Spartina alterniflora invasion alters soil bulk density in coastal wetlands of China

2020 ◽  
Author(s):  
Ren‐Min Yang ◽  
Liu‐Mei Chen
Keyword(s):  
Bulk Density ◽  
Spartina Alterniflora ◽  
Coastal Wetlands ◽  
Soil Bulk Density ◽  
Spartina Alterniflora Invasion

Using a causal‐based function to estimate soil bulk density in invaded coastal wetlands

2021 ◽  
Author(s):  
Ren‐Min Yang ◽  
Liang‐Jie Wang ◽  
Liu‐Mei Chen ◽  
Zhong‐Qi Zhang
Keyword(s):  
Bulk Density ◽  
Coastal Wetlands ◽  
Soil Bulk Density

MEASUREMENT AND PALEOCLIMATIC SIGNIFICANCE OF SOIL BULK DENSITY FOR LOESS IN EXTREME ARID REGION

2010 ◽  
Vol 30 (2) ◽  
pp. 127-132
Author(s):  
Jinbo ZAN ◽  
Shengli YANG ◽  
Xiaomin FANG ◽  
Xiangyu LI ◽  
Yibo YANG ◽  
...  
Keyword(s):  
Bulk Density ◽  
Arid Region ◽  
Soil Bulk Density ◽  
Paleoclimatic Significance

scholarly journals Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4408
Author(s):  
Iman Salehi Hikouei ◽  
S. Sonny Kim ◽  
Deepak R. Mishra
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Salt Marsh ◽  
Bulk Density ◽  
Remote Sensing Data ◽  
Soil Bulk Density ◽  
Soil Samples ◽  
Marsh Soil ◽  
Landsat 7 ◽  
Salt Marsh Soil

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

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