Identification of Ecological Priority Areas Based on Nested-Scale Analysis: A Case Study of Metropolitan Nanjing, China

Rapid urbanization has led to severe fragmentation of ecological spaces in high-density metropolitan regions, threatening urban ecological security and environmental well-being. While cities explore various restoration strategies, the systematic identification of ecological priority areas remains an urgent challenge, particularly due to the limitations of multi-scale evaluation methods. This study develops an integrated nested-scale analytical approach to examine ecological elements at metropolitan and central urban levels, using Metropolitan Nanjing as a case study. The framework combines Morphological Spatial Pattern Analysis (MSPA), Landscape Connectivity Analysis, and INVEST Habitat Quality Assessment to identify ecological sources while employing a multi-dimensional ecological resistance evaluation system and Circuit Theory Model for critical node assessment. The findings reveal a notable spatial overlap between ecological pinch points and barrier points across scales, demonstrating the importance of nested-scale coupling in maintaining network stability. Through this analysis, 3297 ecological priority areas are identified and classified into three hierarchical categories, offering a practical framework for optimizing ecological networks in high-density metropolitan regions.

Authors

Institutions

Publication Details

Journal
Land
Published
2024-12-31
DOI
https://doi.org/10.3390/land14010060
Citations
2
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.57

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identification of Ecological Priority Areas Based on Nested-Scale Analysis: A Case Study of Metropolitan Nanjing, China

Zhu Le, Yuxi Zhu, Liping Sun, Jianqiang Yang
2 citations
Land
Land Use and Ecosystem Services
0.57
article

Identification of Ecological Priority Areas Based on Nested-Scale Analysis: A Case Study of Metropolitan Nanjing, China

Zhu Le, Yuxi Zhu, Liping Sun, Jianqiang Yang
article en
2 citations

Abstract

Rapid urbanization has led to severe fragmentation of ecological spaces in high-density metropolitan regions, threatening urban ecological security and environmental well-being. While cities explore various restoration strategies, the systematic identification of ecological priority areas remains an urgent challenge, particularly due to the limitations of multi-scale evaluation methods. This study develops an integrated nested-scale analytical approach to examine ecological elements at metropolitan and central urban levels, using Metropolitan Nanjing as a case study. The framework combines Morphological Spatial Pattern Analysis (MSPA), Landscape Connectivity Analysis, and INVEST Habitat Quality Assessment to identify ecological sources while employing a multi-dimensional ecological resistance evaluation system and Circuit Theory Model for critical node assessment. The findings reveal a notable spatial overlap between ecological pinch points and barrier points across scales, demonstrating the importance of nested-scale coupling in maintaining network stability. Through this analysis, 3297 ecological priority areas are identified and classified into three hierarchical categories, offering a practical framework for optimizing ecological networks in high-density metropolitan regions.

LandVol. 14(1)
Chang'an University (CN), Southeast University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 31%
Land Use and Ecosystem Services
0.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.