| 县域尺度下景观指数的粒度效应 |
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| 引用本文： | 崔杨林,董斌,位慧敏,徐文瑞,杨斐,彭亮,方磊,王裕婷.县域尺度下景观指数的粒度效应[J].浙江农林大学学报,2020,37(4):778-786. |
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| 作者姓名： | 崔杨林 董斌 位慧敏 徐文瑞 杨斐 彭亮 方磊 王裕婷 |
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| 作者单位： | 1.安徽农业大学 理学院，安徽 合肥 2300362.中国科学院 测量与地球物理研究所 环境与灾害监测评估湖北省重点实验室，湖北 武汉 4300773.中国科学院大学，北京 100049 |
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| 基金项目： | 国家自然科学基金资助项目(41571101，41401022)；北京林业大学青年教师科学研究中长期项目计划(2015ZCQ-LX-01) |
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| 摘 要： | 目的 尺度的合理确定是景观格局和生态研究过程的关键。研究县域尺度下景观指数的粒度效应，并计算景观指数的适宜粒度范围对分析景观格局变化具有重要意义。 方法 以2017年安徽省宿松县的景观分布图为数据源，从类型和景观水平分析了各个景观指数在20~500 m粒度范围内的粒度效应，并选取适宜的粒度范围；通过拟合函数揭示不同景观格局指数随粒度增大的变化特征；结合景观面积损失精度评价模型确定宿松县景观格局变化的最适宜空间粒度值。 结果 景观指数具有一定的空间粒度效应性，其中大部分景观指数的可预测性强，但景观总面积、平均面积分维数、平均形状指数、Simpson多样性、Simpson均匀度指数对空间粒度响应不敏感；景观指数的粒度效应曲线可分为单调递减、单调递增、无变化、复杂变化4种类型；景观指数的拐点主要集中在70和200 m；在景观水平下景观指数粒度效应曲线拟合后的函数主要为幂函数，且拟合程度高。 结论 宿松县景观格局变化的适宜粒度为100~110 m，最佳粒度为100 m。图3表1参27
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| 关 键 词： | 景观生态学 景观指数 粒度效应 地理国情 斑块 宿松县 |
| 收稿时间： | 2019-08-21 |
Granularity effect of landscape index at the county scale |
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| CUI Yanglin,DONG Bin,WEI Huimin,XU Wenrui,YANG Fei,PENG Liang,FANG Lei,WANG Yuting.Granularity effect of landscape index at the county scale[J].Journal of Zhejiang A&F University,2020,37(4):778-786. |
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| Authors: | CUI Yanglin DONG Bin WEI Huimin XU Wenrui YANG Fei PENG Liang FANG Lei WANG Yuting |
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| Affiliation: | 1.School of Science, Anhui Agricultural University, Hefei 230036, Anhui, China2.Key Laboratory for Environment and Disaeter Monitoring and Evaluation of Hubei, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, Hubei, China3.University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Objective Reasonable scale determination is the key to the research of landscape pattern and ecology and has become one of the research focuses in landscape ecology and geography. Hence, the present research is focused on the granularity effect of landscape index at the county scale and appropriate granularity range of landscape index which is of great significance to the analysis of landscape pattern changes. Method First, with the landscape distribution map of Susong County, Anhui Province in 2017 was used as the data, an analysis was conducted of the granularity effect of each landscape index in the granularity range of 20?500 m from the two levels of type and landscape with the appropriate granularity range selected. Then, the fitting function was used to reveal the variation features of different landscape pattern indexes with the increase of granularity. Lastly, the optimal spatial granularity value of landscape pattern changes in Susong County was determined by the results of the precision evaluation model of landscape area loss. Result (1) The landscape indexes had a certain spatial granularity effect and most of them were highly predictable; (2) Landscape total area index, average area fractal dimension, average shape index, Simpson diversity index and Simpson evenness index demonstrated low response sensitivity to spatial granularity; (3) There were four different types of granularity effect curves of the landscape index: monotonically decreasing, monotonically increasing, no change, and complex change and their inflection points were mainly occurred at 70 and 200 m; (4) Power function was the main function upon the favorable fitting of the landscape index granularity effect curve. Conclusion The suitable grain size range landscape pattern change was 100?110 m in Susong County, Anhui Province, and the optimal grain size value was 100 m. Ch, 3 fig. 1 tab. 27 ref.] |
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