Physical interpretation of the correlation between multi‐angle spectral data and canopy height

Numerous studies have demonstrated the ability of hyper-spectral data to discriminate crop types, however most methods rely on empirical data and are therefore site specific. In this brief proceeding we provide a physically based approach for separation of crop types using multiangle hyperspectral data. We use the radiative transfer theory of spectral invariants which allows for the parameterization of the canopy reflectance into two spectrally invariant and structurally varying parameters-recollision and escape probabilities. The spectral invariant parameters are retrieved from the CHRIS/PROBA multiangle hyperspectral sensor. We present the spectral invariant parameters in spectral invariant space. The horizontal axis provides information about macro scale features such as plant shape and size as well as ground cover. The vertical axis provides information about microscale features such as leaf density as well as portion of sunlit to shaded leaves. These features allow for the natural separation of crops. In addition we illustrate the potential for further separation of crop types based on angular information. Results suggest that multiangle information is important for canopies with similar structural features in the nadir direction.

There have been few approaches developed for the estimation of height using waveform LiDAR data. Unlike any existing methods, we illustrate how the new Moment Distance (MD) framework can characterize the canopy height based on the geometry and return power of the LiDAR waveform without having to go through curve modeling processes. Our approach offers the possibilities of using the raw waveform data to capture vital information from the variety of complex waveform shapes in LiDAR. We assess the relationship of the MD metrics to the key waveform landmarks—such as locations of peaks, power of returns, canopy heights, and height metrics—using synthetic data and real Laser Vegetation Imaging Sensor (LVIS) data. In order to verify the utility of the new approach, we use field measurements obtained through the DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice) campaign. Our results reveal that the MDI can capture temporal dynamics of canopy and segregate generations of stands based on curve shapes. The method satisfactorily estimates the canopy height using the synthetic (<em>r</em>²= 0.40) and the LVIS dataset (<em>r</em>²= 0.74). The MDI is also comparable with existing RH75 (relative height at 75%) and RH50 (relative height at 50%) height metrics. Furthermore, the MDI shows better correlations with ground-based measurements than relative height metrics. The MDI performs well at any type of waveform shape. This opens the possibility of looking more closely at single-peaked waveforms that usually carries complex extremes.

Characterizing leaf-scale fluorescence with spectral invariants

Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest

Expert system-based mineral mapping in northern death valley, California/Nevada, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi-angle optical data

Forest canopy height is an important variable to the modeling of energy over regional and global scales. This paper first examined the relationship between field-surveyed canopy height and LiDAR-derived canopy height, regression between them had an RMSE and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.94 m and 0.64. To extrapolate the LiDAR height to a continuous area, we compared the ability of four sources of optical remote sensing data (MODIS BRFs, MODIS NBAR, MISR and SPOT data) in predicting the LiDAR measured canopy height. Multivariate linear regression and single variable nonlinear regression models were developed, and the best model accurately predicted the LiDAR height using MODIS BRFs data (RMSE=1.2 m, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.67). This model was applied to the whole study area and finally the canopy height map of the study area was generated.

Forest canopy height reflects the vertical structure of forests and gives indications on the growth capacity of trees and the level of forest biomass. Despite an increasing availability of global canopy height maps, there is a lack of maps reflecting temporal dynamics, which is required to understand forest carbon sink capacity. This study uses publicly available global maps and Landsat data to construct a long time series of canopy height maps in southern China (1986 to 2019). Our predictions are in the same range as National Forest Inventory data, both spatially and temporally. The dataset shows clear signs of tree growth, with small trees dominating the first period, which grow taller over the 3 decades. In numbers, the mean canopy height of the forests in southern China increased from 6.4 ± 3.99 m in 1986 to 10.3 ± 5.54 m in 2019, which is an increase of 61.25%. Secondary forests have an overall higher canopy height (17.8 ± 2.12 m) as compared to plantations (14.6 ± 2.05 m), but plantations grow faster (0.20 ± 0.08 m/year) as compared to secondary forests (0.13 ± 0.08 m/year). A driver analysis shows that management is the dominating factor for the canopy height increase, while natural factors play a minor role. Our study reaffirms the human made surge in forest areas in China, quantifies their canopy height dynamics, and demonstrates that spectral data can indeed be used to track canopy height changes at scale.

Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data

Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data

In this paper, we carried out a case study with a multidirectional and hyperspectral data, CHRIS (the Compact High Resolution Imaging Spectrometer) on board platform PROBA (Project for On Board Autonomy). After orthocorrection and atmospheric correction, we analyzed the directional reflectance (HDRF) of a dense conifer forest to get two CSI, i.e. effective value of re-collision probability, pr, and escape probability R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> at different angles. These two invariants at all five angles were regressed to LVIS_H100 data, which is relevant to canopy height, and demonstrated a comparable statistics to the regression with spectral reflectances. In this sense, CSI method can act as an effective tool to reduce the dimension of hyperspectral remote sensing data by a ratio of N/2 (N is the number of spectral bands). Re-collision probability, p <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</inf> , and escape probability R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> can be understood as two principle components in PCA, however, they are superior to principle components because the transformation is based on radiation transfer physics and reduction result have explicit interpretation. In similar consideration to reduce the redundancy as above, we also calculated the deviation of each spectral band to the fitted value from p <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</inf> and R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and make a statistics for all the sampling pixels. We picked out the best fitted bands, as most of them locate in NIR range, we employed statistics to determine which to supplement in visible range, and discussed the potential of these bands to be selected as optimal VNIR bands combination of future vegetation sensor design.

Carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) and methane (CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) are the two primary anthropogenic greenhouse gases contributing to positive radiative forcing that causes global warming. Plumes emitted from point sources such as power plants (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) and fossil fuel production (CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) may be detectable using high spectral and spatial resolution airborne imaging spectrometer data. We used the MODTRAN radiative transfer model to simulate changes in reflected solar radiance caused by increasing CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> or CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> concentration within a 500 m layer at the base of the atmosphere. Radiance residuals were calculated for varying surface reflectance, solar zenith angle, and column water vapor concentration. Radiance residuals were compared to noise equivalent delta radiance (NEdL) for the Classic (10 nm spectral resolution) and Next Generation (5 nm spectral resolution) Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensors. Detection of a CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> plume emitted by a fossil fuel power plant is demonstrated using AVIRIS Classic data.

Introduction: This study, which was first conducted in Vietnam, aimed to develop a multivariable and simple-variable linear regression model from the direct measurement of the UV‒Vis absorption of copper(II) ions in aqueous solution without using other reagents (chelating agents and solvents), which reduces environmental pollution and analysis fees. Methods: Simple-variable and multivariable linear regression models were developed from UV‒Vis spectral data of copper(II) ion solutions with concentrations ranging from 0.2 to 50 ppm. Results: Four multivariable regression models were developed and modified, and the optimal simple variable regression model was selected. This study analyzed the suitability of single and multivariable models for the analysis of copper(II) ions in aqueous solution at low concentrations. Conclusion: This study successfully built and adjusted linear regression models for predicting the copper(II) ion content in aqueous solution via a photometric method. The multivariable model with odd variables (model No. 2’) and the simple-variable model at a wavelength of 221 were optimized for use in the prediction of the concentration at an acceptable level of 0.5 ppm. These results were verified by the graph of the correlation between the true concentration and the predicted concentration in both selected models. In particular, the multivariate model yields significantly more accurate prediction results than does the simple-variable model.

High spectral resolution imagery produced by imaging spectrometers enables the discrimination of geologic materials whose surface expression is subpixel in scale. Moreover, the use of such data makes it possible to distinguish materials which are characterized only by subtle differences in the spectral continuum. We define the “continuum” as the reflectance or radiance spanning the space between spectral features. The capability to distinguish subpixel targets will prove invaluable in studies of the geology of the Earth and planets from airborne and spaceborne imaging spectrometers. However, subpixel targets can only be uniquely identified in a truly optimal sense through the application of data reduction techniques that model the spectral contribution of both target and background materials. Two such techniques are utilized herein. They are a spectral mixture analysis approach and a low probability detection routine based on orthogonal subspace projection. These techniques were applied to the problem of detecting two different volcanic tuff units, one basaltic and one rhyolitic, in two different scenes of data measured by the airborne visible/infrared imaging spectrometer (AVIRIS). These tuff units have limited exposures from an overhead perspective and have spectral signatures which differ from those of background materials only in terms of subtle slope changes in the reflectance continuum. Of the two approaches, it was found that the low probability detection algorithm was more effective in highlighting those pixels that contained the target tuff units while suppressing the response of undesired background materials.

Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great significance. Visible-near infrared (Vis-NIR) spectroscopy has been shown to be a rapid, timely and efficient tool for the prediction of soil organic carbon (SOC). In this study, 104 soil samples were collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and soil organic carbon content were then measured under laboratory conditions. The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method or the multiple scattering correction (MSC) method, after which the spectral reflectance (R) was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). The results showed that: (1) The SOC content in the mining area was generally low (at the below-average level) and exhibited great variability. (2) The spectral reflectance increased with the decrease of soil organic carbon content. In addition, the sensitivity of the spectrum to the change in SOC content, especially that in the near-infrared band of the original reflectance, decreased when the SOC content was low. (3) The modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R2 = 0.86, RMSE = 2.00 g/kg, verification R2 = 0.78, RMSE = 1.81 g/kg, and RPD = 2.69). In addition, the first-order differential of R combined with SG, MSC with R, SG together with MSC and R also produced better modeling results than other pretreatment combinations. Vis-NIR modeling with specific spectral preprocessing methods could predict SOC content effectively.