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

doi:10.12302/j.issn.1000-2006.202204048

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (6): 183-191.doi: 10.12302/j.issn.1000-2006.202204048

Previous Articles Next Articles

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

Abstract

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

CLC Number:

P237
TP7

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, 2023, 47(6): 183-191.

Figures/Tables 9

Fig. 1

Table 1

Table 2

Fig. 2

Table 3

Fig. 3

Fig. 4

Fig. 5

Table 4

References 29

[1]	WEN Z M, LEES B G, JIAO F, et al. Stratified vegetation cover index:a new way to assess vegetation impact on soil erosion[J]. CATENA, 2010, 83(1):87-93.DOI:10.1016/j.catena.2010.07.006.
[2]	MARSETT R C, QI J G, HEILMAN P, et al. Remote sensing for grassland management in the arid southwest[J]. Rangel Ecol Manag, 2006, 59(5):530-540.DOI:10.2111/05-201R.1.
[3]	JIA K, LI Y W, LIANG S L, et al. Combining estimation of green vegetation fraction in an arid region from Landsat 7 ETM+ data[J]. Remote Sens, 2017, 9(11):1121.DOI:10.3390/rs9111121.
[4]	DE ASIS A M, OMASA K. Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data[J]. ISPRS J Photogramm Remote Sens, 2007, 62(4):309-324.DOI:10.1016/j.isprsjprs.2007.05.013.
[5]	ROUJEAN J L. Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions:a pragmatic method and its validation[J]. J Geophys Res, 2002, 107(D12):4150.DOI:10.1029/2001jd000751.
[6]	张洪达, 赵传普, 戴玉婷, 等. 群落尺度植被水土保持功能评价模型的构建:以大别山北麓安徽省金寨县为例[J]. 水土保持通报, 2022, 42(1):122-129.
	ZHANG H D, ZHAO C P, DAI Y T, et al. Construction of evaluation model for soil and water conservation function of vegetation at community scale-taking Jinzhai County,Anhui Province at northern foot of Dabie Mountains as an example[J]. Bull Soil Water Conserv, 2022, 42(1):122-129.DOI:10.13961/j.cnki.stbctb.2022.01.017.
[7]	张骁, 赵文武, 刘源鑫. 遥感技术在土壤侵蚀研究中的应用述评[J]. 水土保持通报, 2017, 37(2):228-238.
	ZHANG X, ZHAO W W, LIU Y X. Application of remote sensing technology in research of soil erosion:a review[J]. Bull Soil Water Conserv, 2017, 37(2):228-238.DOI:10.13961/j.cnki.stbctb.2017.02.035.
[8]	杨翔惟, 张洪达, 刘霞, 等. 面向多源异构数据环境的区域水土流失野外调查技术研究与应用[J]. 干旱区资源与环境, 2020, 34(10):139-146.
	YANG X W, ZHANG H D, LIU X, et al. Research and application of field investigation technology of regional soil and water loss in multi-source heterogeneous data environment[J]. J Arid Land Resour Environ, 2020, 34(10):139-146.DOI:10.13448/j.cnki.jalre.2020.280.
[9]	邱凤婷, 过志峰, 张宗科, 等. 大湄公河次区域植被覆盖时空变化特征及其与气象因子的关系[J]. 南京林业大学学报(自然科学版), 2022, 46(2): 187-195.
	QIU F T, GUO Z F, ZHANG Z K, et al. Spatio-temporal change characteristics of vegetation coverage and its relationship with meteorological factors in the Greater Mekong Subregion[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2022, 46(2):187-195.DOI:10.12302/j.issn.1000-2006.202010020.
[10]	BOLTON D K, FRIEDL M A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics[J]. Agric For Meteorol, 2013, 173:74-84.DOI:10.1016/j.agrformet.2013.01.007.
[11]	QI J, MARSETT R C, MORAN M S, et al. Spatial and temporal dynamics of vegetation in the San Pedro River basin area[J]. Agric For Meteorol, 2000, 105(1/2/3):55-68.DOI:10.1016/S0168-1923(00)00195-7.
[12]	张婧, 刘咏梅, 徐健, 等. 影像分辨率对植被覆盖度提取的影响[J]. 水土保持研究, 2014, 21(3):120-124.
	ZHANG J, LIU Y M, XU J, et al. Scale effect analysis of the extraction about vegetation coverage[J]. Res Soil Water Conserv, 2014, 21(3):120-124.DOI:10.13869/j.cnki.rswc.2014.03.023.
[13]	MU X H, HUANG S, REN H Z, et al. Validating GEOV₁ fractional vegetation cover derived from coarse-resolution remote sensing images over croplands[J]. IEEE J Sel Top Appl Earth Obs Remote Sens, 2015, 8(2):439-446.DOI:10.1109/JSTARS.2014.2342257.
[14]	TIAN Y H, WOODCOCK C E, WANG Y J, et al. Multiscale analysis and validation of the MODIS LAI product[J]. Remote Sens Environ, 2002, 83(3):414-430.DOI:10.1016/S0034-4257(02)00047-0.
[15]	LI P, HE Z W, HE D, et al. Fractional vegetation coverage response to climatic factors based on grey relational analysis during the 2000-2017 growing season in Sichuan Province,China[J]. Int J Remote Sens, 2020, 41(3):1170-1190.DOI:10.1080/01431161.2019.1657605.
[16]	TUCKER C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sens Environ, 1979, 8(2):127-150.DOI:10.1016/0034-4257(79)90013-0.
[17]	中华人民共和国水利部. 土壤侵蚀分类分级标准:SL 190—2007[S]. 北京: 中国水利水电出版社, 2008.
	Ministry of Water Resources of the People’s Republic of China. Standards for classification and gradation of soil erosion:SL 190—2007[S]. Beijing: China Water & Power Press, 2008.
[18]	毋亭, 张勇, 侯西勇. 基于半变异函数的环渤海地区NDVI空间格局特征研究[J]. 生态科学, 2014, 33(2):313-320.
	WU T, ZHANG Y, HOU X Y. Analysis of spatial patterns of NDVI over the Circum-Bohai Sea region based on semi-variogram[J]. Ecol Sci, 2014, 33(2):313-320.DOI:10.3969/j.issn.1008-8873.2014.02.018.
[19]	卫春阳, 徐丹丹, 董凯凯, 等. 遥感影像空间格局变异函数分析研究进展[J]. 地球信息科学学报, 2017, 19(4):540-548.
	WEI C Y, XU D D, DONG K K, et al. Advances in analysis of remote sensing image pattern based on semi-variogram[J]. J Geo Inf Sci, 2017, 19(4):540-548.DOI:10.3969/j.issn.1560-8999.2017.04.012.
[20]	周静平, 李存军, 胡海棠, 等. 森林植被遥感监测影像最佳分辨率选择[J]. 浙江农林大学学报, 2018, 35(4):716-723.
	ZHOU J P, LI C J, HU H T, et al. Optimal resolution selection for monitoring forest vegetation using remote sensing images[J]. J Zhejiang A F Univ, 2018, 35(4):716-723.DOI:10.11833/j.issn.2095-0756.2018.04.017.
[21]	ATKINSON P M, CURRAN P J. Defining an optimal size of support for remote sensing investigations[J]. IEEE Trans Geosci Remote Sens, 1995, 33(3):768-776.DOI:10.1109/36.387592.
[22]	MEISEL J E, TURNER M G. Scale detection in real and artificial landscapes using semivariance analysis[J]. Landsc Ecol, 1998, 13(6):347-362.DOI:10.1023/A:1008065627847.
[23]	徐岚, 赵羿. 利用马尔柯夫过程预测东陵区土地利用格局的变化[J]. 应用生态学报, 1993, 4(3):272-277.
	XU L, ZHAO Y. Forecast of land use pattern change in Dongling District of Shenyang:an application of Markov process[J]. Chin J Appl Ecol, 1993, 4(3):272-277.
[24]	鲁春阳, 齐磊刚, 桑超杰. 土地利用变化的数学模型解析[J]. 资源开发与市场, 2007, 23(1):25-27.
	LU C Y, QI L G, SANG C J. Analysis on mathematic model of land-use changes[J]. Resour Dev Mark, 2007, 23(1):25-27.DOI:10.3969/j.issn.1005-8141.2007.01.008.
[25]	胡鸿, 许延丽, 鞠洪波, 等. 基于遥感影像的福建省长汀县级植被覆盖变化监测及分析[J]. 南京林业大学学报(自然科学版), 2019, 43(3):92-98.
	HU H, XU Y L, JU H B, et al. Monitoring and analysis of county-level vegetation cover change in Changting,Fujian based on remote sensing images[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2019, 43(3):92-98.DOI:10.3969/j.issn.1000-2006.201805013.
[26]	MILNE B T, COHEN W B. Multiscale assessment of binary and continuous landcover variables for MODIS validation,mapping,and modeling applications[J]. Remote Sens Environ, 1999, 70(1):82-98.DOI:10.1016/S0034-4257(99)00059-0.
[27]	SILVESTRI S, MARANI M, SETTLE J, et al. Salt marsh vegetation radiometry[J]. Remote Sens Environ, 2002, 80(3):473-482.DOI:10.1016/S0034-4257(01)00325-X.
[28]	江淼, 张显峰, 孙权, 等. 不同分辨率影像反演植被覆盖度的参数确定与尺度效应分析[J]. 武汉大学学报·信息科学版, 2011, 36(3):311-315.
	JIANG M, ZHANG X F, SUN Q, et al. Vegetation coverage retrieval scale effect analysis using multi-sensor data[J]. Geomat Inf Sci Wuhan Univ, 2011, 36(3):311-315.DOI:10.13203/j.whugis2011.03.010.
[29]	BARET F, HAGOLLE O, GEIGER B, et al. LAI,fAPAR and fCover CYCLOPES global products derived from VEGETATION, Part 1: principles of the algorithm[J]. Remote Sens Environ, 2007, 110(3):275-286.DOI:10.1016/j.rse.2007.02.018.

Metrics

Comments

www.nldxb.njfu.edu.cn

Edited ＆ Published by Editorial Department of Nanjing Forestry University(Natural Sciences Edition)
Address： No.159 Longpan Road,Nanjing 210037 Jiangsu,P.R.China
E-mail ：xuebao@njfu.edu.cn , xuebao@njfu.com.cn
Telephone +86-25-85428247,85427076
Distributed by China International Book Trading Corporation P.O. Box 399, Beijing ,P.R. China

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

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

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 29

Related Articles 7

Recommended Articles

Metrics

Comments

Author

Reader

Reviewer

[1]	HAN Sen, RUAN Renzong, FU Qiaoni, XU Hanwei, HENG Xuebiao. Extraction of aquatic vegetation in Hongze Lake National Wetland Park based on Sentinel-1 and Sentinel-2 images [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(2): 19-26.
[2]	ZHANG Wenwen, WANG Jin, WANG Qiuhua, ZHANG Xiyan, CAO Hengmao, LONG Tengteng. Analyses on spatial and temporal characteristics of forest fires in Yunnan Province based on MODIS from 2001 to 2020 [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(5): 73-79.
[3]	ZHAO Yinghui, GUO Xinlong, ZHEN Zhen. Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(4): 49-57.
[4]	CUI Yang, DI Haiting, XING Yanqiu, CHANG Xiaoqing, SHAN Wei. Spatial and temporal distributions of forest fires in Heilongjiang Province from 2001 to 2018 based on MODIS data [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(1): 205-211.
[5]	PAN Lei, SUN Yujun, WANG Yifu, CHEN Liping, CAO Yuanshuai. Estimation of aboveground biomass in a Chinese fir (Cunninghamia lanceolata)forest combining data of Sentinel⁃1 and Sentinel⁃2 [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(3): 149-156.
[6]	DU Xuehui, MENG Chun, LIU Meishuang. Research on feature selection based on single feature classification accuracy [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(04): 109-116.
[7]	YU Zhengxiang, HAN Guangzhong, CAI Tijiu, JU Cunyong, ZHU Binbin. Comparison between GF-1 data and Landsat-8 data in inversing SWE of the forest in the northern of Daxing’an Mountain [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2017, 41(03): 105-111.