Mapping global lake aquatic vegetation dynamics using 10-m resolution satellite observations.

Aquatic vegetation is crucial for improving water quality, supporting fisheries and preserving biodiversity in lakes. Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems. Nevertheless, a comprehensive global assessment of lacustrine aquatic vegetation is lacking. This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022. Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years, covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 km2. The global median aquatic vegetation occurrence (VO, in %) is 3.0%, with notable higher values observed in South America (7.4%) and Africa (4.1%) compared with Asia (2.7%) and North America (2.4%). High VO is also observed in lakes near major rivers such as the Yangtze, Ob, and Paraná Rivers. Integrating historical data with our calculated MVA, the aquatic vegetation changes in 170 lakes worldwide were analyzed. It shows that 72.4% (123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022, encompassing both submerged and overall aquatic vegetation. The most substantial decrease is observed in Asia and Africa. Our findings suggest that, beyond lake algal blooms and temperature, the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution. Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.

A benchmark GaoFen-7 dataset for building extraction from satellite images.

Accurate building extraction is crucial for urban understanding, but it often requires a substantial number of building samples. While some building datasets are available for model training, there remains a lack of high-quality building datasets covering urban and rural areas in China. To fill this gap, this study creates a high-resolution GaoFen-7 (GF-7) Building dataset utilizing the Chinese GF-7 imagery from six Chinese cities. The dataset comprises 5,175 pairs of 512 × 512 image tiles, covering 573.17 km2. It contains 170,015 buildings, with 84.8% of the buildings in urban areas and 15.2% in rural areas. The usability of the GF-7 Building dataset has been proved with seven convolutional neural networks, all achieving an overall accuracy (OA) exceeding 93%. Experiments have shown that the GF-7 building dataset can be used for building extraction in urban and rural scenarios. The proposed dataset boasts high quality and high diversity. It supplements existing building datasets and will contribute to promoting new algorithms for building extraction, as well as facilitating intelligent building interpretation in China.

Continued spring phenological advance under global warming hiatus over the Pan-Third Pole.

The global surface temperature has witnessed a warming hiatus in the first decade of this century, but how this slowing down of warming will impact spring phenology over Pan-Third Pole remains unclear. Here, we combined multiple satellite-derived vegetation indices with eddy covariance datasets to evaluate the spatiotemporal changes in spring phenological changes over the Pan-Third Pole. We found that the spring phenology over Pan-Third Pole continues to advance at the rate of 4.8 days decade-1 during the warming hiatus period, which is contrasted to a non-significant change over the northern hemisphere. Such a significant and continued advance in spring phenology was mainly attributed to an increase in preseason minimum temperature and water availability. Moreover, there is an overall increasing importance of precipitation on changes in spring phenology during the last four decades. We further demonstrated that this increasingly negative correlation was also found across more than two-thirds of the dryland region, tentatively suggesting that spring phenological changes might shift from temperature to precipitation-controlled over the Pan-Third Pole in a warmer world.

Characterization and attribution of vegetation dynamics in the ecologically fragile South China Karst: Evidence from three decadal Landsat observations.

Plant growth and its changes over space and time are effective indicators for signifying ecosystem health. However, large uncertainties remain in characterizing and attributing vegetation changes in the ecologically fragile South China Karst region, since most existing studies were conducted at a coarse spatial resolution or covered limited time spans. Considering the highly fragmented landscapes in the region, this hinders their capability in detecting fine information of vegetation dynamics taking place at local scales and comprehending the influence of climate change usually over relatively long temporal ranges. Here, we explored the spatiotemporal variations in vegetation greenness for the entire South China Karst region (1.9 million km2) at a resolution of 30m for the notably increased time span (1987-2018) using three decadal Landsat images and the cloud-based Google Earth Engine. Moreover, we spatially attributed the vegetation changes and quantified the relative contribution of driving factors. Our results revealed a widespread vegetation recovery in the South China Karst (74.80%) during the past three decades. Notably, the area of vegetation recovery tripled following the implementation of ecological engineering compared with the reference period (1987-1999). Meanwhile, the vegetation restoration trend was strongly sustainable beyond 2018 as demonstrated by the Hurst exponent. Furthermore, climate change contributed only one-fifth to vegetation restoration, whereas major vegetation recovery was highly attributable to afforestation projects, implying that anthropogenic influences accelerated vegetation greenness gains in karst areas since the start of the new millennium during which ecological engineering was continually established. Our study provides additional insights into ecological restoration and conservation in the highly heterogeneous karst landscapes and other similar ecologically fragile areas worldwide.

Towards an open and synergistic framework for mapping global land cover.

Global land-cover datasets are key sources of information for understanding the complex inter-actions between human activities and global change. They are also among the most critical variables for climate change studies. Over time, the spatial resolution of land cover maps has increased from the kilometer scale to 10-m scale. Single-type historical land cover datasets, including for forests, water, and impervious surfaces, have also been developed in recent years. In this study, we present an open and synergy framework to produce a global land cover dataset that combines supervised land cover classification and aggregation of existing multiple thematic land cover maps with the Google Earth Engine (GEE) cloud computing platform. On the basis of this method of classification and mosaicking, we derived a global land cover dataset for 6 years over a time span of 25 years. The overall accuracies of the six maps were around 75% and the accuracy for change area detection was over 70%. Our product also showed good similarity with the FAO and existing land cover maps.

ARRDC1 and ARRDC3 act as tumor suppressors in renal cell carcinoma by facilitating YAP1 degradation.

The α-arrestins domain-containing 1 and 3 (ARRDC1 and ARRDC3) are two members of the α-arrestins family. Yes-associated protein 1 (YAP1) is a key downstream transcription co-activator of the Hippo pathway essential for cancer initiation, progression, or metastasis in clear cell renal cell carcinoma (ccRCC). The aim of this work was to elucidate the role of the α-arrestins in ccRCC tumorigenesis by identifying molecular interacting factors and exploring potential mechanisms. In this study, we identified YAP1 as a novel ARRDC3 interacting protein in RCC cells through tandem affinity purification and mass spectrometry. We confirmed that ARRDC1 and ARRDC3, but not other α-arrestin family proteins, interact with YAP1. Binding of ARRDC1/3 to YAP1 is mediated through the WW domains of YAP1 and the PPXY motifs of ARRDC1/3. Functional analysis of ARRDC1/3 by lentiviral shRNA revealed a role for ARRDC1/3 in suppression of cell growth, migration, invasion and epithelial-mesenchymal transition in ccRCC cells, and these effects were mediated, at least in part, through YAP1. Mechanically, ARRDC1/3 negatively regulates YAP1 protein stability by facilitating E3 ubiquitin ligase Itch-mediated ubiquitination and degradation of YAP1. Moreover, ARRDC1/3 mRNA levels were significantly downregulated in ccRCC specimens. A negative correlation was identified between ARRDC3 and YAP1 expression in ccRCC specimens by immunohistochemistry. This study revealed a novel mechanism for ARRDC1/3 in the regulation of YAP1 stability and provided insight in understanding the relationship between ARRDC1/3 downregulation and aberrant Hippo-YAP1 pathway activation in ccRCC.

The changing pattern of urban flooding in Guangzhou, China.

Extensive research has focused on modelling and analysis of urban flooding in relatively small catchments. Findings in small areas tend to be site-specific, and may not be applicable to larger study areas. Larger scale studies can identify general patterns and influential factors; however, few studies have investigated urban flooding on a larger scale such as a metropolitan area. In this study, we explored the spatial-temporal patterns of urban flooding during the period of 2009-2015 in the central area of Guangzhou, China. Under the framework of Pressure (precipitation and impervious surface)-State (urban flooding)-Response (drainage improvement), we evaluated reasons for the State change and effectiveness of the Response. Overall the urban flooding state showed a fluctuating and improving trend. The fluctuation of the flooding state trend is due to precipitation variation, and the improving trend is attributed to drainage improvement. Furthermore, drainage improvement in the upstream area had led to new flooding in the downstream area. It is evident that the mitigation effect of urban flooding in Guangzhou varied significantly across the city. It is further suggested to regularly collect urban flooding records in cities with flood risk, so that more appropriate policies and measures about urban flooding mitigation can be developed.

Moisture-induced greening of the South Asia over the past three decades.

South Asia experienced a weakening of summer monsoon circulation in the past several decades, resulting in rainfall decline in wet regions. In comparison with other tropical ecosystems, quantitative assessments of the extent and triggers of vegetation change are lacking in assessing climate-change impacts over South Asia dominated by crops. Here, we use satellite-based Normalized Difference Vegetation Index (NDVI) to quantify spatial-temporal changes in vegetation greenness, and find a widespread annual greening trend that stands in contrast to the weakening of summer monsoon circulation particularly over the last decade. We further show that moisture supply is the primary factor limiting vegetation activity during dry season or in dry region, and cloud cover or temperature would become increasingly important in wet region. Enhanced moisture conditions over dry region, coinciding with the decline in monsoon, are mainly responsible for the widespread greening trend. This result thereby cautions the use of a unified monsoon index to predict South Asia's vegetation dynamics. Current climate-carbon models in general correctly reproduce the dominant control of moisture in the temporal characteristics of vegetation productivity. But the model ensemble cannot exactly reproduce the spatial pattern of satellite-based vegetation change mainly because of biases in climate simulations. The moisture-induced greening over South Asia, which is likely to persist into the wetter future, has significant implications for regional carbon cycling and maintaining food security.

The first all-season sample set for mapping global land cover with Landsat-8 data.

We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data. Prior to this, such samples were only available at a single date primarily from the growing season. It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year. To answer this question, we selected available Landsat-8 images from four seasons and collected training and validation samples from them. We compared the performances of training samples in different seasons using Random Forest algorithm. We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season. The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) classification system. The use of training samples from all seasons (named all-season training sample set hereafter) produced an overall accuracy of 67.0%. We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%. This indicates that properly grouped subsamples in space can help improve classification accuracies. All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.

Size discrepancy: not a formidable problem in a mouse heterotopic aortic transplant.

OBJECTIVES: We sought to develop a better approach in establishing a heterotopic aortic transplant model. MATERIALS AND METHODS: We used "sleeve " operation and set up 2 heterotopic aortic transplant experimental groups (groups 1 and 2). In group 1, donors (BALB/c (H-2d) mice; weight, 25-30 g) had the same weight as the recipients (C57BL/6 (H-2d) mice; weight, 25-30 g); in group 2, donors had lower weights (BALB/c (H-2d) mice, weight, 15-20 g). Grafts were examined macroscopically and histologically 60 days after the transplant. RESULTS: The thrombosis incidence was 10% in group 1 (5 of 50) and 2% in group 2 (1 of 50) (P < .05). Intestinal obstructions led to a high mortality rate in both groups: 8% in group 1 and 10% in group 2 (P < .05). CONCLUSIONS: The issue of size discrepancy between donor and recipient aortas is not a problem in a heterotopic aortic transplant model with the use of a sleeve technique, while the high incidence of intestinal obstructions must be considered.

Spatio-temporal distribution of malaria in Yunnan Province, China.

The spatio-temporal distribution pattern of malaria in Yunnan Province, China was studied using a geographic information system technique. Both descriptive and temporal scan statistics revealed seasonal fluctuation in malaria incidences in Yunnan Province with only one peak during 1995-2000, and two apparent peaks from 2001 to 2005. Spatial autocorrelation analysis indicated that malaria incidence was not randomly distributed in the province. Further analysis using spatial scan statistics discovered that the high risk areas were mainly clustered at the bordering areas with Myanmar and Laos, and in Yuanjiang River Basin. There were obvious associations between Plasmodium vivax and Plasmodoium falciparum malaria incidences and climatic factors with a clear 1-month lagged effect, especially in cluster areas. All these could provide information on where and when malaria prevention and control measures would be applied. These findings imply that countermeasures should target high risk areas at suitable times, when climatic factors facilitate the transmission of malaria.

Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data.

Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data.

Phase behaviors and supra-molecular structures of a series of symmetrically tapered bisamides.

A series of symmetrically tapered 1,4-bis[3,4,5-tris(alkan-1-yloxy)benzamido] benzene bisamides (CPhBA, where is the number of carbon atoms in the alkyl chains, = 10, 12 and 16), was synthesized in order to investigate the effect of alkyl chain length on supra-molecular ordered structures induced by hydrogen (H)-bonding and micro-phase separation. These bisamides consist of a rigid aromatic bisamide core with three flexible alkyl chains at each end of the core. Major phase transitions and their origins in CPhBA bisamides were studied with differential scanning calorimetry, one-dimensional (1D) wide angle X-ray diffraction (WAXD), infrared spectroscopy, and solid-state carbon-13 nuclear magnetic resonance experiments. The structures of these compounds in different phases were identified using 2D WAXD from oriented samples and were also confirmed by selected area electron diffractions in transmission electron microscopy from stacked single crystals and by computer simulations. All of the CPhBA bisamides in this series formed a highly ordered oblique columnar () phase and a low-ordered oblique columnar () phase, similar to a recent report on C14PhBA. The two main driving forces in the formation of these two supra-molecular columnar structures were identified: One was the H-bond formation between N-H and C[double bond, length as m-dash]O groups, and the other was the micro-phase separation between the bisamide cores and the alkyl chains. With increasing the length of alkyl tails, the isotropization temperature decreased, while the disordering temperature of the alkyl tails increased. The 2D lattice structures perpendicular to the columnar axis also increasingly deviated from the pseudo-hexagonal packing with increasing the alkyl tail length. However, the alkyl tail length did not have a significant influence on the packing along the columnar axis direction. Utilizing polarized optical microscopy, the phase identifications were also supported by the observation of texture changes and molecular arrangements inside of the micro-sized domains.