那曲典型草地植被光谱特征分析

遥感是大尺度生态研究的重要工具之一,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。地面实测数据由于其高空间分辨率和高光谱分辨率,能够准确反映地物光谱信息,可以用来指导卫星遥感解译工作,同时为遥感监测草地退化、草地模型建立等提供数据支持。选取西藏那曲地区的优势植被类型作为研究对象,利用ASD FieldSpec 3便携式光谱仪测定优势种的冠层光谱并进行比较,并取其中一种优势种测量其在不同覆盖度和不同生长期的光谱反射特点。研究结果表明:①不同植被群落冠层光谱具有特殊的光谱曲线,可见光波段光谱反射率依次是紫花针茅、小嵩草和藏北嵩草,近红外波段光谱反射率则依次是小嵩草、藏北嵩草和紫花针茅;红边位置可以识别藏北嵩草,但是不能区分小嵩草和紫花针茅;②不同覆盖度的小嵩草红边、“绿峰”位置不随覆盖度的变化而发生变化;连续统去除后得到吸收深度随覆盖度的增加而变大,吸收峰面积随覆盖度的增加而增加;③小嵩草衰退期内,在可见光波段和红边波段,冠层光谱反射率随着叶绿素含量的减少而下降,出现“红边蓝移,绿峰下降”的现象。     

焦岗湖沉水植被对水体反射光谱的影响

针对沉水植被冠层对光学浅水区离水辐射信号影响不清楚的问题,以浅水湖泊焦岗湖为研究区,根据水面光谱实测资料探究了沉水植物菹草生长对湖泊光学浅水区水体反射光谱特征的影响。结果表明,草型与淤泥底质在可见光波段对水中光均具有吸收作用,但草型底质的吸收作用明显强于淤泥底质;在近红外波段,2种类型底质对水中光都具有一定的反射作用,而淤泥底质的反射作用要强于草型底质;对于同一种草型底质,随着植被盖度增加、冠层水下深度减小,沉水植被冠层对湖泊水体反射光谱的贡献度逐渐增加,其中0.65～0.75um、0.7～0.9um分别为水体反射光谱对冠层盖度、冠层水下深度的光学敏感波段。该研究可为提高湖泊水质参数反演精度、改进沉水植物信息提取方法提供科学参考。     

滨海盐土重金属含量高光谱遥感研究

高光谱遥感凭借其极高的光谱分辨率在获取有机质、矿物质等土壤组分定量信息的研究中表现出非凡的潜力。以如东县洋口镇为研究区,通过对土壤反射光谱的测量和同步的土壤化学分析,研究了土壤重金属Cr、Cu、Ni与土壤粘土矿物、铁锰氧化物以及碳酸盐之间的赋存关系。利用光谱一阶微分、倒数对数和连续统去除法对土壤光谱的处理,获得了土壤成分的特征波段,通过土壤重金属与土壤光谱变量的相关分析,并利用逐步回归分析方法,确立了3种重金属元素的最佳遥感模型。结果表明,研究区3种重金属与波长429 nm、470 nm、490 nm、1 430 nm、2 398 nm、2 455 nm处光谱变量具有很好的相关性,在所建立的逐步回归模型中,以一阶微分处理的模型精度最高。研究结果可以为高光谱遥感技术反演土壤重金属含量,进一步应用空间或航空遥感进行大尺度环境污染遥感、遥测信息提取和反演提供技术支撑。     

滨海盐渍土可见近红外高光谱特征

土壤光谱是遥感监测的物理基础,盐渍化土壤光谱特征研究对于盐渍化土地的监测有着重要的意义。以黄河三角洲地区的滨海盐渍土为研究对象,通过野外土壤样品采集和高光谱测量,研究在去除水分以及剔除土壤质地影响后,不同盐渍化程度的滨海盐渍土在350~1 100 nm区间的高光谱反射和吸收特征,并且试图构建光谱预测模型。结果表明: 平滑后的光谱曲线能更准确有效地描述光谱的反射特征及吸收峰。不同盐化程度的土壤光谱曲线形态一致,但反射率大小差异较大。连续统去除后,490 nm处轻度盐化土吸收最小,在760~920 nm区间重度盐化土的吸收更强烈。原始光谱不能预测土壤盐渍化信息,但是二阶微分变换能够提高波段敏感性,建立的光谱预测模型能够基本满足预测要求。     

滨海盐渍土可见近红外高光谱特征

土壤光谱是遥感监测的物理基础，盐渍化土壤光谱特征研究对于盐渍化土地的监测有着重要的意义。以黄河三角洲地区的滨海盐渍土为研究对象，通过野外土壤样品采集和高光谱测量，研究在去除水分以及剔除土壤质地影响后，不同盐渍化程度的滨海盐渍土在350~1 100 nm区间的高光谱反射和吸收特征，并且试图构建光谱预测模型。结果表明: 平滑后的光谱曲线能更准确有效地描述光谱的反射特征及吸收峰。不同盐化程度的土壤光谱曲线形态一致，但反射率大小差异较大。连续统去除后，490 nm处轻度盐化土吸收最小,在760~920 nm区间重度盐化土的吸收更强烈。原始光谱不能预测土壤盐渍化信息，但是二阶微分变换能够提高波段敏感性，建立的光谱预测模型能够基本满足预测要求。     

滨海盐渍土可见近红外高光谱特征

土壤光谱是遥感监测的物理基础,盐渍化土壤光谱特征研究对于盐渍化土地的监测有着重要的意义。以黄河三角洲地区的滨海盐渍土为研究对象,通过野外土壤样品采集和高光谱测量,研究在去除水分以及剔除土壤质地影响后,不同盐渍化程度的滨海盐渍土在350～1 100 nm区间的高光谱反射和吸收特征,并且试图构建光谱预测模型。结果表明:平滑后的光谱曲线能更准确有效地描述光谱的反射特征及吸收峰。不同盐化程度的土壤光谱曲线形态一致,但反射率大小差异较大。连续统去除后,490 nm处轻度盐化土吸收最小,在760～920 nm区间重度盐化土的吸收更强烈。原始光谱不能预测土壤盐渍化信息,但是二阶微分变换能够提高波段敏感性,建立的光谱预测模型能够基本满足预测要求。     

在水分胁迫下棉花冠层叶片全氮含量的高光谱遥感估算模型研究

研究利用美国产ASD地物光谱仪,获取新疆北部地区棉花冠层关键生育时期的高光谱数据,采用红边积分面积变量估测棉花冠层叶片的全氮含量,对反射光谱进行一阶微分,应用一阶微分光谱数据,衍生出基于光谱位置变量的分析方法,以红边积分面积(SDr)为自变量,冠层全氮(TN)含量为因变量,做相关分析与处理,构建新陆早6号红边积分面积与冠层叶片TN含量的相关数学模型。研究在不同水处理条件下,对棉花冠层单叶叶绿素含量和单叶全氮含量做相关分析,结果表明:叶绿素含量与TN含量呈显著的正相关(R=0.8723,n=39),叶绿素含量能有效的估计棉花单叶TN含量;红边积分面积变量与冠层TN含量呈显著的相关性,相关系数是0.7394(n=40),利用构建的相关模型可以较为精确地估测棉花两个品种新陆早6号与8号冠层叶片的全氮含量,RMSE分别为0.3859和0.4272。研究认为红边积分面积变量具有预测棉花冠层全氮含量的应用潜力,研究得出利用3边面积变量构造的数学模型对反演作物冠层TN含量有较高应用价值。研究认为,红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。研究结果证明:①随着棉花的生长发育,叶片的生理生化参数发生变化,冠层的生理生化参数随之发生变化;②.棉花叶片叶绿素含量与叶片的全氮含量相关性显著(R=0.8723,n=38),通过建立数学模型,可以估测叶片中全氮的含量;③由一阶微分光谱衍生出基于光谱“红边”位置变量的分析方法,使我们认识到“红边”的变幅、形状和面积包含了各个波段的信息,这些波段综合产生的变量所构造的模型,为棉花氮素营养参数的估计提供了预测能力;④如果棉花叶绿素含量高,说明水分充足、氮代谢旺盛,植株处于生长旺盛时期,红边向蓝光方向发生了位移。利用红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。     

在水分胁迫下棉花冠层叶片全氮含量的高光谱遥感估算模型研究

研究利用美国产ASD地物光谱仪,获取新疆北部地区棉花冠层关键生育时期的高光谱数据,采用红边积分面积变量估测棉花冠层叶片的全氮含量,对反射光谱进行一阶微分,应用一阶微分光谱数据,衍生出基于光谱位置变量的分析方法,以红边积分面积(SDr)为自变量,冠层全氮(TN)含量为因变量,做相关分析与处理,构建新陆早6号红边积分面积与冠层叶片TN含量的相关数学模型。研究在不同水处理条件下,对棉花冠层单叶叶绿素含量和单叶全氮含量做相关分析,结果表明:叶绿素含量与TN含量呈显著的正相关(R=0.8723,n=39),叶绿素含量能有效的估计棉花单叶TN含量;红边积分面积变量与冠层TN含量呈显著的相关性,相关系数是0.7394(n=40),利用构建的相关模型可以较为精确地估测棉花两个品种新陆早6号与8号冠层叶片的全氮含量,RMSE分别为0.3859和0.4272。研究认为红边积分面积变量具有预测棉花冠层全氮含量的应用潜力,研究得出利用3边面积变量构造的数学模型对反演作物冠层TN含量有较高应用价值。研究认为,红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。研究结果证明:①随着棉花的生长发育,叶片的生理生化参数发生变化,冠层的生理生化参数随之发生变化;②.棉花叶片叶绿素含量与叶片的全氮含量相关性显著(R=0.8723,n=38),通过建立数学模型,可以估测叶片中全氮的含量;③由一阶微分光谱衍生出基于光谱“红边”位置变量的分析方法,使我们认识到“红边”的变幅、形状和面积包含了各个波段的信息,这些波段综合产生的变量所构造的模型,为棉花氮素营养参数的估计提供了预测能力;④如果棉花叶绿素含量高,说明水分充足、氮代谢旺盛,植株处于生长旺盛时期,红边向蓝光方向发生了位移。利用红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。     

磷营养胁迫对冬小麦冠层光谱的影响

因为磷素重要的营养作用,其胁迫的存在影响冬小麦的正常生长。借助地面遥感仪器获取冬小麦在磷营养胁迫下的多个生育期里的冠层光谱数据并对其影响特征进行了分析。利用因子分析方法提取主因子与含有丰富信息的光谱变量,并结合极显著水平(0.01)的均值比较与检验过程考察了冬小麦冠层光谱,确定了对磷营养胁迫敏感的光谱波段:760nm,810nm和870nm与950nm,并在此基础上结合冬小麦对磷素的吸收利用特征选定了运用冠层光谱敏感波段反射率探测和区分磷营养胁迫的关键生育期:拔节期。结果同时表明,对冬小麦磷营养胁迫而言,近红外区间(760nm~1100nm)光谱反射特征的区分能力要强于可见光区。本文同时指出了研究与发展利用遥感技术进行营养胁迫监测的方法和着重点。     

基于地面实测高光谱数据的三江源中东部草地植被光谱特征研究

草地不仅是畜牧业的生产基地,而且是生态安全屏障保护和牧民生活与草原文化传承的基础,具有生态、生产和生活功能。然而,草地日益退化导致的生态经济问题越来越突出。因此,实时、准确地监测草地的退化具有重要意义。根据所测定的各种地面植被的光谱数据,分析了三江源中东部典型草原区常见草种的光谱特性;利用一阶微分法、连续统去除法和归一化微分比的方法对草地植被光谱反射曲线进行了处理,提取了典型草地植被的光谱特征;通过光谱分析法能准确识别藏嵩草和小嵩草优势种,取得了较好的精度。为高光谱遥感草地监测提供了有力依据。     

Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features

The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, more investigation is required to assess their capabilities in the field, where inconsistent results have been obtained so far. We investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species. Canopy spectral measurements were taken in the field using a GER 3700 spectroradiometer. We tested the utility of using four variables derived from continuum-removed absorption features for predicting canopy nitrogen, phosphorus, potassium, calcium and magnesium concentration: (i) continuum-removed derivative reflectance (CRDR), (ii) band depth (BD), (iii) band depth ratio (BDR) and (iv) normalised band depth index (NBDI). Stepwise linear regression was used to select wavelengths from the absorption-feature-based variables. Univariate correlation analysis was also done between the first derivative reflectance and biochemicals. Using a training data set, the variables derived from continuum-removed absorption features could predict biochemicals with R² values ranging from 0.43 to 0.80. Results were highest using CRDR data, which yielded R² values of 0.70, 0.80, 0.64, 0.50 and 0.68 with root mean square errors (RMSE) of 0.01, 0.004, 0.03, 0.01 and 0.004 for nitrogen, phosphorous, potassium, calcium and magnesium, respectively. Predicting biochemicals on a test data set, using regression models developed from a training data set, resulted in R² values ranging from 0.15 to 0.70. The error of prediction (RSE) in the test data set was 0.08 (±10.25% of mean), 0.05 (±5.2% of mean), 0.02 (±11.11% of mean), 0.05 (±11.6% of mean) and 0.03 (±15% of mean) for nitrogen, potassium, phosphorous, calcium and magnesium, respectively, using CRDR. When data was partitioned into species groups, the R² increased significantly to >0.80. With high-quality radiometric and geometric calibration of hyperspectral imagery, the techniques applied in this study (i.e. continuum removal on absorption features) may also be applied on data acquired by airborne and spaceborne imaging spectrometers to predict and ultimately to map the concentration of macronutrients in tropical rangelands.     

Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data

View angle effects present in spectral vegetation indices can either be regarded as an added source of uncertainty for variable retrieval or as a source of additional information, enhancing the variable retrieval; however, the magnitude of these angular effects remains for most indices unknown or unquantified. We use the ESA-mission CHRIS-PROBA (Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy) providing spaceborne imaging spectrometer and multiangular data to assess the reflectance anisotropy of broadband as well as recently developed narrowband indices. Multiangular variability of Hemispherical Directional Reflectance Factor (HDRF) is a prime factor determining the indices´ angular response. Two contrasting structural vegetation types, pine forest and meadow, were selected to study the effect of reflectance anisotropy on the angular response. Calculated indices were standardized and statistically evaluated for their varying HDRF. Additionally we employ a coupled radiative transfer model (PROSPECT/FLIGHT) to quantify and substantiate the findings beyond an incidental case study. Nearly all tested indices manifested a prominent anisotropic behaviour. Apart from the conventional broadband greenness indices [e.g. Simple Ratio Index (SRI), Normalized Difference Vegetation Index (NDVI)], light use efficiency and leaf pigment indices [e.g. Structure Insensitive Pigment Index (SIPI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index (ARI)] did express significant different angular responses depending on the vegetation type. Following the quantification of the impact, we conclude that the angular-dependent fraction of non-photosynthetic material is of critical importance shaping the angular signature of these VIs. This work highlights the influence of viewing geometry and surface reflectance anisotropy, particularly when using light use efficiency and leaf pigment indices.     

辽东地区矿化蚀变遥感信息提取的研究和应用

针对工作区(辽东-吉南)植被覆盖率高,河流水体及冲积物等干扰信息多的特点,本文采用比值法、主成分分析法和光谱角制图法相结合,进行大面积遥感矿化蚀变异常信息的提取;在矿化信息分割过程中,引入面向对象的思想,基于实地考察的蚀变信息提取模型,结合矿点、地质构造和遥感图像光谱特征和色彩等多种信息,对图像波段和像元统一进行因子分析和处理,确立切割阈值,克服了主成分变换后主成分分量物理意义不明确的缺点。本次工作建立起植被覆盖地区的遥感矿化蚀变异常提取的一套有效的技术体系,在遥感应用于找矿具有很强的现实意义。     

Spectral characteristics of copper-stressed vegetation leaves and further understanding of the copper stress vegetation index

This study analysed the changing pattern of the spectral features of copper-stressed leaves for several vegetation types, and explored the mechanism of the Copper Stress Vegetation Index (CSVI). First, the change of seven key spectral features (Green Peak, Red Valley, Red Shoulder, NIR (Near Infrared) Reflectance Platform, Blue-Edge, Yellow-Edge, and Red-Edge) with copper stress level from low to high, were presented and analysed. Second, the chlorophyll contents in leaves were investigated to explain the spectral characteristics at the visible band. Third, the leaf structure and absorption related to copper were analysed to explore the reason of changing pattern at NIR band. The results showed that there are significant changing trends at Blue-Edge, Green Peak, and Red-Edge while the changing pattern at NIR band depends on the vegetation type. The analysis on chlorophyll content, leaf structure, and absorption related to copper, gave an overall mechanism explanation for the spectral characteristics of copper-stressed vegetation and the wavelengths used in CSVI. The results and conclusions in this paper, contribute new knowledge of copper-stressed vegetation reflectance and the CSVI, and provide mechanism basement for the remote sensing of copper-stressed vegetation.     

Detecting photosynthetic algal pigments in natural populations using a high-spectral-resolution spectroradiometer

Reflectance data from a high spectral resolution spectroradiometer were obtained onboard a ship in Plymouth coastal waters. These data were analysed to detect algal photosynthetic accessory pigments for comparison with absorption spectra as measured in the laboratory by a spectrophotometer. The overall spectral characteristics of Plymouth waters allowed identification as to population composition. Derivative analysis of the spectra was used to resolve characteristic peaks of specific pigments. It was determined that chlorophyll pigments, a specific carotenoid and sea water absorption bands were detectable in the reflectance data. Absorption bands of photosynthetic and accessory pigments were assessed through chromatographic pigment analysis.     

Multiple-level defoliation assessment with hyperspectral data: integration of continuum-removed absorptions and red edges

Hyperspectral data were collected from 40 canopies of saltcedar (Tamarix ramosissima): 10 healthy canopies and 30 canopies defoliated by an introduced biological control agent, the saltcedar leaf beetle (Diorhabda carinata). These data assessed multiple-level defoliations in response to the process of biological control. Two important characteristics of the hyperspectral data – red edges and continuum-removed absorptions – were used to discriminate four defoliation categories of saltcedar (healthy plants, newly defoliated plants, completely defoliated plants and refoliating plants) at the canopy level. The red edge positions were located at ranges of 711–716 nm, 706–712 nm, 694–698 nm and 715–719 nm for the four defoliation stages described above, respectively. These red edge positions alone could not clearly judge the four defoliation categories associated with feeding by the beetles. Only the completely defoliated canopies had distinct red edge positions that could be differentiated from the other three types of canopies. While using a classification tree to integrate the red edge positions and their derivatives with the central band depths of these five continuum-removed absorptions, it was found that only two band depths of the continuum-removed absorptions were selected, which were the red absorption between 570 and 716 nm and the water absorption between 936 and 990 nm in the near-infrared region (NIR). This implied that the continuum-removed absorptions outperformed the red edges for identifying the defoliation categories. The resulting overall accuracy was 87.5%. The producer accuracy was 100%, 70%, 100% and 80% for the healthy plants, newly defoliated, completely defoliated plants and refoliating canopies, respectively. The corresponding user accuracy was 90.91%, 77.78%, 100% and 80%. Therefore, we concluded that single spectral data based variable failed to separate the four stages but a combination of the two continuum-removed absorptions located in the blue absorption and the first water absorption in the NIR improved the identification of defoliated canopies associated with the dynamic defoliation process of the biological control agent. This study developed the defoliation detection techniques of commonly used binary levels (i.e. defoliation and non-defoliation) to multiple vegetation defoliation levels. We anticipate applying these assessment techniques to wide-area collections of hyperspectral data covering the two spectral regions as described above to further evaluate the effectiveness of these biological control beetles and their impact on saltcedar management in the Western United States.     

高光谱技术提取不同作物叶片类胡萝卜素信息

以棉花、玉米、大豆、甘薯四种作物为材料，各采集叶片30张(处于不同部位、不同功能期)，分别测定其反射光谱和叶绿素、类胡萝卜素含量。目的在于探讨利用高光谱技术提取类胡萝卜素信息的可行性方法。结果表明，由于叶绿素与类胡萝卜素间存在显的相关性，在叶片水平，利用高光谱反射率估测叶片类胡萝卜素绝对量是可行的。与类胡萝卜素／叶绿素比值或类胡萝卜素含量相比，类胡萝卜素密度(单位叶片面积类胡萝卜素总量，Cardens)与光谱反射率间的相关性更为稳定。类胡萝卜素光谱吸收峰(470nm)附近的反射光谱与Cardens间的相关性较差，基于类胡萝卜素吸收峰附近反射光谱的光谱指数(如PSSRc、PSNDc)与Cardens间也表现出较弱的相关性。叶绿素光谱指数(如SR705、ND705)与Cardens间存在良好的相关性，红边光谱区的微分光谱、包络线归一化吸收深度等高光谱指数与Cardens间也表现出了良好的相关性。     

Spectroscopic characterization of oils yielded from Brazilian offshore basins: Potential applications of remote sensing

Spectroscopy is the basis to detect and characterize offshore hydrocarbon (HC) seeps through optical remote sensing. Diagnostic spectral features of HCs are linked to their chemical composition and fundamental molecular vibrations (SWIR-TIR features), as well as overtones and combinations of these vibrations (VNIR-SWIR). These features allow for the characterization of oil, oil on water and emulsified oil. This work shows the results of lab and field spectral measurements of 17 petroleum samples yielded from key, oil-rich sedimentary basins in Brazil. Measurements comprised reflectance data (VNIR- SWIR), Attenuated Total Reflectance (ATR), Directional Hemispherical Reflectance (DHR), and emissivity data (TIR). These spectra were analyzed by multivariate techniques, such as Principal Components Analysis (PCA) and Partial Least-Square analysis (PLS). The experimental results indicate that for the VNIR-SWIR range: (i) spectral features can be recognized for crude oil, emulsified oil and oil on ocean water; (ii) different oil types can be qualitatively distinguished based on these features (i.e. light or heavy), even considering oil on water; (iii) the same applies for oil measurements simulated at the spectral resolution of hyperspectral (357-bands/ProSpecTIR) and multispectral (9-bands/ASTER) sensors. Within TIR wavelengths (3-14 μm), typical HC spectral features can also be resolved and oil types qualitatively discriminated using PCA/PLS, including both full-resolution spectra and spectra resampled to hyperspectral sensor (128-bands/SEBASS). However, despite the fact that oil emissivity is always lower than that of water, such separation seems unfeasible using 8-12 μm TIR features only; emissivity spectra are essentially flat for all samples in this interval. This research demonstrated that oil can be qualitatively distinguished based on both VNIR-SWIR and TIR spectroscopy data, with important implications for remote off-shore oil exploration and classification of oil leakages.     

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).     

Mapping the Buraburi granite in the Himalaya of Western Nepal: Remote sensing analysis in a collisional belt with vegetation cover and extreme variation of topography

The aim of the present study is to define the most suitable methodologies for ASTER data pre-processing and analysis in order to enhance peraluminous granitoid rocks in rugged and vegetated areas.The research started with raw image data pre-processing and continued with a comparison of satellite, field and laboratory data. The masking technique adopted to isolate rocky pixels was of fundamental importance to perform further analysis. An integration of density-sliced images and false colour composite images of Band Ratio, Relative Absorption Band Depth and Principal Component Analysis allowed us to generate a geological map that highlights a new granitoid body (Buraburi Granite) and the surrounding host rocks in the Dolpo region (western Nepal). The Buraburi Granite was mapped and sampled integrating remotely sensed ASTER data with analysis of rocks and minerals spectral signatures.The innovative approach that we have adopted considers the absorption features of particular lichen species (acidophilic). The results highlight the importance of considering acidophilic lichen means of detecting granitoid rocks. Furthermore, since peraluminous granitoids (i.e. Buraburi granite) have a considerable Al2O3 bulk rock content, the Muscovite Al-OH absorption peaks centred in the 6th ASTER band were also considered an important parameter for their detection.Field observations confirm the results of remote sensing analysis showing the intrusive relationship between the newly discovered 110 km² granitoid body and the wall rocks of the Higher Himalayan Crystalline and the Tibetan Sedimentary Sequence.In conclusion, the proposed methods have great potential for granitoid mapping in vegetated and rough terrains, particularly those with climatic and geological conditions similar to the ones of the Southern Himalayan belt.