基于多源遥感数据的溧阳市林地植被覆盖度时空差异研究

doi:10.12302/j.issn.1000-2006.202204048

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南京林业大学学报（自然科学版） ›› 2023, Vol. 47 ›› Issue (6): 183-191.doi: 10.12302/j.issn.1000-2006.202204048

基于多源遥感数据的溧阳市林地植被覆盖度时空差异研究

王艳芳¹(), 谭露¹, 郭红丽², 吴芳², 齐斐³, 蒙雯婷¹, 徐雁南¹^,^*()

1.南京林业大学林草学院,南方现代林业协同创新中心,江苏南京 210037
2.江苏省水文水资源勘测局,江苏南京 210009
3.江苏省水利科学研究院,江苏南京 210017

收稿日期:2022-04-20 修回日期:2022-06-07 出版日期:2023-11-30 发布日期:2023-11-23
通讯作者: *徐雁南(nfuxyn@126.com),教授。
基金资助:
江苏省水利厅项目(2020003);江苏省水利厅项目(2021060);江苏省水利厅项目(2021061);国家自然科学基金项目(32071840);国家留学基金委项目(202008680003)

Study on spatial and temporal variation of forest fractional vegetation cover in Liyang based on multi-source remote sensing data

WANG Yanfang¹(), TAN Lu¹, GUO Hongli², WU Fang², QI Fei³, MENG Wenting¹, XU Yannan¹^,^*()

1. College of Forestry and Grassland, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
2. Jiangsu Hydrology and Water Resources Investigation Bureau,Nanjing210009, China
3. Jiangsu Hydraulic Science Institute,Nanjing 210017, China

Received:2022-04-20 Revised:2022-06-07 Online:2023-11-30 Published:2023-11-23

摘要/Abstract

摘要：

【目的】分析不同空间分辨率影像各季节林地植被覆盖度 (fractional vegetation cover,FVC) 提取的精度差异,为选择遥感影像提取林地植被覆盖因子提供理论支持和参考。【方法】选择江苏溧阳市林地为研究区,以Sentinel-2、Landsat-8、MODIS等几种常用的不同空间分辨率的遥感影像为数据源,基于像元二分模型提取FVC,利用半变异函数阐明不同季节FVC的空间变异特征,通过混淆矩阵对比分析各个季节不同分辨率影像提取FVC的精度差异。【结果】①用半变异函数可以有效描述FVC的空间变异特征,空间变异程度的季节差异表现为冬季>春季>秋季>夏季。②用不同分辨率影像提取的FVC在水陆交界处及林地斑块边缘差异最为显著,各个季节FVC的提取精度表现为夏季>秋季>春季>冬季。其中,夏季30、250以及500 m空间分辨率影像提取FVC的精度分别为90.99%、76.28%、76.71%。③植被盖度等级对FVC提取精度有显著影响,高覆盖等级提取精度较高,低覆盖等级提取精度较低。低分辨率影像FVC提取精度在植被生长茂盛的夏季显著提升。【结论】不同FVC等级地表对高分辨率影像的监测需求有较大的分异性,夏季利用低分辨率影像提取FVC的可替代性最高,本研究结果有利于提高植被动态监测的精度和效率。

关键词: 植被覆盖度, Sentinel-2, Landsat-8, MODIS, 半变异函数, 江苏溧阳

Abstract:

【Objective】This research aims to study the accuracy variation of forest fractional vegetation cover (FVC) extraction using images with different spatial resolutions in each season. The results can provide theoretical support and serve as a useful reference for choosing suitable remote sensing images to extract forest FVC. 【Method】 Selecting the forest area in Liyang, Jiangsu Province for the study, several commonly used remote sensing images with different spatial resolutions, such as Sentinel-2, Landsat-8, and MODIS were used to extract FVC based on the dimidiate pixel model. The spatial variability of FVC in different seasons was analyzed by using the semi-variance function. In addition, the confusion matrix was adopted to compare the accuracy of FVC extracted with different resolutions in each season. 【Result】 (1) The semi-variance function could effectively depict the spatial variability of FVC. The seasonal differences in spatial variability of FVC indicated the following order: winter>spring>autumn>summer. (2) Significant differences between FVC extracted using images with different spatial resolution were detected at the water-land junction and the edge of forest patch. The extraction accuracy of FVC with different resolutions indicated the following order: summer>autumn>spring>winter. In summer, the extraction accuracies of FVC using 30, 250 and 500 m resolutions were 90.99%, 76.28% and 76.71%, respectively. (3) The level of vegetation cover could affect the accuracy of FVC extraction significantly. The extraction accuracy for high vegetation cover was greater than that for other cover levels, and accordingly, the accuracy of FVC extracted from low-resolution images was enhanced in summer when vegetation grows abundantly. 【Conclusion】 The need for high-resolution imagery varied with the levels of FVC. It was more practical to extract FVC using low-resolution images in summer. The study findings are useful to improve the accuracy and efficiency of dynamic monitoring for vegetation.

Key words: fractional vegetation cover, Sentinel-2, Landsat-8, moderate-resolution imaging spectroradiometer(MODIS), semi-variance, Liyang,Jingsu Privince

中图分类号:

P237
TP7

王艳芳,谭露,郭红丽,等. 基于多源遥感数据的溧阳市林地植被覆盖度时空差异研究[J]. 南京林业大学学报（自然科学版）, 2023, 47(6): 183-191.

WANG Yanfang, TAN Lu, GUO Hongli, WU Fang, QI Fei, MENG Wenting, XU Yannan. Study on spatial and temporal variation of forest fractional vegetation cover in Liyang based on multi-source remote sensing data[J].Journal of Nanjing Forestry University (Natural Science Edition), 2023, 47(6): 183-191.DOI: 10.12302/j.issn.1000-2006.202204048.

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参考文献 29

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季节 season	时间(年-月-日)data(year-month-day)
季节 season	Sentinel-2 (10 m)	Landsat-8 (30 m)	MOD13Q1 (250 m)	MOD13A1 (500 m)
春spring	2017-04-29	2017-04-23	2017-04-23	2017-04-23
夏summer	2017-07-28	2017-07-28	2017-07-28	2017-07-28
秋autumn	2017-10-31	2017-11-01	2017-11-01	2017-11-01
冬winter	2017-12-20	2017-12-19	2017-12-19	2017-12-19

序号 No.	等级 levels	植被覆盖度/% FVC
1	低覆盖	<30
2	中低覆盖	[30,45)
3	中覆盖	[45,60)
4	中高覆盖	[60,75)
5	高覆盖	≥75

季节 season	R²	基台值(c) sill	块金值(c₀) nugget	变程(a) range
春spring	0.954	0.007 46	0.001 30	169
夏summer	0.949	0.004 10	0.000 50	122
秋autumn	0.933	0.009 47	0.001 17	171
冬winter	0.948	0.021 19	0.006 59	252

FVC等级 coverage level		面积/hm² area					生产者精度/% producer’s accuracy	使用者精度/% user’s accuracy
FVC等级 coverage level		低 low	中低 medium-low	中 medium	中高 medium-high	高 high	生产者精度/% producer’s accuracy	使用者精度/% user’s accuracy
低覆盖 low	FVC_30m	17.13	11.57	5.49	2.46	14.18	33.70	57.20
	FVC_250m	0.61	0.26	3.47	15.53	30.96	1.20	1.44
	FVC_500m	0.04	0.22	5.32	22.84	22.41	0.08	0.45
中低覆盖 medium-low	FVC_30m	11.68	54.79	48.26	15.44	64.08	28.21	55.77
	FVC_250m	1.14	2.92	11.77	56.74	121.68	1.50	2.14
	FVC_500m	0.31	3.69	11.19	68.66	110.40	1.90	5.90
中覆盖 medium	FVC_30m	0.85	24.98	98.48	94.87	78.92	33.04	38.30
	FVC_250m	1.17	4.62	17.66	89.73	184.92	5.92	6.08
	FVC_500m	0.41	5.92	14.22	99.99	177.56	4.77	4.54
中高覆盖 medium-high	FVC_30m	0.25	5.56	63.66	230.19	267.64	40.58	30.30
	FVC_250m	2.27	7.27	29.59	151.65	376.52	26.73	7.34
	FVC_500m	0.58	8.08	26.14	165.20	367.30	29.12	7.71
高覆盖 high	FVC_30m	0.04	1.35	41.23	416.81	11 403.65	96.13	96.41
	FVC_250m	37.06	121.29	227.81	1 753.16	9 723.76	81.97	93.16
	FVC_500m	7.54	44.58	256.06	1 785.59	9 769.31	82.35	93.51

基于多源遥感数据的溧阳市林地植被覆盖度时空差异研究

Study on spatial and temporal variation of forest fractional vegetation cover in Liyang based on multi-source remote sensing data

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