闽东南沿海地区景观指数粒度效应的高分辨率遥感分析

The Effects of Changing Grain on Landscape Metrics with High-resolution Image in the Southeastern Coastal Region of Fujian

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。

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 2.5m were aggregated through majority filters. We then compared the differences between scaling functions fitted with the data from2.5 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.