Multi-Source Domain Adaptation for Medical Image Segmentation.

Unsupervised domain adaptation(UDA) aims to mitigate the performance drop of models tested on the target domain, due to the domain shift from the target to sources. Most UDA segmentation methods focus on the scenario of solely single source domain. However, in practical situations data with gold standard could be available from multiple sources (domains), and the multi-source training data could provide more information for knowledge transfer. How to utilize them to achieve better domain adaptation yet remains to be further explored. This work investigates multi-source UDA and proposes a new framework for medical image segmentation. Firstly, we employ a multi-level adversarial learning scheme to adapt features at different levels between each of the source domains and the target, to improve the segmentation performance. Then, we propose a multi-model consistency loss to transfer the learned multi-source knowledge to the target domain simultaneously. Finally, we validated the proposed framework on two applications, i.e., multi-modality cardiac segmentation and cross-modality liver segmentation. The results showed our method delivered promising performance and compared favorably to state-of-the-art approaches.

Urbanization expands the fluctuating difference in gross primary productivity between urban and rural areas from 2000 to 2018 in China.

Urban and rural vegetation are affected by both climate change and human activities, but the role of urbanization in vegetation productivity is unclear given the dual impacts. Here, we delineated urban area (UA) and rural area (RA), quantified the relative impacts of climate change and human activities on gross primary production (GPP) in 34 major cities (MCs) in China from 2000 to 2018, and analyzed the intrinsic impacts of urbanization on GPP. First, we found that the total urban impervious surface coverage (ISC) of the 34 MCs increased by 13.25 % and the mean annual GPP increased by 211 gC m-2 during the study period. GPP increased significantly in urban core areas, but decreased significantly in urban expansion areas, which was mainly due to a large amount of vegetation loss due to land use conversion. Second, the variability of GPP in UA was generally lower than in RA. Both climate change and human activities had a positive impact on GPP in UA and RA in the 34 MCs, of which the contribution was 49 % and 51 % in UA, and 76 % and 24 % in RA, respectively. Third, under climate change and human activities, the increase in GPP offset 4.96 % and 12.35 % of the impact of land use conversion on GPP in 2000 and 2018, respectively, which indicated that the offset strengthened over time. These findings emphasize the role of human activities in promoting carbon sequestration in urban vegetation, which is crucial for better understanding the processes and mechanisms of urban carbon cycles. Decision-makers can manage urban vegetation based on vegetation carbon sequestration potential as regions urbanize, aiding comprehensive decision-making.

Characterizing the 2020 summer floods in South China and effects on croplands.

Floods occur more frequently in the context of climate change; however, flood monitoring capacity has not been well established. Here, we used a synergic mapping framework to characterize summer floods in the middle and lower reaches of the Yangtze River Plain and the effects on croplands in 2020, from both flood extent and intensity perspectives. We found that the total flood extent was 4936 km2 from July to August, and for flood intensity, 1658, 1382, and 1896 km2 of areas experienced triple, double, and single floods. A total of 2282 km2 croplands (46% of the flooded area) were inundated mainly from Poyang and Dongting Lake Basins, containing a high ratio of moderate damage croplands (47%). The newly increased flooding extent in 2020 was 29% larger than the maximum ever-flooded extent in 2015-2019. This study is expected to provide a reference for rapid regional flood disaster assessment and serving mitigation.

How Social Considerations Improve the Equity and Effectiveness of Ecosystem Restoration.

Ecosystem restoration is an important means to address global sustainability challenges. However, scientific and policy discourse often overlooks the social processes that influence the equity and effectiveness of restoration interventions. In the present article, we outline how social processes that are critical to restoration equity and effectiveness can be better incorporated in restoration science and policy. Drawing from existing case studies, we show how projects that align with local people's preferences and are implemented through inclusive governance are more likely to lead to improved social, ecological, and environmental outcomes. To underscore the importance of social considerations in restoration, we overlay existing global restoration priority maps, population, and the Human Development Index (HDI) to show that approximately 1.4 billion people, disproportionately belonging to groups with low HDI, live in areas identified by previous studies as being of high restoration priority. We conclude with five action points for science and policy to promote equity-centered restoration.

High emissions could increase the future risk of maize drought in China by 60-70.

Drought events have considerable direct and indirect economic, environmental, and social impacts, but few studies have analyzed and assessed future changes in drought disasters from a risk perspective to guide responses and adaptations thoroughly. Studying the potential climate-related impacts on future crop yield is therefore urgently needed. Intercomparison of the three Shared Socio-economic Pathway (SSP) scenarios based drought risks and yield loss of China was carried out using the climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and the hotspots of high drought risk regions were identified. This study found that the areas affected by severe maize drought (loss ratio larger than 0.2) accounted for 16.13 %, 20.79 %, and 18.87 % of the total national corn areas under three low, medium-to-high and high emission scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) respectively. The northwest China maize region, the ecotone between agriculture and animal husbandry, and the western central northern China maize region have relatively high loss risk. Compared with SSP1-2.6, the yield loss rates increased with 70.73 % and 61.52 % of national corn areas for SSP3-7.0 and SSP5-8.5, respectively. There is a decrease in the areas with low-risk and a significant increase in the areas with high-risk for SSP3-7.0 and SSP5-8.5 compared to the SSP1-2.6. These results may provide theoretical support for agricultural drought risk reduction and adaptation planning to ensure food security under climate change.

Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products.

As a routine agricultural practice, irrigation is fundamental to protect crops from water scarcity and ensure food security in China. However, consistent and reliable maps about the spatial distribution and extent of irrigated croplands are still unavailable, impeding water resource management and agricultural planning. Here, we produced annual 500-m irrigated cropland maps across China for 2000-2019, using a two-step strategy that integrated statistics, remote sensing, and existing irrigation products into a hybrid irrigation dataset. First, we generated intermediate irrigation maps (MIrAD-GI) by fusing the MODIS-derived greenness index and statistical data. Second, we collected all existing available irrigation maps over China and integrated them with MIrAD-GI into an improved series of annual irrigation maps, using constrained statistics and a synergy mapping method. The resultant maps had moderate overall accuracies (0.732~0.819) based on nationwide reference ground samples and outperformed existing irrigation products by inter-comparison. As the first of this kind in China, the annual maps delineated the spatiotemporal pattern of irrigated croplands and could contribute to sustainable water use and agricultural development.

Annual 30-m big Lake Maps of the Tibetan Plateau in 1991-2018.

Lake systems on the Tibetan Plateau (TP) are important for the supply and storage of fresh water to billions of people. However, previous studies on the dynamics of these lakes focused on monitoring on multi-year scales and therefore lack sufficient temporal information. Here we present a new dataset comprising annual maps of big lakes (>10 km2) on the TP for 1991-2018, generated by utilizing all available Landsat images in conjunction with Google Earth Engine. The annual lake maps with high overall accuracy (~96%) highlight distinctive lake distribution and lake changes: (1) about 70% number and area of lakes concentrated in the Inner basin; (2) generally increasing trends in both the area (by 33%) and number (by 30%) of lakes from 1991 to 2018; (3) the total area changes were dominated by larger lakes (>50 km2) while more fluctuations in the lake number changes were found in medium lakes (10-50 km2). Our dataset infills temporal gaps in long-term inter-annual variations of big lakes, contributing towards enhanced knowledge of TP lake systems.

Ten facts about land systems for sustainability.

Land use is central to addressing sustainability issues, including biodiversity conservation, climate change, food security, poverty alleviation, and sustainable energy. In this paper, we synthesize knowledge accumulated in land system science, the integrated study of terrestrial social-ecological systems, into 10 hard truths that have strong, general, empirical support. These facts help to explain the challenges of achieving sustainability in land use and thus also point toward solutions. The 10 facts are as follows: 1) Meanings and values of land are socially constructed and contested; 2) land systems exhibit complex behaviors with abrupt, hard-to-predict changes; 3) irreversible changes and path dependence are common features of land systems; 4) some land uses have a small footprint but very large impacts; 5) drivers and impacts of land-use change are globally interconnected and spill over to distant locations; 6) humanity lives on a used planet where all land provides benefits to societies; 7) land-use change usually entails trade-offs between different benefits-"win-wins" are thus rare; 8) land tenure and land-use claims are often unclear, overlapping, and contested; 9) the benefits and burdens from land are unequally distributed; and 10) land users have multiple, sometimes conflicting, ideas of what social and environmental justice entails. The facts have implications for governance, but do not provide fixed answers. Instead they constitute a set of core principles which can guide scientists, policy makers, and practitioners toward meeting sustainability challenges in land use.

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling.

Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007-2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007-2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.

Cropland redistribution to marginal lands undermines environmental sustainability.

Cropland redistribution to marginal land has been reported worldwide; however, the resulting impacts on environmental sustainability have not been investigated sufficiently. Here we investigated the environmental impacts of cropland redistribution in China. As a result of urbanization-induced loss of high-quality croplands in south China (∼8.5 t ha-1), croplands expanded to marginal lands in northeast (∼4.5 t ha-1) and northwest China (∼2.9 t ha-1) during 1990-2015 to pursue food security. However, the reclamation in these low-yield and ecologically vulnerable zones considerably undermined local environmental sustainability, for example increasing wind erosion (+3.47%), irrigation water consumption (+34.42%), fertilizer use (+20.02%) and decreasing natural habitats (-3.11%). Forecasts show that further reclamation in marginal lands per current policies would exacerbate environmental costs by 2050. The future cropland security risk will be remarkably intensified because of the conflict between food production and environmental sustainability. Our research suggests that globally emerging reclamation of marginal lands should be restricted and crop yield boost should be encouraged for both food security and environmental benefits.

The 10-m crop type maps in Northeast China during 2017-2019.

Northeast China is the leading grain production region in China where one-fifth of the national grain is produced; however, consistent and reliable crop maps are still unavailable, impeding crop management decisions for regional and national food security. Here, we produced annual 10-m crop maps of the major crops (maize, soybean, and rice) in Northeast China from 2017 to 2019, by using (1) a hierarchical mapping strategy (cropland mapping followed by crop classification), (2) agro-climate zone-specific random forest classifiers, (3) interpolated and smoothed 10-day Sentinel-2 time series data, and (4) optimized features from spectral, temporal, and texture characteristics of the land surface. The resultant maps have high overall accuracies (OA) spanning from 0.81 to 0.86 based on abundant ground truth data. The satellite estimates agreed well with the statistical data for most of the municipalities (R2 ≥ 0.83, p < 0.01). This is the first effort on regional annual crop mapping in China at the 10-m resolution, which permits assessing the performance of the soybean rejuvenation plan and crop rotation practice in China.

Community Composition and Structure Affect Ecosystem and Canopy Water Use Efficiency Across Three Typical Alpine Ecosystems.

Unique ecosystems distributed in alpine areas of the Qinghai-Tibetan Plateau play important roles in climate change mitigation, local food supply, and conservation of species diversity. To understand the water use efficiency (WUE) of this fragile and sensitive region, this study combined observed data from the eddy covariance system and the Shuttleworth-Wallace (S-W) model to measure the continuous mass exchange, including gross primary productivity (GPP), evapotranspiration (ET), and canopy transpiration (T) throughout 2 or 3 years (2016-2018) in three common alpine ecosystems (i.e., alpine steppe, alpine meadow, and alpine swamp). These ecosystems represent a water availability gradient and thus provide the opportunity to quantify environmental and biological controls on WUE at various spatiotemporal scales. We analyzed the ecosystem WUE (WUEe; defined as the ratio of GPP to ET) and canopy WUE (WUEc; defined as the ratio of GPP and canopy T). It was found that the yearly WUEe was 1.40, 1.63, and 2.16 g C kg-1 H2O, and the yearly WUEc was 8.93, 2.46, and 5.19 g C kg-1 H2O in the three typical ecosystems, respectively. The controlling factors of yearly WUE diverged between WUEe and WUEc. We found that plant functional group proportion (e.g., gramineous and Cyperaceae) highly explained the yearly WUEe variation across sites, and a good correlation was observed between community species diversity and WUEc. These findings suggest that community composition and trait change are critical in regulating WUEe and WUEc across different alpine ecosystems and that the regulation mechanisms may differ fundamentally between WUEe and WUEc.

Tracking annual changes of coastal tidal flats in China during 1986-2016 through analyses of Landsat images with Google Earth Engine.

Tidal flats (non-vegetated area), along with coastal vegetation area, constitute the coastal wetlands (intertidal zone) between high and low water lines, and play an important role in wildlife, biodiversity and biogeochemical cycles. However, accurate annual maps of coastal tidal flats over the last few decades are unavailable and their spatio-temporal changes in China are unknown. In this study, we analyzed all the available Landsat TM/ETM+/OLI imagery (~ 44,528 images) using the Google Earth Engine (GEE) cloud computing platform and a robust decision tree algorithm to generate annual frequency maps of open surface water body and vegetation to produce annual maps of coastal tidal flats in eastern China from 1986 to 2016 at 30-m spatial resolution. The resulting map of coastal tidal flats in 2016 was evaluated using very high-resolution images available in Google Earth. The total area of coastal tidal flats in China in 2016 was about 731,170 ha, mostly distributed in the provinces around Yellow River Delta and Pearl River Delta. The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986-1992, an increasing period during 1993-2001 and a decreasing period during 2002-2016. The resulting annual coastal tidal flats maps could be used to support sustainable coastal zone management policies that preserve coastal ecosystem services and biodiversity in China.

Gainers and losers of surface and terrestrial water resources in China during 1989-2016.

Data and knowledge of the spatial-temporal dynamics of surface water area (SWA) and terrestrial water storage (TWS) in China are critical for sustainable management of water resources but remain very limited. Here we report annual maps of surface water bodies in China during 1989-2016 at 30m spatial resolution. We find that SWA decreases in water-poor northern China but increases in water-rich southern China during 1989-2016. Our results also reveal the spatial-temporal divergence and consistency between TWS and SWA during 2002-2016. In North China, extensive and continued losses of TWS, together with small to moderate changes of SWA, indicate long-term water stress in the region. Approximately 569 million people live in those areas with deceasing SWA or TWS trends in 2015. Our data set and the findings from this study could be used to support the government and the public to address increasing challenges of water resources and security in China.

Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine.

Coastal wetlands, composed of coastal vegetation and non-vegetated tidal flats, play critical roles in biodiversity conservation, food production, and the global economy. Coastal wetlands in China are changing quickly due to land reclamation from sea, aquaculture, industrialization, and urbanization. However, accurate and updated maps of coastal wetlands (including vegetation and tidal flats) in China are unavailable, and the detailed spatial distribution of coastal wetlands are unknown. Here, we developed a new pixel- and phenology-based algorithm to identify and map coastal wetlands in China for 2018 using time series Landsat imagery (2,798 ETM+/OLI images) and the Google Earth Engine (GEE). The resultant map had a very high overall accuracy (98%). There were 7,474.6 km2 of coastal wetlands in China in 2018, which included 5,379.8 km2 of tidal flats, 1,856.4 km2 of deciduous wetlands, and 238.3 km2 of evergreen wetlands. Jiangsu Province had the largest area of coastal wetlands in China, followed by Shandong, Fujian, and Zhejiang Provinces. Our study demonstrates the high potential of time series Landsat images, pixel- and phenology-based algorithm, and GEE for mapping coastal wetlands at large scales. The resultant coastal wetland maps at 30-m spatial resolution serve as the most current dataset for sustainable management, ecological assessments, and conservation of coastal wetlands in China.

Large increases of paddy rice area, gross primary production, and grain production in Northeast China during 2000-2017.

China is the largest rice producer and consumer in the world. Accurate estimations of paddy rice planting area and rice grain production is important for feeding the increasing population in China. However, Southern China had substantial losses in paddy rice area over the last three decades in those regions where paddy rice has traditionally been produced. Several studies have shown increased paddy rice area in Northeast China. Here we document the annual dynamics of paddy rice area, gross primary production (GPP), and grain production in Northeast China (Heilongjiang, Jilin and Liaoning provinces) during 2000-2017 using agricultural statistical data, satellite images, and model simulations. Annual maps derived from satellite images show that paddy rice area in Northeast China has increased by 3.68 million ha from 2000 to 2017, which is more than the total combined paddy rice area of North Korea, South Korea, and Japan. Approximately 82% of paddy rice pixels had an increase in annual GPP during 2000-2017. The expansion of paddy rice area slowed down substantially since 2015. Annual GPP from those paddy rice fields cultivated continuously over the 18 years were moderately higher than that from other paddy rice fields, which suggested that improved management practices could increase grain production in the region. There was a strong linear relationship between annual GPP and annual rice grain production in Northeast China by province and year, which illustrates the potential of using satellite-based data-driven model to track and assess grain production of paddy rice in the region. Northeast China is clearly an emerging rice production base and plays an increasing role in crop production and food security in China. However, many challenges for the further expansion and sustainable cultivation of paddy rice in Northeast China remain.