climate change impacts on vegetation cover in China from 1982 to 2015

Assessing the spatiotemporal variation in anthropogenic heat and its impact on the surface thermal environment over global land areas