stract provided there is a strong correlation between
Soil organic carbon (SOC) and total
soil nitrogen (TSN) are the significant indicators of soil fertility and
biogeochemical cycle. Spatial distribution and variation of SOC and TSN estimation
is central to climate change and sustainable soil management studies. Little
research on spatial prediction of SOC and TSN based on geostatistical
techniques employing secondary variables (sampling location) and auxiliary
information (topographic factors and type of vegetation) has been conducted
globally and under Himalayas in particular. To attempt this ninety-six soils
samples of 0-20 cm depths were taken from small forest area of North Kashmir
Himalayas. The effect of topographic factors-elevation, slope, compound
topographic index (CTI), stream power index (SPI), sediment transport index
(STI), normalized difference vegetation index (NDVI) and vegetation type on SOC
and TSN spatial distribution were studied using regression kriging. Results
indicated regression kriging as better predictor of SOC and TSN spatial
distribution than ordinary kriging with residuals moderately auto-correlated.
Semi-variogram test indicated topographic factors- elevation and slope and
vegetation type as major factors of SOC and TSN spatial variation. The negative
correlation of topographic elevation and slope with spatial distribution of SOC
and TSN reveal better stabilization of SOC and TSN at lower degrees of slope
and lower altitudes. Our study suggest
regression kriging can provide better estimations at larger scale, provided
there is a strong correlation between environmental variables and the SOC and
TSN contents, and residuals are spatially auto-correlated.
Soil organic carbon, total soil nitrogen, spatial distribution, regression
kriging, Kashmir Himalayas
world climate change studies are centric to carbon-nitrogen cycling. Soil
organic carbon (SOC) and total soil nitrogen (TSN) play an important role in
ecosystem functioning (Gregorich et al., 1994). They act as an important factor
in food and fuel security, reclamation of degraded lands and mitigation of
climate change (Lal, 2004). They act as driving force of agro-ecosystem functions-
controlling soil fertility, water holding capacity and other soil quality factors
(Kosmas et al., 2000; Bangroo et al., 2013).
soil biodiversity and soil physical stability is controlled by the spatial
variability of SOC and TSN (Stevenson and Cole, 1999). Therefore, their precise
estimation and spatial distribution is important to comprehend the
carbon-nitrogen dynamics and assist in the decision support system for the
spatial and temporal SOC and TSN variation with soil and atmosphere is affected
by topographic factors (altitude, aspect and slope), land use/ management,
temperature and soil moisture (Bangroo et al., 2017). Appreciable research is
available on factors affecting SOC and TSN under different physiographic, land
use/management and climatic conditions (Zhang et al., 2012; Peng et al., 2013;
Mondal et al., 2017). A non-uniform SOC and TSN spatial distribution and
correlation with auxiliary information (topography, land use/management,
vegetation and parent material) show a changing continuum on different scales (Tan
and Lal, 2005; Su et al., 2006; Liu et al., 2006).
in recent past have been made to assess the SOC
and TSN spatial distribution in relation to these factors by
employing the geostatistical techniques (Kerry and Oliver, 2007; Chai et al.,
2008; Marchetti et al., 2012). Many geospatial prediction models have been developed
to interpolate soil variables into spatially distributed continuum surface from
target sampling points (Harries et al., 2010; Kumar et al., 2012). Not all take
into account the large uncertainty inherent soil spatial heterogeneity such as
ordinary kriging. More recently regression kriging has been extensively used
that combines multiple linear regression using auxiliary information with
kriging and thus incorporates the topography, vegetation and other factors for
higher prediction accuracy.
this study, we selected small forest area of Kashmir Himalayan region as a
research site. We used the regression kriging capability to accomplish these objectives
i) to estimate the SOC and TSN spatial
distribution; ii) to evaluate the impact of topographic attributes
and vegetation indices on spatial interpolation accuracy; and iii) to analyze
the spatial prediction accuracy for SOC and TSN using regression and ordinary
Materials and Methods
Mawer forest range lies between 34° 17? to 34° 22? N and 73° 19? to 74° 59? E
in Kupwara District of Jammu & Kashmir, India (Fig. 1). The area is lacustrine
in origin crossed by the Mawar river. It is spread to an area of 26.1 sq. km
with slope ranging from 15–30% to 30–50%. Being pleistocene and
post-pleistocene in nature the area has good fertility levels. The forests of
the Mawer range is dominated by the coniferous species (Table 1) like Deodar (Cedrus deodara), Himalayan Pine (Pinus wallichiana) and Fir (Abies pindrow). The distribution pattern
of the principal species is influenced mainly by the factors such as altitude,
aspect, and soil. The Deodar and Himalayan Pine on lower belts occur both in
mixtures and in pure stands. The Deodar and Himalayan Pine covers about 44% and
19% of the total area of the commercial forest area of the division. The
broad-leaved species are irregularly distributed throughout the division and
are mainly confined to natural drains, moist depressions, and damp localities.
Soil sampling and soil
total of ninety-six soil samples were selected from Mawer forest range. A 10m x
10m gird sampling design was generated from digital topographic map of forest
range at 1:10,000 scale. The locations of the sampling sites were recorded
using a global positioning system (GPS) receiver (Garmin3790T). Three soil samples
were collected at depth 0–20 cm over a circle of radius 10m surrounding the
specified sampling location and mixed thoroughly. The samples were air-dried
and ground to pass a 2-mm sieve. Nitrogen was determined by Kjeldahl method
(Bremner, 1996) and OC by Walkley and Black method (Nelson and Sommers, 1982).
Acquisition of auxiliary information
normalized difference vegetation index (NDVI) was procured from Landsat 8 OLI
and topographic factors like elevation, slope, compound topographic index
(CTI), stream power index (SPI) and sediment power index (SPI) were calculated
from cartosat DEM.
is the classical indication of plant health and is used to monitor the changes
in vegetation. The NDVI is closely related to vegetation cover, biomass and the
leaf area index (LAI). The NDVI is given as
the near-infrared and red band spectral reflectance, respectively in the
LANDSAT 8 satellite data. The NDVI ranges from -1 to +1 (NOAA Coastal Service
Centre, 2007). The NDVI derived from the Landsat data along with the terrain
attributes are utilized in the prediction of the soil organic carbon.
Slope and elevation are
derived from the DEM. Both the factors have a strong correlation with the SOC
stabilization (Perruchound et al., 2000; Bangroo et al., 2017).
is an important aspect of hydrologic system model and provides an indirect
information on land cover and agriculture potential. It is a function of both
slope and upstream contributing area per unit width extraneous to the flow
direction. CTI is defined as
? represents the catchment area per unit width extraneous to the direction of
the flow direction and ? refers to the slope.
a measure of the potential flow erosion of water flow based on the assumption
that flow is proportional to the specific topographic surface (Moore et al.,
1991). It takes into account a local slope geometry and site location in the
landscape and measures the erosive power of flowing water at a given point of
the topographic surface. SPI is defined as
represents the upstream drainage area (m2/m) and ? refers to the
STI takes into account the upslope contributing area assuming it to
be directly related to discharge, and slope. STI is defined as (Moore et al.,
? is the specific catchment area (m2/m), and ? is the slope
Regression Kriging Methodology
points of SOC and TSN were interpolated in spatial domain by the regression
kriging method (Fig. 2). Regression kriging method can perceive the auxiliary variables
for interpolation of the output at those locations points which is restrained
in the simple kriging method (Hengl et al., 2007). Remote sensing images,
vegetation type, and elevation were considered as common auxiliary predictors
and topographic parameters elevation and slope, normalized difference
vegetation index (NDVI), compound topographic index (CTI), stream power index
(SPI) and sediment power index (SPI) have been used as the predictor variables
here. Regression kriging combines the two approaches of regression and kriging
where regression is applied to fit the explanatory variation and the simple
kriging with an expected value of 0 is applied to fit the residuals, i.e.
unexplained variation (Hengl et al., 2004; Mukherjee et al., 2015):
denotes the interpolated value of the
the fitted drift,
denotes the interpolated residual,
stands for the estimated drift model
the estimated intercept),
denotes the kriging weights that is determined
by the spatial dependence structure of the residual and
the residual at location
the model validation 29 soil samples out of total 96 samples were randomly
extracted from the data. The mean error (ME) and root mean square error (RMSE)
were used for the model efficiency estimated by comparing SOC and TSN observed
and predicted values from the validation point location. Prediction accuracy
improvement was computed by comparing RK with OK.
ME is the mean error; RMSE is the root mean square error; n represents sampling
are observed and predicted values of the
sampling points, respectively; and R’ is the prediction accuracy improvement from
comparing RK with OK. For positive value of R’, RK has higher prediction
accuracy than that of OK and vice-versa for the negative value of R’.
the root mean square error of OK, and
that of RK.
and regression analysis of SOC and TSN data was performed in SPSS 20.0 software.
Study area was delineated and topographic factors were extracted from watershed
DEM using ArcGIS 10.2. Spatial and semi-variogram analysis between regression
prediction and residual values of OK were computed and lastly spatial
prediction SOC and TSN distribution maps were produced.
SOC and TSN Descriptive Statistics
coefficient of variation (CV), standard deviation, and basic statistical
parameters of mean, range, minimum and maximum are shown in Table 2. The
average SOC and TSN concentration in the study area were 17.74 g kg-1 and
2.31g kg-1 respectively. Both the moderate CV 26.21% and 23.32 % could
be linked to uniform land use pattern, and/or soil erosion.
Correlation between SOC and TSN with
the environmental variables
SOC and TSN showed a negative correlation with the elevation (Table 3). This
indicates that the concentration of both SOC and TSN deceases with the
elevation. Similar, correlation was observed with the slope which is an
important soil erosion factor. This reveals that greater the slope more intense
is the soil erosion which results in decrease in SOC and TSN concentrations.
or no correlation of SOC and TSN was observed with CTI, SPI or STI. Correlation
of average SOC and TSN content along the elevation with NDVI was also analyzed
and found to be significant (r2 = 0.673, p<0.001). This indicates that SOC and TSN increases with an increase in vegetation NDVI. Spatial variability and distribution of SOC and TSN Topographic factors (elevation, slope, SPI, STI and CTI) and NDVI were used to predict the spatial variability of SOC and TSN through multiple linear regression method. Among these, elevation and slope proved to be the optimal factors for the prediction of SOC and TSN with determination coefficients (r2) of 0.426 (at P<0.05) and 0.406 (at P<0.001) respectively. The regression kriging provided better results for spatial autocorrelation of SOC and TSN than that of ordinary kriging (Fig. 3). The Nugget/Sill ratio for regression kriging and ordinary kriging for SOC were 0.28 and 9.81 and for TSN were 0.24 and 4.59 respectively (Table 4). The semi-variogram analysis showed that environmental factors such as topography and vegetation were the primary causes of SOC and TSN spatial variance. SOC and TSN were also found to be strongly correlated, with a correlation coefficient of 0.7121 (P<0.05) and have highly significant linear relationship. Prediction accuracy of OK and RK Location points of SOC and TSN samples were interpolated in spatial domain by the regression kriging method and using topographic factors (elevation, slope, SPI, STI and CTI) and NDVI as predictor variables. Regression was applied to fit the explanatory variation and simple kriging with an expected value of 0 was applied to fit the residuals, i.e., unexplained variation in regression kriging method. Sixty-seven samples were randomly selected to conduct ordinary kriging interpolation for regression residual error of SOC and TSN in the study area. In the meantime, ordinary kriging interpolation was also conducted on these samples as a control. From the results of prediction errors, regression-kriging was found better than that of ordinary kriging (Fig. 4). The 29 training samples were used for model validation and comparison of the two prediction methods (Table 5). Satisfactory results were obtained with regression kriging with predicted values close to observed ones and much more detailed concerning the partly variation and topographical relationships than that of ordinary kriging. The improvements of prediction accuracy (R') of SOC and TSN were 17.82% and 19.44%, respectively (Table 5). Discussion Effect of vegetation type on SOC and TSN The type of vegetation has a significant effect on corresponding changes in micro-climate in an ecologically fragile environment like of Kashmir Himalayas which subsequently alter soil nutrient dynamics (Bangroo et al., 2017). The SOC and TSN concentration in existing dominant vegetation types of the study area ranked as Pinus wallichina > Cedrus
deodara > Abies pindrow. This suggests that vegetation
type had a significant impact on spatial SOC and TSN patterns. Similar, trend
was observed by Peng et al., 2013 and Garcia et al 2016.
differences in SOC and TSN in varying vegetation types were observed
(P<0.05), this may be attributed to species composition, stand structure, and management history (Dar and Sundarapandian, 2015). The thicker forest litter and well flourished soil plant root system of P. wallichina and C. deodara fix and more SOC and TSN which cause high accumulation. The shrub biomass was also found highest under C. deodara in Western Himalayas (Wani et al., 2016). The study area being a protected forest had less human intervention and less soil erosion in P. wallichina and C. deodara belt which favored SOC and TSN accumulation. Effect of topographic parameters on SOC and TSN Topographic parameters have a significant effect on the spatial distribution of SOC and TSN (Mondal et al., 2017). Research indicate that SOC is primarily controlled by the variation in temperature, and soil moisture which vary with elevation gradients (Griffiths et al., 2009), aspect (Måren et al., 2015; Garcia et al., 2016) and slope (Perruchoud et al., 2000). While, the N stock variation with altitude are partly influenced by vegetation type and partly by altitude (Bangroo et al., 2017). The correlation analysis revealed negative correlation of SOC and TSN in our study area with the elevation (Table 3). This may be attributed to the 1) lower mineralization rate and net nitrification rate at the higher altitude, 2) decline in total tree density, and species richness with increasing altitude, and 3) better stabilization of SOC at lower altitudes. A characteristic decline in vegetation was observed across altitudinal strata. The decrease in species richness in high elevation strata significant in Himalayan forests could be due to eco-physiological constraints, low temperature, and productivity (Gairola et al., 2008; Hardy et al., 2001). The characteristic decline in vegetation with increasing altitude results in less accumulation of litter and low input of organic carbon in soils. We observed negative correlation of SOC and TSN spatial distribution with the slope (Table 3). This is attributed to 1) higher rates of erosion with the slope which increases with increasing rainfall, 2) poor soil development which results in poor retention of SOC and 3) soil temperature gradients along the slope under different aspects which affect the rate of SOC decomposition. These results concur with other findings, Bookhagen et al., 2005, observed lowest rates of soil erosion at less than 2% of slope and highest at more than 20% of slope resulting in highest SOC loss. High erosion rate in steep slopes along with low carbon stock causes further depletion of SOC whereas lower areas have better retention of the SOC stock. Conclusion The spatial distribution of SOC and TSN across the complex topography of small forest area of Kashmir Himalaya is better predicted by regression kriging as compared to ordinary kriging with a prediction accuracy of 17.82 % and 19.44 % respectively. The spatial autocorrelation of SOC and TSN is better explained by regression kriging with Nugget/Sill ratio of 0.28 and 0.24 respectively. It is important to select appropriate environmental variables for interpolation techniques and semi-variogram analyses showed topographic parameters of elevation, slope and vegetation type/ land use as major factors influencing the spatial distribution of SOC and TSN. Both elevation and slope has significant influence on spatial distribution of SOC and TSN concentrations. Negative correlation of SOC and TSN with elevation indicate better stabilization at lower altitudes. High degree of slope has low vegetation and high soil erosion rate which leads to low SOC and TSN concentrations. In conclusion, regression kriging can provide better estimations at larger scale, provided there is a strong correlation between environmental variables and the SOC and TSN concentrations, and residuals are spatially autocorrelated.