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Land use model research in agro-pastoral ecotone in northern China: A case study of Horqin Left Back Banner
Journal of Environmental Management 237 (2019) 139–146
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Research article
Land use model research in agro-pastoral ecotone in northern China: A case study of Horqin Left Back Banner
T
Jian Zhoua,∗, Yan Xub, Yang Gaob, Zhen Xieb a b
Northwest Land and Resources Research Center, Shaanxi Normal University, Xi'an, 710119, China College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Land surface temperature Vegetation growth Soil moisture Dune–interdune Land use model Agro-pastoral ecotone in northern China
The agro-pastoral ecotone in northern China is an ecological fragile region, where desertification is a prominent problem and mainly induced by extensive cultivation. The typical topography of the area is dune–interdune. The irrational land use model of dune–interdune should be investigated to benefit crop production and anti-desertification. This study investigated vegetation growth status by calculating land surface temperature and soil moisture differentiation along dune–interdune. Dry land maize growth status worsened, and grass growth status showed a trend of worsening first and then improving with increasing elevation of the dune–interdune topography; meanwhile, soil moisture decreased. A land use model was designed according to soil moisture differentiation and vegetation growth condition to obtain crop production and anti-desertification. In the top region of the dune–interdune, water tolerance grass should be maintained for anti-desertification. In the middle region, cultivated land infrastructures should be established to obtain sustainable cultivated land use and avoid frequent change in land use. In the bottom region, rice can be planted, and drainage facilities should be constructed if maize is planted. Crop production and anti-desertification can be completed simultaneously in the land use model of dune–interdune.
1. Introduction Desertification was defined as “land degradation in arid, semiarid, and dry sub-humid areas resulting from various factors, including climatic variability and human activities” in the Convention to Combat Desertification and Drought in 1994 (Chen and Tang, 2005). Desertification is a major ecological problem worldwide (Kassas, 1995; Moran et al., 2009; Zhao et al., 2014). Desertified land accounts for 3.6 × 107 km2, covering 24.1% of the Earth's land surface, and affects approximately one-sixth of the world's population, many of whom live in poverty (Xue et al., 2013). Natural factors, including precipitation, wind, and human activities, such as extensive reclamation and overgrazing, are the driving forces of desertification (Dawelbait and Morari, 2012; Ge et al., 2013; Li et al., 2013; Ma et al., 2007). However, the main driving force of desertification varies among different regions (Wang et al., 2005; Wu, 2001). The agro-pastoral ecotone in Northern China has semi-arid continental monsoon climate. Precipitation in this area cannot meet the water demand for normal growth of dry land maize, which is the main crop in the region. Moreover, precipitation fluctuation is dramatic and considerably influences vegetation growth (Zhou et al., 2018). Aeolian
∗
sandy soil is the main soil type in this region. Under natural conditions, the agro-pastoral ecotone in Northern China is an ecological fragile area, which makes it prone to desertification (Jiang et al., 2005; Peng et al., 2016; Xu et al., 2014). Agriculture production and animal husbandry coexist in the region. Land resource is vast in the agro-pastoral ecotone in Northern China, and it was 0.0281 km2 per capita for Horqin Left Back Banner (HLBB) in 2013. On the one hand, plantation can provide fodder and forage for animal husbandry. On the other hand, animal husbandry can make up the economic risk of plantation induced by drought (Yang and Xu, 2016). Under the complementarity condition of plantation and animal husbandry, farmers reclaim land as much as possible to increase their income (Zhou et al., 2017b). Su et al. (2004) revealed that the soil organic matter, total nitrogen, and total phosphorus contents in Horqin Sandy Land declined to approximately 18%–38% in 3 years after the grass land was reclaimed into cultivated land; the soil particle distribution of the reclaimed cultivated land caused it to be prone to desertification. Zhao et al. (2005) found soil deterioration symbolized by soil structure deterioration, and soil fertilization declined evidently; the recovery of soil properties would take considerable time. Thus, soil deterioration of reclaimed cultivated land, which is unsuitable for cultivation, is the main driving force of
Corresponding author. E-mail address: [email protected] (J. Zhou).
https://doi.org/10.1016/j.jenvman.2019.02.046 Received 4 September 2018; Received in revised form 1 December 2018; Accepted 7 February 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Environmental Management 237 (2019) 139–146
J. Zhou, et al.
desertification in the agro-pastoral ecotone in Northern China (Dong et al., 2011; Ge et al., 2013; Ma et al., 2007). Farmer's ability and reclamation incentive are enhanced with the rapid development of agriculture mechanism (Luo et al., 2016). Thus, anti-desertification remains prominent in the agro-pastoral ecotone in northern China. For anti-desertification, several ecological restoration projects have been launched; these projects include the “Three-north Shelterbelt Project” in 1978, “Grain for Green Project” in 1999, and “Beijing and Tianjin Sandstorm Source Controlling Project” in 2000. High and low vegetation coverage land increased and decreased, respectively (Qiao et al., 2006). The development process of desertification has been primarily curbed by ecological restoration engineering (Li et al., 2009; State Forestry Administration, 2011, 2016; Zhang et al., 2012). However, Ge et al. (2016) found a linear correlation between agricultural GDP per capita and severe desertified land, which indicated that farmers increased their income by cultivating considerable land with a small input in the agro-pastoral ecotone. Vegetation was mainly degraded in fixed and semi-fixed sandy dunes, which had high vegetation coverage after ecological restoration (Zhang et al., 2012). This phenomenon reflects that land with good vegetation coverage caused by ecological restoration is reclaimed again into cultivated land, thereby inducing vegetation degradation and desertification once again. Dune–interdune topography is typical (Liu et al., 2016), and water resource is a key factor in determining land use in the agro-pastoral ecotone in Northern China (Tao et al., 2001; Wang and Qin, 2017). Precipitation flows to the low area of this terrain, and a large difference in soil moisture can be observed in various locations along dune–interdune (Zhou et al., 2017c). Farmers reclaimed land at the top and upper regions of dune–interdune, where water condition was poor and land was unsuitable for cultivation, to yield a high economic income. This method of land use induced desertification and prevented ecology rehabilitation. Thus, irrational land use arrangement along dune–interdune topography is the key factor in determining ecological restoration under the condition of desertification curbed in general and expanding in partial locations. According to the ecological characteristics of different regions, relevant studies have proposed different land use models for anti-desertification. In the Hexi Corridor, which is a region of saline land of desert in an arid area, a control desertification model of integrated “fixing, closing, defending” has been established by fixing moving sand with a sandy barrier, closing to increase vegetation, and making up for defending belt and establishing a wind break belt (Yang et al., 2004). In Maowusu Sandy Land, a grass and agroforestry model for low land, a semi-artificial model for soft hills, and a natural grazing model for hard hills have been suggested (Zhang, 1994). Moreover, a “three circle” model has been proposed for Maowusu Sandy Land (Zheng, 1998). The two land use models were proposed for different topographic regions in Maowusu Sandy Land, and micro-topography differences in different topographic regions were not considered in the two land use models. A ‘‘tri-circle’’ land use paradigm has been implemented around Yulin City to address the soil wind erosion that surrounds the urban area (Yue et al., 2016). Different land use models have been proposed for antidesertification in consideration of the different regional natural characteristics. Study on land use model at dune–interdune topography is rare and urgent for anti-desertification in the agro-pastoral ecotone in Northern China. Thus, this study aims to 1) investigate vegetation growth difference from the top to the bottom of dune–interdune; 2) explore soil moisture difference along dune–interdune; and 3) prompt an irrational land use model of dune–interdune according to soil moisture difference and vegetation growth condition for anti-desertification in the agro-pastoral ecotone in Northern China.
Fig. 1. 030/119 and 030/120 remote sensing images of HLBB in 2013.
2. Material and methods 2.1. Study area HLBB (121°30′E−123°42′E, 42°40′N–43°42′N) is a county in Inner Mongolia that is located in the agro-pastoral ecotone in Northern China (Fig. 1). The area of HLBB is 1.1 × 104 km2. As a part of Horqin Sandy Land, which is the largest sandy land in China, desertification is a prominent eco-environmental problem in HLBB. Desertified land reached 7042 km2 in 1996, which accounted for 61.3% of the total land area. From 2001 to 2010, desertified land accounted for an average of 38.7%. HLBB has a temperate continental monsoon climate. The average annual precipitation is 428 mm, which is mainly concentrated in July–September (accounting for 70% of the total). The average annual evaporation is 3.9–4.5 multiples of the average annual rainfall. The average annual wind velocity is between 3 and 4 m/s, and sanddriving windy days (average wind velocity is higher than 5 m/s in one day) can reach up to 40 days in one year. These days are concentrated in winter and spring when vegetation coverage is low. Elevation is between 88.5 and 208.4 m. Aeolian sandy soil, which is prone to desertification, is the main type of soil, accounting for 68.9%. The dune–interdune landscape is the main topography and widely spreads in HLBB (Fig. 2). Land at the top and upper regions of the terrain was cultivated for high income. This method of land use resulted in serious
Fig. 2. Dune–interdune topography. 140
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water vapor content values of remote sensing images was used as the atmospheric water vapor content in LST calculation to make the atmospheric water vapor content close to the acquisition time of Landsat8 remote sensing images. The coordinate of Modis05_L2 data was converted into WGS84_UTM coordinate using HEGTool (Hdf-Eos to Gis Conversion Tool) software. The spatial resolution of Modis05_L2 data with a spatial resolution of 1 km was resampled to 30 m using nearest neighbor resample method. The atmospheric water vapor content of 030/120 remote sensing image on September 12, 2013 and that of 030/ 119 remote sensing image on September 5, 2013 were between 1.275 and 1.948 g/cm2 and 1.032–2.047 g/cm2. Thus, the LSEs of TIRS-10 and 11 bands of Landate-8 were calculated according to the following equations:
desertification. The population in 2016 was 405,500, in which 74.9% accounted for Mongolian population. The main crop is dry land maize, and its planting area, 1890 km2, accounted for 85.7% of all sown areas in 2016. The numbers of sheep and cattle were 1,501,500 and 771,100 in 2016, respectively. 2.2. Methods 2.2.1. Land surface temperature (LST) Remotely sensed LST is the radiometric temperature of the ground or the canopy surface and is sensitive to land surface properties, such as land cover, albedo, and soil moisture. Therefore, LST is an effective parameter relevant to the physics of land surface processes at a local scale (Jin and Dickinson, 2015; Mostovoy et al., 2006; Zhao et al., 2017). LST increases rapidly when land is not covered by vegetation and increases slowly when land is covered by vegetation. Especially in sandy land area, LST is sensitive to vegetation coverage. High vegetation coverage and low LST can be observed in a certain area. Thus, this study used LST to reflect the vegetation growth condition in HLBB. LST was calculated from 030/120 and 030/119 Landsat-8 remote sensing images, which were acquired on September 12, 2013 and September 5, 2013, respectively (Table 1). Parts covered by cloud were deleted to eliminate the influences of cloud on LST in 030/119 remote sensing image (Fig. 1). Split window method was used to calculate LST from Landsat-8 remote sensing images (Rozenstein et al., 2014).
(2)
A1 = 1 + A + b10 E1
(3)
τ11 = −0.1546w + 1.0078
(12)
T10(11) = K2/ln(1 + K1/ Tb10(11) )
where Ts is the LST (K); T10 and T11 are the brightness temperatures of TIRS bands 10 and 11 of Landsat-8, respectively; and A0, A1, and A2 are the coefficients determined by the atmospheric transmittance and land surface emissivity (LSE) in both TIRS bands.
A0 = a10 E1 − a11 E2
(11)
where w is the atmospheric water vapor content (g/cm2). According to the proximity principle, the a10, a11, b10, and b11 values of 030/120 and 030/119 remote sensing images were determined by the temperatures of HLBB and ShuangLiao weather stations (Fig. 1). The average, maximum, and minimum temperatures of the HLBB weather station were 20 °C, 27.9 °C, and 13.9 °C, respectively, and those of the ShuangLiao weather station were 17.9 °C, 24.7 °C, and 9.2 °C, respectively, on September 12, 2013. Thus, the a10, b10, a11, and bl1 values of 030/120 and 030/119 remote sensing images were −62.8065, 0.4338, −67.1728, 0.4694 and −59.1391, 0.4213, −63.3921, 0.4565, respectively.
(1)
Ts = A0 + A1 T10 − A2 T11
τ10 = −0.1134 w+ 1.0335
T10(11) = K2/ln(1 + K1/ Tb10(11) )
(13) 2
where T10(11) is the brightness temperature (K); K1 is 774.89 w/m /sr/ um and K2 is 1321.08 K for TIRS band 10; and K1 is 480.89 w/m2/sr/ um and K2 is 1201.14 K for TIRS band 11.
Tb10(11) = (gain×DN ) + offset
(14) 2
A2 = A + b11 E2
(4)
E1 = D11 × (1 − C10 − D10 )/ E0
(5)
E2 = D10 × (1 − C11 − D11)/ E0
(6)
A = D10 / E0
(7)
E0 = D11 C10 − D10 C11
(8)
Ci = εi τi
(9)
Di = (1 − τi ) × (1 + (1 − εi ) × τi )
where Tb10(11) is the radiation brightness temperature (w/m /sr/um), gain is 0.0003342 w/m2/sr/um, offset is 0.1 w/m2/sr/um, and DN is the digital number of remote sensing. In the calculation of LSE, land surface was divided into three types, namely, water body, natural surface, and urban area based on the types of land use/cover. Land use/cover map was classified from Landsat-8 remote sensing images (Table 1) using the maximum likelihood classification method. Two remote sensing images acquired on July 3, 2013 and September 5, 2013 were applied to eliminate the influences of cloud on interpretation accuracy in 030/119 remote sensing image. The classification result was then adjusted by man–machine interaction. Land use/cover map was obtained with a kappa coefficient of 0.94 (Zhou et al., 2017a). The kappa coefficient was calculated on the basis of random sampling using 2 m-resolution remote sensing data. Land use/cover types included cultivated land, forest land, grass land, sandy land, water body, and construction land. The LSE of water body at the thermal infrared range is very high, which is close to the LSE of black body. The vegetation coverage of sandy land is 0, and the LSE of sandy land is equal to that of sandy soil. Therefore, the LSEs of water body and sand land were set according to Table 2. The LSEs of construction area and natural land were calculated by
(10)
where εi is the LSE of TIRS-10 or TIRS-11, and τi is the atmospheric transmittance of TIRS-10 or TIRS-11. Modis05_L2 data (https://ladsweb.nascom.nasa.gov/data/search. html) were used as atmospheric water vapor content. 030/120 and 030/119 Landsat-8 remote sensing images used for LST calculation were acquired at 10:36 on September 12, 2013 and at 10:30 on September 5, 2013 (Beijing Time). Modis05_L2 had two remote sensing images that cover 030/120 at 10:00 and 11:40 on September 12, 2013 and two remote sensing images that cover 030/119 at 09:55 and 11:30 on September 5, 2013 (Beijing Time). The average of two atmospheric
Table 2 LSE of typical object in TIRS band 10 and 11of Landsat-8 (Song et al., 2015).
Table 1 Remote sensing data. Path/row
Data acquired
Cloud/%
Data resource
030/119 030/119 030/120
Jul. 3, 2013 Sep. 5, 2013 Sep. 12, 2013
11.53 12.91 0.02
Gscloud Gscloud Gscloud
141
LSE
TIRS band 10
TIRS band 11
Vegetation/εv Bare soil/εs Construction/εm Water body/εw
0.98672 0.96767 0.964885 0.99683
0.98990 0.97790 0.975115 0.99254
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Table 3 ALST of land use/cover type. Land use/cover type
ALST in 030/120 remote sensing image (K)
ALST in 030/119 remote sensing image (K)
Land use/cover type
ALST in 030/120 remote sensing image (K)
ALST in 030/119 remote sensing image (K)
Water body Cultivated land Forest land
294.25 299.62 300.86
298.87 301.77 300.79
Grass land Sandy land Construction land
303.83 306.69 301.31
301.66 304.28 302.67
was then calculated. TI was finally calculated by using Equation (22). For each part, the TI was small for a grid, which indicated that the grid was low in that region.
the following equations (Qin et al., 2005):
εcity = fc Rv ε v + (1 − fc ) Rm εm + dε
(15)
εnature = fc Rv ε v + (1 − fc ) Rs εs + dε
(16)
EI = 1 + E / E
Rv = 0.9332 + 0.0585fc
(17)
where E is the elevation of a grid, and‾E is the average elevation of one part of 2 km × 2 km.
Rs = 0.9902 + 0.1068fc
(18)
Rm = 0.9886 + 0.1287fc
(19)
dε =
fc ≤ 0.5 ⎧ 0.0038fc ⎨ 0.0038(1 − fc ) fc ≥ 0.5 ⎩
2.2.4. Relative LST (RLST) RLST was calculated to reflect the differences in vegetation growth in local areas. HLBB was divided into many 2 km × 2 km parts. The minimum surface temperature of each land use/cover type of each part was calculated. RLST was obtained by using Equation (23). The RLST was small for a grid, which indicated that the grid had a relatively remarkable vegetation growth in one part of 2 km × 2 km.
(20)
where εcity is the LSE of construction surface; εnature is the LSE of natural surface, and when εnature is greater than εv, εnature is replaced with εv; Rv is the temperature ratio of vegetation; Rs is the temperature ratio of bare soil; Rm is the temperature ratio of construction surface; the values of εv, εs, and εm are shown in Table 2; fc is the vegetation coverage; and dε is the interaction value of LSEs.
∇T = Ti − Ti, min
(23)
where ∇T is the RLST, Ti is the LST for a grid of land use/cover type i, and Ti, min is the minimum LST for land use/cover type i in one 2 km × 2 km part.
2.2.2. Dimidiate pixel model Vegetation coverage was calculated by using the dimidiate pixel model. Minimum noise separation was conducted, and pixel pure index was calculated using the first five bands, which represented 95.59% and 99.47% information of 030/120 and 030/119 remote sensing images, respectively. Pure pixels were selected. The average normalized difference vegetation index (NDVI) values of pure pixels were used as the values of NDVIsoil and NDVIveg according to land use/cover types. Sandy land was without vegetation coverage, and its pure pixel NDVI value could be used as NDVIsoil value. Cultivated land was a strongly artificial intervention ecological system, and its vegetation growth was relatively fine and uniform. The pure pixel NDVI value could be used as NDVIveg value. According to the reflection characteristics of vegetation and sandy land at near infrared and red band, the sandy land pure pixels with a pixel purity index greater than 50 and an NDVI value of more than 0 were selected, and the minimum NDVI value was regarded as NDVIsoil value. The pure pixels of cultivated land with a pixel purity index greater than 50 and an NDVI value less than 1 were selected, and the maximum NDVI value was regarded as NDVIveg value. The NDVIsoil and NDVIveg values of 030/119 remote sensing image were 0.0666 and 0.9996, respectively. The NDVIsoil and NDVIveg values of 030/120 remote sensing image were 0.0213 and 0.9227, respectively. Vegetation coverage was calculated by using Equation (21).
fc = (NDVI − NDVIsoil )/(NDVIveg − NDVIsoil )
(22)
3. Results 3.1. Average LST (ALST) of land use/cover type For 030/120 remote sensing image, the ALST of water body (294.25 K) was the lowest (Table 3). The ALST of sandy land (306.69 K) was the highest. The ALSTs of cultivated land, forest land, and grass land increased gradually. For 030/119 remote sensing image, the ALSTs of water body and sandy land were the lowest and highest at 298.87 and 304.28 K, respectively. The ALST of forest land was the lowest, and the ALSTs of forest land and grass land were almost equal compared with those of 030/120 remote sensing image. 3.2. LST changes of cultivated land along dune–interdune For 030/120 remote sensing image, the ALST of cultivated land increased gradually with the increase in TI. When TIs were distributed at [1.40, 1.60), [1.60, 1.80), [1.80, 2.00), [2.00, 2.20), [2.20, 2.40), and [2.40, 2.60), the ALSTs of cultivated land were 298.15, 299.06, 299.49, 299.79, 299.92, and 301.49 K, respectively (Fig. 3a). When TIs were distributed at less than 1.86, [1.86, 2.06), [2.06, 2.26) and greater than 2.26 in 030/119 remote sensing image, the ALSTs of cultivated land were 301.09, 301.74, 301.94, and 302.23 K, respectively (Fig. 4a). The ALST changes of cultivated land indicated that the dry land maize growth status worsened with increasing TI of dune–interdune in
(21)
where fc is the vegetation coverage; and NDVIsoil and NDVIveg are the NDVI values of bare land and completely vegetation covered land, respectively. 2.2.3. Topography index (TI) TI was calculated to display the fluctuation state of dune–interdune quantitatively. Elevation data, including N42E121, N42E122, N42E123, N43E121, N43E122, and N43E123 images, were collected from GDEMV2 30 m database (http://www.gscloud.cn/). The DEM of HLBB was obtained after mosaicking and cutting. HLBB was first divided into many 2 km × 2 km parts. The average elevation of each part
Fig. 3. ALST and ARLST changes of cultivated land with increasing TI in 030/ 120 remote sensing image. 142
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Fig. 4. ALST and ARLST changes of cultivated land with increasing TI in 030/ 119 remote sensing image.
Fig. 5. ALST and ARLST changes of grass land with increasing TI in 030/120 remote sensing image.
general. As the TIs increased, the average relative LST (ARLST) of cultivated land also increased gradually. When TIs were distributed at [1.40, 1.60), [1.60, 1.80), [1.80, 2.00), [2.00, 2.20), [2.20, 2.40), and [2.40, 2.60) in 030/120 remote sensing image, the ARLSTs of cultivated land were 0.70, 1.49, 1.63, 1.97, 2.22, and 3.97 K, respectively (Fig. 3b). When TIs were distributed at less than 1.86, [1.86, 2.06), [2.06, 2.26) and greater than 2.26 in 030/119 remote sensing image, the ARLSTs of cultivated land were 2.20, 2.35, 2.69, and 3.37 K, respectively (Fig. 4b). Thus, the ARLST changes of cultivated land indicated that the dry land maize growth condition worsened with increasing TI of dune–interdune in local areas.
4. Discussion 4.1. Analysis of ALSTs of land use/cover types The ALSTs of water body and sandy land were the lowest and highest in 030/120 and 030/119 remote sensing images, respectively. The specific heat capacities of water and sandy soil were the highest and lowest among cultivated, forest, grass, and sandy lands and water body. The temperature increment of water body was the smallest and that of sandy land was the highest at the condition of absorbing the same amount of heat. The ALSTs of cultivated land, forest land, and grass land increased successively in 030/120 remote sensing image, whereas the ALST of forest land was the smallest, and those of cultivated and grass lands were almost equal in 030/119 remote sensing image. Cultivated land had a higher degree of human intervention and management than forest and grass lands had. The vegetation coverage of cultivated land was higher than those of forest and grass lands. Thus, the ALST of the cultivated land was the lowest in 030/120 remote sensing image. Forest land was arbor mainly without sparse forest land. Grass land included low vegetation glass land, such as shrub grass land, and sparse grass land. The vegetation coverage of forest land was higher than that of grass land. The ALST of forest land was lower than that of grass land in 030/120 remote sensing image. The region covered by 030/119 remote sensing image is located at the east of HLBB. On the one hand, precipitation decreases from east to west in HLBB (Fig. 6). On the other hand, East and West Liaohe Rivers go through the region. The water resource in the region covered by 030/119 remote sensing image is much richer than that in the region covered by 030/120 remote sensing image. Forest and grass grow well in the region covered by 030/119 remote sensing image. Thus, the ALST of forest land was lower than that of cultivated land, and the ALSTs of grass and cultivated lands were almost equal.
3.3. LST changes of grass land along dune–interdune The region covered by cloud in 030/119 remote sensing image was deleted (Fig. 1), and the land use/cover structure of the remnant part was mainly cultivated land that accounted for 85.88% (Table 4). The grass land area was 13.4568 km2, which accounted for the grass land area in the remnant part of 030/119 remote sensing image, which was 3.17%, and that in HLBB, which was only 0.12%. TI was calculated in each part area of 4 km2. The grass growth condition in 030/119 remote sensing image was ignored, and that in 030/120 remote sensing image was analyzed. For the 030/120 remote sensing image, the ALST of grass land increased first and then decreased with increasing TI. When TIs were distributed at [160, 1.80), [1.80, 2.00), [2.00, 2.20), and equal or greater than 2.20 in the 030/120 remote sensing image, the ALSTs of grass land were 300.25, 303.42, 304.09, and 301.66 K, respectively (Fig. 5a). The ALST changes of grass land indicated that grass growth status worsened first and then improved with increasing TI of dune–interdune in general. The ARLST of grass land increased first and then decreased with increasing TI. When TIs were distributed at [160, 1.80), [1.80, 2.00), [2.00, 2.20), and equal or greater than 2.20 in the 030/120 remote sensing image, the ARLSTs of grass land were 2.32, 3.56, 4.36, and 3.30 K, respectively (Fig. 5b). The ARLST changes of cultivated land indicated that grass growth status worsened first and then improved with increasing TI of dune–interdune in local areas.
Table 4 Land use/cover structure of the remnant part of 030/119 remote sensing image. Land use/cover type
Area (km2)
Taking proportion in the remnant part of 030/119 remote sensing image (%)
Taking proportion in HLBB (%)
Cultivated land Forest land Grass land Construction land Water body Sandy land
364.2804 2.2581 13.4568 28.1421 10.4328 5.5818
85.88 0.53 3.17 6.63 2.46 1.32
3.17 0.03 0.12 0.24 0.09 0.05
Fig. 6. Precipitation of HLBB and east and west Liaohe Rivers. 143
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Fig. 9. Locations of soil profiles 1-1 and 1–2. Fig. 7. Water condition of dune–interdune in HLBB.
4.2. Dry land maize growth status analysis along dune–interdune Dry land maize is the main crop in HLBB. Maize plantation area accounted for 85.7% of all sown area in 2016. The water demanded for the normal growth of maize was approximately 500 mm. First, precipitation in HLBB ranged from 358.0 mm to 462.8 mm from west to east (Fig. 6). The precipitation of maize growth period from May 5 to September 17 accounted for 74%. Thus, dry land maize had insufficient growth due to water stress in HLBB. Second, soil was mainly sandy soil, which had coarse soil particles and could not retain water. Third, precipitation seeped into low-lying areas rapidly under dune–interdune topography. Thus, soil moisture decreased with increasing TI of dune–interdune, and the water stress degree of maize growth became heavy (Fig. 7). Dry land maize growth status worsened with increasing TI of dune–interdune. According to our survey investigation, dry land maize at the upper region of dune–interdune had no yield at dry years, which induced desertification (Fig. 8). From the top to the bottom of dune–interdune, soil profiles were excavated and soil moistures were measured from July to September in 2015 and 2016. Two soil profiles along one dune–interdune were excavated (Fig. 9), and soil moisture was measured in July to September 2015. Moreover, three soil profiles along another dune–interdune were excavated (Fig. 10) and soil moisture was measured in July to September 2016. According to temperature and precipitation conditions, the first measurement period was divided into three phases, namely, high temperature and low precipitation (from middle July to middle August), high temperature and high precipitation (from middle August to last August), and low temperature and low precipitation (September). At the three periods, the soil moistures of profile 1-1 were approximately 10.48%, 14.41%, and 12.74%, and those of profile 1–2 were approximately 35.20%, 41.03%, and 41.06%. Soil profile 1-1 was at the top of dune–interdune, and soil profile 1–2 was at the bottom. The distance between the two profiles was 74 m, and the LSTs of soil profiles 1-1 and 1–2 were 308.03 K and 307.56 K, respectively. According to precipitation condition, the second measurement period was divided into two phases, namely low precipitation (from middle July to middle August) and high precipitation (from middle August to middle September). At the first period, the soil moistures of profiles 2-1, 2-2, and 2–3 were approximately 9.14%, 13.52%, and 15.03%, respectively. At the second period, the soil moistures of profiles 2-1, 2-2, and 2–3
Fig. 10. Locations of soil profiles 2-1, 2-2, and 2–3.
were approximately 12.38%, 20.08%, and 25.20%, respectively. Thus, soil water content decreased with increasing TI of dune–interdune. Dry land maize growth worsened with increasing TI of dune–interdune.
4.3. Grass growth status analysis along dune–interdune Land use/cover maps for 2001 and 2008 were obtained from ETM+ and TM5 data, respectively, using the maximum likelihood classification method. Land use/cover changes from 2001, 2008, and 2013 were then analyzed. The mutual conversions of cultivated, forest, and grass lands were mainly distributed at the middle region of dune–interdune with TIs between [1.80, 2.20). Changed area of cultivated, forest, and grass lands at [1.80, 2.00) accounted for 56.26% of the total changed area of cultivated, forest, and grass lands (Table 5). The changed area of cultivated, forest, and grass lands at [2.00, 2.20) accounted for 43.66% of the total changed area of cultivated, forest, and grass lands. Both changed areas of cultivated, forest, and grass lands at [1.80, 2.20) and [2.00, 2.20) accounted for 99.92% of changed area of cultivated, forest, and grass lands. At the middle region of dune–interdune, dry land maize could obtain much production when precipitation was rich, and no production was obtained when precipitation was less. Farmers adopted extensive cultivation to obtain high yield by extensive reclamation. Frequent changes among cultivated, forest, and grass lands destroyed vegetation growth, and land surface was exposed. Thus, ALST of grass land was highest and grass growth was lower at the middle region of dune–interdune. Frequent conversion among cultivated, forest, and grass lands must be immediately prevented to realize ecological restoration and crop production. Although the upper area of dune–interdune had the lowest soil moisture, anthropogenic interference was weakest, and conversions among land use/cover were also rare. Changed area of cultivated, forest, and grass lands with TI equal or greater than 2.20 was 154.71 hm2, which accounted for 0.057% of all changed area of cultivated, forest, and grass lands. Precipitation in HLBB ranges from 358 mm to 462.8 mm. When precipitation is between 330–550 mm and 300–400 mm, zonal vegetation is temperate meadow steppe and typical grassland. Thus, precipitation in HLBB can meet the water demand of grass normal growth (Liu, 1992). Therefore, grass can grow well even at the upper region of dune–interdune. At the low region of dune–interdune, soil moisture is higher, and grass grows best.
Fig. 8. Dry land maize growth condition at the upper region of dune–interdune under dry year. 144
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Table 5 Land use/cover changes at different TI groups in 2001, 2008, and 2013. Land use/cover type in 2001
Grass land Grass land Grass land Grass land Grass land Grass land Grass land Grass land Forest land Forest land Forest land Forest land Forest land Forest land Forest land Forest land Cultivated land Cultivated land Cultivated land Cultivated land Cultivated land Cultivated land Cultivated land Cultivated land Total
Land use/cover type in 2008
Grass land Grass land Forest land Forest land Forest land Cultivated land Cultivated land Cultivated land Grass land Grass land Grass land Forest land Forest land Cultivated land Cultivated land Cultivated land Grass land Grass land Grass land Forest land Forest land Forest land Cultivated land Cultivated land
Land use/cover type in 2013
Forest land Cultivated land Grass land Forest land Cultivated land Grass land Forest land Cultivated land Grass land Forest land Cultivated land Grass land Cultivated land Grass land Forest land Cultivated land Grass land Forest land Cultivated land Grass land Forest land Cultivated land Grass land Forest land
Changed area at different TI groups (km2) [1.40, 1.60)
[1.60, 1.80)
[1.80, 2.00)
[2.00, 2.20)
≥2.20
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0054 0.0000 0.0000 0.0054
0.0081 0.0414 0.0216 0.0117 0.0072 0.0117 0.0000 0.1278 0.0018 0.0009 0.0126 0.0099 0.0090 0.0063 0.0000 0.0216 0.0243 0.0252 0.0810 0.0189 0.0153 0.0936 0.0324 0.0306 0.6129
122.9346 184.8339 51.7662 46.6740 58.4172 81.4266 43.7877 239.8455 13.3965 3.4614 8.0226 12.3219 17.6463 11.9619 6.4593 66.3786 200.2581 44.7300 104.9571 16.7652 18.4797 55.2681 61.6221 56.1024 1527.5169
124.6860 145.7730 50.5260 54.9873 35.8452 52.9281 32.3640 131.5836 14.6034 3.5163 6.0516 12.0258 11.5614 8.5401 5.7681 38.3769 197.7345 42.0543 82.1619 12.9159 18.5301 28.2240 38.2815 36.4896 1185.5286
0.0711 0.2691 0.0450 0.0333 0.0171 0.0459 0.0279 0.1188 0.0423 0.0018 0.0135 0.0099 0.0063 0.0837 0.0009 0.0333 0.2817 0.0126 0.1485 0.0297 0.0414 0.0405 0.1116 0.0612 1.5471
years. Thus, rice can be planted in the area, and drainage facilities should be constructed when maize is planted. Although the land use model was proposed according to soil moisture differentiation and vegetation growth condition at the dune–interdune topography, land use boundaries will be further investigated in the future.
4.4. Land use model of dune–interdune Soil moisture differentiation caused the growth difference of vegetation at the dune–interdune topography. Soil moisture decreased with increasing TI of dune–interdune. According to our field investigation, dry land maize production at the marshy land could reach up to more than 750,000 kg/km2; otherwise, dry land maize probably has no production in dry years at the upper region of dune–interdune. Thus, the upper region of dune–interdune was intended for desertification. However, grass could grow well even if it is located at the top region of dune–interdune. On the contrary, grass grows worst at the middle region because of the frequent conversion among cultivated, forest, and grass lands, which induced desertification. Land use must therefore be designed according to the soil moisture differentiation of dune–interdune to obtain crop production and anti-desertification. At the top and upper regions of dune–interdune, water tolerance grass should be maintained for anti-desertification (Fig. 11). At the middle region of dune–interdune, cultivated land infrastructures, such as irrigation facilities and shelters, should be established to obtain sustainably cultivated land use and avoid frequent land use change. Only in this way can crop production be sustained and grass grows well. Lastly, agriculture production and anti-desertification can be completed simultaneously at the middle region of dune–interdune. At the low region of dune–interdune, soil moisture is richest, and the area is suitable for agriculture. According to relevant studies (Zhou et al., 2018), the risk of water logging is perceived in this region in wet
5. Conclusion The ALSTs of water body and cultivated, forest, grass, and sandy lands in 030/120 remote sensing image increased gradually. The ALSTs of water body and sandy land were lowest and highest in 030/119 remote sensing image, respectively. The ALST of forest land was lowest and the ALSTs of grass and cultivated lands were nearly equal among the ALSTs of cultivated, forest, and grass lands, which were different from those of 030/120 remote sensing image. This result was mainly attributed to the water resource differentiation of two regions covered by two remote sensing images. Dry land maize growth condition worsened with increasing TI of dune–interdune. First, precipitation could not meet the water demand of dry land maize normal growth in the agro-pastoral ecotone in Northern China. Second, soil moisture decreased and the water stress of dry land maize growth became increasingly serious with increasing TI of dune–interdune. Grass growth worsened first and then improved with increasing TI of dune–interdune, indicating that grass could grow well even at the upper region of the dune–interdune as long as land use was stable. According to soil moisture differentiation and vegetation growth condition at dune–interdune, land use model was designed to obtain crop production and anti-desertification. At the top region, water tolerance grass should be kept for anti-desertification. At the middle region, cultivated land infrastructures should be established to obtain sustainably cultivated land use and avoid frequent land use conversation. Crop production and anti-desertification could be completed at the middle region simultaneously. At the bottom region, rice can be planted, and if maize is planted, drainage facilities should be constructed.
Fig. 11. Land use model based on soil water differentiation and vegetation growth condition at dune–interdune. 145
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Acknowledgment
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Research article
Land use model research in agro-pastoral ecotone in northern China: A case study of Horqin Left Back Banner
T
Jian Zhoua,∗, Yan Xub, Yang Gaob, Zhen Xieb a b
Northwest Land and Resources Research Center, Shaanxi Normal University, Xi'an, 710119, China College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Land surface temperature Vegetation growth Soil moisture Dune–interdune Land use model Agro-pastoral ecotone in northern China
The agro-pastoral ecotone in northern China is an ecological fragile region, where desertification is a prominent problem and mainly induced by extensive cultivation. The typical topography of the area is dune–interdune. The irrational land use model of dune–interdune should be investigated to benefit crop production and anti-desertification. This study investigated vegetation growth status by calculating land surface temperature and soil moisture differentiation along dune–interdune. Dry land maize growth status worsened, and grass growth status showed a trend of worsening first and then improving with increasing elevation of the dune–interdune topography; meanwhile, soil moisture decreased. A land use model was designed according to soil moisture differentiation and vegetation growth condition to obtain crop production and anti-desertification. In the top region of the dune–interdune, water tolerance grass should be maintained for anti-desertification. In the middle region, cultivated land infrastructures should be established to obtain sustainable cultivated land use and avoid frequent change in land use. In the bottom region, rice can be planted, and drainage facilities should be constructed if maize is planted. Crop production and anti-desertification can be completed simultaneously in the land use model of dune–interdune.
1. Introduction Desertification was defined as “land degradation in arid, semiarid, and dry sub-humid areas resulting from various factors, including climatic variability and human activities” in the Convention to Combat Desertification and Drought in 1994 (Chen and Tang, 2005). Desertification is a major ecological problem worldwide (Kassas, 1995; Moran et al., 2009; Zhao et al., 2014). Desertified land accounts for 3.6 × 107 km2, covering 24.1% of the Earth's land surface, and affects approximately one-sixth of the world's population, many of whom live in poverty (Xue et al., 2013). Natural factors, including precipitation, wind, and human activities, such as extensive reclamation and overgrazing, are the driving forces of desertification (Dawelbait and Morari, 2012; Ge et al., 2013; Li et al., 2013; Ma et al., 2007). However, the main driving force of desertification varies among different regions (Wang et al., 2005; Wu, 2001). The agro-pastoral ecotone in Northern China has semi-arid continental monsoon climate. Precipitation in this area cannot meet the water demand for normal growth of dry land maize, which is the main crop in the region. Moreover, precipitation fluctuation is dramatic and considerably influences vegetation growth (Zhou et al., 2018). Aeolian
∗
sandy soil is the main soil type in this region. Under natural conditions, the agro-pastoral ecotone in Northern China is an ecological fragile area, which makes it prone to desertification (Jiang et al., 2005; Peng et al., 2016; Xu et al., 2014). Agriculture production and animal husbandry coexist in the region. Land resource is vast in the agro-pastoral ecotone in Northern China, and it was 0.0281 km2 per capita for Horqin Left Back Banner (HLBB) in 2013. On the one hand, plantation can provide fodder and forage for animal husbandry. On the other hand, animal husbandry can make up the economic risk of plantation induced by drought (Yang and Xu, 2016). Under the complementarity condition of plantation and animal husbandry, farmers reclaim land as much as possible to increase their income (Zhou et al., 2017b). Su et al. (2004) revealed that the soil organic matter, total nitrogen, and total phosphorus contents in Horqin Sandy Land declined to approximately 18%–38% in 3 years after the grass land was reclaimed into cultivated land; the soil particle distribution of the reclaimed cultivated land caused it to be prone to desertification. Zhao et al. (2005) found soil deterioration symbolized by soil structure deterioration, and soil fertilization declined evidently; the recovery of soil properties would take considerable time. Thus, soil deterioration of reclaimed cultivated land, which is unsuitable for cultivation, is the main driving force of
Corresponding author. E-mail address: [email protected] (J. Zhou).
https://doi.org/10.1016/j.jenvman.2019.02.046 Received 4 September 2018; Received in revised form 1 December 2018; Accepted 7 February 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.
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desertification in the agro-pastoral ecotone in Northern China (Dong et al., 2011; Ge et al., 2013; Ma et al., 2007). Farmer's ability and reclamation incentive are enhanced with the rapid development of agriculture mechanism (Luo et al., 2016). Thus, anti-desertification remains prominent in the agro-pastoral ecotone in northern China. For anti-desertification, several ecological restoration projects have been launched; these projects include the “Three-north Shelterbelt Project” in 1978, “Grain for Green Project” in 1999, and “Beijing and Tianjin Sandstorm Source Controlling Project” in 2000. High and low vegetation coverage land increased and decreased, respectively (Qiao et al., 2006). The development process of desertification has been primarily curbed by ecological restoration engineering (Li et al., 2009; State Forestry Administration, 2011, 2016; Zhang et al., 2012). However, Ge et al. (2016) found a linear correlation between agricultural GDP per capita and severe desertified land, which indicated that farmers increased their income by cultivating considerable land with a small input in the agro-pastoral ecotone. Vegetation was mainly degraded in fixed and semi-fixed sandy dunes, which had high vegetation coverage after ecological restoration (Zhang et al., 2012). This phenomenon reflects that land with good vegetation coverage caused by ecological restoration is reclaimed again into cultivated land, thereby inducing vegetation degradation and desertification once again. Dune–interdune topography is typical (Liu et al., 2016), and water resource is a key factor in determining land use in the agro-pastoral ecotone in Northern China (Tao et al., 2001; Wang and Qin, 2017). Precipitation flows to the low area of this terrain, and a large difference in soil moisture can be observed in various locations along dune–interdune (Zhou et al., 2017c). Farmers reclaimed land at the top and upper regions of dune–interdune, where water condition was poor and land was unsuitable for cultivation, to yield a high economic income. This method of land use induced desertification and prevented ecology rehabilitation. Thus, irrational land use arrangement along dune–interdune topography is the key factor in determining ecological restoration under the condition of desertification curbed in general and expanding in partial locations. According to the ecological characteristics of different regions, relevant studies have proposed different land use models for anti-desertification. In the Hexi Corridor, which is a region of saline land of desert in an arid area, a control desertification model of integrated “fixing, closing, defending” has been established by fixing moving sand with a sandy barrier, closing to increase vegetation, and making up for defending belt and establishing a wind break belt (Yang et al., 2004). In Maowusu Sandy Land, a grass and agroforestry model for low land, a semi-artificial model for soft hills, and a natural grazing model for hard hills have been suggested (Zhang, 1994). Moreover, a “three circle” model has been proposed for Maowusu Sandy Land (Zheng, 1998). The two land use models were proposed for different topographic regions in Maowusu Sandy Land, and micro-topography differences in different topographic regions were not considered in the two land use models. A ‘‘tri-circle’’ land use paradigm has been implemented around Yulin City to address the soil wind erosion that surrounds the urban area (Yue et al., 2016). Different land use models have been proposed for antidesertification in consideration of the different regional natural characteristics. Study on land use model at dune–interdune topography is rare and urgent for anti-desertification in the agro-pastoral ecotone in Northern China. Thus, this study aims to 1) investigate vegetation growth difference from the top to the bottom of dune–interdune; 2) explore soil moisture difference along dune–interdune; and 3) prompt an irrational land use model of dune–interdune according to soil moisture difference and vegetation growth condition for anti-desertification in the agro-pastoral ecotone in Northern China.
Fig. 1. 030/119 and 030/120 remote sensing images of HLBB in 2013.
2. Material and methods 2.1. Study area HLBB (121°30′E−123°42′E, 42°40′N–43°42′N) is a county in Inner Mongolia that is located in the agro-pastoral ecotone in Northern China (Fig. 1). The area of HLBB is 1.1 × 104 km2. As a part of Horqin Sandy Land, which is the largest sandy land in China, desertification is a prominent eco-environmental problem in HLBB. Desertified land reached 7042 km2 in 1996, which accounted for 61.3% of the total land area. From 2001 to 2010, desertified land accounted for an average of 38.7%. HLBB has a temperate continental monsoon climate. The average annual precipitation is 428 mm, which is mainly concentrated in July–September (accounting for 70% of the total). The average annual evaporation is 3.9–4.5 multiples of the average annual rainfall. The average annual wind velocity is between 3 and 4 m/s, and sanddriving windy days (average wind velocity is higher than 5 m/s in one day) can reach up to 40 days in one year. These days are concentrated in winter and spring when vegetation coverage is low. Elevation is between 88.5 and 208.4 m. Aeolian sandy soil, which is prone to desertification, is the main type of soil, accounting for 68.9%. The dune–interdune landscape is the main topography and widely spreads in HLBB (Fig. 2). Land at the top and upper regions of the terrain was cultivated for high income. This method of land use resulted in serious
Fig. 2. Dune–interdune topography. 140
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water vapor content values of remote sensing images was used as the atmospheric water vapor content in LST calculation to make the atmospheric water vapor content close to the acquisition time of Landsat8 remote sensing images. The coordinate of Modis05_L2 data was converted into WGS84_UTM coordinate using HEGTool (Hdf-Eos to Gis Conversion Tool) software. The spatial resolution of Modis05_L2 data with a spatial resolution of 1 km was resampled to 30 m using nearest neighbor resample method. The atmospheric water vapor content of 030/120 remote sensing image on September 12, 2013 and that of 030/ 119 remote sensing image on September 5, 2013 were between 1.275 and 1.948 g/cm2 and 1.032–2.047 g/cm2. Thus, the LSEs of TIRS-10 and 11 bands of Landate-8 were calculated according to the following equations:
desertification. The population in 2016 was 405,500, in which 74.9% accounted for Mongolian population. The main crop is dry land maize, and its planting area, 1890 km2, accounted for 85.7% of all sown areas in 2016. The numbers of sheep and cattle were 1,501,500 and 771,100 in 2016, respectively. 2.2. Methods 2.2.1. Land surface temperature (LST) Remotely sensed LST is the radiometric temperature of the ground or the canopy surface and is sensitive to land surface properties, such as land cover, albedo, and soil moisture. Therefore, LST is an effective parameter relevant to the physics of land surface processes at a local scale (Jin and Dickinson, 2015; Mostovoy et al., 2006; Zhao et al., 2017). LST increases rapidly when land is not covered by vegetation and increases slowly when land is covered by vegetation. Especially in sandy land area, LST is sensitive to vegetation coverage. High vegetation coverage and low LST can be observed in a certain area. Thus, this study used LST to reflect the vegetation growth condition in HLBB. LST was calculated from 030/120 and 030/119 Landsat-8 remote sensing images, which were acquired on September 12, 2013 and September 5, 2013, respectively (Table 1). Parts covered by cloud were deleted to eliminate the influences of cloud on LST in 030/119 remote sensing image (Fig. 1). Split window method was used to calculate LST from Landsat-8 remote sensing images (Rozenstein et al., 2014).
(2)
A1 = 1 + A + b10 E1
(3)
τ11 = −0.1546w + 1.0078
(12)
T10(11) = K2/ln(1 + K1/ Tb10(11) )
where Ts is the LST (K); T10 and T11 are the brightness temperatures of TIRS bands 10 and 11 of Landsat-8, respectively; and A0, A1, and A2 are the coefficients determined by the atmospheric transmittance and land surface emissivity (LSE) in both TIRS bands.
A0 = a10 E1 − a11 E2
(11)
where w is the atmospheric water vapor content (g/cm2). According to the proximity principle, the a10, a11, b10, and b11 values of 030/120 and 030/119 remote sensing images were determined by the temperatures of HLBB and ShuangLiao weather stations (Fig. 1). The average, maximum, and minimum temperatures of the HLBB weather station were 20 °C, 27.9 °C, and 13.9 °C, respectively, and those of the ShuangLiao weather station were 17.9 °C, 24.7 °C, and 9.2 °C, respectively, on September 12, 2013. Thus, the a10, b10, a11, and bl1 values of 030/120 and 030/119 remote sensing images were −62.8065, 0.4338, −67.1728, 0.4694 and −59.1391, 0.4213, −63.3921, 0.4565, respectively.
(1)
Ts = A0 + A1 T10 − A2 T11
τ10 = −0.1134 w+ 1.0335
T10(11) = K2/ln(1 + K1/ Tb10(11) )
(13) 2
where T10(11) is the brightness temperature (K); K1 is 774.89 w/m /sr/ um and K2 is 1321.08 K for TIRS band 10; and K1 is 480.89 w/m2/sr/ um and K2 is 1201.14 K for TIRS band 11.
Tb10(11) = (gain×DN ) + offset
(14) 2
A2 = A + b11 E2
(4)
E1 = D11 × (1 − C10 − D10 )/ E0
(5)
E2 = D10 × (1 − C11 − D11)/ E0
(6)
A = D10 / E0
(7)
E0 = D11 C10 − D10 C11
(8)
Ci = εi τi
(9)
Di = (1 − τi ) × (1 + (1 − εi ) × τi )
where Tb10(11) is the radiation brightness temperature (w/m /sr/um), gain is 0.0003342 w/m2/sr/um, offset is 0.1 w/m2/sr/um, and DN is the digital number of remote sensing. In the calculation of LSE, land surface was divided into three types, namely, water body, natural surface, and urban area based on the types of land use/cover. Land use/cover map was classified from Landsat-8 remote sensing images (Table 1) using the maximum likelihood classification method. Two remote sensing images acquired on July 3, 2013 and September 5, 2013 were applied to eliminate the influences of cloud on interpretation accuracy in 030/119 remote sensing image. The classification result was then adjusted by man–machine interaction. Land use/cover map was obtained with a kappa coefficient of 0.94 (Zhou et al., 2017a). The kappa coefficient was calculated on the basis of random sampling using 2 m-resolution remote sensing data. Land use/cover types included cultivated land, forest land, grass land, sandy land, water body, and construction land. The LSE of water body at the thermal infrared range is very high, which is close to the LSE of black body. The vegetation coverage of sandy land is 0, and the LSE of sandy land is equal to that of sandy soil. Therefore, the LSEs of water body and sand land were set according to Table 2. The LSEs of construction area and natural land were calculated by
(10)
where εi is the LSE of TIRS-10 or TIRS-11, and τi is the atmospheric transmittance of TIRS-10 or TIRS-11. Modis05_L2 data (https://ladsweb.nascom.nasa.gov/data/search. html) were used as atmospheric water vapor content. 030/120 and 030/119 Landsat-8 remote sensing images used for LST calculation were acquired at 10:36 on September 12, 2013 and at 10:30 on September 5, 2013 (Beijing Time). Modis05_L2 had two remote sensing images that cover 030/120 at 10:00 and 11:40 on September 12, 2013 and two remote sensing images that cover 030/119 at 09:55 and 11:30 on September 5, 2013 (Beijing Time). The average of two atmospheric
Table 2 LSE of typical object in TIRS band 10 and 11of Landsat-8 (Song et al., 2015).
Table 1 Remote sensing data. Path/row
Data acquired
Cloud/%
Data resource
030/119 030/119 030/120
Jul. 3, 2013 Sep. 5, 2013 Sep. 12, 2013
11.53 12.91 0.02
Gscloud Gscloud Gscloud
141
LSE
TIRS band 10
TIRS band 11
Vegetation/εv Bare soil/εs Construction/εm Water body/εw
0.98672 0.96767 0.964885 0.99683
0.98990 0.97790 0.975115 0.99254
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Table 3 ALST of land use/cover type. Land use/cover type
ALST in 030/120 remote sensing image (K)
ALST in 030/119 remote sensing image (K)
Land use/cover type
ALST in 030/120 remote sensing image (K)
ALST in 030/119 remote sensing image (K)
Water body Cultivated land Forest land
294.25 299.62 300.86
298.87 301.77 300.79
Grass land Sandy land Construction land
303.83 306.69 301.31
301.66 304.28 302.67
was then calculated. TI was finally calculated by using Equation (22). For each part, the TI was small for a grid, which indicated that the grid was low in that region.
the following equations (Qin et al., 2005):
εcity = fc Rv ε v + (1 − fc ) Rm εm + dε
(15)
εnature = fc Rv ε v + (1 − fc ) Rs εs + dε
(16)
EI = 1 + E / E
Rv = 0.9332 + 0.0585fc
(17)
where E is the elevation of a grid, and‾E is the average elevation of one part of 2 km × 2 km.
Rs = 0.9902 + 0.1068fc
(18)
Rm = 0.9886 + 0.1287fc
(19)
dε =
fc ≤ 0.5 ⎧ 0.0038fc ⎨ 0.0038(1 − fc ) fc ≥ 0.5 ⎩
2.2.4. Relative LST (RLST) RLST was calculated to reflect the differences in vegetation growth in local areas. HLBB was divided into many 2 km × 2 km parts. The minimum surface temperature of each land use/cover type of each part was calculated. RLST was obtained by using Equation (23). The RLST was small for a grid, which indicated that the grid had a relatively remarkable vegetation growth in one part of 2 km × 2 km.
(20)
where εcity is the LSE of construction surface; εnature is the LSE of natural surface, and when εnature is greater than εv, εnature is replaced with εv; Rv is the temperature ratio of vegetation; Rs is the temperature ratio of bare soil; Rm is the temperature ratio of construction surface; the values of εv, εs, and εm are shown in Table 2; fc is the vegetation coverage; and dε is the interaction value of LSEs.
∇T = Ti − Ti, min
(23)
where ∇T is the RLST, Ti is the LST for a grid of land use/cover type i, and Ti, min is the minimum LST for land use/cover type i in one 2 km × 2 km part.
2.2.2. Dimidiate pixel model Vegetation coverage was calculated by using the dimidiate pixel model. Minimum noise separation was conducted, and pixel pure index was calculated using the first five bands, which represented 95.59% and 99.47% information of 030/120 and 030/119 remote sensing images, respectively. Pure pixels were selected. The average normalized difference vegetation index (NDVI) values of pure pixels were used as the values of NDVIsoil and NDVIveg according to land use/cover types. Sandy land was without vegetation coverage, and its pure pixel NDVI value could be used as NDVIsoil value. Cultivated land was a strongly artificial intervention ecological system, and its vegetation growth was relatively fine and uniform. The pure pixel NDVI value could be used as NDVIveg value. According to the reflection characteristics of vegetation and sandy land at near infrared and red band, the sandy land pure pixels with a pixel purity index greater than 50 and an NDVI value of more than 0 were selected, and the minimum NDVI value was regarded as NDVIsoil value. The pure pixels of cultivated land with a pixel purity index greater than 50 and an NDVI value less than 1 were selected, and the maximum NDVI value was regarded as NDVIveg value. The NDVIsoil and NDVIveg values of 030/119 remote sensing image were 0.0666 and 0.9996, respectively. The NDVIsoil and NDVIveg values of 030/120 remote sensing image were 0.0213 and 0.9227, respectively. Vegetation coverage was calculated by using Equation (21).
fc = (NDVI − NDVIsoil )/(NDVIveg − NDVIsoil )
(22)
3. Results 3.1. Average LST (ALST) of land use/cover type For 030/120 remote sensing image, the ALST of water body (294.25 K) was the lowest (Table 3). The ALST of sandy land (306.69 K) was the highest. The ALSTs of cultivated land, forest land, and grass land increased gradually. For 030/119 remote sensing image, the ALSTs of water body and sandy land were the lowest and highest at 298.87 and 304.28 K, respectively. The ALST of forest land was the lowest, and the ALSTs of forest land and grass land were almost equal compared with those of 030/120 remote sensing image. 3.2. LST changes of cultivated land along dune–interdune For 030/120 remote sensing image, the ALST of cultivated land increased gradually with the increase in TI. When TIs were distributed at [1.40, 1.60), [1.60, 1.80), [1.80, 2.00), [2.00, 2.20), [2.20, 2.40), and [2.40, 2.60), the ALSTs of cultivated land were 298.15, 299.06, 299.49, 299.79, 299.92, and 301.49 K, respectively (Fig. 3a). When TIs were distributed at less than 1.86, [1.86, 2.06), [2.06, 2.26) and greater than 2.26 in 030/119 remote sensing image, the ALSTs of cultivated land were 301.09, 301.74, 301.94, and 302.23 K, respectively (Fig. 4a). The ALST changes of cultivated land indicated that the dry land maize growth status worsened with increasing TI of dune–interdune in
(21)
where fc is the vegetation coverage; and NDVIsoil and NDVIveg are the NDVI values of bare land and completely vegetation covered land, respectively. 2.2.3. Topography index (TI) TI was calculated to display the fluctuation state of dune–interdune quantitatively. Elevation data, including N42E121, N42E122, N42E123, N43E121, N43E122, and N43E123 images, were collected from GDEMV2 30 m database (http://www.gscloud.cn/). The DEM of HLBB was obtained after mosaicking and cutting. HLBB was first divided into many 2 km × 2 km parts. The average elevation of each part
Fig. 3. ALST and ARLST changes of cultivated land with increasing TI in 030/ 120 remote sensing image. 142
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Fig. 4. ALST and ARLST changes of cultivated land with increasing TI in 030/ 119 remote sensing image.
Fig. 5. ALST and ARLST changes of grass land with increasing TI in 030/120 remote sensing image.
general. As the TIs increased, the average relative LST (ARLST) of cultivated land also increased gradually. When TIs were distributed at [1.40, 1.60), [1.60, 1.80), [1.80, 2.00), [2.00, 2.20), [2.20, 2.40), and [2.40, 2.60) in 030/120 remote sensing image, the ARLSTs of cultivated land were 0.70, 1.49, 1.63, 1.97, 2.22, and 3.97 K, respectively (Fig. 3b). When TIs were distributed at less than 1.86, [1.86, 2.06), [2.06, 2.26) and greater than 2.26 in 030/119 remote sensing image, the ARLSTs of cultivated land were 2.20, 2.35, 2.69, and 3.37 K, respectively (Fig. 4b). Thus, the ARLST changes of cultivated land indicated that the dry land maize growth condition worsened with increasing TI of dune–interdune in local areas.
4. Discussion 4.1. Analysis of ALSTs of land use/cover types The ALSTs of water body and sandy land were the lowest and highest in 030/120 and 030/119 remote sensing images, respectively. The specific heat capacities of water and sandy soil were the highest and lowest among cultivated, forest, grass, and sandy lands and water body. The temperature increment of water body was the smallest and that of sandy land was the highest at the condition of absorbing the same amount of heat. The ALSTs of cultivated land, forest land, and grass land increased successively in 030/120 remote sensing image, whereas the ALST of forest land was the smallest, and those of cultivated and grass lands were almost equal in 030/119 remote sensing image. Cultivated land had a higher degree of human intervention and management than forest and grass lands had. The vegetation coverage of cultivated land was higher than those of forest and grass lands. Thus, the ALST of the cultivated land was the lowest in 030/120 remote sensing image. Forest land was arbor mainly without sparse forest land. Grass land included low vegetation glass land, such as shrub grass land, and sparse grass land. The vegetation coverage of forest land was higher than that of grass land. The ALST of forest land was lower than that of grass land in 030/120 remote sensing image. The region covered by 030/119 remote sensing image is located at the east of HLBB. On the one hand, precipitation decreases from east to west in HLBB (Fig. 6). On the other hand, East and West Liaohe Rivers go through the region. The water resource in the region covered by 030/119 remote sensing image is much richer than that in the region covered by 030/120 remote sensing image. Forest and grass grow well in the region covered by 030/119 remote sensing image. Thus, the ALST of forest land was lower than that of cultivated land, and the ALSTs of grass and cultivated lands were almost equal.
3.3. LST changes of grass land along dune–interdune The region covered by cloud in 030/119 remote sensing image was deleted (Fig. 1), and the land use/cover structure of the remnant part was mainly cultivated land that accounted for 85.88% (Table 4). The grass land area was 13.4568 km2, which accounted for the grass land area in the remnant part of 030/119 remote sensing image, which was 3.17%, and that in HLBB, which was only 0.12%. TI was calculated in each part area of 4 km2. The grass growth condition in 030/119 remote sensing image was ignored, and that in 030/120 remote sensing image was analyzed. For the 030/120 remote sensing image, the ALST of grass land increased first and then decreased with increasing TI. When TIs were distributed at [160, 1.80), [1.80, 2.00), [2.00, 2.20), and equal or greater than 2.20 in the 030/120 remote sensing image, the ALSTs of grass land were 300.25, 303.42, 304.09, and 301.66 K, respectively (Fig. 5a). The ALST changes of grass land indicated that grass growth status worsened first and then improved with increasing TI of dune–interdune in general. The ARLST of grass land increased first and then decreased with increasing TI. When TIs were distributed at [160, 1.80), [1.80, 2.00), [2.00, 2.20), and equal or greater than 2.20 in the 030/120 remote sensing image, the ARLSTs of grass land were 2.32, 3.56, 4.36, and 3.30 K, respectively (Fig. 5b). The ARLST changes of cultivated land indicated that grass growth status worsened first and then improved with increasing TI of dune–interdune in local areas.
Table 4 Land use/cover structure of the remnant part of 030/119 remote sensing image. Land use/cover type
Area (km2)
Taking proportion in the remnant part of 030/119 remote sensing image (%)
Taking proportion in HLBB (%)
Cultivated land Forest land Grass land Construction land Water body Sandy land
364.2804 2.2581 13.4568 28.1421 10.4328 5.5818
85.88 0.53 3.17 6.63 2.46 1.32
3.17 0.03 0.12 0.24 0.09 0.05
Fig. 6. Precipitation of HLBB and east and west Liaohe Rivers. 143
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Fig. 9. Locations of soil profiles 1-1 and 1–2. Fig. 7. Water condition of dune–interdune in HLBB.
4.2. Dry land maize growth status analysis along dune–interdune Dry land maize is the main crop in HLBB. Maize plantation area accounted for 85.7% of all sown area in 2016. The water demanded for the normal growth of maize was approximately 500 mm. First, precipitation in HLBB ranged from 358.0 mm to 462.8 mm from west to east (Fig. 6). The precipitation of maize growth period from May 5 to September 17 accounted for 74%. Thus, dry land maize had insufficient growth due to water stress in HLBB. Second, soil was mainly sandy soil, which had coarse soil particles and could not retain water. Third, precipitation seeped into low-lying areas rapidly under dune–interdune topography. Thus, soil moisture decreased with increasing TI of dune–interdune, and the water stress degree of maize growth became heavy (Fig. 7). Dry land maize growth status worsened with increasing TI of dune–interdune. According to our survey investigation, dry land maize at the upper region of dune–interdune had no yield at dry years, which induced desertification (Fig. 8). From the top to the bottom of dune–interdune, soil profiles were excavated and soil moistures were measured from July to September in 2015 and 2016. Two soil profiles along one dune–interdune were excavated (Fig. 9), and soil moisture was measured in July to September 2015. Moreover, three soil profiles along another dune–interdune were excavated (Fig. 10) and soil moisture was measured in July to September 2016. According to temperature and precipitation conditions, the first measurement period was divided into three phases, namely, high temperature and low precipitation (from middle July to middle August), high temperature and high precipitation (from middle August to last August), and low temperature and low precipitation (September). At the three periods, the soil moistures of profile 1-1 were approximately 10.48%, 14.41%, and 12.74%, and those of profile 1–2 were approximately 35.20%, 41.03%, and 41.06%. Soil profile 1-1 was at the top of dune–interdune, and soil profile 1–2 was at the bottom. The distance between the two profiles was 74 m, and the LSTs of soil profiles 1-1 and 1–2 were 308.03 K and 307.56 K, respectively. According to precipitation condition, the second measurement period was divided into two phases, namely low precipitation (from middle July to middle August) and high precipitation (from middle August to middle September). At the first period, the soil moistures of profiles 2-1, 2-2, and 2–3 were approximately 9.14%, 13.52%, and 15.03%, respectively. At the second period, the soil moistures of profiles 2-1, 2-2, and 2–3
Fig. 10. Locations of soil profiles 2-1, 2-2, and 2–3.
were approximately 12.38%, 20.08%, and 25.20%, respectively. Thus, soil water content decreased with increasing TI of dune–interdune. Dry land maize growth worsened with increasing TI of dune–interdune.
4.3. Grass growth status analysis along dune–interdune Land use/cover maps for 2001 and 2008 were obtained from ETM+ and TM5 data, respectively, using the maximum likelihood classification method. Land use/cover changes from 2001, 2008, and 2013 were then analyzed. The mutual conversions of cultivated, forest, and grass lands were mainly distributed at the middle region of dune–interdune with TIs between [1.80, 2.20). Changed area of cultivated, forest, and grass lands at [1.80, 2.00) accounted for 56.26% of the total changed area of cultivated, forest, and grass lands (Table 5). The changed area of cultivated, forest, and grass lands at [2.00, 2.20) accounted for 43.66% of the total changed area of cultivated, forest, and grass lands. Both changed areas of cultivated, forest, and grass lands at [1.80, 2.20) and [2.00, 2.20) accounted for 99.92% of changed area of cultivated, forest, and grass lands. At the middle region of dune–interdune, dry land maize could obtain much production when precipitation was rich, and no production was obtained when precipitation was less. Farmers adopted extensive cultivation to obtain high yield by extensive reclamation. Frequent changes among cultivated, forest, and grass lands destroyed vegetation growth, and land surface was exposed. Thus, ALST of grass land was highest and grass growth was lower at the middle region of dune–interdune. Frequent conversion among cultivated, forest, and grass lands must be immediately prevented to realize ecological restoration and crop production. Although the upper area of dune–interdune had the lowest soil moisture, anthropogenic interference was weakest, and conversions among land use/cover were also rare. Changed area of cultivated, forest, and grass lands with TI equal or greater than 2.20 was 154.71 hm2, which accounted for 0.057% of all changed area of cultivated, forest, and grass lands. Precipitation in HLBB ranges from 358 mm to 462.8 mm. When precipitation is between 330–550 mm and 300–400 mm, zonal vegetation is temperate meadow steppe and typical grassland. Thus, precipitation in HLBB can meet the water demand of grass normal growth (Liu, 1992). Therefore, grass can grow well even at the upper region of dune–interdune. At the low region of dune–interdune, soil moisture is higher, and grass grows best.
Fig. 8. Dry land maize growth condition at the upper region of dune–interdune under dry year. 144
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Table 5 Land use/cover changes at different TI groups in 2001, 2008, and 2013. Land use/cover type in 2001
Grass land Grass land Grass land Grass land Grass land Grass land Grass land Grass land Forest land Forest land Forest land Forest land Forest land Forest land Forest land Forest land Cultivated land Cultivated land Cultivated land Cultivated land Cultivated land Cultivated land Cultivated land Cultivated land Total
Land use/cover type in 2008
Grass land Grass land Forest land Forest land Forest land Cultivated land Cultivated land Cultivated land Grass land Grass land Grass land Forest land Forest land Cultivated land Cultivated land Cultivated land Grass land Grass land Grass land Forest land Forest land Forest land Cultivated land Cultivated land
Land use/cover type in 2013
Forest land Cultivated land Grass land Forest land Cultivated land Grass land Forest land Cultivated land Grass land Forest land Cultivated land Grass land Cultivated land Grass land Forest land Cultivated land Grass land Forest land Cultivated land Grass land Forest land Cultivated land Grass land Forest land
Changed area at different TI groups (km2) [1.40, 1.60)
[1.60, 1.80)
[1.80, 2.00)
[2.00, 2.20)
≥2.20
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0054 0.0000 0.0000 0.0054
0.0081 0.0414 0.0216 0.0117 0.0072 0.0117 0.0000 0.1278 0.0018 0.0009 0.0126 0.0099 0.0090 0.0063 0.0000 0.0216 0.0243 0.0252 0.0810 0.0189 0.0153 0.0936 0.0324 0.0306 0.6129
122.9346 184.8339 51.7662 46.6740 58.4172 81.4266 43.7877 239.8455 13.3965 3.4614 8.0226 12.3219 17.6463 11.9619 6.4593 66.3786 200.2581 44.7300 104.9571 16.7652 18.4797 55.2681 61.6221 56.1024 1527.5169
124.6860 145.7730 50.5260 54.9873 35.8452 52.9281 32.3640 131.5836 14.6034 3.5163 6.0516 12.0258 11.5614 8.5401 5.7681 38.3769 197.7345 42.0543 82.1619 12.9159 18.5301 28.2240 38.2815 36.4896 1185.5286
0.0711 0.2691 0.0450 0.0333 0.0171 0.0459 0.0279 0.1188 0.0423 0.0018 0.0135 0.0099 0.0063 0.0837 0.0009 0.0333 0.2817 0.0126 0.1485 0.0297 0.0414 0.0405 0.1116 0.0612 1.5471
years. Thus, rice can be planted in the area, and drainage facilities should be constructed when maize is planted. Although the land use model was proposed according to soil moisture differentiation and vegetation growth condition at the dune–interdune topography, land use boundaries will be further investigated in the future.
4.4. Land use model of dune–interdune Soil moisture differentiation caused the growth difference of vegetation at the dune–interdune topography. Soil moisture decreased with increasing TI of dune–interdune. According to our field investigation, dry land maize production at the marshy land could reach up to more than 750,000 kg/km2; otherwise, dry land maize probably has no production in dry years at the upper region of dune–interdune. Thus, the upper region of dune–interdune was intended for desertification. However, grass could grow well even if it is located at the top region of dune–interdune. On the contrary, grass grows worst at the middle region because of the frequent conversion among cultivated, forest, and grass lands, which induced desertification. Land use must therefore be designed according to the soil moisture differentiation of dune–interdune to obtain crop production and anti-desertification. At the top and upper regions of dune–interdune, water tolerance grass should be maintained for anti-desertification (Fig. 11). At the middle region of dune–interdune, cultivated land infrastructures, such as irrigation facilities and shelters, should be established to obtain sustainably cultivated land use and avoid frequent land use change. Only in this way can crop production be sustained and grass grows well. Lastly, agriculture production and anti-desertification can be completed simultaneously at the middle region of dune–interdune. At the low region of dune–interdune, soil moisture is richest, and the area is suitable for agriculture. According to relevant studies (Zhou et al., 2018), the risk of water logging is perceived in this region in wet
5. Conclusion The ALSTs of water body and cultivated, forest, grass, and sandy lands in 030/120 remote sensing image increased gradually. The ALSTs of water body and sandy land were lowest and highest in 030/119 remote sensing image, respectively. The ALST of forest land was lowest and the ALSTs of grass and cultivated lands were nearly equal among the ALSTs of cultivated, forest, and grass lands, which were different from those of 030/120 remote sensing image. This result was mainly attributed to the water resource differentiation of two regions covered by two remote sensing images. Dry land maize growth condition worsened with increasing TI of dune–interdune. First, precipitation could not meet the water demand of dry land maize normal growth in the agro-pastoral ecotone in Northern China. Second, soil moisture decreased and the water stress of dry land maize growth became increasingly serious with increasing TI of dune–interdune. Grass growth worsened first and then improved with increasing TI of dune–interdune, indicating that grass could grow well even at the upper region of the dune–interdune as long as land use was stable. According to soil moisture differentiation and vegetation growth condition at dune–interdune, land use model was designed to obtain crop production and anti-desertification. At the top region, water tolerance grass should be kept for anti-desertification. At the middle region, cultivated land infrastructures should be established to obtain sustainably cultivated land use and avoid frequent land use conversation. Crop production and anti-desertification could be completed at the middle region simultaneously. At the bottom region, rice can be planted, and if maize is planted, drainage facilities should be constructed.
Fig. 11. Land use model based on soil water differentiation and vegetation growth condition at dune–interdune. 145
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Acknowledgment
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