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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 221-230     DOI: 10.6046/gtzyyg.2020100
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An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information
HU Suliyang1,2,3(), LI Hui1,2(), GU Yansheng4,5, HUANG Xianyu1,2, ZHANG Zhiqi6, WANG Yingchun6
1. China University of Geosciences(Wuhan), School of Geography and Information Engineering, Wuhan 430074, China
2. China University of Geosciences(Wuhan),Hubei Key Laboratory of Critical Zone Evolution, Wuhan 430074, China
3. Geological Surveying and Mapping Institute, Kunming 650000, China
4. State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
5. Hubei Key Laboratory of Wetland Evolution and Eco-Restoration(WEER), China University of Geosciences, Wuhan 430074, China
6. Shennongjia National Park Management Bureau, Shennongjia 442421, China
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Abstract  

Dajiu lake wetland is a rare subtropical alpine wetland in Central China. The wetland has experienced several periods of large-scale development since the founding of the People’s Republic of China, which has led to the serious destruction of the wetland. The “Dajiu Lake wetland protection and restoration and park construction project” implemented in 2005 has made the wetland function recovered to a certain extent. To understand the land use changes in Dajiuhu wetland, the authors identified nine land use types of Dajiu lake wetland based on field investigation and previous work. The high-resolution remote sensing images acquired in 2005, 2011 and 2017 and UAV images in 2018 were used to visually interpret the land use types. The dynamic change and type conversion of land use in three periods were examined and the driving forces were explored. The results show that, from 2005 to 2011, new lakes (84.41 hm2) were added, and the most decreased area was farmland, which mainly transformed into xerophytic meadow and wet herbaceous swamp. From 2011 to 2017, a new type of mesophytic meadow (80.07 hm2) was added, which was mainly transformed from wet peat swamp, wet herbaceous swamp and xerophytic meadow. Most of the reduction was in farmland, which was mainly converted to xerophytic meadow. In a word, during the research period, the wetland types and areas of Dajiu Lake were increasing, the wetland landscape was restored to a certain extent, and the wetland ecological environment was improved. The analysis of driving forces shows that the establishment of Wetland Nature Reserve and a series of effective wetland ecological restoration projects are the main driving forces of land use change in Dajiu Lake wetland. The results of this study can provide reference and suggestions for wetland restoration and protection.

Keywords Shennongjia Dajiu Lake      high resolution remote sensing image      land use      wetland      driving forces     
ZTFLH:  TP79  
Fund:CUGCJ1806
Corresponding Authors: LI Hui     E-mail: jacobxiaosu@foxmail.com;leelmars@gmail.com
Issue Date: 18 March 2021
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Suliyang HU
Hui LI
Yansheng GU
Xianyu HUANG
Zhiqi ZHANG
Yingchun WANG
Cite this article:   
Suliyang HU,Hui LI,Yansheng GU, et al. An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information[J]. Remote Sensing for Land & Resources, 2021, 33(1): 221-230.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020100     OR     https://www.gtzyyg.com/EN/Y2021/V33/I1/221
Fig.1  Location and satellite image of the study area
地物 遥感影像特征 解译标志
旱生草甸 影像整体呈深绿色,轮廓清晰 沿保护区主干道分布
中生草甸 影像整体呈灰褐色和浅绿色,轮廓较模糊 分布于湿生草本沼泽和旱生草甸的交界处
河渠 影像整体呈不规则白色,有水时呈深蓝色,轮廓清晰 特征较明晰
湖泊 影像整体呈棕灰色和深蓝色,轮廓清晰 特征较明晰
建设用地 影像整体呈白色和棕红色,轮廓清晰 分散于保护区四周
林地 影像整体呈斑点状深绿色,轮廓清晰 分布于保护区中部
道路 影像整体呈线状白色,轮廓清晰 沿保护区四周分布
农田 影像整体呈浅绿色,轮廓清晰 沿保护区周围分布
湿生泥炭沼泽 整体呈灰绿色和浅绿色,轮廓模糊 分布于保护区东南方向
湿生草本沼泽 整体呈棕红色,轮廓模糊 分布于保护区中部
Tab.1  Remote sensing interpretation signatures of land use types in Dajiuhu Wetland
Fig.2  Maps of land uses of Dajiuhu wetland
地物类别 2005 2011 2017 2005—2011 2011—2017
面积/hm2 比例/% 面积/hm2 比例/% 面积/hm2 比例/% 变化量/hm2 动态度/% 变化量/hm2 动态度/%
旱生草甸 124.37 11.48 217.49 20.08 456.11 42.11 93.12 12.48 238.62 18.29
河渠 8.53 0.79 14.35 1.32 9.39 0.87 5.82 11.37 -4.96 -5.76
湖泊 0① 0 84.41 7.79 87.53 8.08 84.41 0 3.12 0.62
建设用地 49.76 4.59 98.27 9.07 84.99 7.85 48.52 16.25 -13.29 -2.25
林地 187.68 17.33 142.23 13.13 157.07 14.5 -45.45 -4.04 14.84 1.74
农田 511.83 47.25 244.83 22.6 2.79 0.26 -266.99 -8.69 -242.05 -16.48
湿生草本沼泽 5.3 0.49 147.8 13.65 90.64 8.37 142.5 447.74 -57.16 -6.45
湿生泥炭沼泽 195.66 18.06 133.74 12.35 114.53 10.57 -61.92 -5.27 -19.21 -2.39
中生草甸 0 0 0 0 80.07 7.39 0 0 80.07 0
合计 1 083.13 100.00 1 083.13 100.00 1 083.13 100
Tab.2  Statistical table of land use change results of Dajiuhu wetland in 2005-2017
地类 旱生草甸 河渠 湖泊 建设用地 林地 农田 湿生草本沼泽 湿生泥炭沼泽 转出合计
旱生草甸 40.52 2.08 26.96 6.81 7.12 5.97 25.09 9.82 83.85
河渠 1.29 6.69 0.00 0.03 0.08 0.29 0.05 0.11 1.84
建设用地 4.67 0.62 4.09 32.42 0.94 3.37 2.17 1.49 17.34
林地 4.36 0.54 24.22 8.22 119.29 1.35 11.18 18.52 68.39
农田 125.79 3.58 19.14 6.19 47.28 233.68 75.45 0.71 278.15
湿生草本沼泽 0.20 0.00 0.00 0.03 0.09 0.00 4.98 0.00 0.32
湿生泥炭沼泽 40.67 0.84 10.00 8.95 3.07 0.17 28.87 103.09 92.57
转入合计 176.97 7.66 84.41 30.23 58.58 11.15 118.92 30.65 542.47
Tab.3  Land use transfer matrix of Dajiuhu wetland in 2005—2011(hm2)
地类 旱生草甸 河渠 湖泊 建设用地 林地 农田 湿生草本沼泽 湿生泥炭沼泽 中生草甸 转出合计
旱生草甸 150.07 1.31 2.34 8.56 4.73 0.93 23.50 11.78 14.26 67.41
河渠 2.93 6.01 0.00 0.13 0.22 0.00 2.64 0.02 2.41 8.34
湖泊 2.60 0.29 74.66 0.15 1.02 0.00 3.76 1.63 0.30 9.75
建设用地 40.88 0.18 0.50 46.19 4.81 0.03 3.00 1.04 1.65 52.08
林地 8.88 0.21 0.96 1.39 122.72 0.00 3.31 4.01 0.76 19.51
农田 201.74 0.96 0.00 21.64 2.23 1.82 6.86 0.00 9.59 243.01
湿生草本沼泽 44.59 0.35 7.57 6.73 10.66 0.00 19.23 81.53 25.87 177.30
湿生泥炭沼泽 4.43 0.08 1.49 0.20 10.69 0.00 28.35 14.53 25.24 70.49
转入合计 306.04 3.38 12.87 38.80 34.35 0.97 71.41 100.01 80.07 647.89
Tab.4  Land use transfer matrix of Dajiuhu wetland in 2011—2017(hm2)
Fig.3  Remote sensing image interpretation map of lakes in the study area
Fig.4  Remote sensing image interpretation map of farmland in the study area
Fig.5  Remote sensing image interpretation map of xerophytic meadow and mesophytic meadow in the study area
Fig.6  Remote sensing image interpretation map of wet peat swamp and wet herbaceous swamp in the study area
Fig.7  Remote sensing image interpretation map of roads and build ups in the study area
Fig.8  Remote sensing image interpretation map of forest land and river in the study area
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