Comprehensive evaluation of natural landscapes before and after earthquake based on GST-FAHP-GIS analytical method: A case study of Jiuzhaigou Nature Reserve, China

Luo, Xiyu; Li, Xin; Liu, Wei; Wang, Minxi; Liu, Chonghao

doi:10.1016/j.ecolind.2023.110850

Cited by 4 publications

(3 citation statements)

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Section: Fragmentation Of Landscape Patternsmentioning

confidence: 99%

“…Major earthquakes and their subsequent geological hazards caused huge damage to natural landscapes and ecosystems, resulting in heavy loss of life and property, ecological degradation, as well as landscape fragmentation [12,52,53]. It has been proven that landscape fragmentation had a high importance degree in assessing the extent of earthquake-induced landscape destruction due to its negative effects on biodiversity and the ecological process of species [12]. Our result showed that human activities and gully erosion across the Longxi River Basin were the main factors controlling landscape fragmentation in the basin before the earthquake in 2005.…”

Section: Fragmentation Of Landscape Patternsmentioning

confidence: 99%

“…They also reported the automatic detection of earthquake-triggered landscape changes [9], landscape evolution [10], and changes in ecosystem services in the earthquake-stricken area [11]. For example, the Jiuzhaigou earthquake occurred on 8 August 2017, with a magnitude of 7.0, which caused the overall landscape grade of Jiuzhaigou Nature Reserve to decrease by 15% (Luo et al, 2023) [12]. In the 2004 Mw 6.6 Chuetsu earthquake-affected areas in Japan, natural succession improved landscape structure, resulting in good vegetation recovery (85.5%) by 2021 (Xiang et al, 2023) [13].…”

Section: Introductionmentioning

confidence: 99%

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Landscape Fragmentation and Spatial Autocorrelation of a Typical Watershed in the Wenchuan Earthquake-Affected Area—A Case Study in the Longxi River Basin

Tian,

Yang,

et al. 2023

Forests

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The 2008 8.0 Ms Wenchuan earthquake caused serious ecological degradation in the northwest of China’s Sichuan Province. Elucidating landscape fragmentation and spatial pattern of post-earthquake landscape is of great interest in order to improve ecological restoration and predict the spatial distribution of damaged ecosystems in earthquake-affected areas. We used four real-time remote sensing images to analyze the landscape pattern characteristics and spatial autocorrelation of the Longxi River Basin before and after the Wenchuan earthquake during the period from 2005 to 2015. In the study period, the degree of landscape fragmentation increased 1 year after the earthquake in 2009 but decreased in 2011 and 2015. The spatial distribution of forest, farmland, and shrub-grassland had significantly positive spatial correlation and the characteristics of spatial aggregation from 2005 to 2015. Construction land had no significant spatial correlation before the earthquake in 2005, but significantly positive spatial correlation after the earthquake, while traffic land had no significant spatial correlation across the study period. Unlike the other landscape types, geological disasters changed from a significantly negative spatial correlation before the earthquake to a significantly positive spatial correlation after the earthquake. However, the positive autocorrelation of all landscape types decreased with the increase of space distance, but in different distance-decay rates. The High-high spatial aggregation areas of geological disasters and construction land clustered gradually upstream of the basin and distributed in Longchi town, respectively; those of farmland distributed in the southeast of the basin increased in 2009 but then decreased, while those of forests had the opposite pattern as the dominant landscape type after the earthquake. Our results elucidated the spatial structure and distribution features of the Longxi River Basin to give a theoretical foundation for assessing the effects of ecological restoration and reconstruction management in earthquake-affected areas.

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Section: Fragmentation Of Landscape Patternsmentioning

confidence: 99%