A UAV-Based Eddy Covariance System for Measurement of Mass and Energy Exchange of the Ecosystem: Preliminary Results.

Airborne eddy covariance (EC) measurement is one of the most effective methods to directly measure the surface mass and energy fluxes at the regional scale. It offers the possibility to bridge the scale gap between local- and global-scale measurements by ground-based sites and remote-sensing instrumentations, and to validate the surface fluxes estimated by satellite products or process-based models. In this study, we developed an unmanned aerial vehicle (UAV)-based EC system that can be operated to measure the turbulent fluxes in carbon dioxides, momentum, latent and sensible heat, as well as net radiation and photosynthetically active radiation. Flight tests of the developed UAV-based EC system over land were conducted in October 2020 in Inner Mongolia, China. The in-flight calibration was firstly conducted to correct the mounting error. Then, three flight comparison tests were performed, and we compared the measurement with those from a ground tower. The results, along with power spectral comparison and consideration of the differing measurement strategies indicate that the system can resolve the turbulent fluxes in the encountered measurement condition. Lastly, the challenges of the UAV-based EC method were discussed, and potential improvements with further development were explored. The results of this paper reveal the considerable potential of the UAV-based EC method for land surface process studies.

Green polymer hydrogels from a natural monomer with inherent antioxidative capability for efficient wound healing and spinal cord injury treatment.

Development of polymeric hydrogels with multiple functions (adhesiveness, self-healability, anti-oxidation efficiency, etc.) through one-step green polymerization of naturally occurring small molecules in water is critical for various biomedical applications and clinical transformation. In this work, benefiting from the dynamic disulfide bond in α-lipoic acid (LA), we directly obtain an advanced hydrogel (poly(lipoic acid-co-sodium lipoate) (PLAS)) through heat and concentration-induced ring-opening polymerization of LA with the addition of NaHCO3 in an aqueous solution. The presence of COOH, COO- and disulfide bonds endows the resulting hydrogels with comprehensive mechanical properties, facile injectability, fast self-healability and adequate adhesiveness. Moreover, the PLAS hydrogels show promising antioxidative efficiency, inherited from naturally occurring LA, and can efficiently eliminate intracellular reactive oxygen species (ROS). We also verify the advantage of PLAS hydrogels in a rat spinal injury model. Through the regulation of ROS and in situ inflammation, our system can promote the recovery of spinal cord injury. Owing to the natural origin and inherent anti-oxidative capability of LA, and a green preparation method, our hydrogel should be beneficial for clinical transformation and may be a good candidate for various biomedical applications.

Long-term detection and spatiotemporal variation analysis of open-surface water bodies in the Yellow River Basin from 1986 to 2020.

Accurately investigating long-term information about open-surface water bodies can contribute to water resource protection and management. However, due to the limits of big-data calculations for remote sensing, there has been no specific study on the long-term changes in the water bodies in the Yellow River Basin. Thus, in this study, we developed a new combined extraction rule to build an entire annual-scale open-surface water body dataset for 1986-2020 with excellent effectiveness in eliminating the interference of shadows in the Yellow River Basin using all of the available Landsat images. For the first time, the spatial distribution, change trends, conversion processes, and the heterogeneity of the surface water bodies in the Yellow River Basin were analyzed comprehensively to the best of our knowledge. The extraction results had an overall accuracy of 99.70 % and a kappa coefficient of 0.90, which were validated using 34,073 verification points selected on high-resolution Google Earth images and random Landsat images. The total area of water bodies initially decreased (1986-2000) and then increased (2001-2020); however, only the size of the permanent water bodies increased in most areas, while the size of most of the seasonal water bodies decreased. In regions with human-made water bodies, the non-water areas were substantially converted to seasonal and permanent water bodies; however, in areas with natural water bodies, many permanent and seasonal water bodies were gradually converted to non-water areas. Thus, most of the increases in the water bodies occurred in the form of artificial lakes and reservoirs, while most of the decreases in the water body area occurred in natural wetlands and lakes. The areas of both the permanent and seasonal water bodies were positively correlated with precipitation, but only the area of the seasonal water bodies was negatively correlated with temperature.

Excess sulfur and Fe elements drive changes in soil and vegetation at abandoned coal gangues, Guizhou China.

Coal gangue piles accumulate outside mines and can persist for years, negatively impacting the regional environment. To determine the main cause of soil pollution at coal gangues, several coal gangues in Guizhou Province, China that had undergone natural recovery via native plants for 8 years were investigated in summer 2019. Three plots (2 m × 2 m) from the coal gangue area were selected for the treatment (GP). Control plots that were 100 m away from GP were also investigated in contrast (CK-near). In addition, plots from forest, farmland and lake land that were far from GP and largely undisturbed were also investigated as more extreme contrasts (CK-far). A series of soil indicators that can be affected by coal-gangue, such as heavy metals (Mn, Cr, Cd, Ni, Zn, Cu, Pb), As, pH, cation exchange capacity (CEC), sulfur (S) and iron (Fe), were tested for in the plots. Plant species, coverage and height were also analyzed to uncover biodiversity and dominant species information. The results suggested that coal gangue significantly influences soil S, pH and plant species after 8 years of natural recovery. The CK-far plots contained relatively low soil sulfur content, normal pH (close to 7) and abundant plant biodiversity. Generally, pH related positively with both the Patrick (R = 0.79, n = 22, p < 0.001) and Shannon indices (R = 0.67, n = 22, p < 0.001); the soil S related negatively with both the Patrick (R = 0.85, n = 22, p < 0.001) and Shannon indices (R = - 0.79, n = 22, p < 0.001). S content was highest (S = 1.0%) in GP plots, was lower in CK-near plots (S = 0.3%) and was the lowest of all in the plots distant from the coal mine (S = 0.1%, CK-far). S content was negatively correlated with pH. Soil pH decreased significantly, from 7.0 in CK-far, to 5.9 in CK-near, to 4.2 in GP. Soil Fe was 3.4 times higher in GP and CK-near than in CK-far. The excess sulfur and Fe elements and the acidified soil drove changes in soil and vegetation in the coal gangue areas. After 8 years of natural recovery, only a few plants, like Miscanthus floridulus, were able to live near the coal gangue in the area where the soil was still acidic and high in S and Fe.

Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform.

With the rapid development of the economy over 40 years since the initiation of Chinese economic reform, terrestrial ecosystems in China have undergone large-scale changes. In this study, we investigated vegetation dynamics in China and their relationships with climatic factors and anthropogenic drivers over 15 progressive periods of 18-32 years starting in 1982. This was accomplished by using the third-generation global satellite Advanced Very High Resolution Radiometer (AVHRR), Normalized Difference Vegetation Index (NDVI) dataset, night-time satellite data, and climate data. Across China, NDVI increased significantly during 1982-2013; especially significant increases were observed in all periods during the growing season and spring. At the pixel scale, 21-38% of the vegetated area in the 15 periods experienced a significant positive trend in vegetation growth. This increase was mostly located in central and southern China. A significant negative trend was observed in 1-8% of the vegetated area pixels, and this pattern was mainly seen in northwestern China, the Yangtze River Delta region, and the Pearl River Delta region. The contribution of spring NDVI to vegetation improvement increased, while the contribution of summer NDVI decreased. Vegetation activity in China was mainly regulated by thermal factors, especially pronounced in mountainous regions of northern China. However, the restrictive effect of moisture factors was very marked to vegetation growth in areas with less than 400 mm of precipitation. Urbanization in China has led to vegetation degradation in most urban centers and surrounding areas in central and eastern China. The increase of agricultural plantations, the Grain for Green Project, and a series ecological restoration projects in some areas have promoted vegetation coverage.

Variations in vegetation dynamics and its cause in national key ecological function zones in China.

Continued long-term monitoring of vegetation activity in national key ecological function zones (NKEFZs) has implications for national ecological security and sustainability in China. We used Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) dataset to map and analyze the spatiotemporal patterns of change in vegetation growth and their linkage with climate change and human activities in NKEFZs during 1982-2013. Statistically significant increases of growing season, spring, and autumn NDVI were observed during all or most periods while 25 NKEFZs are taken as a whole. Non-significant decreases of NDVI were found in 7 NKEFZs during a few periods, and obvious increases were observed during fifteen periods in all other NKEFZs. Vegetation growth in NKEFZs was mainly regulated by a thermal factor, and the dominant climatic drivers varied across different regions and seasons. The influence of temperature was stronger on vegetation activity in spring and autumn for those NKEFZs located in high latitudes and high elevations, while precipitation was the main climatic control factor for NKEFZs in the arid and semi-arid regions. The effects of human activity on the NDVI of NKEFZs were not ignored; a significant decrease of NDVI in the Sanjiang Plain may be related to the rapid change in land use from wetland into farmland.

Syntheses, structures, and magnetic properties of mixed-ligand complexes based on 3,6-bis(benzimidazol-1-yl)pyridazine.

Six new metal-organic coordination polymers (CPs) [Ni(L)(2,5-TDC)(H2O)] n (1), [Ni(L)(1,3-BDC)(H2O)] n (2), [Ni(L)(1,4-BDC)(H2O)] n (3), [Mn(L)(2,5-TDC)(H2O)] n (4), [Mn(L)(2,6-PYDC)(H2O)] n (5) and [Mn(L)(1,4-NDC)] n (6) were achieved by reactions of the corresponding metal salt with mixed organic ligands (L = 3,6-bis(benzimidazol-1-yl)pyridazine, 2,5-H2TDC = thiophene-2,5-dicarboxylic acid, 1,3-H2BDC = isophthalic acid, 1,4-H2BDC = terephthalic acid, 2,6-H2PYDC = pyridine-2,6-dicarboxylic acid, 1,4-H2NDC = naphthalene-1,4-dicarboxylic acid) under solvothermal condition. CPs 1-6 were characterized by single-crystal X-ray diffraction, IR, TG, XRD and elemental analyses. Their structures range from the intricate 3D CPs 1, 3, 4 and 6 to the 2D coordination polymer 2 and the infinite 1D chain 5. The CPs 1-4 and 6 underlying networks were classified from the topological viewpoint, disclosing the distinct sql (in 1), pcu (in 3 and 6), new topology (in 2), and dia (in 4) topological nets. Moreover, analysis of thermal stability shows that they had good thermal stability. Finally, magnetic properties of CPs 1-6 have been studied, the results showed that complex 2 had ferromagnetic coupling and complexes 1, 3-6 were antiferromagnetic.

[Dynamic changes in vegetation NDVI from 1982 to 2012 and its responses to climate change and human activities in Xinjiang, China].

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominates the provisioning of ecosystem services. Therefore, it has significance to monitor the growth of vegetation. Based on AVHRR GIMMS NDVI and MODIS NDVI datasets, we analyzed the spatiotemporal patterns of change in NDVI and their linkage with climate change and human activity from 1982 to 2012 in the typical arid region, Xinjiang of northwestern China, at pixel and regional scales. At regional scale, although a statistically significant positive trend of growing season NDVI with a rate of 4.09 x 10⁻4· a⁻¹ was found during 1982-2012, there were two distinct periods with opposite trends in growing season NDVI before and after 1998, respectively. NDVI in growing season first significantly increased with a rate of 10 x 10⁻4· a⁻¹ from 1982 to 1998, and then decreased with a rate of -3 x 10⁻4· a⁻¹ from 1998 to 2012. The change in trend of NDVI from increase to decrease mainly occurred in summer, followed by autumn, and the reversal wasn't observed in spring. At pixel scale, the NDVI in farmland significantly increased; the NDVI changes in the growing season and all seasons showed polarization: Areas with significant change mostly increased in size as the NDVI record grown in length. The rate of increase in size of areas with significantly decreasing NDVI was larger than that with significantly increasing NDVI, which led to the NDVI increase obviously slowing down or stopping at regional scale. The vegetation growth in the study area was regulated by both climate change and human activity. Temperature was the most important driving factor in spring and autumn, whereas precipitation in summer. Extensive use of fertilizers and increased farmland irrigated area promoted the vegetation growth. However, the rapid increase in the proportion of cotton cultivation and use of drip irrigation might reduce spring NDVI in the part of farmlands, and the increase in stocking levels of livestock might lead to a decrease in NDVI in some grasslands.

[Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].

Consistent NDVI time series are basic and prerequisite in long-term monitoring of land surface properties. Advanced very high resolution radiometer (AVHRR) measurements provide the longest records of continuous global satellite measurements sensitive to live green vegetation, and moderate resolution imaging spectroradiometer (MODIS) is more recent typical with high spatial and temporal resolution. Understanding the relationship between the AVHRR-derived NDVI and MODIS NDVI is critical to continued long-term monitoring of ecological resources. NDVI time series acquired by the global inventory modeling and mapping studies (GIMMS) and Terra MODIS were compared over the same time periods from 2000 to 2006 at four scales of Qinghai-Tibet Plateau (whole region, sub-region, biome and pixel) to assess the level of agreement in terms of absolute values and dynamic change by independently assessing the performance of GIMMS and MODIS NDVI and using 495 Landsat samples of 20 km x20 km covering major land cover type. High correlations existed between the two datasets at the four scales, indicating their mostly equal capability of capturing seasonal and monthly phenological variations (mostly at 0. 001 significance level). Simi- larities of the two datasets differed significantly among different vegetation types. The relative low correlation coefficients and large difference of NDVI value between the two datasets were found among dense vegetation types including broadleaf forest and needleleaf forest, yet the correlations were strong and the deviations were small in more homogeneous vegetation types, such as meadow, steppe and crop. 82% of study area was characterized by strong consistency between GIMMS and MODIS NDVI at pixel scale. In the Landsat NDVI vs. GIMMS and MODIS NDVI comparison of absolute values, the MODIS NDVI performed slightly better than GIMMS NDVI, whereas in the comparison of temporal change values, the GIMMS data set performed best. Similar with comparison results of GIMMS and MODIS NDVI, the consistency across the three datasets was clearly different among various vegetation types. In dynamic changes, differences between Landsat and MODIS NDVI were smaller than Landsat NDVI vs. GIMMS NDVI for forest, but Landsat and GIMMS NDVI agreed better for grass and crop. The results suggested that spatial patterns and dynamic trends of GIMMS NDVI were found to be in overall acceptable agreement with MODIS NDVI. It might be feasible to successfully integrate historical GIMMS and more recent MODIS NDVI to provide continuity of NDVI products. The accuracy of merging AVHRR historical data recorded with more modern MODIS NDVI data strongly depends on vegetation type, season and phenological period, and spatial scale. The integration of the two datasets for needleleaf forest, broadleaf forest, and for all vegetation types in the phenological transition periods in spring and autumn should be treated with caution.