Vanni Nardino

Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV

Abstract Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but their application is limited by the spectral and temporal resolution available from these systems, which is often not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remote sensing platforms has recently gained increasing attention, but their applications in forestry are still at an experimental stage. In this study we described a methodology to obtain rapid and reliable estimates of forest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digital numbers were converted to the green leaf algorithm (GLA) and to the CIE L*a*b* colour space to obtain estimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributes were compared with in situ estimates obtained from two digital canopy photographic techniques (cover and fisheye photography). The method was tested in beech forests. UAV images accurately quantified canopy cover even in very dense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images, leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photography. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, but the accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution. We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and meaningful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage of high resolution imagery with quick turnaround series, being therefore suitable for routine forest stand monitoring and real-time applications.

Theoretical aspects of Fourier Transform Spectrometry and common path triangular interferometers

Recent investigations have induced relevant advancements of imaging interferometry, which is becoming a viable option for Earth remote sensing. Various research programs have chosen the Sagnac configuration for new imaging interferometers. Due to the growing diffusion of this technique, we have developed a self-contained theory for describing the signal produced by triangular FTSs and its optimal processing. We investigate the relevant disadvantages of multiplexing, and compare dispersive with FTS instruments. The paper addresses some methods for correcting the phase error, and the non-unitary transformation performed by a Sagnac interferometer. The effect of noise on spectral estimations is discussed.

Radiometric and signal-to-noise ratio properties of multiplex dispersive spectrometry

Recent theoretical investigations have shown important radiometric disadvantages of interferential multiplexing in Fourier transform spectrometry that apparently can be applied even to coded aperture spectrometers. We have reexamined the methods of noninterferential multiplexing in order to assess their signal-to-noise ratio (SNR) performance, relying on a theoretical modeling of the multiplexed signals. We are able to show that quite similar SNR and radiometric disadvantages affect multiplex dispersive spectrometry. The effect of noise on spectral estimations is discussed.

Developing a new hyperspectral imaging interferometer for earth observation

Abstract. The Aerospace Leap-frog Imaging Stationary interferometer for Earth Observation (ALISEO) is a hyperspectral imaging interferometer for Earth remote sensing. The instrument belongs to the class of Sagnac stationary interferometers and acquires the image of the target superimposed to the pattern of autocorrelation functions of the electromagnetic field coming from each pixel. The ALISEO sensor together with the data processing algorithms that retrieve the at-sensor spectral radiance are discussed. A model describing the instrument OPD and interferogram center is also discussed, improving the procedures for phase retrieval and spectral estimation. Images acquired by ALISEO are shown, and examples of retrieved reflectance spectra are presented.

Emissivity and Temperature Assessment Using a Maximum Entropy Estimator: Structure and Performance of the MaxEnTES Algorithm

In this paper, we discuss the structure and performance of the maximum entropy temperature-emissivity separation (MaxEnTES) algorithm for assessing land temperature and emissivity from thermal infrared hyperspectral images. This procedure derives the emissivity spectrum and the temperature of the target adopting the maximum entropy (MaxEnt) estimation approach. The main advantage of the MaxEnt statistical inference is the absence of any external hypothesis, which is, instead, the main critical point characterizing any other temperature-emissivity separation (TES) algorithm. The MaxEnTES algorithm carries out the TES task adopting a modified version of the subgradient Shors r-algorithm adopted for numerical optimization of a MaxEnt objective function. For this purpose, we have utilized the C/C++ Solvopt code from the University of Gratz to develop a practical data processing implementation. In this paper, we discuss the mathematical structure of the MaxEnTES algorithm and analyze its performance in depth using numerical simulations and remote sensing Multispectral Infrared/Visible Imaging Spectrometer images. We show that the MaxEnTES algorithm provides improved accuracy for temperature and emissivity estimation, lowering the standard estimation error to a fraction of degree Kelvin. In agreement with previous investigations, we find that the estimation accuracy grows when increasing the number of available spectral channels. The systematic errors affecting the temperature estimates (e.g., bias) are thoroughly evaluated. We prove that the MaxEnTES algorithm retrieves the correct shape of the target emissivity spectrum even in presence of a significant temperature estimation error.

Passive remote sensing of solar-induced fluorescence spectra of crude oil

The solar-induced fluorescence (SIF) of a simulated oil slick is remotely retrieved from the infilling of the solar Fraunhofer lines of its radiance spectrum acquired by means of a sub-nanometric spectral resolution fluorescence lidar operated in passive mode (i.e. as a spectroradiometer). SIF data, retrieved from the infilling of several Fraunhofer lines selected in the 390–660 nm range, were exploited to reconstruct the oil fluorescence spectrum. This spectrum was finally compared with laser-induced fluorescence spectra acquired on the same oil slick by using the fluorescence lidar. The results open new prospects for the remote sensing of oils and their classification.

McCART: Monte Carlo Code for Atmospheric Radiative Transfer

McCART is a numerical procedure to solve the radiative transfer equation for light propagation through the atmosphere from visible to near-infrared wavelengths. The procedure has been developed to study the effect of the atmosphere in the remote sensing of the Earth, using aerospace imaging spectrometers. The simulation is run for a reference layered plane nonabsorbing atmosphere and a plane ground with uniform reflectance. For a given distribution of ground reflectance and for a specific profile of scattering and absorption properties of the atmosphere, the spectral response of the sensor is obtained in a short time from the results of the Monte Carlo simulation by using scaling relationships and symmetry properties. The procedure also includes an accurate analysis of the adjacency and trapping effects due to multiple scattering of photons coming from neighboring pixels. McCART can generate synthetic images of the Earths surface for arbitrary viewing conditions. The results can be used to establish the limits of applicability of approximate algorithms for the processing and analysis of hyperspectral images acquired by imaging spectrometers. In addition, the algorithm can be used to develop procedures for atmospheric correction for the accurate retrieval of the spectral ground reflectance.

Assessing the daedalus sensor's performance by means of spectral mixture analysis in the Migliarino, San Rossore, Massaciuccoli Regional Park (Italy)

Coastal areas represent relevant zones for environmental monitoring. They are characterized by several habitats that coexist and interact in a condition of dynamic equilibrium. Moreover they are sites of human settlements and important economic and commercial activities. Therefore, an accurate environmental characterization of these complex systems require a large amount of information and different levels of analysis. To date, the contribution by remote sensing to study coastal zones is widely accepted as it provides high quality tools and products to investigate and monitor these fragile ecosystems. In this framework, the aim of this work is to test the performance of the multispectral Daedalus Airborne Thematic Mapper (ATM-2) sensor for the interpretation and analysis of geo-environmental features of the Migliarino, San Rossore, Massaciuccoli Regional Parks coastal area.

OPTIMA: advanced methods for the analysis, integration and optimization of PRISMA mission products

PRISMA is an Earth observation system that combines a hyperspectral sensor with a panchromatic, medium-resolution camera. OPTIMA is one of the five independent scientific research projects funded by the Italian Space Agency in the framework of PRISMA mission for the development of added-value algorithms and advanced applications. The main goal of OPTIMA is to increase and to strengthen the applications of PRISMA through the implementation of advanced methodologies for the analysis, integration and optimization of level 1 and 2 products. The project is comprehensive of several working packages: data simulation, data quality, data optimization, data processing and integration and, finally, evaluation of some applications related to natural hazards. Several algorithms implemented during the project employ high-speed autonomous procedures for the elaboration of the upcoming images acquired by PRISMA. To assess the performances of the developed algorithms and products, an end-to-end simulator of the instrument has been implemented. Data quality analysis has been completed by introducing noise modeling. Stand-alone procedures of radiometric and atmospheric corrections have been developed, allowing the retrieval of at-ground spectral reflectance maps. Specific studies about image enhancement, restoration and pan-sharpening have been carried out for providing added-value data. Regarding the mission capability of monitoring environmental processes and disasters, different techniques for estimating surface humidity and for analyzing burned areas have been investigated. Finally, calibration and validation activities utilizing the CAL/VAL test site managed by CNR-IFAC and located inside the Regional Park of San Rossore (Pisa), Italy have been considered.

Implementation of a hyperspectral image simulation tool and analysis of the impact of instrumental noise on vegetation fluorescence retrieval using the telluric O2-A and O2-B lines

This paper presents an analysis of the main artifacts introduced by the non- uniformity of the instrumental characteristics in an image dataset simulated by taking into account the main technical features of the FLORIS sensor. The dataset was produced by using a hyperspectral image simulation tool – named FLISM (Fluorescence Image Simulator for space Missions) – specifically implemented to produce images of fluorescent and non-fluorescent targets acquired by a pushbroom hyperspectral instrument. In this specific case, the available technical specifications of the FLORIS sensor were taken into account to investigate some critical issues concerning Solar Induced Fluorescence (SIF) retrieval in vegetated areas by means of the FLD (Fraunhofer Line Discriminator) method, which relies on the telluric O2-A and O2-B lines to decouple the weak SIF signal of vegetation from the backscattered radiance.