Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China
Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China
复制标题
了解中国江苏南京崂山区城市森林覆盖现状和未来破碎化动态
DOI：
10.3390/rs12010155
复制
发表时间：
2020-01
期刊：
影响因子：
5
通讯作者：
Li Mingshi
中科院分区：
文献类型：
--
作者：
Shen Wenjuan;Mao Xupeng;He Jiaying;Dong Jinwei;Huang Chengquan;Li Mingshi
准确获取城市森林的时空分布和破碎化（例如，内陆和未受破坏的地区）对促进减缓气候变化和保护生境生物多样性具有重要意义。然而，从现在到未来的城市森林覆盖变化的时空格局与内部和完整的森林的动态很少有特点。利用卫星观测和模拟模型，研究了2002-2023年江苏省江北新区南京老山地区城市森林覆盖的破碎化特征。利用元胞自动机-马尔可夫链（CA-Markov）模型和状态转移模拟模型，建立了基于面向对象分类的土地覆盖图，模拟土地覆盖变化。然后，我们量化的森林覆盖变化的形态变化检测算法和估计的森林面积密度为基础的破碎化模式。通过空间分析和统计方法建立了它们之间的关系。结果表明，实际土地覆盖图的总体精度约为83.75-92.25%（2012-2017）。CA-Markov模型用于模拟土地覆盖图的实用性得到了证明。在27像素× 27像素的高空间分辨率数据中，随着破碎化程度从81.1%下降到64.1%，低破碎化程度的森林所占比例最大，沿着下降。据报道，在完整森林和碎片化森林之间的变化中，碎片化的增幅最大（从2016年到2023年为3%）。然而，完整的森林被模拟为在2023年恢复，并恢复到2002年的破碎化水平。此外，我们发现58.07 km 2和0.35 km 2的内部和完整的森林已经从森林面积损失中删除，并从森林面积收益中增加。森林内部和完整面积的损失率超过了森林总面积的损失率。然而，它们的近似比率（1）意味着森林内部和完整面积的损失将对剩余的森林产生轻微的破碎化影响。这一分析说明了保护和恢复森林内部的成就，更重要的是，避免了周围地区过度的人类活动。这项研究为大城市地区未来的森林保护和管理提供了战略。
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.
登录
查看更多内容
影响因子：
13.5
作者：
M. Shimada;T. Itoh;T. Motooka;Manabu Watanabe;T. Shiraishi;R. Thapa;R. Lucas
通讯作者：
M. Shimada;T. Itoh;T. Motooka;Manabu Watanabe;T. Shiraishi;R. Thapa;R. Lucas
使用超高分辨率图像比较基于对象的土地覆盖分类的机器学习分类器
影响因子：
5
作者：
Qian Yuguo;Zhou Weiqi;Yan Jingli;Li Weifeng;Han Lijian
通讯作者：
Han Lijian
影响因子：
13.6
作者：
Haddad NM;Brudvig LA;Clobert J;Davies KF;Gonzalez A;Holt RD;Lovejoy TE;Sexton JO;Austin MP;Collins CD;Cook WM;Damschen EI;Ewers RM;Foster BL;Jenkins CN;King AJ;Laurance WF;Levey DJ;Margules CR;Melbourne BA;Nicholls AO;Orrock JL;Song DX;Townshend JR
通讯作者：
Townshend JR
DOI：
10.3390/rs11070823
发表时间：
2019-04
期刊：
Remote. Sens.
影响因子：
--
作者：
C. Voight;Karla Hernandez-Aguilar;Christina Garcia;S. Gutierrez
通讯作者：
C. Voight;Karla Hernandez-Aguilar;Christina Garcia;S. Gutierrez
影响因子：
2.6
作者：
Myint, Soe W.;Wang, Le
通讯作者：
Wang, Le