Performance Considerations for the SIMPL Single Photon, Polarimetric, Two-Color Laser Altimeter as Applied to Measurements of Forest Canopy Structure and Composition

The Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) is a multi-beam, micropulse airborne laser altimeter that acquires active and passive polarimetric optical remote sensing measurements at visible and near-infrared wavelengths. SIMPL was developed to demonstrate advanced measurement approaches of potential benefit for improved, more efficient spaceflight laser altimeter missions. SIMPL data have been acquired for wide diversity of forest types in the summers of 2010 and 2011 in order to assess the potential of its novel capabilities for characterization of vegetation structure and composition. On each of its four beams SIMPL provides highly-resolved measurements of forest canopy structure by detecting single-photons with 15 cm ranging precision using a narrow-beam system operating at a laser repetition rate of 11 kHz. Associated with that ranging data SIMPL provides eight amplitude parameters per beam unlike the single amplitude provided by typical laser altimeters. Those eight parameters are received energy that is parallel and perpendicular to that of the plane-polarized transmit pulse at 532 nm (green) and 1064 nm (near IR), for both the active laser backscatter retro-reflectance and the passive solar bi-directional reflectance. This poster presentation will cover the instrument architecture and highlight the performance of the SIMPL instrument with examples taken from measurements for several sites with distinct canopy structures and compositions. Specific performance areas such as probability of detection, after pulsing, and dead time, will be highlighted and addressed, along with examples of their impact on the measurements and how they limit the ability to accurately model and recover the canopy properties. To assess the sensitivity of SIMPL's measurements to canopy properties an instrument model has been implemented in the FLIGHT radiative transfer code, based on Monte Carlo simulation of photon transport. SIMPL data collected in 2010 over the Smithsonian Environmental Research Center, MD are currently being modelled and compared to other remote sensing and in situ data sets. Results on the adaptation of FLIGHT to model micropulse, single'photon ranging measurements are presented elsewhere at this conference. NASA's ICESat-2 spaceflight mission, scheduled for launch in 2016, will utilize a multi-beam, micropulse, single-photon ranging measurement approach (although non-polarimetric and only at 532 nm). Insights gained from the analysis and modelling of SIMPL data will help guide preparations for that mission, including development of calibration/validation plans and algorithms for the estimation of forest biophysical parameters

Upscaling of solar induced chlorophyll fluorescence from leaf to canopy using the DART model and a realistic 3D forest scene

Non peer reviewe

Radiative transfer modelling reveals why canopy reflectance follows function

Optical remote sensing is potentially highly informative to track Earth’s plant functional diversity. Yet, causal explanations of how and why plant functioning is expressed in canopy reflectance remain limited. Variation in canopy reflectance can be described by radiative transfer models (here PROSAIL) that incorporate plant traits affecting light transmission in canopies. To establish causal links between canopy reflectance and plant functioning, we investigate how two plant functional schemes, i.e. the Leaf Economic Spectrum (LES) and CSR plant strategies, are related to traits with relevance to reflectance. These traits indeed related to both functional schemes, whereas only traits describing leaf properties correlated with the LES. In contrast, traits related to canopy structure showed no correlation to the LES, but to CSR strategies, as the latter integrates both plant economics and size traits, rather than solely leaf economics. Multiple optically relevant traits featured comparable or higher correspondence to the CSR space than those traits originally used to allocate CSR scores. This evidences that plant functions and strategies are directly expressed in reflectance and entails that canopy ‘reflectance follows function’. This opens up new possibilities to understand differences in plant functioning and to harness optical remote sensing data for monitoring Earth´s functional diversity

The Laegeren site: an augmented forest laboratory combining 3-D reconstruction and radiative transfer models for trait-based assessment of functional diversity

Given the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity

Recovery of forest canopy parameters by inversion of multispectral LiDAR data

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion

Evaluating the use of Beer's law for estimating light interception in canopy architectures with varying heterogeneity and anisotropy

Light interception in plant canopies is most commonly estimated using a simple one-dimensional turbid medium model (i.e., Beer's law). Inherent in this class of models are assumptions that vegetation is uniformly distributed in space (homogeneous) and in many cases that vegetation orientation is uniformly distributed (isotropic). It is known that these assumptions are violated in a wide range of canopies, as real canopies commonly have heterogeneity at multiple scales and almost always have highly anisotropic leaf angle distributions. However, it is not quantitatively known under what conditions these assumptions become problematic given the difficulty of robustly evaluating model results for a range of canopy architectures. In this study, assumptions of vegetation homogeneity and isotropy were evaluated under clear sky conditions for a range of virtually-generated crop canopies with the aid of a detailed three-dimensional, leaf-resolving radiation model. Results showed that Beer's law consistently over predicted light interception for all canopy configurations. For canopies where the plant spacing was comparable to the plant height, Beer's law performed poorly, and over predicted daily intercepted sunlight by up to ∼115%. For vegetation with a highly anisotropic leaf inclination distribution but a relatively isotropic leaf azimuth distribution, the assumption of canopy isotropy (i.e., G = 0.5) resulted in relatively small errors. However, if leaf elevation and azimuth were both highly anisotropic, the assumption of canopy isotropy could introduce significant errors depending on the orientation of the azimuthal anisotropy with respect to the sun's path

An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth’s surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We hereby present an “Emulator toolbox” that enables analysing multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. The toolbox is included in the free-access ARTMO’s MATLAB suite for parameter retrieval and model inversion and currently contains both linear and non-linear MO-MLRAs, namely partial least squares regression (PLSR), kernel ridge regression (KRR) and neural networks (NN). These MO-MLRAs have been evaluated on their precision and speed to approximate the soil vegetation atmosphere transfer model SCOPE (Soil Canopy Observation, Photochemistry and Energy balance). SCOPE generates, amongst others, sun-induced chlorophyll fluorescence as the output signal. KRR and NN were evaluated as capable of reconstructing fluorescence spectra with great precision. Relative errors fell below 0.5% when trained with 500 or more samples using cross-validation and principal component analysis to alleviate the underdetermination problem. Moreover, NN reconstructed fluorescence spectra about 50-times faster and KRR about 800-times faster than SCOPE. The Emulator toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together

Estimation of intercepted radiation on row-structured orchards with remote sensing and radiative transfer models

The light energy absorbed by plant leaves drives fundamental physiological processes such as photosynthesis. The absorption of light occurs within the 400-700 nm spectral region, so it is called Photosynthetic Active Radiation, PAR. Thus, the fraction of intercepted PAR is called fIPAR. This thesis studies the estimation of fIPAR with high spatial resolution sensors and radiative transfer models in heterogeneous orchards. The objective is to obtain maps showing the spatial variability of fIPAR within the field. In previous works, relationships between spectral vegetation indices (SVI) and fIPAR have been obtained for homogeneous crops. However, studies were lacking where these kind of relationships were explored for heterogeneous orchards. The heterogeneous orchards are more structurally complex than homogeneous crops; therefore previous relationships might not be applicable in a general way. This work explored these relationships in heterogeneous canopies. This study required extensive field measurements of architecture of the canopy, fIPAR as well as analysis of airborne imagery acquired by a sensor on board and unmaned aerial vehicle (UAV). The different studied canopies were orange, peach, olive and vineyard orchards. Moreover, the use of radiativa transfer models allowed the evaluation of the influence of different parameters such as, solar geometry, row orientation on SVI, fIPAR as well as the relation between them.La radiación solar interceptada por un cultivo es un factor determinante en numerosos procesos de importancia para la planta como lo es la fotosíntesis. La energía absorbida por el cultivo para tal es la comprendida en la región del espectro 400 – 700 nm, y se denomina radiación fotosintéticamente activa o de su traducción al inglés radiación PAR. Así, la fracción de radiación PAR interceptada es llamada fIPAR. En este trabajo se aborda la estimación de fIPAR en cultivos discontinuos, como árboles frutales, mediante sensores de alta resolución espacial y modelos de transferencia radiativa. El objetivo es crear mapas de variabilidad espacial de fIPAR útiles para el manejo del cultivo en agricultura de precisión. En trabajos previos se han obtenido índices de vegetación (IV) mediante teledetección demostrando su relación con fIPAR en cultivos homogéneos. Sin embargo no existen muchos trabajos donde se investiguen este tipo relaciones en cultivos heterogéneos, cuya estructura más compleja hace que relaciones obtenidas en estos trabajos anteriores, puedan no ser aplicables de forma general. Para estudiar estas relaciones en este tipo de cubiertas se llevaron a cabo medidas estructurales y de radiación interceptada en fincas de naranjo, melocotón, olivar y viñedo. Así como el procesamiento y análisis de imágenes obtenidas mediante métodos innovadores basados en vehículos aéreos no tripulados. Con el fin de evaluar la estimación de radiación interceptada se usaron modelos de transferencia radiativa. Estos modelos permitieron simular distintos escenarios de plantación y estudiar cómo parámetros, como la posición del sol, la orientación del cultivo o el tipo de suelo, influyen a la variación de índices de vegetación, fIPAR, y la relación entre ellos