Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C‐band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low‐density vegetation, using low‐incidence‐angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high‐density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal‐ and vertical‐polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual‐polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture.     

Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS Wind Scatterometer and SAR data

This paper presents an original methodology to retrieve surface (<5 cm) soil moisture over low vegetated regions using the two active microwave instruments of ERS satellites. The developed algorithm takes advantage of the multi-angular configuration and high temporal resolution of the Wind Scatterometer (WSC) combined with the SAR high spatial resolution. As a result, a mixed target model is proposed. The WSC backscattered signal may be represented as a combination of the vegetation and bare soil contributions weighted by their respective fractional covers. Over our temperate regions and time periods of interest, the vegetation signal is assumed to be principally due to forests backscattered signal. Then, thanks to the high spatial resolution of the SAR instrument, the forest contribution may be quantified from the analysis of the SAR image, and then removed from the total WSC signal in order to estimate the soil contribution. Finally, the Integral Equation Model (IEM, [IEEE Transactions on Geoscience and Remote Sensing, 30 (2), (1992) 356]) is used to estimate the effect of surface roughness and to retrieve surface soil moisture from the WSC multi-angular measurements. This methodology has been developed and applied on ERS data acquired over three different Seine river watersheds in France, and for a 3-year time period. The soil moisture estimations are compared with in situ ground measurements. High correlations (R² greater than 0.8) are observed for the three study watersheds with a root mean square (rms) error smaller than 4%.     

Measurements of land surface features using an airborne laser altimeter: the HAPEX-Sahel experiment

An airborne laser profiling altimeter was used to measure surface features and properties of the landscape during the HAPEX-Sahel Experiment in Niger, Africa in September 1992. The laser altimeter makes 4000 measurements per second with a vertical resolution of 5 cm. Airborne laser and detailed field measurements of vegetation heights had similar average heights and frequency distribution. Laser transects were used to estimate land surface topography, gully and channel morphology, and vegetation properties ( height, cover and distribution). Land surface changes related to soil erosion and channel development were measured. For 1 km laser transects over tiger bush communities, the maximum vegetation height was between 4-5 and 6-5 m, with an average height of 21 m. Distances between the centre of rows of tiger bush vegetation averaged 100 m. For two laser transects, ground cover for tiger bush was estimated to be 225 and 301 per cent for vegetation greater than 0-5m tall and 190 and 25-8 per cent for vegetation greater than 10m tall. These values are similar to published values for tiger bush. Vegetation cover for 14 and 18 km transects was estimated to be 4 per cent for vegetation greater than 0-5 m tall. These cover values agree within 1-2 per cent with published data for short transects (? 100 m) for the area. The laser altimeter provided quick and accurate measurements for evaluating changes in land surface features. Such information provides a basis for understanding land degradation and a basis for management plans to rehabilitate the landscape.     

Cover: Remote sensing based detection of defensive minefields

Crop surface temperature (CST) is an important parameter to monitor crop status. Satellite data in thermal region provide an opportunity to estimate CST over large regions at frequent intervals. In the present study, various split‐window algorithms are employed to estimate CST over rice areas in irrigation projects of Krishna basin, South India using multi‐resolution MODIS satellite data. NDVI is used to approximate the mean pixel emissivity, by taking known values for emissivity and NDVI for pure vegetation and bare soil pixels. Diurnal ground measurements are made to evaluate satellite‐derived CST. CST values obtained using the Sobrino method have been found to be closer to the ground‐measured values compared with other algorithms, as it takes into account view angle, atmospheric transmittance, and water vapour corrections. It has been observed that the error in estimating CST is comparatively lower for well‐grown crops.     

Application of passive microwave and optical signatures to monitor submerging of vegetation due to floods

In this article, passive microwave observations in synergy with optical data are exploited to monitor floods and estimate vegetation submerging. The selected site is Sundarban Delta, at the borders between India and Bangladesh. The area is subject to severe monsoon in summer, producing heavy floods and vegetation submerging. Because of their high spatial resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) signatures are used to evaluate the coverage fractions of bare soil, vegetated fields, and permanent water. Multifrequency Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) signatures are used to monitor vegetation submerging during monsoon. Results are compared with ground measurements of water level and plant biomass in both agriculture areas and wetlands. Previous studies indicated that, during monsoon, there is a clear effect of brightness temperature decrease and polarization index increase in the C, X and Ka bands over the areas affected by floods. X band data prove to be particularly useful since the sensitivity to flood effects is appreciable and the spatial resolution is better than at C band. In this article, the vegetation submerging effect is estimated with the aid of a radiative transfer model. In the pre-monsoon season, the retrieved value of emerged biomass is close to that of the measured total biomass. During monsoon, it is estimated that up to 3 kg m^?2 of vegetation biomass is submerged by flood. For both agricultural fields and wetlands, obtained results are consistent with ground measurements of water level.     

Radiometric measurements over bare and vegetated fields at 1.4-GHz and 5-GHz frequencies

Results of radiometric measurements over bare and vegetated fields with dual-polarized microwave radiometers at 1.4-GHz and 5-GHz frequencies are presented. The measured brightness temperatures over bare fields are shown to compare favorably with those calculated from radiative transfer theory with two constant parameters characterizing surface roughness effect. The presence of vegetation cover is found to reduce the sensitivity to soil moisture variation. This sensitivity reduction is generally more pronounced the denser the vegetation cover and the higher the frequency of observation. The effect of vegetation cover is also examined with respect to the measured polarization factor at both frequencies. With the exception of dry corn fields, the measured polarization factor over vegetated fields is found appreciably reduced compared to that over bare fields. A much larger reduction in this factor is found at 5 GHz than at 1.4 GHz.     

基于混合像元分解的喀斯特石漠化地物丰度估测

我国西南喀斯特地区长期存在以石漠化为特征的土地退化问题,是我国三大生态问题之一。喀斯特地区地表复杂度高,具有高度时空异质性,像元混合现象严重,植被、裸岩和裸土为喀斯特地区典型地物,使得评价喀斯特石漠化的关键指标(如裸岩率、植被覆盖度)获取比较困难,高光谱遥感在混合像元分解方面有独特优势,可以获取地物端元的丰度。通过地面试验表明光谱指数能够表征地物覆盖度,进而以Hyperion高光谱影像为数据源,利用连续最大角凸锥方法从影像中提取这3类地物的端元,运用半约束和全约束线性光谱分解方法估算其丰度。研究表明:半约束线性分解得到的丰度优于全约束分解结果,其反演的植被、裸土和裸岩的丰度与相应的光谱指数间具有显著线性相关性,确定系数R²分别为0.92、0.66与0.84,表明地物丰度能够表征其覆盖度。因此,通过混合像元分解算法反演地物丰度来提取喀斯特石漠化因子具有一定的可行性,这为高光谱遥感在喀斯特石漠化中的评价和监测奠定了理论和算法基础。     

Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically unmixing coarse resolution signals through the inversion of linear mixture modelling on heterogeneous regions of natural vegetation (Bidi-Bahn) in Burkina-Faso and in Niger (Hapex site). In a first step, simulated coarse resolution data (NOAA-AVHRR) obtained from the degradation of SPOT images are used to assess the method. In a second step, real NOAA-AVHRR data are used and some elements of validation are given by comparing the results to airborne reflectance measurements.     

Fusing optical and radar data to estimate sagebrush, herbaceous, and bare ground cover in Yellowstone

The arid and semi-arid sagebrush-grass ecosystem occupies a substantial portion of rangelands in the western United States. Using remote sensing techniques to map the percent of sagebrush, grass/forb, and bare ground components is necessary for forage production estimation and natural resource management over large areas. However optical data have significant deficiencies in these ecosystems because of exposed bright soil, spectrally-indeterminate vegetation, and a large dead vegetation component. Radar data also have deficiencies caused by factors such as antenna pattern calibration, local incidence angle (LIA), soil moisture, and surface roughness. With the complementary vegetation information gained from optical data and radar data, these two datasets were fused to estimate 10-m sagebrush, grass, and bare ground percent cover in the non-forested areas of Yellowstone National Park, which is a representative native western rangeland ecosystem of the US. The datasets were processed to resolve the issues of antenna pattern calibration and LIA effect. Peak green Landsat, late fall Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AirSAR) data were fused in this analysis. AVIRIS, Landsat, AirSAR and elevation data were used to segment the study area into two main subcategories of “pure grass” and “mixed sagebrush and grass”. Landsat Tasseled Cap Greenness (LTCG) was used to retrieve bare land and grass percentages in pure grass areas. In the areas with mixed grass and sagebrush, standardized LTCG and radar C_vv were used to derive the vegetation cover percentage, and the ratio of standardized LTCG and radar L_hv was further used to calculate the relative abundance of sagebrush and grass. Comparison between the field and remote sensing estimations shows the correlation coefficients were 0.838, 0.746, and 0.830 for bare land, grass, and sagebrush, respectively. When grouped into three discrete categories of “low”, “medium”, and “high”, the overall accuracies were 79.4%, 75.9%, and 77.6%, respectively. Our study shows the potential for application of global spaceborne C- and L-band radar and optical data fusion for large-area rangeland monitoring.     

Mapping vegetation community types in a highly disturbed landscape: integrating hierarchical object-based image analysis with lidar-derived canopy height data

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island (SCI), California, we test the effectiveness of incorporating a hierarchical object-based image analysis (OBIA) approach with high-spatial resolution imagery and canopy height surfaces derived from light detection and ranging (lidar) data for mapping vegetation communities. The hierarchical approach entailed segmentation and classification of fine-scale patches of vegetation growth forms and bare ground, with shrub species identified, and a coarser-scale segmentation and classification to generate vegetation community maps. Such maps were generated for two areas of interest on SCI, with and without vegetation canopy height data as input, primarily to determine the effectiveness of such structural data on mapping accuracy. Overall accuracy is highest for the vegetation community map derived by integrating airborne visible and near-infrared imagery having very high spatial resolution with the lidar-derived canopy height data. The results demonstrate the utility of the hierarchical OBIA approach for mapping vegetation with very high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accurately mapping vegetation communities within highly disturbed landscapes.     

针对MODIS 数据的地表温度非线性迭代反演方法

地表温度是气象、水文、生态等研究领域中的一个重要参数。构建了MODIS31/ 32 波段的热辐射传输方程, 讨论了方程的数值迭代解法, 提出了针对MODIS 数据地表温度的非线性迭代反演方法, 并介绍了大气透过率和地表比辐射率这两个中间参数的估计方法。误差及敏感性分析表明,提出的方法对大气透过率和地表比辐射率都不敏感, 反演精度优于传统的线性分裂窗算法。     

Review of: “ Télédétection spatiale: Université d' été européenne Toulouse 1990”. By MICHEL VAUZELLE (Cépadues-Éditions. Toulouse. 1992) [Pp. 309.] Price FF300.

In this paper, a simple model is proposed for measuring the vegetation cover over soil surfaces from radar signals acquired in semi-arid regions. In such regions, vegetation is characterized by the presence of clumps which partially cover the soil surface. The proposed model describes the relationship between the percentage of covered surface and the measured radar signal. Model simulations over Tunisian test areas, where ground parameters are controlled, are performed and compared with actual ERS2 radar measurements. A very good agreement is found. The model is then used to derive a map of the vegetation cover density for the whole studied site (in Tunisia). The approach used here is based upon supervised classification with classes defined by inverting the model and taking into account ERS calibration error. Each of the four classes thus defined exhibits a good classification rate, greater than 85%. Finally, two important applications for natural resources management are presented: vegetation monitoring and soil moisture monitoring.     

现代黄河三角洲类圆形植被斑块时空动态遥感分析

斑状植被在世界范围内的干旱半干旱区、海岸滩涂均有分布。近年来,有关斑状植被形成、时空格局演替的研究受到了越来越多的关注。斑状植被分布及其时空动态是其中重要的研究方向之一,它是植被演替机制研究的基础,也是表征生态系统中植被长期变化的关键指标之一。以现代黄河三角洲类圆形植被斑块为研究对象,将其分为裸斑区、明显类圆形植被斑块区和隐性类圆形植被斑块区等三大类,通过1996、2005、2007、2010和2012年5个时相多源遥感影像的人机交互目视解译,首次给出了现代黄河三角洲这三大类区域的分布范围,在此基础上分析了其时空动态。结果表明:现代黄河三角洲类圆形植被斑块在空间分布上存在明显的梯度分布和动态演替规律,具有海域—光滩—裸斑区—明显类圆形植被斑块区—隐性类圆形植被斑块区高程梯度分布的普遍特征;类圆形植被斑块的直径、植被覆盖度、植被平均高度以及土壤全盐量可作为判别新老斑块的直观标志;10m、5~6m分辨率的多光谱图像能够较好划分3种类圆形植被斑块区,但对于斑块面积变化的测量精度仍显不足,1m甚至更高分辨率图像的运用将会弥补这一缺陷。研究结果可为将来现代黄河三角洲斑状植被格局及演替机制的深入研究提供参考。     

Evaluating MODIS soil fractional cover for arid regions,using albedo from high-spatial resolution satellite imagery

Broad-scale high-temporal frequency satellite imagery is increasingly used for environmental monitoring. While the normalized difference vegetation index (NDVI) is the most commonly used index to track changes in vegetation cover, newer spectral mixture approaches aim to quantify sub-pixel fractions of photosynthesizing vegetation, non-photosynthesizing vegetation, and exposed soil. Validation of the unmixing products is essential to enable confident use of the products for management and decision-making. The most frequently used validation method is by field data collection, but this is very time consuming and costly, in particular in remote regions where access is difficult.

This study developed and demonstrates an alternative method for quantifying land-cover fractions using high-spatial resolution satellite imagery. The research aimed to evaluate the bare soil fraction in a sub-pixel product, MODIS Fract-G, for the natural arid landscapes of the far west of South Australia. Twenty-two sample regions, of 3400 sampling points each, were investigated across several arid land types in the study area. Albedo thresholds were carefully determined in Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument Stereo Mapping (ALOS PRISM) images (2.5 m spatial resolution), which separated predominantly bare soil from predominantly vegetated or covered soil, and created classified images. Correlation analysis was carried out between MODIS Fract-G bare soil fractional cover and ALOS PRISM bare soil proportions for the same areas. Results showed much lower correlations than expected, though limited agreement was found in some specific areas. It is posited that the Moderate Resolution Imaging Spectroradiometer (MODIS) fractional cover product, which is based on unmixing using the NDVI and a cellulose absorption index (CAI) proxy, may be generally unable to separate soil from vegetation in situations where both indices are low. In addition, separation is hampered by the lack of ‘pure pixels’ in this heterogeneous landscape. This suggests that the MODIS fractional cover product, at least in its present form, is unsuited to monitor sparsely vegetated arid landscapes.     

Review of: “Glaciers-Ocean-Atmosphere Interactions”. Edited by V. M. KOTLYAKOV,A. USHAKOV and A. GLAZOVSKY,Proceedings of the International Symposium,St Petersburg, 24-29 September 1990 (Wallingford,UK: IAHS Press, 1991.) [Pp. 549.] Price US$60.

Data gathered during the NASA sponsored Multisensor Aircraft Campaign Hydrology (MACHYDRO) experiment in central Pennsylvania (U.S.A.) in July, 1990 have been analysed to study the combined use of active and passive microwave sensors for estimating soil moisture from vegetated areas. These data sets were obtained during an eleven-day period with NASA's Airborne Synthetic Aperture Radar (AIRSAR), and Push-Broom Microwave Radiometer (PBMR) over an instrumented watershed, which included agricultural fields with a number of different crop covers. Simultaneous ground truth measurements were also made in order to characterize the state of vegetation and soil moisture under a variety of meteorological conditions. Various multi-sensor techniques are currently under investigation to improve the accuracy of remote sensing estimates of the soil moisture in the presence of vegetation and surface roughness conditions using these data sets. One such algorithm involving combination of active and passive microwave sensors is presented here, and is applied to representative corn fields in the Mahantango watershed that was the focus of study during the MACHYDRO experiment. In this algorithm, a simple emission model is inverted to obtain Fresnel reflectivity in terms of ground and vegetation parameters. Since Fresnel reflectivity depends on soil dielectric constant, soil moisture is determined from reflectivity using dielectric-soil moisture relations. The algorithm requires brightness temperature, vegetation and ground parameters as the input parameters. The former is measured by a passive microwave technique and the later two are estimated by using active microwave techniques. The soil moisture estimates obtained by this combined use of active and passive microwave remote sensing techniques, show an excellent agreement with the in situ soil moisture measurements made during the MACHYDRO experiment.     

A re‐examination of perpendicular drought indices

In this letter, the performance of newly developed drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional surface dryness monitoring to provide clear guidance on appropriate implementation of these indices over different eco‐systems through in‐depth analysis of their advantages and constraints. Spatio‐temporal patterns of surface drought derived by MODerate Resolution Imaging Spectroradiometer (MODIS)‐based PDI and MPDI are compared against field‐measured soil moisture (SM), rainfall, and regional hydrological conditions. Results indicate that there are significant negative correlations between the PDI, the MPDI, and mean 0–20 cm SM content and rainfall. The PDI and the MPDI provide similar results at the early stage of vegetation growth, but a greater agreement between the drought information extracted by the MPDI and field measurements is observed for vegetated surfaces where the PDI fails. Therefore, it is recommended that PDI be used for bare soil applications, since it does not require calculation of additional information such as the fraction of vegetation which may contain some uncertainties, but the MPDI should be used for vegetated regions.     

Estimating life-form cover fractions in California sage scrub communities using multispectral remote sensing

The California sage scrub (CSS) community type in California's Mediterranean-type ecosystems is known for its high biodiversity and is home to a large number of rare, threatened, and endangered species. Because of extensive urban development in the past fifty years, this ecologically significant community type is highly degraded and fragmented. To conserve endangered CSS communities, monitoring internal conditions of communities is as crucial as monitoring distributions of the community type in the region. Vegetation type mapping and field sampling of individual plants provide ecologically meaningful information about CSS communities such as spatial distribution and species compositions, respectively. However, both approaches only provide spatially comprehensive information but no information about internal conditions or vice versa. Therefore, there is a need for monitoring variables which fill the information gap between vegetation type maps and field-based data. A number of field-based studies indicate that life-form fractional cover is an effective indicator of CSS community health and habitat quality for CSS-obligated species. This study investigates the effectiveness of remote sensing approaches for estimating fractional cover of true shrub, subshrub, herb, and bare ground in CSS communities of southern California. Combinations of four types of multispectral imagery ranging from 0.15 m resolution scanned color infrared aerial photography to 10 m resolution SPOT 5 multispectral imagery and three image processing models - per-pixel, object-based, and spectral mixture models - were tested.An object-based image analysis (OBIA) routine consistently yielded higher accuracy than other image processing methods for estimating all cover types. Life-form cover was reliably predicted, with error magnitudes as low as 2%. Subshrub and herb cover types required finer spatial resolution imagery for more accurate predictions than true shrub and bare ground types. Positioning of sampling grids had a substantial impact on the reliability of accuracy assessment, particularly for cover estimates predicted using multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery. Of the approaches tested in this study, OBIA using pansharpened QuickBird imagery is one of the most promising approaches because of its high accuracy and processing efficiency and should be tested for more heterogeneous CSS landscapes. MESMA applied to SPOT imagery should also be examined for effectiveness in estimating factional cover over more extensive habitat areas because of its low data cost and potential for conducting retrospective studies of vegetation community conditions.     

Satellite remote sensing of rangelands in Botswana I. Landsat MSS and herbaceous vegetation

Field measurements of the cover and biomass of live and dead herbaceous vegetation, the cover of trees and shrubs and the area of bare ground were made for rangelands in three study sites in eastern Botswana between September 1983 and April 1984. The sites were selected to be representative of Terminalia sericea, Cotophospermum mopane and Combretum apiculatum-Acacia nigrescens woodland savannas, which, taken together, occupy a large part of eastern and northern Botswana. Mean herbaceous biomass varied from 0 to 563?kg ha^?1, cover from 0 to 21 per cent and bare ground from 57 to 85 per cent. The mean tree canopy cover in each site was approximately 30 per cent, with a range of 0-50. Landsat miiltispectral scanner (MSS) data were obtained for May, August, November 1983, January and March-April 1984. Nine MSS pixels were registered with 20 sample plots in each site and the ratios of mean band-7 to band-5 digital numbers were calculated. The variation in these ratios between the three sites and four dates on which the data were acquired was analyzed with respect to the field measurements.

The results indicate that the biomass and cover of live herbaceous vegetation and the bare ground individually account for quite small, but significant proportions of the variation in band ratio for all four observation dates taken together. However, when factors that specified site and date were included in the multiple regression models, 75·7, 77·9 and 64·1 per cent of the variation in herb biomass, cover and bare ground respectively were accounted for. Multitemporal integration of the band ratios accounted for 70·3 per cent of the variation in the end-of-season biomass of herbaceous vegetation, without the need to use a site factor. These highly significant relationships were achieved without including measurements of the canopy cover of trees in the models. Analysis of the individual site data revealed some negative relationships between band ratios and both biomass and Cover of herbaceous vegetation in one site, which seem to be a result of a strong negative relationship between the cover of herbaceous vegetation and trees in this vegetation type.

It was concluded that predictive equations could be constructed which enable cover and biomass of herbaceous vegetation and the area of bare ground to be estimated from Landsat MSS band-7/band-5 ratios, but only if the relationship is applied to sites having the same type of vegetation as that for which the equations were derived. Stratification of the scene using vegetation maps is therefore an important requirement for the application of remote sensing by Landsat MSS to the monitoring of the rangelands in Botswana     

Evaluate Quantitatively Effects of Understory on Leaf Area Index (LAI) Estimation Combining Active and Passive Remote Sensing Data

Natural forests have the vertical three\|dimensional structure of canopy and understory vegetation (shrubs,grasslands,and bare soil).Accurate and quantitative separation of understory vegetation is of great scientific significance and practicality on improving the precision of inversion of forest canopy leaf area index.value.Due to the limitations of traditional passive optical remote sensing data on directly acquiring 3D information,this study intends to combine active and passive ALS and HyperMap data with the Washington Botanic Garden as the key research area.On the basis of individual tree segmentation,the vertical stratification of the forest (forest canopy and undergrowth vegetation layer) is achieved.On this basis,the forest canopy laser point cloud data was used to remove the understory information from the optical image data.By comparing the results of the forest effective leaf area index obtained from aerial optical images and ground measurements,it was found that：(1) forest canopy density has a significant impact on the penetration of ALS data;(2) removal of understory information can effectively improve the forest crown accuracy of LAIe estimated.The correlation between Normalized Difference Vegetation Index (NDVI) and ground surface measured effective leaf area index increased from 0.087 to 0.591.In addition,the optical remote sensing image based on the removal of understory vegetation information was compared with the Simple Ratio vegetation index (SR) (the correlation increased from 0.209 to 0.559) and the simplified simple Ratio vegetation index (RSR) (the correlation increased from 0.147 to 0.358).The NDVI was most sensitive to changes in canopy leaf area index (correlation increased by 0.5).The method of quantitatively separating understory vegetation with the combined active and passive remote sensing data proposed in this study can effectively improve the accuracy of inversion of forest canopy leaf area index,and provide a solid foundation for quantitative and accurate estimate of forest biophysical parameters and study of carbon and water cycle processes.