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Regional soil erosion assessment from remote sensing data in rehabilitated high density canopy forests of southern China
Catena 123 (2014) 106–112
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Regional soil erosion assessment from remote sensing data in rehabilitated high density canopy forests of southern China Haidong Zhang a,b, Dongsheng Yu a,b,⁎, Linlin Dong a,b, Xuezheng Shi a,b, Eric Warner c, Zhujun Gu a, Jiajia Sun a a b c
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China University of Chinese Academy of Sciences, Beijing 100049, China Office for Remote Sensing of Earth Resources, the Pennsylvania State University, University Park, PA 16802-4900, USA
a r t i c l e
i n f o
Article history: Received 6 November 2012 Received in revised form 19 February 2014 Accepted 22 July 2014 Available online xxxx Keywords: Vegetation restoration Plot experiment Vegetation fractional coverage Leaf area index Vegetation restoration degree
a b s t r a c t Soil erosion is the most severe environmental problem in the red soil region of southern China. Erosion has been significantly reduced over the last 30 years through the deployment of a massive government re-forestation program. Nevertheless, soil erosion is still severe in some areas, and an efficient method for assessing soil erosion under secondary forest canopy is needed. Traditionally the vegetation indices derived from remotely sensed imagery have been used for identifying eroded areas. However, under high density forest canopy (HDFC) their applicability suffers due to biomass light absorption, which varies by canopy structure. A mapping method was developed by integrating remote sensing parameters to identify eroded areas. The remotely sensed vegetation indices, vegetation fractional canopy (VFC) and leaf area index (LAI) were calibrated based on soil erosion measurements from previous runoff plot experiments and extrapolated from USLE modeling to a regional scale. Using vegetation restoration degree (VRD), based on VFC (VRDVFC = VFC/VITVFC) and LAI (VRDLAI = LAI/VITLAI), soil erosion under HDFC was identified. Results indicate that the threshold value of VFC (VITVFC) and LAI (VITLAI) ranged from 0.45 to 0.60, and from 1.3 to 2.7, respectively. Secondary forest associated with VRDVFC N 100% or VRDLAI N 100% occupied 75.8% and 37.8% of the total study area, respectively. About 50% of the area distinguished as being eroded by LAI, was mapped as having no obvious erosion by VFC. LAI based mapping had a precision of 96.7% according to field validation. The eroded areas were primarily distributed in locations with elevation between 300 to 500 m and slope angles below 25°. The present method for distinguishing soil erosion under HDFC by combining VRDVFC and VRDLAI is effective and can be used for operational erosion monitoring in the red soil region of southern China. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Red soils (Humic Acrisols) are widely distributed in southern China, covering about 1/5 of the land area of China. Soil erosion is a serious environmental hazard in the region, once referred to as the “Red Desert”, and is characterized by annual disasters and an extremely deteriorated environment (Zhang et al., 2004). Hetian Town, in Changting County, Fujian Province, is representative of the eroded red soil areas in southern China, where soil profiles in some severely eroded areas are characterized by an A horizon (humus horizon) that is almost completely washed away, leaving an exposed B horizon (illuvial horizon) (Zhang et al., 2004). The native primary vegetation of southern China has been rare since the 1940s because of deforestation and burning, which resulted in the ⁎ Corresponding author at: Institute of Soil Science, Chinese Academy of Sciences, 71 East Beijing Road, Nanjing, 210008, China. Tel.: +86 25 8688 1272; fax: + 86 25 86881000. E-mail address: [email protected] (D. Yu).
http://dx.doi.org/10.1016/j.catena.2014.07.013 0341-8162/© 2014 Elsevier B.V. All rights reserved.
described soil erosion. Vegetation restoration is accepted as the most efficient and promising strategy to combat soil erosion over the long term (Albadalejo et al., 1998; Cammeraat, 2004; Elwell and Stocking, 1976; Marques et al., 2007; Mohammad and Mohammad, 2010). Since the early 1980s, a decade-long large-scale re-vegetation program has been carried out. Vegetation has regenerated, and the forest coverage had increased to 52.9% by 2004 (China statistical yearbook, 2004). However, red soils are acidic, having developed from granitic parent material, which can be low in nutrients, and the iron oxide constituents inhibit shrub and grass growth. Overall, the area and intensity of soil erosion, determined from remote sensing (RS) survey, in the red soil region are trending downward with forest cover increases (Liang et al., 2008). Field survey results indicate that soil erosion remains a serious problem, especially in secondary forests where no or sparse grasses and shrubs grow under the canopy (Zhang et al., 2010; Zhou et al., 2002). Sun (2010), a researcher affiliated with our institute, has established the relationship between soil loss, and VFC, and LAI, using data from twelve runoff plots with varying vegetation cover in Hetian Town. Among their findings are that Pinus
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massoniana forests without ground cover had little influence on surface runoff, while a combined vegetation cover of trees and grass reduced surface runoff of 25% and sediment of 90%. Field surveys are of limited spatial extent resulting in low applicability outside the studied area. As a result, RS based methods are the preferred erosion monitoring approach at regional scales, while field surveys are secondary, usually reserved for more specialized applications. RS surveys use vegetation indices (VIs), in combination with land use and topography data, to assess soil erosion and vegetation restoration status (Alejandro and Kenji, 2007; Guerschman et al., 2009; Zhou et al., 2008). VIs are produced by mathematical operations performed on combinations of selected bands in image data sets to aid the distinction of vegetation properties, and support reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations (Bannari et al., 1995). Among them, vegetation fractional coverage (VFC) derived from normalized difference vegetation index, defined as the fraction of vegetation after projection onto the ground plane (North, 2002), is the most widely used index (Fiona et al., 2010; Meusburger et al., 2010a; Meusburger et al., 2010b). Despite the extensive utility of VFC, problems were encountered when using VFC as the sole index to assess soil erosion (Sun et al., 2010), and likely the source of the difference between RS and field surveys. Previous studies have shown that the on- and near-ground vegetation layer in forests is the key to preventing soil erosion (Zhou et al., 2002). VFC has a relatively reduced sensitivity to vertical vegetation structure, which is more reliably documented with field surveys (Wen et al., 2010). Sun et al. (2010) and Zhang et al. (2011) found that LAI, defined as half of the total needle surface area per unit horizontal ground area for coniferous trees (Chen and Black, 1992), overcomes VFC shortcomings and may be more reliable and stable for soil erosion assessment. Recently, Gu (2005) put forward the concept of vegetation restoration degree (VRD), defined as the ratio of the current vegetation condition (expressed as VIs) to the standard value of the vegetation condition (expressed as vegetation indices threshold, VIT), determined from plot experiments, as the value relative no obvious erosion (annual amount of soil erosion less than soil loss tolerance). VRD indicates the degree to which vegetation limit soil erosion, ranging from 0 to 1. Elevated VRD indicates a stronger vegetation influence on soil erosion prevention, and resulting in reduced soil erosion. The VRD method connects vegetation and degree of soil erosion based on the plot experiments.
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The key to determining soil erosion under high density forest canopy (HDFC) is the estimation of near ground vegetation. However, methods using light detection and ranging (LiDAR) or multi-angular imaging are difficult to apply, owing to the processing complexity, and high expense. Thus, more feasible methods are needed. In this study, the remotely sensed VIs (VFC and LAI) were calibrated to soil erosion measurements from previous runoff plot experiments conducted by Sun (2010) to assess soil erosion. Soil erosion under the forest canopy in the red soil region of southern China was described with VRD derived from VFC (VRDVFC) and LAI (VRDLAI), supporting the objectives of this paper, specifically: (i) extrapolate VIT by USLE modeling to a regional scale, (ii) assess the difference between the VRDVFC and VRDLAI, (iii) distinguish the soil erosion spatial distribution in rehabilitated forest land with high density canopy, and (iv) validate a feasible method for assessing soil erosion under HDFC in southern China. 2. Materials and methods 2.1. Study area Hetian Town (116°18′-116°31′E, 25°33′-25°48′N) is located in the central part of Changting County, Fujian Province, China (Fig. 1), surrounded by low mountains and hills resulting in a cauldron shaped landform. It is the largest valley basin in Changting County. Elevations vary from 149 m to 1013 m above mean sea level. A subtropical humid monsoon climate prevails in the area with an average annual temperature of 18 °C. The average annual precipitation is approximately 1700 mm, the majority of which falls between April and June, accounting for about 50% of the annual rainfall. This study examines the secondary forests around Hetian Town. Analysis of remotely sensed data acquired in 1983 revealed that the eroded area around Hetian Town was 158.4 km2 (accounting for 55.7% of total area), with seriously, moderately and slightly eroded area being 58.9%, 20.0% and 20.5% of the total respectively. The degree of soil erosion ranked as the worst in China. With continuous ecological management over the past 20 years, revegetation reclaimed 136.2 km2 of severely eroded land with an 8% drop in 2003. 2.2. Experiment design The experiment design is mainly composed of two parts: runoff plot experiments and remotely sensed image inversion (Fig. 2). The
Fig. 1. Location of the Hetian Town of Changting County, Fujian Province, southern China.
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Fig. 2. Design tree of experiment design in Hetian Town, southern China. DEM is digital elevation models, RS is remote sensing, VFC is vegetation fractional canopy, LAI is leaf area index, LS is the slope length and slope steepness, VI is the vegetation indices derived from remote sense image.
parameters such as soil loss amount, VFC and LAI were obtained from plot experiments by Sun (2010), to establish the relationships between them, to determine the threshold of VFC (VITVFC) and LAI (VITLAI) with no obvious soil erosion (A0 ≤ 500 t/km2 · a). At A0 ≤ 500 t/km2 · a is considered to be threshold at which erosion control is marginally productive, and the erosion intensity could be controlled in the range of light or slight erosion. The spatial distribution of VITVFC and VITLAI for the whole study area was then extrapolated from USLE modeling to the extent of the study area. The aim of remotely sensed image inversion was to obtain the spatial distribution of VFC and LAI across the study area. Finally, the VRDVFC and VRDLAI based on the results of two parts were obtained to assess the soil erosion under HDFC. 2.3. Methodology 2.3.1. Acquisition method of VITs in runoff plots Twelve runoff plots of three types (forest and grass, pure forest and pure grass plots) were established in 2007, which mirror the soil erosion and vegetation characteristics in the study area (Sun et al., 2010). Plot slope angles were all 14%, and the horizontal projection area was 20 m × 5 m. Soils in the plots were all acid red soil developed from granitic parent material. Trees grown in the plots were Pinus massoniana, the most widely grown species in the eroded region in southern China, having an average height of 6.2 m and average diameter at breast height of 6.7 cm. The near ground vegetation, Paspalurn wettsteinii, was replanted in March every year. From 2007 to 2009, 180 rainfall experiments were conducted in the plots using rainfall simulation. Two rainfall intensities, 40 mm · h-1 and 54 mm · h- 1, were identified from natural rainfall data for the study area. Care was taken to irrigate the entire plot (20 m). Runoff and sediment samples were measured after each rainfall event (Sun et al., 2010). Runoff was directly determined by measuring the water volume in the container. A representative runoff sample containing sediment was taken by mixing vigorously and homogeneously and taken to the laboratory for gravimetric processing. The samples were then oven-dried at a temperature of 105 °C until constant weight was achieved (Vásquez-Méndez et al., 2010). Finally, the sediment yield (t/km2 · a) was calculated. A total of 74 VFC (calculated from digital pictures taken with a digital camera) and LAI (measured with LP80 AccuPAR Ceptometer) valid measurements were obtained weekly (Gu et al., 2009). The relationships between soil loss amount (A) and VFC and LAI were then
established from Eqs. (1)–(2) (Sun, 2010). According to the soil loss tolerance (A0) for the red soil region set by the Ministry of Water Resources of the People's Republic of China (Chen et al., 2000), plot threshold VFC (VIT0VFC) and LAI (VIT0LAI) with no obvious soil erosion were determined to be 0.5 and 1.5 with Eqs. (1)–(2). Plot VIT of C was also determined as 0.11 with Eqs. (3)–(4) also developed by Sun (2010). A ¼ 9514 expð−5:8918 VFC Þ
ð1Þ
A ¼ 12358 expð−2:1383 LAI Þ
ð2Þ
A is soil loss amount, VFC and LAI are the values for those variables relative to the complete canopy within the plots, VFC is the vegetation fraction coverage, LAI is the leaf area index. When A equals soil loss tolerance (A0) for the red soil region in southern China (500 t/km2 · a), the value of VFC and LAI are the VIT0VFC and VIT0LAI with no obvious soil erosion. C ¼ −0:603 ln ðVFC Þ−0:302
ð3Þ
C ¼ −0:177 ln ðLAIÞ þ 0:184
ð4Þ
C is the USLE cover and management factor, VFC is the vegetation fraction coverage, LAI is the leaf area index, all derived from the plot measurements. When VFC and LAI equals VIT0VFC and VIT0LAI, the value of C is the threshold with no obvious soil erosion. 2.3.2. Mapping VITs of the study area with LS factor and plot VITs According to the USLE/RUSLE model soil erosion is influenced by rainfall erosivity (R), soil erodibility (K), slope length and slope steepness (LS), cover and management (C), and support practice (P) factors (Wischmeier and Smith, 1978). Because the plots were established mirroring the soil erosion and vegetation characteristics in the study area (Sun et al., 2010), the K, R and P factors of the experimental plots were similar to the study area (Fang et al., 1997). Differences between C and LS factors exist between the study area and experiment plots. According to the transformation equation of USLE: C = A/(R · K · LS · P), the equation about the ratio of C of the study area (Ci) to C of the plots (C) was deduced as Eq. (5). Then the VFC and LAI thresholds with no obvious soil erosion (Eqs. (6)–(7))
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could be deduced using Eqs. (3)–(5), while the parameters were all set as the threshold value. Finally, the VITs for each pixel that compose a remotely sensed image of the study area could be mapped by calculating attribute via ArcGIS 9.2 software. Ci LS ¼ C LSi
ð5Þ
Ci is the C value for each pixel of the study area, C is the C value of plots, LSi is the slope length and steepness factor for each pixel that compose a remotely sensed image of the study area, LS is the plot slope length and steepness factors, and the value is calculated as 1.89 according to Eq. (8), i is the pixel of the remote sensed image of the study area. VIT VFC
−1 ¼ exp −0:345 LSi −0:501
VIT LAI
−1 ¼ exp −1:175 LSi þ 1:039
ð6Þ
ð7Þ
VITLAI is the leaf area index threshold of each pixel in the remotely sensed image of the study area, VITVFC is the vegetation fractional coverage threshold of each pixel in the remotely sensed image of the study area. LSi is the slope length and slope steepness factor for each pixel in the remotely sensed image of the study area, being calculated from Eq. (8) (Wischmeier and Smith, 1978). The spatial distribution of the LS factor was obtained from processing 10 meter resolution ASTER GDEM data (http://datamirror.csdb.cn) with analytical programs implemented with ARC Macro Language (AML) via ArcGIS 9.2 software (Van Remortel et al., 2001). m
LS ¼ ðλ=22:13Þ
2 65:41 sin β þ 4:56 β þ 0:065
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thought of as an area with no obvious soil erosion. Based on the VRDVFC and VRDLAI pixel data calculated above, ArcGIS 9.2 software was used to identify soil erosion for each pixel based on HDFC by the logical expression as Eq. (13). In addition, thirty verification locations were chosen in a subset of the study area (Fig. 2), and a field investigation was carried out in August, 2010 to determine the reliability and precision of the mapping method. VRDVFC ≥1 and VRDLAI b1
ð13Þ
3. Results and discussion 3.1. VIT spatial patterns of the secondary forest land The VITVFC and VITLAI values were high on the edge of the study area and low in the central basin (Fig. 3). The maximum threshold values of VFC and LAI were 0.6 and 2.7 respectively. There was 59.3 km2 secondary forest land (33.6% of total area) associated with the elevated VIT levels, mainly distributed in the northern part of the study area. The minimum threshold values of VFC and LAI were 0.45 and 1.3 respectively, with the area of the lowest VIT encompassing about 8.7% of the total, which was concentrated in the center of the town. 3.2. VRD spatial patterns for secondary forests The general spatial distribution of VRDVFC and VRDLAI were similar when describing the various vegetation restoration levels (Fig. 4). The values of VRD were high in the northern, southeastern, and
ð8Þ
λ is the cumulative slope length (m), β is the uninterrupted slope steepness, m is the slope length parameter. 2.3.3. Acquisition of VFC and LAI from satellite image A SPOT 5 image of the study area (acquired on April 22, 2009, 10 meter spatial resolution) was radiometrically corrected, and then processed to render the spatial distribution of VFC and LAI using Eqs. (9)–(10) proposed by Gu et al. (2011). VFC ¼ −1:14 NDVI þ 0:58 RVI þ 0:01 V−0:01 L−0:10 LAI ¼ −0:012 B þ 0:01 V þ 5:36 NDVI þ 0:08
ð9Þ ð10Þ
NDVI is normalized difference vegetation index, RVI is ratio vegetation index, V is band radiance variation, L is general radiance level, B is visible-infrared radiation balance. 2.3.4. Mapping soil erosion under HDFC The VRDVFC (Eq. (11)) or VRDLAI (Eq. (12)) reflects the degree of vegetation restoration relative to an erosion base line established by either the VFC or LAI thresholds, respectively. VRDVFC ¼ VFC=VIT VFC
ð11Þ
VRDLAI ¼ LAI=VIT LAI
ð12Þ
VRDVFC and VRDLAI are the degree of vegetation restoration of a pixel derived from VFC and LAI, VFC is the vegetation fraction coverage, LAI is the leaf area index, VITVFC and VITLAI are the pixel value of VFC and LAI threshold, respectively. In this study, the vegetation status of the secondary forest with high density canopy is defined as VRDVFC not less than 100%, and typically
Fig. 3. Spatial distribution of two vegetation indices threshold with no soil erosion in Hetian Town, southern China. VITVFC is the vegetation fractional coverage threshold for each pixel that composed a remotely sensed image of the study area. VITLAI is the leaf area index threshold for each pixel that composed a remotely sensed image of the study area. Both VITVFC and VITLAI are derived from the plot experiment according to the slope length and slope steepness factor.
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Fig. 4. Spatial distribution of VRDVFC (a) and VRDLAI (b) at each level in Hetian Town, southern China. VRDVFC is the degree of vegetation restoration derived from VFC for each pixel that compose a remotely sensed image of the study area, VRDLAI is the degree of vegetation restoration derived from LAI for each pixel that compose a remotely sensed image of the study area LAI.
southwestern parts of the study area, which indicated that the reinitiation of soil erosion there was relatively rare. However, there was a difference in the distribution pattern between the two maps. For example, the low VRDVFC values were primarily distributed in the central basin of the study area, where soil erosion was previously high. Conversely, the low VRDLAI values were distributed more widely, not only in the central basin, but also in other areas, such as southeastern and northern parts of the study area (Fig. 4b). In the areas of elevated VRD (VRD ≥ 1.0) values, the distribution of VRDVFC was continuous while VRDLAI was fragmented. The areas associated with VRDVFC and VRDLAI values b 1 were 42.7 km2 and 109.8 km2, respectively (Table 1). The distinct difference between the mapping with the two variables primarily exists in locations with VRD b 0.7. When VRD ≥ 1, VRDVFC encompassed 75.8% of the study area; while the area of VRDLAI only encompassed one half of the former (37.8%). The mapped difference between the two indices is related to the influence of subcanopy vegetation diversity, as vertical vegetation structure is not taken into account for the VRD derived from VFC. When the canopy is dense, the value of VFC is high, but the biomass occurs as a single layer (i.e. pure forest). Given the same vegetation condition as
Table 1 Area statistics for VRDVFC and VRDLAI at each level in Hetian Town, southern China. VRDVFC
Area 2
≤0.4 0.4–0.7 0.7–1.0 ≥1
Percentage
(km )
(%)
11.5 13.4 17.8 133.7
6.5 7.6 10.1 75.8
VRDLAI
Area 2
≤ 0.4 0.4–0.7 0.7–1.0 ≥1
Percentage
(km )
(%)
30.4 50.8 28.6 66.6
17.2 28.8 16.2 37.8
VRDVFC is the degree of vegetation restoration derived from VFC, VRDLAI is the degree of vegetation restoration derived from LAI. VRDVFC or VRDLAI b1 indicates that low degree of vegetation restoration and soil erosion still exists, VRDVFC or VRDLAI b1 indicates high degree of vegetation restoration and no soil erosion exists. Area is the area distribution corresponding with the VRDVFC or VRDLAI. Percentage is the ratio of area to total area of the study.
measured by LAI, the value of VRDLAI would be lower. Demonstrating increased LAI sensitivity to the total vegetation in the profile (Gutman and Ignatov, 1998; Wen et al., 2010). LAI sensitivity is not only influenced by vegetation coverage, but also the number and density of vegetation layers. For VRD values between 0.7 and 1.0, the difference between VRDVFC and VRDLAI did not vary distinctly. The lack of distinction indicates that when VRD was close to saturation, the sensitivity of VRDVFC and VRDLAI to the degree of vegetation restoration decreases slightly. In general, LAI was more sensitive than VFC to the vertical vegetation structure, which enabled a better mapping of subcanopy soil erosion. 3.3. Soil erosion spatial patterns by HDFC areas Soil erosion occurred throughout the whole study area, and was especially concentrated in the mountains (Fig. 5). The total area with HDFC affected by soil erosion was 67.0 km2, accounting for 38.0% of the total forest land in the study area. Thirty survey points were chosen at random in the areas with HDFC mapped as eroded (Fig. 5). Subsequent field survey and validation reveal that, twenty-nine points occurred in locations with no or rare vegetation under the forest canopy, and exhibited various degrees of soil erosion, resulting in a precision of 96.7%. Deviation from these results were found in orchards where red bayberry (Myrica rubra Sieb. et Zucc.) was planted, but its fruit was harvested. The difference could be caused by the growth of grass under the bayberry trees. During the blooming and fruit-bearing periods, the grasses are usually manually weeded, but after harvest the subcanopy grasses were ignored. Due to its high precision, the method in this study could be used to distinguish eroded area under HDFC in the red soil region of southern China. 3.4. Interpretation of distinguished soil erosion spatial patterns Soil erosion under HDFC is influenced to some extent by elevation and slope, a similar result was reported by Koulouri and Giourga
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Fig. 5. Distribution of eroded areas under high density forest canopy and survey points in Hetian Town, southern China. HDFC is high density forest canopy. Erosion area under HDFC is the eroded areas under high density forest canopy derived from vegetation restoration degree. Survey points are the points chosen for field investigation.
(2007). The eroded area increased in locations within the 200 to 400 m elevation range, and reached a maximum extent for the 300 to 400 m range. However, the extent of eroded land decreased with increasing elevation above 400 m. Similarly, the eroded area trended upward on land with slope less than 15°, reaching a maximum on land with slopes 8° to 15°, and trended down when the slope was higher than 15° (Fig. 6). Soil erosion under HDFC primarily occurred in places in the 300 to 500 m elevation range, accounting for 71.7% of the total eroded area (Fig. 6a). Orchards classified as HDFC were widely distributed at the same elevation zone. Human factors such as weeding and firewood collection were soil erosion drivers in these areas. Eroded areas in the b 300 m or N 500 m elevation range were only 6.8% and 21.5% of the total, respectively (Fig. 6a). The erosion patterns are linked to human activity, which also limited high density canopy
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forest land as human habitation, and land cultivation are common in the b 300 m elevation range. On the contrary, in mountain areas with elevation N 500 m, the access is poor and people have difficulty getting to, and moving through the region. The limited access and mobility mean small anthropogenic disturbances result in fragmented vegetation disturbances, promoting good vegetation restoration over large areas (Shi et al., 2008), such that most of the high density canopy forest land is characterized by no obvious erosion. However, in the N 500 m elevation zone, soil erosion under HDFC still exists, and is associated with poor soil quality. For example, quartz sand is often exposed in these areas, which is of poor native fertility, and is compounded with elevated surface temperatures up to 70 °C, impeding grass and shrub growth in some areas, especially around ridges. Slope steepness is another factor affecting soil erosion (Defersha and Melesse, 2012; Fu et al., 2011; Sheng and Liao, 1997). In our study, slope steepness associated with eroded areas under HDFC was primarily below 25°, accounting for 88.8% of the total eroded area (Fig. 6b). Soil erosion was primarily characterized as being of light or moderate severity. Eroded areas with slopes below 15° accounted for 60.1% of the total eroded area. Soil erosion area tended to decrease in the slope range above 15°. Soil erosion in areas with slopes N 25° was limited, accounting for 11.2% of the total eroded area, which contrasts to the patterns of erosion associated with cultivated land (Shi et al., 2012). The contrasting soil erosion results might be explained by the way people live and cultivate. The residential areas and cultivated lands were generally located in the flat areas, where slope is low. Weeding under economic forests and fuel wood collection etc. could lead to poor understory vegetation and increase runoff. In the steep areas, natural factors such as parent material and topography rather than human factors mainly impacted soil erosion. In general, the soil erosion spatial pattern determined for high density canopy secondary forests using the approach described here is rational and consistent with conditions on the ground. The present method for assessing soil erosion under secondary forest canopy using VRDVFC and VRDLAI is effective and can be used for operational erosion monitoring in the red soil region of southern China. Furthermore, the LAI measure was more sensitive than VFC, making it the more preferred indicator of subcanopy soil erosion status.
4. Conclusions Restoration now finds that the area of soil erosion is trending downward. However, due to poor near ground vegetation, soil erosion still exists under HDFC. The eroded areas totaled 42.7 km2 when mapped by VFC, a distinct difference from the eroded areas mapped by LAI, which totaled 109.8 km2. Field survey and validation demonstrates LAI, which is more sensitive than VFC to the vertical vegetation structure,
Fig. 6. Relationship between soil erosion area and topography under high density forest canopy: elevation(a) and slope (b) in Hetian Town, southern China. Area percentage is the ratio of the eroded area at different range of elevation and slope to total eroded area under high density forest canopy.
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and enables a better mapping of subcanopy soil erosion. An increase in vertical vegetation structure reduces soil erosion; however, VFC was less sensitive to canopy variability than LAI. In regards to soil erosion area under HDFC (where the VRDVFC ≥ 1, and the forest is assumed to have a high density canopy), the eroded area was 67.0 km2, accounting for 38.0% of the secondary forest land. Soil erosion under HDFC is influenced by natural and human factors, and primarily distributed in the northern part of the study area. In addition, some areas with elevation between 300 to 500 m and slope angles below 25°, primarily in the southeastern and southwestern parts of the study area, also continue to suffer from soil erosion. This study provided a quick and straightforward approach to monitoring soil erosion in the secondary forest land with high density canopy. Efficient erosion monitoring over large areas has been an important and unresolved scientific problem for many years. Field verification established the precision and reliability of the RS soil erosion mapping method. Therefore, the method examined in this study has important practical significance and societal value for addressing the erosion problems that hinder economic and environmental progress in the red soil region of southern China. Acknowledgements We gratefully acknowledge support for this research from the National Basic Research Program of China (973 Program) (2010CB950702; 2007CB407206), the Foundation of National Natural Science Foundation of China (No. 41001126) and the “Strategic Priority Research Program— Climate Change: Carbon Budget and Related Issues” of the Chinese Academy of Sciences (Grant No. XDA05050507). References Albadalejo, J.,Martínez-Mena, M.,Roldán, A.,Castillo, V., 1998. Soil degradation and desertification induced by vegetation removal in semiarid environment. Soil Use Manag. 14, 1–5. Alejandro, M., Kenji, O., 2007. Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. ISPRS J. Photogramm. RS 62, 309–324. Bannari, A., Morin, D., Bonn, F., Huete, A., 1995. A review of vegetation indices. Remote Sens. Rev. 13, 95–120. Cammeraat, L.H., 2004. Scale dependent thresholds in hydrological and erosion response of a semi-arid catchment in southeast Spain. Agric. Ecosyst. Environ. 104, 317–332. Chen, J., Black, T.A., 1992. Defining leaf area index for non-flat leaves. Plant Cell Environ. 15, 421–429. Chen, Q., Qi, S., Sun, L., 2000. Progress and trend in the research on the amount of soil allowed loss. Bull. Soil Water Conserv. 14, 9–13 (in Chinese). China statistical yearbook [M]. Beijing: China Statistics Press, 2004. Defersha, M., Melesse, A., 2012. Effect of rainfall intensity, slope and antecedent moisture content on sediment concentration and sediment enrichment ratio. Catena 90, 47–52. Elwell, H.A., Stocking, M.A., 1976. Vegetal cover to estimate soil erosion hazard in Rhodesia. Geoderma 15, 61–70. Fang, G.Q.,Ruan, F.S.,Wu, X.H.,Guo, Z.M., 1997. Discussion of soil erodibility characteristics in Fujian Province. Fujian Soil Water Conserv. 2, 19–23 (in Chinese). Fiona, P.M., Saskia, M.V., Stroosnijder, L., 2010. A tool for rapid assessment of erosion risk to support decision-making and policy development at the Ngenge watershed in Uganda. Geoderma 160, 165–174. Fu, S., Liu, B., Liu, H., Xu, L., 2011. The effect of slope on interrill erosion at short slopes. Catena 84, 29–34.
Gu, Z., 2005. Occurrence mechanism of under-forest soil erosion and RS of vegetation restoration degree on water-eroded area [D]. Institute of Soil Science, Chinese Academy of Sciences, Beijing. Gu, Z., Zeng, Z., Shi, X., Li, L., Yu, D., Zheng, W., Zhang, Z., Hu, Z., 2009. Assessing factors influencing vegetation coverage calculation with RS imagery. Int. J. Remote Sens. 30, 2479–2489. Gu, Z.,Shi, X.,Li, L.,Yu, D.,Liu, L.,Zhang, W., 2011. Using multiple radiometric correction images to estimate leaf area index. Int. J. Remote Sens. 32, 9441–9454. Guerschman, J.P., Hill, M.J., Renzullo, L.J., Barrett, D.J., Marks, A.S., Botha, E.J., 2009. Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sens. Environ. 113, 928–945. Gutman, G.,Ignatov, A., 1998. The derivation of the green vegetation fraction from NOAA/ AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens. 19, 1533–1543. Koulouri, M.,Giourga, C., 2007. Land abandonment and slope gradient as key factors of soil erosion in Mediterranean terraced lands. Catena 69, 274–281. Liang, Y., Zhang, B., Pan, X.Z., Shi, D.M., 2008. Current status and comprehensive control strategies of soil erosion for hilly region in the Southern China. Sci. Soil Water Conserv. 6 (1), 22–27 (in Chinese). Marques, M.J., Bienes, R., Jiménez, L., Pérez-Rodríguez, R., 2007. Effect of vegetal cover on runoff and soil erosion under light intensity events. Rainfall simulation over USLE plots. Sci. Total Environ. 378, 161–165. Meusburger, K., Baninger, D., Alewell, C., 2010a. Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery. Int. J. Appl. Earth Obs. Geoinformation 12, 201–207. Meusburger, K., Konz, N., Schaub, M., Alewell, C., 2010b. Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment. Int. J. Appl. Earth Obs. Geoinformation 12, 208–215. Mohammad, A., Mohammad, A., 2010. The impact of vegetative cover type on runoff and soil erosion under different land uses. Catena 81, 97–103. North, P.R.J., 2002. Estimation of fAPAR, LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sens. Environ. 80, 114–121. Sheng, J., Liao, A., 1997. Erosion control in South China. Catena 29, 211–221. Shi, X.,Wang, K.,Warner, E.D.,Yu, D.,Wang, H.,Yang, R.,Liang, Y.,Shi, D., 2008. Relationship between soil erosion and distance to roadways in undeveloped areas of China. Catena 72, 305–313. Shi, Z.,Fang, N.,Wu, F.,Wang, L.,Yue, B.,Wu, G., 2012. Soil erosion processes and sediment sorting associated with transport mechanisms on steep slopes. J. Hydrol. 454–455, 123–130. Sun, J., 2010. Using LAI express the effect of vegetation in preventing soil erosion and vegetation recovering degree [D]. Institute of Soil Science, Chinese Academy of Sciences, Beijing (in Chinese). Sun, J.,Yu, D.,Shi, X.,Gu, Z.,Zhang, W.,Yang, H., 2010. Comparison of between LAI and VFC in relationship with soil erosion in the red soil hilly region of south China. Acta Pedol. Sin. 47, 1060–1066 (in Chinese). Van Remortel, R.,Hamilton, M.,Hickey, R., 2001. Estimating the LS factor for RUSLE through iterative slope length processing of DEM elevation data. Cartography 30 (1), 27. Vásquez-Méndez, R., Ventura-Ramos, E., Oleschko, K., Hernández-Sandoval, L., Parrot, J.F., Nearing, M.A., 2010. Soil erosion and runoff in different vegetation patches from semiarid Central Mexico. Catena 80, 162–169. Wen, Z.,Lees, Brian G.,Jiao, F.,Lei, W.,Shi, H., 2010. Stratified vegetation cover index: a new way to assess vegetation impact on soil erosion. Catena 83, 87–93. Wischmeier, W.H.,Smith, D.D., 1978. Predicting rainfall erosion losses: a guide to conservation planning. U.S. Department of Agriculture, Agriculture (Handbook No. 537). Zhang, B., Yang, Y., Zepp, H., 2004. Effect of vegetation restoration on soil and water erosion and nutrient losses of a severely eroded clayey Plinthudult in southeastern China. Catena 57, 77–90. Zhang, W.,Yu, D., Shi, X., Tan, M.,Liu, L., 2010. Variation of sediment concentration and its drivers under different soil management systems. Pedosphere 20, 78–585. Zhang, W., Yu, D., Shi, X., Wang, H., Gu, Z., Zhang, X., Tan, M., 2011. The suitability of using leaf area index to quantify soil loss under vegetation cover. J. Mt. Sci. 8, 564–570. Zhou, G., Morris, J.D., Yan, J., Yu, Z., Peng, S., 2002. Hydrological impacts of reafforestation with eucalyptus and indigenous species: a case study in southern China. Forest Ecol. Manag. 167, 209–222. Zhou, P.,Luukkanen, O.,Tokola, T.,Nieminen, J., 2008. Effect of vegetation cover on soil erosion in a mountainous watershed. Catena 75, 319–325.
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Regional soil erosion assessment from remote sensing data in rehabilitated high density canopy forests of southern China Haidong Zhang a,b, Dongsheng Yu a,b,⁎, Linlin Dong a,b, Xuezheng Shi a,b, Eric Warner c, Zhujun Gu a, Jiajia Sun a a b c
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China University of Chinese Academy of Sciences, Beijing 100049, China Office for Remote Sensing of Earth Resources, the Pennsylvania State University, University Park, PA 16802-4900, USA
a r t i c l e
i n f o
Article history: Received 6 November 2012 Received in revised form 19 February 2014 Accepted 22 July 2014 Available online xxxx Keywords: Vegetation restoration Plot experiment Vegetation fractional coverage Leaf area index Vegetation restoration degree
a b s t r a c t Soil erosion is the most severe environmental problem in the red soil region of southern China. Erosion has been significantly reduced over the last 30 years through the deployment of a massive government re-forestation program. Nevertheless, soil erosion is still severe in some areas, and an efficient method for assessing soil erosion under secondary forest canopy is needed. Traditionally the vegetation indices derived from remotely sensed imagery have been used for identifying eroded areas. However, under high density forest canopy (HDFC) their applicability suffers due to biomass light absorption, which varies by canopy structure. A mapping method was developed by integrating remote sensing parameters to identify eroded areas. The remotely sensed vegetation indices, vegetation fractional canopy (VFC) and leaf area index (LAI) were calibrated based on soil erosion measurements from previous runoff plot experiments and extrapolated from USLE modeling to a regional scale. Using vegetation restoration degree (VRD), based on VFC (VRDVFC = VFC/VITVFC) and LAI (VRDLAI = LAI/VITLAI), soil erosion under HDFC was identified. Results indicate that the threshold value of VFC (VITVFC) and LAI (VITLAI) ranged from 0.45 to 0.60, and from 1.3 to 2.7, respectively. Secondary forest associated with VRDVFC N 100% or VRDLAI N 100% occupied 75.8% and 37.8% of the total study area, respectively. About 50% of the area distinguished as being eroded by LAI, was mapped as having no obvious erosion by VFC. LAI based mapping had a precision of 96.7% according to field validation. The eroded areas were primarily distributed in locations with elevation between 300 to 500 m and slope angles below 25°. The present method for distinguishing soil erosion under HDFC by combining VRDVFC and VRDLAI is effective and can be used for operational erosion monitoring in the red soil region of southern China. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Red soils (Humic Acrisols) are widely distributed in southern China, covering about 1/5 of the land area of China. Soil erosion is a serious environmental hazard in the region, once referred to as the “Red Desert”, and is characterized by annual disasters and an extremely deteriorated environment (Zhang et al., 2004). Hetian Town, in Changting County, Fujian Province, is representative of the eroded red soil areas in southern China, where soil profiles in some severely eroded areas are characterized by an A horizon (humus horizon) that is almost completely washed away, leaving an exposed B horizon (illuvial horizon) (Zhang et al., 2004). The native primary vegetation of southern China has been rare since the 1940s because of deforestation and burning, which resulted in the ⁎ Corresponding author at: Institute of Soil Science, Chinese Academy of Sciences, 71 East Beijing Road, Nanjing, 210008, China. Tel.: +86 25 8688 1272; fax: + 86 25 86881000. E-mail address: [email protected] (D. Yu).
http://dx.doi.org/10.1016/j.catena.2014.07.013 0341-8162/© 2014 Elsevier B.V. All rights reserved.
described soil erosion. Vegetation restoration is accepted as the most efficient and promising strategy to combat soil erosion over the long term (Albadalejo et al., 1998; Cammeraat, 2004; Elwell and Stocking, 1976; Marques et al., 2007; Mohammad and Mohammad, 2010). Since the early 1980s, a decade-long large-scale re-vegetation program has been carried out. Vegetation has regenerated, and the forest coverage had increased to 52.9% by 2004 (China statistical yearbook, 2004). However, red soils are acidic, having developed from granitic parent material, which can be low in nutrients, and the iron oxide constituents inhibit shrub and grass growth. Overall, the area and intensity of soil erosion, determined from remote sensing (RS) survey, in the red soil region are trending downward with forest cover increases (Liang et al., 2008). Field survey results indicate that soil erosion remains a serious problem, especially in secondary forests where no or sparse grasses and shrubs grow under the canopy (Zhang et al., 2010; Zhou et al., 2002). Sun (2010), a researcher affiliated with our institute, has established the relationship between soil loss, and VFC, and LAI, using data from twelve runoff plots with varying vegetation cover in Hetian Town. Among their findings are that Pinus
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massoniana forests without ground cover had little influence on surface runoff, while a combined vegetation cover of trees and grass reduced surface runoff of 25% and sediment of 90%. Field surveys are of limited spatial extent resulting in low applicability outside the studied area. As a result, RS based methods are the preferred erosion monitoring approach at regional scales, while field surveys are secondary, usually reserved for more specialized applications. RS surveys use vegetation indices (VIs), in combination with land use and topography data, to assess soil erosion and vegetation restoration status (Alejandro and Kenji, 2007; Guerschman et al., 2009; Zhou et al., 2008). VIs are produced by mathematical operations performed on combinations of selected bands in image data sets to aid the distinction of vegetation properties, and support reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations (Bannari et al., 1995). Among them, vegetation fractional coverage (VFC) derived from normalized difference vegetation index, defined as the fraction of vegetation after projection onto the ground plane (North, 2002), is the most widely used index (Fiona et al., 2010; Meusburger et al., 2010a; Meusburger et al., 2010b). Despite the extensive utility of VFC, problems were encountered when using VFC as the sole index to assess soil erosion (Sun et al., 2010), and likely the source of the difference between RS and field surveys. Previous studies have shown that the on- and near-ground vegetation layer in forests is the key to preventing soil erosion (Zhou et al., 2002). VFC has a relatively reduced sensitivity to vertical vegetation structure, which is more reliably documented with field surveys (Wen et al., 2010). Sun et al. (2010) and Zhang et al. (2011) found that LAI, defined as half of the total needle surface area per unit horizontal ground area for coniferous trees (Chen and Black, 1992), overcomes VFC shortcomings and may be more reliable and stable for soil erosion assessment. Recently, Gu (2005) put forward the concept of vegetation restoration degree (VRD), defined as the ratio of the current vegetation condition (expressed as VIs) to the standard value of the vegetation condition (expressed as vegetation indices threshold, VIT), determined from plot experiments, as the value relative no obvious erosion (annual amount of soil erosion less than soil loss tolerance). VRD indicates the degree to which vegetation limit soil erosion, ranging from 0 to 1. Elevated VRD indicates a stronger vegetation influence on soil erosion prevention, and resulting in reduced soil erosion. The VRD method connects vegetation and degree of soil erosion based on the plot experiments.
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The key to determining soil erosion under high density forest canopy (HDFC) is the estimation of near ground vegetation. However, methods using light detection and ranging (LiDAR) or multi-angular imaging are difficult to apply, owing to the processing complexity, and high expense. Thus, more feasible methods are needed. In this study, the remotely sensed VIs (VFC and LAI) were calibrated to soil erosion measurements from previous runoff plot experiments conducted by Sun (2010) to assess soil erosion. Soil erosion under the forest canopy in the red soil region of southern China was described with VRD derived from VFC (VRDVFC) and LAI (VRDLAI), supporting the objectives of this paper, specifically: (i) extrapolate VIT by USLE modeling to a regional scale, (ii) assess the difference between the VRDVFC and VRDLAI, (iii) distinguish the soil erosion spatial distribution in rehabilitated forest land with high density canopy, and (iv) validate a feasible method for assessing soil erosion under HDFC in southern China. 2. Materials and methods 2.1. Study area Hetian Town (116°18′-116°31′E, 25°33′-25°48′N) is located in the central part of Changting County, Fujian Province, China (Fig. 1), surrounded by low mountains and hills resulting in a cauldron shaped landform. It is the largest valley basin in Changting County. Elevations vary from 149 m to 1013 m above mean sea level. A subtropical humid monsoon climate prevails in the area with an average annual temperature of 18 °C. The average annual precipitation is approximately 1700 mm, the majority of which falls between April and June, accounting for about 50% of the annual rainfall. This study examines the secondary forests around Hetian Town. Analysis of remotely sensed data acquired in 1983 revealed that the eroded area around Hetian Town was 158.4 km2 (accounting for 55.7% of total area), with seriously, moderately and slightly eroded area being 58.9%, 20.0% and 20.5% of the total respectively. The degree of soil erosion ranked as the worst in China. With continuous ecological management over the past 20 years, revegetation reclaimed 136.2 km2 of severely eroded land with an 8% drop in 2003. 2.2. Experiment design The experiment design is mainly composed of two parts: runoff plot experiments and remotely sensed image inversion (Fig. 2). The
Fig. 1. Location of the Hetian Town of Changting County, Fujian Province, southern China.
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Fig. 2. Design tree of experiment design in Hetian Town, southern China. DEM is digital elevation models, RS is remote sensing, VFC is vegetation fractional canopy, LAI is leaf area index, LS is the slope length and slope steepness, VI is the vegetation indices derived from remote sense image.
parameters such as soil loss amount, VFC and LAI were obtained from plot experiments by Sun (2010), to establish the relationships between them, to determine the threshold of VFC (VITVFC) and LAI (VITLAI) with no obvious soil erosion (A0 ≤ 500 t/km2 · a). At A0 ≤ 500 t/km2 · a is considered to be threshold at which erosion control is marginally productive, and the erosion intensity could be controlled in the range of light or slight erosion. The spatial distribution of VITVFC and VITLAI for the whole study area was then extrapolated from USLE modeling to the extent of the study area. The aim of remotely sensed image inversion was to obtain the spatial distribution of VFC and LAI across the study area. Finally, the VRDVFC and VRDLAI based on the results of two parts were obtained to assess the soil erosion under HDFC. 2.3. Methodology 2.3.1. Acquisition method of VITs in runoff plots Twelve runoff plots of three types (forest and grass, pure forest and pure grass plots) were established in 2007, which mirror the soil erosion and vegetation characteristics in the study area (Sun et al., 2010). Plot slope angles were all 14%, and the horizontal projection area was 20 m × 5 m. Soils in the plots were all acid red soil developed from granitic parent material. Trees grown in the plots were Pinus massoniana, the most widely grown species in the eroded region in southern China, having an average height of 6.2 m and average diameter at breast height of 6.7 cm. The near ground vegetation, Paspalurn wettsteinii, was replanted in March every year. From 2007 to 2009, 180 rainfall experiments were conducted in the plots using rainfall simulation. Two rainfall intensities, 40 mm · h-1 and 54 mm · h- 1, were identified from natural rainfall data for the study area. Care was taken to irrigate the entire plot (20 m). Runoff and sediment samples were measured after each rainfall event (Sun et al., 2010). Runoff was directly determined by measuring the water volume in the container. A representative runoff sample containing sediment was taken by mixing vigorously and homogeneously and taken to the laboratory for gravimetric processing. The samples were then oven-dried at a temperature of 105 °C until constant weight was achieved (Vásquez-Méndez et al., 2010). Finally, the sediment yield (t/km2 · a) was calculated. A total of 74 VFC (calculated from digital pictures taken with a digital camera) and LAI (measured with LP80 AccuPAR Ceptometer) valid measurements were obtained weekly (Gu et al., 2009). The relationships between soil loss amount (A) and VFC and LAI were then
established from Eqs. (1)–(2) (Sun, 2010). According to the soil loss tolerance (A0) for the red soil region set by the Ministry of Water Resources of the People's Republic of China (Chen et al., 2000), plot threshold VFC (VIT0VFC) and LAI (VIT0LAI) with no obvious soil erosion were determined to be 0.5 and 1.5 with Eqs. (1)–(2). Plot VIT of C was also determined as 0.11 with Eqs. (3)–(4) also developed by Sun (2010). A ¼ 9514 expð−5:8918 VFC Þ
ð1Þ
A ¼ 12358 expð−2:1383 LAI Þ
ð2Þ
A is soil loss amount, VFC and LAI are the values for those variables relative to the complete canopy within the plots, VFC is the vegetation fraction coverage, LAI is the leaf area index. When A equals soil loss tolerance (A0) for the red soil region in southern China (500 t/km2 · a), the value of VFC and LAI are the VIT0VFC and VIT0LAI with no obvious soil erosion. C ¼ −0:603 ln ðVFC Þ−0:302
ð3Þ
C ¼ −0:177 ln ðLAIÞ þ 0:184
ð4Þ
C is the USLE cover and management factor, VFC is the vegetation fraction coverage, LAI is the leaf area index, all derived from the plot measurements. When VFC and LAI equals VIT0VFC and VIT0LAI, the value of C is the threshold with no obvious soil erosion. 2.3.2. Mapping VITs of the study area with LS factor and plot VITs According to the USLE/RUSLE model soil erosion is influenced by rainfall erosivity (R), soil erodibility (K), slope length and slope steepness (LS), cover and management (C), and support practice (P) factors (Wischmeier and Smith, 1978). Because the plots were established mirroring the soil erosion and vegetation characteristics in the study area (Sun et al., 2010), the K, R and P factors of the experimental plots were similar to the study area (Fang et al., 1997). Differences between C and LS factors exist between the study area and experiment plots. According to the transformation equation of USLE: C = A/(R · K · LS · P), the equation about the ratio of C of the study area (Ci) to C of the plots (C) was deduced as Eq. (5). Then the VFC and LAI thresholds with no obvious soil erosion (Eqs. (6)–(7))
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could be deduced using Eqs. (3)–(5), while the parameters were all set as the threshold value. Finally, the VITs for each pixel that compose a remotely sensed image of the study area could be mapped by calculating attribute via ArcGIS 9.2 software. Ci LS ¼ C LSi
ð5Þ
Ci is the C value for each pixel of the study area, C is the C value of plots, LSi is the slope length and steepness factor for each pixel that compose a remotely sensed image of the study area, LS is the plot slope length and steepness factors, and the value is calculated as 1.89 according to Eq. (8), i is the pixel of the remote sensed image of the study area. VIT VFC
−1 ¼ exp −0:345 LSi −0:501
VIT LAI
−1 ¼ exp −1:175 LSi þ 1:039
ð6Þ
ð7Þ
VITLAI is the leaf area index threshold of each pixel in the remotely sensed image of the study area, VITVFC is the vegetation fractional coverage threshold of each pixel in the remotely sensed image of the study area. LSi is the slope length and slope steepness factor for each pixel in the remotely sensed image of the study area, being calculated from Eq. (8) (Wischmeier and Smith, 1978). The spatial distribution of the LS factor was obtained from processing 10 meter resolution ASTER GDEM data (http://datamirror.csdb.cn) with analytical programs implemented with ARC Macro Language (AML) via ArcGIS 9.2 software (Van Remortel et al., 2001). m
LS ¼ ðλ=22:13Þ
2 65:41 sin β þ 4:56 β þ 0:065
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thought of as an area with no obvious soil erosion. Based on the VRDVFC and VRDLAI pixel data calculated above, ArcGIS 9.2 software was used to identify soil erosion for each pixel based on HDFC by the logical expression as Eq. (13). In addition, thirty verification locations were chosen in a subset of the study area (Fig. 2), and a field investigation was carried out in August, 2010 to determine the reliability and precision of the mapping method. VRDVFC ≥1 and VRDLAI b1
ð13Þ
3. Results and discussion 3.1. VIT spatial patterns of the secondary forest land The VITVFC and VITLAI values were high on the edge of the study area and low in the central basin (Fig. 3). The maximum threshold values of VFC and LAI were 0.6 and 2.7 respectively. There was 59.3 km2 secondary forest land (33.6% of total area) associated with the elevated VIT levels, mainly distributed in the northern part of the study area. The minimum threshold values of VFC and LAI were 0.45 and 1.3 respectively, with the area of the lowest VIT encompassing about 8.7% of the total, which was concentrated in the center of the town. 3.2. VRD spatial patterns for secondary forests The general spatial distribution of VRDVFC and VRDLAI were similar when describing the various vegetation restoration levels (Fig. 4). The values of VRD were high in the northern, southeastern, and
ð8Þ
λ is the cumulative slope length (m), β is the uninterrupted slope steepness, m is the slope length parameter. 2.3.3. Acquisition of VFC and LAI from satellite image A SPOT 5 image of the study area (acquired on April 22, 2009, 10 meter spatial resolution) was radiometrically corrected, and then processed to render the spatial distribution of VFC and LAI using Eqs. (9)–(10) proposed by Gu et al. (2011). VFC ¼ −1:14 NDVI þ 0:58 RVI þ 0:01 V−0:01 L−0:10 LAI ¼ −0:012 B þ 0:01 V þ 5:36 NDVI þ 0:08
ð9Þ ð10Þ
NDVI is normalized difference vegetation index, RVI is ratio vegetation index, V is band radiance variation, L is general radiance level, B is visible-infrared radiation balance. 2.3.4. Mapping soil erosion under HDFC The VRDVFC (Eq. (11)) or VRDLAI (Eq. (12)) reflects the degree of vegetation restoration relative to an erosion base line established by either the VFC or LAI thresholds, respectively. VRDVFC ¼ VFC=VIT VFC
ð11Þ
VRDLAI ¼ LAI=VIT LAI
ð12Þ
VRDVFC and VRDLAI are the degree of vegetation restoration of a pixel derived from VFC and LAI, VFC is the vegetation fraction coverage, LAI is the leaf area index, VITVFC and VITLAI are the pixel value of VFC and LAI threshold, respectively. In this study, the vegetation status of the secondary forest with high density canopy is defined as VRDVFC not less than 100%, and typically
Fig. 3. Spatial distribution of two vegetation indices threshold with no soil erosion in Hetian Town, southern China. VITVFC is the vegetation fractional coverage threshold for each pixel that composed a remotely sensed image of the study area. VITLAI is the leaf area index threshold for each pixel that composed a remotely sensed image of the study area. Both VITVFC and VITLAI are derived from the plot experiment according to the slope length and slope steepness factor.
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Fig. 4. Spatial distribution of VRDVFC (a) and VRDLAI (b) at each level in Hetian Town, southern China. VRDVFC is the degree of vegetation restoration derived from VFC for each pixel that compose a remotely sensed image of the study area, VRDLAI is the degree of vegetation restoration derived from LAI for each pixel that compose a remotely sensed image of the study area LAI.
southwestern parts of the study area, which indicated that the reinitiation of soil erosion there was relatively rare. However, there was a difference in the distribution pattern between the two maps. For example, the low VRDVFC values were primarily distributed in the central basin of the study area, where soil erosion was previously high. Conversely, the low VRDLAI values were distributed more widely, not only in the central basin, but also in other areas, such as southeastern and northern parts of the study area (Fig. 4b). In the areas of elevated VRD (VRD ≥ 1.0) values, the distribution of VRDVFC was continuous while VRDLAI was fragmented. The areas associated with VRDVFC and VRDLAI values b 1 were 42.7 km2 and 109.8 km2, respectively (Table 1). The distinct difference between the mapping with the two variables primarily exists in locations with VRD b 0.7. When VRD ≥ 1, VRDVFC encompassed 75.8% of the study area; while the area of VRDLAI only encompassed one half of the former (37.8%). The mapped difference between the two indices is related to the influence of subcanopy vegetation diversity, as vertical vegetation structure is not taken into account for the VRD derived from VFC. When the canopy is dense, the value of VFC is high, but the biomass occurs as a single layer (i.e. pure forest). Given the same vegetation condition as
Table 1 Area statistics for VRDVFC and VRDLAI at each level in Hetian Town, southern China. VRDVFC
Area 2
≤0.4 0.4–0.7 0.7–1.0 ≥1
Percentage
(km )
(%)
11.5 13.4 17.8 133.7
6.5 7.6 10.1 75.8
VRDLAI
Area 2
≤ 0.4 0.4–0.7 0.7–1.0 ≥1
Percentage
(km )
(%)
30.4 50.8 28.6 66.6
17.2 28.8 16.2 37.8
VRDVFC is the degree of vegetation restoration derived from VFC, VRDLAI is the degree of vegetation restoration derived from LAI. VRDVFC or VRDLAI b1 indicates that low degree of vegetation restoration and soil erosion still exists, VRDVFC or VRDLAI b1 indicates high degree of vegetation restoration and no soil erosion exists. Area is the area distribution corresponding with the VRDVFC or VRDLAI. Percentage is the ratio of area to total area of the study.
measured by LAI, the value of VRDLAI would be lower. Demonstrating increased LAI sensitivity to the total vegetation in the profile (Gutman and Ignatov, 1998; Wen et al., 2010). LAI sensitivity is not only influenced by vegetation coverage, but also the number and density of vegetation layers. For VRD values between 0.7 and 1.0, the difference between VRDVFC and VRDLAI did not vary distinctly. The lack of distinction indicates that when VRD was close to saturation, the sensitivity of VRDVFC and VRDLAI to the degree of vegetation restoration decreases slightly. In general, LAI was more sensitive than VFC to the vertical vegetation structure, which enabled a better mapping of subcanopy soil erosion. 3.3. Soil erosion spatial patterns by HDFC areas Soil erosion occurred throughout the whole study area, and was especially concentrated in the mountains (Fig. 5). The total area with HDFC affected by soil erosion was 67.0 km2, accounting for 38.0% of the total forest land in the study area. Thirty survey points were chosen at random in the areas with HDFC mapped as eroded (Fig. 5). Subsequent field survey and validation reveal that, twenty-nine points occurred in locations with no or rare vegetation under the forest canopy, and exhibited various degrees of soil erosion, resulting in a precision of 96.7%. Deviation from these results were found in orchards where red bayberry (Myrica rubra Sieb. et Zucc.) was planted, but its fruit was harvested. The difference could be caused by the growth of grass under the bayberry trees. During the blooming and fruit-bearing periods, the grasses are usually manually weeded, but after harvest the subcanopy grasses were ignored. Due to its high precision, the method in this study could be used to distinguish eroded area under HDFC in the red soil region of southern China. 3.4. Interpretation of distinguished soil erosion spatial patterns Soil erosion under HDFC is influenced to some extent by elevation and slope, a similar result was reported by Koulouri and Giourga
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Fig. 5. Distribution of eroded areas under high density forest canopy and survey points in Hetian Town, southern China. HDFC is high density forest canopy. Erosion area under HDFC is the eroded areas under high density forest canopy derived from vegetation restoration degree. Survey points are the points chosen for field investigation.
(2007). The eroded area increased in locations within the 200 to 400 m elevation range, and reached a maximum extent for the 300 to 400 m range. However, the extent of eroded land decreased with increasing elevation above 400 m. Similarly, the eroded area trended upward on land with slope less than 15°, reaching a maximum on land with slopes 8° to 15°, and trended down when the slope was higher than 15° (Fig. 6). Soil erosion under HDFC primarily occurred in places in the 300 to 500 m elevation range, accounting for 71.7% of the total eroded area (Fig. 6a). Orchards classified as HDFC were widely distributed at the same elevation zone. Human factors such as weeding and firewood collection were soil erosion drivers in these areas. Eroded areas in the b 300 m or N 500 m elevation range were only 6.8% and 21.5% of the total, respectively (Fig. 6a). The erosion patterns are linked to human activity, which also limited high density canopy
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forest land as human habitation, and land cultivation are common in the b 300 m elevation range. On the contrary, in mountain areas with elevation N 500 m, the access is poor and people have difficulty getting to, and moving through the region. The limited access and mobility mean small anthropogenic disturbances result in fragmented vegetation disturbances, promoting good vegetation restoration over large areas (Shi et al., 2008), such that most of the high density canopy forest land is characterized by no obvious erosion. However, in the N 500 m elevation zone, soil erosion under HDFC still exists, and is associated with poor soil quality. For example, quartz sand is often exposed in these areas, which is of poor native fertility, and is compounded with elevated surface temperatures up to 70 °C, impeding grass and shrub growth in some areas, especially around ridges. Slope steepness is another factor affecting soil erosion (Defersha and Melesse, 2012; Fu et al., 2011; Sheng and Liao, 1997). In our study, slope steepness associated with eroded areas under HDFC was primarily below 25°, accounting for 88.8% of the total eroded area (Fig. 6b). Soil erosion was primarily characterized as being of light or moderate severity. Eroded areas with slopes below 15° accounted for 60.1% of the total eroded area. Soil erosion area tended to decrease in the slope range above 15°. Soil erosion in areas with slopes N 25° was limited, accounting for 11.2% of the total eroded area, which contrasts to the patterns of erosion associated with cultivated land (Shi et al., 2012). The contrasting soil erosion results might be explained by the way people live and cultivate. The residential areas and cultivated lands were generally located in the flat areas, where slope is low. Weeding under economic forests and fuel wood collection etc. could lead to poor understory vegetation and increase runoff. In the steep areas, natural factors such as parent material and topography rather than human factors mainly impacted soil erosion. In general, the soil erosion spatial pattern determined for high density canopy secondary forests using the approach described here is rational and consistent with conditions on the ground. The present method for assessing soil erosion under secondary forest canopy using VRDVFC and VRDLAI is effective and can be used for operational erosion monitoring in the red soil region of southern China. Furthermore, the LAI measure was more sensitive than VFC, making it the more preferred indicator of subcanopy soil erosion status.
4. Conclusions Restoration now finds that the area of soil erosion is trending downward. However, due to poor near ground vegetation, soil erosion still exists under HDFC. The eroded areas totaled 42.7 km2 when mapped by VFC, a distinct difference from the eroded areas mapped by LAI, which totaled 109.8 km2. Field survey and validation demonstrates LAI, which is more sensitive than VFC to the vertical vegetation structure,
Fig. 6. Relationship between soil erosion area and topography under high density forest canopy: elevation(a) and slope (b) in Hetian Town, southern China. Area percentage is the ratio of the eroded area at different range of elevation and slope to total eroded area under high density forest canopy.
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and enables a better mapping of subcanopy soil erosion. An increase in vertical vegetation structure reduces soil erosion; however, VFC was less sensitive to canopy variability than LAI. In regards to soil erosion area under HDFC (where the VRDVFC ≥ 1, and the forest is assumed to have a high density canopy), the eroded area was 67.0 km2, accounting for 38.0% of the secondary forest land. Soil erosion under HDFC is influenced by natural and human factors, and primarily distributed in the northern part of the study area. In addition, some areas with elevation between 300 to 500 m and slope angles below 25°, primarily in the southeastern and southwestern parts of the study area, also continue to suffer from soil erosion. This study provided a quick and straightforward approach to monitoring soil erosion in the secondary forest land with high density canopy. Efficient erosion monitoring over large areas has been an important and unresolved scientific problem for many years. Field verification established the precision and reliability of the RS soil erosion mapping method. Therefore, the method examined in this study has important practical significance and societal value for addressing the erosion problems that hinder economic and environmental progress in the red soil region of southern China. Acknowledgements We gratefully acknowledge support for this research from the National Basic Research Program of China (973 Program) (2010CB950702; 2007CB407206), the Foundation of National Natural Science Foundation of China (No. 41001126) and the “Strategic Priority Research Program— Climate Change: Carbon Budget and Related Issues” of the Chinese Academy of Sciences (Grant No. XDA05050507). References Albadalejo, J.,Martínez-Mena, M.,Roldán, A.,Castillo, V., 1998. Soil degradation and desertification induced by vegetation removal in semiarid environment. Soil Use Manag. 14, 1–5. Alejandro, M., Kenji, O., 2007. Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. ISPRS J. Photogramm. RS 62, 309–324. Bannari, A., Morin, D., Bonn, F., Huete, A., 1995. A review of vegetation indices. Remote Sens. Rev. 13, 95–120. Cammeraat, L.H., 2004. Scale dependent thresholds in hydrological and erosion response of a semi-arid catchment in southeast Spain. Agric. Ecosyst. Environ. 104, 317–332. Chen, J., Black, T.A., 1992. Defining leaf area index for non-flat leaves. Plant Cell Environ. 15, 421–429. Chen, Q., Qi, S., Sun, L., 2000. Progress and trend in the research on the amount of soil allowed loss. Bull. Soil Water Conserv. 14, 9–13 (in Chinese). China statistical yearbook [M]. Beijing: China Statistics Press, 2004. Defersha, M., Melesse, A., 2012. Effect of rainfall intensity, slope and antecedent moisture content on sediment concentration and sediment enrichment ratio. Catena 90, 47–52. Elwell, H.A., Stocking, M.A., 1976. Vegetal cover to estimate soil erosion hazard in Rhodesia. Geoderma 15, 61–70. Fang, G.Q.,Ruan, F.S.,Wu, X.H.,Guo, Z.M., 1997. Discussion of soil erodibility characteristics in Fujian Province. Fujian Soil Water Conserv. 2, 19–23 (in Chinese). Fiona, P.M., Saskia, M.V., Stroosnijder, L., 2010. A tool for rapid assessment of erosion risk to support decision-making and policy development at the Ngenge watershed in Uganda. Geoderma 160, 165–174. Fu, S., Liu, B., Liu, H., Xu, L., 2011. The effect of slope on interrill erosion at short slopes. Catena 84, 29–34.
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