高光谱技术在汝箕沟煤田烧变岩和Fe~(3+)丰度信息提取中的方法研究

介绍了宁夏汝箕沟煤田煤系地层和烧变岩的反射光谱特征。通过采样分析烧变岩的铁含量,借助多元回归分析,确定了烧变岩中Fe3+含量与某些波段反射率的定量关系,提出了利用高光谱遥感图像提取Fe3+的方法。文中以汝箕沟煤田大岭湾火区、阴坡火区为试点,证明了该方法是有效的。该成果再次证明了高光谱遥感技术在矿产资源调查和定量分析中的有效性。     

煤田火区烧变岩光谱特征分析及其信息提取

中国北方煤田煤层自燃现象非常严重.利用遥感手段可以快速实现火区的动态监测,及时为灭火工程提供信息.烧变岩作为火区的地表指示性特征,是火区解译最直观的信息.本文从烧变岩的反射光谱曲线特征入手,对几种提取烧变岩信息的方法进行比较,从而确定解译烧变岩的最佳方法,为圈定火区的范围和位置奠定基础.     

宁夏汝箕沟煤田火区高光谱定量遥感探测研究

通过航空高光谱遥感地面定标、波谱特征分析、火区特征地物和热异常信息提取,确定了用于温度反演的热红外波段、拟合关系式和热异常对应辐射温度,探测精度达到了1∶2 000比例尺精度;准确圈定了宁夏汝箕沟煤田火区范围,查清了火区燃烧强度,分析了热扩散规律以及遥感探测热异常与地下煤火热异常的对应关系,实现了将遥感定量调查结果直接用于灭火工程设计的目标。     

新疆拜城地区煤田煤层自燃的陆地卫星遥感探测方法研究

利用TM图像,结合区域实测、地质和区域能源分布资料,分析了煤田煤层自燃的光谱特征,对煤田地火燃烧区进行定位;在此基础上对新疆拜城地区TM图像进行线性变换、边界增强、波段运算、多波段假彩色合成等增强处理,识别并提取影像中煤田煤层自燃引起的地表热信息、地表植被异常和岩石烧变信息等,通过分析达到探测煤田火区的目的。     

煤田火区遥感四层空间探测方法

煤层自燃是一个动态变化的过程,随着自燃向不同方向扩大或缩小,其热场随着燃烧过程而发生空间变化。本文在研究煤田煤层自燃现象及其火区灾害特征的基础上,基于宁夏汝箕沟地区的四层空间遥感探测试验,总结了不同平台遥感方法的探测效果,提出了利用遥感方法实现地下煤田火区监测的有效方法。     

海水背景下不同浓度的甲烷含量高光谱定量反演

在油气资源遥感探测中,通过烃渗漏引起的海表面甲烷气浓度异常来探测海底气藏是最直接的方法之一。为了更好地识别海表甲烷异常,提高遥感反演精度,对海表甲烷气含量进行定量光谱分析研究。设计室内甲烷波谱测试平台,获取海水背景下不同含量甲烷高光谱数据为数据,对光谱数据预处理及进行比值导数光谱法,并提取光谱吸收特征参数,对甲烷含量与光谱参数之间进行相关性分析,构建甲烷含量的反演模型。比值导数光谱法确实抑制了海水背景信息,突出了甲烷特征。1650—1664 nm和2180—2210 nm波段范围的光谱参数与甲烷含量相关性显著;其中,波谷、波深、面积、斜率与甲烷含量显著相关。基于2180—2210 nm波段范围建立的波谷、波深、面积、斜率四元回归方程y=-14.356 - 5931.796x₁ - 4325.081x₂+241.481x₃+7531.973x₄拟合效果最好,R²为0.9817;且在此波段范围内基于波深建立的单变量甲烷反演模型y = 2047.571x - 9.758,R²为0.9741,比基于其他变量所建立的反演模型效果要好。成功获取了和海水背景下甲烷含量线性相关显著的对应波段和吸收特征,可为利用多/高光谱遥感预测勘探海表面甲烷气浓度提供一定的理论和技术依据。     

陕西神府煤田新民烧变区煤层自燃边界范围的遥感调查

本课题以可见光黑白航空像片为主要信息源,对陕西神府煤田新民烧变区进行了1:5万航空遥感地质调查,圈定了该区烧变岩分布范围及煤层自燃边界线。文中着重介绍了遥感调查煤层烧变区的技术方法与工作成果。调查区为煤层自燃死火区。调查首先从烧变岩的基本地质特征入手,划分了烧变岩的宏观类型,确定了烧变岩的主要形成时代,探讨了烧变岩的形成机理;第二,根据掌握资料选取已知区,研究烧变岩及煤层自燃边界线的影像特征,建立初步解译标志;第三,在全区范围内进行煤层自燃边界线的遥感调查,并对解译标志进行补充、修改与完善。在调查中,解译与调绘相结合,遥感与地面调查方法相结合,最终圈定了区内各煤层的自燃边界线,并经钻孔验证,精度达到要求。     

辽西肖家营子地区金矿遥感信息提取及成矿预测

对TM图像作比值及主成分分析,选择能突出金矿蚀变的高特征向量值所在的主分量进行彩色合成,通过空间滤波及非监督分类处理,图像上显示出似晕圈状的彩色异常区。经野外验证,部分彩色异常区与化探异常分布范围相叠合,新发现的3处金矿点金品位分别达到2.74×10^-6、9.61×10^-6和9.6×10^-6。结合图像上的彩色异常信息和取样分析结果,在进行成矿条件分析后,提出了三处金(银铜)遥感综合成矿预测区。     

孤东油田土壤石油类含量的高光谱反演模型

土壤中石油类含量的检测对石油污染的预防与治理具有重要的实际意义。本文首先进行孤东油田土壤样品高光谱反射率的室内测定及石油含量的检测,然后利用单变量预测模型和逐步回归方法分析了土壤光谱特征参数与石油类含量之间的线性和非线性关系,结果表明:包络线分析的第三折线段斜率与石油类含量相关性最好,该段斜率的三次曲线函数为石油类含量的最佳单变量估算模型。标准正态变量变换对光谱的预处理效果最好,利用变换后光谱建立多元模型,其调整的判定系数R²是0.826,总均方根RMSE是0.531,且自变量个数较少,为最优预测模型。本文提出的利用高光谱数据检测土壤中石油类含量的方法,为土壤石油类污染检测提供了一种有效的新思路。     

应用遥感技术调查研究煤层自燃灾害

本文主要论述利用遥感技术对我国北方煤层已经熄灭的自燃火区及正在燃烧的活火区进行圈定的方法。死人区范围是以烧变岩反射光谱特征为依据,利用黑白及彩红外航空摄影图像进行解译圈定;大面积活火区则是以其热辐射特征为依据,利用热红外扫描图像解译圈定分布范围,小面积活火区则利用地面红外测温技术圈定分布范围,并取得了明显效果。     

Visible-Near Infrared Reflectance Spectroscopy for Rapid Characterization of Salt-Affected Soil in the Indo-Gangetic Plains of Haryana,India

Management of salt-affected soils is a challenging task in the input intensive rice-wheat cropping zone of the Indo-Gangetic plains (IGP). Timely detection of salt-affected areas and assessment of the degree of severity are vital in order to narrow down the potential gap in yield. Conventional laboratory techniques of saturation extract electrical conductivity (EC_e) and sodium adsorption ration (SAR) for soil salinity assessment are time-consuming and labour intensive; the VNIR (visible-near infrared) reflectance spectroscopy technique provides ample information on salinity and its attributes in an efficient and cost-effective way. This study aims to develop robust soil reflectance spectral models for rapid assessment of soil salinity in the salt affected areas of the IGP region of Haryana using VNIR reflectance spectroscopy. The results indicated that the spectral region between 1390 and 2400 nm was highly sensitive to measure changes in salinity. The developed hyperspectral models explained more than 80 % variability in EC_e, and other salinity related attributes (saturated extract Na⁺, Ca²⁺ + Mg²⁺, Cl^? and SAR) in the validation datasets. With the increasing availability of data from hyperspectral sensors in near future, the study will be very useful in real time monitoring of soils in the spatio-temporal context; enabling the farmers of IGP area to deal with salt degradation more effectively and efficiently.     

Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®

This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS^® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS^® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP.     

RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits

Information about pigment and water contents provides comprehensive insights for evaluating photosynthetic potential and activity of agricultural crops. In this study, we present the concept of using spectral integral ratios (SIR) to retrieve three biochemical traits, namely chlorophyll a and b (C_ab), carotenoids (C_cx), and water (C_w) content, simultaneously from hyperspectral measurements in the wavelength range 460−1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The algorithm was tested on two synthetically established databases using a physiologically constrained look-up-table (LUT) generated by (i) the leaf optical properties model PROSPECT and (ii) the canopy radiative transfer model (RTM) PROSAIL. LUT constraints were realized based on natural C_cx-C_ab relations and green peak locations identified in the leaf optical database ANGERS. Linear regression between obtained SIRs and model parameters resulted in coefficients of determination (R²) of 0.66 (i and ii) for C_cx, R² = 0.85 (i) and 0.53 (ii) for C_ab, and R² = 0.97 (i) and 0.67 (ii) for C_w, respectively. Using the model established from the PROSPECT LUT, leaf level validation was carried out based on ANGERS data with reasonable results both in terms of goodness of fit and root mean square error (RMSE) (C_cx: R² = 0.86, RMSE = 2.1 μg cm⁻²; C_ab: R² = 0.67, RMSE = 12.5 μg cm^-2; C_w: R² = 0.89, RMSE = 0.007 cm). The algorithm was applied to airborne spectrometric HyMap data acquired on 12th July 2003 in Barrax, Spain and to AVIRIS-NG data recorded on 2nd July 2018 southwest of Munich, Germany. Mapping of the SIR results as multiband images (3-segment SIR) allows for intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Barrax in situ validation using linear regression models derived from PROSAIL LUT showed satisfactory results regarding C_ab (R² = 0.84; RMSE = 9.06 μg cm^-2) and canopy water content (CWC, R² = 0.70; RMSE = 0.05 cm). Retrieved C_cx values were reasonable according to C_ab-C_cx-dependence plausibility analysis. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is ready to be applied on satellite imaging spectroscopy data available in the near future. The algorithm is publicly available as an interface supported tool within the 'Agricultural Applications' of the EnMAP-Box 3 hyperspectral remote sensing software suite.     

Estimation of forest leaf water content through inversion of a radiative transfer model from LiDAR and hyperspectral data

The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R² = 0.87, RMSE = 0.0022 g/cm², nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data.     

Monitoring nitrogen concentration of oilseed rape from hyperspectral data using radial basis function

Remote sensing technology is the important tool of digital earth, it can facilitate nutrient management in sustainable cropping systems. In the study, two types of radial basis function (RBF) neural network approaches, the standard radial basis function (SRBF) neural networks and the modified type of RBF, generalized regression neural networks (GRNN), were investigated in estimating the nitrogen concentrations of oilseed rape canopy using vegetation indices (VIs) and hyperspectral reflectance. Comparison analyses were performed to the spectral variables and the approaches. The Root Mean Square Error (RMSE) and determination coefficients (R²) were used to assess their predictability of nitrogen concentrations. For all spectral variables (VIs and hyperspectral reflectance), the GRNN method produced more accurate estimates of nitrogen concentrations than did the SRBF method at all ranges of nitrogen concentrations, and the better agreements between the measured and the predicted nitrogen concentration were obtained with the GRNN method. This indicated that the GRNN method is prior to the SRBF method in estimation of nitrogen concentrations. Among the VIs, the Modified Chlorophyll Absorption in Reflectance Index (MCARI), MCARI₁₅₁₀, and Transformed Chlorophyll Absorption in Reflectance Index are better than the others in estimating oilseed rape canopy nitrogen concentrations. Compared to the results from VIs, the hyperspectral reflectance data also gave an acceptable estimation. The study showed that nitrogen concentrations of oilseed rape canopy could be monitored using remotely sensed data and the RBF method, especially the GRNN method, is a useful explorative tool for oilseed rape nitrogen concentration monitoring when applied on hyperspectral data.     

Studies on Textural and Compositional Characteristics of Sand and Clay Mixtures Using Hyperspectral Radiometry

This paper examines the hyperspectral signatures (in the Visible Near Infrared (VNIR)-Shortwave Infrared (SWIR) regions) of soil samples with varying colour and minerals. 36 samples of sands (from river and beach) with differing clay contents were examined using a hyperspectral radiometer operating in the 350–2,500 nm range, and the spectral curves were obtained. Analysis of the spectra indicates that there is an overall increase in the reflectance in the VNIR-SWIR region with an increase in the content of kaolinite clay in the sand samples. As regards the red and black clays and sand mixtures, the overall reflectance increases with decreasing clay content. Several spectral parameters such as depth of absorption at 1,400 nm and 1,900 nm regions, radius of curvature of the absorption troughs, slope at a particular wavelength region and the peak reflectance values were derived. There exists a correlation between certain of these spectral parameters (depth, slope, position, peak reflectance, area under the curve and radius of the curve) and the compositional and textural parameters of the soils. Based on these well-defined relations, it is inferred that hyperspectral radiometry in the VNIR and SWIR regions can be used to identify the type of clay and estimate the clay content in a given soil and thus define its geotechnical category.     

Investigating relationships between Landsat-7 ETM data and spatial segregation of LULC types under shifting agriculture in southern Cameroon

This paper investigates statistical relationships between land use/land cover (LULC), Landsat-7 ETM⁺ imagery and landscape mosaic structure in southern Cameroon where the conversion of tropical rain forest to shifting cultivation leads to dynamic processes, acting on the spatial aggregation of various LULC types. A Global Positioning System (GPS) was used in the field to identify a total of 171 shifting cultivation patches representing eight LULC types in two sub-areas. Because of the lack of a cloud-free image for the date of field sampling, the ETM⁺ imagery was acquired 2 months after field survey, during which it was assumed that no significant changes in LULC occurred (all dry season). Per pixel correlations were developed between spectral reflectance data, vegetation indices and LULC. As an exploratory study, several statistical methods (analysis of variance, means separations (Tukey HSD), principal component analysis (PCA), geo-statistical analysis, image classification and landscape metrics) were applied on point data and sensor images for evaluating the spatial variability within the landscape. Most variables explained 30–72% of LULC variation in the whole dataset. Those variables with high information content of LULC (infrared bands 4, 5, 7 and derived indices and PC1) also showed long ranges (6 km) spatial dependence as compared to those varying only within 1 km range. The results of these statistical analyses suggested the need to group some LULC types and the application of the Maximum Likelihood Classifier (MLC) for supervised classification provided a LULC map with the highest accuracy (81%) after consolidation of perennial LULC types, such as bush fallow, forest fallow and cocoa plantations. Landscape metrics computed from this map showed a high level of patch diversity and connectivity within the landscape and provided input data that can further be used to simulate predictive maps as substitute to cloud-covered sensor imageries. Landsat-7 ETM⁺ imagery proved to be useful in discriminating (with about 80% accuracy) the most dynamic LULC types such cropped plots and young fallow patches (shifting every season) and the extension front of the agricultural landscape.     

Leaf to canopy upscaling approach affects the estimation of canopy traits

In remote sensing applications, leaf traits are often upscaled to canopy level using sunlit leaf samples collected from the upper canopy. The implicit assumption is that the top of canopy foliage material dominates canopy reflectance and the variability in leaf traits across the canopy is very small. However, the effect of different approaches of upscaling leaf traits to canopy level on model performance and estimation accuracy remains poorly understood. This is especially important in short or sparse canopies where foliage material from the lower canopy potentially contributes to the canopy reflectance. The principal aim of this study is to examine the effect of different approaches when upscaling leaf traits to canopy level on model performance and estimation accuracy using spectral measurements (in-situ canopy hyperspectral and simulated Sentinel-2 data) in short woody vegetation. To achieve this, we measured foliar nitrogen (N), leaf mass per area (LMA), foliar chlorophyll and carbon together with leaf area index (LAI) at three vertical canopy layers (lower, middle and upper) along the plant stem in a controlled laboratory environment. We then upscaled the leaf traits to canopy level by multiplying leaf traits by LAI based on different combinations of the three canopy layers. Concurrently, in-situ canopy reflectance was measured using an ASD FieldSpec-3 Pro FR spectrometer, and the canopy traits were related to in-situ spectral measurements using partial least square regression (PLSR). The PLSR models were cross-validated based on repeated k-fold, and the normalized root mean square errors (nRMSE_cv) obtained from each upscaling approach were compared using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Results of the study showed that leaf-to-canopy upscaling approaches that consider the contribution of leaf traits from the exposed upper canopy layer together with the shaded middle canopy layer yield significantly (p < 0.05) lower error (nRMSE_cv < 0.2 for canopy N, LMA and carbon) as well as high explained variance (R² > 0.71) for both in-situ hyperspectral and simulated Sentinel-2 data. The widely-used upscaling approach that considers only leaf traits from the upper illuminated canopy layer yielded a relatively high error (nRMSE_cv>0.2) and lower explained variance (R² < 0.71) for canopy N, LMA and carbon. In contrast, canopy chlorophyll upscaled based on leaf samples collected from the upper canopy and total canopy LAI exhibited a more accurate relationship with spectral measurements compared with other upscaling approaches. Results of this study demonstrate that leaf to canopy upscaling approaches have a profound effect on canopy traits estimation for both in-situ hyperspectral measurements and simulated Sentinel-2 data in short woody vegetation. These findings have implications for field sampling protocols of leaf traits measurement as well as upscaling leaf traits to canopy level especially in short and less foliated vegetation where leaves from the lower canopy contribute to the canopy reflectance.     

Evaluation of EO-1 hyperion data for agricultural applications

The present study was carried out to evaluate the satellite-based hyperspectral data available from Hyperion onboard EO-1 of NASA for agricultural applications. The study was carried out for Daurala block of Meerut district, using data of March 2005. The preliminary data analysis showed that there are 196 usable bands out of a total of 242 bands. Principal component (PC) analysis showed that about 99% of the information explained in 10 PCs. The atmospherically corrected reflectance, derived from satellite data had good agreement with the ground reflectance, observed using handheld spectroradiometer, with r² ranging from 0.85 to 0.98. A set of twenty most usable bands was selected by the criteria of maximum contribution to first five PCs and the band combinations with least inter-band correlations.     

Relationship of Hyperspectral Reflectance Indices with Leaf N and P Concentration,Dry Matter Accumulation and Grain Yield of Wheat

A field experiment was conducted on wheat during rabi season of year 2010–2011 and 2011–2012 at IARI, New Delhi to study the reflectance response of wheat to the nutrient omissions and identify the appropriate indices for assessing the nutrient deficiencies. Treatments comprised omission of N, P, K, S and Zn, 50% omission of N, P, and K, absolute control and optimum dose of nutrition (150–26.4–50–15–3 kg/ha N–P–K–S–Zn). The R² were significant and higher for the hyperspectral indices than the broad band vegetation indices. GMI-I, RI-2 dB and RI-3d, GNDVI, VOGa, VOGb, VOGc, ND₇₀₅, PRI, PSNDc and REIP had higher R² (>0.61) for the leaf N concentration. The hyperspectral indices having highly significant correlation with leaf P concentration were PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR and REIP. Among the indices analysed PSSRc, GMI-I, VOGa, RI-2 dB, RI-3 dB, GNDVI, VOGb, VOGc and ND₇₀₅ had almost a similar degree of relationship with DM accumulation with R² values ranging from 0.70 to 0.73. However, REIP displayed a higher degree of relationship with leaf N concentration, drymatter accumulation and grain yield as indicated by R² of 0.85, 0.81 and 0.95 (P = ≤0.01), respectively. It can be concluded from the study that among the hyperspectral indices REIP had a highly significant relationship with leaf N concentration, DM accumulation and grain yield. However, for leaf P concentration several hyperspectral indices viz PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR had though significant but almost similar R² values.