Modelizzazione spazio-temporale delle dinamiche di uso del suolo ed analisi comparativa di differenti approcci predittivi. Uso integrato di SIG e telerilevamento nello studio dei processi di deforestazione nella regione La Joyanca in Peten, Guatemala

L’uso frequente dei modelli predittivi per l’analisi di sistemi complessi, naturali o artificiali, sta cambiando il tradizionale approccio alle problematiche ambientali e di rischio. Il continuo miglioramento delle capacità di elaborazione dei computer facilita l’utilizzo e la risoluzione di metodi numerici basati su una discretizzazione spazio-temporale che permette una modellizzazione predittiva di sistemi reali complessi, riproducendo l’evoluzione dei loro patterns spaziali ed calcolando il grado di precisione della simulazione. In questa tesi presentiamo una applicazione di differenti metodi predittivi (Geomatico, Reti Neurali, Land Cover Modeler e Dinamica EGO) in un’area test del Petén, Guatemala. Durante gli ultimi decenni questa regione, inclusa nella Riserva di Biosfera Maya, ha conosciuto una rapida crescita demografica ed un’incontrollata pressione sulle sue risorse naturali. L’area test puó essere suddivisa in sotto-regioni caratterizzate da differenti dinamiche di uso del suolo. Comprendere e quantificare queste differenze permette una migliore approssimazione del sistema reale; é inoltre necessario integrare tutti i parametri fisici e socio-economici, per una rappresentazione più completa della complessità dell’impatto antropico. Data l’assenza di informazioni dettagliate sull’area di studio, quasi tutti i dati sono stati ricavati dall’elaborazione di 11 immagini ETM+, TM e SPOT; abbiamo poi realizzato un’analisi multitemporale dei cambi uso del suolo passati e costruito l’input per alimentare i modelli predittivi. I dati del 1998 e 2000 sono stati usati per la fase di calibrazione per simulare i cambiamenti nella copertura terrestre del 2003, scelta come data di riferimento per la validazione dei risultati. Quest’ultima permette di evidenziare le qualità ed i limiti per ogni modello nelle differenti sub-regioni.

Development of the optimal estimation method for inversion of stratospheric emission spectra and validation of HNO3 profiles

This doctoral thesis focuses on ground-based measurements of stratospheric nitric acid (HNO3)concentrations obtained by means of the Ground-Based Millimeter-wave Spectrometer (GBMS). Pressure broadened HNO3 emission spectra are analyzed using a new inversion algorithm developed as part of this thesis work and the retrieved vertical profiles are extensively compared to satellite-based data. This comparison effort I carried out has a key role in establishing a long-term (1991-2010), global data record of stratospheric HNO3, with an expected impact on studies concerning ozone decline and recovery. The first part of this work is focused on the development of an ad hoc version of the Optimal Estimation Method (Rodgers, 2000) in order to retrieve HNO3 spectra observed by means of GBMS. I also performed a comparison between HNO3 vertical profiles retrieved with the OEM and those obtained with the old iterative Matrix Inversion method. Results show no significant differences in retrieved profiles and error estimates, with the OEM providing however additional information needed to better characterize the retrievals. A final section of this first part of the work is dedicated to a brief review on the application of the OEM to other trace gases observed by GBMS, namely O3 and N2O. The second part of this study deals with the validation of HNO3 profiles obtained with the new inversion method. The first step has been the validation of GBMS measurements of tropospheric opacity, which is a necessary tool in the calibration of any GBMS spectra. This was achieved by means of comparisons among correlative measurements of water vapor column content (or Precipitable Water Vapor, PWV) since, in the spectral region observed by GBMS, the tropospheric opacity is almost entirely due to water vapor absorption. In particular, I compared GBMS PWV measurements collected during the primary field campaign of the ECOWAR project (Bhawar et al., 2008) with simultaneous PWV observations obtained with Vaisala RS92k radiosondes, a Raman lidar, and an IR Fourier transform spectrometer. I found that GBMS PWV measurements are in good agreement with the other three data sets exhibiting a mean difference between observations of ~9%. After this initial validation, GBMS HNO3 retrievals have been compared to two sets of satellite data produced by the two NASA/JPL Microwave Limb Sounder (MLS) experiments (aboard the Upper Atmosphere Research Satellite (UARS) from 1991 to 1999, and on the Earth Observing System (EOS) Aura mission from 2004 to date). This part of my thesis is inserted in GOZCARDS (Global Ozone Chemistry and Related Trace gas Data Records for the Stratosphere), a multi-year project, aimed at developing a long-term data record of stratospheric constituents relevant to the issues of ozone decline and expected recovery. This data record will be based mainly on satellite-derived measurements but ground-based observations will be pivotal for assessing offsets between satellite data sets. Since the GBMS has been operated for more than 15 years, its nitric acid data record offers a unique opportunity for cross-calibrating HNO3 measurements from the two MLS experiments. I compare GBMS HNO3 measurements obtained from the Italian Alpine station of Testa Grigia (45.9° N, 7.7° E, elev. 3500 m), during the period February 2004 - March 2007, and from Thule Air Base, Greenland (76.5°N 68.8°W), during polar winter 2008/09, and Aura MLS observations. A similar intercomparison is made between UARS MLS HNO3 measurements with those carried out from the GBMS at South Pole, Antarctica (90°S), during the most part of 1993 and 1995. I assess systematic differences between GBMS and both UARS and Aura HNO3 data sets at seven potential temperature levels. Results show that, except for measurements carried out at Thule, ground based and satellite data sets are consistent within the errors, at all potential temperature levels.