Jonathan Rizzi
Forsker
(+47) 483 47 537
jonathan.rizzi@nibio.no
Sted
Ås - Bygg O43
Besøksadresse
Oluf Thesens vei 43, 1433 Ås (Varelevering: Elizabeth Stephansens vei 21)
Vedlegg
List of PublicationsBiografi
De viktigste forskningsområdene er bruk av GIS i flere miljøsektorer, inkludert klimaendringer, forurenset grunn og vannkvalitet. Bidratt til utvikliing av GIS-baserte verktøy som Spatial Decision Support System konsekvensutredning for klimaendringer (DESYCO) og WebGIS for klimadata. Har også jobbet med metoder og tilpasningstiltak for å møte klimaendringene ved kystsoner. Arbeidserfaring fra MultiCriteria Decision Analysis (MCDA).
I de siste årene har han deltatt i internasjonale samarbeidsprosjekter i utviklingsland.
Sammendrag
Urban green structures (UGS) play important roles in enhancing urban ecosystems by providing benefits such as mitigating the urban heat island effect, improving air quality, supporting biodiversity, and aiding in stormwater management. Accurately mapping UGS is important for sustainable urban planning and management. Traditional methods of mapping such as manual mapping, aerial photography interpretation and pixel-based classification have limitations in terms of coverage, accuracy, and efficiency. Object-based image analysis (OBIA) has gained prominence due to its ability to incorporate both spectral and spatial information making it particularly effective for classification of high-resolution satellite data. This paper reviews the application of OBIA on satellite images for UGS mapping, focusing on various data sources, popular segmentation methods, and classification techniques, highlighting their respective advantages and limitations. Key segmentation methodologies discussed include multi-resolution segmentation and watershed segmentation. For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. The review also addresses the challenges associated with OBIA, such as dealing with heterogenous urban landscapes, data sources and with OBIA methods itself. Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. By providing a detailed analysis of the current state-of-the-art in object-based UGS mapping, this review aims to guide future research and practical applications in UGS management.
Forfattere
Siri Svendgård-Stokke Eva Solbjørg Flo Heggem Anne B. Nilsen Svein Olav Krøgli Sebastian Eiter Henrik Forsberg Mathiesen Jonathan Rizzi Torgeir Tajet Ole Einar TveitoSammendrag
Rapporten beskriver resultatene av arbeidet som er gjort i arbeidspakke 1 «Økt verdiskaping i norsk grøntnæring – Veivalg GS35 (GrøntStrategi mot 2035)», oppgavene 1.1 og 1.4. De naturlige betingelsene for grønnsaksdyrking er identifisert ved hjelp av stedfestet informasjon om jordsmonnets egenskaper, klimadata og terreng. Resultatene er framstilt i kartløsninga prosjektkilden Veivalg (https://kart19.nibio.no/kilden3). I tillegg er den geografiske utbredelsen av eksisterende grønnsaksdyrking undersøkt og sammenliknet med den geografiske fordelinga av de arealene som har høyt potensial for dyrking av grønnsaker. Det har også blitt utført bærekraftsanalyser på gårdsnivå.
Sammendrag
Det er ikke registrert sammendrag

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