英国环境工程学毕业论文：The Study on the Effects of Land Use and Cover C

发布时间：2017-09-04 10:03

1 Introduction介绍

1.1 Background背景
土壤作为陆地生态系统碳库的重要组成部分，在调节碳氧化物浓度和减缓全球变暖方面发挥着重要作用。人类活动引起的土地利用/覆被变化（LUCC）是影响土壤有机碳库的关键因素之一。随着城市化进程的加快和城市化水平的提高，邻近的土壤经历了快速变化。因此，对城市土地利用系统的SOC、空间分布及其与LUCC的时间变化进行评估，可以为政府制定有效的减排措施和增加碳汇提供科学依据。此外，它有助于全球SOC循环研究的研究和全球变化的预测未来。
上海是中国经济最发达的城市。近年来，随着经济的快速发展，工业化、城镇化快速发展已经成为上海最重要的土地利用格局的变化在中国的地区，对城市土壤碳库的巨大影响。此外，上海地处东部沿海地区，，使其成为未来影响全球气候变化最为严重的地区。
As an important component of terrestrial ecosystem carbon Pool, soils play a significant role in regulating the concentration of carbon oxide (CO2) and mitigating global warming. Land use and cover change (LUCC) caused by the human activities becomes one of the key factors affecting the soil organic carbon (SOC) pool. With the development of urbanization and increasing urbanized area, soils adjacent to cites experience rapid change. Therefore an assessment of SOC in urban area, their spatial distribution and temporal variation associated with LUCC, can provide scientific basis for government to make effective measures to reduce carbon emission and increase carbon sink. In addition, it can contribute to the research of global SOC cycling research and the prediction of global change in the future.  
Shanghai is one of China's most economically developed cities. In recent years, with the rapid development of economy, rapid industrialization and urbanization have made Shanghai become the area with the most significant land use pattern changes in China, making great impacts on the urban soil carbon pool. In addition, Shanghai locates in the eastern coastal region, which makes it become the area most greatly affected the global climate change in the future. 
1.2 Definition of Soil Carbon Sequestration (SCS)
    What is Soil Carbon Sequestration (SCS)? The so-called carbon dioxide from the atmosphere into the soil through crop residues and other organic solids, and in a form that is not immediately reemitted. 
2)  
Figure 1-1 The sources and sink of carbon and its interplay in pedosphere, atmosphere and hydrosphere. The carbon stocks in various pools (including fossil fuels) were obtained from Batjes (1996), Lal (2004a, b,2008), and the carbon efflux (including fossil fuel burning) data were from IPCC (2000, 2007). 
1.3 Soil Carbon Sequestration and Land-Use Change
Various land-uses result in very rapid declines in soil organic matter (Jenny 1941, Davidson and Ackerman 1993, Mann 1986, Schlesinger 1985, Post and Mann 1990). Much of this loss in soil organic carbon can be attributed to reduced inputs of organic matter, increased decomposability of crop residues, and tillage effects that decrease the amount of physical protection to decomposition. When agricultural land is no longer used for cultivation and allowed to revert to natural vegetation or replanted to perennial vegetation, soil organic carbon can accumulate by processes that essentially reverse some of the effects responsible for soil organic carbon losses from when the land was converted from perennial vegetation (Post and Kwon, 2000, pp.317) . 
2 Research questions
On the basis of combined methods involving geographical information system (GIS), remote sensing (RS) and soil carbon analyze, the objectives of this studies are: 1) the land use and cover change of Shanghai during 1990-2010; 2) estimates of soil carbon pools in different periods Shanghai; 3) Dynamic changes caused by land use change in soil carbon pool. 

3. The study area
3.1 Location and area of Shanghai 
 
Shanghai (N31°14’, E121°29’) locates in the Yangtze River delta, adjacent to the East China Sea, Hangzhou Bay, Zhejiang and Jiangshu province (Figure 3-1). 
4. The research contents and methodology 
5. Temporal and spatial variations of land use in Shanghai 
5.1 Research significance of regional land use for the carbon cycle
Land is the basic natural and material resources for the survival and development of humanity. Land use/land cover change is increasingly being recognized as a critical and urgent research topic in the studies about global environmental change. With the joint promotion of the “International Geosphere-Biosphere Programme" (IGBP) and the "Global Environmental Change humanities program", land Use/Land Cover Change (LUCC) has become one of the core areas of global environmental change research. Especially with the progress in studies about global change and the carbon cycle, the relationship between the land use change and the carbon cycle is suggested to be more and more closed. 
Shanghai is one of the most of economically developed cities. In recent years, with the rapid development of Shanghai's economy, rapid industrialization and urbanization has made shanghai become one of the areas with the most significant changes in land use patterns. Dramatic changes in land use patterns will inevitably make important impacts the carbon fixation of the regional soils. Therefore, in this context, the dynamic changes of soil carbon fixation should be studied combined with land use change. Up to date, some research has been conducted about the land use change in Shanghai (Han, et al., 2009). 
5.2 Methodology
5.2.1 Data collection
For almost three decades, China has been undergoing significant transition from a planned economy to a market economy. Fast-paced economic growth and urbanization, interacting with market-oriented reforms in land resources allocation, have caused profound spatial restructuring of Chinese cities (Xu, et al., 2007, pp.19). In 1990s, Shanghai experienced the fastest-growing urbanization and dramatic changes in land use. The time span of the land use data is 1980-2010. The data contains the interpretation data of aerial remote sensing in 1980, 1985, 1990, 1995, 2000, 2005, 2010 

5.2.2 Data processing
5.2.2.1 Land use classification system
According to the research purpose of this study, urban green fields, farmland, woodland, garden plot and tidal-flat areas, etc. are the most important component of Shanghai's soil carbon pool. Therefore, the land use classification system in this paper focuses on the information extraction of these types of land use. At the same time, in order to analyze the effects of the distribution and change of other land use types such as industrial land, residential land use, land for roads on the Shanghai's soil carbon pool, thus the information of these land use types will also be classified and extracted. Combined with the characteristics of remote sensing survey, the land use classification system was developed as following Table 5-1
No. of classification Name Introduction
1 Industrial warehouse Production workshop, warehouse, yard and ancillary facilities of Mining and warehousing enterprises the dedicated railway land, docks and roads and other land
2 Traffic land Including roads, railways, airports, ports and lands for external internal transport, and squares, parking lots and other lands
3 Urban residential land Various types of land and (except of green land) in the urban and rural residential area, and land for utilities including buildings and other facilities 
4 Urban green land Including various of public green land, parks, road greening, the affiliated green space of landscape, the affiliated green space of landscape of institutions, residential district green area, green land for production and industrial green land
5 Other landuse Including sanitation facilities land, land for sewage treatment, etc. 
6 Water Including land for rivers, lakes, ponds and aquaculture farms
7 Farmland Crop land, including cooked farmland, newly reclaimed land, fallow land, swidden land, grass and crop rotation land, and bottomland and tidal-flat areas farmed for more than three years 
8 Woodland Forest land for trees, shrubs, bamboo, and tree belts
9 Garden Including land for orchards, tea and mulberry
10 Unused land Untapped land except tidal-flat areas
11 Tidal-flat areas Untapped land on the verge of river sea and perennial above the surface of the water
5.2.2.2 The information extraction of land use change
5.2.3 Analysis of land use change
5.2.3.1 Models of land use change
Land use dynamic indexes can be used to quantitatively describe the speed of regional land use change. Land use dynamic index can be divided into the single land using dynamic degree indexes and comprehensive land using dynamic degree indexes, which can be described as follows:
1) Single land using dynamic degree indexes
K=(U2-U1)/U1×1/(t2-t1) ×100%
Where, K is the single land using dynamic degree, t2-t1 is time interval, U1 means the initial area of certain types of land use within a certain region, 
2) Comprehensive land using dynamic degree indexes
Using aerial remote sensing data of Shanghai in 1980, 1985, 1990, 1995, 2000, 2005 and 2010, combined with 1:50000 topographic map and 1:10000 digital map, we collected the information of various types of land use through visual interpretation and field survey methods according to developed land use classification system with the help of Aicgis9.2. 
The interpreted Shanghai land use map was then transformed into vector format and saved, 

5.3 Dynamic analysis of Shanghai’s land use change in different periods 

5.3.1 Dynamic analysis of Shanghai’s land use change during 1980-1985
Figure 5-2 Statistical data of Shanghai’s land use change during 1980-1985 
Unit: hm2
Land use type 1980 1985 Changes in the area Annual variation in the area Dynamic degrees (%)
Area Proportions Area Proportions
Industrial land 18082.34 2.37 22091.18 3.12 4008.84 801.77 4.43
Traffic land 6708.29 0.88 8150.46 1.15 1442.17 288.43 4.30
Urban residential land 51092.57 6.70 57281.14 8.10 6188.57 1237.71 2.42
Urban Green Space 1892.23 0.25 2109.53 0.30 217.3 43.46 2.30
Other land use 5230.28 0.69 5609.87 0.79 379.59 75.92 1.45
Water 109290.14 14.32 95603.26 13.52 -13686.9 -2737.38 -2.50
Farmland 538920.9 70.64 490210.3 69.33 -48710.65 -9742.13 -1.81
Woodland 15780.82 2.07 14092.07 1.99 -1688.75 -337.75 -2.14
Garden 1428.17 0.19 1234.23 0.17 -193.94 -38.79 -2.72
Unused land 4692.52 0.62 3018.18 0.43 -1674.34 -334.87 -7.14
Tidal-flat areas 9829.01 1.29 7624.21 1.08 -2204.8 -440.96 -4.49

5.3.2 Dynamic analysis of Shanghai’s land use change during 1985-1990
Figure 5-3 Statistical data of Shanghai’s land use change during 1985-1990
Unit: hm2
Land use type 1985 1990 Changes in the area Annual variation in the area Dynamic degrees (%)
Area Proportions Area Proportions
Industrial land 22091.18 3.12 26098.32 3.79 4007.14 801.428 3.63
Traffic land 8150.46 1.15 10890.15 1.58 2739.69 547.938 6.72
Urban residential land 57281.14 8.10 62092.31 9.01 4811.17 962.234 1.68
Urban Green Space 2109.53 0.30 2865.14 0.42 755.61 151.122 7.16
Other land use 5609.87 0.79 6243.52 0.91 633.65 126.73 2.26
Water 95603.26 13.52 89081.89 12.92 -6521.37 -1304.27 -1.36
Farmland 490210.3 69.33 468900.12 68.01 -21310.13 -4262.03 -0.87
Woodland 14092.07 1.99 12098.13 1.75 -1993.94 -398.788 -2.83
Garden 1234.23 0.17 1478.86 0.21 244.63 48.926 3.96
Unused land 3018.18 0.43 2878.09 0.42 -140.09 -28.018 -0.93
Tidal-flat areas 7624.21 1.08 6780.91 0.98 -843.3 -168.66 -2.21
    Figure 5-3 shows that 
5.3.3 Dynamic analysis of Shanghai’s land use change during 1990-1995
Figure 5-3 Statistical data of Shanghai’s land use change during 1990-1995
Unit: hm2
Land use type 1990 1995 Changes in the area Annual variation in the area Dynamic degrees (%)
Area Proportions Area Proportions
Industrial land 26098.32 3.79 34008.11 5.26 7909.79 1581.96 6.06
Traffic land 10890.15 1.58 15059.53 2.33 4169.38 833.88 7.66
Urban residential land 62092.31 9.01 93705.18 14.49 31612.87 6322.57 10.18
Urban Green Space 2865.14 0.42 4028.27 0.62 1163.13 232.63 8.12
Other land use 6243.52 0.91 7333.79 1.13 1090.27 218.05 3.49
Water 89081.89 12.92 75801.22 11.72 -13280.7 -2656.13 -2.98
Farmland 468900.12 68.01 403099.34 62.35 -65800.8 -13160.16 -2.81
Woodland 12098.13 1.75 6190 0.96 -5908.13 -1181.63 -9.77
Garden 1478.86 0.21 1985.48 0.31 506.62 101.32 6.85
Unused land 2878.09 0.42 1098.36 0.17 -1779.73 -355.95 -12.37
Tidal-flat areas 6780.91 0.98 4208.8 0.65 -2572.11 -514.42 -7.59
5.3.4 Dynamic analysis of Shanghai’s land use change during 1995-2000
Figure 5-3 Statistical data of Shanghai’s land use change during 1995-2000
Unit: hm2
Land use type 1995 2000 Changes in the area Annual variation in the area Dynamic degrees (%)
Area Proportions Area Proportions
Industrial land 34008.11 5.26 39620.71 6.07 5612.6 1122.52 3.300742
Traffic land 15059.53 2.33 21305.02 3.26 6245.49 1249.098 8.294402
Urban residential land 93705.18 14.49 98461.46 15.08 4756.28 951.256 1.015158
Urban Green Space 4028.27 0.62 6659.92 1.02 2631.65 526.33 13.06591
Other land use 7333.79 1.13 11962.55 1.83 4628.76 925.752 12.6231
Water 75801.22 11.72 83445.38 12.78 7644.16 1528.832 2.016896
Farmland 403099.34 62.35 376249.8 57.62 -26849.5 -5369.91 -1.33215
Woodland 6190 0.96 7549.97 1.16 1359.97 271.994 4.394087
Garden 1985.48 0.31 2245.71 0.34 260.23 52.046 2.621331
Unused land 1098.36 0.17 3423.59 0.52 2325.23 465.046 42.34003
Tidal-flat areas 4208.8 0.65 2030.93 0.31 -2177.87 -435.574 -10.3491
5.3.5 Dynamic analysis of Shanghai’s land use change during 2000-2005
Figure 5-3 Statistical data of Shanghai’s land use change during 2000-2005
Unit: hm2
Land use type 2000 2005 Changes in the area Annual variation in the area Dynamic degrees (%)
Area Proportions Area Proportions
Industrial land 39620.71 6.07 59439.9 7.16 19819.2 3963.84 10.00 
Traffic land 21305.02 3.26 32268.72 3.89 10963.7 2192.74 10.29 
Urban residential land 98461.46 15.08 109686.33 13.22 11224.87 2244.974 2.28 
Urban Green Space 6659.92 1.02 13020.68 1.57 6360.76 1272.152 19.10 
Other land use 11962.55 1.83 221890.23 26.74 10217.68 2043.536 17.08 
Water 83445.38 12.78 87667.95 10.56 4222.57 844.514 1.01 
Farmland 376249.8 57.62 289991.6 34.94 -76258.2 -15251.6 -4.05 
Woodland 7549.97 1.16 7149.10 0.86 -400.87 -80.174 -1.06 
Garden 2245.71 0.34 3699.72 0.45 1454.01 290.802 12.95 
Unused land 3423.59 0.52 3049.21 0.37 -374.38 -74.876 -2.19 
Tidal-flat areas 2030.93 0.31 2019.32 0.24 -11.61 -2.322 -0.11 
5.3.6 Dynamic analysis of Shanghai’s land use change during 2005-2010
Figure 5-3 Statistical data of Shanghai’s land use change during 2005-2010
Unit: hm2

Land use type 2005 2010 Changes in the area Annual variation in the area Dynamic degrees (%)
Area Proportions Area Proportions
Industrial land 59439.9 7.16 76251.82 11.98 16811.92 3362.38 5.66
Traffic land 32268.72 3.89 41082.32 6.45 8813.6 1762.72 5.46
Urban residential land 109686.33 13.22 120072.21 18.86 10385.88 2077.18 1.89
Urban Green Space 13020.68 1.57 16721.18 2.63 3700.5 740.10 5.68
Other land use 221890.23 26.74 25910.10 4.07 3729.87 745.97 3.36
Water 87667.95 10.56 73801.97 11.59 -13866 -2773.20 -3.16
Farmland 289991.6 34.94 268901.14 42.25 -31090.5 -6218.09 -2.07
Woodland 7149.10 0.86 6329.16 0.99 -819.94 -163.99 -2.29
Garden 3699.72 0.45 3104.15 0.49 -595.57 -119.11 -3.22
Unused land 3049.21 0.37 2456.21 0.39 -593 -118.60 -3.89
Tidal-flat areas 2019.32 0.24 1877.13 0.29 -142.19 -28.44 -1.41

6. Driving force of land use change in Shanghai

7. Carbon fixation of soils under different land use patterns in Shanghai
 
Figure 7.1 Map sampling sites (provided by Dr. Yu Lizhong of East China Normal University)
7. 1 Data collection 
The data about the soil carbon concentrations under different land use patterns in Shanghai were provided by Dr. Yu Lizhong of East China Normal University. The sample collection, treatment and analysis methods would be described in detail here. 
Shanghai's soil carbon pools include five kinds of land use types such as farmland, woodland, urban areas, green space, garden and tidal land. Therefore, these five kinds of land use types were investigated in our study. Taking the regional distribution of various soil and land use types in Shanghai, surface soil (0-20cm) in several regions such as urban districts, Qingpu district, Nanhui district, Minxing district, Fengxian district, Pudong district, Chongming district were collected with the help of GPS. The sampling sites are showed in Table 7.1. The overall 190 sampling sites cover urban green space (mainly lawn, 47 sampling sites), paddy soils (30 sampling sites), dry land (24 sampling sites), woodland (51 sampling sites), garden (25 sampling sites), and tidal land (10 sampling sites). The cutting rings were used to collect soil samples and soil bulk density was determined. After the soil samples were collected, they were put in ziplock bags and taken back and analyzed in the laboratory. 
The samples collected were air-dried inthe laboratory, and the animal, plant residues were picked out and discarded. At the same time, dust, acid, alkali and dirty gas pollution were prevented in the process of the soil samples. 20 gram dried samples were ground by mortar and sieved with a 80-mesh sieve. The gravel and plant roots were removed. The sieved samples were placed in sealed bags, labeled and saved in the dryer for determination of soil carbon and nitrogen. 
The soil bulk density, soil organic carbon (SOC) was determined. Soil bulk density was measured using cutting ring method. Soil organic carbon (SOC) was determined by carbon and nitrogen elemental analyzer. Before instrumental determination, all samples were acidified with hydrochloric acid to eliminate the inorganic matter. It can be described in detail as follows: about 0.5 gram of each sample was weighed and placed in the weighing bottle, and then added 5 microgram 1 mol/L hydrochloric acid using 5ml pipette, and allowed to stand for 8 hours. After that, we washed it with Millipore water 3 to 4 times, and dried it by distillation. Then it was cooled, and then determined by the machine. 
7.2 Soil organic carbon content under different land use
Table 7.2 Soil organic carbon content under different land use
Unit: g/kg dry soil
Land use types Minimum Maximum Mean value STDa Variation coefficients Number of samples
Urban lawn 2.46 24.83 10.32 5.14 50.10% 47
Paddy 6.23 31.78 14.56 6.45 44.62% 30
Upland 2.13 28.85 12.52 6.09 48.92% 24
Woodland 5.23 30.92 12.57 6.09 49.61% 51
Garden 5.72 15.24 9.45 3.91 40.94% 25
Abandoned land 7.18 13.87 11.24 3.52 30.91% 3
Tidal land 2.63 8.31 5.46 1.93 35.68% 10
a STD means standard deviation 
Table 7.2 shows the soil organic carbon (SOC) content under different land use. The average content of SOC under different land use patterns in Shanghai showed significant differences. The average SOC concentrations of paddy were 14.56 g/kg with a range of 6.23-31.78 g/kg, significantly higher than those of the other five kinds of land-use types. Woodlands and upland followed with average SOC concentrations of 12.57 g/kg and 12.52 g/kg, ranging between 5.23-30.92 g/kg and 2.13-28.85 g/kg. The SOC concentrations of tidal land were lowest, only 5.46 g/kg (ranging between 263 - 8.31g/kg), which was equivalent to 37.5% of paddy. The explanation for the lowest SOC concentrations observed in the tidal land was that the tidal land was strongly affected by the tidal water, causing the relatively weak growth of ground vegetation, resulting in less organic matter input. The low organic matter input into the tidal land caused the low SOC concentrations of it. The average SOC concentrations of the remaining three kinds of land use types decreased in an order: abandoned land (11.24 g/kg) > urban lawn (10.32 g/kg) > garden (9.45 g/kg), with variation range of 7.18-13.87 g/kg, 2.46-24.83 g/kg and 5.72-15.24 g/kg. In all kinds of land use types, the SOC concentration of paddy was highest, suggesting the highest carbon sequestration capacity of paddy. The reason may be that paddy fields were submerged in a long time, inhibiting the decomposition of organic matter. At the same time, various external fertilizers were continually input into the paddy. All of these caused the higher SOC concentrations of paddy. Due to the high canopy, the soil humidity of woodland was much higher. And there were much surface litter, causing large amount of organic matter input. In addition, the decomposition rate of soil organic matter was slow, which was conducive to the accumulation of soil organic matter. Due to human disturbance, the SOC of the dry land was exposed to the air, accelerating the decomposition of SOC in the dry land. Meanwhile, the farming process can increase the number of microorganisms in the soil, enhancing their activity, thereby increasing the rate of degradation of organic matter. In addition, the fertilization also increased SOC content in the upland. After the abandonment of land, the soil fertility of the upland was restored mainly through the natural succession of vegetation. But in the short term, due to the less external input of organic matter, the SOC concentrations of the abandoned land was relatively lower than those of paddy and upland. Due to the high degree of urbanization, large population, many tall buildings of Shanghai city, in addition, the soil of urban green space was not fertile and the urban lawns were regularly pruned, thus the surface transport of organic matter was greatly reduced, resulting in low SOC concentration of the urban lawns. 
According to the evaluation criteria of Chinese second soil survey (see Table 7.3), the SOC concentrations in the soils of various types of land in Shanghai were on the medium level. Moreover, the SOC concentrations of paddy, upland and woodland were on the medium level, and the SOC concentrations of the abandoned land, urban green space and garden were on or below the medium level, while the SOC concentrations of the tidal land were below the medium level. 
Table 7.3 The evaluation criteria of Chinese second soil survey
Order Levels SOC concentrations (g/kg)
1 Extremely high > 23.2
2 High 17.4-23.2
3 Medium level 11.6-17.4
4 Relatively low 5.8-11.6
5 Low 3.5-5.8
6 Extremely low < 3.5

7.3 Soil bulk density under different land use
Table 7.4 Soil bulk density under different land use
Unit: g/cm3
Land use types Minimum Maximum Mean value STDa Variation coefficient Number of samples
Urban lawn 0.93 1.71 1.35 0.15 11.42% 47
Paddy 1.01 1.66 1.31 0.21 15.36% 30
Upland 1.14 1.64 1.34 0.17 11.21% 24
Woodland 0.89 1.45 1.27 0.14 9.42% 51
Garden 0.66 1.52 1.13 0.28 26.89% 25
Abandoned land 1.22 1.29 1.25 0.06 3.78% 3
Tidal land 1.37 8.31 1.32 1.93 9.12% 10
Soil bulk density is the weight of the soil per unit volume (including porosity), reflecting the soil variability within a longer time, which is affected by the texture, tightness and structure. It is also an important indicator to calculate the density of soil carbon. The statistical analysis of soil bulk density data (see Table 7.4) indicates that the soil bulk density under different land use also showed significant variation.  Judging from the mean soil bulk density, the soil bulk density with various land use decreased in an order: urban lawns (1.35 g/cm3) > upland (1.34 g/cm3) > tidal land (1.32 g/cm3) > paddy (1.31 g/cm3) > woodland (1.27 g/cm3) > abandoned land (1.25 g/cm3) > garden (1.13 g/cm3). Judging from the variation coefficients, the variation coefficient (26.89%) of garden was highest, but the variation coefficients of the soil bulk density of other five kinds of land use types were low. Moreover, compared to the variation coefficients of SOC (see Table 7.2), the variation coefficients of the soil bulk density of various land use types were much lower. 
The soil bulk density of the urban lawns was highest. That was because that the urban lawns were always distributed in the regions with the internal population agglomeration and huge person flow. Compared to other types of land, the urban lawns suffered much more anthropogenic disturbances, making the soil to be compacted resulting in increased bulk density and reduced porosity. In addition, the tidal land was formed by the deposition of the intertidal sediments, and its parent soils were fluvial marine sediments. Affected by the tides in a long time, the soil texture of tidal land was much heavy and clay, resulting high soil bulk density. 
7.4 Soil carbon density under different land use 
Soil organic carbon density is an important indicator reflecting carbon sequestration ability of the soils, which is closely related to the organic carbon content and soil bulk density. Surface (0 - 20cm) soil carbon density can be calculated as follows: 
SOCi= BiCiHi   (7-1)
Where, SOCi means the soil organic carbon density of i type of soils; Bi means soil bulk density (g/cm3); Ci means soil organic carbon (SOC) concentrations of i type of soils; Hi means the soil thickness (20cm). 
According to the formulas 7-1, the soil organic carbon density under different land use patterns were calculated and showed in Table 7.5. 
Table 7.5 Soil carbon density under different land use
Unit: Kg/m2
Land use types Minimum Maximum Mean value STDa Variation coefficient Number of samples
Urban lawn 0.46 8.49 2.79 1.31 45.22% 47
Paddy 1.26 10.55 3.81 2.52 58.72% 30
Upland 0.49 9.46 3.36 1.66 49.19% 24
Woodland 0.93 8.97 3.19 1.23 41.34% 51
Garden 0.76 4.63 2.14 0.37 11.46% 25
Abandoned land 1.75 3.58 2.81 0.83 28.45% 3
Tidal land 0.72 13.811.44 0.41 32.01% 10
The results show that: the soil organic carbon density of paddy topsoil (0-20 cm) was 3.81 kg/m2, significantly higher than other types of land. The SOC concentrations (1.44 kg/m2) of the tidal land were lowest, only equivalent to 37.8% of paddy. The soil organic density of the remaining several types of land use decreased in an order: upland (3.36 kg/m2) > woodland (3.15 kg/m2) > abandoned land (2.81 kg/m2) > urban lawns (2.79 kg/m2) > garden (2.14 kg/m2). Pan et al. (2003) pointed out that the average total organic carbon density of Chinese paddy soil was 2.8 kg/m2. According the results observed in our study, the soil organic carbon density of paddy topsoil in Shanghai was 3.81 kg/m2, higher than the average of Chinese paddy topsoil reported recently. It is suggested that agricultural soils especially paddy soil in Shanghai has high carbon sequestration capability, playing an important role in mitigate the increase of the atmospheric concentrations of greenhouse gases. The SOC concentrations of the artificial forest were higher than those of the upland, but soil dry bulk density of the artificial forest was lower caused by high soil moisture, resulting in the lower SOC density. The soil bulk density of the urban lawns was higher, but their much lower SOC content resulted in lower SOC density of the urban lawns. In addition, due to strong tillage disturbances, the SOC content loss of garden was high, resulting in low SOC content. In addition, its bulk density was low, therefore, the SOC density of garden was very low. 
8. The effects of land use change on the carbon fixation of the soils in Shanghai
In order to investigate the effects of land use change (e.g. turning from paddy into upland farmland abandonment, turning from paddy into woodland) on the soil carbon accumulation, typical sampling sites were also selected, and the adjacent plot method was adopted, so as to provide scientific basis for the in-depth study and assessment of the effect mechanisms of land use change on soil carbon dynamics. 
8.1 The impacts of turning from paddy into upland on the soil carbon
Paddy soil samples and upland soils samples collected in adjacent sampling sites in Liantang town and Xiaozeng town of Qingpu district were selected. The SOC concentrations of these samples were determined and compared, so as to investigate the impacts of changing from paddy to upland on the soil carbon. The number of soil samples was 19, including 9 paddy soil samples and 10 upland soil samples. 
Table 8-1 Soil carbon content of paddy samples in Qingpu district
Indicators Number of samples Minimum Maximum Mean value STD Variation coefficient
SOC (g/kg) 9 9.06 31.51 19.64 8.46 43.04%
Soil bulk density (g/cm3) 9 1.23 1.64 1.50 0.15 10.11%
Soil carbon density (kg/m2) 9 2.36 10.02 6.04 2.88 47.63%
Table 8-2 Soil carbon content of upland samples in Qingpu district
Indicators Number of samples Minimum Maximum Mean value STD Variation coefficient
SOC (g/kg) 10 2.03 28.67 14.02 6.93 49.44%
Soil bulk density (g/cm3) 10 1.08 1.57 1.31 0.16 12.12%
Soil carbon density (kg/m2) 10 0.63 7.25 3.60 1.77 49.09%
Table 8-1 and 8-2 shows the soil carbon content of paddy and upland samples in Qingpu district. From these two tables we can figure out that the SOC concentrations and density of paddy samples in Qingpu district were 19.64 g/kg and 6.04 g/cm3, respectively, much higher than those of the upland. Moreover, the SOC concentrations and density of paddy samples in Qingpu district were 40.09% and 38.28% higher than those of upland soils, suggesting that the change from paddy to upland will reduce the soil carbon content and soil organic carbon density. It was also indicated that the carbon accumulation capacity of paddy was higher than that of upland under the same soil type, topography and climate conditions. When the soil type and other interference factors are constant, land use is the most important factors affecting the soil carbon accumulation capacity. 

8.2 The impacts of farmland abandonment on the soil carbon
8.3 The impacts of turning from paddy into woodland on the soil carbon
9. Conclusions

Reference cited 

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