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20212022

论文（4）

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1Retrieving the Infected Area of Pine Wilt Disease-Disturbed Pine Forests from Medium-Resolution Satellite Images Using the Stochastic Radiative Transfer Theory

Xiaoyao Li, Tong, Tao Luo,Jingxu Wang,Yueming Rao,Linyuan Li,Decai Jin,Dewei Wu,Huaguo Huang

Pine wilt disease (PWD) is a global destructive threat to forests which has been widely spread and has caused severe tree mortality all over the world. It is important to establish an effective method for forest managers to detect the infected area in a large region. Remote sensing is a feasible tool to detect PWD, but the traditional empirical methods lack the ability to explain the signals and can hardly be extended to large scales. The studies using physically-based models either ignore the within-canopy heterogeneity or rely too much on prior knowledge. In this study, we propose an approach to retrieve PWD infected areas from medium-resolution satellite images of two phases based on the simulations of an extended stochastic radiative transfer model for forests infected by pests (SRTP). A small amount of prior knowledge was used, and a change of background soil was considered in this approach. The performance was evaluated in different study sites. The inversion method performs best in the three-dimensional model LESS simulation sample plots (R2 = 0.88, RMSE = 0.059), and the inversion accuracy decreases in the real forest sample plots. For Jiangxi masson pine stand with large coverage and serious damage, R2 = 0.57, RMSE = 0.074; and for Shandong black pine stand with sparse and a small number of single plant damage, R2 = 0.48, RMSE = 0.063. This study indicates that the SRTP model is more feasible for pest damage inversion over different regions compared with empirical methods. The stochastic radiative transfer theory provides a potential approach for future monitoring of terrestrial vegetation parameters.

2022REMOTE SENSING（2022）引用：10

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2Inversion of canopy water content of fire scars in Genhe county based on INFORM model and Google Earth Engine

RAO Yueming,LIU Kaijun,HUANG Huaguo,LIN Qinan,WANG Chuan

[目的]提出一种结合辐射传输模型与遥感云平台反演火烧迹地冠层含水量(Canopy water content,CWC)的新方法,弥补目前对火烧迹地恢复阶段植被含水量的监测,为定量监测植被水分与火灾预警提供理论参考.[方法]以内蒙古自治区根河市火烧迹地为研究对象,基于INFORM辐射传输模型,使用查找表的方法反演森林冠层含水量,并结合Google Earth Engine(GEE)大数据遥感平台与Mann-kendall模型分析了火烧迹地的冠层含水量时序性变化,最后绘制根河2018年8月森林冠层含水量分布图.[结果]1)基于样地的CWC反演精度较高(R2为0.79);2)大范围的归一化水分指数(Normalized difference water index,NDWI)和CWC呈指数关系(R2为0.77),但CWC比NDWI的饱和点更高;3)CWC可作为火烧迹地恢复的生态指标,获得了34 a的CWC时序性反演结果,表明火灾后CWC明显降低,并基于Mann-kendall模型得到各样地CWC和LAI的恢复速率.[结论]联合INFORM模型与GEE反演并监测火烧迹地冠层含水量,方法通用且高效.火烧迹地在恢复过程中面临再次发生火烧和虫害病害的风险,研究结果可为森林防火和森林病虫害监测提供技术支持,对该区域森林火灾与森林虫害的预警有一定意义.

2021Journal of Central South University of Forestry & Technology（2021）引用：1

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3Study of the Correlation Between Optical Vegetation Index and SAR Data and the Main Affecting Factors

饶月明, 范景辉,林思美,黄华国,王川

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(nor-malized difference vegetation index,NDVI)、增强型植被指数(enhanced vegetation index,EVI)、绿度植被指数(green-ness 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(polariza-tion 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.

2020引用：2

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4Forest Fire Monitoring Based on Multisensor Remote Sensing Techniques in Muli County, Sichuan Province

饶月明,王川,黄华国

2020引用：34

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