基于多角度遥感的植被指数与叶面积指数的线性关系研究

以辐射传输方程PROSAIL为基础,模拟不同观测天顶角和不同叶面积指数(LAI)下的植被冠层光谱。利用模拟的冠层光谱构建3种常用的植被指数,并分析不同观测天顶角下叶面积指数变化对3种植被指数的影响。结果表明,MSR能较好解决由于LAI变化而引起的饱和现象。观测天顶角为-30°时,3种植被指数与叶面积指数的线性关系较30°和0°时好。     

植被覆盖地表土壤水分遥感反演

以地域特色突出的新疆渭干河-库车河三角洲绿洲为研究区,联合使用雷达数据和光学遥感数据,对干旱区绿洲土壤和植被水分信息进行提取。在同期光学遥感影像数据提取植被归一化差分水分指数基础上,利用"水-云模型"从雷达数据总的后向散射中去除植被影响,建立土壤后向散射系数与土壤含水量的关系,相关系数为HH极化R2=0.5227,HV极化R2=0.3277。结果表明利用C波段HH极化雷达影像数据结合光学影像数据,进行干旱半干旱地区棉花、玉米等农作物种植区地表土壤水分反演时,在中等覆盖条件下去除植被影响有较好的效果。     

草原植被覆盖度遥感估算模型的适用性比较

植被覆盖度及其变化对区域生态系统的稳定性具有直接影响,且这种影响在草原地区更加明显。为探寻草原植被覆盖度的最佳遥感估算方法,本文对像元二分模型、Carlson模型和Baret模型的估算精度和适用性进行了比较,优化了Baret模型的参数,以提高其在草原地区的估算精度。内蒙古呼伦贝尔地区的草地计算结果表明：像元二分模型有高估植被覆盖度的现象;Carlson模型在低植被覆盖区低估了植被覆盖度,而在高植被覆盖区高估了植被覆盖度;Baret模型在草原地区的估算精度最高。对Baret模型进行参数优化后,其在高植被覆盖度区域的估算精度提升了4.9%。     

滑坡蠕变与遥感影像上植被异常关系

以滑坡蠕变阶段坡体的蠕变会引起环境条件的改变，进而影响植被生长状况的野外考察客观现实为依据，提出一种间接监测滑坡变化的新方法。利用高分辨率光学遥感技术，对滑坡蠕变阶段遥感影像上坡体上覆植被的异常特征进行判识，建立遥感影像上植被异常与滑坡蠕变的关系，反映滑坡的演化过程，弥补GPS技术、InSAR技术及部分地面监测手段在地势高、地形陡峭、植被茂盛等条件下监测工作的不足，为后续的滑坡预测研究提供帮助。以植被覆盖度较高的新磨村山体高位滑坡为例，首先，对研究区域进行分区；其次，计算各分区的植被覆盖度；最后，利用植被覆盖度分析遥感影像上的植被异常与滑坡蠕变的关系，并根据滑后遥感影像和实地考察情况进行验证。结果显示，2014年—2016年，滑坡的主要物源区、变形体上方细长局部崩滑区和泉眼及冲沟周边的植被覆盖度出现明显的下降，即随着滑坡发生时间的临近，植被受滑坡蠕变的影响变大，植被生长状况变差；而且随着距裸地等滑坡风险较大区域的距离增大，植被受滑坡蠕变的影响变小，植被生长状况变好。这表明，植被异常与滑坡蠕变存在明显的时空相关性，体现了滑坡蠕变阶段遥感影像上植被异常与滑坡蠕变的内在联系，反映了滑坡逐步失稳的演化过程，为进一步预测滑坡的发生提供依据。     

基于多源遥感数据的物种多样性及陆表植被恢复力监测研究

生物多样性和生态系统恢复力是生态系统健康的关键因素和重要指标。物种多样性指一定区域内物种数量的总和,物种多样性监测是生物多样性监测与保护的核心部分。作为生态系统恢复力的重要组成部分,植被恢复力是陆表生态系统服务和生态系统功能的重要指标,直接决定了陆表植被变化、陆表生态系统稳定性等关键生态过程和生态参量。目前,受到观测手段的限制,物种多样性与陆表植被恢复力监测普遍空间范围小,时间范围不连续,其中热带地区植物物种多样性变化对植被影响效果争议较大。论文基于长时间序列高精度遥感数据,从动物物种多样性遥感监测、植物物种多样性变化对热带雨林影响的遥感评估、遥感植被恢复力监测三方面出发开展相关工作。主要工作和成果包括:     

Advances in atmospheric, hydrographic and vegetation remote sensing with lidar

Lidar has evolved as one of the powerful and versatile techinqne for atmospheric monitoring and geophysical and hydrographic studies from different observational platforom. This paper discusses the recent advances that have taken place in the lidar technology for rnulti-dimensional atmospheric, hydragraphic and vegetation monitoring together with the results of some specific atmospheric studies carried our for the past ten years using the lider systems built at the Indian Institute of Tropical Meteorotogy (IITm) Pune.     

Mapping of soil degradation hazards by remote sensing in Hanumangarh district (western Rajasthan)

In the present study, efforts have been made to identify and map areas affected by various soil degradation processes in Hanumangarh district of western Rajasthan. Soil degradation processes were identified by using IRS-1B satellite image of the year 1998, SOI toposheets, ground truth verification and soil studies. The kind, extent and degree of soil degradation have been mapped in an area of 9703 km². The study reveals that the soil degradation problems were mainly due to wind erosion/deposition and water-logging, followed by salinity/alkalinity, water erosion and wind and water erosion combinedly. Nearly 38.7% area is subjected to slight and moderate degradation, which can easily be combated by adopting the suggested techniques and 17.1% area is free of hazard. Soil degradation processes have resulted in the loss of organic carbon, available Phosphorus and Potassium. Soil degradation due to water logging/salinization has also shown a significant increase in electrical conductivity and available potassium content of soil.     

GF-2支持下的干旱区稀疏植被区植被覆盖度估算

现有像元二分模型MODIS植被覆盖度模型因其形式简单、适用性较强的特点被广泛应用于区域植被覆盖度（FVC）的估算。然而,研究表明在沙漠和低植被覆盖的西部干旱区,从250 m的影像上很难精准地获取NDVI_veg（全植被覆盖植被指数）和NDVI_soil（全裸土区植被指数）参数。利用常用的直方图累计法获取模型所需参数NDVI_veg和NDVI_soil,估算结果存在普遍高估现象。为此,本文首先引入同期获取的GF-2号卫星数据,从GF-2号影像上提取植被覆盖像元;然后,利用Pixel Aggregate方法重采样至250 m分辨率,获取250 m空间分辨率下纯植被和纯裸土像元;最后,将纯植被和纯裸土像元各自空间位置相对应的MODIS NDVI数据最大值作为模型所需NDVI_veg和NDVI_soil参数,实现研究区内植被覆盖度的估算。试验通过与线性回归法、多项式回归法和直方图累计像元二分模型法估算结果进行精度对比,结果表明：利用GF-2影像辅助的像元二分模型,精准地获取了低植被覆盖区NDVI_veg和NDVI_soil模型参数,提高了干旱区植被覆盖度的估算精度,并有效地抑制了受稀疏植被影响NDVI在干旱区普遍偏高问题导致的FVC高估的现象。     

Use of high spectral resolution remote sensing to determine leaf palatability of eucalypt trees for folivorous marsupials

In this study we assess the feasibility of remotely measuring canopy biochemistry, and thus the potential for conducting large-scale mapping of habitat quality. A number of studies have found nutrient composition of eucalypt foliage to be a major determinant of the distribution of folivorous marsupials. More recently it has been demonstrated that a specific group of secondary plant chemicals, the diformylphloroglucinols (DFPs), are the most important feeding deterrents, and are thus vital determinants of habitat quality. We report on the use of laboratory spectroscopy to attempt to identify one such DFP, sideroxylonal-A, in the foliage of Eucalyptus melliodora, one of the few eucalypt species browsed by folivorous marsupials. Reflectance spectra were obtained for freeze-dried, ground leaves using near infrared spectroscopy (NIRS) and for both oven-dried and fresh whole leaves using a laboratory-based (FieldSpec) spectroradiometer. Modified partial least squares (MPLS) regression was used to develop calibration equations for sideroxylonal-A concentration based on the reflectance spectra transformed as both the first and second difference of absorbance (Log 1/R). The predictive ability of the calibration equations was assessed using the standard error of calibration statistic (SECV). Coefficients of determination (r²) were highest for the ground leaf spectra (0.98), followed by the fresh leaf and dry leaf spectra (0.94 and 0.87, respectively). When applied to independent validation sub-sets, sideroxylonal-A was most accurately predicted from the ground leaf spectra (r² = 0.94), followed by the dry leaf and fresh leaf spectra (0.72 and 0.53, respectively). Two spectral regions, centred on 674 nm and 1394 nm, were found to be highly correlated with sideroxylonal-A concentration for each of the three spectral data sets studied. Results from this study suggest that calibration equations derived from modified partial least squares regression may be used to predict sideroxylonal-A concentration, and hence leaf palatability, of Eucalyptus melliodora trees, thereby indicating that the remote estimation of habitat quality of eucalypt forests for marsupial folivores is feasible.     

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll with points at (670 nm, R₆₇₀), (550 nm, R₅₅₀), and (480 nm, R₄₈₀), where R_λ is the spectral reflectance at wavelengths of 670, 550 and 480, respectively. The equation is TGI = −0.5[(670 − 480)(R₆₇₀ − R₅₅₀) − (670 − 550)(R₆₇₀ − R₄₈₀)]. In 1999, investigators funded by NASA's Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated maize in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper (TM) data were acquired along with leaf chlorophyll meter and other data on three dates in July during late vegetative growth and early reproductive growth. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Landsat TM, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms.     

高分辨率定量遥感的偏振光效应与偏振遥感新领域

随着航空航天高分辨率观测手段的逐步成熟,定量遥感在有效解决了光学地表多尺度效应后,更高分辨率的光学偏振效应在地表、大气、仪器3大要素方面逐步全面凸显出来,并直接决定或影响高分辨率观测系统的定量遥感效能实现和质量保障。例如,植被冠层极其微弱的多次散射反射偏振效应,如果不加扣除其模型误差达到136%;大气衰减本质是大气偏振效应,是遥感反演的最大误差源,误差达到5%—30%,偏振手段扣除大气误差目前已可以降低一半以上的误差;观测仪器扣除多次散射透射偏振光后,可以实现5 nm分辨率下0.1—0.3 nm高光谱定标能力,并借助偏振强化光噪声分离出中心波长偏移和带宽退化的误差根源。借助偏振"强光弱化,弱光强化",能够实现稳定度达10~(-8)月球辐亮度基准观测,为遥感辐亮度定标不确定度由7%到1%—2%跨越提供可能。     

Satellite remote sensing in soil resource inventory of Dibrugarh district (part), Assam

Remote sensing techniques have been employed to identify and delineate soils in a part of Dibrugarh district of Assam. Landsat-4 MSS data in the form of FCC (4, 5, 7) were interpreted visually for physiographic analysis in conjunction with Survey of India topographic maps. Ground data were translated in terms of soils using composite interpretation map as base. The abstraction level attained was Families of Soil Taxonomy. Four major physiographic units were delineated, viz. active flood plain, recent alluvial plain, gently undulating old alluvial plain and gently sloping to undulating piedmont plain. Dominant soils identified are: coarse loamy Aeric Fluvaquents and fine loamy Typic Udifluvents in active flood plain; fine Typic Haplaquepts and fine loamy Aquic Dystrochrepts in recent alluvial plain; fine loamy Umbric Dystrochrepts and fine Ultic Hapludalfs in old alluvial plain; coarse loamy Typic Udorthents and fine Mollic Hapludalfs in piedmont plain.     

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).     

顾及植被的复杂艰险地区多光谱遥感岩性识别

针对复杂艰险地区修建道路时,地质岩性难以现场获取的问题,该文利用ASTER多光谱遥感影像,研究和设计了一种顾及植被覆盖的复杂艰险地区多光谱遥感岩性信息识别提取方法.该方法根据植被覆盖情况先分类后进行岩性识别:对中高植被覆盖区进行植被信息的抑制处理,再对中低植被覆盖区采用主成分分析法进行岩性信息提取;对低植被地表裸露区域采用端元提取、光谱角填图的方法进行岩性信息的提取.最后将岩性识别提取结果与现场地勘结果进行对比验证.结果表明该方法进行岩性信息提取结果与已有的现场勘察结果基本一致,具有重要的参考和应用价值.     

Assessment of soil degradation Hazards in Jalor and Ahor Tehsil of Jalor district (Western Rajasthan) by remote sensing

In the present study efforts have been made to identify and map areas affected by various land degradation processes with the aid of Landsat TM imagery data of 1988 and ground truth verification. The kind, extent and degree of land degradation have been mapped. In an area of over 4,124 sq. km. 51% was affected by water erosion and 30% area by wind erosion. Nearly 1.14% area is affected by salinity. Degradation due to combined effect of water and wind erosion and water erosion and salinization has affected 8.20% of the study area. 1.53% area is free from any hazard. Remaining 7.85% area comes under hills and rivers. Nearly 44 percent of the affected area is subjected to moderate and severe degradation which can easily be combatted by techniques referred.     

Suitability of the DCS460c colour digital camera for quantitative remote sensing analysis of vegetation

The Kodak DCS460c is a high resolution, colour digital camera with a specific CCD array mosaic structure that requires a dedicated processing algorithm for production of a three-band image. Kodak's Active Interpolation (KAI) algorithm is compared with a simpler nearest neighbour interpolation method (NNI) with regard to applicability in scientific investigations of target features in imagery. Detailed quantitative analyses of flat-field imagery were undertaken in order to provide insight into artefacts and to correct for the off nadir reduction in brightness. Characteristics of the aerial photography were also studied and aspects of processing and analysis of the imagery for scientific investigations are discussed. We present a novel method for correction of brightness fall-off (due to the vignette effects and increasing view angle) which is generally applicable to remote sensing of rural scenes. Quantitative spectral comparisons between related imagery from the DCS460c (e.g. for interpretation of relative vegetation cover) are only scientifically reliable when the raw (i.e. non-interpolated) DN values for green and red are below about 67% of their maximum range. This is due to preferential vertical leakage or charge diffusion in the CCD. A combination of CCD characteristics leads to possibly poor spatial resolution for the infrared band. The imagery after KAI shows good spatial resolution and has a natural looking colour reproduction useful for discerning features by eye. However, when using KAI, reliable spectral measurements can probably only be acquired from portions of objects that are at least four pixels from the object's boundary. In general, quantitative spatial and spectral analyses can be carried out with the DCS460c but the KAI, the broad band response and the vertical leakage require specific solutions to produce quantitative results.     

Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands

Water and energy balance interactions with vegetation in mountainous terrain are influenced by topographic effects, spatial variation in vegetation type and density, and water availability. This is the case for the mountainous areas of northern Portugal, where ancestral irrigated meadows (lameiros) are a main component of a complex vegetation mosaic. The widely used surface energy balance model METRIC was applied to four Landsat images to determine the spatial and temporal distribution of the energy balance terms in the identified land cover types (LCT). A discussion on the variability of evapotranspiration (ET) through the various vegetation types was supported by a comparison between the respective crop coefficients and those available in the literature corresponding to the LCT, which has shown the appropriateness of METRIC estimates of ET. METRIC products derived from images of May and June – NDVI, surface temperature, net radiation, soil heat flux, sensible heat flux, and ET – were used to characterize the LCTs, through application of principal component analysis. Three principal components explained the variance of observed variables and their varimax rotated loadings allowed a good explanation of the behaviour of the explanatory variables in association with the LCTs. Information gained contributes to improve the characterization of the study area and may further support conservation and management of these mountain landscapes.     

光学与微波数据协同反演农田区土壤水分

光学和微波协同遥感反演对于提高农田土壤水分遥感反演精度十分重要。本文采用SMEX02数据集,研究了L波段土壤发射率与地表土壤水分之间的关系,分析了地面植被覆盖对L波段土壤发射率与地表水分之关系的影响规律,推导了以L波段土壤发射率和归一化植被指数NDVI为自变量的土壤水分反演模型。研究表明:L波段土壤发射率与地表土壤水分之间的相关性随NDVI的增加而下降。验证结果表明,本文算法相对常规经验算法,土壤水分反演精度明显提高,H极化条件下,土壤水分的反演精度RMSE由0.0553提高到0.0407,相关系数R2由0.70提高到0.81;V极化条件下,反演精度RMSE由0.0452提高到0.0348,相关系数R2由0.79提高到0.86。     

基于遥感的植被长时序趋势特征研究进展及评价

基于遥感的植被长时序变化特征是植被生态学研究的核心领域, 也是全球变化研究的重点方向。AVHRR、SPOT VGT和MODIS是当前研究植被长时序趋势变化的主要数据源。海量数据不断积累的同时, 植被长时序趋势特征研究方法却缺乏对比评价和分析。当前常用的方法有代数运算法、傅里叶变换、主成分分析、小波变换法、回归分析法和相关系数分析法等。在对各种方法评述和分析的基础上, 重点讨论和对比了主流方法中的回归分析法和相关系数分析与新兴方法Sen＋Mann-Kendall法。结果表明, Sen＋Mann-Kendall能克服主流方法的不足, 不需要数据服从某一特定分布, 并且对数据的误差具有较强的抵抗能力。