Trajectory based detection of forest-change impacts on surface soil moisture at a basin scale [Poyang Lake Basin, China]

Journal of Hydrology 514 (2014) 337–346

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Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol

Trajectory based detection of forest-change impacts on surface soil moisture at a basin scale [Poyang Lake Basin, China] Huihui Feng, Yuanbo Liu ⇑ Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 21008, China

a r t i c l e

i n f o

Article history: Received 19 October 2013 Received in revised form 14 April 2014 Accepted 17 April 2014 Available online 24 April 2014 This manuscript was handled by Andras Bardossy, Editor-in-Chief, with the assistance of Purna Chandra Nayak, Associate Editor Keywords: Soil moisture Forest Land cover change trajectory Tree age AMSR-E MODIS

s u m m a r y Surface soil moisture plays a critical role in hydrological processes, but varies with both natural and anthropogenic influences. Land cover change unavoidably alters surface property and subsequent soil moisture, and its contribution is yet hard to isolate from the mixed influences. In combination with trajectory analysis, this paper proposes a novel approach for detection of forest-change impacts on surface soil moisture variation with an examination over the Poyang Lake Basin, China from 2003 to 2009. Soil moisture in permanent forest trajectory represents a synthetic result of natural influences and serves as a reference for isolating soil moisture alternation due to land cover change at a basin scale. Our results showed that soil moisture decreased in all forest trajectories, while the absolute decrease was lower for permanent forest trajectory (2.53%) than the whole basin (2.61%), afforestation trajectories (2.70%) and deforestation trajectories (2.81%). Moreover, afforestation has a high capacity to hold more soil moisture, but may take more than 6 years to reach its maximum capacity. Soil moisture increased from 14.09% to 14.94% for the afforestation trajectories with tree aging from 1 to 6 years. Finally, land cover change may affect soil moisture alternation toward different transformation directions. Absolute soil moisture decreases by 0.08% for the whole basin, 0.17% for afforestation and 0.28% for deforestation trajectories, accounting for 3.13%, 6.47% and 10.07% of the total decrease in soil moisture. More specifically, the transformation from woody Savannas, cropland and other lands to forest generated absolute soil moisture deceases of 0.20%, 0.08% and 0.27%, accounting for 7.26%, 3.52% and 9.57% of the decreases. On the other hand, the reverse transformation generated soil moisture deceases of 0.29%, 0.21% and 0.35%, accounting for 10.43%, 7.69% and 12.14% of the total decrease. Our findings should be valuable for evaluating the impacts of land cover change on soil moisture alternation and promoting effective management of water resources. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction Surface soil moisture plays a critical role in hydrological processes, for which its spatial and temporal distribution strongly influences evapotranspiration (Liang et al., 2010), precipitation (Koster et al., 2004; Taylor et al., 2012), and hot extremes (Hirschi et al., 2011). Thus, it is very important to understand soil moisture variation and identify its influence factors and their contributions. The influences can be divided into two groups: natural and anthropogenic effects. The former includes climate (Holsten et al., 2009), soil texture (English et al., 2005) and terrain (Zhu and Lin, 2011). The latter is commonly characterized by land cover change (Sterling et al., 2012; Yang et al., 2012). These factors influence soil moisture with complex interactions, and a single factor ⇑ Corresponding author. Tel.: +86 25 8688 2164. E-mail addresses: [email protected], [email protected] (Y. Liu). http://dx.doi.org/10.1016/j.jhydrol.2014.04.044 0022-1694/Ó 2014 Elsevier B.V. All rights reserved.

cannot explain the full variation of soil moisture (Gómez-Plaza et al., 2001). For example, soil moisture responses vary with rainfall events even for the same land cover (He et al., 2012; Wang et al., 2013). A high air temperature may result in soil moisture deficit through evapotranspiration, but its relationship with land cover is highly non-linear (Mahmood and Hubbard, 2005; Lofgren et al., 2011). The complex interactions lead to difficulties in isolating the influence of a single factor on soil moisture at a large scale. Separation of the natural and anthropogenic influences on soil moisture remains as a challenge. Clarification of the issue will be helpful for effective management of water resources and climatic adaptation. The anthropogenic effects on hydrological processes have received increasing attentions in recent decade (Barnett et al., 2008; Savva et al., 2013). Land cover change unavoidably alters soil property including infiltration and field capacity (Mapa, 1995; Wang et al., 2012; Savva et al., 2013), which may influence soil

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moisture strongly. Effects of land cover change on soil moisture have been investigated, but reported results varied with locations and times, largely due to complex variation of soil moisture. For example, Mapa (1995) demonstrated that reforested land had the highest steady infiltration rate and soil moisture retention in Kandy of Sri Lanka. In contrast, Yang et al. (2012) reported soil moisture deficits in all land covers changed with plantation in a semi-arid area of the Loess Plateau, China. Giraldo et al. (2008) showed that soil moisture was higher in grassland than bare soil in south Georgia, USA, but Zhang and Schilling (2006) revealed an opposite trend at the Neal Smith National Wildlife Refuge in the Walnut Creek watershed, USA. Existing studies focused on comparative analysis of soil moisture variability in different land covers, but few have identified soil moisture change generated purely from land cover transformation. Wang et al. (2010) reported that soil moisture decreased by 60–100% in areas transformed from forestland into grassland in the Yongding River Basin of China. Yang et al. (2012) reported that deep soil moisture decreased by more than 35% after vegetation restoration. Yet, few studies clearly differentiated the relative contributions from natural and anthropogenic influences. Models are often adopted to simulate soil moisture variation in different land-cover change scenarios (Sheikh et al., 2009; Li et al., 2009), but the linkages between land-cover change and hydrological processes are too complex to be covered with a single model (Hörmann et al., 2005). Comparative analysis of experimental areas with similar natural condition provides a practical way to evaluate the effects of land cover change on soil moisture. It has been applied in relatively small areas (Venkatesh et al., 2011; Wang et al., 2013). At a large scale, it is usually difficult to find areas with similar condition, which hampers its application. Notably, trajectory analysis has been proposed and applied for studying land cover change (Kasperson et al. 1995; Mertens and Lambin, 2000). Trajectories are defined as the trends among the relationships over time between the factors shaping the changing nature of human-environment relations and their effects within a particular region (Kasperson et al., 1995). A land cover change trajectory refers to the succession areas of land cover types in time series (Lambin, 1997; Mertens and Lambin, 2000; Petit and Lambin, 2001). A particular region can be divided into several groups according to land cover transformation (Liu and Zhou, 2005; Zhou et al., 2008). It provides an effective way to select experimental areas for comparative analysis of environmental alternations in different land covers (Feng et al., 2013). In combination with trajectory analysis, this paper proposed a novel approach to isolate the contribution of land cover change on soil moisture. It was then applied for a case examination over the Poyang Lake Basin, China. The structure of this paper is divided into four parts. Section 2 details study materials and methods used. Section 3 describes spatio-temporal variation of soil moisture and its driving factors, and discusses the effects of land cover change on soil moisture. Section 4 comes to conclusions. The study should be valuable for understanding the effects of land cover change on soil moisture, and effective management of water resources undergoing anthropogenic change.

2. Data and methods 2.1. Study area Hydrological processes had been extensively investigated in the Poyang Lake Basin of China Examples include evapotranspiration (Li and Zhang, 2011), precipitation (Fu et al., 2011) and runoff (Guo et al., 2011). Soil moisture variation and its driving factors remain unknown, leaving a gap for understanding of complete

water cycle at a basin scale (Liu et al., 2012a). The Poyang Lake Basin lies between 24°290 and 30°040 N, 113°340 and 118°280 E, with an area of 1.622  105 km2 (Fig. 1). The basin contains the China’s largest freshwater lake, which plays an important ecological and hydrological role in the middle and lower Yangtze River (Hu et al., 2007). It has a subtropical humid climate with an annual mean air temperature of 17.5 °C and a multi-year mean of annual precipitation of 1635.9 mm for 1960–2010. Poyang Lake receives water flows from five main tributaries, including Ganjiang, Fuhe, Xingjiang, Raohe and Xiushui. Its annual discharge to the Yangtze River comprises 15.6% of the total flows of the River (Zhu and Zhang, 1997). 2.2. Data pre-processing 2.2.1. Soil moisture Remote sensing is capable of capturing soil moisture in its spatial consistent view at a large scale, particularly microwave remote sensing (Liu et al., 2012b). This study selected the Level-3 land surface product (AE_Land3) of the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard NASA’s Aqua satellite. The data sets were acquired from the National Snow & Ice Data Center (NSIDC, http://nsidc.org/data/ae_land3.html). It includes daily surface soil moisture, vegetation water content, brightness temperature and quality control items. Soil moisture data has a declared accuracy within 6% at a spatial resolution of 25 km (Njoku et al., 2003; Njoku and Chan, 2006), which has been examined in the United Sates (Sahoo et al., 2008), Europe (Brocca et al., 2011), Australia (Draper et al., 2009) and China (Zhang et al., 2011). In this study, daily soil moisture was segmented for the Poyang Lake Basin with a spatial analyst tool of ‘‘Extract by Mask’’ in ArcGIS. Then, annual mean soil moisture was calculated from the daily values. In addition, two time series in-situ data sets were used to calibrate and validate the AMSR-E soil moisture. The first one was obtained from the Qianyanzhou Ecology Station (26°440 N, 115°030 E), covered by subtropical evergreen coniferous plantation (Wang et al., 2011). The second one is available from the Nankang station, located at 25°410 N, 114°420 E and covered by paddy and arachis hypogasa (http://cdc.cma.gov.cn/home.do). 2.2.2. Land cover MODIS/Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V005 (MCD12Q1 Version 5) data is available for extracting land covers from 2003 to 2009. The data sets contain the land covers classified from five different classification systems, including the International Geosphere – Biosphere Programme (IGBP), the University of Maryland (UMD), the MODIS LAI/FPAR, the net primary production (NPP) and the plant functional type (PFT) classification (Friedl et al., 2010). This study selected the IGBP classification for its high classification accuracy. The overall accuracy of this classification was 75%, with both the user’s and the producer’s accuracy over 70% for most land cover classes (Herold et al., 2008; Friedl et al., 2010). To analyze the main land cover change, the land cover types were grouped into six categories, namely (1) forest (F for short): lands dominated by trees with a percent cover greater than 60%, including Evergreen Needleleaf Forest, Evergreen Broadleaf Forest, Deciduous Needleleaf Forest, Deciduous Broadleaf Forest and Mixed Forest; (2) grassland (G), lands with herbaceous types of cover and forest cover is less than 30%, including Closed Shrublands, Open Shrublands, Savannas and grasslands; (3) Woody Savannas (WS), the transition zone between forest and grassland with the forest between 30–60% and forest cover height exceeds 2 m; (4) Croplands (C): including Croplands and Cropland/Natural Vegetation Mosaic; (5) water (W), which mainly refers the lakes, reservoirs, and rivers and (6) other lands (O): including Permanent

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Fig. 1. Geographical location of the Poyang Lake Basin, China.

Wetlands, Urban/Built-Up, Snow/Ice and Barren or Sparsely Vegetated covers. Auxiliary land cover data in 2005 (data available at http://wdcrre.geodata.cn/) is adopted to evaluate the classification accuracy of MCD12Q1. Considering the classification criteria in two datasets and the dominant land cover types in study area, only forest and cropland are selected for the evaluation. This paper adopts two indices including area ratio (Zimmerman et al., 2013) and user’s accuracy (Friedl et al., 2010) to evaluate the area and spatial agreement of classification. They are described as follows:

where T and NDVI are the LST and NDVI of MODIS products, and subscripts of min and max stand for the corresponding minimum and maximum values. NDVI* and T* data were subsequently resampled to 25 km (resolution of AMSR-E soil moisture). Relationship between AMSR-E soil moisture with resampled NDVI* and T* was evaluated, and regression equation was subsequently built. The downscaling is described as follows (Carlson et al., 1994):

Ar ¼ AMODIS =AAux

ð1Þ

U a ¼ nii =niþ

ð2Þ

where aij are the coefficients of the independent variables, i and j are the dimensions. According to Carlson et al. (1994) and Chauhan et al. (2003), the second- or third-order polynomial regression provides a convincible representation of the relationship. Since SSM, NDVI* and T* are known, coefficients of aij can be estimated through regression analysis. Finally, taken the original NDVI* and T* data (1 km) as independent variables, and resolution of AMSR-E data was downscaled from 25 km to 1 km through Eq. (5). Subsequently, this paper used time series in-situ data to calibrate and validate the downscaled AMSR-E soil moisture. Previous studies that employed the temporal stability concept showed that point soil moisture data could be a representative of a large area (Vachaud et al., 1985; Martínez-Fernández and Ceballos, 2005; Brocca et al., 2009; Loew and Schlenz, 2011). Temporal pattern of local soil moisture follows closely that of spatial average. Therefore, linear regression correction could effectively remove systematic errors in the AMSR-E soil moisture data (Brocca et al., 2011). Since seasonal variation of vegetation significantly influences the AMSR-E land observations (Njoku and Chan, 2006), we calibrated the AMSR-E data at monthly scale. First, a linear regression was built between soil moisture values of Qianyanzhou data and of the corresponding AMSR-E pixel for each month during 2003– 2009, which can be described as follows:

where Ar and Ua are area ratio and user’s accuracy; AMODIS and AAux are area of the MCD12Q1 and auxiliary data for each land cover type; nii is overlap area in spatial of a land cover type between two data sets, ni+ is area of MCD12Q1. Results showed the area ratio and user’s accuracy are 92.97% and 73.05% for forest area, 80.62% and 61.86% for cropland. 2.3. Methods 2.3.1. Downscaling of AMSR-E data Image pixels with resolution of 25 km lose much spatial information of detailed surface. The AMSR-E soil moisture is therefore required to be downscaled to an appropriate resolution. Jia et al. (2011) recommended that resolution with 1 km was appropriate for a basin of 100,000 km2. This paper downscaled AMSR-E soil moisture with resolution from 25 km to 1 km through the approach proposed by Carlson et al. (1994). The 1 km Land surface temperature (LST) data from MODIS MOD11A2 and the 1 km normalized difference vegetation index (NDVI) from MOD13A2 were used for the downscaling. Both NDVI and LST data were normalized to eliminate the effects of dimension unit with following equations (Chauhan et al., 2003):

T  ¼ ðT  T min Þ=ðT max  T min Þ

ð3Þ

NDVI ¼ ðNDVI  NDVImin Þ=ðNDVImax  NDVImin Þ

ð4Þ

SSM ¼

n X n X

aij  NDVIðiÞ  T ðjÞ

ð5Þ

i¼0 j¼0

SSMinsitu;i ¼ ai  SSM AMSR;i þ bi

ð6Þ

where SSMin-situ,i represents the Qianyanzhou data and SSMAMSR,i denotes the corresponding AMSR-E pixel for month i; ai and bi are the regression coefficients. The regression was then used to calibrate the AMSR-E soil moisture for all the pixels over the whole

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basin for the month. After the calibration, the in-situ soil moisture of Nankang data was used to evaluate the accuracy of the calibrated AMSR-E soil moisture. Specifically, the accuracy was described in terms of coefficient of determination (R2) and root mean square error (RMSE). 2.3.2. Trajectory based change detection To generate the land cover trajectories in the Poyang Lake Basin, all classified images were integrated in GIS with ArcGIS TM9.3, and calculated with overlay analysis. In ArcGIS, overlay analysis tools allow applying weights to several inputs and combining them into a single output. Trajectory of Trac can be described as:

Trac ¼

2009 X

Cli  10ð2009iÞ

ð7Þ

i¼2003

where Cli is classified land cover for year i. A trajectory code with seven digits can be obtained through Eq. (7). The code contains two meanings. First, it describes the succession among different land cover types. Furthermore, it implies the time of succession happened. For example, the trajectory code No. 2222221 refers to transformation from grassland to forest in the seventh year. This transformation can be described as grassland ? grassland ? grassland ? grassland ? grassland ? grassland ? forest (No. G ? G ? G ? G ? G ? G ? F for short). Since forest is dominant in the study area, its trajectories are of key concern, which include permanent forest, afforestation, and deforestation trajectories. Soil moisture change in the permanent forest trajectory is a synthetic result of natural influences. It can be used as a reference to evaluate soil moisture alteration generated from land cover change, which can be described as follows:

DSSMLC ¼ SSMLC  SSM forest

ð8Þ

where DSSMLC is soil moisture alteration generated from the land cover change, SSMLC and SSMforest are soil moisture change in the land cover change trajectories and the permanent forest trajectory. 3. Results and discussion 3.1. Spatio-temporal variation of soil moisture in the Poyang Lake Basin Fig. 2(a) comprises the calibrated AMSR-E soil moisture and Qianyanzhou data. RMSE and R2 of the calibrated AMSR-E soil moisture are 1.89% and 0.82. Fig. 2(b) shows the agreement

between the calibrated soil moisture and Nankang data, with a RMSE of 3.62% and a R2 of 0.56. Table 1 and Fig. 3 show the spatial and temporal context of P intra-annual variation of soil moisture (SSMannual ¼ 12 i¼1 SSM i =12 for each pixel, SSMi is monthly soil moisture at i-th month). Temporally, the multi-year average of soil moisture over the whole basin (except for water pixels) is 15.06%. On an annual basis, soil moisture at the basin scale decreased from 16.54% in 2003 to 13.93% in 2009. The decreasing trend is described with y = 0.30x + 16.27 (R2 = 0.53, p < 0.001), where x is the calendar year and y is the corresponding annual mean soil moisture. Droughts occurred in the last decade accounted for the serious soil moisture deficit. Annual precipitation, evapotranspiration and outflow discharge were 1574.7 mm (167.4 mm lower than the multiyear mean), 845.9 mm (118.8 mm) and 743.5 mm (214.4 mm lower than the multi-year mean) in the last decade, which caused a negative water budget (91.1 mm) in the Poyang Lake Basin (Liu et al., 2012a, 2013). Thus, the basin would be expected to be dry because of the low input and the high output of water. Spatially, soil moisture presents a high variability. The driest areas occurred surrounding the lake. The five sub-basins outside the lake region were relatively wet. Two important factors of land covers (discussed in the following sections) and lake shrinkage could contribute to this pattern. Poyang Lake has shrunk in size in recent decades, which can be described as y = 6.15x + 2508.8, where x is the calendar year (Liu et al., 2013). This shrinkage also influenced soil moisture adjacent to the lake. It may have led to decrease of groundwater level and water table (Whltman et al., 1999; Liu et al., 2013), which could generate soil moisture drop through groundwater water discharge (Price, 1997; Woodland et al., 1998). For example, Jarsjö and Destouni (2004) illustrated that the dramatic shrinkage of the Aral Sea after 1960 increased groundwater discharge up to about 200%. Thus, soils adjacent to the lake became drier. 3.2. Soil moisture in different land covers Fig. 4 presents the spatial patterns of land covers. Fig. 5 shows the area ratios of land cover types during the study period. Grassland, cropland and water are mainly located in the central areas, while forest and woody Savannas are in the surrounding area. Forest is dominant, occupying more than 40% of the study area, followed by woody Savannas and croplands (about 20%). Grassland and water body cover approximately 1% of the study area. Temporary areas of forest, woody Savanna and croplands changed during the study period. Forest was similar with woody Savanna, but contrary to the croplands. It implies that the three

Fig. 2. Comparison of (a) AMSR-E and (b) downscaled soil moisture with field measures.

H. Feng, Y. Liu / Journal of Hydrology 514 (2014) 337–346 Table 1 The annual average of soil moisture in the Poyang Lake Basin and its sub-basins (%). Data

Basin

Lake area

Xiushui

Ganjiang

Fuhe

Xinjiang

Raohe

2003 2004 2005 2006 2007 2008 2009

16.54 14.48 15.46 15.70 14.73 14.58 13.93

14.46 12.50 14.07 14.06 12.50 12.13 11.89

16.96 14.78 16.09 16.18 14.87 14.90 14.59

17.01 14.95 15.75 16.07 15.33 15.07 14.23

16.59 14.79 15.43 15.83 14.55 14.69 14.11

16.71 14.69 15.61 15.81 14.79 14.71 14.20

17.16 14.78 16.11 16.07 15.17 15.46 14.82

Average

15.06

13.09

15.48

15.49

15.14

15.22

15.65

land cover types transformed into each other. Grassland changed slightly. Water body and others land covers decreased significantly, with an annual decreasing rates of 0.0584% (R2 = 0.9107) for water and 1.087% (R2 = 0.9129) for others land. Several reasons account for the decline of Poyang Lake. Liu et al. (2013) illustrated that the lake decline was principally ascribed to the decreased stage of the Yangtze River. The stage decrease was a combined result of both water impoundments of the Three Gorges Dam established upstream in 2003 and climatic change in the upper reaches of the Yangtze River (Guo et al., 2012). Fig. 6 shows soil moisture variations in different land covers for 2003–2009. In general, soil moisture presented similar trends. It decreased from 2003 to 2004 and then increased from 2004 to 2006. After then, soil moisture decreased continuously to the end of the study period. Specifically, soil moisture was highest in forestland with a multi-year average of 16.24%, followed by the woody Savannas (14.73%). The lowest value appeared in grassland (12.11%). Two reasons can interpret the difference of soil moisture. First, the area with a high NDVI and a low LST are expected to be

341

wetter than other areas (Tucker, 1979; Mapa, 1995; Wang et al., 2010). As shown in Fig. 7, the maximum NDVI (multi-year average of 0.72) and the minimum LST (294.50 K) appeared in forestland that held the highest soil moisture. Since LST and NDVI are related to the chlorophyll abundance, energy absorption and soil water deficit, they may represent soil moisture condition (Mallick et al., 2009; Wang et al., 2010). Previous studies revealed that NDVI was positively correlated with soil moisture, while LST was negatively correlated (Nemani et al., 1993). For example, Patel et al. (2007) pointed that topsoil moisture increased from 17% to 34% with an increase of NDVI from 0.3 to 0.8 (R2 = 0.15) or with an decrease of LST from 306 K to 300 K (R2 = 0.32). Second, forest has a powerful capacity of water holding. Specifically, forest could increase infiltration (Mapa, 1995) and reduce runoff (Huang et al., 2012) for its developed root systems, organic matter and litter floor. For example, Neris et al. (2012) showed that infiltration rate under green forest story (796 mm/h) was much higher than that of cropped soils (67 mm/h). Mao and Cherkauer (2009) found that the transformation from deciduous forest to grassland and crop resulted in a 10–30% increase in total runoff in the Great Lakes region. Further investigation shows that soil moisture presented decreasing trend in all the land cover types over the study period. Annual decreasing rates were -0.26% (R2 = 0.43) for forest, 0.42% (R2 = 0.64) for grassland, 0.21% (R2 = 0.41) for woody Savannas, 0.36% (R2 = 0.61) for cropland and 0.74% (R2 = 0.86) for others land. It indicates that each land cover has its own responses under the same climatic variations. The minimum decreasing rate in forest (0.26% per year) and woody Savannas (0.21% per year) suggests that trees had a strong ability to hold more soil water, which is consistent with the results of prior studies (Mapa, 1995; Wang et al., 2010, 2013).

Fig. 3. Soil moisture variation from 2003 to 2009.

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Fig. 4. Spatial patterns of land cover from 2003 to 2009.

3.3. Effects of land cover change on soil moisture

Fig. 5. Land cover change of the Poyang Lake basin from 2003 to 2009.

3.3.1. Land cover change trajectories from 2003 to 2009 Land cover change trajectories were extracted with Eq. (7). Each trajectory represents a succession among different land cover types over the basin. Considering classification errors, trajectories of pixels less than 100 were removed from the present study. Furthermore, we assumed that one land cover type would not occur after its transformation into other types. For example, trajectory of F ? G ? G ? F ? G ? G ? G, which refers to forest occur in the fourth year after it transforms into grassland, was removed according the assumption. As a result, 1 permanent forest trajectory, 22 deforestation trajectories and 22 afforestation trajectories were finally determined. Specifically, permanent forest trajectory (NO. F ? F ? F ? F ? F ? F ? F) occupied 30.0% of the study area.

Fig. 6. Soil moisture variation for different land covers.

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Fig. 7. NDVI and LST in different land cover types from 2003 to 2009. Character of F, G, WS, C and O represent land cover type of forest, grassland, woody savannas, croplands and other lands.

Table 2 lists the percentages of deforestation and afforestation trajectories. It shows that 41.1%, 7.1% and 51.8% of the afforestation trajectories converted from woody Savannas, cropland and others land covers. Moreover, 87.4%, 8.8% and 3.8% of the deforestation trajectories transformed into woody Savannas, croplands and others land covers. The transformations between forest, water and grassland were quite small and negligible. Fig. 8 shows spatial patterns of forest change trajectories. The permanent forest shows a concentrated distribution over the mountainous areas (Fig. 8(a)). The afforestation trajectories from the woody Savannas and others land covers were mainly located in the mid-west and south-east areas. The trajectories from croplands were distributed in the lake region (Fig. 8(b)). The trajectories from forest to woody Savannas appeared in the south region (Fig. 8(c)). Other trajectories (forest ? croplands and forest ? others land) occupied small areas and appeared inconspicuous in Fig. 8. 3.3.2. Effects of land cover change on soil moisture dynamics Soil moisture in each trajectory was extracted through mosaic calculation. Fig. 9 shows soil moisture changes for the whole basin and the forest trajectories. Its trend is consistent with precipitation. Permanent forest trajectory had the highest soil moisture, followed

by the afforestation trajectories, the deforestation trajectories and the basin had lowest. Furthermore, all the trajectories presented decreasing trends from 2003 to 2009, but the decreasing rates were quite different. The absolute decrease was lower for permanent forest trajectory (2.53%) than for the whole basin (2.61%), afforestation trajectories (2.70%) and deforestation trajectories (2.81%). It demonstrates that forest could slow down soil moisture decrease, while deforestation could accelerate soil moisture decrease. Soil moisture in afforestation trajectories was lower than that in permanent forest trajectory. It implies a shift in soil moisture from low to high. The main reason was attributed to tree aging (Bauhus et al., 1998; Inagaki et al., 2004). Based on the definition of trajectory, this study divided afforestation trajectories into 6 groups according to succession time. For example, the trajectory of NO. ****** ? F (* is one of the land cover types except forest) means forest with 1 year old, while the NO. ***** ? F ? F means 2 years old. Fig. 10 shows soil moisture change at different tree ages, which increased from 14.09% to 14.94% with tree aging from 1 to 6 years. Yet, before trees mature, its soil moisture would be lower than that in the permanent forest trajectories (15.57%). It indicates that afforestation has a capacity to hold more soil moisture, but it may take at least 6 years to reach its maximum capacity. The

Table 2 The afforestation and deforestation trajectories. Afforestation trajectories

a

a

Index

ID

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20 21 22

O?O?F?F?F?F?F O?F?F?F?F?F?F WS ? WS ? WS ? F ? F ? F ? F O?O?O?F?F?F?F WS ? WS ? WS ? WS ? WS ? WS ? F WS ? F ? F ? F ? F ? F ? F O?O?O?O?F?F?F WS ? WS ? WS ? WS ? WS ? F ? F WS ? WS ? F ? F ? F ? F ? F WS ? WS ? WS ? WS ? F ? F ? F C?C?C?F?F?F?F O ? O ? WS ? F ? F ? F ? F WS ? O ? O ? O ? F ? F ? F C?C?C?C?C?F?F O ? WS ? WS ? F ? F ? F ? F O ? O ? WS ? WS ? WS ? F ? F WS ? O ? O ? F ? F ? F ? F WS ? C ? C ? C ? F ? F ? F C?F?F?F?F?F?F O ? WS ? F ? F ? F ? F ? F O ? O ? WS ? WS ? F ? F ? F C?C?C?C?F?F?F

Deforestation trajectories Area (%)

Index

ID

Area (%)

0.707 0.633 0.358 0.341 0.324 0.293 0.286 0.262 0.239 0.164 0.111 0.101 0.101 0.086 0.085 0.081 0.068 0.064 0.064 0.063 0.063 0.062

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20 21 22

F ? F ? WS ? WS ? WS ? WS ? WS F ? F ? F ? F ? F ? F ? WS F ? F ? F ? F ? F ? WS ? WS F ? WS ? WS ? WS ? WS ? WS ? WS F ? F ? F ? WS ? WS ? WS ? WS F ? F ? F ? F ? WS ? WS ? WS F?F?F?F?F?F?C F ? C ? C ? C ? WS ? WS ? WS F ? C ? C ? WS ? WS ? WS ? WS F ? O ? O ? O ? WS ? WS ? WS F ? F ? C ? C ? WS ? WS ? WS F ? C ? WS ? WS ? WS ? WS ? WS F ? F ? WS ? WS ? WS ? WS ? C F?F?F?F?F?C?C F?F?F?F?O?O?O F?F?F?F?F?F?O F ? F ? C ? WS ? WS ? WS ? WS F ? O ? O ? WS ? WS ? WS ? WS F ? F ? F ? C ? WS ? WS ? WS F?F?F?F?F?O?O F ? F ? F ? WS ? WS ? WS ? C F?C?C?C?C?C?C

1.129 0.937 0.838 0.556 0.486 0.443 0.213 0.13 0.128 0.126 0.101 0.1 0.098 0.086 0.082 0.079 0.076 0.073 0.067 0.063 0.063 0.062

F: forest; G: grassland; WS: woody Savannas; C: cropland and O: other lands.

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Fig. 8. Spatial pattern of (a) permanent forest trajectory, (b) afforestation trajectories and (c) deforestation trajectories in the Poyang Lake Basin.

Fig. 9. Soil moisture changes in forest trajectories.

reason was mainly attributed to age-related decline of transpiration, due to a decrease in stomatal conductance in taller trees. For example, Delzon and Loustau (2005) pointed out that transpiration decline from 508 mm per year for 10-years-old stands to 144 mm per year for 54-years-old stands, and transpiration per unit leaf area also decreased by 55%. Since soil moisture variation of permanent forest trajectory reflects the synthetic result of natural influences (e.g. precipitation and evapotranspiration), the differences between the permanent

Fig. 10. Soil moisture change with tree aging in afforestation trajectories.

forest and other trajectories would be the contribution from land cover change. For the examined case, land cover change contributed an absolute decrease of 0.08% to soil moisture for the whole basin, 0.17% for afforested lands and 0.28% for deforested lands, accounting for 3.13%, 6.47% and 10.07% of the total decrease in soil moisture. In general, permanent forest had the strongest ability to hold soil moisture, and both deforestation and afforestation could lead to decrease in soil moisture. Newly planted trees had a limit ability to hold soil moisture at their early ages, but the capacity would enhance with aging. Land cover transformation makes influences on soil moisture. On the one hand, soil moisture decreased by 2.73%, 2.45% and 2.80% due to the afforestation from woody Savannas, cropland and other lands. The corresponding contributions from the land cover change were 0.20%, 0.08% and 0.27% after eliminating the natural influences, accounting for 7.26%, 3.52% and 9.57% of the total decrease in soil moisture. On the other hand, soil moisture decreased by 2.82%, 2.74% and 2.88% due to deforestation from forest to woody Savannas, cropland and other lands. There were 0.29%, 0.21% and 0.35% of the decrease generated by the land cover change, accounting for 10.43%, 7.69% and 12.14% of the total decrease in soil moisture. It demonstrates that the afforestation could effectively increase soil moisture, and deforestation could decrease soil moisture.

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4. Conclusions Because of the complex interactions between environmental factors and soil moisture, it remains difficult to isolate the effect of a single influence on soil moisture. Hydrological models and comparative analysis of experimental areas are often adopted to analyze the relationship between land cover and soil moisture change. However, neither of the approaches can quantify with a high confidence the effect of land cover change on soil moisture due to theoretical and practical weaknesses in the approaches. In combination with trajectory analysis, this paper proposes a novel approach for detection of forest-change impacts on surface soil moisture with an examination over the Poyang Lake Basin, China from 2003 to 2009. Soil moisture change in the permanent forest trajectory represents a synthetic result of natural influences and serves as a reference to evaluate soil moisture alteration generated from land cover change. This paper used the trajectory-based approach to illustrate and quantify land cover change impacts on soil moisture in the Poyang Lake Basin. Quantitative assessment revealed that soil moisture decreased in all the trajectories. The minimum decrease appeared in the permanent forest and the maximum decrease in the deforestation trajectories. It confirms that the permanent forest slows down soil moisture decrease, and the deforestation accelerates the trend. The afforestation presented a faster decreasing rate than the permanent forest trajectory, but its soil moisture increased with tree aging. Afforestation has a high capacity to hold more soil moisture, but it may take more than 6 years to reach its maximum capacity. Several extensive studies should be operated in future. Firstly, multi- spatial (soil moisture in entire soil profile) and temporal scales (annual, monthly or daily) are needed to reveal comprehensive characteristics of soil moisture. Secondly, since different tree species also influence hydrological processes (Zhou et al., 2002; Jost et al., 2012), the effects of subclass land covers should be also taken into account. Acknowledgments This work was partly supported by the 973 Program of the National Basic Research Program of China (2012CB417003), China Postdoctoral Science Foundation (2013M541742), Key Program of Nanjing Institute of Geography and Limnology of the Chinese Academy of Sciences (NIGLAS2012135001) and the Chinese Academy of Sciences (CAS) 100-Talents Project. We highly appreciate the anonymous reviewers for their constructive comments on this manuscript. References Barnett, T.P., Pierce, D.W., Hidalgo, H.G., et al., 2008. Human-induced changes in the hydrology of the Western United States. Science 319, 1080–1083. Bauhus, J., Paré, D., Côté, L., 1998. Effects of tree species, stand age and soil type on soil microbial biomass and its activity in a southern boreal forest. Soil Biol. Biochem. 30 (8-9), 1077–1089. Brocca, L., Melone, F., Moramarco, T., et al., 2009. Soil moisture temporal stability over experimental areas in Central Italy. Geoderma 148 (3–4), 364–374. Brocca, L., Hasenauer, S., Lacava, T., et al., 2011. Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe. Remote Sens. Environ. 115 (12), 3390–3408. Carlson, T.N., Gillies, R.R., Perry, E.M., 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens. Rev. 9 (1–2), 161–173. Chauhan, N.S., Miller, S., Ardanuy, P., 2003. Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach. Int. J. Remote Sens. 24 (22), 4599–4622. Delzon, S., Loustau, D., 2005. Age-related decline in stand water use: sap flow and transpiration in a pine forest chronosequence. Agric. For. Meteorol. 129 (3–4), 105–119.

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