闽东南沿海地区景观指数粒度效应的高分辨率遥感分析

doi:10.3724/SP.J.1047.2016.00824

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (6): 824-832.doi: 10.3724/SP.J.1047.2016.00824

闽东南沿海地区景观指数粒度效应的高分辨率遥感分析

冉建波¹(), 陈兴伟^1,^2,^3,^*()

1. 福建师范大学地理科学学院,福州 350007
2. 湿润亚热带山地生态国家重点实验室培育基地,福州 350007
3. 福建省陆地灾害监测评估工程技术研究中心,福州 350007

收稿日期:2014-11-25 修回日期:2015-09-09 出版日期:2016-06-10 发布日期:2016-06-10
通讯作者: 陈兴伟 E-mail:ran_wl@163.com;cxwchen215@163.com
作者简介:
作者简介：冉建波（1989-）,男,重庆武隆人,硕士生,主要从事资源生态与遥感应用。E-mail: ran_wl@163.com
基金资助:
福建省高校产学合作项目（2015Y4002）

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

RAN Jianbo¹(), CHEN Xingwei^1,^2,^3,^*()

1. College of Geographic Sciences, Fujian Normal University, Fuzhou 350007, China
2. Cultivation Base of State Key Laboratory of Humid Subtropical Mountain Ecology, Fuzhou 350007, China
3. Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters, Fuzhou 350007, China

Received:2014-11-25 Revised:2015-09-09 Online:2016-06-10 Published:2016-06-10
Contact: CHEN Xingwei E-mail:ran_wl@163.com;cxwchen215@163.com

摘要/Abstract

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。

关键词: 景观指数, 粒度效应, 尺度函数, SPOT 5, 闽东南沿海地区

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.

Key words: landscape metrics, grain effect, scaling functions, SPOT5, south-eastern coast region of Fujian

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]. 地球信息科学学报, 2016, 18(6): 824-832.DOI:10.3724/SP.J.1047.2016.00824

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]. Journal of Geo-information Science, 2016, 18(6): 824-832.DOI:10.3724/SP.J.1047.2016.00824

图/表 7

图1

表1

景观水平上所用到的28种指数"

	景观指数	英文名称	缩写	单位
组成特征	斑块密度	Patch density	PD	个/100 hm2
	边界密度	Edge density	ED	m/hm2
	最大斑块指数	Largest patch index	LPI	%
	平均斑块面积	Patch area distribution_Mean	AREA_MN	hm2
	加权平均斑块面积	Patch area distribution_Area-weighted Mean	AREA_AM	hm2
	斑块面积标准差	Patch area distribution_Standard Deviation	AREA_SD	hm2
	斑块面积变异系数	Patch area distribution_Coeffient of Variation	AREA_CV	%
	平均斑块回旋半径	Radius of gyration	GYRATE_MN	m
	加权平均斑块回旋半径		GYRATE_AM	m
形状特征	平均斑块形状指数	Shape index distribution_Mean	SHAPE_MN
	加权平均斑块形状指数	Shape index distribution_Area-weighted Mean	SHAPE_AM
	平均斑块邻近指数	Contiguity index distribution_Mean	CONTIG_MN
	加权平均斑块邻近指数	Contiguity index distribution_Area-weighted Mean	CONTIG_AM
	周长面积分维数	Perimeter-area fractal dimension index	PAFRAC
	平均周长面积比值	Perimeter-area ratio distribution_Mean	PARA_MN
	加权平均周长面积比值	Perimeter-area ratio distribution_Area-weighted Mean	PARA_AM
	平均斑块分维数	Patch fractal dimension distribution_Mean	FRAC_MN
	加权平均斑块分维数	Patch fractal dimension distribution_Area-weighted Mean	FRAC_AM
	斑块分维数标准差	Patch fractal dimension distribution_Standard Deviation	FRAC_SD
	斑块分维数数变异系数	Patch fractal dimension distribution_Coeffient of Variation	FRAC_CV	%
聚散性特征	蔓延度	Contagion index	CONTAG	%
	聚合度	Aggregation index	AI	%
	散布与并列指数	Interspersion and juxtaposition index	IJI	%
	分离度	Landscape division index	DIVISION	%
	破碎度	Splitting index	SPLIT
多样性特征	香农多样性指数	Shannon's diversity index	SHDI
	辛普森多样性指数	Simpson's diversity index	SIDI
	香农均匀度指数	Shannon's evenness index	SHEI

表1

图2

图3

图4

表2

表3

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景观指数	拟合函数	粒度分析范围/m	坑仔口溪			诗溪				九十九溪
景观指数	拟合函数	粒度分析范围/m	a		b	R2	a	b	R2	a	b	R2
ED	对数	30~150	-31.889	200.463	1.000	-43.729	260.818	0.996		-44.987	264.643	0.996
		2.5~150	-26.682	178.560	0.972	-40.624	248.552	0.982		-42.196	253.778	0.983
SHAPE_MN	幂函数	30~150	2.339	-0.136	0.987	1.577	-0.046	0.960		1.517	-0.045	0.936
		2.5~150	2.517	-0.153	0.993	2.129	-0.115	0.909		2.024	-0.111	0.907
CONTIG_MN	幂函数	30~150	2.907	-0.575	0.990	0.750	-0.301	0.994		0.799	-0.326	0.982
		2.5~150	1.436	-0.414	0.972	1.165	-0.402	0.985		1.151	-0.409	0.990
FRAC_MN	幂函数	30~150	1.179	-0.028	0.992	1.104	-0.013	0.968		1.102	-0.014	0.969
		2.5~150	1.197	-0.031	0.993	1.181	-0.029	0.936		1.176	-0.029	0.938
CONTAG	幂函数	30~150	75.679	-0.090	0.972	69.552	-0.085	0.986		59.985	-0.098	0.965
		2.5~150	75.487	-0.088	0.985	71.891	-0.092	0.993		64.261	-0.113	0.989
AI	对数	30~150	-11.019	123.938	0.999	-10.178	117.515	1.000		-8.946	113.148	0.998
		2.5~150	-8.147	111.623	0.956	-8.452	110.197	0.977		-7.925	108.905	0.983
SHDI	线性	30~150	-0.001	1.352	0.986	0.000	1.424	0.973		-0.001	1.778	0.997
		2.5~150	-0.001	1.345	0.977	0.000	1.422	0.985		-0.001	1.775	0.995

景观指数	研究区	估计值			实测值				相对误差/（%）
景观指数	研究区	2.5 m		5 m	10 m	2.5 m	5 m	10 m	2.5 m	5 m	10 m
ED	坑仔口溪	171.243	149.140	127.036	136.167	132.554	122.277		25.8	12.5	3.9
	诗溪	220.756	190.451	160.147	189.370	180.905	162.781		16.6	5.3	-1.6
	九十九溪	223.422	192.239	161.057	192.410	184.338	164.612		16.1	4.3	-2.2
SHAPE_MN	坑仔口溪	2.065	1.879	1.710	2.153	2.041	1.778		-4.1	-7.9	-3.8
	诗溪	1.512	1.464	1.419	2.071	1.893	1.632		-27.0	-22.7	-13.1
	九十九溪	1.456	1.411	1.368	1.976	1.811	1.559		-26.3	-22.1	-12.3
CONTIG_MN	坑仔口溪	1.716	1.152	0.773	0.851	0.713	0.537		101.7	61.6	44.2
	诗溪	0.569	0.462	0.375	0.847	0.679	0.470		-32.8	-32.0	-20.2
	九十九溪	0.593	0.473	0.377	0.823	0.650	0.452		-28.0	-27.3	-16.5
FRAC_MN	坑仔口溪	1.149	1.127	1.105	1.161	1.146	1.116		-1.0	-1.6	-1.0
	诗溪	1.091	1.081	1.071	1.164	1.145	1.108		-6.3	-5.6	-3.3
	九十九溪	1.088	1.077	1.067	1.160	1.139	1.102		-6.2	-5.4	-3.1
CONTAG	坑仔口溪	69.688	65.474	61.514	67.065	65.209	62.410		3.9	0.4	-1.4
	诗溪	64.341	60.659	57.189	64.493	62.219	58.945		-0.2	-2.5	-3.0
	九十九溪	54.833	51.232	47.868	56.338	53.893	50.406		-2.7	-4.9	-5.0
AI	坑仔口溪	113.841	106.204	98.566	98.305	96.698	93.905		15.8	9.8	5.0
	诗溪	108.189	101.134	94.079	97.642	95.492	91.883		10.8	5.9	2.4
	九十九溪	104.951	98.750	92.549	97.605	95.409	91.799		7.5	3.5	0.8
SHDI	坑仔口溪	1.350	1.347	1.342	1.337	1.337	1.337		0.9	0.7	0.4
	诗溪	1.424	1.424	1.424	1.418	1.418	1.418		0.4	0.4	0.5
	九十九溪	1.776	1.773	1.768	1.768	1.768	1.767		0.4	0.3	0.0

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 South-eastern Coastal Region of Fujian

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