scholarly journals Spatial Variation in Surface Soil Total Carbon and Its Relationship with Soil Color in a River Floodplain Ecosystem of Northern Ghana

2018 ◽  
Vol 52 (3) ◽  
pp. 219-228 ◽  
Author(s):  
Keisuke KATSURA ◽  
Takenori WATANABE ◽  
Naoki MORITSUKA ◽  
Yasuhiro TSUJIMOTO ◽  
Baba INUSAH ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Surface Soil ◽  
Total Carbon ◽  
Northern Ghana ◽  
Soil Color ◽  
River Floodplain

scholarly journals Spatial and Temporal Variation in Microbial Diversity and Community Structure in a Contaminated Mangrove Wetland

2020 ◽  
Vol 10 (17) ◽  
pp. 5850
Author(s):  
Jiaojiao Ma ◽  
Ting Zhou ◽  
Chunyu Xu ◽  
Dawen Shen ◽  
Songjun Xu ◽  
...  
Keyword(s):  
Community Structure ◽  
Spatial Variation ◽  
Temporal Variation ◽  
Bacterial Diversity ◽  
Diversity Index ◽  
Total Carbon ◽  
Spatial And Temporal Variation ◽  
Rrna Gene ◽  
Mangrove Wetland ◽  
Mangrove Soils

Field and laboratory investigations were conducted to characterize bacterial diversity and community structure in a badly contaminated mangrove wetland adjacent to the metropolitan area of a megacity in subtropical China. Next-generation sequencing technique was used for sequencing the V4–V5 region of the 16s rRNA gene on the Illumina system. Collectively, Proteobacteria, Chloroflexi, Planctomycetes, Actinobacteria and Bacteroidetes were the predominant phyla identified in the investigated soils. A significant spatial variation in bacterial diversity and community structure was observed for the investigated mangrove soils. Heavy metal pollution played a key role in reducing the bacterial diversity. The spatial variation in soil-borne heavy metals shaped the spatial variation in bacterial diversity and community structure in the study area. Other environmental factors such as total carbon and total nitrogen in the soils that are affected by seasonal change in temperature could also influence the bacterial abundance, diversity and community structure though the temporal variation was relatively weaker, as compared to spatial variation. The bacterial diversity index was lower in the investigated site than in the comparable reference site with less contaminated status. The community structure in mangrove soils at the current study site was, to a remarkable extent, different from those in the tropical mangrove wetlands around the world.

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>

Seasonal and spatial variation of nitrogen dynamics in the litter and surface soil layers on a tropical dry evergreen forest slope

2010 ◽  
Vol 259 (8) ◽  
pp. 1502-1512 ◽  
Author(s):  
Naoyuki Yamashita ◽  
Seiichi Ohta ◽  
Hiroyuki Sase ◽  
Jesada Luangjame ◽  
Thiti Visaratana ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Surface Soil ◽  
Nitrogen Dynamics ◽  
Evergreen Forest ◽  
Soil Layers ◽  
Dry Evergreen Forest ◽  
Tropical Dry Evergreen Forest

Temporal and spatial variation of surface soil water content on karst slopes in dry season

2015 ◽  
Vol 35 (19) ◽  
Author(s):  
张川 Zhang Chuan ◽  
张伟 Zhang Wei ◽  
陈洪松 Chen Hongsong ◽  
聂云鹏 Nie Yunpeng ◽  
叶莹莹 Ye yingying ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Surface Soil ◽  
Dry Season ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial

Spatial variation in soil physical properties and effects on soil NO3– production on forest hillslopes

2020 ◽  
Author(s):  
Tomoki Oda ◽  
Megumi Kuroiwa ◽  
Naoya Fujime ◽  
Kazuo Isobe ◽  
Naoya Masaoka ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Physical Properties ◽  
Spatial Variation ◽  
Volcanic Ash ◽  
Surface Soil ◽  
Soil Physical Properties ◽  
Slope Position ◽  
Surface Soil Moisture ◽  
Slope Bottom

<p>Ammonium (NH<sub>4</sub><sup>+</sup>) and nitrate (NO<sub>3</sub><sup>–</sup>) concentrations and production rates in forest soil vary by hillslope position due to variation in ammonia-oxidizing microorganism concentrations, soil chemistry, and surface soil moisture. These spatial distributions have a significant effect on nutrient cycles and streamwater chemistry. Soil moisture conditions significantly restrict microbial activity, influencing the spatial distribution of NO<sub>3</sub><sup>–</sup> concentrations on forest hillslopes. However, studies linking forest hydrological processes to nitrogen cycling are limited. Therefore, we investigated the determinants of spatial variation in soil moisture and evaluated the effects of soil moisture fluctuations on spatial variation in NO<sub>3</sub><sup>–</sup> concentration and production rate.</p><p>The study sites were the Fukuroyamasawa Experimental Watershed (FEW) and Oyasan Experimental Watershed (OEW) in Japan. The two have similar topographies, climates, and tree species. In each watershed, a 100 m transect was set up from the ridge to the base of the slope, and soil moisture sensors were installed at soil depths of 10 cm and 30 cm at both the top and bottom of the slope. We collected surface soil samples at a depth of 10 cm at the top, middle, and bottom of the slopes using 100 cm<sup>3</sup> cores, and measured soil physical properties, particle size distribution, volcanic ash content, chemical properties (pH, NO<sub>3</sub><sup>–</sup>, NH<sub>4</sub><sup>+</sup>, nitrification rate, and mineralization rate), and microbial content (archaeal content). Spatial and temporal changes in soil moisture on the hillslope were calculated using HYDRUS-2D to examine contributing factors of soil moisture.</p><p>At FEW, high NO<sub>3</sub><sup>–</sup> concentrations and nitrification rates were observed only at the slope bottom and middle, and no NO<sub>3</sub><sup>–</sup> concentrations were detected at up slope. By contrast, at OEW, high NO<sub>3</sub><sup>–</sup> concentrations and nitrification rates were observed at all points. NH<sub>4</sub><sup>+</sup> concentrations were similar at all points in both watersheds. At FEW, 10 cm surface soil moisture fluctuated within 25–40% at the slope top but was within 40–50% at the slope bottom. At OEW, surface soil moisture was 30–40% at both the slope top and bottom, with no significant differences according to slope position. It was confirmed that soil moisture was significantly involved in NO<sub>3</sub><sup>– </sup>concentration and nitrification rates. Model simulations showed that the difference in soil moisture fluctuations between FEW and OEW was mainly explained by the spatial variation in soil physical properties. In particular, volcanic ash influenced soil moisture along the entire slope at OEW, resulting in high water retention, but only influenced soil moisture at the slope bottom at FEW. These findings indicate that spatial variability in soil physical properties has a significant effect on soil moisture fluctuation and leads to a spatial distribution of NO<sub>3</sub><sup>–</sup> production.</p>

Control of soil development on the Tanana River floodplain, interior Alaska

1993 ◽  
Vol 23 (5) ◽  
pp. 941-955 ◽  
Author(s):  
K. Van Cleve ◽  
C.T. Dyrness ◽  
G.M. Marion ◽  
R. Erickson
Keyword(s):  
Particle Size ◽  
Organic Matter ◽  
Surface Soil ◽  
Chemical Properties ◽  
Physical And Chemical Properties ◽  
Vegetation Development ◽  
River Floodplain ◽  
Soil Particle Size ◽  
Physical And Chemical ◽  
And Chemical Properties

Alluvial soils on the Tanana River floodplain near Fairbanks, Alaska, were examined for development of physical and chemical properties in relation to soil depth and across a 200-year vegetation development sequence. Development was mediated by ecosystem controls including successional time, vegetation, terrace height, soil physical and chemical properties, and microclimate. These controls interact and are conditioned by the state factors time, flora, topography, parent material, and climate, respectively. On early-successional (<5 years) lower alluvial surfaces, terrace height above groundwater, soil particle size, and microclimate (through soil surface evaporation) interacted through capillary rise to produce salt-affected surface soil. Calcium salts of carbonate and sulfate were the principal chemicals encountered in these soils. Establishment of a vegetation cover between 5 and 10 years introduced evapotranspiration as a new mechanism, along with capillarity, to control moisture suction gradients. In addition, newly formed surface litter layers further helped eliminate evaporation and formation of high salt content surface soil. Continued sedimentation raised terrace elevation, so on older terraces only infrequent flood events influenced soil development. Moreover, in these successional stages, only the highest river stages raised groundwater levels, so transpiration and capillarity influenced water movement to tree root systems. During the first 25–30 years of succession, plant deposition of organic matter and nitrogen, associated with the growth of alder, markedly changed soil properties. Nearly 60% (or 240 g•m−2) of the 400 g•m−2 nitrogen encountered at 100 years was accumulated during this early period. After 100 years of vegetation development, soil carbonate content dropped to about half the peak values of about 1600 g•m−2 encountered between 4 and 25 years. By the time white spruce was the dominant forest type at 180 years, carbonate carbon declined to about 500 g•m−2, one-third that of the 1600 g•m−2 high. By this time surface soil pH declined from high values of 7.5 to between 5.5 and 6.0. Organic carbon continued to accumulate to about 6300 g•m−2 in the white spruce stage, twice that encountered in the alder–poplar stage at 25 years. Indices of moisture retention were most strongly related to either soil particle size (low moisture tension and available moisture range) or vegetation-mediated soil organic matter content (high moisture tension). Cation exchange capacity was most strongly related to a vegetation-mediated index of organic matter (OM) content (%N, %C, or %OM).

Soil pH and aluminium and their spatial variation in Western Australian acidic soils

1990 ◽  
Vol 30 (5) ◽  
pp. 637 ◽  
Author(s):  
PJ Dolling ◽  
WM Porter ◽  
AD Robson
Keyword(s):  
Spatial Variation ◽  
Soil Ph ◽  
Surface Soil ◽  
Acid Soils ◽  
Poor Correlation ◽  
Ph Values ◽  
Coefficients Of Variation ◽  
Subsurface Soil ◽  
The Relationship ◽  
Surface Soil Ph

Thirty-eight sites on acid soils (pH<5.5, 1:5 in water) in the medium rainfall region of Western Australia were sampled to examine spatial variation in soil pH and 0.01 mol/L CaCl2-extractable aluminium. We also examined the relationship between (i) the A1 and A2 horizon soil pH, (ii) the A1 and A2 horizon extractable aluminium, (iii) surface and subsurface soil pH and (iv) surface soil and subsurface soil-extractable Al. Soil at each site generally had a light-textured layer overlying a clay layer at varying depths (30-70 cm) and was classified as either Dy 5.21 or Dy 5.41 (Northcote 1979). Over 80% of the sites had surface soil pH values 4.8 or lower and extractable aluminium concentrations 2 �g/g or higher. There was a very poor correlation (r2 = 0.21) between the A1 horizon soil aluminium extracted in 0.01 mol/L CaCl2 and the pH measured in 0.01 mol/L CaCl2 over 1 ha sites. The relationship was slightly improved in the A2 horizon (r2 = 0.49). The coefficients of variation of soil pH varied from 1.2 to 5.1%, while the coefficients of variation for CaCl2-extractable aluminium varied from 10 to 50%. At many of the sites, low pH values and high aluminium concentrations extended down to 35-45 cm. At the B horizon the pH values generally increased and the aluminium concentrations decreased. The surface soil pH and extractable aluminium were not good indicators (r2 = 0.09-0.60) of the subsurface soil pH and extractable aluminium.

scholarly journals Ecological controls on N<sub>2</sub>O emission in surface litter and near-surface soil of a managed grassland: modelling and measurements

2016 ◽  
Vol 13 (12) ◽  
pp. 3549-3571 ◽  
Author(s):  
Robert F. Grant ◽  
Albrecht Neftel ◽  
Pierluigi Calanca
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Surface Soil ◽  
N Uptake ◽  
N2o Emissions ◽  
N Availability ◽  
Area Index ◽  
Large Variability ◽  
Near Surface ◽  
Intensively Managed

Abstract. Large variability in N2O emissions from managed grasslands may occur because most emissions originate in surface litter or near-surface soil where variability in soil water content (θ) and temperature (Ts) is greatest. To determine whether temporal variability in θ and Ts of surface litter and near-surface soil could explain this in N2O emissions, a simulation experiment was conducted with ecosys, a comprehensive mathematical model of terrestrial ecosystems in which processes governing N2O emissions were represented at high temporal and spatial resolution. Model performance was verified by comparing N2O emissions, CO2 and energy exchange, and θ and Ts modelled by ecosys with those measured by automated chambers, eddy covariance (EC) and soil sensors on an hourly timescale during several emission events from 2004 to 2009 in an intensively managed pasture at Oensingen, Switzerland. Both modelled and measured events were induced by precipitation following harvesting and subsequent fertilizing or manuring. These events were brief (2–5 days) with maximum N2O effluxes that varied from  <  1 mgNm−2h−1 in early spring and autumn to  >  3 mgNm−2h−1 in summer. Only very small emissions were modelled or measured outside these events. In the model, emissions were generated almost entirely in surface litter or near-surface (0–2 cm) soil, at rates driven by N availability with fertilization vs. N uptake with grassland regrowth and by O2 supply controlled by litter and soil wetting relative to O2 demand from microbial respiration. In the model, NOx availability relative to O2 limitation governed both the reduction of more oxidized electron acceptors to N2O and the reduction of N2O to N2, so that the magnitude of N2O emissions was not simply related to surface and near-surface θ and Ts. Modelled N2O emissions were found to be sensitive to defoliation intensity and timing which controlled plant N uptake and soil θ and Ts prior to and during emission events. Reducing leaf area index (LAI) remaining after defoliation to half that under current practice and delaying harvesting by 5 days raised modelled N2O emissions by as much as 80 % during subsequent events and by an average of 43 % annually. Modelled N2O emissions were also found to be sensitive to surface soil properties. Increasing near-surface bulk density by 10 % raised N2O emissions by as much as 100 % during emission events and by an average of 23 % annually. Relatively small spatial variation in management practices and soil surface properties could therefore cause the large spatial variation in N2O emissions commonly found in field studies. The global warming potential from annual N2O emissions in this intensively managed grassland largely offset those from net C uptake in both modelled and field experiments. However, model results indicated that this offset could be adversely affected by suboptimal land management and soil properties.

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