Predictive Mapping of Topsoil Organic Carbon in an Alpine Environment Aided by Landsat TM

How do tree species characteristics affect the bacterial community structure of subtropical natural mixed forests?

The effects of tree species on bacterial community structure have attracted much attention, but few studies have been done in natural mixed forests. In this study, we selected 12 sampling sites in the subtropical natural mixed forest (mainly distributed by Chinese sweet gum, chestnut, Oriental oak, Masson pine, Chinese fir, etc.). The fermentation layer (OF) and humified layer (OH) were mixed as forest floor samples, and the topsoil samples (0-10 cm) were taken. Bacterial composition was studied by 16S rRNA gene sequencing. Coniferous canopy area ratio (P_c), broadleaved and shrubby canopy area ratio (P_hwd), elevation, soil properties were tested. The objective is to reveal which soil properties are significantly affected by tree species characteristics, which soil properties significantly affect bacterial community structure, and whether the bacterial community structure is the same in forest floor and topsoil samples at the same sampling site. The results showed that: (1) P_c and P_hwd could be used to represent tree species characteristics of natural mixed forests, and they significantly (P=0.05) affected the soil C/N ratio; (2) the soil C/N ratio was the main factor affecting the soil bacterial community composition, especially for the dominant heterotrophic bacteria (Acidothermus, Variibacter, Candidatus Solibacter, Acidibacter, and Bryobacter). The relative abundance (1.11-26.27%) of the dominant heterotrophic bacteria increases with an increase in the C/N ratio (6.33-10.76) within a certain range; (3) the dominant bacteria in topsoil samples were Nitrospira, Acidothermus, Arthrobacter, Bradyrhizobium, and Variibacter, while that in forest floor samples were Jatrophihabitans, Acidothermus, Burkholderia-Paraburkholderia, and Bradyrhizobium. Although the forest floor bacteria came from the topsoil at the same sampling site, the bacterial community structure had changed significantly. This study indicated that tree species drive the change of soil bacterial community by changing the soil C/N ratio, which may provide a new perspective for maintaining the stability of regional ecosystem structure.

High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms

Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil health and play a key role in the global carbon and nitrogen cycles. High-resolution radar Sentinel-1 and multispectral Sentinel-2 images have the potential to investigate soil spatial distribution information over a large area, although Sentinel-1 and Sentinel-2 data have rarely been combined to map either SOC or STN content. In this study, we applied machine learning techniques to map both SOC and STN content in the southern part of Central Europe using digital elevation model (DEM) derivatives, multi-temporal Sentinel-1 and Sentinel-2 data, and evaluated the potential of different remote sensing sensors (Sentinel-1 and Sentinel-2) to predict SOC and STN content. Four machine-learners including random forest (RF), boosted regression trees (BRT), support vector machine (SVM) and Bagged CART were used to construct predictive models of SOC and STN contents based on 179 soil samples and different combinations of environmental covariates. The performance of these models was evaluated based on a 10-fold cross-validation method by three statistical indicators. Overall, the BRT model performed better than RF, SVM and Bagged CART, and these models yielded similar spatial distribution patterns of SOC and STN. Our results showed that multi-source sensor methods provided more accurate predictions of SOC and STN contents than individual sensors. The application of radar Sentinel-1 and multispectral Sentinel-2 images proved useful for predicting SOC and STN. A combination of Sentinel-1/2-derived predictors and DEM derivatives yielded the highest prediction accuracy. The prediction accuracy changed with and without the Sentinel-1/2-derived predictors, with the R² for estimating both SOC and STN content using the BRT model increasing by 12.8% and 18.8%, respectively. Topographic variables were the main explanatory variables for SOC and STN predictions, where elevation was assigned as the variable with the most importance by the models. The results of this study illustrate the potential of free high-resolution radar Sentinel-1 and multispectral Sentinel-2 data as input when developing SOC and STN prediction models.

An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China

Soil organic carbon (SOC) and soil total nitrogen (STN) are major soil indicators for soil quality and fertility. Accurate mapping SOC and STN in soils would help both managed and natural soils and ecosystem management. This study developed an improved similarity-based approach (ISA) to predicting and mapping topsoil (0-20 cm soil depth) SOC and STN in a coastal region of northeastern China. Six environmental variables including elevation, slope gradient, topographic wetness index, the mean annual temperature, the mean annual temperature, and normalized difference vegetation index were used as predictors. Soil survey data in 2012 was designed based on the clustering of the study area into six climatic vegetation landscape units. In each landscape unit, 20-25 sampling points were determined at different landform positions considering local climate, soil type, elevation and other environmental factors, and finally 126 sampling points were obtained. Soil sampling from the depth of 0-20 cm were used for model prediction and validation. The ISA model performance was compared with the geographically weighted regression (GWR), regression kriging (RK), boosted regression trees (BRT) considering mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R ²), and maximum relative difference (RD) indices. We found that the ISA method performed best with the highest R² and lowest MAE, RMSE compared to GWR, RK, and BRT methods. The ISA method could explain 76% and 83% of the total SOC and STN variability, respectively, 12-40% higher than other models in the study area. Elevation had the largest influence on SOC and STN distribution. We conclude that the developed ISA model is robust and effective in mapping SOC and STN, particularly in the areas with complex vegetation-landscape when limited samples are available. The method needs to be tested for other regions in our future research.

Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0–30 cm) samples (10.32 kg m−2 (±0.53) for SOC, 1.21 kg m−2 (±0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m−2 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m−2 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.

Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms

Fog is an important component of the water cycle in northern coastal regions of Iran. Having accurate tools for mapping the precise spatial distribution of fog is vital for water harvesting within integrated water resources management in this semi-humid region. In this study, environmental variables were considered in prediction mapping of areas with high concentrations of fog in the Vazroud watershed, Iran. Fog probability maps were derived from four artificial intelligence algorithms (Generalized Linear Model, Generalized Additive Model, Generalized Boosted Model, and Generalized Dissimilarity Model). Models accuracy were assessed using Receiver Operating characteristic Curve (ROC). Three social variables were also selected according to their relevance for fog suitability mapping. Finally, Fog-water harvesting Capability Index (FCI) maps were produced by multiplying fog probability by fog suitability maps. The results showed high accuracy in fog probability mapping for the study area, with all models proving capable of identifying areas with high fog concentrations in the south and southeast. For all models, the highest values of importance were obtained for sky view factor and the lowest for slope curvature. Analytic Hierarchy Process results showed the relative importance of social conditioning factors in fog suitability mapping, with the highest weight given to distance to residential area, followed by distance to livestock buildings and distance to road. Based on the fog suitability map, southeast and southern parts of the study area are most suitable for fog water harvesting. The fog spatial distribution maps obtained can increase fog water harvesting efficiency. They also indicate areas for future study with regions where fog is a critical component in the water cycle.

Climatic and socioeconomic effects on land cover changes across Europe: Does protected area designation matter?

Land cover change is a dynamic phenomenon driven by synergetic biophysical and socioeconomic effects. It involves massive transitions from natural to less natural habitats and thereby threatens ecosystems and the services they provide. To retain intact ecosystems and reduce land cover change to a minimum of natural transition processes, a dense network of protected areas has been established across Europe. However, even protected areas and in particular the zones around protected areas have been shown to undergo land cover changes. The aim of our study was to compare land cover changes in protected areas, non-protected areas, and 1 km buffer zones around protected areas and analyse their relationship to climatic and socioeconomic factors across Europe between 2000 and 2012 based on earth observation data. We investigated land cover flows describing major change processes: urbanisation, afforestation, deforestation, intensification of agriculture, extensification of agriculture, and formation of water bodies. Based on boosted regression trees, we modelled correlations between land cover flows and climatic and socioeconomic factors. The results show that land cover changes were most frequent in 1 km buffer zones around protected areas (3.0% of all buffer areas affected). Overall, land cover changes within protected areas were less frequent than outside, although they still amounted to 18,800 km² (1.5% of all protected areas) from 2000 to 2012. In some parts of Europe, urbanisation and intensification of agriculture still accounted for up to 25% of land cover changes within protected areas. Modelling revealed meaningful relationships between land cover changes and a combination of influencing factors. Demographic factors (accessibility to cities and population density) were most important for coarse-scale patterns of land cover changes, whereas fine-scale patterns were most related to longitude (representing the general east/west economic gradient) and latitude (representing the north/south climatic gradient).

Using time-series Sentinel-1 data for soil prediction on invaded coastal wetlands

Coastal soils are particularly sensitive to nonnative species invasion. In this context, spatially explicit soil information is essential for improving the knowledge of the role of soil in changing environments, supporting coastal sustainable management. Synthetic-aperture radar (SAR) data provides an attractive opportunity to monitor soil because the acquisition of images is independent of weather and daylight. However, SAR has not been commonly used for soil prediction. In this study, we firstly investigated the temporal variation of vegetation canopy and the soil-vegetation relationship using Sentinel-1 data in an invaded coastal wetland. And then we built 3D models to predict soil properties at multiple depths. A total of 16 Sentinel-1 images were acquired in a growing season. A series of soil physicochemical properties were examined including soil bulk density, texture, organic/inorganic carbon, pH, salinity, total nitrogen, and C/N ratio, relating to three depth layers in the top 1-m depth. Our results showed that time-series Sentinel-1 data can capture temporal characteristics of vegetation, and VH/VV was more sensitive to the vegetation growth than VH and VV. The soil-vegetation relationship captured by time-series SAR data was beneficial to predict soil properties, especially for soil chemical properties. The models provided permissible prediction accuracy, with an average RPD of 0.99. We concluded that the prior understanding of the temporal variation of SAR data is essential for developing practical soil prediction strategy. Our results highlight that SAR has the potential to predict a diverse set of soil properties in coastal wetlands with dense vegetation cover.

Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions. This study was conducted in a hilly region of Central Vietnam with the following objectives: (i) to evaluate the best environmental variables to estimate soil organic carbon (SOC), total nitrogen (TN), and soil reaction (pH) with a regression kriging (RK) model, and (ii) to compare the accuracy of the ordinary kriging (OK) and RK methods. SOC, TN, and soil pH data were measured at 155 locations within the research area with a sampling grid of 2 km × 2 km for a soil layer from 0 to 30 cm depth. From these samples, 117 were used for interpolation, and the 38 randomly remaining samples were used for evaluating accuracy. The chosen environmental variables are land use type (LUT), topographic wetness index (TWI), and transformed soil adjusted vegetation index (TSAVI). The results indicate that the LUT variable is more effective than TWI and TSAVI for determining TN and pH when using the RK method, with a variance of 7.00% and 18.40%, respectively. In contrast, a combination of the LUT and TWI variables is the best for SOC mapping with the RK method, with a variance of 14.98%. The OK method seemed more accurate than the RK method for SOC mapping by 3.33% and for TN mapping by 10% but the RK method was found more precise than the OK method for soil pH mapping by 1.81%. Further selection of auxiliary variables and higher sampling density should be considered to improve the accuracy of the RK method.

Spatiotemporal Variability of Soil Nitrogen in Relation to Environmental Factors in a Low Hilly Region of Southeastern China

Soil total nitrogen (TN) plays a major role in agriculture, geochemical cycles and terrestrial ecosystem functions. Knowledge regarding the TN distribution is crucial for the sustainable use of soil resources. This paper therefore aims to characterize the spatiotemporal distribution of soil TN and improve the current understanding of how various factors influence changes in TN. Natural characteristics and remote sensing (RS) variables were used in conjunction with the random forest (RF) model to map the TN distribution in a low hilly region of southeastern China in 1979, 2004 and 2014. The means and changes of TN in different geographic regions and farmland protection regions were also analyzed. The results showed that: (1) the TN showed an increasing trend in the early periods and exhibited a decreasing trend from 2004 to 2014; (2) the geographic and RS variables played more important roles in predicting TN distribution than did the other variables; and (3) changes in the fertilization and crop planting structure caused by soil testing and formulated fertilization techniques (STFFT-Soil Testing and Formulated Fertilization Techniques) as well as farmland protection policies influenced the spatiotemporal variability of TN. Evidently, more attention should be focused on improving the quality and soil fertility in the surrounding low mountainous areas.

High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia

Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0-5cm and 0-30cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R² of 0.32 for SOC stock at 0-5cm and 0.44 at 0-30cm, RMSE of 3.51MgCha^-1 at 0-5cm and 9.16MgCha^-1 at 0-30cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4-12.7% at 0-5cm, and by 2.8-5.9% at 0-30cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring.