Functional analysis of landscape connectivity at the landscape, component, and patch levels: A case study of Minqing County, Fuzhou City, China

Ke, Qifa; Fan, Zhengqiu; Ng, Cho Nam; Wang, Xiangrong; Xie, Yujing

doi:10.1016/j.apgeog.2017.01.009

Cited by 30 publications

(30 citation statements)

References 55 publications

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“…Probability of Connectivity (PC), which is based on a probabilistic connection model, and Integral Index of Connectivity (IIC), which is based on a binary connection model, were calculated based on a graph-theoretic approach [37]. The former is suitable for studying the actual movement of organisms of specific species while the latter is suitable for studying the topology of the network [22,38]. In this study, we used two indices to evaluate the overall forest landscape connectivity in Zhuhai and the IIC index to examine the structure of forestland and the general functional connectivity pattern.…”

Section: Index Of Landscape Connectivitymentioning

confidence: 99%

The Effects of Rapid Urbanization on Forest Landscape Connectivity in Zhuhai City, China

et al. 2018

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Urban forests can provide the necessary ecosystem services for their residents and play an important part in improving the urban environment. Forest landscape connectivity is a vital indicator reflecting the quality of the ecological environment and ecological functions. Detecting changes in landscape connectivity is, therefore, an important step for providing sound scientific evidence for the better urban planning. Using remote sensing images of a study area in Zhuhai City in 1999, 2005, 2009 and 2013, the dynamic forest landscape connectivity of Zhuhai city can be evaluated based on a graph-theoretic approach. The aims of our study were to discover and interpret the effect of rapid urbanization on forest landscape connectivity. The construction of ecological corridors helps us specifically compare the landscape connectivity of three parts of urban forests. On the basis of functional landscape metrics, the correlation of these metrics and patch area was discussed in order to comprehensively identify the key patches. The analysis showed that the total areas of forestlands reduced from 1999 to 2009 and then increased from 2009 to 2013, and the same trend was found in overall forest landscape connectivity. To improve the overall landscape connectivity, construct urban ecological network and appropriately protect biodiversity in the future, the existing important patches with large areas or key positions should be well protected. This study revealed that urbanization reduced the area of key patches and consequently reduced the forest landscape connectivity, which increased while the patch areas increased due to the environmental protection policy. Functional connectivity indicators could provide more comprehensive information in the development of environmental protection strategies.

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Section: Index Of Landscape Connectivitymentioning

confidence: 99%

The Effects of Rapid Urbanization on Forest Landscape Connectivity in Zhuhai City, China

et al. 2018

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“…Two derived indices were used in the Conefor 2.6 program (Saura & Torne´, 2009): the integral connectivity index (IIC) and the probability of connectivity (PC). The first is based on a binary connection model and is recommended for analyzing the structure and general pattern of long-term functional connectivity, while the second uses a probabilistic connection model and is useful for studying the flow of organisms regardless of their origin (Qi, Fan, Nam, Wang, & Xie, 2017).…”

Section: Functional Connectivitymentioning

confidence: 99%

Assessment of Habitat Quality and Landscape Connectivity for Forest-Dependent Cracids in the Sierra Madre del Sur Mesoamerican Biological Corridor, México

Ocampo

Castillo-Santiago

Ochoa-Gaona

et al. 2019

Tropical Conservation Science

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Assessing landscape connectivity allows us to identify critical areas that impede or facilitate the movement of organisms and their genes and to plan their conservation and management. In this article, we assessed landscape connectivity and ecological condition of the habitat patches of a highly biodiverse region in Chiapas, Mexico. We employed data of three cracid species with different characteristics in habitat use and mobility. The habitat map of each species was derived from a spatial intersection of the models of potential distribution and a high-resolution map of current land cover and land use. The ecological condition of vegetation types was evaluated using 75 field plots. Structure of landscape was estimated by fragmentation metrics, while functional connectivity was assessed using spatially explicit graph analysis. The extent of suitable habitat for Oreophasis derbianus, Penelopina nigra, and Penelope purpurascens correspond to 25%, 46%, and 55% of the study area (5,185.6 km 2 ), respectively. Although the pine-oak forests were the most fragmented vegetation type, habitats of the three species were well connected, and only 4% to 9% of the fragments located on the periphery of the corridor had low connectivity. Landscape connectivity depends mainly on land uses with an intermediate and lower ecological condition (secondary forests and coffee agroforestry systems). Therefore, we suggest that in addition to promoting the improvement in connectivity in fragmented forests, conservation efforts should be aimed at preventing the conversion of mature forests into agricultural uses and maintaining agroforestry systems.

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“…The Probability of Connectivity for each patch (dPC) was an index of patch importance. The dPC could be used to determine the extent to which a patch could influence the connectivity of the study area [51]. Moreover, it was applied to evaluate the importance of individual patches as connectivity providers [52].…”

Section: Construction Of Ecological Landscape Key Area Recognition Inmentioning

confidence: 99%

“…The connection distances and nodes text file between patches were calculated by the Conefor Inputs for ArcGIS 10.6 module. The evaluation of the optimal threshold distance and the classification of functional types could be achieved by statistical methods [51]. Results were then imported into Conefor Sensinode 2.2 to calculate dPC.…”

Section: Construction Of Ecological Landscape Key Area Recognition Inmentioning

confidence: 99%

Recognition of Landscape Key Areas in a Coal Mine Area of a Semi-Arid Steppe in China: A Case Study of Yimin Open-Pit Coal Mine

Shou-gang

Huang

et al. 2020

Sustainability

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The ecological integrity and biodiversity of steppes were destroyed under the long-term and high-intensity development of open-pit coal mines in China, causing desertification, steppe degradation, landscape function defect, and so on. As a source of species maintenance and dispersal, an ecological source is a key area for preservation in order to restore the ecological security pattern of the larger landscape. The purpose of this study was to establish a landscape key area recognition model to identify the landscape key areas (LKA) surrounding an open pit coalmine located in semi-arid steppe. This study takes the Yimin open pit mining area as a case study. We assessed Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) remote sensing images taken during the peak season of vegetation growth from July to August in 1999, 2006, 2011, and 2017. From these images, we identified the main landscape types and vegetation coverage grades in order to identify the ecological land. Next, we applied the three indices of Importance of Patch Connectivity, Habitat Quality, and Ecosystem Service Value to calculate the comprehensive results that identify ecological land. Finally, the ecological land quality results of different years are superimposed and averaged, and then Very Important Patch (VIMP), Important Patch (IMP), and General Patch (GEP) areas were used for LKA extraction. Our results showed LKA to cover 177.35 km2, accounting for 20.01% of the total study area. The landscape types identified as LKA are primarily grassland (47.37%), wetland (40.27%), and shrubland (11.88%), indicating that landscape type correlates strongly with its value as a landscape key area. The proposed landscape key area recognition model could enrich the foundations for ecological planning and ecological security pattern construction in order to support ecological protection and restoration in semi-arid steppe areas affected by coal mining.

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Functional analysis of landscape connectivity at the landscape, component, and patch levels: A case study of Minqing County, Fuzhou City, China

Cited by 30 publications

References 55 publications

The Effects of Rapid Urbanization on Forest Landscape Connectivity in Zhuhai City, China

The Effects of Rapid Urbanization on Forest Landscape Connectivity in Zhuhai City, China

Assessment of Habitat Quality and Landscape Connectivity for Forest-Dependent Cracids in the Sierra Madre del Sur Mesoamerican Biological Corridor, México

Recognition of Landscape Key Areas in a Coal Mine Area of a Semi-Arid Steppe in China: A Case Study of Yimin Open-Pit Coal Mine

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