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ABSTRACTRemote sensing technology plays a critical role in evaluating large-scale ecological restoration efforts. However, previous studies tend to focus on specific aspects of ecological restoration, such as water conservation, vegetation restoration, or land rehabilitation. As a sustainable development demonstration city in China, Taiyuan has undertaken numerous ecological projects to mitigate environmental degradation caused by its long-standing energy-driven economic expansion. To comprehensively evaluate Taiyuan’s ecological restoration efforts, this study used remote sensing technology to assess its progress from the SDGs perspectives, such as water conservation (SDG 6.3.2), air quality improvement (SDG 11.6.2), and ecosystem preservation (SDG 6.6.1, 15.1.1, 15.3.1, 15.4.2). Firstly, ecological restoration indicators were selected from the aforementioned SDGs perspectives. Secondly, a new vegetation growth condition index (VGCI) was developed to accurately monitor changes in vegetation growth. Finally, Taiyuan’s ecological restoration efforts were evaluated based on the selected indicators. The results showed that Taiyuan has made significant progress in ecological restoration, including improved water and air quality, increased forest coverage, and restored mine’s vegetation, likely due to its ecological projects. This study provides a useful framework for evaluating ecological restoration efforts and advancing sustainability goals.

Achieving land degradation neutrality (LDN) worldwide is a significant target of the Sustainable Development Goal (SDG15.3). Inner Mongolia, as a typical dryland region in northern China, has carried out several large-scale ecological restoration programs to combat land degradation. However, there is a lack of comprehensive assessment of its land degradation situation after ecological programs implementation, which is of great significance to supporting SDG15.3 in China. This study analyzed the land degradation situation using the improved SDG15.3.1 calculation framework based on fine resolution data in Inner Mongolia from 2000 to 2020, and finally comprehensively evaluated the land status of the whole region and those subject to ecological programs. The results show that net land restoration proportion of various ecological project regions and whole region continues to increase. The scope of the Grain for Green Program (GGP) had the largest proportion of net land restoration while the Natural Reserve Program (NRP) had the lowest proportion from 2000 to 2020. The net land restoration area of Inner Mongolia during 2000–2010 and 2010–2020 was 35,800 km² and 65,300 km², respectively. Overall, Inner Mongolia has achieved statistically zero growth in land degradation under the governance of ecological restoration programs. Therefore, reasonable planning, well monitoring, and timely assessment of ecological restoration programs are crucial to support SDG15.3.

Fractional vegetation cover (FVC) is a critical land surface parameter, and several large-scale FVC products have been generated based on remote sensing data. Among these existing products, the global land surface satellite (GLASS) FVC product, derived from moderate resolution imaging spectroradiometer (MODIS) 500 m reflectance data (MOD09A1), has achieved complete spatial-temporal continuity and satisfying accuracy. To further improve the spatial resolution of GLASS FVC product, this study developed a novel FVC estimation algorithm for MODIS 250 m reflectance data based on a recurrent neural network with the long short-term memory unit (RNN-LSTM). The RNN-LSTM was established using sequence training samples derived from the MODIS 250 m reflectance and GLASS FVC products, which were conducted over three vegetation types in mid-West China. Additionally, two machine learning methods, including the back propagation neural network (BPNN) and multivariate adaptive regression splines (MARS), were used to compare with the proposed method. The evaluation results showed that RNN-LSTM derived FVC had reliable spatial-temporal continuity and good consistency with the GLASS FVC product. Furthermore, the smooth temporal profiles of the RNN-LSTM FVC estimation indicated that the proposed method was capable of capturing the temporal characteristics of vegetation growth and reducing the uncertainties from the atmosphere and radiation. Finally, an independent validation case in the Heihe area indicated that the RNN-LSTM algorithm achieved the best accuracy (R2 = 0.8081, rmse = 0.0951) compared with the BPNN (R2 = 0.7320, rmse = 0.1127) and MARS (R2 = 0.7361, rmse = 0.1117). This study provides a new approach by showing the potential of the RNN-LSTM method for land surface parameter estimation and related research.

Ecosystem authenticity is a concept for understanding the status of an ecosystem and provides useful information for making environmental management plans. However, approaches for quantitative ecosystem authenticity evaluation over large area are still rare. In this study, a conceptual framework of “Pattern-Process-Function-Resilience” is proposed to quantitatively evaluate regional ecosystem authenticity. Firstly, a composite index is calculated to estimate the self-adjustment ability and the level of biodiversity of the ecosystem. Then, the ratio between the composite indices are computed at the final and initial periods, and used to determine whether the ecosystem authenticity is protected or damaged, as well as the degrees to which the authenticity is protected or damaged by categorizing the ratio into five levels. A case study was conducted to quantify and evaluate the ecosystem authenticity over the Qinghai-Tibet Plateau (QTP) and the alternative locations of its twenty national parks from 2000 to 2015. Results show that regions with protected or damaged ecosystem authenticity cover about 95% and 5% of the entire QTP, respectively. Five of the twenty national parks alternative locations have damaged ecosystem authenticity. Areas with damaged authenticity are mainly distributed over the southeastern QTP, which suffered from deforestation and increased frequency of mountain hazards, including debris flow, flash flood, landslide, collapse, and snow avalanche. The approach presented in this study is instrumental to support future case-specific management and restoration strategies.

Spatiotemporal fusion has provided a feasible way to generate fractional vegetation cover (FVC) data with high spatial and temporal resolution. However, when the currently available spatiotemporal fusion methods are applied over agricultural regions, they usually underestimate high FVC values at the peak vegetation growth stage with medium FVC values as base data. This mainly results from inconsistencies in the temporal variations between fine- and coarse-resolution data if substantial temporal changes occur in vegetation. Therefore, a Spatial and Temporal Fusion method combining with Vegetation Growth Models (STF-VGM) was proposed to address this problem in this study, which incorporates vegetation growth models into the fusion process. Unlike other spatiotemporal fusion methods that mainly rely on changes in coarse-resolution data for prediction, STF-VGM fully utilizes available coarse- and fine-resolution time series data, including uncontaminated information in cloud/cloud shadow contaminated images. By establishing vegetation growth models with time series data, a conversion relationship between coarse- and fine-resolution FVC that changes along with the nonlinear vegetation change process can be extracted. STF-VGM makes prediction based on this variable relationship. A typical agricultural region located in the North China Plain was selected as the study area. The validation results indicated that the prediction accuracy for high FVC values was significantly improved using STF-VGM compared to the commonly used Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and Flexible Spatiotemporal DAta Fusion (FSDAF) methods (STF-VGM: coefficient of determination (R2) = 0.9491, root mean square error (RMSE) = 0.0650, average difference (AD) = -0.0092; ESTARFM: R2 = 0.9341, RMSE = 0.1127, AD = -0.0631; FSDAF: R2 = 0.9224, RMSE = 0.1110, AD = -0.0599). The satisfactory performance of STF-VGM was also achieved in predicting FVC values at other vegetation growth stages (early growth stage: R2 = 0.8277, RMSE = 0.0440, AD = 0.0027; rapid growth stage: R2 = 0.9183, RMSE = 0.0844, AD = 0.0500). In addition, STF-VGM also has the potential to improve the spatiotemporal fusion accuracy of other vegetation parameters and vegetation indices, which will be evaluated in the future.

The Qinghai-Tibetan Plateau (QTP) has the most fragile ecosystems in the world. Over the past decades, QTP is suffering from increasing external pressures of climate change, human activities, and natural hazards, thus ecological vulnerability assessment is crucial for its sustainable development. This study proposes an objective and automatic framework to assess the ecological vulnerability in the QTP under the threats of mountain hazards, ecosystem degradation and human economic activities and then analyze its spatio-temporal patterns from 2000 to 2015. An ecological vulnerability index (EVI) is established by integrating natural and anthropic factors based on sub-systems of land resources, hydro-meteorology, topography, and social economics. Seventeen indicators are selected to reflect ecological conditions and their weights are determined by principle component analysis and entropy weighting methods. Then, the EVI values are automatically categorized into five vulnerability levels of potential, light, moderate, heavy, and very heavy to illustrate their spatio-temporal patterns across the QTP. Results indicated that spatial distributions of EVI across the QTP exhibited similar patterns during the study period at an overall heavy level. Among all the indicators, vegetation was the dominant driver for ecological vulnerability. Based on trend analyses during the study period, approximately 10.43% of the QTP, mainly distributed in Tibet Autonomous Region, experienced significant increase in ecological vulnerability, while 7.38%, mainly distributed in Qinghai Province, experienced significant ecological vulnerability declination. However, more detailed analyses showed that after the implementation of several ecological protection programs, the increasing trend of ecological vulnerability was eased and more regions experienced significantly decreasing vulnerability. This indicated the ecological restoration projects conducted by the government were efficient in reducing ecological vulnerability.

As an important land surface vegetation parameter, fractional vegetation cover (FVC) has been widely used in many Earth system ecological and climate models. In particular, high-quality and reliable FVC products on the global scale are important for the Earth surface process simulation and global change studies. Recently, the FengYun-3 (FY-3) series satellites, which are the second generation of Chinese meteorological satellites, launched with the polar orbit and provide continuous land surface observations on a global scale. However, there is rare studying on the FVC estimation using FY-3 reflectance data. Therefore, the FY-3B reflectance data were selected as the representative data to develop a FVC estimation algorithm in this study, which would investigate the capability of the FY-3 reflectance data on the global FVC estimation. The spatial–temporal validation over the regional area indicated that the FVC estimations generated by the proposed algorithm had reliable continuities. Furthermore, a satisfactory accuracy performance (R2 = 0.7336, RMSE = 0.1288) was achieved for the proposed algorithm based on the Earth Observation LABoratory (EOLAB) reference FVC data, which provided further evidence on the reliability and robustness of the proposed algorithm. All these results indicated that the FY-3 reflectance data were capable of generating a FVC estimation with reliable spatial–temporal continuities and accuracy.

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.

Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS successor. Therefore, developing an FVC estimation algorithm for VIIRS data is important for maintaining continuous FVC estimates in case of MODIS failure. In this study, a global FVC estimation algorithm for VIIRS surface reflectance data was proposed based on machine learning methods, which investigated the performances of back propagating neural networks (BPNNs), general regression networks (GRNNs), multivariate adaptive regression splines (MARS), and Gaussian process regression (GPR). The training samples were extracted from the GLASS FVC product and corresponding reconstructed VIIRS surface reflectance in 2013 over the global sampling locations. The VIIRS reflectances of red and near infrared (NIR) bands were the input variables for these machine learning methods. The theoretical performances and independent validation results indicated that the four machine learning methods could achieve similar and reliable FVC estimates. Regarding the FVC estimation accuracy, the GPR method achieved the best performance (R2 = 0.9019, RMSE = 0.0887). The MARS method had the obvious advantage of computational efficiency. Furthermore, the FVC estimates achieved good spatial and temporal continuities. Therefore, the proposed FVC estimation algorithm for VIIRS data can potentially generate reliable global FVC data for related applications.

Acetylcholinesterase (AChE) is an important hydrolase in cholinergic synapses and a candidate target in the treatment of Alzheimer's disease. The lithium treatment widely used in neurological disorders can alter the AChE activity, yet the underlying mechanism of how the ion species regulate the enzymatic activity remains unclear. In this work, we performed combined quantum mechanics/molecular mechanics (QM/MM) and molecular dynamics (MD) simulations and well-tempered metadynamics to understand the modulation of human AChE (hAChE) activity using three alkali metal ions (Li+, Na+, and K+) in different concentrations. Our simulations show that the binding affinity and catalytic activity are affected by different ion species through allosteric ion coordination geometries on the hAChE complex and distant electrostatic screening effect. A Li+ cluster involving D330, E393, and D397 residues and three Li+ ions was found to be highly conserved and can be critical to the enzyme activity. Binding energy calculations indicate that the electrostatic screening from allosterically bound cations can affect the key residues at the catalytic site and active-site gorge, including E199. Furthermore, an increase in ion concentration can lead to lower reactivity, especially for Li+ ions, which exhibit more cation-hAChE contacts than Na+ and K+. The selective ion binding and their preferred modulation on hAChE are highly related to ion species. This work provides a molecular perspective on selective modulation by different ion species of the enzyme catalytic processes. [ABSTRACT FROM AUTHOR]

Abstract [Objective] The study aims to investigate the effects of different light exposure durations and intensities on the sugar, acid, and flavonoid contents of Cerasus humilis fruits, and to provide a reference for further exploration of the molecular mechanisms underlying the influence of light exposure on fruit quality. [Methods] The C. humilis varieties ‘Nongda 6’ and ‘Nongda 7’ were used as experimental materials. Fruit bags with three different light transmission rates (30%, 55%, and 100%) were applied during fruit enlargement period and color conversion period, respectively, and measured the fruit mass and titratable acid, soluble solid, and flavonoid content. [Results] (1) The single fruit mass and soluble solid content of both varieties were lower during fruit enlargement period than during color conversion period, and both were decreased gradually with increasing light-blocking rate of fruit bags. (2) The titratable acid content of ‘Nongda 6’ was significantly reduced under bagging treatment, and the higher the light-blocking rate of the fruit bag and the longer the bagging time, the more pronounced the reduction in acidity. In contrast, the titratable acid content of ‘Nongda 7’ was less affected. (3) The flavonoid content of bagged ‘Nongda 6’ was higher than the control, increasing first and then decreasing with the increase in lightblocking rate of the fruit bag, and the content during fruit enlargement period was higher than that during color conversion period. The flavonoid content of bagged ‘Nongda 7’ was significantly higher than the control only at a 30% light-blocking rate, and the content during fruit enlargement period was significantly lower than during color conversion period. [Conclusion] Bagging can effectively improve the sugar, acid, and flavonoid content of C. humilis fruits. Furthermore, bagging during fruit enlargement period is suitable for ‘Nongda 6’, while bagging during color conversion period is more effective for ‘Nongda 7’. Both varieties show the best overall improvement in sugar, acid, and flavonoid content with a 55% light-blocking rate of the fruit bag.