Jonathan Rizzi

Research Scientist

(+47) 483 47 537
jonathan.rizzi@nibio.no

Place
Ås O43

Visiting address
Oluf Thesens vei 43, 1433 Ås

Biography

See attachement for a complete list of scientific publications.
 
PhD in Environmental Sciences, working since more than 15 years in the GIS sector. Experience as consultant, teacher, researcher and project manager of national and international project, in international groups and work experience in countries such as China and Ecuador.

The main research activities are concerned with the use of GIS in several environmental sectors, including climate change, contaminated sites and water quality. Development of GIS-based tools such as a Spatial Decision Support System for climate change impact assessment (DESYCO) and WebGIS for climate data. He also worked on the definition of methodologies addressing climate change impacts of coastal zones useful to support the definition of adaptation measures and he has experience in MultiCriteria Decision Analysis (MCDA).

In the last years, he has also participated and managed international cooperation projects in developing countries.

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Abstract

Sustainable water resources management roots in monitoring data reliability and a full engagement of all institutions involved in the water sector. When competences and interests are overlapping, however, coordination may be difficult, thus hampering cooperative actions. This is the case of Santa Cruz Island (Galápagos, Ecuador). A comprehensive assessment on water quality data (physico-chemical parameters, major elements, trace elements and coliforms) collected since 1985 revealed the need of optimizing monitoring efforts to fill knowledge gaps and to better target decision-making processes. A Water Committee (Comité de la gestión del Agua) was established to foster the coordinated action among stakeholders and to pave the way for joint monitoring in the island that can optimize the efforts for water quality assessment and protection. Shared procedures for data collection, sample analysis, evaluation and data assessment by an open-access geodatabase were proposed and implemented for the first time as a prototype in order to improve accountability and outreach towards civil society and water users. The overall results reveal the high potential of a well-structured and effective joint monitoring approach within a complex, multi-stakeholder framework.

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Abstract

There are neither volume nor velocity thresholds that define big data. Any data ranging from just beyond the capacity of a single personal computer to tera- and petabytes of data can be considered big data. Although it is common to use High Performance Computers (HPCs) and cloud facilities to compute big data, migrating to such facilities is not always practical due to various reasons, especially for medium/small analysis. Personal computers at public institutions and business companies are often idle during parts of the day and the entire night. Exploiting such computational resources can partly alleviate the need for HPC and cloud services for analysis of big data where HPC and cloud facilities are not immediate options. This is particularly relevant also during testing and pilot application before implementation on HPC or cloud computing. In this paper, we show a real case of using a local network of personal computers using open-source software packages configured for distributed processing to process remotely sensed big data. Sentinel-2 image time series are used for the testing of the distributed system. The normalized difference vegetation index (NDVI) and the monthly median band values are the variables computed to test and evaluate the practicality and efficiency of the distributed cluster. Computational efficiencies of the cluster in relation to different cluster setup, different data sources and different data distribution are tested and evaluated. The results demonstrate that the proposed cluster of local computers is efficient and practical to process remotely sensed data where single personal computers cannot perform the computation. Careful configurations of the computers, the distributed framework and the data are important aspects to be considered in optimizing the efficiency of such a system. If correctly implemented, the solution leads to an efficient use of the computer facilities and allows the processing of big, remote, sensing data without the need to migrate it to larger facilities such as HPC and cloud computing systems, except when going to production and large applications.

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Abstract

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.

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Abstract

Rapporten utforsker og diskuterer potensialet for økt bruk av Stordata (engelsk: big data) teknologi og metode innenfor instituttets arbeidsområder. I dag benyttes Stordata-tilnærminger til å løse forvaltningsstøtteoppgaver, samt til forskningsformål, særlig i sentrene for presisjonslandbruk og presisjonsjordbruk. Potensialet for økt bruk av Stordata innenfor instituttet er stort. For å realisere potensialet er det behov for god samordning mellom organisasjonsenhetene og utvikling av strategisk kompetanse på fagområdet.

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Abstract

Rapporten dokumenterer utvalgte eksempler på bruk av stordata (engelsk: big data) teknologi og metode i NIBIO. Det første eksemplet er knyttet til oppdatering av arealressurskartet AR5, hvor det undersøkes om stordata-tilnærming kan benyttes for å identifisere lokaliteter der kartet må oppdateres. De neste eksemplene er hentet fra fagområdet plantehelse og tar for seg mulighetene for å bruke stordata-metode for å bedre prediksjonsmodeller og gjenkjenning av for skadegjørere.

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Abstract

Protected Areas (PAs) in Tanzania had been established originally for the goal of habitat, landscape and biodiversity conservation. However, human activities such as agricultural expansion and wood harvesting pose challenges to the conservation objectives. We monitored a decade of deforestation within 708 PAs and their unprotected buffer areas, analyzed deforestation by PA management regimes, and assessed connectivity among PAs. Data came from a Landsat based wall-to-wall forest to non-forest change map for the period 2002–2013, developed for the definition of Tanzania’s National Forest Reference Emissions Level (FREL). Deforestation data were extracted in a series of concentric bands that allow pairwise comparison and correlation analysis between the inside of PAs and the external buffer areas. Half of the PAs exhibit either no deforestation or significantly less deforestation than the unprotected buffer areas. A small proportion (10%; n = 71) are responsible for more than 90% of the total deforestation; but these few PAs represent more than 75% of the total area under protection. While about half of the PAs are connected to one or more other PAs, the remaining half, most of which are Forest Reserves, are isolated. Furthermore, deforestation inside isolated PAs is significantly correlated with deforestation in the unprotected buffer areas, suggesting pressure from land use outside PAs. Management regimes varied in reducing deforestation inside PA territories, but differences in protection status within a management regime are also large. Deforestation as percentages of land area and forested areas of PAs was largest for Forest Reserves and Game Controlled areas, while most National Parks, Nature Reserves and Forest Plantations generally retained large proportions of their forest cover. Areas of immediate management concern include the few PAs with a disproportionately large contribution to the total deforestation, and the sizeable number of PAs being isolated. Future protection should account for landscapes outside protected areas, engage local communities and establish new PAs or corridors such as village-managed forest areas.

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Abstract

Coastal erosion is an issue of major concern for coastal managers and is expected to increase in magnitude and severity due to global climate change. This paper analyzes the potential consequences of climate change on coastal erosion (e.g., impacts on beaches, wetlands and protected areas) by applying a Regional Risk Assessment (RRA) methodology to the North Adriatic (NA) coast of Italy. The approach employs hazard scenarios from a multi-model chain in order to project the spatial and temporal patterns of relevant coastal erosion stressors (i.e., increases in mean sea-level, changes in wave height and variations in the sediment mobility at the sea bottom) under the A1B climate change scenario. Site-specific environmental and socio-economic indicators (e.g., vegetation cover, geomorphology, population) and hazard metrics are then aggregated by means of Multi-Criteria Decision Analysis (MCDA) with the aim to provide an example of exposure, susceptibility, risk and damage maps for the NA region. Among seasonal exposure maps winter and autumn depict the worse situation in 2070–2100, and locally around the Po river delta. Risk maps highlight that the receptors at higher risk are beaches, wetlands and river mouths. The work presents the results of the RRA tested in the NA region, discussing how spatial risk mapping can be used to establish relative priorities for intervention, to identify hot-spot areas and to provide a basis for the definition of coastal adaptation and management strategies.

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Abstract

This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000–2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.

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Abstract

Northern latitudes are experiencing faster warming than other regions in the world, which is partly explained by the snow albedo feedback. In Norway, mean temperatures have been increasing since the 1990s, with 2014 being the warmest year on record, 2.2 °C above normal (1961–1990). At the same time, a concurrent reduction in the land area covered by snow has been reported. In this study, we present a detailed spatial and temporal (monthly and seasonal) analysis of trends and changes in snow indices based on a high resolution (1 km) gridded hydro-meteorological dataset for Norway (seNorge). During the period 1961–2010, snow cover extent (SCE) was found to decrease, notably at the end of the snow season, with a corresponding decrease in snow water equivalent except at high elevations. SCE for all Norway decreased by more than 20,000 km2 (6% of the land area) between the periods 1961–1990 and 1981–2010, mainly north of 63° N. Overall, air temperature increased in all seasons, with the highest increase in spring (particularly in April) and winter. Mean monthly air temperatures were significantly correlated with the monthly SCE, suggesting a positive land–atmosphere feedback enhancing warming in winter and spring.

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Abstract

Runoff prediction in ungauged catchments has been a challenging topic over recent decades. Much research have been conducted including the intensive studies of the PUB (Prediction in Ungauged Basins) Decade of the International Association for Hydrological Science. Great progress has been made in the field of regionalization study of hydrological models; however, there is no clear conclusion yet about the applicability of various methods in different regions and for different models. This study made a comprehensive assessment of the strengths and limitations of existing regionalization methods in predicting ungauged stream flows in the high latitudes, large climate and geographically diverse, seasonally snow-covered mountainous catchments of Norway. The regionalization methods were evaluated using the water balance model – WASMOD (Water And Snow balance MODeling system) on 118 independent catchments in Norway, and the results show that: (1) distance-based similarity approaches (spatial proximity, physical similarity) performed better than regression-based approaches; (2) one of the combination approaches (combining spatial proximity and physical similarity methods) could slightly improve the simulation; and (3) classifying the catchments into homogeneous groups did not improve the simulations in ungauged catchments in our study region. This study contributes to the theoretical understanding and development of regionalization methods.

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Division of Survey and Statistics

Considering the Environment and Nature when Building and Operating Ground Mounted Solar Power Plants in Norway


EnviSol's mission is to harmonize the growth of ground-mounted solar power plants in Norway with the imperative to protect biodiversity and ecosystem services. With renewable energy production, preserving nature, and supporting ecosystems all in mind, EnviSol aims to pinpoint the ideal methods and locations for these solar installations, mitigating clashes over land use.

Active Updated: 30.01.2024
End: jul 2027
Start: aug 2023
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Division of Environment and Natural Resources

CANALLS Agroecological practices for sustainable transition


Agroecology covers all activities and actors involved in food systems. It also places the well-being of people (producers and consumers of crops and products) at its core. The EU-funded CANALLS project will focus on the agroecological zones and diverse farming systems in the humid tropics of Central and Eastern Africa. It will explore the complex environmental, social and economic challenges, which in some cases are exacerbated by conflict and high vulnerability. Moreover, it will advance agroecological transitions in these regions through multi-actor transdisciplinary agroecology Living Labs at eight sites in four countries. The focus will be on crops such as cocoa, coffee and cassava, which are vital for subsistence and economic development.

Active Updated: 30.01.2024
End: dec 2026
Start: jan 2023