scholarly journals OBIA-Based Extraction of Artificial Terrace Damages in the Loess Plateau of China from UAV Photogrammetry

2021 ◽  
Vol 10 (12) ◽  
pp. 805
Author(s):  
Xuan Fang ◽  
Jincheng Li ◽  
Ying Zhu ◽  
Jianjun Cao ◽  
Jiaming Na ◽  
...  
Keyword(s):  
High Resolution ◽  
Loess Plateau ◽  
Water Conservation ◽  
Ground Truth ◽  
Field Investigation ◽  
The Loess Plateau ◽  
Flow Process ◽  
Local Flow ◽  
Object Based ◽  
Damage Mapping

Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China.

scholarly journals Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

2021 ◽  
Vol 13 (5) ◽  
pp. 1021
Author(s):  
Hu Ding ◽  
Jiaming Na ◽  
Shangjing Jiang ◽  
Jie Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Loess Plateau ◽  
Water Conservation ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
The Loess Plateau ◽  
Object Based ◽  
Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

scholarly journals Effects of Vegetation Restoration on Soil Erosion on the Loess Plateau: A Case Study in the Ansai Watershed

2021 ◽  
Vol 18 (12) ◽  
pp. 6266
Author(s):  
Hui Wei ◽  
Wenwu Zhao ◽  
Han Wang
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Loess Plateau ◽  
Water Conservation ◽  
Large Scale ◽  
Land Use Changes ◽  
Vegetation Restoration ◽  
The Loess Plateau ◽  
Local Soil ◽  
Erosion Environment

Large-scale vegetation restoration greatly changed the soil erosion environment in the Loess Plateau since the implementation of the “Grain for Green Project” (GGP) in 1999. Evaluating the effects of vegetation restoration on soil erosion is significant to local soil and water conservation and vegetation construction. Taking the Ansai Watershed as the case area, this study calculated the soil erosion modulus from 2000 to 2015 under the initial and current scenarios of vegetation restoration, using the Chinese Soil Loess Equation (CSLE), based on rainfall and soil data, remote sensing images and socio-economic data. The effect of vegetation restoration on soil erosion was evaluated by comparing the average annual soil erosion modulus under two scenarios among 16 years. The results showed: (1) vegetation restoration significantly changed the local land use, characterized by the conversion of farmland to grassland, arboreal land, and shrub land. From 2000 to 2015, the area of arboreal land, shrub land, and grassland increased from 19.46 km2, 19.43 km2, and 719.49 km2 to 99.26 km2, 75.97 km2, and 1084.24 km2; while the farmland area decreased from 547.90 km2 to 34.35 km2; (2) the average annual soil erosion modulus from 2000 to 2015 under the initial and current scenarios of vegetation restoration was 114.44 t/(hm²·a) and 78.42 t/(hm²·a), respectively, with an average annual reduction of 4.81 × 106 t of soil erosion amount thanks to the vegetation restoration; (3) the dominant soil erosion intensity changed from “severe and light erosion” to “moderate and light erosion”, vegetation restoration greatly improved the soil erosion environment in the study area; (4) areas with increased erosion and decreased erosion were alternately distributed, accounting for 48% and 52% of the total land area, and mainly distributed in the northwest and southeast of the watershed, respectively. Irrational land use changes in local areas (such as the conversion of farmland and grassland into construction land, etc.) and the ineffective implementation of vegetation restoration are the main reasons leading to the existence of areas with increased erosion.

scholarly journals Quantifying the Impacts of Climate Change, Coal Mining and Soil and Water Conservation on Streamflow in a Coal Mining Concentrated Watershed on the Loess Plateau, China

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1054 ◽  
Author(s):  
Qiaoling Guo ◽  
Yaoyao Han ◽  
Yunsong Yang ◽  
Guobin Fu ◽  
Jianlin Li
Keyword(s):  
Climate Change ◽  
Coal Mining ◽  
Loess Plateau ◽  
Water Conservation ◽  
Soil And Water Conservation ◽  
The Loess Plateau ◽  
Annual Streamflow ◽  
Significant Downward Trend ◽  
The Impact ◽  
Soil And Water

The streamflow has declined significantly in the coal mining concentrated watershed of the Loess Plateau, China, since the 1970s. Quantifying the impact of climate change, coal mining and soil and water conservation (SWC), which are mainly human activities, on streamflow is essential not only for understanding the mechanism of hydrological response, but also for water resource management in the catchment. In this study, the trend of annual streamflow series by Mann-Kendall test has been analyzed, and years showing abrupt changes have been detected using the cumulative anomaly curves and Pettitt test. The contribution of climate change, coal mining and SWC on streamflow has been separated with the monthly water-balance model (MWBM) and field investigation. The results showed: (1) The streamflow had an statistically significant downward trend during 1955–2013; (2) The two break points were in 1979 and 1996; (3) Relative to the baseline period, i.e., 1955–1978, the mean annual streamflow reduction in 1979–1996 was mainly affected by climate change, which was responsible for a decreased annual streamflow of 12.70 mm, for 70.95%, while coal mining and SWC resulted in a runoff reduction of 2.15 mm, 12.01% and 3.05mm, 17.04%, respectively; (4) In a recent period, i.e., 1997–2013, the impact of coal mining on streamflow reduction was dominant, reaching 29.88 mm, 54.24%. At the same time, the declining mean annual streamflow induced through climate change and SWC were 13.01 mm, 23.62% and 12.20 mm, 22.14%, respectively.

scholarly journals Effects of climatic variability and human activity on runoff in the Loess Plateau of China

2013 ◽  
Vol 89 (02) ◽  
pp. 153-161 ◽  
Author(s):  
Yang Zhao ◽  
Xinxiao Yu
Keyword(s):  
Soil Water ◽  
Loess Plateau ◽  
Water Conservation ◽  
Human Activities ◽  
Climatic Variability ◽  
Land Use Changes ◽  
Annual Runoff ◽  
Central China ◽  
Vegetative Cover ◽  
The Loess Plateau

The Loess Plateau in north-central China has a long history of human activities. As a result of climate change, deforestation and sparse vegetative cover, the region suffers from water shortages and severe soil erosion, significantly influencing efforts for sustainable social development. In order to understand the impacts of climatic variability and human activities on runoff and other hydrological factors in this region, the Luoyugou catchment and its paired catchments (Qiaozidong and Qiaozixi) were selected. Statistical analysis indicated precipitation did not vary significantly whereas the annual runoff decreased from 1986 to 2008, with an abrupt change in 1994. Actual evapotranspiration (AET) increased slightly but not significantly. A comparison of runoff in the paired catchments showed land use changes reduced runoff by more than 38% under the same rainfall conditions. Human activities were the strongest contributor to changes in runoff and AET, at 67% and 90% respectively, while the remaining contributor was climate variation. The influence of various human activities on runoff is quite different, and soil-water conservation initiatives have a greater impact on runoff (about 41%). Thus, human activities were the primary reason for the reduction in runoff in the study catchment compared to climate. Greater emphasis should be given to afforestation and soil-water conservation measures.

Erosion characteristics of loess tunnels on the Loess Plateau: A field investigation and experimental study

2020 ◽  
Vol 45 (9) ◽  
pp. 1945-1958 ◽  
Author(s):  
Xi‐An Li ◽  
Li Wang ◽  
Bo Hong ◽  
Lin‐Cui Li ◽  
Jia Liu ◽  
...  
Keyword(s):  
Experimental Study ◽  
Loess Plateau ◽  
Field Investigation ◽  
The Loess Plateau
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