J. Mt. Sci. (2014) 11(2): 507-518 e-mail: jms@imde.ac.cn http://jms.imde.ac.cn DOI: 10.1007/s11629-013-2645-1

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Abstract: This study was conducted to determine the changes in the soil carbon stocks as influenced by land use in a humid zone of Deylaman district (10,876 ha), a mountainous region of northern Iran. For this, land use maps were produced from TM and ETM+ images for 1985, 2000 and 2010 years; and this was supplemented by field measurement of soil carbon in 2010. The results showed that the mean soil organic carbon (SOC) density was 6.7±1.8 kg C m-2, 5.2±3.4 kg C m-2 and 3.2±1.8 kg C m-2 for 0-20 cm soil layer and 4.8±1.9 kg C m-2, 3.1±2 kg C m-2 and 2.7±1.8 kg C m-2 for 20-40 cm soil layer in forest, rangeland and cultivated land, respectively. During the past 25 years, 14.4% of the forest area had been converted to rangeland; and 28.4% of rangelands had been converted to cultivated land. According to the historical land use changes in the study area, the highest loss of SOC stocks resulted from the conversion of the forest to rangeland (0.45×104 Mg C in 0-40 cm depth layer); and the conversion of rangeland to cultivated land (0.37×104 Mg C in 0-40 cm), which typically led to the loss of soil carbon in the area studied. The knowledge on the historical land use changes and its influence on overall SOC stocks could be helpful for making management decision for

farmers and policy managers in the future, for enhancing the potential of C sequestration in northern Iran. Keywords: Soil organic carbon stocks; Land cover; Land use; Iran

Introduction

Land use change, mainly through the conversion of natural vegetation to cropland or grazing, may influence ecological processes and natural phenomena (Turner 1989), leading to changes in soil properties (Yimer et al. 2007; Ayoubi et al. 2011; Karchegani et al. 2012). Land-use change has been identified as a major cause of soil carbon (C) loss, and as a source of atmospheric carbon dioxide over the last two centuries (Wu et al. 2003; Houghton 2003; Brannstrom and Filippi 2008; Wilson et al. 2010). Land use change significantly affects soil organic carbon stocks (Guo and Gifford 2002; Wilson et al. 2010; Ayoubi et al. 2012). It has been reported that land use change is considered the second most important cause of C emission after

Soil Organic Carbon Stock as Affected by Land Use/Cover Changes in the Humid Region of Northern Iran

Samereh FALAHATKAR1, Seyed Mohsen HOSSEINI1*, Abdolrassoul SALMAN MAHINY2, Shamsollah AYOUBI3, WANG Shao-qiang 4

1 Faculty of Natural Resource and Marine Science, Tarbiat Modares University, Noor, Mazandaran 46414, Iran

2 Department of Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49138-15749, Iran

3 Department of Soil Sciences, College of Agriculture, Isfahan University of Technology, 8415683111, Isfahan 84156-93111, Iran

4 Key Lab of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China

*Corresponding author, e-mail: Hosseini@modares.ac.ir; First author, e-mail: Samereh.falahatkar@modares.ac.ir; Second author, e-mail: Rassoulmahiny@gmail.com; Third author, e-mail: ayoubi@cc.iut.ac.ir; Fourth author, e-mail: sqwang@igsnrr.ac.cn

Citation: Falahatkar S, Hosseini SM, Salman Mahiny A, et al. (2014) Soil organic carbon stock as affected by land use/cover changes in the humid region of northern Iran. Journal of Mountain Science 11(2). DOI: 10.1007/s11629-013-2645-1

© Science Press and Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2014

Received: 4 January 13 Accepted: 22 September 2013

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fuel consumption (Watson et al. 2000). Soil Organic Carbon (SOC) is the most

important parameter for the sustainability and quality of ecosystem (Ogle and Paustian 2005; Khormali et al. 2009). Natural and anthropogenic factors affect the total SOC storage; and hence the decision for managing agricultural and forest ecosystems influence SOC stocks as a means to mitigating the effects of climate change (Alvaro-Fuentes and Paustian 2011).

Remote sensing is a powerful tool for monitoring land cover change at different scales. In remote sensing, the change detection methods based on multi-temporal and multi-spectral image data have been extensively applied to monitor forests, cultivated lands and urban areas among many other applications (Zanotta and Haertal 2012). For example, Sharma and Ria (2002) reported that across the land-use cover, total mean C densities ranged from 46 Mg ha−1 in open cropped area temperate to a high of 669 Mg ha−1 in temperate natural dense forest in Mamlay watershed of Sikkim Himalaya, India.

Poeplau and Don (2013) found that the mean SOC stock changes following the land use change were 18±11 Mg ha−1 (cultivated land to grassland), 21±13 Mg ha−1 (cultivated land to forest), −19±7 Mg ha−1 (grassland to cultivated land), and −10±7 Mg ha−1 (grassland to forest) with the main changes occurring in the topsoil (0–30 cm depth) at 24 study sites across Europe. Khormali and Ajami (2011) showed that deforestation caused a loss of SOC, a reduction in cation exchange capacity, and an increase in carbonates in the surface soil in Golestan province, northern Iran. Moreover, Ayoubi et al. (2012) in west of Iran showed that deforestation of Oak in steep slopes led to changes in soil organic pools in bulk soil of different aggregate sizes.

Hyrcanian forest is an important ecosystem globally, and it is located in Alborz Mountain. The area under Hyrcanian forest is around 1.9 million ha in northern Iran along the southern coasts of the Caspian Sea; and around 20,000 ha in the Republic of Azerbaijan. (Marvie Mohadjer 2004). Unfortunately, Hyrcanian forest is increasingly fragmented and converted to other land uses in recent years (Mohammadi and Shataee 2010). Due to an increasing demand for timber, firewood, pasture, food and residential dwelling; the forest

ecosystem is being degraded or converted to rangeland or agricultural production systems at an alarming rate in majority of the areas of the Guilan province. This conversion of forest to other land uses or covers, such as rangeland or cultivated land, has created serious problems including increased soil erosion and change in soil properties. It is observed in the study area that the conversion of forest land to rangeland has been extended to increasing elevation gradually. Unfortunately, there is no special grazing management in rangeland; and these lands have been converted to cropland mostly for growing food crops like wheat and other cereals.

Little attention however, seems to have been paid to link land use changes with SOC stocks in Hyrcanian forests, especially by including land uses such as natural forest, rangeland and cultivated ecosystems. The information on the influence of land cover change on SOC stocks is important in the north of Iran for decision making for effective management of soils in this region. Therefore, a representative area in the Deylaman region, with a range of land uses, was selected. We have observed the conversion of forest to rangeland, and of rangeland to cultivated land in recent years; therefore, we focused on the three main land uses (forest, rangeland and cultivated agricultural land) in Deylaman region, and an attempt has been made to establish the relationships among land uses and SOC density. The specific objectives of our study were: i) to determine the extent of land uses change during the last 25 years using remote sensing data, and ii) to estimate and compare soil organic carbon stocks under different land use change in a humid region located in northern Iran.

1 Materials and Methods

1.1 Study area

The study area is located in the Deylaman region of Guilan Province northern Iran, between 49°49'19.36" and 49°57'49.38" E longitudes and 36°50'47.29" and 37°00'1.76" N latitudes (Figure 1). The study area has an altitude between 500 m and 2100 m with an area of 10,876 ha. Climate is moderate and humid in northern Iran. The mean annual temperature and precipitation at the site

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are 12.7°C and 1173 mm, respectively. Soil texture of the studied area varied from loam to clay loam and predominantly classified as acidic forest soils. There are three major land covers and land uses including forest, rangeland, and cultivated land. Some of parts of forest land are used for harvesting wood. Different crops are cultivated in croplands such as wheat and other cereals. Rangeland of Deylaman has been subjected to human activities such as traditional animal husbandry and cutting bushes and shrubs for expanding cropland. There

is evidence and we observe signs of sheet erosion in all of the study area.

1.2 Soil Sampling

The first step in the study was land stratification, which allowed the study area to be subdivided into Land Unit Tracts1 (LUT) according to specified criteria including topographic attributes (slope and elevation) and land use type.

Figure 1 Location of the studied area and Land Unit Tracts for sampling. [In all Land Unit Tracts, the left number shows land use classes (1: Forest, 2: Rangeland, 3: Cultivated land), the middle number shows elevation class (2: <1,500 m, 3: >1,500 m) and the right number shows the slope classes (1: 0-15%, 2: 15-30%, 3: >30%)]

1 Land Unit Tract (LUT) is defined as an area of land where the attribute values are sufficiently uniform and distinct from those of neighboring areas to justify its delineation in a map or image.

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The stratifying procedure was conducted using land use maps topography maps, and Geographical Information System (GIS) technology, which allowed map digitizing, data analysis and production of a Digital Elevation Model (DEM) and subsequently thematic map layers. In a randomized design considering the three main land use types (forest, rangeland and elevation), two elevation classes <1500 m and >1500 m, three slope classes (0-15%, 15-30%, and >30%), a total of 18 different LUTs (3×2×3=18) were created. At least three replicated sites for each small LUT and more than three replicated site for larger LUT were sampled, with replicates located in quite different parts of the study area in order to produce a measure of SOCD within each LUT. We selected 124 sampling sites randomly; and soil samples were collected from two depths of 0-20 and 20- 40 cm in October 2010 covering the whole area. In the study, 30, 33 and 61 soil samples are located in forest, rangeland and cultivated land respectively. A global positioning system (GPS) was used to precisely locate the sampling sites. At each site, soil samples were collected from four replicated points in 3- meter radius to reduce the variability due to chance factor. Soil samples from four replicated points in each depth were mixed for reducing the number of soil samples. Elevation of the sampling sites was measured at the location by GPS. Slope gradient and aspect of the terrain of the sampling site were also measured by clinometers and compass, respectively.

1.3 Laboratory analyses

All soil samples were air-dried and crushed to pass through a 2 mm sieve before use. Walkley-Black method was used to determine soil organic carbon. Several studies have shown that the recovery of organic C using the Walkley-Black procedure ranges from 60% to 86% with a mean recovery being 76% (Walkley and Black 1934). As a result of the incomplete oxidation; and in the absence of a site-specific correction factor, a correction factor of 1.33 is commonly applied to the results to adjust the organic C recovery. Particle size distribution and bulk density were determined with the hydrometer technique and core method, respectively (Sheldrick and Wang 1993; Soil Survey Staff 1996).

1.4 Calculation of soil organic carbon stocks

The SOC density for each sampling site, SOCD (kg m-2), was calculated using the following equation (Fang et al. 2012)

(1)

where, SOCi is the SOC content of the ith layer (g kg-1), BDi is the bulk density of the ith layer (g cm-3), Di is the depth of the ith layer (m), m is number of the layers. The occurrence of gravels (particles larger than 2 mm in diameter) was rare in the studied soils; hence, the percent of gravel was not considered in calculations.

The total SOC storage in the study area, TSOC (kg) can be expressed as follows:

(2)

where ASOCDj is the average SOC density of the jth class (kg m-2), Sj is the area of the jth class (m2), m is number of the land use categories.

1.5 Remote sensing data and preprocessing

To assess the trend in land use and terrestrial carbon stock changes in Deylaman region, we produced land use maps using TM and ETM+ images for the years of 1985, 2000, 2010; and conducted carbon field measurements in 2010. To analyze the land use dynamics, 1:40,000 scale aerial photos for the years of 1983 and 1999 and Google earth image in 2010 were visually interpreted for producing road, village and small city layer as a feature class (Road and Urban) in study area.

Pre-processing of satellite images is essential prior to detecting the changes; and this has the unique aim to establishing a more direct linkage between the data and biophysical phenomena (Coppin et al. 2004). The 1985 and 2000 images were geometrically corrected to the 2010 image using the root mean square error of less than 0.4 pixels. The 2010 image and aerial photos had previously been georeferenced using topographic maps (RMSE < 0.38 pixel).

After geometric correction, a hybrid classification method was used for producing the land use maps for different years. We took

ii

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advantage of both the supervised (Maximum likelihood algorithm) and unsupervised (Kmeans clustering algorithm) classification methods (Pradhan et al. 2010). Land use maps included 6 classes in our study: forest, rangeland, cultivated land, rocks, urban area and road.

An error matrix was used to evaluate the produced land use maps resulting from the processing of remotely sensed images. Spectral mixing and spectral confusion between different land cover such as forest and rangeland or urban and fallow agriculture fields are the most prevalent errors in image classification (Guindon et al. 2004). The Kappa coefficient (ranges between 0 and 1) is a conservative measure of the difference between the actual agreement between the reference data and an automated classifier, and the chance agreement between the reference data and a random classifier (Congalton and Green 1999). A Kappa coefficient of 0.88 thus means that the classification accuracy was 88% greater than chance.

1.6 Geostatistical and statistical analysis

Geostatistics uses the semi-variogram to measure the spatial variability of a regionalized variable, and provides the input parameters for the spatial interpolation method of kriging (McGrath and Zhang 2003). In this study, Ordinary Kriging (OK) was applied to produce a spatial distribution map of SOC density based on 2010 data because it provides information on the spatial structure as well as on the input parameters. The interpolation procedure was preceded by the selection of a suitable variogram model specific to each dataset. This was accomplished by fitting each of the several theoretical variogram models (e.g., linear, Gaussian, spherical, and exponential) to the empirical ones using the least-square method and by doing cross-validation (e.g., Isaaks and Srivastava 1989). Cross-validation was done to compare the Ordinary Krigged predictions with the observed values (Goovaerts 1997).

In order to compare the results of changes in SOC stocks within the land use, the analysis of variance (ANOVA) was done in the SPSS statistical program (SPSS 2005). Comparison of the means was performed using the Duncan's

multiple range method. Statistical significant was accessed at the p< 0.05 probability levels.

1.7 Land use change detection

Following the production of land use maps, post classification comparison (PCC) method was applied for detecting land use change. The principal advantage of the post classification comparison lies in the fact that the two dates of imagery, are separately classified; thereby minimizing the problem of radiometric calibration between dates (Coppin et al. 2004).

2 Results and Discussion

2.1 SOCD for different depths under various land use

Results showed that the mean SOC density in the 0-20 cm soil layer was 6.7±1.8 kg C m-2, 5.2±3.4 kg C m-2 and 3.2±1.8 kg C m-2 for forest, rangeland and cultivated land, respectively (Table 1). Mean SOC density in the 20-40 cm soil layer was 4.8±1.9 kg C m-2, 3.1±2 kg C m-2 and 2.7±1.8 kg C m-2 for forest, rangeland and cultivated land, respectively (Figure 2). We calculated arithmetic means for soil samples located in special land use. The lowest SOC content was found in cultivated land and the highest SOC density was observed in forest land. Fang et al. (2012) reported from their study that terrace cultivated land had the lowest SOC density as compared with other land uses. Mendoza-vega et al. (2003) showed that Oak–evergreen cloud forest had the largest amounts of SOC at all depths. Karchegani et al. (2012) showed that natural Oak forest had higher SOC content compared to the disturbed forest and cultivated soils in steep lands of Lordegan district, western Iran.

Table 1 Descriptive statistics for SOCD in different land use.

C.V. S.D.Min Max MeanNo.land use Depth (cm)

0.26 1.8 3.4 11.7 6.7 30 Forest 0-20 0.66 3.4 0.9 12.5 5.1 33 Rangeland

0.56 1.8 1.05 10.2 3.2 61 Cultivated land 0.39 1.9 1.9 9.5 4.8 30 Forest

20-40 0.64 2 0.9 11.2 3.1 33 Rangeland 0.66 1.8 0.4 9.4 2.7 61 Cultivated land

Notes: S.D.= Standard Deviation; C.V.= Coefficient of Variation.

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1.06% of rangeland changed to forest and urban, respectively. The urban area has increased from 14.6 ha to 93.8 ha in the past 25 years. According to conservation plan such as movements of livestock from forest and reorganization of forest settlers, 12% of the cultivated land has been converted to rangeland (445.5 ha) in recent years. In this section, we report significant changes among different land use especially in forest, rangeland and cultivated land, ignoring the minor changes. Geometric correction error, omission and commission errors and misclassification of some pixels might have caused some minor changes (Lunetta et al. 2006) by not detecting or adding by error.

2.4 Soil organic carbon stocks change

How much Soil organic carbon stock has been lost in relation to land use change?

Since no data on SOC are available for the past years to assess the change in soil organic carbon stocks due to land use changes (increasing cultivated land and decreasing forest and rangeland), we assumed SOC to be static based on the 2010 field survey. For this assumption we unavoidably assumed that the environmental conditions (for example erosion, climate and ...) had been stable for the past 25 years. Stevens and Van Wesemael (2008) made appropriate assumptions based on land use change in their research as no earlier data were available. Absolutely, we were confronted with errors like image classification error, geometric correction error and uncertainty of our assumptions. But these errors did not fundamentally change the outcome.

The loss or accumulation of carbon depends on the standing stock of carbon in vegetation and soils, and on the rate of land cover change (Ostle et al. 2009). According to our results, SOC stocks in forest land in the 0-40 cm soil layer were 4.06×105 Mg C, 3.85×105 Mg C and 3.63×105 Mg C for the years 1985, 2000 and 2010, respectively. A similar trend was obtained for other land uses (see Table 4). Figure 5 shows the changes in SOC stocks in different land use in the 0-20 cm and 20-40 cm soil layers. The

decrease in forest area is about 9.3% in the last 25 year, SOC stock declined by about 0.43×105 Mg C in 0-40 cm depth in the area studied. We conclude that during this period, rangeland area has decreased by about 214.3 ha. Accordingly, 0.19×105

Mg of SOC stocks has been lost from the study area (in 0-40 cm). Khormali et al. (2009) showed that the average organic carbon stock in the 0–60 cm depth of the forest and deforested area were 184.8 and 58.8 ton ha−1, respectively in Golestan province, north of Iran. Shahriari et al. (2011) reported that the minimum and maximum SOC decreases were 4 and 51.14 Mg C ha-1/30 cm during 34 years cultivation in Golestan province, northern Iran. Conversion of rangeland to other land use categories was also prominent during these years, which played a significant role in the loss of SOC from soils; and a reduction of carbon storage in the soils of the studied region. In this duration, other land use have been converted to cultivated land (477.6 ha), which resulted in increased SOC stocks in this land use (0.29×105 Mg C in 0-40 cm). According to table 4, we observed that total SOC stock in 0-40 cm soil layer was 9.06×105 Mg C in 1985 and it has decreased by 8.85×105 Mg C and 8.73×105 Mg C in 2000 and 2010 in Deylaman region, respectively. Here, the degradation ratio was defined as the ratio of carbon lost to the area lost (Sharma and Rai 2007).The total loss of soil organic carbon stocks were 4.23×104 Mg C and 1.95×104 Mg C in 0-40 cm from forest and rangeland, and the total loss of forest and rangeland area were 365.5 ha and 224.3 ha,

Figure 5 The changes in the SOC stocks from 1985 to 2010 in relation to land use change.

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respectively (Table 5). Therefore, this ratio for SOC stocks is 115.7 Mg C ha-1 and 86.9 Mg C ha-1 for forest and rangeland in the past 25 years, respectively.

Although the Hyrcanian forest ecosystem was recognized as a source for higher carbon accumulation in northern Iran, the present study shows that this ecosystem has been subjected to significant land use changes and serious environmental problems. Martin et al. (2010) showed that the Hymalian forest ecosystem has been subjected to climate change and other environmental threats; and the effect was attributed to increasing carbon emission from this ecosystem.

In terms of organic carbon stock, an average of 3.1% decline was found through the past 25 years. It’s approximately equal to 0.124% decrease in SOC stock each year in the 0-40 cm soil layer in Deylaman region. Lal (2002) reported 30%–50% of the carbon pool loss in the Midwestern USA due to land use changes from natural ecosystems to croplands. Martin et al. (2010) recorded an average decline of 0.36% in the past 25 years in Himalayan ecosystem due to land use change. The accumulation and loss ratios of carbon varied among the land use. Sharma and Rai (2007) showed that the largest changes in SOC stocks were related to the conversion of forest to cropland. However, it is clear that with the high exploitation and continuous land conversion, land use changes have become a source of carbon release to the atmosphere. Wang et al. (2011) reported that overgrazing and conversion of freely grazed grassland to cropland led to an annual average

decline of 2.3%–2.8% in SOC in their study area in China. Guo and Gifford (2002) showed that there was a decline in SOC stocks after land use conversion from grassland to cropland (−59%), grassland to plantation forest (−10%), native forest to cropland (−42%), and native forest to plantation forest (−13%). Therefore, careful assessment of the land resources is essential to make any change in the natural ecosystem. Encroachment by agriculture into such areas to provide food may lead to deterioration of soil health through rapid loss of organic carbon from the soil (Martin et al. 2010).

3 Conclusions

The land use change in particular impacts vegetation cover and soil carbon stocks by altering the balance between carbon loss and accumulation. Our results provide estimates of the effects of land use changes on SOC stocks in a representative area in the humid region of Alborz Mountain, north of Iran. The results show a strong relationship between land use conversion and SOC stocks changes. Our results also showed that deforestation has significantly reduced the SOC density in the study area. Based on the historical land use changes in the study area, the highest changes in the SOC stocks resulted from the conversion of forest to rangeland (0.45×104 Mg C in 0-40 cm), and the conversion of rangeland to cultivated (0.37×104 Mg C in 0-40 cm), which typically led to the loss of accumulated soil organic carbon. According to our findings, conservation of natural

Table 5 Total loss of area and SOC stocks changes for different land use (symbols of positive and negative show the increase and decrease) in 1985-2010

Land cover NC S1×104 S2×104 2010

To Rangeland To Cultivated land To Forest

1985

Ran

gela

nd

-224.3 -1.17 -0.78

(28.4%) (6.3%) 63.7 ha 8.1 ha 0.20×104 Mg (0-20 cm) 0.54×103 Mg (0-20 cm)0.17×104 Mg (20-40 cm) 0.39×103 Mg (20-40 cm)

Cul

tiva

ted

land

+477.6 +1.53 +1.31

(12.1%)

(0.07%) 57.8 ha 0.33 ha 0.30 ×104 Mg (0-20) 0.22 ×102 Mg (0-20 cm)0.20×104 Mg (20-40) 0.16×102 Mg (20-40 cm)

Fore

st

-365.5 -2.45 -1.78

(14.5%) (1.1%)

52.9 ha 4.02 ha0.27×104 Mg (0-20 cm) 0.12×103 Mg (0-20 cm)0.18×104 Mg (20-40 cm) 0.11 ×103Mg (20-40 cm)

Notes: NC=Net change of area (ha) Changes; S1=SOC stocks 0-20(Mg C); S2= SOC stocks 20-40 (Mg C)

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resources like forest and rangeland, and prevention of their change to other land use by applying management plans, can be helpful in arresting a decline SOC density as affected by land use change. Rangelands provided high potential for carbon sequestration; and it covers about 28% of study area and regrettably had been subjected to severe changes because of their availability to farmers. Hence, rangelands must be conserved more than other land use by managers in Deylaman region. To enhance the potential of carbon sequestration in different land uses in Deylaman region, it is

suggested to reduce the grazing intensity, and increase the rangeland productivity using appropriate management practices and decision-making tools. Moreover, soil conserving practices such as contour farming, green manure application and residue management would improve the SOC stocks in the cultivated soils. It is also suggested that some additional practices such as planting of native hardwood species, reducing harvest, and protection against disturbance could also improve the SOC stocks in natural Hyrcanian forest in northern Iran.

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