| 闽东南沿海地区景观指数粒度效应的高分辨率遥感分析 |
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| 引用本文： | 冉建波,陈兴伟.闽东南沿海地区景观指数粒度效应的高分辨率遥感分析[J].地球信息科学,2016,18(6):824-832. |
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| 作者姓名： | 冉建波 陈兴伟 |
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| 作者单位： | 1. 福建师范大学地理科学学院,福州 3500072. 湿润亚热带山地生态国家重点实验室培育基地,福州 3500073. 福建省陆地灾害监测评估工程技术研究中心,福州 350007 |
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| 基金项目： | 福建省高校产学合作项目（2015Y4002） |
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| 摘 要： | 以闽东南沿海地区3个流域SPOT 5影像解译所得的土地覆被类型分布图为原始矢量数据,通过优势规则聚合为一系列不同粒度大小的栅格类型图,统计各粒度下28种常用景观指数值;采用尺度检测图分析各种景观指数的粒度变化响应规律,对比中分辨率数据与高分辨率数据所建立尺度函数的差异,评价预测响应型指数尺度下推的相对误差。结果表明：闽东南沿海地区景观指数的粒度效应明显,可分为预测响应型、分段预测型、阶梯变化型和波动变化型4类,其中分段预测型指数由已有报道的预测响应型分化而来,其尺度转折点位于5 m 、7.5 m或10 m等粒度下,即高分辨率影像对景观指数粒度效应的分类有重要影响。在预测响应型指数中,高分辨率影像对ED、SHAPE_MN、CONTIG_MN和AI 4种指数的尺度函数影响较大,由中分辨率数据所建立的尺度函数进行尺度下推,会带来较大误差;这些指数的区域差异性也较大,对同一指数来说,不同区域可选用不同的尺度函数进行尺度下推。
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| 关 键 词： | 景观指数 粒度效应 尺度函数 SPOT 5 闽东南沿海地区 |
| 收稿时间： | 2014-11-25 |
The Effects of Changing Grain on Landscape Metrics with High-resolution Image in the South-eastern Coastal Region of Fujian |
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| RAN Jianbo,CHEN Xingwei.The Effects of Changing Grain on Landscape Metrics with High-resolution Image in the South-eastern Coastal Region of Fujian[J].Geo-information Science,2016,18(6):824-832. |
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| Authors: | RAN Jianbo CHEN Xingwei |
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| Institution: | 1. College of Geographic Sciences, Fujian Normal University, Fuzhou 350007, China2. Cultivation Base of State Key Laboratory of Humid Subtropical Mountain Ecology, Fuzhou 350007, China3. Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters, Fuzhou 350007, China |
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| Abstract: | The effects of changing grain on landscape metrics is an important topic in landscape ecology studies. The original data in the most previous studies were derived from moderate/low resolution data and how the high-resolution data could affect the grain effect has not yet been well investigated. Therefore, we selected the land cover datasets produced from SPOT 5 imagery (with a spatial resolution of 2.5 m) in three watersheds located in the south-eastern coastal region of Fujian Province, China. We examined the behaviors of 28 landscape metrics in varying the range of grain size varing from 2.5 m to 150 m, where the grains coarser than 2.5 m were aggregated through majority filters. We then compared the differences between scaling functions fitted with the data from 2.5 m to 150 m and 30 m to 150 m. The results show that the effects of changing grain on 28 landscape metrics are obvious in the three watersheds examined. The responses of the metrics to changing grain size can be divided into four categories, while previous studies reported only three categories. The newly category is named as TypeⅡ including eight metrics that behaved split-up predictable responses with scale inflexion at 5 m, 7.5 m or 10 m respectively. This indicates that the high-resolution data can reveal more detailed effects of changing grain on landscape metrics. The results also show that the scaling functions for ED, SHAPE_MN, CONTIG_MN and AI, are sensitive to the spatial resolution of the raw data. Those scaling functions obtained from the moderate-resolution data may not be applicable to estimating the landscape metrics for grains finer than 10 m. |
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| Keywords: | landscape metrics grain effect scaling functions SPOT5 south-eastern coast region of Fujian |
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