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Preface—Evaluating the response of critical zone processes to human impacts with sediment source fingerprinting

1) Background: Critical Zone Processes in the Anthropocene The Earth’s Critical Zone encompasses a suite of interconnected processes in the near-surface lithosphere, pedosphere, biosphere, atmosphere, and hydrosphere (Brantley et al., 2007; Lin, 2010) (Fig. 1). Processes and interactions both within and between these various Critical Zone components supports life-sustaining ecosystem services and resources that establish the foundation for humanity (NRC, 2001). This includes the formation production of fertile soils, flourishing vegetation, productive rivers, lakes and oceans, and our life-sustaining atmosphere (Gaillardet, 2014; Guo and Lin, 2016). Rapid population growth, land use intensification, and global environmental change are disturbing many of these fundamental Critical Zone processes. More than half of the Earth’s terrestrial surface is now impacted by anthropogenic activities (e.g., clearing, grazing, plowing, mining, and logging) (Hooke et al., 2012; Richter and Mobley, 2009). These changes are so widespread and pervasive that the great acceleration of socioeconomic development that occurred around 1950 (Fig. 2) has been recommended to delineate the dawn of the Anthropocene (Waters et al., 2016). Although the utility of adopting and delineating the Anthropocene as the current epoch is subject to debate (Crutzen, 2002; Ruddiman et al., 2015; Smith and Zeder, 2013), the concept effectively highlights both the nature and the extent of our global impact on Earth’s Critical Zone. Soil forming processes and ecosystem services provided by the pedosphere are central to the Critical Zone (Banwart et al., 2011; Lin, 2010). Many of these processes have been disturbed by the agricultural intensification that coincided with the great acceleration resulting in unsustainable land use practices now outpacing soil formation processes (Brantley et al., 2007). As agricultural landscapes now cover an area equivalent to what was scoured during the last glacial maximum (Amundson et al., 2007), the broad-scale intensification of anthropogenic activities has resulted in significant on- and off-site impacts. On-site, soil loss has resulted in decreases in soil fertility and agricultural yields (Ladha et al., 2009) threatening the ability to feed the world’s growing population (Brantley et al., 2007). Off-site, the excess delivery of particulate matter downstream is degrading riverine, lacustrine, and estuarine ecosystems (Bilotta and Brazier, 2008; Clark, 1985; Owens et al., 2005). The challenge, as noted by Brantley et al., (2007), is that despite our society having over 10,000 years of experience working with soils, our conceptual and quantitative models remain inadequate at predicting Critical Zone dynamics under current conditions. Notwithstanding growing pressure for improved environmental management, we still have a limited capacity to predict changes in the Critical Zone in response to anthropogenic activities owing to the multiple spatial and temporal scales at which these complex processes and feedbacks are manifest. As river basin systems are impacted by many of these processes, a deep understanding of soil-sediment continuum dynamics may provide a valuable framework for evaluating the disturbance response of Critical Zone processes. Understanding these processes may also provide land and resource managers with the information necessary to manage both the on-site and off-site effects of accelerated soil erosion.

Journal of Soils and Sediments

Improving land resource evaluation using fuzzy neural network ensembles

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts' experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network (BPNN), a fuzzy radial basis function neural network (RBFNN), a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators' experiences was considerably reduced. ?? 2007 Soil Science Society of China.

Pedosphere

Soil quality assessment using weighted fuzzy association rules

Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.

Pedosphere

A spectral index for estimating soil salinity in the Yellow River Delta region of China using EO-1 Hyperion data

Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2 052 and 2 203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation ( r =0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R 2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R 2 =0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.

Yellow River