Rice is one of the most important crops all over the world and it represents the staple food for more than half of the human population. One fifth of the world's population, more than a billion people, depend on rice cultivation for their livelihoods. Asia, where about 90% of rice is grown, has more than 200 million rice farms (IRRI). Even in Europe rice is cultivated and Italy holds the primacy in the production representing an important farming activity. Rice is unique because it can grow in wet environments and on flooded fields that other crops cannot survive in. This feature is very important because the base of the study is to take advantage of the physic behavior of water for detecting flooding in the paddy rice fields. In this study we tested the approach given by Xiao et al. (2005) as it is, verifying the adaptability in a Mediterranean environment. By using MODIS (Moderate Resolution Imaging Spectroradiometer) imagery of 2006 (46 of 8-days composite images) a number of vegetation indices (NDVI-EVI-LSWI) has been calculated for rice detection in the main rice district in the northern Italy situated between Lombardia and Piemonte. The accuracy at ground level of the derived rice map has been evaluated by using a European Land cover map, the Corine-land cover of 2006 (CLC06), and statistically calculated. After which by modifying the methodology in order to adapt it to the temperate climatic conditions and Italian agronomic techniques, the accuracy of the newest derived maps has been analyzed and compared. The result of this study shows if the MODIS-based paddy rice mapping algorithm could potentially be applied in the Mediterranean environment as it is or if some calibrations are needed.
Il riso è la coltura cerealicola più importante al mondo e rappresenta l'alimento base per oltre la metà della popolazione mondiale. Anche in Europa, e soprattutto in Italia, che detiene il primato delle produzioni, questa coltivazione ha una grande importanza sia economica che paesaggistico culturale. Per garantire questa produzione oggigiorno si richiedono metodi sostenibili e razionali di analisi nonché sistemi di supporto alle decisioni per la gestione e pianificazione degli interventi. In quest'ottica la conoscenza della distribuzione delle aree coltivate e il loro monitoraggio divengono attività fondamentali per evidenziare eventuali criticità. Nella presente ricerca si è valutata l'estendibilità del metodo di mappatura delle aree a riso dei paesi asiatici proposto da Xiao et. al (2005) al bacino del mediterraneo. Tale metodo si basa sull'analisi congiunta di serie temporali di indici di vegetazione (EVI - NDVI - LSWI) derivati dal sensore MODIS. Lo studio ha previsto l'implementazione del metodo e la produzione di mappe per l'anno 2006. Si sono testate modifiche per migliorare l'algoritmo in zone temperate definendo opportuni adattamenti per le condizioni mediterranee. I risultati sono stati confrontati con mappe di uso del suolo, Corine Land Cover, ed Il lavoro ha confermato la validità dell'approccio basato su analisi di serie temporali anche in ambiente mediterraneo. Lo studio ha permesso di evidenziare con quali coperture si hanno maggiori problemi di commissione e quali problemi debbano essere ulteriormente affrontati per un uso operativo del metodo nelle aree temperate. In prospettiva futura si intende indagare quale sia il limite di accuratezza del prodotto in relazione al low resolution bias e quali modifiche possano essere introdotte nell'algoritmo.
Individuazione automatica di aree coltivate a riso da serie temporale MODIS: primi risultati in ambiente mediterraneo
Alberto Crema;Mirco Boschetti;Giacinto Manfron;Mauro Musanti
2011
Abstract
Rice is one of the most important crops all over the world and it represents the staple food for more than half of the human population. One fifth of the world's population, more than a billion people, depend on rice cultivation for their livelihoods. Asia, where about 90% of rice is grown, has more than 200 million rice farms (IRRI). Even in Europe rice is cultivated and Italy holds the primacy in the production representing an important farming activity. Rice is unique because it can grow in wet environments and on flooded fields that other crops cannot survive in. This feature is very important because the base of the study is to take advantage of the physic behavior of water for detecting flooding in the paddy rice fields. In this study we tested the approach given by Xiao et al. (2005) as it is, verifying the adaptability in a Mediterranean environment. By using MODIS (Moderate Resolution Imaging Spectroradiometer) imagery of 2006 (46 of 8-days composite images) a number of vegetation indices (NDVI-EVI-LSWI) has been calculated for rice detection in the main rice district in the northern Italy situated between Lombardia and Piemonte. The accuracy at ground level of the derived rice map has been evaluated by using a European Land cover map, the Corine-land cover of 2006 (CLC06), and statistically calculated. After which by modifying the methodology in order to adapt it to the temperate climatic conditions and Italian agronomic techniques, the accuracy of the newest derived maps has been analyzed and compared. The result of this study shows if the MODIS-based paddy rice mapping algorithm could potentially be applied in the Mediterranean environment as it is or if some calibrations are needed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
