光学遥感植被指数与SAR遥感参数的相关性及其主要影响因素研究

doi:10.6046/gtzyyg.2020.02.17

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建立合成孔径雷达(synthetic aperture Radar,SAR)数据和光学植被指数的定量关系有助于融合这两种数据源,提高山区森林遥感的时序监测能力。为此,以内蒙古大兴安岭根河林区为例,首先分析了归一化植被指数(normalized difference vegetation index,NDVI)、增强型植被指数(enhanced vegetation index,EVI)、绿度植被指数(greenness vegetation index,GVI)和归一化水分指数(normalized difference water index,NDWI)与C波段雷达数据的相关性,接着对比了不同森林干扰下NDVI,NDWI与X,C,L波段雷达数据的相关性差异。结果表明: ①极化比(polarization ratio,PR)和干涉相干系数与各植被指数呈显著负相关,PR与NDVI,EVI,GVI线性趋势好(R²=0.40~0.49),VH的干涉相干系数与各植被指数线性趋势好(R²=0.43~0.51); ②地表类型会影响VH与NDVI线性回归结果,在植被密集的灌草、火烧迹地和森林内线性趋势好(R²=0.64~0.76); ③不同森林干扰下相关性存在差异: 在火烧迹地内NDVI与X波段HH和C波段PR呈显著负相关,NDWI与C波段VH呈显著正相关; 在未受干扰林地内NDVI和NDWI与C波段PR呈显著正相关; 在采伐迹地内L波段PR与NDVI呈显著负相关,L波段VV和VH的PR与NDWI呈显著负相关。

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	王川
	范景辉
	林思美
	饶月明
	黄华国

关键词 ：植被指数, SAR遥感参数, 火烧迹地, 森林采伐, 相关性

Abstract：

Vegetation index is an important approach in vegetation monitoring and investigation. SAR data are free with weather condition observing data day and night. Building relationships between SAR data and vegetation indices can contribute to fusing two data to improve temporal monitoring in forest of mountain areas. Therefore, the authors made a statistical analysis between vegetation indices including NDVI, EVI, GVI, NDWI and C band SAR data and then made a comparison about difference of correlation between NDVI, NDWI and X, C, L band SAR data in different forest disturbances in Genhe forest region of Da Hinggan Mountains in Inner Mongolia. The results are as follows: ①PR and interferometry coefficients both have significant negative correlations with optical vegetation indices, PR has strong linear correlations with NDVI, EVI, GVI (R²=0.40~0.49), and interferometry coefficients have strong linear correlations with all optical indices (R²=0.43~0.51). ②Ground cover can affect linear regression between VH and NDVI. Scrub-grass land and fires scars with thick vegetation layer and forest land have a strong linear correlation with NDVI (R²=0.64~0.76). ③The correlations are different for different forest disturbances: In fires scars, NDVI has significant negative correlations with X- band HH, and C band PR and NDWI have a significant positive correlation with C band VH. In deforestation areas, L-band PR has significant negative correlations with NDVI, and L band VV and VH have significant positive correlations with NDWI. In undisturbed forest land, C-band PR has significant negative correlations with NDVI and NDWI.

Key words： vegetation index SAR remote sensing parameter fire scar deforestation correlation

收稿日期: 2019-07-12 出版日期: 2020-06-18

TP79

基金资助:国家重点研发计划项目“大兴安岭火烧及采伐迹地植被恢复遥感监测及其辅助决策技术”(2017YFC0504003-4);国家自然科学基金项目“基于相关生长理论的森林光学微波信息互补机理研究”(41971289)

通讯作者: 黄华国

作者简介: 王川(1994-),男,硕士研究生,研究方向为植被定量遥感。Email: W3170099@bjfu.edu.cn。

引用本文:

王川, 范景辉, 林思美, 饶月明, 黄华国. 光学遥感植被指数与SAR遥感参数的相关性及其主要影响因素研究[J]. 国土资源遥感, 2020, 32(2): 130-137.
Chuan WANG, Jinghui FAN, Simei LIN, Yueming RAO, Huaguo HUANG. Study of the correlation between optical vegetation index and SAR data and the main affecting factors. Remote Sensing for Land & Resources, 2020, 32(2): 130-137.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.02.17 或 https://www.gtzyyg.com/CN/Y2020/V32/I2/130

Fig.1 研究区卫星遥感影像
(底图为Google Earth B3 (R),B2(G),B1(B)影像)

Tab.1 火烧迹地基本信息

Tab.2 采伐迹地基本信息

Tab.3 各卫星传感器参数和分析用途

Fig.2 光学植被指数与极化SAR数据相关分析流程

Fig.3 森林干扰对光学植被指数与极化SAR数据相关性影响分析

植被指数	$R 1 ①$			$R 2 ②$
植被指数	VV	VH	PR	VV	VH	PR
NDVI	0.06	0.49^**	-0.71^**	-0.03	0.37^**	-0.67^**
EVI	0.09	0.47^**	-0.64^**	0.04	0.43^**	-0.68^**
GVI	0.10	0.48^**	-0.63^**	-0.03	0.36^**	-0.66^**
NDWI	0.31^**	0.59^**	-0.54^**	0.31^**	0.54^**	-0.49^**

Tab.4 Sentinel-1B影像SAR数据与Landsat8影像植被指数的相关性分析

植被指数	8月14日		8月30日
植被指数	γ_VV	γ_VH	γ_VV	γ_VH
NDVI	-0.5 $1 ** ①$	-0.72^**	-0.49^**	-0.70^**
EVI	-0.49^**	-0.68^**	-0.47^**	-0.66^**
GVI	-0.50^**	-0.67^**	-0.48^**	-0.66^**
NDWI	-0.50^**	-0.68^**	-0.42^**	-0.59^**

Tab.5 干涉相干系数与Landsat8影像植被指数的相关性分析

植被指数	$R 12$		$R 22$		$R 32$		$R 42$
植被指数	VH	PR	VH	PR	γ_VV	γ_VH	γ_VV	γ_VH
NDVI	0.23	0.50	0.14	0.45	0.26	0.51	0.24	0.47
EVI	0.21	0.39	0.18	0.44	0.24	0.46	0.21	0.43
GVI	0.22	0.39	0.13	0.44	0.24	0.45	0.22	0.43
NDWI	0.34	0.28	0.29	0.23	0.24	0.46	0.18	0.35

Tab.6 Sentinel-1B影像VH极化、PR、干涉相干系数与Landsat8影像植被指数的相关性分析^①

Fig.4 后向散射系数与NDVI的散点分布

Fig.5 AH类型和BH类型不同地物特征像元频数统计结果

Fig.6 灌草和火烧迹地内NDVI分布特征

Fig.7 火烧迹地恢复时间和NDVI分布特征

[1]	田庆久, 闵祥军. 植被指数研究进展[J]. 地球科学进展, 1998,13(4):327-333.
	Tian Q J, Min X J. Advances in study on vegetation indices[J]. Advances in Earth Sciences, 1998,13(4):327-333.
[2]	薛娟, 俞琳锋, 林起楠, 等. 基于Sentinel-1多时相InSAR影像的云南松切梢小蠹危害程度监测[J]. 国土资源遥感, 2018,30(4):111-117.doi: 10.6046/gtzyyg.2018.04.17.
	Xue J, Yu L F, Lin Q N, Using Sentinel-1 multi-temporal InSAR data to monitor the damage degree of shoot beetle in Yunnan pine forest.Remote Sensing for Land and Resources. 2018,30(4):111-117.doi: 10.6046/gtzyyg.2018.04.17.
[3]	Reiche J, Verbesselt J, Hoekman D, et al. Fusing Landsat and SAR time series to detect deforestation in the tropics[J]. Remote Sensing of Environment, 2015,156(156):276-293. doi: 10.1016/j.rse.2014.10.001
[4]	Capodici F, D’urso G, Maltese A. Investigating the relationship between X-band SAR data from COSMO-SkyMed satellite and NDVI for LAI detection[J]. Remote Sensing, 2013,5(3):1389-1404.
[5]	Gebhardt S, Huth J, Nguyen L D, et al. A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta,Vietnam[J]. International Journal of Remote Sensing, 2012,33(24):7644-7661.
[6]	Stendardi L, Karlsen S R, Niedrist G, et al. Exploiting time series of Sentinel-1 and Sentinel-2 imagery to detect meadow phenology in mountain regions[J]. Remote Sensing, 2019,11(5):542.
[7]	Baghdadi N, Boyer N, Todoroff P, et al. Potential of SAR sensors TerraSAR-X,ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island[J]. Remote Sensing of Environment, 2009,113(8):1724-1738.
[8]	Wang X, Ge L, Li X. Pasture monitoring using SAR with Cosmo-skymed,Envisat ASAR,and ALOS PALSAR in Otway,Australia[J]. Remote Sensing, 2013,5(7):3611-3636. doi: 10.3390/rs5073611
[9]	Tanase M A, Santoro M, De l R J, et al.Sensitivity of X-,C-,and L-band SAR backscatter to burn severity in mediterranean pine forests[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(10):3663-3675.
[10]	高永年, 张万昌. 遥感影像地形校正研究进展及其比较实验[J]. 地理研究, 2008,27(2):467-477.
	Gao Y N, Zhang W C. Comparison test and research progress of topographic correction on remotely sensed data[J]. Geographical Research, 2008,27(2):467-477.
[11]	Lievens H, Verhoest N E C.On the Retrieval of soil moisture in wheat fields from L-band SAR based on water cloud modeling,the IEM,and effective roughness parameters[J]. IEEE Geoscience and Remote Sensing Letters, 2011,8(4):740-744. doi: 10.1109/LGRS.2011.2106109
[12]	Rouse J W, Haas R H, Schell J A, et al. Monitoring vegetation systems in the great plains with ERTS[J]. NASA Special Publication, 1973,351:309.
[13]	Hui Q L, Huete A. Feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995,33(2):457-465. doi: 10.1109/TGRS.36
[14]	Baig M H A, Zhang L, Tong S, et al. Derivation of a tasselled cap transformation based on Landsat8 at-satellite reflectance[J]. Remote Sensing Letters, 2014,5(5):423-431. doi: 10.1080/2150704X.2014.915434
[15]	Gao B C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote sensing of Environment, 1996,58(3):257-266. doi: 10.1016/S0034-4257(96)00067-3
[16]	侯学会, 刘思含, 高帅, 等. 基于Prosail模型和Landsat8数据的小麦冠层含水量反演比较[J]. 麦类作物学报, 2018,38(4):493-497.
	Hou X H, Liu S H, Gao S, et al. Comparison between Prosail model and Landsat8 images in inversion of water content of wheat canopy[J]. Journal of Triticeae Crops, 2018,38(4):493-497.
[17]	Woodhouse I H. 微波遥感导论[M]. 北京: 科学出版社, 2014.
	Woodhouse I H. Introduction to microwave remote sensing[M]. Beijing: Science Press, 2014.
[18]	王超, 张红, 刘智. 星载合成孔径雷达干涉测量[M]. 北京: 科学出版社, 2002.
	Wang C, Zhang H, Liu Z. Spaceborne synthetic aperture Radar interferometry[M]. Beijing: Science Press, 2002.
[19]	Jung J, Yun S H, Kim D J, et al. Damage-mapping algorithm based on coherence model using multitemporal polarimetric-interferometric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018,56(3):1520-1532. doi: 10.1109/TGRS.2017.2764748
[20]	Ulaby F T. Handbook of Radar scattering statistics for terrain[M]. Norwood,MA:Artech House, 1989.
[21]	Enquist B J, Niklas K J. Global allocation rules for patterns of biomass partitioning in seed plants[J]. Science, 2002,295(5559):1517-1520.
[22]	West G B, Enquist B J, Brown J H. A general quantitative theory of forest structure and dynamics[J]. Proceedings of the National Academy of Sciences, 2009,106(17):7040-7045.
[23]	Huang H, Zhang Z, Ni W, et al. Extending RAPID model to simulate forest microwave backscattering[J], Remote Sensing of Environment, 2018,217:272-291. doi: 10.1016/j.rse.2018.08.011

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