中老铁路建设对沿线景观格局影响的尺度效应

doi:10.18402/resci.2021.12.08

摘要/Abstract

摘要：

线性交通廊道建设对区域景观格局产生深刻影响,科学分析廊道建设对景观格局的影响及尺度效应,明确景观格局的变化趋势,从而构建线性交通廊道建设与生态保护均衡发展格局,对铁路所在区域实现可持续发展具有重要意义。本文以中老（中国—老挝）铁路为研究对象,基于2015和2020年的土地利用数据,采用土地利用转移矩阵及回归分析等研究方法,分析铁路沿线整体和不同类型的景观格局指数变化特征及其空间差异,揭示中老铁路建设对沿线景观格局的影响及尺度效应。研究表明：①中老铁路沿线区域土地利用以林地为主,2020年铁路廊道土建工程完工后,林地面积减少,耕地面积有所增加,部分减少的林地面积转化为耕地。②铁路建设导致沿线缓冲区整体景观格局的异质性增强,斑块连通程度减弱,斑块面积缩减,林地破碎度加剧。③整体景观和林地的斑块密度、分散指数均与铁路影响尺度呈倒“U”型关系,铁路对整体景观和林地的景观格局产生显著影响的拐点分别位于铁路两侧6 km和8 km附近。本文结果可用于评估铁路等线性交通廊道的建设对沿线景观带来的影响,对未来中国与澜沧江—湄公河流域的其他国家在实现互联互通的同时,如何制定更具针对性的景观格局保护政策与措施提供科学依据。

关键词: 线性交通廊道, 景观格局指数, 尺度效应, 中老铁路, 可持续发展, 澜湄流域

Abstract:

The construction of linear transport corridors has profound impacts on regional landscape patterns. Thus, analyzing the scale effects of corridor construction on landscape patterns, identifying the change trend of landscape affected by railway construction, and achieving a balanced development of linear transport corridor construction and ecological protection are significant for the sustainable development in the region where a railway is located. Taking the China-Laos Railway as an example and based on the land use data between 2015 and 2020, this study applied the land use transfer matrix and regression analysis to analyze the characteristics and spatial differences of the overall and different landscape along the railway and revealed the scale effects of the China-Laos Railway construction on the landscape patterns. The results indicate that: (1) The land use along the China-Laos Railway is mainly forestland. After the completion of the railway construction in 2020, the forest area decreased and cultivated land area increased, and part of the forestland has been transformed into cultivated land. (2) The railway construction led to an increased heterogeneity of the overall landscape pattern, while connectivity among the patches became weakened, patch area decreased, and forest fragmentation increased. (3) The patch density (PD) and dispersion index (SPLIT) of the overall landscape and the forest land are in an inverted U-shaped relationship with the scale effects of the railway. The inflection points of the railway that have a significant impact on the overall and forest landscapes are about 6 km and 8 km respectively from the railway on both sides. The results of this study can be applied to evaluate the impacts of the construction of linear transport corridors such as railways on landscape and guide other countries to formulate more specific landscape protection policies while realizing interconnection between China and other developing countries in the Lancang-Mekong Basin in the future.

Key words: linear transport corridor, landscape pattern index, scale effect, China-Laos Railway, sustainable development, Lancang-Mekong Basin

翁凌飞, 白昊男, 沈镭. 中老铁路建设对沿线景观格局影响的尺度效应[J]. 资源科学, 2021, 43(12): 2451-2464.

WENG Lingfei, BAI Haonan, SHEN Lei. Scaling impacts of China-Laos Railway construction on landscape patterns[J]. Resources Science, 2021, 43(12): 2451-2464.

图/表 14

图1

表1

景观格局指数及含义

景观格局指数	计算公式	变量意义	说明
斑块密度	$PD = n i A$	$n i$ 为组成第i类景观的斑块总数;A为景观总面积	反映景观破碎化程度及斑块空间分布差异^[8,15,24]
最大斑块指数	$LPI = max (a 1, a 2, …, a n) / A$	$a 1, a 2, ⋯, a n$ 为各斑块面积	景观中最大面积斑块所占比例,反映景观优势度^[17,32]
分散指数	$SPLIT = A 2 / ∑ i = 1 m ∑ j = 1 n a ij 2$	$a ij$ 为第i类景观中第j个斑块的面积	反映景观斑块集聚与分散程度,数值越大,斑块越分散^[29,31]
香农多样性指数	$SHDI = - ∑ k = 1 l P k lo g 2 P k$	$P k$ 为斑块类型k在景观中的比例; $l$ 为景观中斑块类型的总数	反映斑块类型与各类型斑块比例变化,测度景观丰富程度,数值越大,斑块越丰富^[10,33]
斑块平均面积	$AREA_MN = ∑ j = 1 n a j / n$	$a j$ 为斑块j的面积;n为斑块数量	反映景观斑块的平均大小^[32,34],对斑块扩散与缩减变化敏感

表1

图2

表2

图3

图4

图5

表3

表4

图6

表5

表6

图7

图8

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2015年	2020年
2015年	草地	耕地	建设用地	林地	水域和湿地	其他	总计
草地	170.20	607.33	106.67	500.65	7.43	-	1392.28
耕地	93.96	843.74	123.66	220.39	12.23	-	1293.99
建设用地	1.85	67.56	113.62	7.51	1.29	-	191.83
林地	978.57	2145.91	63.99	12262.83	21.33	0.04	15472.68
水域和湿地	5.42	28.07	4.33	27.13	94.35	-	159.30
其他	1.75	4.21	9.27	1.47	0.14	-	16.83
总计	1251.76	3696.82	421.54	13019.99	136.77	0.04	18526.91

变量	显著性指标	缓冲区半径	PD变化量	LPI变化量	SPLIT变化量	SHDI变化量
缓冲区半径	Pearson相关性	1.000	-0.937**	-0.079	-0.782**	-0.889**
缓冲区半径	显著性（双尾）		0.000	0.828	0.008	0.001
PD变化量	Pearson相关性	-0.937**	1.000	-0.059	0.710*	0.925**
PD变化量	显著性（双尾）	0.000		0.870	0.021	0.000
LPI变化量	Pearson相关性	-0.079	-0.059	1.000	0.478	-0.067
LPI变化量	显著性（双尾）	0.828	0.870		0.163	0.854
SPLIT变化量	Pearson相关性	-0.782**	0.710*	0.478	1.000	0.685*
SPLIT变化量	显著性（双尾）	0.008	0.021	0.163		0.029
SHDI变化量	Pearson相关性	-0.889**	0.925**	-0.067	0.685*	1.000
SHDI变化量	显著性（双尾）	0.001	0.000	0.854	0.029

景观指数	年份	回归模型	决定系数R²	F值	P值
PD	2015	Y₁=3.952+0.189X-0.02X²	0.885	26.834	0.001
PD	2020	Y₂=2.137+0.204X-0.017X²	0.857	20.992	0.001
SPLIT	2015	Y₃=11.1+9.434X-0.892X²	0.496	3.446	0.091
SPLIT	2020	Y₄=31.429+25.432X-1.975X²	0.759	11.028	0.007
SHDI	2015	Y₅=1.825-0.015X-0.003X²	0.899	31.317	0.000
SHDI	2020	Y₆=17.03-4.641X+0.409X²	0.344	1.834	0.229

变量	显著性指标	缓冲区半径	PD变化量	AREA_MN变化量	SPLIT变化量	LPI变化量
缓冲区半径	Pearson相关性	1.000	-0.941**	0.950**	-0.778**	-0.079
缓冲区半径	显著性（双尾）		0.000	0.000	0.008	0.828
PD变化量	Pearson相关性	-0.941**	1.000	-0.988**	0.778**	0.077
PD变化量	显著性（双尾）	0.000		0.000	0.008	0.832
AREA_MN变化量	Pearson相关性	0.950**	-0.988**	1.000	-0.772**	-0.109
AREA_MN变化量	显著性（双尾）	0.000	0.000		0.009	0.765
SPLIT变化量	Pearson相关性	-0.778**	0.778**	-0.772**	1.000	0.489
SPLIT变化量	显著性（双尾）	0.008	0.008	0.009		0.152
LPI变化量	Pearson相关性	-0.079	0.077	-0.109	0.489	1.000
LPI变化量	显著性（双尾）	0.828	0.832	0.765	0.152

因变量	年份	回归模型	决定系数R²	F值	P值
PD	2015	Y₁=0.339+0.045X-0.005X²	0.776	12.116	0.005
PD	2020	Y₂=0.265+0.07X-0.004X²	0.940	55.021	0.000
AREA_MN	2015	Y₃=240.912-23.887X+2.509X²	0.826	16.658	0.002
AREA_MN	2020	Y₄=242.527-31.969X+2.203X²	0.898	30.760	0.000
SPLIT	2015	Y₅=11.171+9.457X-0.895X²	0.494	3.421	0.092
SPLIT	2020	Y₆=33.155+26.39X-2.042X²	0.755	10.808	0.007