【作者】 王蕾；

【导师】 骆有庆； 张晓丽； 马钦彦；

【作者基本信息】 北京林业大学 ， 森林保护学， 2009， 博士

【摘要】 近十多年来,遥感在森林虫害监测中的应用研究得到了逐步开展,尤其是监测模型的研究,取得了显著的研究进展。但大多数模型几乎都集中在利用特定地区的数据经统计分析而建立,并没有从机理上对模型进行分析、研究和调整。这种仅仅源于特定地域数据建立的模型,其应用也常受到地域的限制,所以虫害遥感监测建模研究结果的通用性问题急需解决。同时,由于大多数虫害遥感监测多集中在对林木受虫害后叶片颜色发生变化和产生落叶这两种生理特征的研究,而应用遥感监测到林木因虫害而产生这两种特征时,通常已是林木受害的中后期,无法对林木受害的早期进行监测。在应用的遥感数据方面,多集中在可见光和近红外数据,而热红外波段的应用研究较少。本文将以生态学、定量遥感及森林保护等相关理论为指导,从机理上研究应用遥感监测虫害的关键科学问题,从林木受虫害后的生理特征分析入手,在研究了虫害发生早期阶段遥感可监测因子的基础上,初步确立了具有较强通用性和明确物理含义的三种虫害早期遥感监测指示因子,即:林冠层含水量（CWC）、土壤相对含水量和叶面积指数（LAI）。为验证应用Landsat TM数据提取林冠层含水量的可行性,应用针叶物理反射模型LIBERTY,结合实地调查数据对不同含水量条件下的针叶反射率特征进行了研究,经研究得知三种同水分相关的光谱指数MSI、NDWI和GVMI与针叶含水量高度相关,且GVMI光谱指数对针叶是否为簇状并不敏感,能够显著反映针叶的水分变化。同时,由于GVMI是由两个宽波段（SWIR和NIR）数据计算得来,应用Landsat TM数据即可获得,所以间接验证了应用Landsat TM/ETM+数据反演冠层含水量的可行性,并为后续进一步研究落叶松冠层水分含量的反演提供了理论基础和研究途径。随后,结合物理辐射传输模型,综合利用可见光、近红外和热红外数据,研究了三种因子的遥感反演方法和模型。首先,在研究了以往叶面积指数和冠层含水量的反演方法的基础上,提出了应用辐射传输模型——森林五尺度传输模型（5-scale）和人工神经网络——BP网络反演LAI和CWC的方法,并就如何应用5-scale模型和BP网络对陆地卫星数据反演LAI和CWC进行了研究,通过与实地测量数据和MODIS产品数据的比较,得知反演结果吻合较好。应用该方法对多期图像进行了反演,获得了伊尔施林场10年来12期影像的CWC和LAI的时空分布图。其次,选择TVDI（Temperature/Vegetation dryness Index）作为表征土壤相对含水量的指数,并对其反演进行研究。该指数需要地表温度（TS）和归一化植被指数（NDVI）构成的特征空间共同确定。NDVI可以应用红光和近红外数据计算得到,TS需要从Landsat TM₆波段数据中反演得到。经研究表明,LandsatTM₆热红外波段需要经过大气效应校正和发射率效应校正,得到的地表温度才能更有效地提取TVDI指数。将反演得到的10年的TVDI数据同实地降水量数据对比可以看出,趋势吻合较好,该指数对于地表干旱的指示作用较好,基本能够满足监测土壤相对含水量的要求。最后,在反演了三种虫害发生早期阶段遥感监测指示性因子的基础上,对落叶松林虫害遥感监测规则进行了研究,建立了基于三种因子进行虫害遥感监测的规则,并以内蒙古阿尔山地区虫害监测为例,对该方法进行了应用和评价。经验证,结果与实地情况吻合较好。考虑到实用性,论文进一步简化了三个参数的反演计算,提出了一套较为通用、有效的基于物理模型的落叶松林虫害遥感监测的方法。更多还原

【Abstract】 In the recent ten years,gradual research progresses have been made on the remote sensing application in the forest pest monitoring area,especially on the monitoring model.However, most monitoring models were developed based on statistical analysis,which is highly depending on the specific field data.Therefore,these models are limited for only small regions and not suitable for other study areas.Hence,it is necessary and urgent to develop more general models. Meanwhile,most research used two physiological characteristics,leaf litter and the change of the leaf colour,to monitor forest status after the trees being attacked.Although they are suitable for the loss assessment,these researches are not so good to detect the insect pests in the early stage.That is because it’s usually in the middle and latter stage of the insects attacking when the two physiological characteristics changes can be detectable by remote sensing data.Moreover, most VIS and NIR data were applied in the pest monitoring research,while,the application of TIR data are rare.In this paper,the key scientific problems of the forest insect pests monitoring by remote sensing were studied based on the theories of the ecology,forest protection and quantitative remote sensing.Through the analysis on the physiological characteristics changes after the trees being attacked,three indicative factors of the early forest insect pests monitoring were brought forward,which are CWC（represents the total water content of forest canopy）,TVDI（represents the relative water content of soil） and the LAI（leaf area index）.In order to evaluate the feasibility of the CWC inversion from the Landsat data,the sensitivity of the reflectance characteristics of coniferous needles on the needle water content was studied based on the filed data and the LIBERTY（Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields） model.The results show that three spectrum indices,which are MSI （Moisture Stress Index）,NDWI（Normal Difference Water Index） and GVMI（Global Vegetation Moisture Index） were highly correlated with the water content.Especially,GVMI is not sensitive to whether the needle is clustered or not.Moreover,GVMI uses two broad bands （NIR and SWIR）,which can be calculated from Landsat TM/ETM+ data.Therefore,it is confirmed that Landsat TM/ETM+ data is feasible to inverse the forest canopy water content, which lays a solid theoretical foundation for the following research.Then,the inversion research on the three indicative factors was performed using VIR,NIR and TIR data,combined with physical-based RT（Radiative Transfer） model and ANN（Artificial Neural Network）.Firstly,the inversion method（RT-ANN） for LAI and CWC by combining the 5-scale RT model and BP ANN was put forward based on the historical research.And the method was customized on Landsat data to inverse the two indicative factors.Field data and MODIS data were both validate the reasonable inversion precision.Finally,twelve images of LAI and CWC were obtained based on twelve Landsat images during the ten years.The images show that LAI and CWC decrease gradually.Then,the inversion of TVDI（Temperature/Vegetation dryness Index） which can repsent the soil relative water content was studied.TVDI is determined by using the Ts/NDVI featrue space.Pre-analysis results show that the Landsat TM₆ data must be corrected to remove atmopsheric effects and emissivity effects before retriving TVDI.By combining the field rainfall data,the inversed TVDI image series show that this index is good to indicate the soil dryness and usefull for monitoring soil relative water content.Eventually,the decision rules for the larch forest insect pests monitor were performed based on the three inversed indices.The rules are then applied in the YIERSHI forest farm.The insect pest forest compartments which extract from the images is highly consistent with the ground field data.Considering practicability,a general and physically-based forest insect pest monitoring model is developed by speeding the inversion algorithm.更多还原