Abstract

Accurate acquisition of the spatiotemporal distribution of urban forests and fragmentation (e.g., interior and intact regions) is of great significance to contributing to the mitigation of climate change and the conservation of habitat biodiversity. However, the spatiotemporal pattern of urban forest cover changes related with the dynamics of interior and intact forests from the present to the future have rarely been characterized. We investigated fragmentation of urban forest cover using satellite observations and simulation models in the Nanjing Laoshan Region of Jiangbei New Area, Jiangsu, China, during 2002–2023. Object-oriented classification-based land cover maps were created to simulate land cover changes using the cellular automation-Markov chain (CA-Markov) model and the state transition simulation modeling. We then quantified the forest cover change by the morphological change detection algorithm and estimated the forest area density-based fragmentation patterns. Their relationships were built through the spatial analysis and statistical methods. Results showed that the overall accuracies of actual land cover maps were approximately 83.75–92.25% (2012–2017). The usefulness of a CA-Markov model for simulating land cover maps was demonstrated. The greatest proportion of forest with a low level of fragmentation was captured along with the decreasing percentage of fragmented area from 81.1% to 64.1% based on high spatial resolution data with the window size of 27 pixels × 27 pixels. The greatest increase in fragmentation (3% from 2016 to 2023) among the changes between intact and fragmented forest was reported. However, intact forest was modeled to have recovered in 2023 and restored to 2002 fragmentation levels. Moreover, we found 58.07 km2 and 0.35 km2 of interior and intact forests have been removed from forest area losses and added from forest area gains. The loss rate of forest interior and intact area exceeded the rate of total forest area loss. However, their approximate ratio (1) implying the loss of forest interior and intact area would have slight fragmentation effects on the remaining forests. This analysis illustrates the achievement of protecting and restoring forest interior; more importantly, excessive human activities in the surrounding area had been avoided. This study provides strategies for future forest conservation and management in large urban regions.

Highlights

  • Natural and anthropogenic disturbances during the process of urbanization strongly affect biodiversity and ecosystem dynamics in urban ecosystems, through the alteration of species richness, carbon stocks, and microclimates [1,2]

  • We focus on: (1) mapping recent land cover based on high resolution imagery and simulating future land cover change by using two landscape predictive models; (2) comparing forest area density (FAD)-based fragmentation patterns derived from medium or high resolution data; and (3) quantifying the spatial pattern of forest loss and gain based on the dynamics of forest interior and intact areas

  • We proposed a method to understand the relationship between forest cover change and forest fragmentation in the Nanjing Laoshan Region of Jiangsu, China from 2002 to 2023

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Summary

Introduction

Natural and anthropogenic disturbances during the process of urbanization strongly affect biodiversity and ecosystem dynamics in urban ecosystems, through the alteration of species richness, carbon stocks, and microclimates [1,2]. Forested urban ecosystems are vulnerable to forest cover loss and fragmentation [3,4,5]. As two types of fragmentation (little or no fragmented forests), can be defined as the forest areas that exist in forest-dominated neighborhoods of a specified size, and are not affected by nearby non-forested or disturbed forest habitats [8]. Assessing current and future forest fragmentation patterns related to forest cover change in densely populated and rapidly developing regions, is critical for understanding the impacts of economic development on urban ecosystems and designing policies to reduce the ecological risks [12,13,14,15,16]

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