scholarly journals Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China

2020 ◽  
Vol 12 (3) ◽  
pp. 487 ◽  
Author(s):  
Biwei Wang ◽  
Zengxiang Zhang ◽  
Xiao Wang ◽  
Xiaoli Zhao ◽  
Ling Yi ◽  
...  
Keyword(s):  
Regional Scale ◽  
Black Soil ◽  
Google Earth ◽  
Gully Erosion ◽  
Image Feature ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Optical Images ◽  
Black Soil Region ◽  
Object Based

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.

scholarly journals Effect of farmland shelterbelts on gully erosion in the black soil region of Northeast China

2015 ◽  
Vol 26 (4) ◽  
pp. 941-948 ◽  
Author(s):  
Rongxin Deng ◽  
Wenjuan Wang ◽  
Haiyan Fang ◽  
Zhihong Yao
Keyword(s):  
Northeast China ◽  
Black Soil ◽  
Gully Erosion ◽  
Black Soil Region

The impact of sheet and gully erosion on soil aggregate losses in the black soil region of Northeast China

2013 ◽  
Vol 33 (24) ◽  
Author(s):  
姜义亮 JIANG Yiliang ◽  
郑粉莉 ZHENG Fenli ◽  
王彬 WANG Bin ◽  
温磊磊 WEN Leilei ◽  
沈海鸥 SHEN Hai'ou ◽  
...  
Keyword(s):  
Northeast China ◽  
Black Soil ◽  
Soil Aggregate ◽  
Gully Erosion ◽  
Black Soil Region ◽  
The Impact

scholarly journals Effect of Land Use Change on Gully Erosion Density in the Black Soil Region of Northeast China From 1965 to 2015: A Case Study of the Kedong County

2021 ◽  
Vol 9 ◽  
Author(s):  
Maojuan Li ◽  
Tianqi Li ◽  
Lianqi Zhu ◽  
Michael E. Meadows ◽  
Wenbo Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Northeast China ◽  
Black Soil ◽  
Gully Erosion ◽  
Cultivated Land ◽  
Topographic Map ◽  
Degraded Land ◽  
Landsat Images ◽  
Black Soil Region ◽  
Gully Density

Kedong County is typical of the black soil region of northeast China in being highly susceptible to accelerated soil erosion by gullying. Using data sourced from Corona satellite imagery for 1965, SPOT5 for 2005 and GF-1 for 2015, the spatial distribution of gullies in the research area was mapped. Land use data for 1965, 2005, and 2015 were obtained from the topographic map of 1954, and from Landsat images for 2005 and 2015. Over the last 50 years, the extent of gully erosion in the study area has increased markedly, most notably on cultivated land, while gully density rose from 2,756.16 m2/km2 to 14,294.19 m2/km2. Cultivating land on slopes, especially on slopes greater than ∼4°, may rapidly aggravate gully erosion. The greatest increases in gully density occurred in situations when cultivated land and other/degraded land were transformed, which gully erosion density increased by 49,526.69 m2/km2. Other/degraded land is the most vulnerable land in the study area, with the highest gully erosion density. In these cases, gully density initially increases and, although the “Grain for Green” project has been implemented, gully erosion density has not always declined in the recent past.

scholarly journals Using Machine Learning Algorithms Based on GF-6 and Google Earth Engine to Predict and Map the Spatial Distribution of Soil Organic Matter Content

2021 ◽  
Vol 13 (24) ◽  
pp. 14055
Author(s):  
Zhishan Ye ◽  
Ziheng Sheng ◽  
Xiaoyan Liu ◽  
Youhua Ma ◽  
Ruochen Wang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Computing Time ◽  
Organic Matter Content ◽  
Google Earth ◽  
Matter Content ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Soil Organic Matter Content ◽  
Light Gradient

The prediction of soil organic matter is important for measuring the soil’s environmental quality and the degree of degradation. In this study, we combined China’s GF-6 remote sensing data with the organic matter content data obtained from soil sampling points in the study area to predict soil organic matter content. To these data, we applied the random forest (RF), light gradient boosting machine (LightGBM), gradient boosting tree (GBDT), and extreme boosting machine (XGBoost) learning models. We used the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) to evaluate the prediction model. The results showed that XGBoost (R2 = 0.634), LightGBM (R2 = 0.627), and GBDT (R2 = 0.591) had better accuracy and faster computing time than that of RF (R2 = 0.551) during training. The regression model established by the XGBoost algorithm on the feature-optimized anthrosols dataset had the best accuracy, with an R2 of 0.771. The inversion of soil organic matter content based on GF-6 data combined with the XGBoost model has good application potential.

Effects of gully erosion and gully filling on soil depth and crop production in the black soil region, northeast China

2012 ◽  
Vol 68 (6) ◽  
pp. 1723-1732 ◽  
Author(s):  
Honghu Liu ◽  
Tianyu Zhang ◽  
Baoyuan Liu ◽  
Gang Liu ◽  
G. V. Wilson
Keyword(s):  
Northeast China ◽  
Crop Production ◽  
Soil Depth ◽  
Black Soil ◽  
Gully Erosion ◽  
Black Soil Region

The gully erosion rates in the black soil region of northeastern China: Induced by different processes and indicated by different indexes

CATENA ◽  
2019 ◽  
Vol 182 ◽  
pp. 104146 ◽  
Author(s):  
Yifan Dong ◽  
Yongqiu Wu ◽  
Wei Qin ◽  
Qiankun Guo ◽  
Zhe Yin ◽  
...  
Keyword(s):  
Black Soil ◽  
Northeastern China ◽  
Gully Erosion ◽  
Erosion Rates ◽  
Black Soil Region

scholarly journals The Suitability of Remote Sensing Images at Different Resolutions for Mapping of Gullies in the Black Soil Region, Northeast China

2021 ◽  
Vol 13 (12) ◽  
pp. 2367
Author(s):  
Biwei Wang ◽  
Zengxiang Zhang ◽  
Xiao Wang ◽  
Xiaoli Zhao ◽  
Ling Yi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Northeast China ◽  
Black Soil ◽  
Google Earth ◽  
Gully Erosion ◽  
Classification Systems ◽  
Practical Significance ◽  
Visual Interpretation ◽  
Remote Sensing Images

Remote sensing images with different spatial resolutions have different performance capabilities for gully extraction, so it is very important to study the suitability of different spatial resolutions for this purpose. In this study, part of the black soil area in Northeast China with serious gully erosion was taken as the study area, and Google Earth images with seven spatial resolutions ranging from 0.51 to 32.64 m, commonly used in gully erosion research, were selected as data sources. Combined with auxiliary data, gullies were extracted by visual interpretation. The interpretation results of images of different spatial resolutions were analyzed qualitatively and quantitatively, and the interpretation suitability of images of different spatial resolutions for different types of gullies under different classification systems was emphatically explored. The results indicate that the image with a spatial resolution of 1.02 m has the best performance when not considering the types of gullies. However, the image with a spatial resolution of 2.04 m is the most cost-effective and, therefore, the most suitable for general research. When it is necessary to distinguish the type of gully, the image with a spatial resolution of 0.51 m can be adapted for all situations. However, research on ephemeral gullies is of little practical significance. Therefore, the image with a spatial resolution of 1.02 m is the most universally useful image, being cheaper and easier to obtain. When the spatial resolution is 2.04 m or lower, it is necessary to select the spatial resolution according to the gully type required for practical application. When the spatial resolution is 8.16 or lower, the interpretation of gullies becomes very difficult or even impossible.

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