Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense ...time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs.
The Mekong River Basin (MRB) is undergoing unprecedented changes due to the recent acceleration in large-scale dam construction. While the hydrology of the MRB is well understood and the effects of ...some of the existing dams have been studied, the potential effects of the planned dams on flood pulse dynamics over the entire Lower Mekong remains unexamined. Here, using hydrodynamic model simulations, we show that the effects of flow regulation on downstream river-floodplain dynamics are relatively predictable along the mainstream Mekong, but flow regulations could potentially disrupt the flood dynamics in the Tonle Sap River (TSR) and small distributaries in the Mekong Delta. Results suggest that TSR flow reversal could cease if the Mekong flood pulse is dampened by 50% and delayed by one-month. While flood occurrence in the vicinity of the Tonle Sap Lake and middle reach of the delta could increase due to enhanced low flow, it could decrease by up to five months in other areas due to dampened high flow, particularly during dry years. Further, areas flooded for less than five months and over six months are likely to be impacted significantly by flow regulations, but those flooded for 5-6 months could be impacted the least.
Remote sensing image products (e.g. brightness of nighttime lights and land cover/land use types) have been widely used to disaggregate census data to produce gridded population maps for large ...geographic areas. The advent of the geospatial big data revolution has created additional opportunities to map population distributions at fine resolutions with high accuracy. A considerable proportion of the geospatial data contains semantic information that indicates different categories of human activities occurring at exact geographic locations. Such information is often lacking in remote sensing data. In addition, the remarkable progress in machine learning provides toolkits for demographers to model complex nonlinear correlations between population and heterogeneous geographic covariates. In this study, a typical type of geospatial big data, points-of-interest (POIs), was combined with multi-source remote sensing data in a random forests model to disaggregate the 2010 county-level census population data to 100 × 100 m grids. Compared with the WorldPop population dataset, our population map showed higher accuracy. The root mean square error for population estimates in Beijing, Shanghai, Guangzhou, and Chongqing for this method and WorldPop were 27,829 and 34,193, respectively. The large under-allocation of the population in urban areas and over-allocation in rural areas in the WorldPop dataset was greatly reduced in this new population map. Apart from revealing the effectiveness of POIs in improving population mapping, this study promises the potential of geospatial big data for mapping other socioeconomic parameters in the future.
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•A population map for China at 100-m spatial resolution was produced by random forests.•Remote sensing and POI data were jointly used to disaggregate census population.•The new population map showed higher accuracy than the Worldop dataset.•The use of POI reduced under-allocation in urban and over-allocation in rural areas.•POIs have more strengths than brightness of nighttime lights for population estimation.
The global warming induced by the emission of greenhouse gases, especially the carbon dioxide, has become the global climate and environmental issues. China has been working in the CO
2
emission ...reduction and carbon sinks with the purpose of becoming the carbon-neutral country by 2060. The CO
2
capture, utilization and storage (CCUS) technologies and the reforestation technology represented by the Conversion of Cropland to Forestland Program (CCFP) have great potential for sinking CO
2
emission. However, the trade-off among CCFP, CCS/CCUS and Water-Energy-Food (WEF) nexus are not well evaluated. In this paper, the remote-sensing data are collected and used to evaluate the sustainability of CCFP by analyzing the variation of land use and land cover (LULC), crop production, etc. The results show that 13.29% of the cropland in 2001 vanished and converted to grassland (8.3%), mosaic cropland (3%) and urban land (0.98%) in 2019, demonstrating that the CCFP is successful in both WEF nexus and carbon sink. The total crop production has increased around 50% between 2001 and 2019, implying that the CCFP will not lead to the food risk during the conversion of croplands into other types of land in China. A sustainable implementation of CCFP and other environmental Payments for Ecosystem Services (PES) policies in 2019–2060 could reach an estimated total growth of 7.462 billion m
3
in comparison of that in 2018 and the total plantation forest stock of about 10.852 billion m
3
in 2060, with a corresponding minimum CO
2
sink of 2.90 billion tons in 2060. The estimated peak of net equivalent CO
2
emissions before 2030 is about 11.0 billion tons and could not be reduced to zero by 2060 without the large-scale application of the CCS/CCUS technologies as geological sequestration of CO
2
. Besides, the application of CCS/CCUS can be beneficial for WEF, e.g., through replacing the water by CO
2
during energy production, especially in the shale gas production in the regions with high water risks in China. In one word, CCS/CCUS and CCFP are two decided pathways of carbon sequestration and should be systematically applied to achieve China’s carbon neutrality by 2060.
Grassland degradation received considerable concern because of its adverse impact on agronomic productivity and its capacity to provide goods and service. Climate change and human activities are ...commonly recognized as the two broad underlying drivers that lead to grassland degradation. In this study, a comprehensive method based on net primary productivity (NPP) was introduced to assess quantitatively the relative roles of climate change and human perturbations on worldwide grassland degradation from 2000 to 2010. The results revealed that at a global scale, 49.25 % of grassland ecosystems experienced degradation. Nearly 5 % of these grasslands experienced strong to extreme significant degradation. Climate change was the dominant cause that resulted in 45.51 % of degradation compared with 32.53 % caused by human activities. On the contrary, 39.40 % of grassland restoration was induced by human interferences, and 30.6 % was driven by climate change. The largest area of degradation and restoration both occurred in Asia. NPP losses ranged between 1.40 Tg C year⁻¹ (in North America) and 13.61 Tg C year⁻¹ (in Oceania) because of grassland degradation. Maximum NPP increase caused by restoration was 17.57 Tg C year⁻¹ (in North America). Minimum NPP was estimated at 1.59 Tg C year⁻¹ (in Europe). The roles of climate change and human activities on degradation and restoration were not consistent at continental level. Grassland ecosystems in the southern hemisphere were more vulnerable and sensitive to climate change. Therefore, climate change issues should be gradually integrated into future policies and plans for domestic grassland management and administration.
Limited evidence is available on the health effects of particulate matter with an aerodynamic diameter of <1 μm (PM1), mainly due to the lack of its ground measurement worldwide.
To identify and ...examine the mortality risks and mortality burdens associated with PM1, PM2.5, and PM10 in Zhejiang province, China.
We collected daily data regarding all-cause (stratified by age and gender), cardiovascular, stroke, respiratory, and chronic obstructive pulmonary disease (COPD) mortality, and PM1, PM2.5, and PM10, from 11 cities in Zhejiang province, China during 2013 and 2017. We used a quasi-Poisson regression model to estimate city-specific associations between mortality and PM concentrations. Then we used a random-effect meta-analysis to pool the provincial estimates. To show the mortality burdens of PM1, PM2.5, and PM10, we calculated the mortality fractions and deaths attributable to these PMs.
Daily concentrations of PM1, PM2.5, and PM10 ranged between 0–199 μg/m3, 0–218 μg/m3, and 0–254 μg/m3, respectively; Mortality effects were significant in lag 0–2 days. The relative risks for all-cause mortality were 1.0064 (95% CI: 1.0034, 1.0094), 1.0061 (95% CI: 1.0034, 1.0089), and 1.0060 (95% CI: 1.0038, 1.0083) associated with a 10 μg/m3 increase in PM1, PM2.5, and PM10, respectively. Age- and gender-stratified analysis shows that elderly people (aged 65+) and females are more sensitive to PMs. The mortality fractions of all-cause mortality were estimated to be 2.39% (95% CI: 1.28, 3.48) attributable to PM1, 2.53% (95% CI: 1.42, 3.63) attributable to PM2.5, and 3.08% (95% CI: 1.95, 4.19) attributable to PM10. The ratios of attributable cause-specific deaths for PM1/PM2.5, PM1/PM10, and PM2.5/PM10 were higher than the ratios of their respective concentrations.
PM1, PM2.5 and PM10 are risk factors of all-cause, cardiovascular, stroke, respiratory, and COPD mortality. PM1 accounts for the vast majority of short-term PM2.5- and PM10-induced mortality. Our analyses support the notion that smaller size fractions of PM have a more toxic mortality impacts, which suggests to develop strategies to prevent and control PM1 in China, such as to foster strict regulations for automobile and industrial emissions.
•PM1, PM2.5 and PM10 are risk factors of all-cause, cardiovascular, stroke, respiratory, and COPD mortality.•PM1 accounts for the vast majority of short-term PM2.5- and PM10-induced mortality.•Smaller size fractions of PM have a more toxic mortality impacts.•PM1 has stronger effects on all-cause, cardiovascular, and stroke mortality in the warm season than in the cold season.
The biomass of the subtropical forests of China is an important component of the global carbon cycle. Recently, several above ground biomass (AGB) maps have been produced using a variety of ...approaches to assess the carbon stock of the subtropical forest in China. However, due to the lack of reliable ground observations and the limitations of AGB mapping methods at regional scales, estimates of the spatial distribution of AGB vary greatly, leading to large uncertainties in the carbon stock estimations. In this study, we produced a new 1-km spatial resolution AGB map by synthesizing an unprecedented number of ground AGB observations from published studies, and developed an AGB mapping method using a combination of ground observations, MODIS data, forest cover/gain/loss maps based on Landsat, GLAS forest canopy height, and climatic and terrain data. In addition, we validated our estimates using independent testing data and compared our estimates with three previous AGB maps. The results indicate that the total AGB stock in the subtropical forest of China is (266 ± 9.1) × 106 Mg, with an average AGB of 123.2 Mg/ha. Based on sixteen explanatory variables, our ensemble mean model explains 75% of the variance in forest AGB, with an RMSE of 45.5 Mg/ha. Comparison using all observation data shows that our map has a significantly lower RMSE and bias than previous maps, where the RMSE and bias tended to vary with forest type. This study not only improved the accuracy of AGB estimation for the subtropical forests but also highlighted the importance of forest type for regional AGB estimation.
•Produced a new AGB map for the subtropical forest by integrating multisource data•Improved the accuracy of AGB mapping compared to previous maps•Revealed the importance of forest types for AGB estimation
Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban-rural differences in the temperature impacts on health outcomes.
We investigated whether ...temperature-mortality relationships vary between urban and rural counties in China.
We collected daily data on 1 km gridded temperature and mortality in 89 counties of Zhejiang Province, China, for 2009 and 2015. We first performed a two-stage analysis to estimate the temperature effects on mortality in urban and rural counties. Second, we performed meta-regression to investigate the modifying effect of the urbanization level. Stratified analyses were performed by all-cause, nonaccidental (stratified by age and sex), cardiopulmonary, cardiovascular, and respiratory mortality. We also calculated the fraction of mortality and number of deaths attributable to nonoptimum temperatures associated with both cold and heat components. The potential sources of the urban-rural differences were explored using meta-regression with county-level characteristics.
Increased mortality risks were associated with low and high temperatures in both rural and urban areas, but rural counties had higher relative risks (RRs), attributable fractions of mortality, and attributable death counts than urban counties. The urban-rural disparity was apparent for cold (first percentile relative to minimum mortality temperature), with an RR of 1.47 95% confidence interval (CI): 1.32, 1.62 associated with all-cause mortality for urban counties, and 1.98 (95% CI: 1.87, 2.10) for rural counties. Among the potential sources of the urban-rural disparity are age structure, education, GDP, health care services, air conditioners, and occupation types.
Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly. The findings suggest past studies using exposure-response functions derived from urban areas may underestimate the mortality burden for the population as a whole. The public health agencies aimed at controlling temperature-related mortality should develop area-specific strategies, such as to reduce the urban-rural gaps in access to health care and awareness of risk prevention. Future projections on climate health impacts should consider the urban-rural disparity in mortality risks. https://doi.org/10.1289/EHP3556.
The spatio-temporal characteristics of remote sensing are considered to be the primary advantage in environmental studies. With long-term and frequent satellite observations, it is possible to ...monitor changes in key biophysical attributes such as phenological characteristics, and relate them to climate change by examining their correlations. Although a number of remote sensing methods have been developed to quantify vegetation seasonal cycles using time-series of vegetation indices, there is limited effort to explore and monitor changes and trends of vegetation phenology in the Monsoon Southeast Asia, which is adversely affected by changes in the Asian monsoon climate. In this study, MODIS EVI and TRMM time series data, along with field survey data, were analyzed to quantify phenological patterns and trends in the Monsoon Southeast Asia during 2001–2010 period and assess their relationship with climate change in the region. The results revealed a great regional variability and inter-annual fluctuation in vegetation phenology. The phenological patterns varied spatially across the region and they were strongly correlated with climate variations and land use patterns. The overall phenological trends appeared to shift towards a later and slightly longer growing season up to 14 days from 2001 to 2010. Interestingly, the corresponding rainy season seemed to have started earlier and ended later, resulting in a slightly longer wet season extending up to 7 days, while the total amount of rainfall in the region decreased during the same time period. The phenological shifts and changes in vegetation growth appeared to be associated with climate events such as EL Niño in 2005. Furthermore, rainfall seemed to be the dominant force driving the phenological changes in naturally vegetated areas and rainfed croplands, whereas land use management was the key factor in irrigated agricultural areas.
•Significant phenological shifts occurred resulting from climate change.•Phenological changes are driven by climate change and human interventions.•Satellites imagery can be used to effectively monitor phenological changes and trends.
The increase in the frequency and intensity of extreme heat events, which are potentially associated with climate change in the near future, highlights the importance of heat health risk assessment, ...a significant reference for heat-related death reduction and intervention. However, a spatiotemporal mismatch exists between gridded heat hazard and human exposure in risk assessment, which hinders the identification of high-risk areas at finer scales.
A human settlement index integrated by nighttime light images, enhanced vegetation index, and digital elevation model data was utilized to assess the human exposure at high spatial resolution. Heat hazard and vulnerability index were generated by land surface temperature and demographic and socioeconomic census data, respectively. Spatially explicit assessment of heat health risk and its driving factors was conducted in the Yangtze River Delta (YRD), east China at 250 m pixel level.
High-risk areas were mainly distributed in the urbanized areas of YRD, which were mostly driven by high human exposure and heat hazard index. In some less-urbanized cities and suburban and rural areas of mega-cities, the heat health risks are in second priority. The risks in some less-developed areas were high despite the low human exposure index because of high heat hazard and vulnerability index.
This study illustrated a methodology for identifying high-risk areas by combining freely available multi-source data. Highly urbanized areas were considered hotspots of high heat health risks, which were largely driven by the increasing urban heat island effects and population density in urban areas. Repercussions of overheating were weakened due to the low social vulnerability in some central areas benefitting from the low proportion of sensitive population or the high level of socioeconomic development. By contrast, high social vulnerability intensifies heat health risks in some less-urbanized cities and suburban areas of mega-cities.
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