Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

Non-photosynthetic vegetation biomass estimation in semiarid Canadian mixed grasslands using ground hyperspectral data,Landsat 8 OLI,and Sentinel-2 images

ABSTRACT

Research on quantifying non-photosynthetic vegetation (NPV) with optical remote-sensing approaches has been focusing on optically distinguishing NPV from green vegetation and bare soil. With a very similar spectral response curve to NPV, dry moss is a significant component in semiarid mixed grasslands and plays a large role in NPV estimation. However, limited attention has been paid to this role. We investigated the potential of optical remote sensing to distinguish NPV biomass in semiarid grasslands characterized by NPV, biological soil crust dominated by moss and lichen, and bare soil. First, hyperspectral spectral indices were examined to determine the most useful spectral wavelength regions for NPV biomass estimation. Second, multispectral red-edge indices and shortwave infrared (SWIR) indices were simulated based on Landsat 8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument band reflectance, respectively, to determine the most suitable multispectral indices for NPV estimation. The potential multispectral indices were then applied to Landsat 8 OLI images and Sentinel-2A images acquired in early, middle, peak, and early senescence growing seasons to investigate the potential of satellite images for quantifying NPV biomass. Our results indicated that hyperspectral red-edge indices, modified simple ratio, modified red-edge normalized difference vegetation index (mNDVI₇₀₅), and normalized difference vegetation index (NDVI₇₀₅) are better than the SWIR hyperspectral indices, including cellulose absorption index for quantifying NPV biomass. The simulated multispectral red-edge spectral indices (NDVI_red-edge and mNDVI_red-edge) demonstrate good and comparable performance on quantifying NPV biomass with SWIR multispectral indices (normalized difference index [NDI5 and NDI7] and soil-adjusted corn residue index). Nevertheless, the multispectral indices derived from Landsat 8 OLI and Sentinel-2 images have limited potential for NPV biomass estimation.     

Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island

Multi-temporal TerraSAR-X, ASAR/ENVISAT and PALSAR SAR data acquired at various incidence angles and polarizations were analyzed to study the potential of these new spaceborne SAR systems for monitoring sugarcane crops. The sensitivity of different radar parameters (wavelength, incidence angles, and polarization) to sugarcane growth stages was analyzed to determine the most suitable radar configuration for better characterisation of sugarcane fields and in particular the monitoring of sugarcane harvest.Correlation between backscattered signals and crop height was also carried out. Radar signal increased quickly with sugarcane height until a threshold height, which depended on radar wavelength and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is higher with longer wavelengths (L-band in comparison with C- and X-bands) and higher incidence angles (~ 40° in comparison with ~ 20°).The radar backscattering coefficients (σ°) were also compared to the Normalized Difference Vegetation Index (NDVI) calculated from SPOT-4/5 images. Results showed a high correlation between the behaviors of σ° and NDVI as a function of sugarcane crop parameters. A decrease in NDVI for fully mature sugarcane fields due to drying of the sugarcane (water stress) was also observed in the radar signal. This decrease in radar signal was of the same order as the decrease in radar signal after the sugarcane harvest. In general, it is more suitable to monitor the sugarcane harvest using high incidence angles regardless of the radar wavelength. SAR data in L- and C-bands showed an ambiguity between the signals of ploughed fields and those of fields in vegetation because of the high sensitivity of the radar signal at these wavelengths to surface roughness of bare soils. Indeed, sometimes the radar signal of ploughed fields was of the same order as that of harvested or mature sugarcane fields. Results showed better discrimination between ploughed fields and sugarcane fields in vegetation (sugarcane canopy) when using TerraSAR-X data (X-band).Concerning the influence of radar polarization, results showed that the co-polarizations channels (HH and VV) were well correlated, but had slightly less potential than cross-polarization channels (HV and VH) for the detection of the sugarcane harvest. Finally, SAR data at high spatial resolution were shown to be useful and necessary for better analysis of SAR images when the fields were of small size.     

The Bothnian Experiment in Preparation for ERS-1, 1988 BEPERS-88) — an overview

Abstract

BEPERS-88 was an extensive field campaign on the use of Synthetic Aperture Radar (SAR) in sea ice remote sensing in the Baltic Sea. This experiment was performed in order to study the possibilities of using the ERS-1 satellite SAR (and radar altimeter) in connection with the brackish ice in the Baltic Sea. The Canada Centre for Remote Sensing CV-580 C/X-band SAR was flown and an extensive validation programme was carried out. The data have been used for SAR image analysis, backscatter investigations, geophysical validation of SAR over sea ice, and evaluation of the potentials of SAR in operational ice information services. The results indicate that SAR can be used to discriminate between ice and open water, classify ice types into thrcc categories, quantify ice ridging intensity, and determine the ice drift. As an operational tool SAR is expected to be an excellent complement to NOAA imagery and ground truth.     

建筑物目标高分辨率SAR图像模拟方法

随着TerraSAR-X,Cosmo-SkyMed和Radarsat-2等高分辨率合成孔径雷达(SAR)卫星系统的升空,星载SAR图像空间分辨率达到了米级。在这些高空间分辨率SAR图像中,单个建筑物结构的散射特征能够得到明显的展现,推动了SAR在城市监测中的应用。而城市环境的复杂性给SAR图像的解译和应用带来了巨大的挑战,由于SAR图像模拟有助于图像的解译和应用,因此城市目标高分辨率SAR图像模拟成为一个研究热点。提出了一种基于射线追踪法的图像模拟方法,它能够模拟城市目标SAR图像上叠掩、阴影和多次散射等主要特征,非常有利于SAR图像的解译。该模拟方法首先构建虚拟SAR传感器,发射电磁波射线与场景中三维模型相互作用,并接收回波信号成像,电磁波的传播以及与场景的多次散射在整个过程中都能够被追踪。为了评价该模拟方法的有效性,首先通过模拟平顶屋、尖顶屋模型的SAR图像,然后选择国家体育馆和大场景小区三维模型作为输入,将生成的模拟图像与真实TerraSAR-X聚束模式图像进行比较。结果表明:该模拟器能够模拟城市目标的散射特征并应用于图像的理解和变化检测。     

Dambo mapping for environmental monitoring using Landsat TM and SAR imagery: Case study in the Zambian Copperbelt

The purpose of this study was to monitor the impact of mining in the Zambian Copperbelt, specifically using dambos as an environmental indicator for pollution. Data fusion using a Brovey transform was used for combining speckle filtered radar data with optical data to effectively map natural dambos and dambos that have degraded due to human impact. Comparative analysis of raw images and fusion product reveals that, whereas natural dambos show low values on Landsat reflective bands and low backscatter response in SAR imagery, degraded dambos have mixed spectral responses. Degraded dambos are difficult to identify in either optical or SAR images alone, but a fusion product highlights complimentary spectral information, making these environmental indicators uniquely identifiable.     

Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data

During recent years, synthetic aperture radar (SAR) data have been increasingly used for flood mapping. New radar satellites especially, such as TerraSAR-X, Radarsat-2 and COSMO-SkyMed, provide high-resolution data with high potential for fast and reliable detection of inundated areas. This article compares three simple approaches to derive water areas from SAR data in relation to the German–Vietnamese project, Water-related Information System for the Sustainable Development of the Mekong Delta (WISDOM). Two methods are pixel based and use histogram-based grey-level thresholds, as well as a homogeneity criterion for classification. The third approach is object based and applies characteristic attributes of water objects such as grey value, texture and relations to neighbouring objects. Further discussed are the influence of a variation of the thresholds and the challenges to validate water masks derived from active remote-sensing data. We implemented one of the introduced approaches for surface water derivation in a water mask processor for automatic water mask calculation from radar satellite imagery (WaMaPro). This fully automatic processing chain was developed to process TerraSAR-X and Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) imagery in order to meet the demands for automatic flood monitoring.     

A two-level Markov random field for road network extraction and its application with optical,SAR, and multitemporal data

This article introduces a method for road network extraction from satellite images. The proposed approach covers a new fusion method (using data from multiple sources) and a new Markov random field (MRF) defined on connected components along with a multilevel application (two-level MRF). Our method allows the detection of roads with different characteristics and decreases by around 30% the size of the used graph model. Results for synthetic aperture radar (SAR) images and optical images obtained using the TerraSAR-X and Quickbird sensors, respectively, are presented demonstrating the improvement brought by the proposed approach. In a second part, an analysis of different types of data fusion combining optical/radar images, radar/radar images, and multitemporal SAR (TerraSAR-X and COSMO-SkyMed) images is described. The qualitative and quantitative results show that the fusion approach improves considerably the results of the road network extraction.     

Towards automated mapping and monitoring of potentially dangerous glacial lakes in Bhutan Himalaya using Sentinel-1 Synthetic Aperture Radar data

The majority of glacial lakes around the world are located in remote and hardly accessible regions. The use of remote sensing data is therefore of high importance to identify and assess their potential hazards. However, the persistence of cloud cover, particularly in high mountain areas such as the Himalayas, limits the temporal resolution of optical satellite data with which we can monitor potentially dangerous glacial lakes (PDGLs). The ability of Synthetic Aperture Radar (SAR) satellites to collect data, irrespective of weather and at day or night, facilitates monitoring of PDGLs by without compromising temporal resolution. In this study, we present a semi-automated approach, based on a radar signal intensity threshold between water and non-water feature classes followed by post-processing including elevations, slopes, vegetation and size thresholds, to delineate glacial lakes in Sentinel-1 SAR images in Bhutan Himalaya. We show the capability of our method to be used for delineating and monitoring glacial lakes in Bhutan Himalaya by comparing our results to 10 m resolution Sentinel-2 multispectral data, field survey data, meteorological data, and a time series of monthly images from January to December 2016 of two lakes. Sentinel-1 SAR data can, moreover, be used for detecting lake surface area changes and open water area variations, at temporal resolution of six days, providing substantial advantages over optical satellite data to continuously monitor PDGLs.     

Agricultural crop condition monitoring using airborne C-band synthetic aperture radar in southern Alberta

Applications of airborne C-band synthetic aperture radar imagery for determining variations in agricultural crop characteristics were investigated at a test site in southern Alberta, Canada. Synthetic aperture radar (SAR) imagery and ground-based crop characteristics data were acquired on 19–20 July 1994 for wheat, canola, beans, peas, and wheat + alfalfa cultivated under a variety of irrigation conditions. The results indicate that the statistically significant relationships that were derived between the ground-based data and SAR imagery are a function of crop type, crop condition parameter, and image processing procedures, and that crop characteristics such as leaf area index and plant height are negatively correlated with radar backscatter.     

Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

ABSTRACT

The complex, dynamic and narrow boundaries between vegetation types make wetland mapping challenging. Hereafter the case study of the Hamoun-e-Hirmand wetland is considered by analysing eight Synthetic Aperture Radar (SAR) Images acquired in dry and wet periods with three wavelengths (X-band ~ 3 cm, C-band ~ 6 cm, and L-band ~ 25 cm), three polarizations (HH, VV and VH), and four incidence angles (22°, 30°, 34° and 53°). Then, the Support Vector Machine (SVM) classification method was applied to classify TerraSAR-X, Sentinel-1, and ALOS-PALSAR images. The final wetland land cover map was created by combining the classification results obtained from each sensor. In the case in question, results show that TerraSAR-X (X-band, HH-53°) and Sentinel-1 data (C-band, VV-34°) were useful for determining the flooded vegetation area in the wet period. This is crucial for the conservation of water bird habitats since flooded vegetation is an ideal environment for the nesting and feeding of water birds. PALSAR data (L-band in both HH and VH polarizations, 30°) were capable of separating the classes of vegetation density in the wetland. In the dry period, Sentinel-1 (VV and VH, 34°) and TerraSAR-X (HH, 22° and 53°) had higher potential in land cover mapping than PALSAR (HH and VH, 30°). Based on these results, Sentinel-1 in VV and VH provides the highest ability to discriminate between dry and green plants. TerraSAR-X is better for separating meadow and bare land. The results obtained in this paper can reduce the ambiguity in selecting satellite data for wetland studies. The results can also be used to produce more accurate data from satellite images and to facilitate wetland investigation, conservation and restoration.     

Applications of Radarsat‐1 synthetic aperture radar imagery to assess hurricane‐related flooding of coastal Louisiana

The Louisiana coast is subjected to hurricane impacts including flooding of human settlements, river channels and coastal marshes, and salt water intrusion. Information on the extent of flooding is often required quickly for emergency relief, repairs of infrastructure, and production of flood risk maps. This study investigates the feasibility of using Radarsat‐1 SAR imagery to detect flooded areas in coastal Louisiana after Hurricane Lili, October 2002. Arithmetic differencing and multi‐temporal enhancement techniques were employed to detect flooding and to investigate relationships between backscatter and water level changes. Strong positive correlations (R ² = 0.7–0.94) were observed between water level and SAR backscatter within marsh areas proximate to Atchafalaya Bay. Although variations in elevation and vegetation type did influence and complicate the radar signature at individual sites, multi‐date differences in backscatter largely reflected the patterns of flooding within large marsh areas. Preliminary analyses show that SAR imagery was not useful in mapping urban flooding in New Orleans after Hurricane Katrina's landfall on 29 August 2005.     

基于GF-1和Sentinel-1A的漓江流域典型地物信息提取

漓江流域是桂林山水的核心,保护漓江流域生态环境已成为国家战略。以漓江流域为研究区域,以GF-1多光谱影像和SAR影像为数据源,采用小波融合算法将GF-1多光谱影像和SAR VV极化的后向散射影像进行影像融合,再利用随机森林算法分别对GF-1多光谱影像、GF-1和Sentinel融合影像构建典型地物高精度识别模型,提取与漓江流域生态环境紧密相关的河流、针叶林、阔叶林、水田、旱地以及居民地等地物类型。研究结果表明:①在95%置信区间内,基于GF-1影像分类的总体分类精度达到96.15%,基于GF-1和Sentinel-1A后向散射系数的影像总体分类精度达到了94.40%;②河流、阔叶林和旱地在基于GF-1多光谱影像的分类精度中分别达到了97.74%、93.20%、90.90%,比基于融合GF-1多光谱和SAR的数据分别高出7.57%、8.96%和1.22%,其余地物类型两者分类精度相近;③GF-1多光谱和SAR数据的融合中,利用了小波变换进行图像融合,发现融合图像的喀斯特地貌突出,增加了地物特征的差异性。     

Multisource ERS-1 and optical data for vegetal cover assessment and monitoring in a semi-arid region of Algeria

This paper highlights advantages of using Synthetic Aperture Radar (SAR) data combined with multispectral data to improve vegetal cover assessment and monitoring in a semi-arid region of southern Algeria. We present a number of preprocessing and processing techniques using multidate optical data analysis alone and SAR ERS-1 and Landsat Thematic Mapper (TM) data integration due to aspects of radar image enhancement techniques and the study of roughness of different types of vegetation in steppic regions. Image data integration has become a valuable approach to integrate multisource satellite data. It has been found that image data from different spectral domains (visible, near-infrared, microwave) provides datasets with complementarity information content and can be used to improve the spatial resolution of satellite images. In this communication, we present a part of the cooperation research project which deals with fusing ERS-1 SAR geocoded images with Landsat TM data, investigating different combinations of integration and classification techniques. The methodology consists of several steps: (1) Speckle noise reduction by comparative performance of different filtering algorithms. Several filtering algorithms were implemented and tested with different window sizes, iterations and parameters. (2) Geometric superposition and geocoding of optical images regarding SAR ERS-1 image and resampling at unique resolution of 25 m. (3) Application of different numerical combinations of integration techniques and unsupervized classifications such as the Forgy method, the MacQueen method and other methods. The results were compared with vegetal cover mapping from aerial photographs of the region of Foum Redad in the south of the Saharian Atlas. The combinations proposed above allow us to distinguish different themes in the arid and semi-arid regions in the south of the Saharian Atlas using a colour composite image and show a good correlation between different types of land cover and land use and radar backscattering level in the SAR data which corresponds essentially to the roughness of the soil surface.     

A study on smearing due to ionospheric phase scintillation in SAR data

Spaceborne synthetic aperture radar (SAR) data suffer from scintillation due to changes in the signal path when travelling through the dispersive ionosphere. During data collection, scintillation translates into phase noise in the received signals, which in turn leads to smearing in the resulting image. This article examines ways of reducing ionosphere-induced smearing in high-resolution spotlight SAR data. In this article, several existing techniques are reviewed and one technique is tested using simulated spotlight data with ionospheric phase noise. The results show that the existing techniques are ineffective in correcting spatially varying ionospheric phase noise. Hence, a 2D phase reconstruction and compensation technique is developed, which results in a 10% reduction in the main lobe in simulated data. This technique is also demonstrated to sharpen images using measured data obtained from the TerraSAR-X satellite mission.     

Identification of Typical Land Features in the Lijiang River Basin with Fusion Optics and Radar

Lijiang River is the core of Guilin's landscape. Protecting the ecological environment of Lijiang River Basin has become a national strategy. In this paper, Lijiang River Basin was used as the research area. The GF-1 multispectral image and SAR image were used as the data source. The wavelet fusion algorithm was used to fuse the GF-1 multispectral image and the SAR VV polarized backscatter image. Using random forest algorithm to construct a high-precision recognition model for GF-1 multispectral imagery, GF-1 and sentinel fusion images. The model can extract rivers, coniferous forests, broad-leaved forests, paddy fields, drylands, residential land and other land types that are closely related to the ecological environment of the Lijiang River. The results show that ①the overall accuracy based on GF-1 image classification reaches 96.15% in the 95% confidence interval, and the overall accuracy based on GF-1 and sentinel-1A backscatter coefficient reaches 94.40%. ②The classification accuracy of rivers, broad-leaved forests and drylands based on GF-1 multispectral images reached 97.74%, 93.20%, and 90.90%. They are 7.57%, 8.96%, and 1.22% higher than those based on the fused GF-1 multispectral and SAR data, respectively. The classification accuracy of the other features is similar. ③In the fusion of GF-1 multispectral and SAR data, wavelet transform was used for image fusion. It was found that the karst topography of the fusion image was prominent, which increased the difference of the features of the ground features.     

Methodology to classify rice cultural types based on water regimes using multi-temporal RADARSAT-1 data

This study presents a methodology to classify rice cultural types based on water regimes using multi-temporal synthetic aperture radar (SAR) data. The methodology was developed based on the theoretical understanding of radar scattering mechanisms with rice crop canopy, considering crop phenology and variation in water depth in the rice field, emphasizing the sensitivity of SAR to crop geometry and water. The logic used was the characteristic decrease in SAR backscatter that is associated with the puddled or transplanted field due to specular reflection for little exposure of crop, with increase in backscatter as the crop growth progresses due to volume scattering. Besides, the multiple interactions between SAR and vegetation/water also lead to an increase in backscatter as the crop growth progresses. Classification thresholds were established based on the information provided by each pixel in each image, the pixel's typical temporal behaviour due to crop phenology and changing water depth in rice field and their corresponding SAR signature. Based on this logic, the study site (i.e. South 24 Paraganas district, West Bengal) was classified into three major rice cultural types, namely shallow water rice (SWR; 5 cm ≤ water depth ≤ 30 cm), intermediate water rice (IWR; 30 cm ≤ water depth ≤ 50 cm) and deep water rice (DWR; water depth > 50 cm) during the kharif season. These three types represent most of the traditional rice-growing areas of India. The methodology was validated with the field data collected synchronously with the satellite passes. Classification results showed an overall accuracy of 98.5% (95.5% kappa coefficient) compared with a maximum-likelihood classifier (MLC) with an overall accuracy of 95.5% (84.2% of kappa coefficient) with 95% confidence interval. The relationship between field parameters, especially exposed plant height and water depth with SAR backscatter, was explored to design empirical models for each of the three rice classes. Significant relationships were observed in all the rice classes (coefficient of determination, R ², value more than 0.85) even though they had similar growth profiles but varied with water depth. The two main conclusions drawn from this study are (i) the importance of multi-temporal SAR data for the classification of rice culture types based on water regimes and (ii) the advantages and flexibility of the knowledge-based classifier for classification of RADARSAT-1 data. However, being empirical, the approach needs modification according to the current rainfall pattern and rice-growing practice.     

Simulation of SAR images of urban areas by using the ray tracing method with measured values of backscatter coefficients

This study uses the ray tracing method to simulate synthetic aperture radar (SAR) images of urban areas. The images are constructed for polarisations: horizontal-horizontal (HH) and vertical-vertical (VV), and different types of buildings, vegetation, and streets. Simulated images of a given area are compared with real SAR images of the same area acquired by the TerraSAR-X satellite. The simulations use the measured backscatter coefficient for HH and VV polarisations and for five different classes of terrain: houses, trees, shrubs, grass, and ground. For multiple reflections, we apply the generalized bistatic Lambertian model. The results show that, despite the limits of the ray tracing method and the approximations involved in modelling three-dimensional objects in the simulated scene, the simulated SAR images correspond well with the actual scene. All features present in the real image are reproduced in the simulated image; in particular, the double reflections of buildings and the surrounding ground appear clearly. However, discrepancies exist, and these are also discussed.     

利用色调—亮度彩色分量的可见光植被指数

目的 无人机遥感具有高时效、高分辨率、低成本、操作简单等优势。但由于无人机通常只携带可见光传感器,无法计算由可见光-近红外波段组合所构造的植被指数。为解决这一问题,提出一种归一化色调亮度植被指数NHLVI （normalized hue and lightness vegetation index）。方法 通过分析HSL （hue-saturation-lightness）彩色空间模型,构建一种基于色调亮度的植被指数,将该植被指数以及其他常用的可见光植被指数,如归一化绿红差值指数NGRDI （normalized green-red difference index）、过绿指数ExG （excess green）、超绿超红差分指数ExGR （excess green minus excess red）等,分别与野外实测光谱数据和无人机多光谱数据的NDVI （normalized difference vegetation index）进行相关性比较;利用受试者工作特征曲线ROC （receiver operating characteristic curve）的特点确定阈值,并进行植被信息提取与分析。结果 NHLVI与NDVI相关性高（R²=0.776 8）,而其他可见光植被指数中,NGRDI与NDVI相关性较高（R²=0.687 4）;ROC曲线下面积大小作为评价不同植被指数区分植被与非植被的指标,NHLVI指数在ROC曲线下面积为0.777,小于NDVI （0.815）,但大于NGRDI （0.681）,区分植被与非植被能力较强。为进一步验证其精度,利用阈值法提取植被,NHLVI提取植被信息的总体精度为82.25%,高于NGRDI （79.75%）,尤其在植被稀疏区,NHLVI的提取结果优于NGRDI。结论 提出的归一化色调亮度植被指数,提取植被精度较高,适用于无人机可见光影像植被信息提取,为无人机可见光影像的应用提供了新方法。