Changes of soil quality induced by different vegetation restoration in the collapsing gully erosion areas of southern China

Wen, Hui; Ni, Shimin; Wang, Junguang; Cai, Chongfa

doi:10.1016/j.iswcr.2020.09.006

Cited by 28 publications

(14 citation statements)

References 48 publications

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“…Both study areas include mountains, hills, and valleys, and are characterized by rugged and fragmented terrain with elevations ranging from 1055 to 1508 m. They have a medium subtropical monsoon climate with an average annual temperature ranging from 18.1 to 19.7 • C and an average annual precipitation of 1522.3 to 1919.6 mm [33], with frequent clouds, rain, and fog. Small areas of primary forest exist on the less accessible high mountains, but most of the existing forests are plantation and secondary forests [34]. The dominant plant species in the study areas are Pinus massoniana Lamb., Camellia oleifera, Dicranopteris dichotoma, Cunninghamia lanceolata, and Phyllostachys heterocycle.…”

Section: Study Areasmentioning

confidence: 99%

“…Stratified random sampling was conducted within the forest landscape, topography, and physical accessibility of the areas, with the rule that the forest landscape should be homogeneous within each sample plot [23]. The types of forest landscape are defined according to the age of vegetation recovery and canopy structure [33,34]. Five types of forest landscapes are classified as (1).…”

Section: Data 221 In Situ Lai Measurementsmentioning

confidence: 99%

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Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

Yang

et al. 2021

Remote Sensing

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The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

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Section: Study Areasmentioning

confidence: 99%

Section: Data 221 In Situ Lai Measurementsmentioning

confidence: 99%

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

Yang

et al. 2021

Remote Sensing

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“…Bangang is a typical gully erosion type in China, with steep wall collapse (steeper than 65°); it is also called collapsing gullies, and it is widely distributed in tropical and subtropical granite areas of South China ( Xu, 1996 ; Wen et al, 2020a ). Survey data show that the total number of Benggang in the seven provinces of Jiangxi, Guangdong and other regions in southeastern China exceed 239,100, and ninety percent of them are highly active and relatively difficult to control ( Zhong et al, 2013 ; Huang et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Study on soil-water characteristic curves in the profiles of collapsing walls of typical granite Benggang in southeast China

Zhang¹,

Sun²,

Lin³

et al. 2022

PeerJ

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Benggang with steep collapsing walls is one of the worst soil erosion problems in South China. The collapse of walls is the most critical process in Benggang development. This is mainly due to the soil water properties. The soil water characteristic curve (SWCC) is a key indicator for analyzing soil moisture, but the SWCC and its mechanism of influence in collapsing walls remain obscure. A pressure plate meter was used for drying experiments to research the SWCCs of undisturbed soils of five layers (from top to bottom: red soil layer, transition layer I, sand soil layer, transition layer II and detrital layer) of two typical collapsing walls. The van Genuchten (VG) model can be fitted to the SWCCs for different layers (NSE ≥ 0.90). With increasing soil depth, the parameters a and θs first decreased and then increased, the parameters n first increased and then decreased, θr declined as the soil depth increased. These findings illustrate that soil water holding capacity decreases with increasing soil depth. The bottom of the soil is weak in water retention and water can easily reach saturation, resulting in a decline in soil stability, thus promoting soil collapse and finally inducing upper soil collapse. Furthermore, gravel content and particle morphology are factors that should not be neglected for SWCCs. The results of this study provide a theoretical basis for understanding the process of wall collapse in Benggang landforms in South China.

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“…Biological measures (including afforestation, planting grass) enhance soil erosion resistance by fixing the soil through plant roots and reducing the energy of raindrops through the plant's canopy (Tang et al., 2021). They can also effectively regulate the underground ecosystem, increase soil nutrient content and microbial biomass, and improve the soil physical structure through the input of plant litter and roots (H. Wen et al., 2021). Engineering measures (such as terraces, horizontal ditches, and check dams) prevent soil erosion by reshaping the land surface and reducing slope length and steepness, thereby stabilizing hillsides and reducing soil losses (Niu et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Biological conservation measures are better than engineering conservation measures in improving soil quality of eroded orchards in southern China

Qin

Zhang

et al. 2022

Soil Science Soc of Amer J

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Both biological and engineering measures have proven to be effective soil and water conservation practices for eroded environments, whereas how the soil quality varies with them remains unclear. In this study, we measured soil properties in the 0-to-100-cm soil layer of eroded orchards in southern China after 17-yr of application of different conservation measures. The purpose of the experiment was to examine the effect of biological (grass planting, intercropping) and engineering measures (terrace) on the soil quality of eroded orchards. We selected an orchard plot with traditional clean-tillage as control. In comparison, soil pH, organic carbon, total phosphorus, microbial carbon, available potassium, and available microelements contents increased significantly in orchards with biological measures, and the improvements were more dramatic in orchards planting grass in strips. However, biological measures significantly reduced the mean weight diameter of aggregates. Engineering measures significantly increased the soil pH of orchards but decreased the contents of available iron and available manganese. The soil quality index (SQI)

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Changes of soil quality induced by different vegetation restoration in the collapsing gully erosion areas of southern China

Cited by 28 publications

References 48 publications

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

Study on soil-water characteristic curves in the profiles of collapsing walls of typical granite Benggang in southeast China

Biological conservation measures are better than engineering conservation measures in improving soil quality of eroded orchards in southern China

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