Impact of interaction of landscape pattern factors on desertification status in Kerqin Sandy Land

doi:10.7522/j.issn.1000-694X.2023.00087

Journal of Desert Research ›› 2024, Vol. 44 ›› Issue (2): 99-108.DOI: 10.7522/j.issn.1000-694X.2023.00087

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Impact of interaction of landscape pattern factors on desertification status in Kerqin Sandy Land

Mingyu Cai¹(), Biao Jia², Xueli Chang³()

^1.Yantai Engineering & Technology College，Yantai 264006，Shandong，China
^2.Water Conservancy Science Research Institute of Inner Mongolia，Hohhot 010051，China
^3.School of Resources and Environmental Engineering，Ludong University，Yantai 264025，Shandong，China

Received:2023-08-02 Revised:2023-09-11 Online:2024-03-20 Published:2024-03-19
Contact: Xueli Chang

Abstract

Abstract:

It is a challenge for landscape ecology research to apply the principles of landscape ecology to the accurate and rapid assessment of desertification status （DS）. Based on the land use type data in the sandy area of the central Naiman Banner in 2000 and 2018， the maximum patch index （LPI）， boundary density （ED）， aggregation index （AI） and Shannon diversity index （SHDI） were selected， and the influence of two-factor interaction and grading of landscape pattern on DS was analyzed with the supports of Geo-detector Model and Moving Window function in Fragstats software. DS analysis showed that the desertification process was reversed from 2000 to 2018 in the study area because the DS is reduced from 0.393 to 0.288. There are two forms of two-factor interaction in landscape pattern： nonlinear enhancement and two-factor enhancement， and the influence on DS was greater than that of each factor. There were differences in the influence of different pattern factors on DS， and the differences reasons were related to the area size and distribution pattern of different dune types. In the years when DS was strong （2000）， mobile dunes predominated （30.6%-42.9%） in the areas with significant DS caused by LPI and ED classifications， while fixed sand dunes predominated （32.0%-44.0%） in insignificant areas. In the years when DS was weak （2018）， both significant and insignificant DS areas caused by SHDI classification were dominated by mobile dunes （both over 79.2%）. This study shows that the influence of two-factor interaction of landscape pattern on DS is related to both temporal and spatial scale， and the influence mechanism of interaction on DS has nonlinear characteristics. The study area size directly affects the interaction form and spatial pattern of landscape factors （scale dependent）. The results of any typical case study should't be deduced across scales， but the methods can be unified.

Key words: geo-detector model, moving window method, landscape pattern, desertification status, Kerqin Sandy Land

CLC Number:

P901

Mingyu Cai, Biao Jia, Xueli Chang. Impact of interaction of landscape pattern factors on desertification status in Kerqin Sandy Land[J]. Journal of Desert Research, 2024, 44(2): 99-108.

Figures/Tables 12

Fig.1 Location of the study area

Table 1 Classification of landscape pattern factors （2000）

级别	因子
级别	最大斑块指数LPI	边界密度ED	聚集度AI	香农指数SHDI
Ⅰ级	LPI ≤46.729	ED≤20.598	AI ≤96.849	LDI≤0.292
Ⅱ级	46.729< LPI ≤89.351	20.598< ED ≤38.918	96.849< AI ≤99.259	0.292< LDI≤0.966
Ⅲ级	LPI>89.351	ED>38.918	AI>99.259	LDI>0.966

Fig.2 The scale dependent characteristics of landscape diversity index

Fig.3 Analysis layer of desertification status and landscape pattern factor

Table 2 Types of interaction between two landscape pattern factor in Geodetector Model

交互作用类型	判断准则	代码
非线性增强	$q (x 1 ? x 2) > q (x 1) + q (x 2)$	A
双因子非线性增强	$q (x 1 ? x 2) > q (x 1) 和 ? q (x 2)$	B
单因子非线性减弱	$M i n (q (x 1) 和 q (x 2)) < q (x 1 ? x 2) < ? M a x (q (x 1) 和 q (x 2))$	C
非线性减弱	$q (x 1 ? x 2) < M i n (q (x 1) 和 q (x 2) ?)$	D
独立	$q (x 1 ? x 2) = q (x 1) + q (x 2)$	E

Table 2 Types of interaction between two landscape pattern factor in Geodetector Model

交互作用类型	判断准则	代码
非线性增强	$q (x 1 ? x 2) > q (x 1) + q (x 2)$	A
双因子非线性增强	$q (x 1 ? x 2) > q (x 1) 和 ? q (x 2)$	B
单因子非线性减弱	$M i n (q (x 1) 和 q (x 2)) < q (x 1 ? x 2) < ? M a x (q (x 1) 和 q (x 2))$	C
非线性减弱	$q (x 1 ? x 2) < M i n (q (x 1) 和 q (x 2) ?)$	D
独立	$q (x 1 ? x 2) = q (x 1) + q (x 2)$	E

Table 3 Significance test of explanatory force of landscape pattern factors on DS （ q value）

研究区范围	年份	显著性检查	因子
研究区范围	年份	显著性检查	最大斑块指数LPI	边界密度ED	聚集度AI	香农指数SHDI
研究区整体	2000	q	0.002	0.001	0.006	0.000
	2000	P	0.000	0.046	0.000	0.718
	2018	q	0.004	0.023	0.006	0.022
	2018	P	0.000	0.000	0.000	0.000
研究区局部	2000	q	0.006	0.002	0.013	0.005
	2000	P	0.000	0.013	0.000	0.000
	2018	q	0.003	0.019	0.003	0.031
	2018	P	0.002	0.000	0.000	0.000

Table 4 Correlation analysis ofecological detected of the pattern factor impact on DS （ q value）

研究区范围	年份		最大斑块块指数LPI	边界密度ED	聚集度AI	香农指数SHDI
研究区整体	2000	最大斑块指数LPI	0.002
		边界密度ED	0.004ⁿ	0.001
		密集度AI	0.007ⁿ	0.009ⁿ	0.006
		香农指数SHDI	0.003ⁿ	0.002ⁿ	0.008ⁿ	0.000
	2018	最大斑块指数LPI	0.004
		边界密度ED	0.027ⁿ	0.023
		密集度AI	0.016ⁿ	0.025ⁿ	0.006
		香农指数SHDI	0.024ⁿ	0.032ⁿ	0.026ⁿ	0.022
研究区局部	2000	最大斑块指数LPI	0.006
		边界密度ED	0.008ⁿ	0.002ⁿ
		密集度AI	0.016ⁿ	0.017ⁿ	0.013
		香农指数SHDI	0.012ⁿ	0.009ⁿ	0.020ⁿ	0.005
	2018	最大斑块指数LPI	0.003
		边界密度ED	0.026ⁿ	0.019
		密集度AI	0.016ⁿ	0.026ⁿ	0.003
		香农指数SHDI	0.033ⁿ	0.037ⁿ	0.033ⁿ	0.031

Table 5 Detection results of interaction of different landscape pattern factors on DS

交互作用组合	研究区		缩小区
交互作用组合	2000年	2018年	2000年	2018年
最大斑块指数LPI∩边界密度ED	A	A	B	A
最大斑块指数LPI∩密集度AI	B	A	B	A
最大斑块指数LPI∩SHDI	A	B	A	B
边界密度ED∩密集度AI	A	B	A	A
边界密度ED∩香农指数SHDI	A	B	A	B
密集度AI∩香农指数SHDI	A	B	A	B

Fig.4 3D scatter diagram of landscape pattern factor and DS

Fig.5 Hierarchical risk detection of different landscape pattern factors

Fig.6 Identification of significant areas based on the detection of the pattern factor graduated interactive

Fig.7 Characteristics of DS area composition in significant and non-significant regions of major pattern factor classification

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Impact of interaction of landscape pattern factors on desertification status in Kerqin Sandy Land

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