Luigi Dini

Retrieval of Leaf Area Index from CHRIS/PROBA data: an analysis of the directional and spectral information content

The use of Earth Observation (EO) data to retrieve biophysical variables of vegetated surfaces has proved to be useful in many operative tools to gather information repetitively, at spatial and temporal resolution, for agricultural and water management applications. The launch of the European Space Agency (ESA) Compact High‐Resolution Imaging Spectrometer/Project for On‐Board Autonomy (CHRIS/PROBA) mission has provided an opportunity to study a multiangular and hyperspectral dataset of images with high spatial resolution. The objective of the study was to use the CHRIS/PROBA data, in both directional and spectral domains, to estimate the Leaf Area Index (LAI). For this purpose, inversion of a canopy reflectance model was performed against CHRIS data. LAI estimates were validated by using ground truth LAI measurements and compared, in terms of accuracy, to a semi‐empirical approach. It was shown that, for a given spectral configuration, the directional information always improved the LAI estimation. For the best case (corn), this was achieved with an LAI root mean square error (RMSE) of 0.41 by using five angles and 62 spectral bands compared to a value of 1.42 by using one angle and four bands, as in the Landsat Thematic Mapper (TM) configuration.

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Abstract Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohens kappa coefficient of 0.84 and an overall accuracy of 85%.

Time series of COSMO-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites

This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop classification and that the integration of maps of SAR-derived surface parameters into crop growth and/or hydrologic models, in general, leads to significant improvements in the model performances.

High-resolution SAR and high-resolution optical data integration for sub-urban land-cover classification

This study shows a comparison between pixel-based and object-based approaches in data fusion of high-resolution multispectral GeoEye-1 imagery and high-resolution COSMO-SkyMed SAR data for land-cover/land-use classification. The per-pixel method consisted of a maximum likelihood classification of fused data based on discrete wavelet transform and a classification from optical images alone. Optical and SAR data were then integrated into an object-oriented environment with the addition of texture measurements from SAR and classified with a nearest neighbor approach. Results were compared with the classification of the GeoEye-1 data alone and the outcomes pointed out that per-pixel data fusion did not improve the classification accuracy, while the object-based data integration increased the overall accuracy from 73% to 89%. According to results, an object-based approach with the introduction of adjunctive information layers proved to be more performing than standard pixel-based methods in landcover/ land-use classification.

Evaluation of different methods for the retrieval of LAI using high resolution airborne data

Earth Observation (E.O.) technologies provide a valuable data base for the monitoring of crop and soil characteristics on a large scale, in a rapid, accurate and cost-effective way. The present work aims at evaluating different methods and models for the estimation of the Leaf Area Index (LAI) by means of hyperspectral data acquired by the optical airborne instrument CASI during the ESA AgriSAR 2006 campaign. Inversion of a physical model using an iterative optimization technique (SQP) and a fast look-up-table (LUT) approach is performed and results are compared with an empirical model based on the relationship between LAI and WDVI. Furthermore, the analyses carried out on the inversion of the physical models provide the opportunity to test the spectral bands proposed for the upcoming E.O. satellite Sentinel-2 developed by ESA in the framework of GMES (Global Monitoring for Environment and Security). The Sentinel-2 spectral sampling is compared with the one proposed by an independent study determining the wavebands best characterizing vegetation and crops. Accuracy of LAI estimation, evaluated with the AgriSAR 2006 field measurements, is discussed in the context of operational agricultural monitoring.

Cost-effectiveness of Vegetation Biophysical Parameters Retrieval from Remote Sensing Data

In the context of vegetation studies Earth Observation (E.O.) data have been extensively used to retrieve biophysical parameters of land surface. In some cases, thanks to the availability of near-real-time data, tools and applications have been developed and implemented in the fields of precision agriculture, water resources monitoring and management. So far, empirical approaches based on vegetation indices (Vis) have been successfully applied. They may provide a satisfactory level of accuracy in the estimation of important vegetation biophysical parameters (e.g. LAI, fractional ground cover, biomass, etc). Such methods, however, require a reliable reference data-set to calibrate empirical formulas on different vegetation types; furthermore, they are generally based on a few spectral bands, with a consistent under-exploitation of the full spectral range available in new generation sensors. Alternative approaches based on inversion of radiative transfer models of vegetation represent a challenging opportunity for the estimation of vegetation parameters from data with high dimensionality. This work evaluates the effectiveness, in terms of accuracy and computational complexity, for retrieving the Leaf Area Index, on one hand, by means of empirical relationships, such as the simple CLAIR model proposed by Clevers (1989) and based on the Weighted Differences Vegetation Index (WDVI), and, on the other hand, by means of mathematical inversion of the combined radiative transfer model PROSPECT and SAILH (PSH). Both approaches, i.e. empirical relationship LAI (WDVI) and radiative transfer model inversion, have been tested by using super- spectral and multi-angular data in the solar domain from the Compact High Resolution Imaging Spectrometer on the PROBA experimental satellite.

Cost effectiveness of vegetation biophysical parameters retrieval from remote sensing data

In the context of vegetation studies Earth Observation (E.O.) data have been extensively used to retrieve biophysical parameters of land surface. In some cases, thanks to the availability of near-real-time data, tools and applications have been developed and implemented in the fields of precision agriculture, water resources monitoring and management. So far, empirical approaches based on vegetation indices (VIs) have been successfully applied. They may provide a satisfactory level of accuracy in the estimation of important vegetation biophysical parameters (e.g. LAI, fractional ground cover, biomass, etc). Such methods, however, require a reliable reference data-set to calibrate empirical formulas on different vegetation types; furthermore, they are generally based on a few spectral bands, with a consistent under-exploitation of the full spectral range available in new generation sensors. Alternative approaches based on inversion of radiative transfer models of vegetation represent a challenging opportunity for the estimation of vegetation parameters from data with high dimensionality.

ASI-Volcanic Risk System (SRV): A pilot project to develop EO data processing modules and products for volcanic activity monitoring, first results

The ASI-SRV (Sistema Rischio Vulcanico) project is devoted to the development of an integrated system based on EO and Non EO data to respond to specific needs of the Italian Civil Protection Department (DPC). ASI-SRV provides the capability to import many different EO and Non EO data into the system, it maintains a repository where the acquired data have to be stored and generates selected products which will be functional to the different volcanic activity phases. The processing modules for Radar and EO Optical sensors data allow to estimate a number of parameters which include: surface thermal proprieties, gas, aerosol and ash emissions and to characterize the volcanic products in terms of composition and geometry, surface deformations in terms of displacements and velocity. All the generated products are related to Italian actives volcanoes and three test sites have been chosen to demonstrate the capability of this integrated system: Vesuvio, Campi Flegrei (Campania region) and Etna (Sicilia region). In this paper the first results obtained by means of modules developed within the ASI-SRV project and dedicated to the processing of EO historical series are presented.

Spatial distribution of soil water content from airborne thermal and optical remote sensing data

Spatial and temporal information of soil water content is of essential importance for modelling of land surface processes in hydrological studies and applications for operative systems of irrigation management. In the last decades, several remote sensing domains have been considered in the context of soil water content monitoring, ranging from active and passive microwave to optical and thermal spectral bands. In the framework of an experimental campaign in Southern Italy in 2007, two innovative methodologies to retrieve soil water content information from airborne earth observation (E.O.) data were exploited: a) analyses of the dependence of surface temperature of vegetation with soil water content using thermal infrared radiometer (TIR), and b) estimation of superficial soil moisture content using reflectance in the visible and near infrared regions acquired from optical sensors. The first method (a) is applicable especially at surfaces completely covered with vegetation, whereas the second method is preferably applicable at surfaces without or with sparse vegetation. The synergy of both methods allows the establishment of maps of spatially distributed soil water content. Results of the analyses are presented and discussed, in particular in view of an operative context in irrigation studies.

Estimation of Land Surface Parameters Through Modeling Inversion of Earth Observation Optical Data

Earth observation (EO) optical data represent one of the main sources of information in the retrieval of land surface parameters (i.e., leaf area index and surface albedo). These parameters are widely used in research and applications in agriculture for improving water and land resources management, especially in the field of precision farming, to monitor crop status, predict crop yield, detect disease and insect infestations, and support the management of farming tasks. During recent years, the technical capabilities of airborne and satellite remote sensing imagery have been improved to include hyperspectral and multiangular observations. In parallel with the advancement of observation techniques, there has been an important development in the study of the interaction of solar radiation with Earth’s surface. This process can be described by using canopy reflectance models of different complexity, which can also be used in operative applications in the field of agricultural water and land management. As such, enhanced EO data and canopy reflectance models can be combined together to reduce the empiricism of traditional methods based on simplified approaches and to increase the estimation accuracy.