共查询到20条相似文献，搜索用时 31 毫秒
1.
受仪器和观测条件限制,高光谱数据易受噪声污染,给数据解译带来挑战。针对传统稀疏解混模型抗噪性能差的问题,本文提出一种截断加权核范数稀疏解混方法,利用高光谱图像像元之间的相关性减轻噪声对丰度估计的干扰。该方法借助低秩表示在挖掘数据内在低维结构方面的优势,在稀疏解混中加入基于截断加权核范数的低秩约束,并结合加权稀疏技术,在稀疏正则项中引入空间邻域权重。截断加权核范数对丰度矩阵的奇异值向量分段处理,可以更好地实现丰度矩阵的低秩逼近,使丰度图像保持空间一致性并保留更多细节信息,空间加权策略则增强了丰度图像的空间连续性。模拟高光谱数据、Cuprite矿区真实数据和红树林高光谱数据实验表明,与其他先进的稀疏解混方法相比,所提方法具有更好的抗噪性,能够提高解混精度。 相似文献
2.
K. Meusburger D. BänningerC. Alewell 《International Journal of Applied Earth Observation and Geoinformation》2010
Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km2). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r2 = 0.64, r2 = 0.71, respectively). Best results were achieved for LSU (r2 = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r2 = 0.39 for LSU, r2 = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands. 相似文献
3.
Monitoring Earth dynamics using current and future satellites is one of the most important objectives of the remote sensing community. The exploitation of image time series from sensors with different characteristics provides new opportunities to increase the knowledge about environmental changes and to support many operational applications. This paper presents an image fusion approach based on multiresolution and multisensor regularized spatial unmixing. The approach yields a composite image with the spatial resolution of the high spatial resolution image while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested using images from Landsat/TM and ENVISAT/MERIS instruments, but is general enough to be applied to other sensor pairs. The potential of the proposed spatial unmixing approach is illustrated in an agricultural monitoring application where Landsat temporal profiles from images acquired over Albacete, Spain, in 2004 and 2009 are complemented with MERIS fused images. The resulting spatial resolution from Landsat allows monitoring small and medium size crops at the required scale while the fine spectral and temporal resolution from MERIS allow a more accurate determination of the crop type and phenology as well as capturing rapidly varying land-cover changes. 相似文献
4.
Harm Bartholomeus Lammert Kooistra Antoine Stevens Martin van Leeuwen Bas van Wesemael Eyal Ben-Dor Bernard Tychon 《International Journal of Applied Earth Observation and Geoinformation》2011
Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation. 相似文献
5.
传统的混合像元分解算法认为每个像元都包含图像中所能提取的全部端元组分,但这并不符合实际情况。实际上图像中大多数混合像元仅由少部分端元混合而成。由于端元提取精度及噪声的影响,采用全部端元对混合像元进行分解,会使得混合像元中实际并不存在的端元的丰度估计值不为零,分解结果存在较大误差。由于混合像元大多存在于不同地物的交界处,基于此,本文提出了一种结合图像的空间信息选取混合像元最优端元子集的方法。利用一个空间结构元素,从混合像元的附近邻域开始搜索,将搜索到的纯净像元光谱与所提取的图像端元光谱进行对比,并确定混合像元的端元子集进行分解。根据RMSE大小和变化情况,逐步扩大结构元素的大小,不断调整搜索范围,直至得到最优端元组合。模拟数据和真实数据的试验结果表明,该方法相比传统的全端元光谱分解方法,在总体上获得了更好的分解效果。 相似文献
6.
Anabella Medina Machín Javier Marcello Antonio I. Hernández-Cordero Javier Martín Abasolo Francisco Eugenio 《地理信息系统科学与遥感》2019,56(2):210-232
Vegetation mapping is a priority when managing natural protected areas. In this context, very high resolution satellite remote sensing data can be fundamental in providing accurate vegetation cartography at species level. In this work, a complete processing methodology has been developed and validated in a complex vulnerable coastal-dune ecosystem. Specifically, the analysis has been carried out using WorldView-2 imagery, which offers spatial and spectral resolutions. A thorough assessment of 5 atmospheric correction models has been performed using real reflectance measures from a field radiometry campaign. To select the classification methodology, different strategies have been evaluated, including additional spectral (23 vegetation indices) and spatial (4 texture parameters) information to the multispectral bands. Likewise, the application of linear unmixing techniques has been tested and abundance maps of each plant species have been generated using the library of spectral signatures recorded during the campaign. After the analysis conducted, a new methodology has been proposed based on the use of the 6S atmospheric model and the Support Vector Machine classification algorithm applied to a combination of different spectral and spatial input data. Specifically, an overall accuracy of 88,03% was achieved combining the corrected multispectral bands plus a vegetation index (MSAVI2) and texture information (variance of the first principal component). Furthermore, the methodology has been validated by photointerpretation and 3 plant species achieve significant accuracy: Tamarix canariensis (94,9%), Juncus acutus (85,7%) and Launaea arborescens (62,4%). Finally, the classified procedure comparing maps for different seasons has also shown robustness to changes in the phenological state of the vegetation. 相似文献
7.
基于高光谱Hyperion数据的线性光谱混合模型与神经网络模型的比较 总被引：1，自引：1，他引：0
混合像元问题是定量遥感中的热点问题之一,为了改进从遥感数据中提取定量信息,人们建立了各种混合光谱分解技术,其中线性光谱混合模型和神经网络模型就是两种比较成熟的方法。以陕西省横山地区的高光谱Hyperion数据为研究基础,通过最小噪声变换(MNF)、像元纯度指数(PPI)转换和RMS误差分析的迭代方法相结合提取影像中的纯净像元作为终端端元。分别运用神经网络模型和线性光谱混合模型对影像进行光谱分解,得到各个组分的分解图像。以标准植被指数(NDVI)影像为衡量标准,选取训练样本点,分别对两种模型进行回归分析,结果显示NDVI影像与线性光谱混合模型植被分解图像的判定系数(R2=0.91)要大于其与神经网络模型的判定系数(R2=0.81)。进一步分析表明在一般情况下,线性光谱混合模型具有比神经网络模型略高的分离精度,但是神经网络模型对细部信息的提取的效果要好于线性光谱混合模型,最后提出了端元均方根误差(EAR)指数,一种新的混合像元分解的思路。 相似文献
8.
On the Performance Evaluation of Pan-Sharpening Techniques 总被引：1，自引：0，他引：1
Qian Du Younan N.H. King R. Shah V.P. 《Geoscience and Remote Sensing Letters, IEEE》2007,4(4):518-522
The limitations of the currently existing pan-sharpening quality indices are analyzed: the absolute difference between pixel values, mean shifting, and dynamic range change is frequently used as spatial fidelity measurement, but they may not correlate well with the actual change of image content; and spectral angle is a widely used metric for spectral fidelity, but the spectral angle remains the same if two vectors are multiplied by two individual constants, which means the average spectral angle between two multispectal images is zero even if pixel vectors are multiplied by different constants. Therefore, it is important to evaluate the quality of a pan-sharpened image under a task of its practical use and to assess spectral fidelity in the context of an image. In this letter, three data analysis techniques in linear unmixing, detection, and classification are applied to evaluate spectral information within a spatial scene context. It is demonstrated that those old but simplest approaches, i.e., Brovey and multiplicative (or after straightforward adjustment) methods, can generally yield acceptable data analysis results. Thus, it is necessary to consider the tradeoff between computational complexity, actual improvement on application, and hardware implementation when developing a pan-sharpening method. 相似文献
9.
空间与谱间相关性分析的NMF高光谱解混 总被引：2，自引：1，他引：1
非负矩阵分解(NMF)技术是高光谱像元解混领域的研究热点。为了充分利用高光谱图像中丰富的空间与光谱相关性特征,改善基于NMF的高光谱解混算法性能,提出一种结合了空间与谱间相关性分析的NMF解混算法。算法针对NMF的通用性和局部极小问题,引入并结合高光谱图像两种典型的相关性特征,具体包括:基于马尔可夫随机场(MRF)模型,建立描述相邻像元空间相关特征的约束;通过复杂度映射技术,建立描述相邻波段谱间相关(光谱分段平滑)特征的约束;并将上述两种约束同时引入NMF解混目标函数中。实验结果表明,对于一般自然地物场景或人造地物场景,相对于分段平滑和稀疏约束的非负矩阵分解(PSNMFSC)、交互投影子梯度的非负矩阵分解(APSNMF)和最小体积约束的非负矩阵分解(MVCNMF)这3种代表性NMF解混参考算法,该算法可进一步提高高光谱解混精度;对于空间相关或谱间相关特征中某一种不显著的特殊场景,也具有更好的适应能力。通过将空间相关和谱间相关特征相结合,较全面地反映了高光谱数据与解混相关的重要特征,能够对绝大多数真实高光谱数据进行高精度解混,对高光谱解混及后续应用领域相关研究均具有参考价值。 相似文献
10.
有效监测人工水产养殖水面的分布变化对于海洋资源管理、生态环境保护、防灾减灾具有重要意义。本文以Landsat 5、SPOT 5和GF-1卫星影像为数据源,选择广东省北莉岛为研究区,使用线性光谱解混方法获取中等空间分辨率卫星影像的人工水产养殖水面面积,通过面向对象多尺度分割的方法结合支持向量机分类算法提取高空间分辨率卫星影像的人工水产养殖水面分布。研究结果表明,与单一卫星影像相比,综合多源中高空间分辨率卫星数据延长了人工水产养殖水面变化分析可追溯的时间跨度,提高了监测精度;联合光谱解混和面向对象分类方法开展人工水产养殖长时序遥感监测是可行的。近20多年来,北莉岛人工水产养殖水面的面积经历了先增加后缓慢减少的变化过程,1995—2000年平均增速为23.39 hm2/a,2000—2006年平均增速为23.95 hm2/a,2006—2019年平均减少速度为1.96 hm2/a。 相似文献
11.
植被遥感辐射传输建模中的异质性研究进展 总被引：1，自引：0，他引：1
遥感辐射传输建模是在研究电磁波与地物相互作用机理的基础上,建立遥感观测信号与地物属性、地物结构和观测几何等参量之间定量关系的模型,是理解遥感观测信号和反演地表参量的理论基础。近年来空间异质性问题引起了定量遥感领域的高度关注,高分辨率卫星及激光雷达等数据的日益丰富给研究空间异质性提供了有力支撑。在异质性植被场景遥感辐射传输建模过程中,像元内部的组分比例、3维结构、空间格局以及端元边界处的阴影效应与散射过程等方面是需要重点考虑的因素。本文在总结非均质地表空间异质性描述的基础上,分别总结了植被二向性反射与热红外辐射方向性建模研究的发展历程,以及非均质地表植被遥感建模研究的最新进展,并指出了地表遥感建模中研究异质性问题的发展方向。 相似文献
12.
ABSTRACTImpervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling. 相似文献
13.
《International Journal of Digital Earth》2013,6(3):194-216
Information on Earth's land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors. In this study, we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery. For this purpose, the spectral angle mapper (SAM), the object-based and the non-linear spectral unmixing based on artificial neural networks (ANNs) techniques were applied. A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification, namely of the pixel purity index (PPI) and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites. Object-based classification outperformed the other techniques with an overall accuracy of 83%. Sub-pixel classification based on the ANN showed an overall accuracy of 52%, very close to that of SAM (48%). SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%. Yet, all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery, which affected the spectral separation among the land use/cover classes. 相似文献
14.
基于相关性分析的高光谱解混算法,通常缺少对高光谱图像空间和光谱相关性特征的综合分析与利用,或对于先验知识的依赖程度较高。本文提出一种基于混合像元空间与谱间相关性模型的NMF线性盲解混算法。具体包括：通过改进马尔科夫随机场（MRF）模型,建立相邻像元间的空间相关模型;利用复杂度映射技术,建立相邻波段间的光谱相关模型;在NMF目标函数外部和内部分别引入上述两种模型,作为盲解混算法的约束条件。试验结果表明,该算法相对于区域相关的NMF解混算法（ACBNMF）、最小化光谱相关度约束的NMF方法（MSCCNMF）和最小体积约束的非负矩阵分解（MVCNMF）等代表性NMF解混参考算法,解混精度有所提高;同时,降低了对于先验知识的依赖程度,拓宽了适用范围。 相似文献
15.
Canada’s urban areas have experienced extensive growth over the past quarter century; however, there has been no consistent, spatially explicit approach for quantifying the loss and gain of greenness in cities nationally. Herein, we developed a novel urban greenness score metric using greenness fractions from a multi-decadal time series (1984–2016) of spectrally unmixed annual Landsat satellite image composites to characterize final year (2016) greenness and its overall change for 18 major Canadian urban areas, summarized by census dissemination area (DA). The applied validation procedure confirmed correlation coefficients (ρ) ranging from 0.67 – 0.85 between reference and estimated greenness fractions, indicating that spectral unmixing is an appropriate method for extracting urban greenness from a time series of medium spatial resolution satellite imagery. Most DAs across Canada sustained a moderate (∼20 % – 40 %) or low (≲ 20 %) level of greenness between 1984 and 2016, but overall there was a decreasing trend in greenness. Eastern urban areas maintained the most greenness over time, while urban areas in the Prairies had the greatest increase in greenness. Densely populated urban areas experienced the greatest loss in greenness (16 % of DAs); whereas, urban areas with a moderately-low density experienced the greatest increase (14 % of DAs). In agreement with previous studies, we found that greenness was negatively associated with urban infilling, with lower greenness levels typically found in urban cores, and greenness loss most often found in the urban periphery in conjunction with urban expansion. Methods presented in this analysis take advantage of the open and longstanding Landsat archive, as well as multiple spatial scales, including sub-pixel unmixing techniques, pixel level greenness faction data summarized for management units, and analysis conducted nationally. The developed urban greenness score provides a comprehensive framework to understand current urban greenness and relate it to its recent past, which supports long-term strategic planning, and can be transferred to other regions across spatial and temporal scales. 相似文献
16.
Habes A. Ghrefat Philip C. Goodell 《International Journal of Applied Earth Observation and Geoinformation》2011
The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method. 相似文献
17.
基于光谱库的高光谱稀疏解混技术近年来得到了人们的关注,该技术利用光谱库中光谱样本作为端元,将解混问题转化为稀疏表示问题。然而,由于测量环境的差异,待解混图像的实际端元往往与光谱库中相应光谱信号存在差异。本文提出了一种光谱差异稀疏约束的联合稀疏回归解混算法。首先,假设光谱差异具有稀疏特性,建立了光谱库校正模型,使得在解混过程中可对光谱库进行自适应地调整;然后,将光谱库校正模型与联合稀疏回归解混模型结合,建立了考虑光谱差异的稀疏解混模型;最后,基于交替方向乘子法得到了迭代优化解决方案。分别利用仿真和真实高光谱数据进行了试验验证,结果表明,在光谱库不匹配的情形下,本文方法能够有效提高稀疏解混算法的解混性能。 相似文献
18.
Desertification is a severe stage of land degradation, manifested by “desert-like” conditions in dryland areas. Climatic conditions together with geomorphologic processes help to mould desert-like soil surface features in arid zones. The identification of these soil features serves as a useful input for understanding the desertification process and land degradation as a whole. In the present study, imaging spectrometer data were used to detect and map desert-like surface features. Absorption feature parameters in the spectral region between 0.4 and 2.5 μm wavelengths were analysed and correlated with soil properties, such as soil colour, soil salinity, gypsum content, etc. Soil groupings were made based on their similarities and their spectral reflectance curves were studied. Distinct differences in the reflectance curves throughout the spectrum were exhibited between groups. Although the samples belonging to the same group shared common properties, the curves still showed differences within the same group.Characteristic reflectance curves of soil surface features were derived from spectral measurements both in the field and in the laboratory, and mean reflectance values derived from image pixels representing known features. Linear unmixing and spectral angle matching techniques were applied to assess their suitability in mapping surface features for land degradation studies. The study showed that linear unmixing provided more realistic results for mapping “desert-like” surface features than the spectral angle matching technique. 相似文献
19.
针对遥感影像在南方丘陵地区典型植被丰度信息提取中存在的大量混合像元问题,为进一步提高线性解混精度,通过计算像元EVI值,构建了Landsat 8时间序列影像南方典型植被（端元）和混合像元的EVI时间序列曲线,分析了不同生育期内各种地物类型的植被指数变化曲线,发现不同地物在植被指数时间序列中具有各自独立的波动规律。选取多个端元及其EVI时间序列曲线,利用光谱匹配方法对匹配EVI时间序列曲线和多个端元进行了匹配,达到利用不同端元组合进行光谱解混的目的。试验结果表明,与传统方法相比,阔叶林解混精度有明显提高,针叶林解混精度也有所提高。该研究成果可以为南方丘陵地区植被环境的研究提供有力支撑。 相似文献
20.
The spread of invasive Australia native Acacia tree species threatens biodiversity and adversely affecting on vegetative structure and function, including plant community composition, quantity and quality worldwide. It is essential to provide researchers and land managers for biological invasion science and management with accurate information of the distribution of invasive alien species and their dynamics. Remotely sensed data that reveal spatial distribution of the earth’s surface features/objects provide great potential for this purpose. Consistent satellite monitoring of alien invasive plants is often difficult because of lack of sufficient spectral contrast between them and co-occurring plants species. Time series analysis of spectral properties of the species can reveal timing of their variations among adjacent species. This information can improve accuracy of invasive species discrimination and mapping using remote sensing data at large scale. We sought to identify and better understand the optimal time window and key spectral features sufficient to detect invasive Acacia trees in heterogeneous forested landscape in South Africa. We explored one-year (January to December 2018) time series spectral bands and vegetation indices derived from optical Copernicus Sentinel-2 data. The attributes correspond to geographical information of invasive Acacia and native species recorded during a field survey undertaken from 21 February to 25 February 2018 over Kwa-Zulu Natal grasslands landscape, in South Africa. The results showed comparable separability prospects between times series of spectral bands and that of vegetation indices.Substantial differences between Acacia species and native species were observed from spectral indices and spectral bands which are sensitive to Leaf Area Index, canopy chlorophyll and nitrogen concentrations. The results further revealed spectral differences between Acacia species and co-occurring native vegetation in April (senescence for deciduous plants), June-July (dry season), September (peak flowering period of Acacia spp) and December (leaf green-up) with vegetation indices (overall accuracy > 80 %). While spectral bands showed the beginning of the growing season (November–January) and peak vegetation productivity (February-March) as the optimal seasons or dates for image acquisition for discriminating Acacias from its co-occurring native species (overall accuracy > 80 %). In general, the use of Sentinel-2 time series spectral bands and vegetation indices has increased our understanding of Australian Acacias spectral dynamics, and proved that the sentinel-2 data is useful for characterization and monitoring Acacias over a large scale. Our results and approach could assist in deriving detailed geographic information of the species and assessment of a spread invasive plant species and severity of invasion. 相似文献