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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (3) : 159-166     DOI: 10.6046/gtzyyg.2018.03.22
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An analysis of landscape pattern spatial grain size effects in Qinghai Lake watershed
Jun ZHAI1, Peng HOU1, Zhiping ZHAO2, Rulin XIAO1(), Changzhen YAN3, Xuemin NIE4
1. Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China
2. Chinese Research Academy of Environmental Sciences, Beijing 100012, China
3. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
4. Ecological and Environmental Monitoring Center of Remote Sensing of Qinghai Province, Xining 810007, China
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Abstract  

Watershed is the basic and important spatial scale unit of ecosystem. The scientific analysis of watershed scale landscape pattern depends heavily on the accuracy of the selection of the optimal grain size. In this paper, Qinghai Lake watershed was selected as the study area. Based on the object-oriented classification method, the authors interpreted satellite remote sensing data to generate watershed landscape data. Resampling method was used to obtain different spatial grain size watershed landscape data. Then the landscape pattern indexes were calculated and statistical relation curve was drawn between each landscape pattern index and grain size. Thus the scale effect of landscape pattern index could be identified. Finally, landscape pattern index information loss caused by increasing grain size was used to determine the optimum spatial grain size of watershed scale landscape pattern analysis. The result showed watershed landscape pattern index changed significantly with the increase of spatial grain size, but laws of change were different. Taking into account the landscape pattern spatial grain size effect and the change characteristics of information loss, the authors hold that the best spatial grain size choice of watershed landscape pattern analysis was 90 m.
Keywords watershed      scale      landscape pattern      optimal grain size     
:  TP79  
Corresponding Authors: Rulin XIAO     E-mail: alexandershaw_84@163.com
Issue Date: 10 September 2018
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Jun ZHAI
Peng HOU
Zhiping ZHAO
Rulin XIAO
Changzhen YAN
Xuemin NIE
Cite this article:   
Jun ZHAI,Peng HOU,Zhiping ZHAO, et al. An analysis of landscape pattern spatial grain size effects in Qinghai Lake watershed[J]. Remote Sensing for Land & Resources, 2018, 30(3): 159-166.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.22     OR     https://www.gtzyyg.com/EN/Y2018/V30/I3/159
Fig.1  Geographical location of Qinghai Lake watershed
Fig.2  Examples of landscape in different grain sizes
景观格局指数 拟合函数 函数模型 R2
景观形状指数 幂函数 y = 352.61x-0.628 0.959 4
边界总长度 幂函数 y = 2e+8x-0.645 0.957 4
边界密度 幂函数 y = 82.876x-0.645 0.957 4
聚集度指数 幂函数 y = 93.324x-0.137 0.994 6
平均斑块分维数 幂函数 y = 1.064 6x-0.013 0.770 5
形状指数平均值 幂函数 y = 1.465 7x-0.059 0.653 9
蔓延度指数 幂函数 y = 62.387x-0.093 0.974 3
景观分割度 幂函数 y = 0.966 7x-0.009 0.397 2
分离度指数 幂函数 y = 28.556x-0.167 0.502 1
斑块数 幂函数 y = 319 685x-1.246 0.933 9
斑块密度 幂函数 y = 10.78x-1.246 0.933 9
面积加权形状指数 幂函数 y = 33.778x-0.411 0.808 7
斑块面积变异系数 幂函数 y = 10 600x-0.541 0.878 3
面积加权平均
斑块分维数		 幂函数 y = 1.270 4x-0.029 0.927 2
平均斑块面积 二次函数 y = 0.519 8x2 +
5.711 1x + 12.88		 0.999 4
边缘面积分维数 对数函数 y = 0.040 8lnx +
1.483 1		 0.820 9
斑块面积标准差 线性函数 y = 322x + 1 252.1 0.974 7
平均欧几里得
最近距离		 线性函数 y = 95.658x - 69.067 0.997 6
Tab.1  Functions of landscape indexes and grain sizes
Fig.3-1  Statistic curves of different landscape indexes with different grain sizes (power function type)
Fig.3-2  Statistic curves of different landscape indexes with different grain sizes (power function type)
Fig.4  Curves of indexes with different grain sizes (quadratic, logarithmic and linear function)
Fig.5  Curves of indexes with different grain sizes (piecewise function and stable type with increasing range of change)
Fig.6  Information loss of landscape indexes caused by the grain size effect
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