Error budget for geolocation of spectroradiometer point observations from an unmanned aircraft system

We investigate footprint geolocation uncertainties of a spectroradiometer mounted on an unmanned aircraft system (UAS). Two microelectromechanical systems-based inertial measurement units (IMUs) and global navigation satellite system (GNSS) receivers were used to determine the footprint location and extent of the spectroradiometer. Errors originating from the on-board GNSS/IMU sensors were propagated through an aerial data georeferencing model, taking into account a range of values for the spectroradiometer field of view (FOV), integration time, UAS flight speed, above ground level (AGL) flying height, and IMU grade. The spectroradiometer under nominal operating conditions (8° FOV, 10 m AGL height, 0.6 s integration time, and 3 m/s flying speed) resulted in footprint extent of 140 cm across-track and 320 cm along-track, and a geolocation uncertainty of 11 cm. Flying height and orientation measurement accuracy had the largest influence on the geolocation uncertainty, whereas the FOV, integration time, and flying speed had the biggest impact on the size of the footprint. Furthermore, with an increase in flying height, the rate of increase in geolocation uncertainty was found highest for a low-grade IMU. To increase the footprint geolocation accuracy, we recommend reducing flying height while increasing the FOV which compensates the footprint area loss and increases the signal strength. The disadvantage of a lower flying height and a larger FOV is a higher sensitivity of the footprint size to changing distance from the target. To assist in matching the footprint size to uncertainty ratio with an appropriate spatial scale, we list the expected ratio for a range of IMU grades, FOVs and AGL heights.Deepak Gautam, Christopher Watson, Arko Lucieer and Zbynĕk Malenovsk

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

The global distribution of leaf chlorophyll content

Leaf chlorophyll is central to the exchange of carbon, water and energy between the biosphere and the atmosphere, and to the functioning of terrestrial ecosystems. This paper presents the first spatially-continuous view of terrestrial leaf chlorophyll content (ChlLeaf) at the global scale. Weekly maps of ChlLeaf were produced from ENVISAT MERIS full resolution (300 m) satellite data using a two-stage physically-based radiative transfer modelling approach. Firstly, leaf-level reflectance was derived from top-of-canopy satellite reflectance observations using 4-Scale and SAIL canopy radiative transfer models for woody and non-woody vegetation, respectively. Secondly, the modelled leaf-level reflectance was input into the PROSPECT leaf-level radiative transfer model to derive ChlLeaf. The ChlLeaf retrieval algorithm was validated using measured ChlLeaf data from 248 sample measurements at 28 field locations, and covering six plant functional types (PFTs). Modelled results show strong relationships with field measurements, particularly for deciduous broadleaf forests (R2 = 0.67; RMSE = 9.25 μg cm-2; p < 0.001), croplands (R2 = 0.41; RMSE = 13.18 μg cm-2; p < 0.001) and evergreen needleleaf forests (R2 = 0.47; RMSE = 10.63 μg cm-2; p < 0.001). When the modelled results from all PFTs were considered together, the overall relationship with measured ChlLeaf remained good (R2 = 0.47, RMSE = 10.79 μg cm-2; p < 0.001). This result is an improvement on the relationship between measured ChlLeaf and a commonly used chlorophyll-sensitive spectral vegetation index; the MERIS Terrestrial Chlorophyll Index (MTCI; R2 = 0.27, p < 0.001). The global maps show large temporal and spatial variability in ChlLeaf, with evergreen broadleaf forests presenting the highest leaf chlorophyll values, with global annual median values of 54.4 μg cm-2. Distinct seasonal ChlLeaf phenologies are also visible, particularly in deciduous plant forms, associated with budburst and crop growth, and leaf senescence. It is anticipated that this global ChlLeaf product will make an important step towards the explicit consideration of leaf-level biochemistry in terrestrial water, energy and carbon cycle modelling

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Early Diagnosis of Vegetation Health From High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned From Empirical Relationships and Radiative Transfer Modelling

[Purpose of Review] We provide a comprehensive review of the empirical and modelling approaches used to quantify the radiation–vegetation interactions related to vegetation temperature, leaf optical properties linked to pigment absorption and chlorophyll fluorescence emission, and of their capability to monitor vegetation health. Part 1 provides an overview of the main physiological indicators (PIs) applied in remote sensing to detect alterations in plant functioning linked to vegetation diseases and decline processes. Part 2 reviews the recent advances in the development of quantitative methods to assess PI through hyperspectral and thermal images.[Recent Findings] In recent years, the availability of high-resolution hyperspectral and thermal images has increased due to the extraordinary progress made in sensor technology, including the miniaturization of advanced cameras designed for unmanned aerial vehicle (UAV) systems and lightweight aircrafts. This technological revolution has contributed to the wider use of hyperspectral imaging sensors by the scientific community and industry; it has led to better modelling and understanding of the sensitivity of different ranges of the electromagnetic spectrum to detect biophysical alterations used as early warning indicators of vegetation health.[Summary] The review deals with the capability of PIs such as vegetation temperature, chlorophyll fluorescence, photosynthetic energy downregulation and photosynthetic pigments detected through remote sensing to monitor the early responses of plants to different stressors. Various methods for the detection of PI alterations have recently been proposed and validated to monitor vegetation health. The greatest challenges for the remote sensing community today are (i) the availability of high spatial, spectral and temporal resolution image data; (ii) the empirical validation of radiation–vegetation interactions; (iii) the upscaling of physiological alterations from the leaf to the canopy, mainly in complex heterogeneous vegetation landscapes; and (iv) the temporal dynamics of the PIs and the interaction between physiological changes.The authors received funding provided by the FluorFLIGHT (GGR801) Marie Curie Fellowship, the QUERCUSAT and ESPECTRAMED projects (Spanish Ministry of Economy and Competitiveness), the Academy of Finland (grants 266152, 317387) and the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P.Peer reviewe

Remote-sensed monitoring of norway spruce forest ecosystems

This contribution informes about methods and preliminary results of the project following objectives:Investigate possibilities of the recognition of individual types and periods of multiple stress response behaviour and spectral properties of Norway spruce (Picea abies (L.) K a r s t.) at the shoot and crown levels through spectrometric measurements of needles, shoots and branches, supported by the foliar pigments and cellular compound laboratory analyses, and through analysis of canopies from remote-sensed hyperspectral imagery.Propose a method for estimation and interpretation of multiple stress response history and crown-status assessment of Norway spruce forest stands by the remote sensing approach, via analysis of hyperspectral images

Effects of woody elements on simulated canopy reflectance: implications for forest chlorophyll content retrieval

An important bio-indicator of actual plant health status, the foliar content of chlorophyll a and b (Cab), can be estimated using imaging spectroscopy. For forest canopies, however, the relationship between the spectral response and leaf chemistry is confounded by factors such as background (e.g. understory), canopy structure, and the presence of non-photosynthetic vegetation (NPV, e.g. woody elements)--particularly the appreciable amounts of standing and fallen dead wood found in older forests. We present a sensitivity analysis for the estimation of chlorophyll content in woody coniferous canopies using radiative transfer modeling, and use the modeled top-of-canopy reflectance data to analyze the contribution of woody elements, leaf area index (LAI), and crown cover (CC) to the retrieval of foliar Cab content. The radiative transfer model used comprises two linked submodels: one at leaf level (PROSPECT) and one at canopy level (FLIGHT). This generated bidirectional reflectance data according to the band settings of the Compact High Resolution Imaging Spectrometer (CHRIS) from which chlorophyll indices were calculated. Most of the chlorophyll indices outperformed single wavelengths in predicting Cab content at canopy level, with best results obtained by the Maccioni index ([R780 - R710] / [R780 - R680]). We demonstrate the performance of this index with respect to structural information on three distinct coniferous forest types (young, early mature and old-growth stands). The modeling results suggest that the spectral variation due to variation in canopy chlorophyll content is best captured for stands with medium dense canopies. However, the strength of the up-scaled Cab signal weakens with increasing crown NPV scattering elements, especially when crown cover exceeds 30%. LAI exerts the least perturbations. We conclude that the spectral influence of woody elements is an important variable that should be considered in radiative transfer approaches when retrieving foliar pigment estimates in heterogeneous stands, particularly if the stands are partly defoliated or long-lived

EVALUATION OF VARIOUS SPECTRAL INPUTS FOR ESTIMATION OF FOREST BIOCHEMICAL AND STRUCTURAL PROPERTIES FROM AIRBORNE IMAGING SPECTROSCOPY DATA

In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab &minus;2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view

Empirical test of the spectral invariants theory using imaging spectroscopy data from a coniferous forest

The spectral invariants theory presents an alternative approach for modeling canopy scattering in remote sensing applications. The theory is particularly appealing in the case of coniferous forests, which typically display grouped structures and require computationally intensive calculation to account for the geometric arrangement of their canopies. However, the validity of the spectral invariants theory should be tested with empirical data sets from different vegetation types. In this paper, we evaluate a method to retrieve two canopy spectral invariants, the recollision probability and the escape factor, for a coniferous forest using imaging spectroscopy data from multiangular CHRIS PROBA and NADIR- view AISA Eagle sensors. Our results indicated that in coniferous canopies the spectral invariants theory performs well in the near infrared spectral range. In the visible range, on the other hand, the spectral invariants theory may not be useful. Secondly, our study suggested that retrieval of the escape factor could be used as a new method to describe the BRDF of a canopy

Geometrical and structural parametrization of forest canopy radiative transfer by LIDAR measurements

A forest canopy is a complex system with a highly structural multi-scale architecture. Physical based radiative transfer (RT)modelling has been shown to be an effective tool for retrieval of vegetation canopy biochemical/physical characteristics from optical remote sensing data. A high spatial resolution RT through a forest canopy requires several geometrical and structural parameters of trees and understory to be specified with an appropriate accuracy. Following attributes on forest canopy are required: i) basic tree allometric parameters (i.e., tree height, stem diameter and length, crown length and projection, simplified crown shape, etc.), ii) parameters describing distribution of green biomass (foliage) (e.g., leaf area index (LAI), leaf angle distribution (LAD) or average leaf angle (ALA), clumping of leaves and density of clumps, air gaps and defoliation, etc.), and iii) parameters describing distribution of woody biomass (branches and twigs) (e.g., number, position and angular orientation of the first order branches – branches growing directly from stem, twig area index (TAI), twig angle distribution (TAD)). At very high spatial resolution (airborne image data), an insufficiently characterized structure of the forest canopy can result in inaccurate RT simulations. Direct destructive methods of measuring canopy structure are unfeasible at large-scales, therefore, in this paper we review the non-invasive Light Detection and Ranging (LIDAR) approaches. We also present some results on tree structure parameters acquired by a commercially available ground-based LIDAR scanner employed in scanning the matured Norway spruce trees