Unusually high‐quality soil seed banks in a Midwestern U.S. oak savanna region: variation with land use history, habitat restoration, and soil properties

2020 ◽  
Vol 28 (5) ◽  
pp. 1100-1112 ◽  
Author(s):  
Scott R. Abella ◽  
Jenella L. Hodel ◽  
Timothy A. Schetter
Keyword(s):  
Land Use ◽  
Soil Properties ◽  
Habitat Restoration ◽  
Seed Banks ◽  
Land Use History ◽  
Oak Savanna ◽  
High Quality ◽  
Soil Seed Banks

Soil Seed Banks in European Grasslands: Does Land Use Affect Regeneration Perspectives?

1997 ◽  
Vol 34 (5) ◽  
pp. 1293 ◽  
Author(s):  
R. M. Bekker ◽  
G. L. Verweij ◽  
R. E. N. Smith ◽  
R. Reine ◽  
J. P. Bakker ◽  
...  
Keyword(s):  
Land Use ◽  
Seed Banks ◽  
Soil Seed Banks

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.

Forest land-use history affects the species composition and soil properties of old-aged hillock forests in Estonia

2011 ◽  
Vol 16 (3) ◽  
pp. 244-252 ◽  
Author(s):  
Jaanus Paal ◽  
Margit Turb ◽  
Tiina Köster ◽  
Elle Rajandu ◽  
Jaan Liira
Keyword(s):  
Land Use ◽  
Species Composition ◽  
Soil Properties ◽  
Land Use History ◽  
Forest Land
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