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

The fourth phase of the radiative transfer model intercomparison (RAMI) exercise : Actual canopy scenarios and conformity testing

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RI model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. (C) 2015 The Authors. Published by Elsevier Inc.Peer reviewe

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS

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

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

Comparison of different ground techniques to map leaf area index of Norway spruce forest canopy

The leaf area index (LAI) of three monocultures of Norway spruce (Picea abies (L.) Karst), different in age and structure, was measured by means of two indirect optical techniques of LAI field mapping: 1/ plant canopy analyser LAI-2000, and 2/ digital hemispherical photographs (DHP). The supportive measurements with the TRAC instrument were conducted to produce mainly the element clumping index. The aim of the study was to compare the performances of LAI-2000 and DHP and to evaluate effect of three different sampling strategies on field estimation of leaf area index. One of the suggested sampling designs introduced spatial oversampling around one-point measurement. The oversampling was expected to reveal the importance of sampling point position with respect to surrounding trees. In general, the LAI-2000 instrument produced higher estimates of effective leaf area index than DHP in all experimental stands. On the other hand, the higher "true" estimates of LAI were obtained from DHP. All three sampling strategies produced consistent estimates of effective and "true" LAI in all forest sites. The spatial oversampling of LAI measurement point did not significantly improve the LAI estimate of the canopy subplots