International Journal of Remote Sensing Vol. 27, No. 12, 20 June 2006, 2411–2422
Development of topsoil grain size index for monitoring desertification in arid land using remote sensing J. XIAO*{, Y. SHEN{, R. TATEISHI{ and W. BAYAER§ {Center for Environment Remote Sensing, Chiba University, 1-33, Yayoi, Inage, Chiba 263-8522, Japan {Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 153-8505, Japan §College of Geography, Inner Mongolia Normal University, 295, Zhaowuda Road, Huhhot 010022, China (Received 6 June 2005; in final form 3 January 2006 ) The grain size composition of topsoil characterizes the soil texture and other physical properties. The coarsening of topsoil grain size is a visible symbol of land degradation; thereby the change in topsoil grain size can be potentially used to monitor desertification using remote sensing. This study proposes a new index for detecting topsoil grain size composition through ground in situ soil spectral reflectance measurements and soil physical analysis in the laboratory. The proposed topsoil grain size index (GSI), which has a positive correlation with fine sand content, was then applied to detect desertification in Siziwang Banner, Inner Mongolia, China, using a Landsat TM (1993) image and a Landsat ETM + image (2000). The result shows the fine sand content of topsoil increased in most places, indicating a coarsening process of the topsoil in the study area. The fast soil coarsening of degradation is largely caused by human activities.
1.
Introduction
Land desertification has attracted wide concern in recent years since hundreds of millions of people in the world are affected and threatened by drought and famine as a result of the degradation of soils and vegetation (Paisley et al. 1991). According to the definition by the United Nations Convention to Combat Desertification (UNCCD) in 1994, desertification is land degradation in arid, semi-arid and dry sub-humid areas, where the ratio of annual precipitation to potential evapotranspiration falls within the range 0.05–0.65, resulting from various factors, including climatic variations and human activities. This definition has been widely used for this theme around the world (e.g. Rubio and Bochet 1998). The degradation of formerly productive lands is a complex process. Many environmental problems such as soil degradation, dust storm, and social economic topics such as food shortage, health issues, transportation problems, are caused by desertification. It is commonly understood that desertification is largely affected by human activities, such as reclamation, over grazing, as well as climate change. Particularly in arid and semiarid regions, ecosystems are vulnerable to human interference. *Corresponding author. Email:
[email protected] International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2006 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/01431160600554363
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In China, desertification was divided into four levels: extremely severe, severe, ongoing, and potential. This classification is based on geomorphological change caused by blowing sand, the percentage of change in relation to the total area, and the annual increasing rate of desertified land (Zhu 1985), and is widely used by the desertification research community of China (Dong et al. 1994, Zhu and Chen 1994, Yang et al. 2005). Annual desertification land in China increased from 1560 km2 per annum in the 1960s to 2100 km2 per annum in the 1980s and then to 2460 km2 per annum in the 1990s (Wang 2003), showing an accelerating trend. Nowadays, the total desertification land in China amounts to about 0.3346106 km2, of which around 40% is distributed in the Inner Mongolian Autonomous Region. Understanding the desertification process, including its status, trend and drivers, is quite important for understanding the environmental change mechanism and improving land management. Therefore, beyond a scientific purpose, establishing indicators to monitor the desertification process in arid land at macro scale using remote sensing is also significant for land resources management. The texture of topsoil is closely related to land degradation. According to Zhu et al. (1989), different extents of desertification have different topsoil texture, the more severe the desertification the coarser the topsoil grain composition. More recently, Fu et al. (2002) found over grazing can accelerate the soil wind erosion and result in topsoil coarsening. Zhao et al. (2005) showed that the sand content of severe eroded cropland is higher than that of the contrasting cropland. Coarsening of topsoil is a visible sign of land degradation; therefore, the grain size composition of topsoil can potentially be used as an indicator of land degradation. So it is possible to monitor desertification by topsoil grain size change in arid and semiarid areas using a remote sensing technique. From the literature, there is much research on grain size and its spectral reflectance from the 1970s. Most of the research is concerned with snow, sediments (Fily et al. 1999, Ryu et al. 2004), or sand, but, on the pure desert surface, pure mineral (Clark 1999), beach sand (Leu 1977), and sand classification (Williams et al. 1998). Salisbury and D’Aria (1992) mentioned that the grain size of topsoil plays a significant role in erosion potential and other mechanical properties, and the ratio of ASTER thermal infrared bands 10/14 can be used to estimate the particle size of soil, if other ASTER bands are used to minimize the confusion factors provided by soil moisture, vegetation cover, soil organic content, and the presence of abundant minerals other than quartz. A grain size predictor model was preliminarily developed (Leu 1977) from the visible and near-infrared reflectance of iron stained quartz beach samples by utilizing regression analysis and this has been found to be independent of moisture content. Williams et al. (1998) quantified the success of classifying the different sand grain populations (Desert quartz; Fire Island, New York beach grains; and Brazilian crushed quartz) by comparative analysis using discriminant analysis and neural networks, and found that their textural algorithm could separate quartz sand grain populations automatically. Okin and Painter (2004) analysed the hyper-spectral Airborne Visible Infrared Imaging Spectrometer (AVIRIS) derived apparent surface reflectance and demonstrated the expected negative relation between the effective grain size of sand in the plume and reflectance, with the most significant correlations in the short-wave infrared. Clark (1999) investigated that the fact that the reflectance decreases as the grain size increasing, using visible and near-infrared spectra of pyroxenes a sample.
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As mentioned above, there are many separate investigations on soil grain size and their reflectance spectra. However, the relations between natural soil grain size and spectral reflectance have seldom been applied to desertification monitoring. The present study attempts to develop an approach for detecting desertification in arid and semi-arid lands from the change in surface soil texture using remote sensing. Through field investigation and laboratory analysis, according to the relationship between topsoil grain size composition and its spectral reflectance, a new topsoil grain size index is proposed and its potential for detecting desertification is then applied to arid and semi-arid regions of Inner Mongolia, China. 2.
Description of the study areas
The study areas are located in the desertification front in the arid to semi-arid regions of Inner Mongolia, China (figure 1). One is in Siziwang Banner with a major agro-pastoral-desert transition landscape. The other is in the west part of Ordos plateau by Qubqi desert to the north and Ulan Buh desert to the west, with a landscape of desert steppe to desert. The two Landsat Thematic Mapper (TM) images in figure 1 show the two study areas and routes of our field survey, which cross through the two desertification fronts. The upper map in figure 1 is a desert and desertification distribution map of northern China. Our study areas are located on the fringe of potential, on-going and severe desertification areas. The landscapes change from farmland–grassland–degraded grassland–Gobi steppe–Gobi desert along the Siziwang route from the southeast to the northwest (figure 1(a)), and from desert–Gobi steppe–desert steppe along the Ordos route from the northwest to the southeast (figure 1(b)). Both study areas are arid or semiarid continental climates; the precipitation is largely controlled by East Asian Monsoon. In Siziwang Banner, the annual mean precipitation ranges from 110–350 mm becoming drier from the southeast to the northwest. Annual evaporation potential is around 8–10 times the precipitation. Annual mean wind speed is about 4–5m s21, strong windy days are recorded as 50–100 with increasing dust/sand blowing days in recent years. As for the west Ordos area, the annual mean precipitation is about 144.6–366 mm with an evaporation potential of around 16 times the precipitation (China Atlas of Physical Geography 1984). Thus the humidity index (P/PET) of the study areas ranges from 0.06–0.13, indicating a very dry environment. Due to over grazing and over reclamation, the major environmental problems in the study areas are land degradation and sand/dust storms. 3.
Field work and laboratory analysis
The field survey was conducted during 17–29 June 2004. In situ measurements include bare soil spectral reflectance, surface soil moisture, vegetation cover, and topsoil sampling. The spectral reflectance in the visible and near infrared bands was measured using a portable spectrometer (Abe-sekkei Ltd, Model 2703, Tokyo, Japan) with a wavelength range from 400 nm to 1050 nm. At each sample site, 2–3 duplicate points were measured and averaged for analysis. The surface soil moisture was measured as the average moisture in the top 10 cm using a handy TDR (Campbell Scientific Inc., Hydrosense CS620, Logan, Utah, USA) for referencing the effect of soil moisture. Vegetation cover was coarsely estimated by experience using a tape measure. Simultaneously, soil samples from the top 1–2 cm were randomly collected from three areas of 20 cm by 20 cm from each observation site
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(a) (b)
(a)
(b)
Figure 1. Top, the distribution of deserts and desertification lands in northern China. Bottom, study areas (a) and (b). Our field survey routes are illustrated as the vector lines on the Landsat TM image (a) (Siziwang route) and (b) (Ordos route). 1 Gurbang Tunget Desert; 2 Taklimakan Desert; 3 Kumtag Desert; 4 Deserts in Chaidam Basin; 5 Deserts in Hexi Corridor; 6 Badain Juran Desert; 7 Tenger Desert; 8 Ulan Buh Desert; 9 Mu Su Sandy Land; 10 Qubqi Desert; 11 Otindag Sandy Land; 12 Horqin Sandy Land; 13 Nenjiang Sandy Land; 14 Hulun Buir Sandy Land (map source: Zhu et al. 1989).
and mixed together for laboratory grain size analysis. A portable GPS system (Empex, FG-0212, Tokyo, Japan) (Garmin, Geko 201) was employed to record the geo-positions of each sampling site. Grain size distribution/composition analyses of topsoil samples were carried out using standard sieving techniques in the laboratory. Each topsoil sample was divided into seven classes: clay (,0.005 mm), silt (0.005–0.0075 mm), fine sand (0.0075–0.425 mm), coarse sand (0.425–2 mm), fine gravel (2–4.75 mm), middle gravel (4.75–19 mm), and coarse gravel (19–75 mm) according to the Japanese Geotechnical Society’s classification standard (JGS 2003), which is a little different to the international standard: clay (,0.002 mm), silt (0.002–0.02 mm), fine sand (0.02–0.2 mm), coarse sand (0.2–2 mm), gravel (.2 mm). Most of the soil samples are composed of clay, silt and fine sand. The samples collected from Gobi or Gobi steppe have much gravel and coarse sand.
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Satellite remote sensing data and preprocessing
In this research, the progress of desertification in the Siziwang area was evaluated using only two satellite remote sensing images from 1993 and 2000. One is a Landsat TM image recorded on 18 June 1993 (path/row 127/32). The other is a Landsat ETM + image recorded on 15 July 2000 (path/row 127/32). Both images, provided by the Global Land Cover Facility, Maryland University (http://glcfapp.umiacs.umd), are cloud free. The two images were transformed from DN value to the reflectance images, which can be considered to be the combined surface and atmospheric reflectance of the Earth, computed using the following equation rp ~
pLl d 2 ðESUN Þl cos hs
ð1Þ
where rp is the planetary reflectance; Ll is the spectral radiance at the sensor’s aperture; d is the Earth–sun distance in astronomical units; (ESUN)l is the mean solar exoatmospheric irradiance; hs is the solar zenith angle in degrees. The two images were then geo-rectified and a relative atmospheric correction was applied to minimize the effect of differences in atmospheric conditions between the two images. 5. 5.1
Results and discussions The spectral reflectance versus topsoil grain size
The in situ spectral measurements illustrate that the surface spectral reflectance has a close relation with the topsoil grain size composition (figure 2). Comparing typical vegetation spectral reflectance, all the curves of soil surface reflectance have relatively similar spectral features: they increase gradually in the visible–nearinfrared wavebands except for a little absorption at 675 nm. In general, as widely known, the reflectance of the soil surface gradually increases with wavelengths in the visible and near-infrared bands. The amount of light scattered and absorbed by an independent grain is dependent on grain size (Clark and Roush 1984, Hapke 1993). According to the United States Geological Survey’s Speclab Report (Clark 1999), for a pure clay mineral material (pyroxene), with a grain size of 5–250 mm, falling on the silt and fine sand range of the present study, the laboratory results show that, ‘as is usually the case in the visible and nearinfrared, the reflectance decreases as the grain size increases.’ However, our in situ measurements show a reverse trend, i.e. the reflectance decreases as the content of clay and silt grains increase, and inversely, the reflectance increases as the total fine sand content increases in the topsoil (figure 2). This phenomenon might be attributed to a mixture effect of the different size particles. Moreover, even though 10–40% of our field samples are silt and clay grains, due to the strong wind erosion conditions, there should be fewer finer grains at the surface exposed to the measurements. Unfortunately, until today, there do not appear to be any reports on the reflectance of samples with a mixture of different grain size particles. Soil moisture can affect land surface reflectance (e.g. Ishiyama et al. 1996). However, in arid and semi-arid areas, especially in sandy lands, the vertical distribution of soil moisture can vary extremely. Surface sandy soil has a very low water holding capacity. Li and Li (2000) reported a soil moisture content of 0.6– 0.7% for the surface layer of a Mu-Us sandy land, near the centre of Ordos plateau. Therefore, the effects of soil moisture on the reflectance of different topsoil were
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Figure 2. Reflectance spectra of the topsoil samples. The samples named with S are collected from the Siziwang region; those named with D or H are collected from Ordos Plateau. The arrow shows a fine-sand increasing and clay-silt decreasing direction. The reflectance decreases as the content of clay and silt grain increases, and inversely, the reflectance increases as the fine sand content increases in the topsoil as shown.
neglected in this study since the soil moisture content is at a very low level of 5–10% even throughout the whole layer of 0–10 cm depth. In addition, the difference in mineral composition of the topsoil may slightly influence the reflectance. 5.2
Topsoil grain size index (GSI)
Even though most researchers used the Normalized Difference Vegetation Index (NDVI), the Bare Soil Index (BSI) (e.g. Rikimaru et al. 1997), or percentage grass cover (e.g. Soyza et al. 1998, Geerken and Haiwi 2004) to evaluate desertification progress by detecting the change in vegetation or bare soil cover, these kinds of indices are strongly dependent on the precipitation, which has large temporal and spatial variability and uncertainty in arid regions. The vegetation cover can be significantly increased even after one rainfall event, thus, these indices have difficulties with calculating the actual degree of desertification. In order to develop a practicable index to associate with the physical properties (mechanical composition) of topsoil for monitoring the change in surface soil texture using remote sensing, we analysed the correlations between several spectral indices mentioned above and topsoil grain size composition. It is found that a new index has the best
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correlation (R250.7387) with fine sand content of topsoil. Figure 3 illustrates the relationship between grain size index (GSI) and fine-sand and silt-clay contents of topsoil. The GSI was specifically designed for using Landsat TM/ETM + data as follows GSI~ðR{BÞ=ðRzBzGÞ
ð2Þ
where, R, B, and G are the reflectance of the red, blue and green bands of the Landsat TM and ETM + sensors. Taking into consideration the reflectance spectra of different soil surfaces and vegetation (figure 2), the difference between band R and B in the GSI equation is designed to distinguish between the vegetated or water surface and bare soil; while the accumulation of the reflectance in the R, G and B
Figure 3. topsoil.
Relationship between GSI and (a) fine sand content, (b) clay and silt content of
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bands can discriminate the topsoil with different grain size composition. For vegetated surfaces, the difference between R and B will be minor or negative. Contrarily, this difference is large for bare soil surfaces. On the other hand, the accumulation of the visible band reflectance has a clear increasing trend when the fine sand content of the topsoil increases. Therefore, the designed GSI can potentially detect surface soil texture or grain size composition. The GSI value is
Figure 4. 2000.
Distribution of soil grain size index (GSI) of Siziwang Banner in (a) 1993 and (b)
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close to 0 in vegetated and water areas, sometimes, it can even be negative. According to the regressed equation between GSI and fine-sand content, the upper limit of GSI of these soil samples is about 0.20, where the land surface is fully covered by fine sand, i.e. desert. The point with the highest fine-sand content in figure 3 is the measurement taken from the Ulan Buh desert. 5.3
Application of GSI to Siziwang Banner
Using the proposed GSI, we produced two GSI distribution maps of the Siziwang area in 1993 and 2000 respectively (figure 4). Figure 4(a) shows a high GSI value area (white colour) concentrated in the northwest part, and decreasing from the northwest to the southeast, which means the fine sand content of the topsoil is decreasing in this direction. On the contrary, clay and silt are increasing in this direction, coherent with land cover type changing from sandy land to grassland to cropland. In the northwest arid region, where the land cover is Gobi-desert to Gobi steppe, there are less fine grains (clay, silt) in the surface soil. In the southeast side, a cropping area, soil is well conserved by vegetation and has a large content of clay-silt grains, so it has a low GSI value. Comparing figure 4(a) and figure 4(b), we produced the topsoil GSI change map during 1993 to 2000 (figure 5). The GSI value increased significantly in the northwest part, especially along the river sides, illustrating that the topsoil in the northwest part has a higher fine sand content than that of seven years ago. There are several big patches of high GSI value that expanded and moved to the southeast of the image, implying that the desertification developed in a southerly and easterly direction, and the desertification degree of the whole area continues turning to be serious during this seven years. Figure 6 shows three selected typical sample areas with different landscapes. It clearly shows the imprint of human activities on the soil degradation. At the cultivated cropland area (Area 1), the soil coarsening occurred on the margins of the fields, where the soil was less protected by crops and exposed
Figure 5.
Grain size index (GSI) change map of the study area during 1993–2000.
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Figure 6. The three typical sample areas for (a) cultivated (Area 1), (b) grassland (Area 2) and (c) Gobi marginal area (Area 3) and their positions on the Landsat TM scene (d ). White colour accounts for the fine sand increased areas and grey for no change or slightly decreased.
to wind erosion. However, in the case of grassland (Area 2), where grazing is the major force from human society, most parts of the grassland show a deteriorated change except for the fenced grassland (the irregular polygons in grey). As for the Gobi marginal area (Area 3), the land surface is always under strong wind erosion, the topsoil change mainly manifests as fine sand deposition along the river channel or the leeward side of Gobi shrubs. Under relatively stable physical environments, it is useful to discuss the topsoil grain size and soil texture change over a long period, because the soil grain size and soil texture change slowly. However, in the north part of China, under high pressure from the need for food for the increasing population, over grazing and cultivation are serious problems. So, the desertification process is largely accelerated by human activities in the study area. The change in GSI during the 7-year period is fairly large. Economic statistics of Siziwang Banner shows that cultivated land increased from 129 000 ha in 1993 to 149 000 ha in 2000 with a pure increase of 15.5%; herd of livestock increased about 28% from 680 000 to 1 048 000; annual meat production increased 228% from 6794 tons to 22 251 tons; and wool production also increased
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26.7% from 2024 tons to 2564 tons. The severe land degradation during such a relatively short period was largely affected by human activities. The economic growth was, to some extent, a trade-off for the environmental deterioration, i.e. desertification. 6.
Concluding remarks
Land desertification is a complex process. In this research we attempt to establish a practicable grain size index to monitor the desertification process by detecting the soil grain size distribution change of the land surface. The GSI proposed in this research can detect the abundant fine-sand area well and shows its potential for monitoring the desertification process in arid regions. It may possibly be used for detecting the source area of sand/dust storms in Asia. The change in topsoil grain size, however, is an integrated result of wind erosion and deposition. Detailed discrimination of fine sand patch formation should take into account the effects of land surface micro-topographical and dominant wind characteristics in order to help understand the mechanism of wind-driven desertification. On the other hand, the current GSI is only effective in bare or sparse vegetated areas, so it is better to apply this index using the images in spring when vegetation is pre-germinating. Moreover, on the gravel vanished land, e.g. the Gobi desert, the reflectance of the soil surface is largely affected by the gravels, which can hinder the soil grains and make the reflectance a complicated process. In the current study we could not discuss the topsoil reflectance under this special situation. Therefore, there is some room to improve the index. In our follow-up field research, a series of more comprehensive experiments will be carried out to evaluate the effect of these factors. Acknowledgement The authors gratefully acknowledge Mr Masahiro Koike, Institute of Industrial Science, The University of Tokyo, for his great assistance in the laboratory analysis of the soil samples. Mr Bilige Bater and Dr Chunxing Hai, Inner Mongolia Normal University gave us great help with the field survey. We also thank two anonymous reviewers for their constructive comments to improve the first manuscript. References CHINA ATLAS OF PHYSICAL GEOGRAPHY, 1984, Sino-maps Press (Beijing), pp. 167–172. CLARK, R., 1999, Spectroscopy of rocks and minerals, and principles of spectroscopy. In Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. Available online at: http://speclab.cr.usgs.gov/PAPERS.refl-mrs/refl4.html#section6.2. CLARK, R.N. and ROUSH, T.L., 1984, Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research, 89, pp. 6329–6340. DONG, G., DONG, Y., JIN, J., JIN, H. and LIU, Y., 1994, Study on the cause and development trend of desertification in the midstream region of Yarlung Zangbo River, Tibet. Journal of Desert Research, 14, pp. 9–17. FILY, M., DEDIEU, J.P. and DURAND, Y., 1999, Comparison between the results of a snow metamorphism model and remote sensing derived snow parameters in the Alps. Remote Sensing of Environments, 68, pp. 254–263. FU, H., WANG, Y., WU, C. and TA, L., 2002, Effects of grazing on soil physical and chemical properties of Alxa desert grassland. Journal of Desert Research, 22, pp. 339–343. GEERKEN, R. and ILAIWI, M., 2004, Assessment of rangeland degradation and development of a strategy for rehabilitation. Remote Sensing of Environment, 90, pp. 490–504.
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