Prof. Massimo Menenti Profile

Prof. Massimo Menenti

at Technische Univ Delft

SPIE Involvement:

Author

Publications (16)

Proceedings Article | 21 August 2020 Presentation

Presentation

Observation of atmospheric ammonia with a compact hyperspectral imager in the thermal infrared

Nathan Vercruyssen, Marco Esposito, Simon Conticello, Agne Paskeviciute, Zeger de Groot, Martin Van Damme, Lieven Clarisse, Pierluigi Foglia Manzillo, Massimo Menenti, Alijafar Mousivand, Pierre-François Coheur, Ilias Manolis

Proceedings Volume 11505, 1150509 (2020) https://doi.org/10.1117/12.2570682

KEYWORDS: Nitrogen, Thermography, Infrared radiation, Satellites, Hyperspectral imaging, Imaging systems, Infrared imaging, Earth's atmosphere, Agriculture, Ecosystems

Read Abstract +

SPIE Journal Paper | 8 December 2017 Open Access

Open Access

Estimation of subpixel snow sublimation from multispectral satellite observations

Ning Wang, Li Jia, Chaolei Zheng, Massimo Menenti

JARS, Vol. 11, Issue 04, 046017, (December 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.046017

KEYWORDS: Satellites, Earth observing sensors, Heat flux, Humidity, Remote sensing, Aerodynamics, Data centers, Spatial resolution, MODIS, Wind energy

Read Abstract +

SPIE Journal Paper | 30 October 2017 Open Access

Open Access

Spectral region identification versus individual channel selection in supervised dimensionality reduction of hyperspectral image data

S. Enayat Hosseini Aria, Massimo Menenti, Ben G. H. Gorte

JARS, Vol. 11, Issue 04, 046010, (October 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.046010

KEYWORDS: Hyperspectral imaging, Image classification, Algorithm development, Scene classification, Near infrared, Feature extraction, Error analysis, Statistical analysis, Earth sciences, Image sensors

Read Abstract +

SPIE Journal Paper | 17 February 2017

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

Nana Li, Li Jia, Jing Lu, Massimo Menenti, Jie Zhou

JARS, Vol. 11, Issue 01, 016028, (February 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.016028

KEYWORDS: Soil science, Remote sensing, Data modeling, Heat flux, Vegetation, Thermal modeling, Error analysis, In situ metrology, Lithium, Composites

Read Abstract +

Proceedings Article | 21 November 2012 Paper

Paper

Analyzing the inundation patterns in Asia floodplains by passive microwave data

Haolu Shang, Li Jia, Massimo Menenti

Proceedings Volume 8524, 85240E (2012) https://doi.org/10.1117/12.977238

KEYWORDS: Vegetation, Reflectivity, Microwave radiation, Floods, Soil science, Sensors, Polarization, Dielectric polarization, Data modeling, Spatial resolution

Read Abstract +

Proceedings Article | 21 November 2012 Paper

Paper

Assessing the sensitivity of two new indicators of vegetation response to water availability for drought monitoring

Li Jia, Guangcheng Hu, Jie Zhou, Massimo Menenti

Proceedings Volume 8524, 85241A (2012) https://doi.org/10.1117/12.977416

KEYWORDS: Vegetation, Satellites, MODIS, Remote sensing, Climatology, Clouds, Meteorology, Reconstruction algorithms, Earth observing sensors, Ecosystems

Read Abstract +

Proceedings Article | 8 November 2012 Paper

Paper

Spectral discrimination based on the optimal informative parts of the spectrum

S. E. Hosseini Aria, M. Menenti, B. Gorte

Proceedings Volume 8537, 853709 (2012) https://doi.org/10.1117/12.975258

KEYWORDS: Matrices, Vegetation, Signal to noise ratio, Distance measurement, Hyperspectral imaging, Sensors, Remote sensing, Statistical analysis, Information theory, Reflectivity

Read Abstract +

Proceedings Article | 12 August 2009 Paper

Paper

Skeleton-based botanic tree diameter estimation from dense LiDAR data

Alexander Bucksch, Roderik Lindenbergh, Massimo Menenti, Muhammad Rahman

Proceedings Volume 7460, 746007 (2009) https://doi.org/10.1117/12.825997

KEYWORDS: Breast, LIDAR, Clouds, Algorithm development, Laser systems engineering, Airborne laser technology, Laser development, Vegetation, Raman spectroscopy, Analytical research

Read Abstract +

Proceedings Article | 9 October 2007 Paper

Paper

Retrieval of canopy moisture content for dynamic fire risk assessment using simulated MODIS bands

Carmine Maffei, Antonio Leone, Giuseppe Meoli, Gaetano Calabrò, Massimo Menenti

Proceedings Volume 6742, 674205 (2007) https://doi.org/10.1117/12.737779

KEYWORDS: MODIS, Vegetation, Reflectivity, Short wave infrared radiation, Remote sensing, Liquids, Near infrared, Multispectral imaging, Absorption, Transmittance

Read Abstract +

Forest fires are one of the major environmental hazards in Mediterranean Europe. Biomass burning reduces carbon fixation in terrestrial vegetation, while soil erosion increases in burned areas. For these reasons, more sophisticated prevention tools are needed by local authorities to forecast fire danger, allowing a sound allocation of intervention resources. Various factors contribute to the quantification of fire hazard, and among them vegetation moisture is the one that dictates vegetation susceptibility to fire ignition and propagation. Many authors have demonstrated the role of remote sensing in the assessment of vegetation equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, fire models rely on the fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the weight of the liquid water in a leaf over the weight of dry matter, and its retrieval from remote sensing measurements might be problematic, since it is calculated from two biophysical properties that independently affect vegetation reflectance spectrum. The aim of this research is to evaluate the potential of the Moderate Resolution Imaging Spectrometer (MODIS) in retrieving both EWT and FMC from top of the canopy reflectance. The PROSPECT radiative transfer code was used to simulate leaf reflectance and transmittance as a function of leaf properties, and the SAILH model was adopted to simulate the top of the canopy reflectance. A number of moisture spectral indexes have been calculated, based on MODIS bands, and their performance in predicting EWT and FMC has been evaluated. Results showed that traditional moisture spectral indexes can accurately predict EWT but not FMC. However, it has been found that it is possible to take advantage of the multiple MODIS short-wave infrared (SWIR) channels to improve the retrieval accuracy of FMC (r² = 0.73). The effects of canopy structural properties on MODIS estimates of FMC have been evaluated, and it has been found that the limiting factor is leaf area index (LAI). The best results are recorded for LAI>2 (r² = 0.83), while acceptable results (r² = 0.58) can still be achieved for lower vegetation cover density.

Proceedings Article | 17 October 2006 Paper

Paper

The use of MODIS-simulated spectral bands for monitoring plant water stress as a help for dynamic fire risk assessment

Carmine Maffei, Antonio Leone, Mauro Vella, Giuseppe Meoli, Massimo Menenti

Proceedings Volume 6359, 63590Q (2006) https://doi.org/10.1117/12.689905

KEYWORDS: Reflectivity, MODIS, Vegetation, Near infrared, Short wave infrared radiation, Absorption, Remote sensing, Satellites, Atmospheric sensing, Liquids

Read Abstract +

Proceedings Article | 19 October 2005 Paper

Paper

Retrieval of aerosol optical thickness from PROBA-CHRIS images acquired over a coniferous forest

Carmine Maffei, Antonio Leone, Massimo Menenti, Ivan Pippi, Fabio Maselli, Paolo Antonelli

Proceedings Volume 5976, 59760J (2005) https://doi.org/10.1117/12.627661

KEYWORDS: Aerosols, Reflectivity, Atmospheric modeling, Atmospheric particles, Vegetation, Ozone, Scattering, Remote sensing, Optical properties, Sensors

Read Abstract +

Proceedings Article | 15 October 2004 Paper

Paper

Multi-angular hyperspectral observations of Mediterranean forest with PROBA–CHRIS

Massimo Menenti, Fabio Maselli, Marta Chiesi, Riccardo Benedetti, Simone Cristofori, Donatella Guzzi, Federico Magnani, Sabrina Raddi, Carmine Maffei

Proceedings Volume 5546, (2004) https://doi.org/10.1117/12.559348

KEYWORDS: Reflectivity, 3D modeling, Vegetation, Ecosystems, Data modeling, Sensors, Bidirectional reflectance transmission function, Earth observing sensors, Process modeling, Carbon

Read Abstract +

Measurements of spectro-directional radiances done with the imaging spectrometer CHRIS on-board the agile platform PROBA are being used to determine key properties of terrestrial vegetation at the appropriate spatial resolution. These data on vegetation properties can then be used to improve the accuracy and the parameterizations of models describing biosphere processes, i.e. photosynthesis and water use by irrigated crops and trees. The vegetation properties considered are: albedo, Leaf Area Index (LAI), fractional cover, fraction of absorbed photosynthetically active radiation (fAPAR) and canopy chlorophyll content. The Natural Park of San Rossore (Pisa, Central Italy) is a primary test site for several national and international research projects dealing with forest ecosystem monitoring. In particular, since 1999 measurements of transpiration and ecosystem gas-exchange have been regularly taken in the park pine forest to characterize its main water and carbon fluxes. In the same period, several aerial flights have been carried out with onboard hyper-spectral sensors (MIVIS, VIRS, AISA), while a series of satellite images have been acquired using both conventional (NOAAAVHRR, Landsat-TM/ETM+) and advanced sensors (CHRIS-PROBA). The final objective of these activities is to calibrate and validate methodologies which integrate remotely sensed and ancillary data for monitoring forest ecosystem. More specifically, a major research effort has been focused on evaluating the additional information content provided by advanced hyper-spectral multi-angular sensors about the main parameters needed for forest characterization (species, LAI, pigment content, etc.). These activities are part of projects which are financed by the Italian and European Space Agencies (ASI and ESA, respectively) within the framework of the CHRIS-PROBA and SPECTRA missions. During 2002 and 2003 nine complete multi-angular acquisitions were successfully performed over the San Rossore site. This paper summarizes first results of the evaluation of data acquired so far, particularly forward modeling of Top Of Canopy (TOC) reflectances. The models KUUSK, SAIL and GeoSAIL were used to simulate spectro-directional reflectance of different stands in the forest and compared with PROBA - CHRIS and airborne hyperspectral observations. Deviations of simulated from observed reflectances were significant.

Proceedings Article | 2 February 2004 Paper

Paper

Understanding vegetation response to climate variability from space: recent advances towards the SPECTRA Mission

Massimo Menenti, Michael Rast, Frederic Baret, Bart van den Hurk, Wolfgang Knorr, Wolfram Mauser, John Miller, Jose Moreno, Michael Schaepman, Michel Verstraete

Proceedings Volume 5234, (2004) https://doi.org/10.1117/12.510967

KEYWORDS: Vegetation, Data modeling, Carbon, Reflectivity, Climatology, Satellites, Image segmentation, Infrared radiation, Bidirectional reflectance transmission function, Algorithm development

Read Abstract +

The monitoring of the carbon stock in terrestrial environments, as well as the improved understanding of the surface-atmosphere interactions controlling the exchange of matter, energy and momentum, is of immediate interest for an improved assessment of the various components of the global carbon cycle. Studies of the Earth System processes at the global scale rely on models that require an advanced understanding and proper characterization of processes at smaller scales. The prime objective of the Surface Processes and Ecosystem Changes Through Response Analysis (SPECTRA) Mission is to determine the amount, assess the conditions and understand the response of terrestrial vegetation to climate variability and its role in the coupled cycles of energy, water and carbon. The amount and state of vegetation will be determined by the combination of observed vegetation properties and data assimilation. Many vegetation properties are related to features of reflectance spectra in the region 400 nm - 2500 nm. Detailed observations of spectral reflectance reveal subtle features related to biochemical components of leaves such as chlorophyll and water. The architecture of vegetation canopies determines complex changes of observed reflectance spectra with view and illumination angle. Quantitative analysis of reflectance spectra requires, therefore, an accurate characterization of the anisotropy of reflected radiance. This can be achieved with nearly - simultaneous observations at different view angles. Exchange of energy between the biosphere and the atmosphere is an important mechanism determining the response of vegetation to climate variability. This requires measurements of the component temperature of foliage and soil. The latter are closely related to the angular variation in thermal infrared emittance. Scientific preparations for SPECTRA are pursued along two avenues: a) the nature of the expected data and candidate algorithms are evaluated by generating and using synthetic hyper - spectral multi - angular\radiometric data; algorithms are evaluated with actual hyper -spectral data collected with a variety of airborne systems and concurrent ground measurements;

Proceedings Article | 13 March 2003 Paper

Paper

Detection of main structures in digital images by expansion of border-containing windows

Isabel Flores-Parra, Fernando Bienvenido, Massimo Menenti

Proceedings Volume 4885, (2003) https://doi.org/10.1117/12.463150

KEYWORDS: Image segmentation, Image filtering, Superposition, Image processing algorithms and systems, Image processing, Filtering (signal processing), Digital filtering, Edge detection, Binary data, MATLAB

Read Abstract +

Proceedings Article | 28 January 2002 Paper

Paper

Assessment of inputs to land surface processes models derived from hyperspectral multiangular data

Jose Moreno, Massimo Menenti, Rolf Richter

Proceedings Volume 4542, (2002) https://doi.org/10.1117/12.454198

KEYWORDS: Data modeling, Vegetation, Reflectivity, Process modeling, Carbon, Absorption, Soil science, Atmospheric modeling, Modeling, Remote sensing

Read Abstract +

Proceedings Article | 24 November 1995 Paper

Paper

Mapping crop coefficients in irrigated areas from Landsat TM images

Guido D'Urso, Massimo Menenti

Proceedings Volume 2585, (1995) https://doi.org/10.1117/12.227167

KEYWORDS: Reflectivity, Vegetation, Associative arrays, Remote sensing, Landsat, Satellites, Surface roughness, Image classification, Agriculture, Earth observing sensors

Read Abstract +

Showing 5 of 16 publications

SIGN IN TO:

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Advertisement

Advertisement