9 results on '"Sallaba, Florian"'
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2. Biophysical and Human Controls of Land Productivity under Global Change : Development and Demonstration of Parsimonious Modelling Techniques
- Author
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Sallaba, Florian
- Subjects
Model coupling ,Ecosystem Modelling ,Net Primary Production ,Global Change ,Geosciences, Multidisciplinary ,Meta-modelling ,Natural Sciences ,Environmental Sciences - Abstract
Net primary production (NPP) serves as an indicator for plant-based resources such as food, timber and biofuel for human appropriation. It is defined by the annual production of plant matter and is mainly controlled by climate and human activities. Climate change in combination with human activities is altering NPP. As the controls of NPP are expected to further change in the future, it is vital to investigate alterations in NPP and their magnitudes. The impacts of climate change and human activities on NPP can be explored in integrated assessment (IA) frameworks, where sectoral models are coupled and interact rapidly. For such frameworks, parsimonious models are desired because they enable rapid estimates and facilitate easy model coupling for explorations of multiple global change scenarios (i.e. large volumes of data). This thesis aims to advance parsimonious modelling techniques for quantifying current and future NPP on land. This is accomplished by developing and testing rapid models that facilitate easy model coupling to explore the impacts of multiple global change scenarios on NPP. The model development is based on the meta-modelling concept, which can be applied to simplify the dynamic vegetation model LPJ-GUESS in a parsimonious model. For this, multiple climate change and [CO2] perturbations are applied to LPJ-GUESS to simulate NPP. The NPP data are then used to define biophysically motivated relationships between NPP and the driving climate variables along with [CO2]. The relationships are then combined in a synergistic function – the meta-model. Thereafter, the meta-models are assessed for their performance in estimating NPP by comparing them to LPJ-GUESS NPP simulations, to independent field observations and to NPP experiments under enriched [CO2] on biome level. The results provide confidence in the modelled NPP estimates for the most productive biomes, which are important for global quantifications of NPP. The meta-models capture NPP enhancement under enhanced [CO2] adequately in the majority of the studied biomes. Finally, the NPP meta-models are coupled with other sectoral models in two IA modelling-frameworks in order to explore the impacts of global change on ecosystem indicators. The first framework enables an IA of climate change impacts and vulnerabilities for a range of sectors on the European level. This thesis conducts a sensitivity analysis on the effects of climatic and socio-economic change drivers on model outputs related to key sectors. This provides better quantification and increased understanding of the complex relationships between input and output variables in IA modelling-frameworks. The second framework addresses the NPP supply-demand balance in the Sahel region by coupling two sectoral models in order to analyze the timings and geographies of NPP shortfalls in the 21st century Sahel under global change. The results show consistent regional NPP shortfalls in the Sahel for the majority of global change scenarios.Overall, the parsimonious modelling techniques developed in this thesis contribute with rapid NPP estimates on the biome and global scale. BME NPP estimates agree reasonably well with NPP observations in the majority of biomes (especially in the most productive biomes). This thesis demonstrates that NPP meta-models facilitate easy model coupling for exploring the impacts of global change on human-environmental systems in IA modelling-frameworks.
- Published
- 2016
3. Future supply and demand of net primary production in the Sahel
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Sallaba, Florian, primary, Olin, Stefan, additional, Engström, Kerstin, additional, Abdi, Abdulhakim M., additional, Boke-Olén, Niklas, additional, Lehsten, Veiko, additional, Ardö, Jonas, additional, and Seaquist, Jonathan W., additional
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- 2017
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4. Future supply and demand of net primary production in the Sahel
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Sallaba, Florian, primary, Olin, Stefan, additional, Engström, Kerstin, additional, Abdi, Abdulahakim M., additional, Boke-Olén, Niklas, additional, Lehsten, Veiko, additional, Ardö, Jonas, additional, and Seaquist, Jonathan W., additional
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- 2016
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5. Fuel fragmentation and fire size distributions in managed and unmanaged boreal forests in the province of Saskatchewan, Canada
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Lehsten, Veiko, primary, de Groot, William, additional, and Sallaba, Florian, additional
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- 2016
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6. A rapid NPP meta-model for current and future climate and CO2 scenarios in Europe
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Sallaba, Florian, primary, Lehsten, Dörte, additional, Seaquist, Jonathan, additional, and Sykes, Martin T., additional
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- 2015
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7. The potential of support vector machine classification of land use and land cover using seasonality from MODIS satellite data
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Sallaba, Florian and Sallaba, Florian
- Abstract
With respect to climate change it is necessary to study land use and land cover (LULC) and their changes. LULC are related directly and indirectly to climatic changes such as rising temperatures that trigger earlier onset of vegetation growing seasons (IPCC 2007). Land surface phenology refers to the seasonal patterns of variation in vegetated land surfaces over large areas using satellite data (Reed et al. 2009). General variations observed from satellite may also be referred to as seasonality (Jönsson and Eklundh 2002, 2004). In this study, seasonality was modeled from normalized difference vegetation index timeseries derived from Moderate Resolution Imaging Spectro-Radiometer (MODIS) satellite data. Seasonality data contain valuable information about vegetation dynamics of LULC, such as the maximum of a season as well as the season start and end. The specific seasonality data signatures of LULC and may improve LULC classifications compared to multi-spectral satellite data approaches. Support vector machine classification (SVC) is a machine learning technique that does not require normal distributed input data. A normal distribution of seasonality data cannot be assumed. SVC is superior in comparison to traditional classification methods using multispectral satellite data (Tso and Mather 2009). Thus, it is feasible to test the potential of SVC separation of LULC using seasonality data. The most common linear and non-linear SVC methods recommended for satellite data were applied in this study. The chosen study area is located in southern Sweden, and its LULC classes are well documented by the latest CORINE land cover 2006 data. Thus, it is a good test area for validation of the performance of seasonality parameters for LULC classification using SVC. In this study, a SVC framework was developed and implemented that: (1) selects the most appropriate input seasonality data, (2) incorporates a direct acyclic graph for multiclassification and (3) validates the SVC outco, Då klimatförändringar studeras är det viktigt att ha markanvändningarna och deras förändringar i åtanke. Markanvändningarna är direkt och indirekt relaterat till klimatförändringar såsom exempelvis stigande temperaturer, vilket påskyndar starten av växtsäsongen. Fjärranalysdata ger en bättre bild av storskaliga förändringar i vegetationen än vad som är möjligt att observera från jordytan. Vegetationen och dess fenologi kan observeras med satellitdata, och kallas även för årstidsvariationer. Denna studie använder tidsserier av vegetationsindex från satellitdata för att modellera årstidsvariationer. Dessa matematiska modeller av årstidsvariationerna används sedan för att extrahera olika årstidsrelaterade parametrar som ger värdefull information om vegetationsdynamiken. Dessa årstidsrelaterade parametrar kan förbättra klassificeringen av markanvändningarna. Studien tillämpar en ny teknik, som kallas ”support vector machine” klassificering, för att klassificera de årstidsrelaterade parametrarna. Studien fokuserade på de vanligaste linjära och olinjära ”support vector machine” tekniker som rekommenderas för satellitdata. Det studerade området ligger i södra Sverige och dess markanvändningsklasser är sedan tidigare väldokumenterade. Det innebär att området är mycket lämpligt för att testa årstidsrelaterade parametrar genom att tillämpa ”support vector machine” klassificering. Studien utgick från att: (1) välja de mest lämpliga årstidsrelaterade parametrarna, (2) använda en multi-klassificering, och (3) utvärdera de klassificeringsutfall med noggrannhetsbedömningar baserat från senast dokumenterad CORINE land cover 2006 data. Resultaten påvisar en måttlig prestanda hos ”support vector machine” klassificering, där den övergripande noggrannheten landar mellan 61 till 64 %, och Kappavärdet varierar mellan 0,49 till 0,52. Skillnaderna mellan de linjära och olinjära ”support vector machine” teknikerna är marginella. Dock bör årstidsrelaterade parametrar klassificeras med tradit
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- 2011
8. Potential of a post-classification change detection analysis to identify land use and land cover changes : a case study in northern Greece
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Sallaba, Florian and Sallaba, Florian
- Abstract
The use of remotely sensed data is an important method to indicate land use and land cover changes. Remote sensing can provide a better picture of monitoring land use and land cover changes. It makes it feasible to locate geographically changed areas in order to employ it further detailed studies on environmental changes (e.g. land degradation). The study area is a heterogeneous and small-structured agriculturally dominated prefecture in northern Greece. The core post-classification change detection analysis was based on two Landsat 5 TM and Landsat 7 ETM+ images. Maximum likelihood classification was applied on the satellite data. A basic arithmetic combination was used to compare the classification outcomes to detect and locate land use and land cover changes over a period of 14 years. The accomplished post-classification change detection analysis performed weakly., Användandet av fjärranalysdata är en viktig metod för att bestämma markanvändning och visa på förändringar i marktäcke. Fjärranalys kan ge en bättre utgångspunkt för övervakning av markytstäcke. Det gör det möjligt att geografiskt lokalisera förändrade markområden för att vidare kunna utföra noggrannare undersökningar om miljöförändringar (t.ex. markdegradering). Studieområdet är ett administrativt distrikt i norra Grekland. Det är ett heterogent jordbruksdominerat område karakteriserat av småskaliga landskapsstrukturer. Markförändringsanalysen efter klassifikation baserades på två bilder från Landsat 5TM och Landsat 7 ETM+. Maximum likelihood classification användes för klassifikation av satellitdatan. En kombination av enkel aritmetisk matematik användes för att jämföra resultatet av klassifikationerna och lokalisera förändringar i markanvändning och markytstäcke över en period på 14 år. Den utförda markförändringsanalysen fungerade inte väl.
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- 2009
9. Future supply and demand of net primary production in the Sahel.
- Author
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Sallaba, Florian, Olin, Stefan, Engström, Kerstin, Abdi, Abdulahakim M., Boke-Olén, Niklas, Lehsten, Veiko, Ardö, Jonas, and Seaquist, Jonathan W.
- Subjects
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PRIMARY productivity (Biology) , *DEMAND forecasting , *GOVERNMENT policy on climate change - Abstract
In the 21st century, climate change in combination with increasing demand, mainly from population growth, will exert greater pressure on the ecosystems of the Sahel to supply food and feed resources. The balance between supply and demand (annual biomass required for human consumption) serves as a key metric for quantifying basic resource shortfalls over broad regions. Here we apply an exploratory modelling framework to analyze the variations in the timing and geography of different NPP (net primary production) supply-demand scenarios (with distinct assumptions determining supply and demand) for the 21st century Sahel. We achieve this by coupling a simple NPP supply model (forced with projections from four representative concentration pathways) with a global, reduced-complexity demand model (driven by socio-economic data and assumptions derived from five shared socio-economic pathways). For the scenario that deviates least from current socio-economic and climate trends, we find that per capita NPP outstrips its supply in the 2070s, while by 2050, half the countries in the Sahel experience NPP shortfalls. We also find that despite variations in the timing of the onset of NPP shortfalls, demand cannot consistently be met across the majority of scenarios. Moreover, large between-country variations are shown across the scenarios where by the year 2050, some countries consistently experience shortage, others surplus while yet others shift from surplus to shortage. At the local level (i.e. grid cell) hotspots of total NPP shortfall consistently occur in the same locations across all scenarios, but vary in size and magnitude. These hotspots are linked to population density and high demand. For all scenarios, total simulated NPP supply doubles by 2050 but is outpaced by increasing demand due to a combination of population growth and adoption of a diets rich in animal products. Finally, variations in the timing of onset and end of supply shortfalls stem from the assumptions that underpin the shared socio-economic pathways rather than the representative concentration pathways. Our results suggest that the UN sustainable development goals for eradicating hunger are at high risk for failure. This emphasizes the importance of policy interventions such as the implementation of sustainable and healthy diets, family planning, reducing yield gaps, and encouraging transfer of resources to impoverished areas via trade relations. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
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