Device-free localization (DFL) based on wireless sensor networks (WSNs) is expected to detect and locate a person without the need for any wireless devices. Radio tomographic imaging (RTI) has attracted wide attention from researchers as an emerging important technology in WSNs. However, there is much room for improvement in localization estimation accuracy. In this paper, we propose a geometry-based elliptical model and adopt the orthogonal matching pursuit (OMP) algorithm. The new elliptical model uses not only line-of-sight information, but also non-line-of-sight information, which divides one ellipse into several areas with different weights. Meanwhile the OMP, which can eliminate extra bright spots in image reconstruction, is used to derive an image estimator. The experimental results demonstrate that the proposed algorithm could improve the accuracy of positioning by up to 23.8% for one person and 33.3% for two persons over some state-of-the-art RTI methods.

An increase in vegetation greenness can improve ecosystem productivity, but also reduce the water supply, creating the potential for conflicting water demands between ecosystems and humans. This problem has been well-assessed and is most evident in dry environments. However, in humid regions, the potential effects of vegetation greenness on water yields under drought conditions are not well understood. To address this gap, we focused on the Poyang Lake watershed in the humid region of southern China. Based on the Standardized Precipitation Evapotranspiration Index and a satellite-derived leaf area index dataset during the growing seasons of 1984 to 2013, several typical dry growing seasons were selected as the study conditions. An existing Water Supply Stress Index model was modified to investigate how the changes in vegetation greenness affected water yield and to explore potentially conflicting water demands between ecosystems and humans under drought conditions. Our results showed that an increase of 20–80% in vegetation greenness generally resulted in a reduction of 3–27% in water yield under drought conditions. Large reductions in water yield mainly were observed in forested areas due to large increases in forest greenness. Moreover, increased vegetation greenness caused a 2 to 3 times greater reduction in water yield during continuing and intensifying droughts than during a short moderate drought period. Thus, in this study, during continuing and intensifying droughts, increased vegetation greenness can cause or aggravate water conflicts in sub-watersheds with high forest cover and high human water demands. Therefore, given the increasing frequency of extreme climatic events, afforestation with a targeted approach should be implemented as it would provide the most benefits. In addition, selective harvesting in forested areas with high density could be an effective strategy to maintain water supply in humid regions.

Quantifying the drivers of terrestrial vegetation dynamics is critical for monitoring ecosystem carbon sequestration and bioenergy production. Large scale vegetation dynamics can be observed using the leaf area index (LAI) derived from satellite data as a measure of ‘greenness’. Previous studies have quantified the effects of climate change and carbon dioxide (CO₂) fertilization on vegetation greenness. In contrast, the specific roles of land-use-related drivers (LURDs) on vegetation greenness have not been characterized. Here, we combined the Interior-Point Method-optimized ecosystem model and the Bayesian model averaging statistical method to disentangle the roles of LURDs on vegetation greenness in China from 2000 to 2014. Results showed a significant increase in growing season LAI (greening) over 35% of the land area of China, whereas less than 6% of it exhibited a significantly decreasing trend (browning). The overall impact of LURDs on vegetation greenness over the whole country was comparatively low. However, the local effects of LURDs on the greenness trends of some specified areas were considerable due to afforestation and urbanization. Southern Coastal China had the greatest area fractions (35.82% of its corresponding area) of the LURDs effects on greening, following by Southwest China. It was because of these economic regions with great afforestation programs. Afforestation effects could explain 27% of the observed greening trends in the forest area. In contrast, the browning impact caused by urbanization was approximately three times of the greening effects of both climate change and CO₂ fertilization on the urban area. And they made the urban area had a 50% decrease in LAI. The effects of residual LURDs only accounted for less than 8% of the corresponding observed greenness changes. Such divergent roles would be valuable for understanding changes in local ecosystem functions and services under global environmental changes.

Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM_2.5. However, the spatiotemporal relationship between AODs and PM_2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM_2.5 concentration across China and its 14 representative regions during 2016–2018 using the correlation coefficient (R), the PM_2.5/AOD ratio (η), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM_2.5 (R > 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM_2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R > 0.5) between the PM_2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM_2.5 per unit of these AODs (η > 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q > 0.4) of PM_2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R > 0.5, q > 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM_2.5 concentration based on AOD in different regions of China.