Dynamic Study of Forest Landscape Morphology and Thermal Environment in Dali City

doi:10.13466/j.cnki.lczyyj.2023.05.015

Abstract

Abstract:

The surface temperature(LST)of Dali City was inverted using Landsat remote sensing images between 2006 and 2016.Using the morphological spatial pattern analysis(MSPA)technique,multiple linear regression and geographically weighted regression were utilized to study the coupling connection between forest landscape morphology and the temperature environment in Dali.The findings found that:1)LST in Dali City grew significantly between 2006 and 2016,and there were seasonal variations in the forest landscape and thermal environment,with the strength of several factors altering according to the season:autumn>summer>spring>winter.2)The spatial morphological pattern of the forest landscape had a substantial impact on the thermal mitigation features,which were:core>branch>edge>bridge>perforation>islet>loop in 2006 and core>branch>edge>perforation>islet>loop in 2016.3)The GWR model outperformed the OLS model in terms of reflecting the dynamic interaction between forest landscape and thermal environment.Throughout the urbanization process,the landscape plan may be altered in conjunction with the real circumstances to alleviate the phenomena of urban thermal environment.

Key words: thermal environment, forest landscape morphology, MSPA, Dali City

CLC Number:

S771.8
X16

ZHANG Shunmin, HUANG Xiaoyuan, LIU Junze, CHEN Rong, LI Xiang. Dynamic Study of Forest Landscape Morphology and Thermal Environment in Dali City[J]. Forest and Grassland Resources Research, 2023, (5): 122-132.

Figures/Tables 12

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References 39

[1]	Lei Weiqian, Jiao Limin, Xu Gang, et al. Urban scaling in rapidly urbanising China[J]. Urban Studies, 2021, 59(9):1889-1908. doi: 10.1177/00420980211017817
[2]	AR6 Synthesis Report Climate Change 2023[EB/OL].(2023-03-20) [2023-04-17]. https://www.ipcc.ch/report/sixth-assessment-report-cycle/.
[3]	Alrteimei H A, Ashaari Z H, Muharram F M. Last decade assessment of the impacts of regional climate change on crop yield variations in the mediterranean region[J]. Agriculture, 2022, 12(11):1787. doi: 10.3390/agriculture12111787
[4]	覃志豪, Zhang Minghua, Arnon K. 用NOAA-AVHRR热通道数据演算地表温度的劈窗算法[J]. 国土资源遥感, 2001(2):33-42.
[5]	Wan Zhengming, Li Zhaoliang. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4):980-996. doi: 10.1109/36.602541
[6]	Rozenstein O, Qin Zhihao, Derimian Y, et al. Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm[J]. Sensors, 2014, 14(4):5768-5780. doi: 10.3390/s140405768 pmid: 24670716
[7]	李喆, 陈圣宾, 陈芝阳. 地表温度与土地利用类型间的空间尺度依赖性——以成都为例[J]. 生态环境学报, 2022, 31(5):999-1007. doi: 10.16258/j.cnki.1674-5906.2022.05.015
[8]	Srivanit M, Iamtrakul P. Spatial patterns of greenspace cool islands and their relationship to cooling effectiveness in the tropical city of Chiang Mai,Thailand[J]. Environmental monitoring and assessment, 2019, 191(9):1-16. doi: 10.1007/s10661-018-7122-4
[9]	黄群芳. 城市空间形态对城市热岛效应的多尺度影响研究进展[J]. 地理科学, 2021, 41(10):1832-1842. doi: 10.13249/j.cnki.sgs.2021.10.015
[10]	袭月, 张志强, 周洁, 等. 城市化进程下地表温度时空变化及其与植被覆盖度的相关性——以北京五环区域为例[J]. 林业科学, 2021, 57(6):1-13.
[11]	刘诗喆, 谢苗苗, 武蓉蓉, 等. 地理单元划分对城市热环境响应规律的影响——以北京为例[J]. 地理科学进展, 2021, 40(6):1037-1047. doi: 10.18306/dlkxjz.2021.06.013
[12]	苏王新, 张刘宽, 常青. 基于最优粒度的城市热环境与景观特征耦合分析[J]. 中国环境科学, 2022, 42(2):954-961.
[13]	张含, 张小伟, 樊高峰. 城市化影响杭州城市热环境的数值模拟研究[J]. 中国环境科学, 2021, 41(9):4107-4119.
[14]	吴健生, 何海珊, 胡甜. 地表温度“源—汇”景观贡献度的影响因素分析[J]. 地理学报, 2022, 77(1):51-65. doi: 10.11821/dlxb202201004
[15]	李程蓉, 陈天. 缓解城市热环境的多层级“源-汇”景观网络构建[J]. 生态学报, 2023, 43(8):3068-3078.
[16]	沈中健, 曾坚, 梁晨. 闽南三市绿地景观格局与地表温度的空间关系[J]. 生态学杂志, 2020, 39(4):1309-1317.
[17]	Soille P, Vogt P. Morphological segmentation of binary patterns[J]. Pattern Recognition Letters, 2009, 30(4):456-459. doi: 10.1016/j.patrec.2008.10.015
[18]	杨佳伟, 许秀环, 胡兴宜, 等. 长江两岸造林绿化工程背景下石首市森林生态格局分析[J]. 长江流域资源与环境, 2022, 31(5):1051-1061.
[19]	Osewe E O, Niţă M D, Abrudan I V. Assessing the fragmentation,canopy loss and spatial distribution of forest cover in kakamega national forest reserve,Western Kenya[J]. Forests, 2022, 13(12):21-27. doi: 10.3390/f13010021
[20]	熊畅, 吴卓, 曾梓瑶, 等. 基于“空间形态-破碎化-聚集度”的粤港澳大湾区森林景观格局时空演变[J]. 生态学报, 2023, 43(8):3032-3044.
[21]	陈瑾, 赵超超, 赵青, 等. 基于MSPA分析的福建省生态网络构建[J]. 生态学报, 2023, 43(2):603-614.
[22]	姚采云, 安睿, 窦超, 等. 基于MSPA与MCR模型的三峡库区林地生态网络构建与评价研究[J]. 长江流域资源与环境, 2022, 31(9):1953-1962.
[23]	张守法, 李翅, 赵凯茜. 基于生态网络构建的贵阳市绿地景观格局优化研究[J]. 中国园林, 2022, 38(5):68-73.
[24]	刘婷, 欧阳帅, 勾蒙蒙, 等. 基于MSPA模型的新型城市热景观连通性分析[J]. 生态学报, 2023, 43(2):615-624.
[25]	乔治, 陈嘉悦, 王楠, 等. 基于MSPA和电路理论的京津冀城市群热环境空间网络[J]. 环境科学, 2023, 44(6):3034-3042.
[26]	大理市人民政府《大理市国土空间总体规划(2021-2035》公示[A/OL].(2023-04-27) [2023-05-01]. http://www.yndali.gov.cn/dlszf/c106753/202304/14f65a1a13254796bc7727bd31719e25.shtml
[27]	大理市人民政府. 市情概要[A/OL].(2023-05-01) [2023-05-07]. http://www.yndali.gov.cn/dlszf/c103380/tydp.shtml
[28]	Zhao Chunhong, Jennifer J, Weng Qihao, et al. A geographically weighted regression analysis of the underlying factors related to the surface urban heat island phenomenon[J]. Remote Sensing, 2018, 10(9):1428. doi: 10.3390/rs10091428
[29]	覃志豪, 李文娟, 徐斌, 等. 陆地卫星TM6波段范围内地表比辐射率的估计[J]. 国土资源遥感, 2004(3):28-32.
[30]	谢哲宇, 黄庭, 李亚静, 等. 南昌市土地利用与城市热环境时空关系研究[J]. 环境科学与技术, 2019, 42(S1):241-248.
[31]	吴征镒, 朱彦丞. 云南植被[M]. 北京: 科学出版社,1987.
[32]	全国标准信息公共服务平台[EB/OL].(2017-11-01) [2023-05-16]. https://std.samr.gov.cn/gb/search/gbDetailed?id=71F772D82516D3A7E05397BE0A0AB82A.
[33]	Riitters K H, Wickham J, O'Neill R, et al. Global-scale patterns of forest fragmentation[J]. Conservation Ecology, 2000, 4(2):3.
[34]	李志芳, 王锐, 沈新磊. 基于质量指数和空间自相关分析的耕地保护分区研究[J]. 土壤通报, 2021,5204):785-792.
[35]	Wang Rongxiang, Min Jie, Li Yuechen, et al. Analysis on seasonal variation and influencing mechanism of land surface thermal environment:A case study of Chongqing[J]. Remote Sensing, 2022, 14(9):2022. doi: 10.3390/rs14092022
[36]	He Juelin, Zhao Wei, Li Ainong, et al. The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas[J]. International Journal of Remote Sensing, 2019, 40(5):1808-1827. doi: 10.1080/01431161.2018.1466082
[37]	乐柯君, 方陆明, 何小兵, 等. 森林城市景观格局与热环境的关系——以龙泉市为例[J]. 应用生态学报, 2019, 30(9):3066-3074.
[38]	黄晓军, 宋涛, 王博, 等. 土地利用规模-结构-形态演变对城市热环境的影响——以西安市主城区为例[J]. 地理科学, 2022, 42(5):926-937. doi: 10.13249/j.cnki.sgs.2022.05.018
[39]	Karunaratne S, Athukorala D, Murayama Y, et al. Assessing surface urban heat island related to land use/land cover composition and pattern in the temperate mountain valley city of Kathmandu,Nepal[J]. Remote Sensing, 2022, 14:4047. doi: 10.3390/rs14164047

序号	传感器	分辨率/m	条带号	日期
1	Landsat 5	30	131/42	2006/01/25
2	Landsat 5	30	131/42	2006/05/17
3	Landsat 5	30	131/42	2006/08/05
4	Landsat 7	30	131/42	2006/11/17
5	Landsat 8	30	131/42	2016/01/05
6	Landsat 8	30	131/42	2016/03/25
7	Landsat 8	30	131/42	2016/10/03
8	Landsat 8	30	131/42	2016/11/20

土地利用类型	2006年		2016年
土地利用类型	生产者精度	用户精度	生产者精度	用户精度
林地	88.89	97.56	88.10	97.37
耕地	96.15	86.21	86.21	89.29
建设用地	82.31	88.89	96.15	83.30
未利用地	100	100	100	75
总体精度	92		90
Kappa系数	88.04		85.29

MSPA 景观类型	定义
核心	前景数据中,面积大于指定边缘宽度的斑块。
孔隙	前景数据与背景数据之间的过渡区域,是核心区的内部边界。
孤岛	相互孤立、破碎化分布的小斑块,连通性低。
桥接	前景数据中,连接 2个或 2个以上的核心斑块,且面积较小的狭长区域。
边缘	前景数据与背景数据之间的过渡区域,是核心区的外部边界。
支线	从核心区域扩张的前景数据,但不会连接到另一个核心区。
环岛	与核心区连接的前景数据,是物种迁移的捷径。

时间	R²	调整后R²	AIC	SC
2006/01/25	0.585	0.583	8 328.152	8 411.432
2006/05/17	0.449	0.445	9 963.414	10 046.701
20060/8/05	0.681	0.678	9 303.423	9 386.713
2006/11/17	0.644	0.642	6 525.121	6 608.415
2016/01/05	0.687	0.685	7 791.990	7 875.274
2016/03/25	0.478	0.474	10 158.810	10 242.106
2016/10/03	0.787	0.786	7 158.411	7 241.692
2016/11/20	0.711	0.708	7 483.320	7 566.583

变量	系数	回归系数的标准差	t统计量	P值
常数	90.391	1.354	303.897	0.000^*
海拔	-0.013	0.000	-106.120	0.000^*
坡度	-0.101	0.032	-23.068	0.924
坡向	-0.093	0.009	10.502	0.196
NDVI	69.345	2.992	72.915	0.000^*
针叶林	-5.239	7.220	-1.807	1.221
常绿阔叶林	5.826	6.139	3.053	1.770
落叶阔叶林	-1.144	6.121	-2.484	0.891
核心	-16.995	6.083	-10.465	0.218
孔隙	-21.905	8.580	-10.608	0.042^*
孤岛	11.272	9.727	3.447	0.089
桥接	-12.365	7.323	-6.782	1.788
边缘	-1.853	6.402	4.400	1.179
支线	25.199	9.335	9.376	0.001^*
环岛	-21.623	9.831	-9.730	0.287