The Extent of Infrastructure Causing Fragmentation in the Hydrocarbon Basin in the Arid and Semi-Arid Zones of Patagonia (Argentina)

Buzzi, Mariana Andrea; Rueter, Barbara Lisa; Ghermandi, Luciana; Lasaponara, Rosa

doi:10.3390/su11215956

Cited by 3 publications

(2 citation statements)

References 39 publications

(30 reference statements)

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“…These metrics have their own focus, and single or multiple metrics used separately cannot comprehensively and systematically describe the overall characteristics of a forest landscape. Additionally, some metrics are strongly correlated and provide redundant information [23,24]. Therefore, it is necessary to build a comprehensive index to evaluate forest fragmentation.…”

Section: Introductionmentioning

confidence: 99%

Detecting Spatiotemporal Dynamics and Driving Patterns in Forest Fragmentation with a Forest Fragmentation Comprehensive Index (FFCI): Taking an Area with Active Forest Cover Change as a Case Study

Zhen,

Zhao,

Liu

et al. 2023

Forests

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Forests play an irreplaceable role in preserving soil and water, as well as realizing carbon neutrality. However, logging and urban expansion have caused widespread forest fragmentation globally, resulting in biodiversity loss and carbon emissions. Therefore, it is a prerequisite to develop a comprehensive index for evaluating the degree of forest fragmentation to propose effective policies for forest protection and restoration. In this study, a forest fragmentation comprehensive index (FFCI) was constructed through principal component analysis (PCA) based on land-use data from 2000 to 2020 in Fujian Province, composed of five commonly used landscape metrics: patch density (PD), largest patch index (LPI), mean patch area (MPA), aggregation index (AI), and division. Then, the semivariogram function and moving windows method were employed to explore the scale effect and spatiotemporal variations of FFCI. The spatial autocorrelation analysis was used to distinguish the spatial relationship of forest fragmentation, while the driving mechanisms were explored using the geographic detector (GD). The results show that the optimal scale to reflect forest fragmentation based on the semivariogram and moving window method was 3500 m. The proposed FFCI could explain more than 85% of the information for all landscape metrics, and the effectivity of FFCI was validated by urban–rural gradient and transect analysis. We also found that, despite having the highest forest coverage in China, Fujian Province has experienced severe forest fragmentation. High and medium fragmentation accounted for over 50% of all types of fragmentation, with decreasing trends in low and very low fragmentation and increasing trends in high fragmentation over time, indicating that the degree of forest fragmentation in the study area was aggravated over time. Moreover, the spatial distribution pattern of FFCI was mainly high–high clusters and low–low clusters, showing a decreasing trend year by year. The areas with high fragmentation were mainly distributed in the urban center of coastal cities, while the internal cities in western and central regions had a relatively low degree of fragmentation. Additionally, the spatial differentiation in the variation in FFCI was mainly influenced by elevation, slope, and nighttime light intensity. The superimposed impact of two factors on the variation in FFCI was greater than the impact of individual factors. These results provide an effective approach for assessing the degree of forest fragmentation and offer scientific support for mitigating forest fragmentation.

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Section: Introductionmentioning

confidence: 99%

Detecting Spatiotemporal Dynamics and Driving Patterns in Forest Fragmentation with a Forest Fragmentation Comprehensive Index (FFCI): Taking an Area with Active Forest Cover Change as a Case Study

Zhen,

Zhao,

Liu

et al. 2023

Forests

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“…[29,30], are only based on the geometry of the LU and of the parts into which the landscape is divided. They are also used by different researchers to determine landscape fragmentation [31][32][33][34][35][36][37]. The authors Schmiedel.…”

Section: Introductionmentioning

confidence: 99%

Impact of Roads on Environmental Protected Areas: Analysis and Comparison of Metrics for Assessing Habitat Fragmentation

Sánchez-Fernández

Morillas

González

et al. 2022

Land

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The present study focuses on evaluating the effect of fragmentation caused by road infrastructures on a territory with singular characteristics such as low population density and a high proportion of its surface area protected by the Natura 2000 network. Based on the IFI, UFI, and DIVI metrics, the state of fragmentation of the landscape units (LU) was studied from two different approaches, considering two different protection figures, and the degree of suitability of the metrics used for the objective pursued was analysed. The results show that the expressions proposed for the indicators which measure the fragmentation of landscape units (LU) originated by road infrastructures (IFI, and DIVI) assess different causes and consequences in the territory than that proposed for fragmentation originated by urban areas (UFI). The combination of all indicators allows for the identification of shortcomings and strengths of the LU analysed and, consequently, evaluation of the effectiveness of the design of the LU and need for improvement. The outcomes of fragmentation analysis of the LU in the area under study varied depending on the criterion applied and the protection figure considered. A general increasing trend for all indicators was found in terms of the number of LU units and LU surface as the level of fragmentation rises. The results of this study are useful for decision-making on territory and road infrastructures management and new approaches to the organisation of the Natura 2000 network.

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Urban Type Classification and Characteristic Analysis through Time-Series Environmental Changes for Land Use Management for 31 Satellite Cities around Seoul, South Korea

Kim

Kwon

2021

Land

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The objective of the present study was to determine changes in land coverage for 31 satellite cities surrounding Seoul and changes in values of MSPA (Morphological Spatial Pattern Analysis) for a time period of about 30 years (from 1988 to 2018). Cities that showed similar environmental changes were grouped utilizing a hierarchical cluster analysis. The results of this study are summarized as follows: First, as a result of analyzing changes in land coverage, urbanized areas in all 31 cities greatly increased, whereas areas of forest, grassland, farmland, wetland, etc., greatly decreased. Second, as a result of carrying out MSPA for green areas in each city, the number of Cores, Islets as stepping-stone green areas, and Branches greatly decreased. As a result of analyzing factors in cluster analysis, 12 variables were classified into four groups. After performing a cluster analysis, the 31 cities were classified into six clusters. Cluster-6 showed the biggest decrease in wetland areas. These results could be used as basic data for establishing differentiated environmental policies for clusters of cities that show similar environmental changes, and for establishing policy priorities that break away from uniform environmental policies at the local level.

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The Extent of Infrastructure Causing Fragmentation in the Hydrocarbon Basin in the Arid and Semi-Arid Zones of Patagonia (Argentina)

Cited by 3 publications

References 39 publications

Detecting Spatiotemporal Dynamics and Driving Patterns in Forest Fragmentation with a Forest Fragmentation Comprehensive Index (FFCI): Taking an Area with Active Forest Cover Change as a Case Study

Detecting Spatiotemporal Dynamics and Driving Patterns in Forest Fragmentation with a Forest Fragmentation Comprehensive Index (FFCI): Taking an Area with Active Forest Cover Change as a Case Study

Impact of Roads on Environmental Protected Areas: Analysis and Comparison of Metrics for Assessing Habitat Fragmentation

Urban Type Classification and Characteristic Analysis through Time-Series Environmental Changes for Land Use Management for 31 Satellite Cities around Seoul, South Korea

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