Analysis of seismic controlling factors of the Danjiangkou reservoir based on remote sensing and aeromagnetic data

2021 ◽  
Author(s):  
Yichuan Li ◽  
Junchuan yu ◽  
xuezhong yu ◽  
jiaojiao li ◽  
jingyan hao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Controlling Factors ◽  
Aeromagnetic Data ◽  
Danjiangkou Reservoir

The research on riverine hydrochemistry and controlling factors in the Danjiangkou Reservoir

2020 ◽  
Vol 324 (2) ◽  
pp. 507-519
Author(s):  
Qianzhu Zhang ◽  
Huoming Zhou ◽  
Yang Lu ◽  
Ke Jin ◽  
Jinsong Shi ◽  
...  
Keyword(s):  
Controlling Factors ◽  
Danjiangkou Reservoir

scholarly journals Drivers of Organic Carbon Stocks in Different LULC History and Along Soil Depth for a 30 Years Image Time Series

2021 ◽  
Vol 13 (11) ◽  
pp. 2223
Author(s):  
Mahboobeh Tayebi ◽  
Jorge Tadeu Fim Rosas ◽  
Wanderson de Sousa Mendes ◽  
Raul Roberto Poppiel ◽  
Yaser Ostovari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Random Forest ◽  
Organic Carbon ◽  
Soil Properties ◽  
Soil Depth ◽  
Controlling Factors ◽  
Land Use History ◽  
The Impact ◽  
Soc Stocks

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.

scholarly journals The Sangmelima granite-greenstone belts (South Cameroon): Integration of remote sensing and aeromagnetic data for structural interpretation

2019 ◽  
Vol 22 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Akame Joseph Martial ◽  
Assembe Stéphane Pactrick ◽  
Zo'o Zame Philemon ◽  
Owona Sébatien ◽  
Ndougsa Mbarga Théophile ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aeromagnetic Data ◽  
Structural Interpretation ◽  
Greenstone Belts

scholarly journals Spatio-Temporal Variation of Total Nitrogen and Ammonia Nitrogen in the Water Source of the Middle Route of the South-To-North Water Diversion Project

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2615
Author(s):  
Guoquan Dong ◽  
Zhenqi Hu ◽  
Xuan Liu ◽  
Yaokun Fu ◽  
Wenjing Zhang
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Total Nitrogen ◽  
Ammonia Nitrogen ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Diversion ◽  
Mountainous Area ◽  
Danjiangkou Reservoir ◽  
N Concentration

The quantitative inversion of the concentrations of water quality parameters could clarify the temporal and spatial distribution characteristic, migration, and conversion of water quality parameters. This study took the Danjiangkou Reservoir as the research object, and established an inversion model based on the reflectance of different band combinations of remote sensing analyses on Sentinel-2 images, combined with the water quality monitoring data of total nitrogen (TN) and ammonia nitrogen (NH3-N) of the sampling sites in February 2016. The inversion results of TN and NH3-N in 2020 were obtained, the variation of TN and NH3-N concentrations in the reservoir area were analyzed, and the factors accounting for the variation were discussed. The results indicated that the fitting accuracy using the established model was high for both TN and NH3-N, and R2 was 0.782 for TN and 0.851 for NH3-N, respectively, showing high predication accuracy, which could be suitable for remote sensing inversion of TN and NH3-N concentrations in the Danjiangkou Reservoir. The NH3-N concentration of the Danjiangkou Reservoir was in line with Class I from 2016 to 2020, while the TN concentration was between Class III and IV. The inter-annual changes indicated that the overall water quality had an upward trend. The main tributary in the northern of the Danjiangkou Reservoir had a heavy load of TN, and after entering the reservoir, the flow velocity decreased, which caused nitrogen to accumulate at the river entrance, leading to a high TN concentration. The large slope of the mountainous area cause soil erosion. The lost soil and water carried a large amount of pesticides and fertilizers, and the ground runoff carried a large amount of nitrogen into water body, which could account for the high NH3-N concentration on the east and west sides of the southern part of the Danjiangkou Reservoir.

scholarly journals GIS-supported evaluation of landslide susceptibility in the karst mountainous area：A case study in Wudang, Guiyang

2020 ◽  
Vol 143 ◽  
pp. 02032
Author(s):  
Song Li ◽  
Yi Bai ◽  
Yongjun Long ◽  
Maoqiang Wang
Keyword(s):  
Remote Sensing ◽  
Rural Areas ◽  
Research Area ◽  
Controlling Factors ◽  
Guizhou Province ◽  
Regional Security ◽  
Mountainous Area ◽  
Landsat Images ◽  
Medium Risk

Landslide is the main disaster in the mountainous area. Based on landslide information content models of remote sensing, the work used the aerial and space remote sensing of UltraCamXp WA, Beijing-1 and Landsat images in Wudang, Guiyang to obtain the relative relief, slope, curvature of bedding slope, LUCC, geology and TWI. Finally, we analyzed the spatial susceptibility in the research area. Results showed that there were 42, 56 and 46 potential landslide groups in the high, higher and medium risk regions. The controlling factors of landslides in Wudang, Guiyang refer to the precipitation and precipitation intensity. The densely-populated regions also have the high risk of landslide, and the risk of landslide generally decreases from cities to rural areas. Through the space prediction research of landslide disasters, it is expected to provide valuable protection for regional security and harmonious development, and then sustainable development of Guizhou Province.

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