Guangjian Yan

Land surface emissivity retrieval from satellite data

As an intrinsic property of natural materials, land surface emissivity (LSE) is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature (LST) and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into (1) (semi-)empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given.

Automatic Extraction of Power Lines From Aerial Images

There has been little investigation for the automatic extraction of power lines from aerial images due to the low resolution of aerial images in the past decades. With increasing aerial photogrammetric technology and sensor technology, it is possible for photogrammetrists to monitor the status of power lines. This letter analyzes the property of imaged power lines and presents an algorithm to automatically extract the power line from aerial images acquired by an aerial digital camera onboard a helicopter. This algorithm first uses a Radon transform to extract line segments of the power line, then uses the grouping method to link each segment, and finally applies the Kalman filter technology to connect the segments into an entire line. We compared our algorithm with the line mask detector method and the ratio line detector, and evaluated their performances. The experimental results demonstrated that our algorithm can successfully extract the power lines from aerial images regardless of background complexity. This presented method has successfully been applied in China National 863 project for power line surveillance, 3-D reconstruction, and modeling.

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.

Woody-to-total area ratio determination with a multispectral canopy imager

Leaf area index (LAI) - defined as one half of the total green leaf area per unit ground surface area - can be determined by direct or indirect methods. Three major sources of errors exist in indirect LAI measurements: within-shoot clumping, beyond-shoot clumping and non-photosynthetic components. The effect of non-photosynthetic components on LAI measurements can be described by the woody-to-total area ratio, alpha; however, no convenient and efficient indirect methods have been developed to estimate alpha, especially the variations in alpha with zenith angle , alpha(theta). We describe the development and use of a multispectral canopy imager (MCI) to estimate alpha and alpha(theta) by considering the effects of non-random distributions of canopy elements and woody components and the overestimation of needle-to-shoot area ratio on woody components. The MCI, which mainly comprises a near-infrared band camera (Fujifilm IS-1), two visible band cameras (Canon 40D), filters and a pan tilt, was developed to measure clumping index, woody-to-total area ratio and geometric parameters of isolated trees. Two typical sampling plots (Plots 1 and 5) chosen from among 16 permanent forest experiment plots were selected for the estimation of alpha and alpha(theta). The non-random distributions of canopy elements and woody components were estimated separately at eight zenith angles (from 0 degrees to 70 degrees in increments of 10 degrees) using MCI images based on the gap size distribution theory. The visible/near-infrared image pairs captured by the MCI were able to discriminate among sky, leaves, cloud and woody components. Based on three methods of estimation, we obtained woody-to-total area ratios of 0.24, 0.19, 0.19 for Plot 1 and 0.23, 0.18, 0.17 for Plot 5. If clumping effects were ignored, alpha values were overestimated by as much as 21% and 24% at Plots 1 and 5, respectively. We demonstrated that alpha(theta) varied with the zenith angle, with variations in the range of 3-33% at Plot 1 and 2-65% at Plot 5. A new formula for the precise determination of LAI is proposed.

Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model

Vegetation index (VI) is an important variable for retrieving the vegetation biophysical parameters. With different kinds of remote sensing data sets, it is easy to get the VI at different spatial and temporal resolutions. However, the main concern is whether the relationship existing at some scale between the VI and biophysical parameters is still applicable to other scales. This paper first presents a method to correct the spatial scaling effect of NDVI by mathematic analysis, and then analyses NDVI scale sensitivity with data from a spectral database. The result shows that the NDVI obtained by reflectance up‐scaling is larger than the up‐scaled NDVI from NDVI itself in most situations. The NDVI scaling effect is more significant when water exists in a pixel, and increases with the increase in the difference of the sum of visible reflectance and near‐infrared (NIR) reflectance between the vegetation and soil. Finally, a method is proposed to estimate the fraction of vegetation cover (FVC) on the basis of the NDVI spatial scaling correction model. The method is accurate enough to assess the FVC taking into account the scaling effect.

Spatial distribution of net primary productivity and evapotranspiration in Changbaishan Natural Reserve, China, using Landsat ETM+ data

Remote sensing has been a useful tool to monitor net primary productivity (NPP) and evapotranspiration (ET). In this paper, based on field measurements and Landsat enhanced thematic mapper plus (ETM+) data, NPP and ET are estimated in 2001 in the Changbaishan Natural Reserve, China. Maps of land cover, leaf area index, and biomass of this forested region are first derived from ETM+ data. With these maps and additional soil texture and daily meteorological data, NPP and ET maps are produced for 2001 using the boreal ecosystem productivity simulator (BEPS). The results show that the estimated and observed NPP values for forest agree fairly well, with a mean relative error of 8.6%. The NPP of mixed forests is the highest, with a mean of 500 g C m–2·a–1, and that of alpine tundra and shrub is the lowest, with a mean of 136 g C m–2·a–1. Unlike the spatial pattern of NPP, the annual ET changes distinctly with altitude from greater than 600 mm at the foot of the mountain to about 200 mm at the top of the mountain. ET is highest for broadleaf forests and lowest for urban and built-up areas.

Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC)

Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification errors. To address this problem, an automatic shadow-resistant algorithm in the Commission Internationale d’Eclairage L*a*b* color space (SHAR-LABFVC) based on a documented FVC estimation algorithm (LABFVC) is proposed in this paper. The hue saturation intensity (HSI) is introduced in SHAR-LABFVC to enhance the brightness of shaded parts of the image. The lognormal distribution is used to fit the frequency of vegetation greenness and to classify vegetation and the background. Real and synthesized images are used for evaluation, and the results are in good agreement with the visual interpretation, particularly when the FVC is high and the shadows are deep, indicating that SHAR-LABFVC is shadow resistant. Without specific improvements to reduce the shadow effect, the underestimation of FVC can be up to 0.2 in the flourishing period of vegetation at a scale of 10 m. Therefore, the proposed algorithm is expected to improve the validation accuracy of remote sensing products.

Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands

Fractional vegetation cover (FVC) is one of the most important criteria for surface vegetation status. This criterion corresponds to the complement of gap fraction unity at the nadir direction and accounts for the amount of horizontal vegetation distribution. This study aims to directly validate the accuracy of FVC products over crops at coarse resolutions (1 km) by employing field measurements and high-resolution data. The study area was within an oasis in the Heihe Basin, Northwest China, where the Heihe Watershed Allied Telemetry Experimental Research was conducted. Reference FVC was generated through upscaling, which fitted field-measured data with spaceborne and airborne data to retrieve high-resolution FVC, and then high-resolution FVC was aggregated with a coarse scale. The fraction of green vegetation cover product (i.e., GEOV1 FVC) of SPOT/VEGETATION data taken during the GEOLAND2 project was compared with reference data. GEOV1 FVC was generally overestimated for crops in the study area compared with our estimates. Reference FVC exhibits a systematic uncertainty, and GEOV1 can overestimate FVC by up to 0.20. This finding indicates the necessity of reanalyzing and improving GEOV1 FVC over croplands.

Separating vegetation and soil temperature using airborne multiangular remote sensing image data

Abstract Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.

Improved Methods for Spectral Calibration of On-Orbit Imaging Spectrometers

Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features around 760, 940, 1140, and 2060 nm without making use of external atmospheric or surface parameters. A sensitivity analysis based on synthetic data demonstrated that, for each of the two methods, the root-mean-square errors to be expected were less than 0.18 nm for the retrieval of channel wavelength center and less than 0.8 nm for channel full-width at half-maximum. The application of the proposed methods to a real Hyperion data set showed quite-similar cross-track variations in the spectral calibration for the two methods, although relatively large differences in magnitude were found near the 940- and 1140-nm H2O absorption features. The significant improvement of the reflectance spectra derived after the refinement of the instrument spectral calibration confirms the good performance of the proposed methods.