Xihan Mu

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.

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.

Plant regeneration from in vitro-cultured seedling leaf protoplasts of Actinidia eriantha Benth

Abstract Newly expanded in vitro leaves of Actinidia eriantha were used for protoplast isolation. Protoplasts were cultured in liquid MS medium (lacking NH4NO3) supplemented with 2,4-D (2,4-dichlorophenoxyacetic acid) and 0.4 M glucose. The plating efficiency after 3 weeks of culture was 19.4%, and calli were recovered without addition of fresh medium. These calli regenerated shoots on transfer to MS medium containing 2.28 μM zeatin and 0.57 μM IAA (indole-3-acetic acid). Regenerated shoots were rooted by immersion in 20 ppm IBA (indole-3-butyric acid) solution before culturing on half-strength MS medium lacking growth regulators. Somaclonal variation, in terms of chromosome number and nuclei per cell of protoplast-derived plants, was estimated.

Evaluation of sampling methods for validation of remotely sensed fractional vegetation cover

Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases.

Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions

The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products.

The complicate observations and multi-parameter land information constructions on allied telemetry experiment (COMPLICATE)

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.

A Radiative Transfer Model for Heterogeneous Agro-Forestry Scenarios

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer (RT) models for predicting the surface reflectance. This paper developed an analytical RT model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into nonboundary regions (NRs) and boundary regions (BRs). The scattering contribution of the NRs can be estimated from the scattering-by-arbitrarily-inclined-leaves-with-the-hot-spot-effect model as homogeneous canopies, whereas that of the BRs is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multiangular airborne observations and discrete-anisotropic-RT model simulations were used to validate and evaluate the RTAF model over an agro-forestry scenario in the Heihe River Basin, China. The results suggest that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous scenarios in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g., leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.

Accuracy evaluation of the ground-based fractional vegetation cover measurement by using simulated images

Digital photography is now the most widely used method to obtain the Fractional Vegetation Cover (FVC) in field measurements. Its accuracy is affected by shooting conditions and classification methods of digital images. In this paper, we chose summer maize as the study plant, used computer simulation method to control the shooting conditions strictly and generate simulated scene. Then a physically based ray-tracing (PBRT) algorithm was used to render the scene to obtain simulated images under different shooting conditions. Supervised classification and CIE L*a*b* color space threshold method were used to extract FVC values from the simulated images. Comparing the extracted FVC values with the scenes true FVC value, we evaluated the FVC accuracy of different shooting conditions and classification methods. The results can act as a guidance of digital photography to obtain the FVC.

A method for leaf gap fraction estimation based on multispectral digital images from Multispectral Canopy Imager

Gap fraction is a very important parameter to the indirect estimation of the true Leaf Area Index. In this paper, we combined the multispectral digital imageries (RGB color imagery and Near-Infrared imagery), which were obtained from a new device called Multispectral Canopy Imager (MCI), to estimate gap fraction. A new method incorporated with CIE L*a*b* color space has also been proposed to segment the multispectral digital imagery. The preliminary results of the estimated gap fraction have been showed in the conclusions section and been proved to be very well.