Search

Based on the two most recent assessments of Chinese higher plants in 2013 and 2020, of 34,450 and 39,330 species, respectively, we analysed the threatened status of Chinese higher plants. In 2020, around 4,088 (10.39%) of the assessed species in China are threatened, 2,875 (7.31%) Near Threatened, 27,593 (70.16%) not currently threatened and categorised as Least Concern and 4,752 (12.08%) categorised as Data Deficient. While in 2013, 3,767 (10.93%) of the assessed higher plants in China are threatened, 2,723 (7.90%) Near Threatened, 24,296 (70.53%) Least Concern and 3,612 (10.48%) Data Deficient. Estimates of the Red List Index in the two years show different patterns when using different weighting methods with the equal steps weighting method showing a slight decrease (0.91675–0.91495) and the extinction risk weighting method showing a slight increase (0.98792–0.98797). We inferred that China's threatened plant species were likely / relatively effectively protected. However, attention should also be given to the non-threatened species in the future as an additional strategy for their conservation, to maintain their non-threatened status. [ABSTRACT FROM AUTHOR]

Land surface albedo analysis and prediction are of great significance for global energy budget research and global change forecasting. Research has been performed on time series albedo analysis but seldom attempt was performed on land surface albedo prediction. This article develops an effective method for land surface albedo prediction from Moderate-Resolution Imaging Spectroradiometer (MODIS) time series albedo data (MCD43A3). It consists of time series data decomposing and time series data forecasting. The ensemble empirical mode decomposition (EEMD) method decomposes the MODIS historical time series albedo data into several intrinsic mode functions (IMFs) and one residual series, then the nonlinear autoregressive neural network (NARnet) method is used to forecast each IMF component and residue. The predictions of all IMFs and residue are summed to obtain a final forecast for the albedo series. The proposed method was performed on monthly and daily albedo prediction both in snow-free and snowy areas. The results showed that the forecast albedo consists of the MODIS albedo data well, with R2 greater than 0.89 and RMSE less than 0.052 for snow-free areas. For snowy areas, the forecasting also performed well during snow cover periods, with R2 greater than 0.76 and RMSE less than 0.076. For irregular change periods of snow falling and melting, it is hard to get very high prediction accuracy due to the irregular land surface change. For this problem, more land surface information should be introduced, or adjusting the model over time is necessary. [ABSTRACT FROM AUTHOR]

Vegetation is a crucial and intuitive index that can be used to evaluate the ecological status. Since the 20th century, land use has changed significantly in Yellow River Basin (YRB), along with great changes of vegetation, serious soil erosion, and gradual ecological deterioration. To improve the ecological environment in the YRB, China has carried out a series of ecological protection projects since the 1970s. Therefore, long-term sequence monitoring of vegetation in YRB is necessary to show the conservation effect and better support the further protection and restoration. This study analysed vegetation changes from 2000 to 2019 based on an annual mean fractional vegetation cover (FVC) dataset. The Theil–Sen median trend analysis method was used to analyse trends in FVC. The results showed that the vegetation in the YRB has improved significantly, with an average annual growth rate of 0.65%, and the 'green line' of vegetation has moved approximately 300 km westward. The influence of climate on vegetation is essential; therefore, this study also analysed the influence of temperature and precipitation on vegetation over time and space. Ecological control and afforestation are important anthropogenic factors that affect vegetation. The growth trend (0.6%/a) in key ecological function regions (KEFRs) was the fastest, and even though the protection measures are not strict, they provide space for afforestation. The China Ecological Conservation Red Line (CECRL) and the national nature reserves (NNRs) showed relatively flat trends. Ecological afforestation projects were closely correlated with the growth trend of the FVC. The correlation between FVC and the intensity of ecological engineering was significant in typical areas. [ABSTRACT FROM AUTHOR]

Land surface albedo (LSA) is an important parameter that affects surface–air interactions and controls the surface radiation energy budget. The spatial and temporal variation characteristics of LSA reflect land surface changes and further influence the local climate. Ganzhou District, which belongs to the middle of the Hexi Corridor, is a typical irrigated agricultural and desert area in Northwest China. The study of the interaction of LSA and the land surface is of great significance for understanding the land surface energy budget and for ground measurements. In this study, high spatial and temporal resolution GF-1 wide field view (WFV) data were used to explore the spatial and temporal variation characteristics of LSA in Ganzhou District. First, the surface albedo of Ganzhou District was estimated by the GF-1 WFV. Then, the estimated results were verified by the surface measured data, and the temporal and spatial variation characteristics of surface albedo from 2014 to 2018 were analyzed. The interaction between albedo and precipitation or temperature was analyzed based on precipitation and temperature data. The results show that the estimation of surface albedo based on GF-1 WFV data was of high accuracy, which can meet the accuracy requirements of spatial and temporal variation characteristic analysis of albedo. There are obvious geographic differences in the spatial distribution of surface albedo in Ganzhou, with the overall distribution characteristics being high in the north and low in the middle. The interannual variation in annual average surface albedo in Ganzhou shows a trend of slow fluctuations and gradual increases. The variation in annual albedo is characterized by "double peaks and a single valley", with the peaks occurring from December to February at the end and beginning of the year, and the valley occurring from June to August. Surface albedo was negatively correlated with precipitation and temperature in most areas of Ganzhou. [ABSTRACT FROM AUTHOR]

An explicit analysis of the impact for the richness of species of the vegetation phenological characteristics calculated from various remote sensing data is critical and essential for biodiversity conversion and restoration. This study collected long-term the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and the Fractional Vegetation Cover (FVC), and calculated the six vegetation phenological characteristic parameters: the mean of the growing season, the mean of the mature season, the mean of the withered season, the annual difference value, the annual cumulative value, and the annual standard deviation in the Xinjiang Uygur Autonomous Region. The relationships between the vegetation phenological characteristics and the species richness of birds and mammals were analyzed in spatial distribution. The main findings include: (1) The correlation between bird diversity and vegetation factors is greater than that of mammals. (2) For remote sensing data, FAPAR is the most important vegetation parameter for both birds and mammals. (3) For vegetation phenological characteristics, the annual cumulative value of the LAI is the most crucial vegetation phenological parameter for influencing bird diversity distribution, and the annual difference value of the NDVI is the most significant driving factor for mammal diversity distribution. [ABSTRACT FROM AUTHOR]

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is an important parameter for vegetation monitoring and land surface ecosystem research. Although a variety of LAI products have been generated, the moderate to coarse spatial resolution and low temporal resolution of these products are insufficient for regional-scale analysis. In this study, a modified ensemble Kalman filter model (MEnKF) was proposed to generate spatio-temporal complete 30 m LAI data. High-quality, filtered historical Moderate-resolution Imaging Spectroradiometer (MODIS) LAI data were used to obtain the LAI background, and an LAI temporal dynamic model was constructed based on it. An improved back-propagation (BP) neural network based on a simulated annealing algorithm (SA-BP) was constructed with paired Landsat surface reflectance data and field LAI data to generate a 30 m LAI. The MEnKF was used to estimate the spatio-temporal complete LAI beginning from the LAI peak value position where Landsat observations were available. The spatio-temporal 30 m LAI was estimated in farmland (Pshenichne), grassland (Zhangbei), and woodland (Genhe) sites. The results indicate that the MEnKF-estimated LAI is consistent with the field measurements for all sites (the coefficient of determination ( R 2 ) = 0.70; root mean squared error (RMSE) = 0.40) and is better than that of the conventional sequence data assimilation algorithm ( R 2 = 0.40; RMSE = 0.78). The regional LAI captures the vegetation growth pattern and is consistent with the Landsat LAI, with an R 2 larger than 0.65 and an RMSE less than 0.51. The proposed MEnKF algorithm, which effectively avoids error accumulation in the data assimilation scheme, is an efficient method for spatio-temporal complete 30 m LAI estimation. [ABSTRACT FROM AUTHOR]

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an "amalgamation albedo" approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model. The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution. [ABSTRACT FROM AUTHOR]

A hybrid inversion technique based on Bayesian network is proposed for estimating the biochemical and biophysical parameters of land surface vegetation from remotely sensed data. A Bayesian network is a unified knowledge-inferring process that can incorporate information derived from multiple sources including remote sensing and information derived from a priori knowledge. Using this inversion approach, content of chlorophyll a and chlorophyll b (Cab) and leaf area index (LAI) of winter wheat were estimated from data derived from simulations as well as field measurements. Estimations from the simulated data proved accurate, with root mean square errors (RMSEs) of 0.54 m2/m2 in LAI and 4.5 μg/cm2 in Cab. In validating the estimates against field measurements, it was found that prior knowledge of target parameters improved the accuracy of estimates, in terms of RMSEs from 0.73 to 0.22 m2/m2 in LAI and 9.6 to 4.0 μg/cm2 in Cab. Bayesian inference in this hybrid inversion scheme produces a posterior probability distribution, which can reveal such properties of the inferred results as updated information contained in the inversion result. Using entropy, the revision of posterior information about the parameters of interest was calculated. Including more data may allow more information to be retrieved about parameters in general. Exceptions were also observed where data from some viewing angles slightly reduced the information on the parameters of interest. It was also found that data from these viewing angles were less sensitive to the parameters. The method proposed here was also validated using LandSat ETM+ imagery provided by the BigFoot project. When used for mapping LAI with ETM+ imagery, the proposed method with an RMSE of 0.70 and a correlation of 0.67 produced a slightly better result than that from empirical regression. [Copyright &y& Elsevier]