Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices.

Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference Quercus wutaishanica index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas.

Effects of anthropogenic and natural environmental factors on the spatial distribution of trace elements in agricultural soils.

The concentrations of trace elements in agricultural soils directly affect the ecological security and quality of agricultural products. A comprehensive study aimed at quantitatively analyze the effects of anthropogenic and natural environmental factors on the spatial distribution of heavy metals (HMs) and selenium (Se) in agricultural soils in a typical grain producing area of China. Factors considered in this study were parent rock, soil physicochemical properties, topography, precipitation, mine activity, and vegetation. Results showed that the median values of Zn, Cd, Cr, and Cu of 111 topsoil samples exceeded the background values of Guangxi province but were lower than the relevant national soil quality standards, and 85% of soil samples were classified as having rich Se levels (0.40 -3.0 mg kg-1). The potential ecological risk index of soil heavy metals as a whole was low, with Cd in 9% of the samples posing moderate ecological risk. The concentrations of heavy metals and Se were relatively high in soils from shale rock. Soil properties, mainly Fe2O3 and Mn played a dominant role on soil HMs and Se concentrations. Based on GeoDetector, we found that the interaction effects of two factors on the spatial differentiation of soil HMs and Se were greater than their sum effect. Among the factors, Mn enhanced the explanatory power of the model the most when interacting with other factors for soil Zn; the greatest interactive effect was between distance from mining area and Mn for Cd (q = 0.70); Fe2O3 significantly promoted the spatial differentiation of soil Cr, Cu and Se when interacting with other factors (q > 0.50). These findings contribute to a better understanding of the factors that drive the distribution of HMs and Se in agricultural soils.

Study on Spatial and Temporal Characteristics of Surface Albedo at the Northern Edge of the Badain Jaran Desert Based on C + STNLFFM Model.

Obtaining surface albedo data with high spatial and temporal resolution is essential for measuring the factors, effects, and change mechanisms of regional land-atmosphere interactions in deserts. In order to obtain surface albedo data with higher accuracy and better applicability in deserts, we used MODIS and OLI as data sources, and calculated the daily surface albedo data, with a spatial resolution of 30 m, of Guaizi Lake at the northern edge of the Badain Jaran Desert in 2016, using the Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM) and topographical correction model (C model). We then compared the results of STNLFFM and C + STNLFFM for fusion accuracy, and for spatial and temporal distribution differences in surface albedo over different underlying surfaces. The results indicated that, compared with STNLFFM surface albedo and MODIS surface albedo, the relative error of C + STNLFFM surface albedo decreased by 2.34% and 3.57%, respectively. C + STNLFFM can improve poor applicability of MODIS in winter, and better responds to the changes in the measured value over a short time range. After the correction of the C model, the spatial difference in surface albedo over different underlying surfaces was enhanced, and the spatial differences in surface albedo between shifting dunes and semi-shifting dunes, fixed dunes and saline-alkali land, and the Gobi and saline-alkali land were significant. C + STNLFFM maintained the spatial and temporal distribution characteristics of STNLFFM surface albedo, but the increase in regional aerosol concentration and thickness caused by frequent dust storms weakened the spatial difference in surface albedo over different underlying surfaces in March, which led to the overcorrection of the C model.

Monitoring Leaf Nitrogen Accumulation With Optimized Spectral Index in Winter Wheat Under Different Irrigation Regimes.

Rapid and non-destructive estimation of leaf nitrogen accumulation (LNA) is essential to field nitrogen management. Currently, many vegetation indices have been used for indicating nitrogen status. Few studies systematically analyzed the performance of vegetation indices of winter wheat in estimating LNA under different irrigation regimes. This study aimed to develop a new spectral index for LNA estimation. In this study, 2 years of field experiments with different irrigation regimes were conducted from 2015 to 2017. The original reflectance (OR) and three transformed spectra [e.g., the first derivative reflectance (FDR), logarithm of the reciprocal of the spectra (Log(1/R)), and continuum removal (CR)] were used to calculate two- and three-band spectral indices. Correlation analyses and univariate linear and non-linear regression between transformed-based spectral indices and LNA were performed. The performance of the optimal spectral index was evaluated with classical vegetation index. The results showed that FDR was the most stable transformation method, which can effectively enhance the relationships to LNA and improve prediction performance. With a linear relationship with LNA, FDR-based three-band spectral index 1 (FDR-TBI1) (451, 706, 688) generated the best performance with coefficient of determination (R 2) of 0.73 and 0.79, the root mean square error (RMSE) of 1.267 and 1.266 g/m2, and the ratio of performance to interquartile distance (RPIQ) of 2.84 and 2.71 in calibration and validation datasets, respectively. The optimized spectral index [FDR-TBI1 (451, 706, 688)] is more effective and might be recommended as an indicator for estimating winter wheat LNA under different irrigation regimes.

Spatial and temporal characteristics of surface soil moisture in a disturbed coal mining area of Chinese Loess Plateau.

Soil water content is an important variable in hydrology and many related disciplines. It affects runoff from precipitation, groundwater recharge, and evapotranspiration. This research used the coal mining area of the Changhe River Basin in the Loess Plateau as a study and using SAR (Synthetic Aperture Radar) data, the surface soil water in 24 days (From Jan 25, 2018 to Dec 10, 2019) was estimated using a radar signal change detection algorithm. The temporal and spatial variation characteristics of surface soil water inside and outside the disturbed area were compared and analyzed. An empirical orthogonal function (EOF) analysis method was used to analyze the potential temporal and spatial variation of surface soil water, and to detect the regional soil water variation under coal mining disturbances to better understand the different potential modes of spatial variation of soil water in the unobserved time. The results showed that the average surface soil water content in the study area changed with season, showing a dry-wet-dry variation. Moreover, it was significantly affected by precipitation factors, and its response to precipitation had a hysteresis effect. From the perspective of spatial variation, the influence of coal mining disturbance on surface soil moisture was not obvious. From the perspective of time series change, moving from wet to dry conditions, the soil in the disturbed area dried faster than the soil in the undisturbed area after soil wetted. When moving from drying to wetting, the soil in the disturbed area was quickly wetted. The EOF analysis showed that most observed spatial variability of soil moisture was stable in time. The study was conducted in a disturbed area and an undisturbed area for single EOF analysis, and the results showed that the EOF mode of the disturbed area was closer to that of the whole study area. By comparing the two subregions and the entire study area, it was found that the changes of correlation values were related to soil texture, bulk density, altitude and slope, indicating that the soil texture of the two subregions may be different at different elevations, and may also be related to the change of the original soil structure in the disturbed area. Overall, the EOF mode of the disturbed area determined the EOF mode of the entire study area.

[Using geographical detection to analyze responses of vegetation growth to climate change in the Loess Pla-teau, China]. / 基于地理探测的黄土高原植被生长对气候的响应.

In order to explore the responses of different vegetation types to climatic change in the Chinese Loess Plateau (CLP), we analzyed the changes of different vegetation types and their relationships with meteorological factors using trend analysis, Hurst index, and geographical detector model based on normalized difference vegetation index (NDVI). The results showed that NDVI of different vegetation types from 2002 to 2019 was dominated by a growing trend and codirectional moderate persistence. The NDVI of crops in the built-up and adjacent areas decreased significantly. Except for grassland or meadow that was affected by mixed pixels, the spatial variation of NDVI was significant in the growing season (from April to October). The mean NDVI of different vegetation types followed an oder: coniferous forest > broadleaved forest > scrub > meadow > grassland > crop > steppe > desert. The interactions between meteorological factors were synergistic and non-linear enhancement in the CLP. Moreover, the interaction was more prominent under steppe and desert where habitat was fragile. The synergistic effect of precipitation and temperature had a great influence on all vegetation types. Water vapor, relative humidity, sunshine duration, atmospheric pressure, and wind speed had different explanatory powers on NDVI through indirectly affec-ting hydrothermal conditions.

Effects of polyacrylamide molecular weight and mass concentration on water transport characteristics of iron tailings.

Iron tailings have few macropores which severely inhibit infiltration and transport of soil water. Polyacrylamide (PAM) can regulate soil water, but it is rarely used when remediating tailings matrix. In this research, PAM of four molecular weights of 300w, 600w, 800w, and 1000w were selected as amendments, and were each applied at five mass concentrations of 0% (CK), 0.01%, 0.04%, 0.08%, and 0.16% to observe their effects on water transport in iron tailings using column simulations in the laboratory. After adding PAM, the water retention and saturated water content of iron tailings increased significantly (P < 0.05). With increases in PAM molecular weight and mass concentration, the saturated hydraulic conductivity showed a downward trend, but the saturated hydraulic conductivity increased after a dry-wet cycle. With the increase of PAM mass concentration, adding PAM of 1000w molecular weight to iron tailing decreased infiltration capacity, but treatments of other molecular weights all initially increased then decreased infiltration capacity. The greatest improvement on infiltration capacity of iron tailings was observed with the addition of PAM of 300w molecular weight and 0.01% mass concentration. Adding PAM increased the vertical depth of the saturation zone of iron tailings (P < 0.05) with a maximum depth of 20.83 cm. The Kostiakov model more accurately simulated water infiltration of iron tailings compared with the Horton and Philip models. On the whole, when PAM of low molecular weight and concentration was added to iron tailings, PAM increased stable infiltration, saturated water content, and water retention. It also inhibited saturated hydraulic conductivity of iron tailings. Therefore, in practice, it is necessary to select the appropriate molecular weight and mass concentration of PAM according to the dominant limiting factors and remediation needs of the matrix.

Estimation of Winter Wheat Yield in Arid and Semiarid Regions Based on Assimilated Multi-Source Sentinel Data and the CERES-Wheat Model.

The farmland area in arid and semiarid regions accounts for about 40% of the total area of farmland in the world, and it continues to increase. It is critical for global food security to predict the crop yield in arid and semiarid regions. To improve the prediction of crop yields in arid and semiarid regions, we explored data assimilation-crop modeling strategies for estimating the yield of winter wheat under different water stress conditions across different growing areas. We incorporated leaf area index (LAI) and soil moisture derived from multi-source Sentinel data with the CERES-Wheat model using ensemble Kalman filter data assimilation. According to different water stress conditions, different data assimilation strategies were applied to estimate winter wheat yields in arid and semiarid areas. Sentinel data provided LAI and soil moisture data with higher frequency (<14 d) and higher precision, with root mean square errors (RMSE) of 0.9955 m2 m-2 and 0.0305 cm3 cm-3, respectively, for data assimilation-crop modeling. The temporal continuity of the CERES-Wheat model and the spatial continuity of the remote sensing images obtained from the Sentinel data were integrated using the assimilation method. The RMSE of LAI and soil water obtained by the assimilation method were lower than those simulated by the CERES-Wheat model, which were reduced by 0.4458 m2 m-2 and 0.0244 cm3 cm-3, respectively. Assimilation of LAI independently estimated yield with high precision and efficiency in irrigated areas for winter wheat, with RMSE and absolute relative error (ARE) of 427.57 kg ha-1 and 6.07%, respectively. However, in rain-fed areas of winter wheat under water stress, assimilating both LAI and soil moisture achieved the highest accuracy in estimating yield (RMSE = 424.75 kg ha-1, ARE = 9.55%) by modifying the growth and development of the canopy simultaneously and by promoting soil water balance. Sentinel data can provide high temporal and spatial resolution data for deriving LAI and soil moisture in the study area, thereby improving the estimation accuracy of the assimilation model at a regional scale. In the arid and semiarid region of the southeastern Loess Plateau, assimilation of LAI independently can obtain high-precision yield estimation of winter wheat in irrigated area, while it requires assimilating both LAI and soil moisture to achieve high-precision yield estimation in the rain-fed area.

Erosion characteristics of different reclaimed substrates on iron tailings slopes under simulated rainfall.

Water-induced erosion of iron tailings is a serious problem affecting ecological restoration, but, little is known about how the occurrence of erosion on tailings slopes and types of reclaimed substrates that are beneficial to reducing slope erosion. This study measured the slope erosion characteristics of six reclaimed substrates including loose tailings (LT), crusty tailings (CT), tailings incorporating mushroom residues (TM), tailings incorporating soil (TS), tailings incorporating soil and mushroom residues (TSM) and soil (S) in experimental soil flumes under three simulated intermittent rainfall events, with intensity of 60, 90 and 120 mm h-1 for the first, second and third event, respectively. Significant differences (p < 0.05) were found in erosion characteristics among the six reclaimed substrates. TM had the lowest sediment yield but the highest runoff volume without obvious rills. LT, CT and TS had the highest sediment yield rates and severe slope erosion morphology. With the increased number of rainfall events, the runoff rates of the six substrates all increased, but only the sediment yield rates of LT, CT and TS increased, the sediment yield rates of other substrates increased first and then decreased. Therefore, adding agricultural organic wastes such as mushroom residues to tailings and reducing soil addition may be an effective way to reduce erosion and promote ecological restoration in soilless tailings areas.

[Spatial and temporal changes of farmland soil acidification and their influencing factors in different regions of Guangdong Province, Chin]. / å¹¿ä¸œçœä¸åŒåŒºåŸŸå†œç”°åœŸå£¤é ¸åŒ–æ—¶ç©ºå˜åŒ–åŠå ¶å½±å“å› ç´ .

Based on the three datasets from 1980s, 2010 and 2015 in Guangdong Province, we analyzed the spatial and temporal variations of soil pH in farmlands in different regions of Guangdong Province and analyzed the driving factors for such variations. The results showed that the spatial distribution of soil pH in Guangdong Province changed significantly in different periods. During 1980s to 2010, soil pH showed an acidification trend with a decline of 0.3, and increased by 0.09 from 2010 to 2015, with more uneven trend and more obvious acid base differentiation. From the perspective of each region, there was generally a trend of acidification from the 1980s to 2010. From 2010 to 2015, the average pH value of farmland soil in the Pearl River Delta increased by 0.27, while that on the east wing and west wing decreased by 0.05 and 0.15 respectively, showing a unapparent change of soil pH in the mountainous area. Our results showed that soil acidification in diffe-rent areas of Guangdong Province was affected by natural factors such as soil itself and precipitation. In addition, anthropogenic factors such as acid rain, unreasonable fertilization and the planting structure of high-yielding crops were also the main causes of soil acidification. Industrialization, urbanization, mining development, and the spread of soil testing formula fertilization increased soil pH in local areas. Our results could provide theoretical guidance for controlling and alleviating soil acidification and improving the quality of cultivated land in different areas.

[Prediction of Distribution of Soil Cd Concentrations in Guangdong Province, China].

Heavy metals are one of the principal soil pollution sources. Contaminated soils affect the quality of agricultural products, and then threaten human health. Prediction of the contaminants distribution in the soil is the foundation of pollution evaluation and risk control. A total of 1000 soil profiles were collected to investigate the spatial variation of soil cadmium (Cd) concentration in Guangdong province. These datasets were divided into two groups, about 900 samples for model training and the other 100 for model validation. Six frequently used GIS spatial interpolation methods including Spline, Natural Neighbor, Ordinary Kriging, Inverse Distance Weighted, Local Polynomial Interpolation and Radial Basis Function, and Cubist which is a type of rule-based model were compared to determine their suitability parameters for estimating soil Cd concentration. Nine different resolutions including 2000, 1500, 1000, 800, 500, 300, 200, 150, and 90 m were selected to calculate, evaluate and compare their accuracy. The results showed that, 1 Quantitative assessment of the continuous surfaces showed that there was a large difference in the accuracy of the seven methods. Cubist was superior to GIS-based spatial interpolation methods at all resolutions. Cubist was the best tool for mapping the spatial distribution of Cd in soils with thirty-seven specific predictors relevant to the source and behavior of Cd (parent material, land use, soil type, soil properties, population density, gross domestic product per capita, and the lengths and classes of the roads surrounding the sampling sites, climatic factors, etc.) at 300 m×300 m resolution. The second was Spline, its accuracy was optimal at the 1500 m×1500 m resolution. 2 Results of Cubist suggested that the soil Cd spatial distribution was primarily dependent on the properties of soil regional parent materials. And soil samples with higher Cd concentration mainly located in Carboniferous and Quaternary areas. 3 Spatially, Cd concentrations were higher in the Pearl River Delta region and north of Guangdong Province. Many hotspots existed throughout the Pearl River Delta region due to transportation and pollution of the river. The major anthropogenic inputs of heavy metals to soils and the environment were metalliferous mining and smelting in the north of Guangdong Province. The soil Cd geometric mean concentration of 0.147 mg·kg-1 was lower than that of China, however it varied from zero to 6.056 mg·kg-1. The areas with soil Cd concentrations greater than 1.0 and 3.0 mg·kg-1 were 160 km2 and 2140 km2 respectively, accounting for 0.09% and 1.18% of the total area of Guangdong Province.

[Statistical characteristic analysis of soil PAHs in a coking contaminated site of China].

Statistical characteristic analysis of pollutants in contaminated sites can help identify the origin, generation, and spatial variation of different pollutants, and can reduce the uncertainty of site survey data. Taking a large and abandoned contaminated coking site of China as the object, 114 surface (0-50 cm) soil samples were collected, with the statistical and spatial characteristics of 16 priority PAHs (sigmaPAHs) analyzed. The descriptive statistical analysis indicated that the sigmaPAH levels varied significantly, and the data were severely skewed. The correlation matrix (CM) and principal component analysis (PCA) showed that the extracted first two principal components (PCs) could effectively represent the whole site pollution data. Four pollutants, i. e., Baa, Bbf&Bkf, Bap, and Inp, were selected for trend analysis and spatial local variance analysis. In the east-west and north-south directions of the site, the pollution showed a low-high-low trend. The variation coefficient of pollution for the site was higher in the central, northwest, and southwest regions, while lower in the other regions.

The use of spatial autocorrelation analysis to identify PAHs pollution hotspots at an industrially contaminated site.

The identification of contamination "hotspots" are an important indicator of the degree of contamination in localized areas, which can contribute towards the re-sampling and remedial strategies used in the seriously contaminated areas. Accordingly, 114 surface samples, collected from an industrially contaminated site in northern China, were assessed for 16 polycyclic aromatic hydrocarbons (PAHs) and were analyzed using multivariate statistical and spatial autocorrelation techniques. The results showed that the PCA leads to a reduction in the initial dimension of the dataset to two components, dominated by Chr, Bbf&Bkf, Inp, Daa, Bgp, and Nap were good representations of the 16 original PAHs; Global Moran's I statistics indicated that the significant autocorrelations were detected and the autocorrelation distances of six indicator PAHs were 750, 850, 1,200, 850, 750, and 1,200 m, respectively; there were visible high-high values (hotspots) clustered in the mid-bottom part of the site through the Local Moran's I index analysis. Hotspot identification and spatial distribution results can play a key role in contaminated site investigation and management.

[Heterogeneous characteristic of PAHs' spatial distribution in a large coking site of China].

A large coking contaminated site was selected to study the PAHs' spatial distribution probability in surface-soil (0-50 cm) through the indicator kriging of the non-parametric geostatistics, and the map of probability distribution with a contaminant target was plotted over the entire site. Results indicated that the indicator semivariograms were stable after the conversion of sample data, but the poor correlation of spatial samples was observed due to the spatial variability. In this site, the distribution of the contamination probability of four PAHs' showed a similar characteristic, and the areas with a probability of more than 45% were mainly distributed in production process workshops for coking, gas purification, tar products etc, of the central, northwest and southeast site with serious contamination, while the areas with a probability of less than 45% were mainly distributed in coal preparation, gas purification workshops of the southwest and northeast site. Based on the above analysis results, we can draw a conclusion that the forecast probability results are consistent with the occurrence and distribution of pollution sources, which is important for defining the remediation boundary and calculating the contaminated soil volume.

[Spatiotemporal variation of land disturbance on large opencast coal mine area].

Based on the Landsat-TM images in 1990 and 2005, and with principal component analysis, this paper studied the land use change on the Antaibu opencast coal mine of Pingshuo mine area in Shanxi Province in 1990-2005. The results showed that the spatial characteristic of spectra on the opencast coal mine varied with land type, area distribution, and landscape pattern. The first and second principal components of the TM images had obvious spatial characteristic, i. e., the first principal component highlighted the characters of excavation and transportation area, slope area, and cumuli and stripping area, while the second principal component highlighted the information of higher and lower vegetation-cover area. According to the land use type, the study area was classified into stripping area, excavated area, land reclamation area, and original landform area. In 1990 -2005, the excavated area had a little change, original landform area reduced by 15.263 km2 reclamation area increased by 8.513 km2, and stripping area increased constantly from 5.522 km2 in 1990 to 11.889 km2 in 2005.