Soil Landscape Pattern Changes in Response to Rural Anthropogenic Activity across Tiaoxi Watershed, China

Xiao, Rui; Jiang, Diwei; Christakos, George; Fei, Xufeng; Wu, Jiaping

doi:10.1371/journal.pone.0166224

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…Besides higher HFI values in areas near large human centres in the PR‐PY basin, there were several cascades of reservoirs that retained sediment and nutrients, seriously affecting the water quality and resident biota (Agostinho et al., 2007; Nogueira et al., 2012; Santos et al., 2020). This basin had additive (and maybe multiplicative) impacts caused by the incorrect use of water resources, such as farming non‐native species of fish (Ortega et al., 2015; Pelicice & Agostinho, 2009), coupled with soil sealing, which decreases infiltration and absorption rates (Xiao et al., 2016).…”

Section: Discussionmentioning

confidence: 99%

Functional erosion and trait loss in fish assemblages from Neotropical reservoirs: The man beyond the environment

et al. 2020

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Anthropogenic impacts are altering ecosystems, especially aquatic habitats, resulting in the loss of critical biodiversity. This study aimed to evaluate the influence of human pressure, as surrogated by the human footprint index (HFI), on the functional diversity of fish assemblages from 109 Neotropical reservoirs while considering covariates such as temperature, flooded area and age since damming. We assessed two components of functional diversity (richness and dispersion) and the specific trait composition of each fish assemblage. We found that both components of fish functional diversity decreased with increasing human pressure and that this relationship was stronger in reservoirs near large cities. There was a positive relationship between functional diversity and reservoir area, and a negative relationship between functional diversity and reservoir age. We also observed strong relationships between all the explanatory variables and functional traits, particularly for the HFI. We can infer that the ichthyofaunas of those reservoirs under higher human pressure are likely undergoing a simplification process along with homogenization of functional diversity.

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Section: Discussionmentioning

confidence: 99%

Functional erosion and trait loss in fish assemblages from Neotropical reservoirs: The man beyond the environment

et al. 2020

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“…The study area is located in Youxian County, eastern Hunan of China (Figure 2), which is a typical subtropical climate zone. There are steep slopes and rugged topography in this region [44], with the elevation ranging between 68 m and 1439 m and the slopes varying from 0 • to 60 • .…”

Section: Study Areamentioning

confidence: 85%

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

et al. 2019

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Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images.

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“…According to the different research areas, the scholars chose different landscape metrics and used different analysis methods to extract the landscape pattern information. For example, Xiao et al (2016) selected five landscape pattern metrics: patch density, edge density, shape metrics, diversity metrics, aggregation metrics and three man-made activity indicators (human activity intensity, distance from town and road) to analyze the relationship between soil landscape pattern dynamics in the study area and human activity, which shows that the soil landscape becomes more fragmented during the research period. Su et al (2014) integrated remote sensing, a geographic information system, landscape metrics analysis and a spatial regression method to quantify the changes of agricultural landscape patterns in response to urbanization at a regional scale.…”

Section: Landscape Pattern Information Extraction Based On Landscape Metricsmentioning

confidence: 99%

“Source–sink” landscape pattern analysis of nonpoint source pollution using remote sensing techniques

Zhang

Cui

et al. 2018

Int. J. Environ. Sci. Technol.

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Research on the "source-sink" landscape pattern of nonpoint source pollution is of great significance to natural resource management, environmental protection, water quality improvement, nonpoint source pollution prevention and control, and ecological security pattern construction. Remote sensing has proven by many scholars as a practical and effective technique to study landscape patterns and nonpoint source pollution. However, there are still many obstacles to the application of remote sensing technology, such as classification errors, scale effects and the issue, whereby landscape metrics cannot describe the landscape information comprehensively. In view of the characteristics of the macroscale and multi-scale of remote sensing, the analysis of landscape patterns is the basis for the study of the relationship research between patterns and ecological processes, and it is also the key to the study of landscape dynamics and functions. This paper attempts to summarize the representative results and the challenges of remote sensing in the study of the source and sink landscape of the nonpoint source pollution landscape and provide corresponding solutions as a reference for future research.

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Soil Landscape Pattern Changes in Response to Rural Anthropogenic Activity across Tiaoxi Watershed, China

Cited by 9 publications

References 39 publications

Functional erosion and trait loss in fish assemblages from Neotropical reservoirs: The man beyond the environment

Functional erosion and trait loss in fish assemblages from Neotropical reservoirs: The man beyond the environment

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

“Source–sink” landscape pattern analysis of nonpoint source pollution using remote sensing techniques

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