Surface Soil Removal and Herbicide Treatment: Effects on Soil Properties and Loblolly Pine Early Growth

1985 ◽  
Vol 49 (6) ◽  
pp. 1558-1562 ◽  
Author(s):  
C. L. Tuttle ◽  
M. S. Golden ◽  
R. S. Meldahl
Keyword(s):  
Soil Properties ◽  
Loblolly Pine ◽  
Treatment Effects ◽  
Soil Removal ◽  
Surface Soil ◽  
Early Growth ◽  
Herbicide Treatment

scholarly journals Effect of Surface Soil Removal and Organic Amendment on Soil Properties Cultivating Sesame (Sesamum indicum L.)

2020 ◽  
Vol 9 (10) ◽  
pp. 756-763
Author(s):  
Aibapynsuk Khongwar ◽  
Manoj Dutta ◽  
Rizongba Kichu ◽  
R. C. Nayak ◽  
Sewak Ram ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Removal ◽  
Surface Soil ◽  
Organic Amendment ◽  
Sesamum Indicum ◽  
Effect Of Surface

Optimization of spectral pre-processing techniques for estimation of surface soil properties from airborne AVIRIS-NG

2020 ◽  
Author(s):  
SAGAR TANEJA ◽  
Raj Setia ◽  
Baban K Bansod ◽  
Rahul Nigam ◽  
Sharad K Gupta ◽  
...  
Keyword(s):  
Soil Properties ◽  
Surface Soil ◽  
Processing Techniques ◽  
Surface Soil Properties

scholarly journals Effects of Surface Soil Removal on Dynamics of Dissolved Inorganic Nitrogen in a Snow-Dominated Forest

2001 ◽  
Vol 1 ◽  
pp. 527-533 ◽  
Author(s):  
M. Ozawa ◽  
H. Shibata ◽  
F. Satoh ◽  
K. Sasa
Keyword(s):  
Soil Solution ◽  
Soil Removal ◽  
Surface Soil ◽  
Water Movement ◽  
Inorganic Nitrogen ◽  
Seasonal Fluctuation ◽  
Early Spring ◽  
Dissolved Inorganic Nitrogen ◽  
And Control ◽  
Treated Site

To clarify the effect of vegetation and surface soil removal on dissolved inorganic nitrogen (N) dynamics in a snow-dominated forest soil in northern Japan, the seasonal fluctuation of N concentrations in soil solution and the annual flux of N in soil were investigated at a treated site (in which surface soil, including understory vegetation and organic and A horizons, was removed) and control sites from July 1998 to June 2000. Nitrate (NO3–) concentration in soil solution at the treated site was significantly higher than that of the control in the no-snow period, and it was decreased by dilution from melting snow. The annual net outputs of NO3–from soil at the treated site and control sites were 257 and –12 mmol m–2year–1, and about 57% of the net output at the treated site occurred during the snowmelt period. NO3–was transported from the upper level to the lower level of soil via water movement during late autumn and winter, and it was retained in soil and leached by melt water in early spring. Removing vegetation and surface soil resulted in an increase in NO3–concentration of soil solution, and snowmelt strongly affected the NO3–leaching from treated soil and the NO3–restoration process in a snow-dominated region.

scholarly journals Global soil-climate-biome diagram: linking surface soil properties to climate and biota

2019 ◽  
Author(s):  
Xia Zhao ◽  
Yuanhe Yang ◽  
Haihua Shen ◽  
Xiaoqing Geng ◽  
Jingyun Fang
Keyword(s):  
Soil Properties ◽  
Bulk Density ◽  
Soil Ph ◽  
Surface Soil ◽  
Global Scale ◽  
Mean Annual Precipitation ◽  
Global Soil ◽  
Soc Density ◽  
Surface Soil Properties ◽  
Soil Climate

Abstract. Surface soils interact strongly with both climate and biota and provide fundamental ecosystem services that maintain food, climate, and human security. However, the quantitative linkages between soil properties, climate, and biota at the global scale remain unclear. By compiling a comprehensive global soil database, we mapped eight major soil properties (bulk density; clay, silt, and sand fractions; soil pH; soil organic carbon [SOC] density; soil total nitrogen [STN] density; and soil C : N mass ratios) in the surface (0–30 cm) soil layer based on machine learning algorithms, and demonstrated the quantitative linkages between surface soil properties, climate, and biota at the global scale (i.e., global soil-climate-biome diagram). On the diagram, bulk density increased significantly with higher mean annual temperature (MAT) and lower mean annual precipitation (MAP); soil clay fraction increased significantly with higher MAT and MAP; Soil pH decreased with higher MAP and lower MAT, and the critical MAP for the transition from alkaline to acidic soil decreased with decreasing MAT; SOC density and STN density both were jointly affected by MAT and MAP, showing an increase at lower MAT and a saturation tendency towards higher MAP. Surface soil physical and chemical properties also showed remarkable variations across biomes. The soil-climate-biome diagram suggests the co-evolution of the soil, climate, and biota under global environmental change.

Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India).

2021 ◽  
Author(s):  
Cécile Gomez ◽  
Dharumarajan Subramanian ◽  
Philippe Lagacherie ◽  
Jean Riotte ◽  
Sylvain Ferrant ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Properties ◽  
South India ◽  
Surface Soil ◽  
Soil Surface ◽  
Calibration Data ◽  
Surface Soil Moisture ◽  
Soil Color ◽  
Color Changes ◽  
Sentinel 2

<p>Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or “tanks”) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over pedological contexts characterized by Vertisol, Ferralsols and Chromic Luvisols in sub-humid and semi-arid Tropical climate, this practice is characterized by an application of black-colored tank silt providing from Vertisol, to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application.</p><p>We used data collected in a cultivated watershed (Berambadi, Karnataka state, South India) including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February 2017 and April 2017. The surface soil color over each Sentinel-2 image was classified into two-class (“Black” and “Red” soils). A change of soil color from “Red” in February 2017 to “Black” in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from “Red” to “Black”, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image.</p><p>The results showed that 1) the CART method allowed to classify the “Red” and “Black” soil with overall accuracy around 0.81 and 0.76 from the Sentinel-2 image acquired on February and April 2017, respectively, 2) a tank silt application was identified over 97 fields with high confidence and over 107 fields with medium confidence, based on the bootstrap results and 3) the identified soil color changes are not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.</p>

scholarly journals Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

2020 ◽  
Vol 12 (8) ◽  
pp. 1242 ◽  
Author(s):  
Sumanta Chatterjee ◽  
Jingyi Huang ◽  
Alfred E. Hartemink
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Soil Properties ◽  
Soil Water ◽  
Surface Soil ◽  
Reference Network ◽  
Surface Soil Moisture ◽  
Ancillary Data ◽  
Terrain Parameters ◽  
The U.S

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.

Sewage Sludge Increases Early Growth and Decreases Fusiform Rust Infection of Nursery-Run and Rust-Resistant Loblolly Pine

1989 ◽  
Vol 13 (2) ◽  
pp. 68-71 ◽  
Author(s):  
Douglas M. Stone ◽  
Harry R. Powers
Keyword(s):  
Sewage Sludge ◽  
Loblolly Pine ◽  
Volume Growth ◽  
Early Growth ◽  
Municipal Sewage ◽  
Municipal Sewage Sludge ◽  
First Year ◽  
Average Height ◽  
Fusiform Rust ◽  
Rust Infection

Abstract An intensively prepared site in a high-rust hazard area was fertilized with municipal sewage sludge to provide 300 or 600 lb/ac total nitrogen before planting nursery-run and fusiform rust-resistant seedlings. Rust-resistant seedlings had significantly greater first-year survival andsignificantly lower rust infection at age 6. The sludge treatments increased 6-year diameter and volume growth and decreased rust infection significantly; there were no differences between the two sludge levels. Sludge fertilization significantly increased average height, diameter, and stemvolume of the largest 300 trees/ac and has begun to stimulate crown class differentiation. The greater growth of the larger trees did not alter the proportion infected by rust. Results indicate that even in areas of high-rust hazard, intensive site preparation and sludge fertilization canincrease early growth and accelerate stand development of loblolly pine if rust-resistant stock is planted. South J. Appl. For. 13(2):68-71.

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