A 33-year NPP monitoring study in southwest China by the fusion of multi-source remote sensing and station data

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a 33-year regional NPP study in southwest China, at a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the respective advantages of the different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated by the improved Yang hybrid model (YHM), using meteorological station data. The verification described in this paper proved the feasibility of all the applied data processes, and a greatly improved accuracy was obtained for the NPP calculated with the final processed NDVI. The spatio-temporal analysis results indicated that 68.07% of the study area showed an increasing NPP trend over the past three decades. Significant heterogeneity was found in the correlation between NPP and precipitation at a monthly scale, specifically, the negative correlation in the growing season and the positive correlation in the dry season. The lagged positive correlation in the growing season and no lag in the dry season indicated the important impact of precipitation on NPP.Comment: 20 pages, 11 figure

Macrophage CGI-58 Deficiency Activates ROS-Inflammasome Pathway to Promote Insulin Resistance in Mice

SummaryOvernutrition activates a proinflammatory program in macrophages to induce insulin resistance (IR), but its molecular mechanisms remain incompletely understood. Here, we show that saturated fatty acid and lipopolysaccharide, two factors implicated in high-fat diet (HFD)-induced IR, suppress macrophage CGI-58 expression. Macrophage-specific CGI-58 knockout (MaKO) in mice aggravates HFD-induced glucose intolerance and IR, which is associated with augmented systemic/tissue inflammation and proinflammatory activation of adipose tissue macrophages. CGI-58-deficient macrophages exhibit mitochondrial dysfunction due to defective peroxisome proliferator-activated receptor (PPAR)γ signaling. Consequently, they overproduce reactive oxygen species (ROS) to potentiate secretion of proinflammatory cytokines by activating NLRP3 inflammasome. Anti-ROS treatment or NLRP3 silencing prevents CGI-58-deficient macrophages from oversecreting proinflammatory cytokines and from inducing proinflammatory signaling and IR in the cocultured fat slices. Anti-ROS treatment also prevents exacerbation of inflammation and IR in HFD-fed MaKO mice. Our data thus establish CGI-58 as a suppressor of overnutrition-induced NLRP3 inflammasome activation in macrophages

Comprehensive Public Transport Service Accessibility Index—A New Approach Based on Degree Centrality and Gravity Model

Public transport accessibility (PTA) is an essential index for evaluating the efficiency of urban public transport resource and public service. Improving public transport accessibility is considered as the most effective way of alleviating urban congestion and promoting urban sustainability. PTA can be divided into three types, which are access to stations, accessibility of networks and access to activities. This paper focuses on evaluating access to public transport service at stations, considering walking time to stations and waiting time for services at stations. Numerous studies have been carried out on evaluating the accessibility of public transport stations. When building accessibility evaluation model, rare has seen different public transport modes as an integrated system. Hence the topological structure and geometrical layout of the system are not considered. In this paper, factors like the configuration of the public transport system and the surrounding environment of stations are included for the evaluation. The centrality of station index (COS) is presented to describe the importance of stations in the integrated public transport system. The COS index is an improved combination of the gravity model and degree centrality index of the complex network. This index improves the degree centrality index by replacing the number of nodes with weighted connections between stations. By modeling public transport operation, configuration and surroundings of stations, a comprehensive public transport service accessibility index (CPTAI) is formulated to quantify accessibility at the community level. To compute this index, a network analysis model is firstly applied to find the nearest station for each point of interest (POI) by using ArcGIS desktop 10.2, and the transport service frequency at the nearest station is measured. Then Baidu Map API is employed to measure the impedance indexes between stations in the integrated public transport network. Activities covered by stations within a given distance are seen as the generation and attraction of trips in between the stations. Then a weighted gravity model and COS is presented to calculate the integrated service frequency (ISF) for each POI afterward. In the end, the index is converted to the community level, which is CPTAI. The experiment is carried out in Wuhan metropolitan area, Hubei, China. Smart card data (SCD) is utilized to evaluate CPTAI and examine the association between commuting trips by public transport and accessibility level within Wuhan metropolitan area. Experimental results show that CPTAI has a significant statistical association with trips by public transport

Assessment of Inundation Changes of Poyang Lake Using MODIS Observations Between 2000 and 2010

China\u27s largest freshwater lake, Poyang Lake, is well known for its ecological and economic importance as well as its rapid changes in lake inundation areas. However, due to technical difficulties, to date long-term records of its dynamic inundation areas are lacking, not to mention how they are affected by climate change and/or human activities. Using Moderate Resolution Imaging Spectroradiometer (MODIS) medium-resolution (250-m) data collected between 2000 and 2010 and an objective water/land delineation method, we documented and studied the short- and long-term characteristics of lake inundation. Significant seasonality and inter-annual variability were found in the monthly and annual mean inundation areas. The inundation area ranged between 714.1 km2 in October 2009 and 3162.9 km2 in August 2010, and the inundation area during any particular year could change by a factor of 2.3–3.2. During the 11-year period, the maximum possible inundation area was 14 times the minimum possible inundation area, indicating extreme variability. Both the annual mean and minimum inundation areas showed statistically significant declining trends from 2000 to 2010 (− 30.2 km2 yr− 1 and−23.9 km2 yr− 1, p \u3c 0.05). The changes of the inundation area were primarily driven by local precipitation during non-summer months, while during summer months of July to September when the outflow into the Yangtze River was impeded the effect of precipitation became less significant. These results provide long-term baseline data to monitor future changes in Poyang Lake\u27s inundation area in a timely fashion, for example quantifying the extreme drought conditions during spring 2011

Evaluation of Multiple Downscaled Microwave Soil Moisture Products over the Central Tibetan Plateau

Downscaled microwave soil moisture (SM) products with a fine resolution are of great importance for both local and regional studies. However, few studies have explored the merits of multiple downscaled microwave SM products. An evaluation of the different products could help to advance knowledge of the downscaled microwave SM products and help researchers to choose the appropriate downscaled SM products for use in further studies. In this research, five microwave SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), AMSR2, and Soil Moisture and Ocean Salinity (SMOS) data were downscaled via the back-propagation neural network (BPNN). The BPNN was chosen because it can effectively simulate the nonlinear relationship between SM and the land surface temperature (LST)/vegetation index (VI). The different downscaled SM products were evaluated with in-situ SM data from the central Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) during the period from 1 August 2010 to 31 December 2012. Compared with the regression technique, the downscaled correlation coefficient (r) is significantly improved by the BPNN. The downscaled root-mean-square error (RMSE) and bias are comparable for the two techniques. As expected, LST and enhanced VI (EVI) are physically related to SM, and this is the most suitable combination for SM downscaling. Except for the ascending node of SMOS and AMSR2, the downscaled r is closely related to the original RMSE, and a lower original RMSE for the SM product results in a higher downscaled r. The BPNN-downscaled SMOS product in descending node is the closest to the in-situ SM among the different downscaled microwave SM products. The temporal variations and ranges of the microwave SM products are well maintained by the BPNN downscaling. Furthermore, the evaluations against in-situ SM reveal that the overall accuracies of the BPNN-downscaled SM products are very close to the original microwave SM products

Promoting Geospatial Service from Information to Knowledge with Spatiotemporal Semantics

With the development of geoscience, users are eager to obtain preferred service from geospatial information intelligently and automatically. However, the information grows rapidly while the service gets more complicated, which makes it difficult to find out the targeted information for an exact service in geospatial issues. In this paper, a novel method is proposed to promote the geospatial service from information to knowledge with spatiotemporal semantics. Both prompted and professional knowledge are further refined to be published as a service. In terms of an exact task, numerous related services are recombined to a service chain under user requirement. Finally, the proposed method is applied to monitor the environment on the Air Quality Index (AQI) and soil moisture (SM) in the Sensor Web service platform, the results of which indicate geospatial knowledge service (GKS) is more efficient to support spatial decision-making