misgandg

• Alemu, Diress Tsegaye; Rizzi, Jonathan; Bayr, Ulrike; Debella-Gilo, Misganu; Adade, Richard & Aheto, Denis W. [Vis alle 7 forfattere av denne artikkelen] (2026). Mapping Potential Mangrove Forest Restoration Areas in Coastal Ghana Using Multi-Model Habitat Suitability Analysis. Land. 15(9). doi: 10.3390/land15091581. Fulltekst i vitenarkiv Vis sammendrag

  Extensive ecosystem degradation along the coastal areas of Ghana highlights the need for targeted landscape restoration. This study identified areas with high restoration potential by evaluating environmental, climatic, and anthropogenic determinants of mangrove distribution. We tested multiple habitat suitability models, including Random Forest (RF), Generalized Additive Model (GAM), Generalized Linear Model (GLM), Maximum Entropy (MaxEnt), and an ensemble approach. Mangrove occurrence records were compiled from field observations, drone-derived data, and publicly available biodiversity and mapped datasets. The ensemble GAM-RF model emerged as the most robust model, with the highest predictive performance. Hydrological and topographic factors were the strongest predictors of mangrove habitat suitability, particularly proximity to rivers and the coastline, low-elevation terrain (<5 m asl), and temperature-related bioclimatic variables, while precipitation metrics and anthropogenic proxies played secondary roles. Using the ensemble approach, the estimated potentially suitable habitat ranged from 417 to 1313 km2, indicating that substantial areas remain available for mangrove restoration. These findings align with ongoing national and international restoration initiatives, including the coastal restoration plan, and have important implications for coastal protection, carbon sequestration, fisheries habitat, and local livelihoods. Our findings demonstrate that multi-model habitat suitability analyses can guide spatially targeted, and evidence-based restoration planning.

• Karan, Shivesh; Borchsenius, Bjørn Tobias; Debella-Gilo, Misganu & Rizzi, Jonathan (2025). Mapping urban green structures using object-based analysis of satellite imagery: A review. Ecological Indicators. ISSN 1470-160X. 170. doi: 10.1016/j.ecolind.2024.113027. Fulltekst i vitenarkiv Vis 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.

• Debella-Gilo, Misganu (2025). Relative performance of super-resolved Sentinel-2 and Copernicus VHR images in mapping built-up areas and building footprints using deep learning. European Journal of Remote Sensing. 58(1), s. 1–17. doi: 10.1080/22797254.2025.2517381. Fulltekst i vitenarkiv Vis sammendrag

  Studies have demonstrated that impressive super-resolution results can be achieved on spaceborne optical images, such as Sentinel-2, using variants of the Generative Adversarial Networks (GAN) among others. However, the practical performance of these super-resolved images in various applications, compared to their original high-resolution and low-resolution counterparts, is less researched. This study explores enhancing the spatial resolution of spaceborne remote sensing images from the Sentinel-2 satellite, using deep learning-based super-resolution methods. It then evaluates the performance of the super-resolved images against Very High-Resolution (VHR) optical satellite images and original Sentinel-2 images (S2) for mapping built-up areas and building footprints. A GAN-based single-image super-resolution model was trained from scratch on S2 tiles to enhance the resolution by a factor of five, creating three datasets: original S2 (10 m resolution), their super-resolved version (S2-SR), and VHR images (2 m resolution). Using a U-Net semantic segmentation model, the study mapped built-up areas and building footprints, employing both raster-based and vector-based evaluation metrics. Results show that S2-SR images perform comparably to VHR images in mapping built-up areas and slightly lower in mapping building footprints but significantly outperform original S2 images in both tasks.

• Debella-Gilo, Misganu (2022). Detecting built-up areas in agricultural fields using deep learning on Sentinel-2 Satellite Image Time Series. Kart og Plan. ISSN 0047-3278. 115(2), s. 136–153. doi: 10.18261/kp.115.2.4. Fulltekst i vitenarkiv Vis sammendrag

  Parts of the limited agricultural land area in Norway are taken up by buildings, roads, and other permanent changes every year. A method that detects such changes immediately after they have taken place is required in order to monitor the agricultural areas closely. To that end, Sentinel-2 satellite image time series (SITS) acquired during the summer of 2019 were used to detect the agricultural areas taken up by permanent changes such as buildings and roads. A deep-learning algorithm using 1D convolutional neural network (CNN), with the convolution in the temporal dimension, was applied to the SITS data. The training data was collected from the building footprints dataset filtered using a mono-temporal image aided with the areal resource map (AR5). The deep-learning model was trained and evaluated before being used for prediction in two regions of Norway. Procedures to reduce overfitting of the model to the training data were also implemented. The trained model showed a high level of accuracy and robustness when evaluated based on a test dataset kept out of the training process. The trained model was then used to predict new built-up areas in agricultural fields in two Sentinel-2 tiles. The prediction was able to detect areas taken by new buildings, roads, parking areas and other similar changes. The prediction was then evaluated with respect to the existing building footprints after a few post-processing procedures. A high percentage of the buildings were detected by the method, except for small buildings. The details of the methods and the results obtained, together with brief discussion, are presented in this paper.

• Debella-Gilo, Misganu & Rizzi, Jonathan (2021). Computing remote sensing big data using local hardware and open-source software packages. Kart og Plan. ISSN 0047-3278. 114(3-4), s. 254–273. doi: 10.18261/issn.2535-6003-2021-03-04-09. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Debella-Gilo, Misganu & Gjertsen, Arnt Kristian (2021). Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series. Remote Sensing. 13(2). doi: 10.3390/rs13020289. Fulltekst i vitenarkiv Vis sammendrag

  The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.

• Krøgli, Svein Olav; Debella-Gilo, Misganu; Dramstad, Wenche; Nørstebø, Vibeke Stærkebye & Perez-Valdes, Gerardo Alfredo (2020). Her, der eller overalt? Bioøkonomiens geografi. I Burton, Rob J.F.; Forbord, Magnar; Fuglestad, Eirik Magnus & Ellingsen, May-Britt (Red.), Etter oljen: Vår bioøkonomiske fremtid. Cappelen Damm Akademisk. ISSN 9788202604905. s. 127–149. Fulltekst i vitenarkiv Vis sammendrag

  Skal bioøkonomien overta når oljen tar slutt? En slik overgang vil innebære en rekke endringer innen blant annet jordbruk, skogbruk, fiskeri, akvakultur, biovitenskap og industri. En «smart» bioøkonomi vil kreve utvikling på tvers av disse biosektorene. Å få til en overgang som også er samfunnsmessig akseptabel for befolkningen, krever kloke politiske beslutninger, og kunnskapsgrunnlaget må styrkes. Satsing på bioøkonomi reiser en rekke nye spørsmål: Hva vil det innebære i praksis at bioøkonomi får en større rolle? Hvordan styrke omstillingen til en mer biobasert økonomi? Hvilke initiativer og virkemidler må til for å gjennomføre et skifte fra olje- til biobasert økonomi? Hva skal man forstå med begrepet bioøkonomi? Etter oljen. Vår bioøkonomiske fremtid diskuterer forskjellige aspekter, muligheter og utfordringer ved en slik overgang. Boka er aktuell for studenter, aktører i næringslivet, politikere, myndigheter, organisasjoner, forskere og ikke minst den interesserte samfunnsborger. Boka er en del av prosjektet BIOSMART, finansiert av Norges forskningsråd og ledet av Ruralis – Institutt for rural- og regionalforskning.

• Dramstad, Wenche; Debella-Gilo, Misganu & Krøgli, Svein Olav (2020). Bærekraftig bruk av biologiske ressurser: Geografisk kunnskap som grunnlag for gode valg. I Burton, Rob J.F.; Forbord, Magnar; Fuglestad, Eirik Magnus & Ellingsen, May-Britt (Red.), Etter oljen: Vår bioøkonomiske fremtid. Cappelen Damm Akademisk. ISSN 9788202604905. s. 150–168. Fulltekst i vitenarkiv Vis sammendrag

  Regjeringens bioøkonomistrategi, Kjente ressurser – uante muligheter, fra 2016, fremhever at den fremtidige bioøkonomien må være bærekraftig. Når en større del av våre behov skal dekkes med utgangspunkt i fornybare biologiske res¬surser, vil vi måtte høste mer av disse ressursene – selv om det også er et mål å utnytte ressursene bedre. Disse ressursene skal imidlertid høstes på det samme landarealet som utgjør leveområder for sjeldne arter og verdifulle naturtyper. Og disse har vi også forpliktelser overfor, gjennom FNs bærekraftsmål og konvensjonen om biologisk mangfold. Her ligger det en mulig konflikt mel¬lom ulike interesser. Hvordan kan vi øke og effektivisere uttaket av fornybare biologiske ressurser på en måte som ikke kommer i konflikt med målet om å sikre naturmangfoldet og målet om en miljømessig bærekraftig bioøkonomi? I denne sammenhengen er det nødvendig å være oppmerksom på at de land¬baserte fornybare biologiske ressursene til en viss grad konkurrerer om det samme arealet. Og det kan være slik at å produsere mer av én ressurs reduserer muligheten for å produsere en annen. Det finnes heller ikke noen omforent fasit for hva som er en miljømessig bærekraftig løsning. Dette er komplekse sammenhenger, og svaret kan vi ikke få gjennom noe enkelt regnestykke. Men ved hjelp av geografiske analyser kan vi belyse noen av de mulige utfordrin¬gene. I dette kapitlet legger vi frem analyser vi har gjort av forholdet mellom areal og potensielt økt uttak av skog. Vi har også analysert hvordan et slikt uttak kan komme til å påvirke rødlistede arter. Avslutningsvis ser vi nærmere på arealkonflikter som kan oppstå dersom skogarealet økes. Målet med analy¬sene er å bidra til en mest mulig kunnskapsbasert forvaltning av de landbaserte fornybare biologiske ressursene, samtidig som vi kommer nærmere målet om å sikre en bioøkonomi som også er miljømessig bærekraftig.

• Debella-Gilo, Misganu; Borchsenius, Bjørn Tobias; Bjørkelo, Knut & Breidenbach, Johannes (2020). AUTOMATED UPDATING OF FOREST COVER MAPS FROM CLOUD-FREE SENTINEL-2 MOSAIC IMAGES USING OBJECT-BASED IMAGE ANALYSIS AND MACHINE LEARNING METHODS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. ISSN 2194-9042. V-3-2020, s. 803–807. doi: 10.5194/isprs-annals-V-3-2020-803-2020. Fulltekst i vitenarkiv Vis sammendrag

  Planning sustainable use of land resources and environmental monitoring benefit from accurate and detailed forest information. The basis of accurate forest information is data on the spatial extent of forests. In Norway land resource maps have been carefully created by field visits and aerial image interpretation for over four decades with periodic updating. However, due to prioritization of agricultural and built-up areas, and high requirements with respect to the map accuracy, forest areas and outfields have not been frequently updated. Consequently, in some part of the country, the map has not been updated since its first creation in the 1960s. The Sentinel-2 satellite acquires images with high spatial and temporal resolution which provides opportunities for creating cloud-free mosaic images over areas that are often covered with clouds. Here, we combine object-based image analysis with machine learning methods in an automated framework to map forest area in Sentinel-2 mosaic images. The images are segmented using the eCogntion™ software. Training data are collected automatically from the existing land resource map and filtered using height and greenness information so that the training samples certainly represent their respective classes. Two machine learning algorithms, namely Random Forest (RF) and the Multilayer Perceptron Neural Network (MLP), are then trained and validated before mapping forest area. The effects of including and excluding some features on the classification accuracy is investigated. The results show that the method produces forest cover map at very high accuracy (up to 97%). The MLP performs better than the RF algorithm both in classification accuracy and in robustness against inclusion and exclusion of features.

• Nørstebø, Vibeke Stærkebye; Krøgli, Svein Olav; Debella-Gilo, Misganu; Valdes, Gerardo Alfredo Perez; Uggen, Kristin Tolstad & Dramstad, Wenche (2020). Identifying Suitable Bioeconomic Cluster Sites—Combining GIS-MCDA and Operational Research Methods. Environmental Modelling and Assessment. ISSN 1420-2026. 25, s. 689–703. doi: 10.1007/s10666-020-09694-x. Fulltekst i vitenarkiv Vis sammendrag

  A transition to a bioeconomy implies an increased focus on efficient and sustainable use of biological resources. A common, but often neglected feature of these resources is their location dependence. To optimize their use, for example in bioeconomic industrial clusters, this spatial aspect should be integrated in analyses. Optimal design and localization of a bioeconomic cluster with respect to the various biological and non-biological resources required for the cluster, the composition of industrial facilities in the cluster, as well as the demands of the outputs of the cluster, is crucial for profitability and sustainability. We suggest that optimal design and location of bioeconomic clusters can benefit from the use of a Multicriteria Decision Analysis (MCDA) in combination with Geographic Information Systems (GIS) and Operations Research modeling. The integration of MCDA and GIS determines a set of candidate locations based on various criteria, including resource availability, accessibility, and usability. A quantitative analysis of the flow of resources between and within the different industries is then conducted based on economic Input-Output analysis. Then, the cluster locations with the highest potential profit, and their composition of industrial facilities, are identified in an optimization model. A case study on forest-based bioeconomic clusters in the Østfold county of Norway is presented to exemplify this methodology, the expectation being that further implementation of the method at the national level could help decision makers in the planning of a smoother transition from a fossil-based economy to a bioeconomy.

• Breidenbach, Johannes; Waser, Lars T.; Debella-Gilo, Misganu; Schumacher, Johannes; Rahlf, Johannes & Hauglin, Marius [Vis alle 8 forfattere av denne artikkelen] (2020). National mapping and estimation of forest area by dominant tree species using Sentinel-2 data. Canadian Journal of Forest Research. ISSN 0045-5067. doi: 10.1139/cjfr-2020-0170. Fulltekst i vitenarkiv Vis sammendrag

  Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) plot data for modelling and subsequent mapping of spruce-, pine-, and deciduous-dominated forest in Norway at a 16 m × 16 m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42 000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by ∼10%. The produced map was subsequently used to generate model-assisted (MA) and poststratified (PS) estimates of species-specific forest area. At the national level, efficiencies of the estimates increased by 20% to 50% for MA and up to 90% for PS. Greater minimum numbers of observations constrained the use of PS. For MA estimates of municipalities, efficiencies improved by up to a factor of 8 but were sometimes also less than 1. PS estimates were always equally as or more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.

• Krøgli, Svein Olav; Debella-Gilo, Misganu & Dramstad, Wenche (2020). Bioøkonomiens geografi og geografiske målkonflikter. Kart og Plan. ISSN 0047-3278. 113(2), s. 104–120. doi: 10.18261/issn.2535-6003-2020-02-04. Fulltekst i vitenarkiv Vis sammendrag

  The development of a new bioeconomy implies an increased need for renewable biological resources. This means that more of the existing biomass will be har vested and a larger land area is likely to be utilized. While it is widely acknowl edged that this increased harvest and production must be sustainable, it must also be acknowledged that there are some potential challenges. For example, there may be different aims targeting the same area. To meet this challenge in an informed approach, we argue that geographical data and spatial analyses are key. We exemplify this through a study in which we utilize the spatial distribution of produced biomass from agriculture and forestry, together with the location of threatened species from the Norwegian Red List of Species. In our analyses we demonstrate that there is a spatial overlap between the most productive land for forestry and agriculture. At the same time, a high occurrence of threatened spe cies is also found in these areas. We conclude that analysing the geography of conflicting aims is important. It documents the importance of spatial data, and findings from this type of analyses need to be included in bioeconomy decision making.

• Astrup, Rasmus Andreas; Rahlf, Johannes; Bjørkelo, Knut; Debella-Gilo, Misganu; Gjertsen, Arnt Kristian & Breidenbach, Johannes (2019). Forest information at multiple scales: development, evaluation and application of the Norwegian forest resources map SR16. Scandinavian Journal of Forest Research. ISSN 0282-7581. 34(6), s. 484–496. doi: 10.1080/02827581.2019.1588989. Fulltekst i vitenarkiv Vis sammendrag

  This paper describes the development and utility of the Norwegian forest resources map (SR16). SR16 is developed using photogrammetric point cloud data with ground plots from the Norwegian National Forest Inventory (NFI). First, an existing forest mask was updated with object-based image analysis methods. Evaluation against the NFI forest definitions showed Cohen's kappa of 0.80 and accuracy of 0.91 in the lowlands and a kappa of 0.73 and an accuracy of 0.96 in the mountains. Within the updated forest mask, a 16×16 m raster map was developed with Lorey's height, volume, biomass, and tree species as attributes (SR16-raster). All attributes were predicted with generalized linear models that explained about 70% of the observed variation and had relative RMSEs of about 50%. SR16-raster was segmented into stand-like polygons that are relatively homogenous in respect to tree species, volume, site index, and Lorey's height (SR16-vector). When SR16 was utilized in a combination with the NFI plots and a model-assisted estimator, the precision was on average 2–3 times higher than estimates based on field data only. In conclusion, SR16 is useful for improved estimates from the Norwegian NFI at various scales. The mapped products may be useful as additional information in Forest Management Inventories.

• Debella-Gilo, Misganu (2016). Bare-earth extraction and DTM generation from photogrammetric point clouds including the use of an existing lower-resolution DTM. International Journal of Remote Sensing. ISSN 0143-1161. 37(13), s. 3104–3124. doi: 10.1080/01431161.2016.1194543. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2013). Monitoring slow-moving landslides using spatially adaptive least squares image matching. I Margottini, Claudio; Canuti, Paolo & Sassa, Kyoji (Red.), Landslide Science and Practice. Volume 2: Early Warning, Instrumentation and Monitoring. Springer. ISSN 9783642314445. s. 301–307. doi: 10.1007/978-3-642-31445-2_39. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2012). Locally adaptive template sizes for matching repeat images of Earth surface mass movements. ISPRS Journal of Photogrammetry and Remote Sensing (P&RS). ISSN 0924-2716. 69, s. 10–28. doi: 10.1016/j.isprsjprs.2012.02.002. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2012). Measurement of Surface Displacement and Deformation of Mass Movements Using Least Squares Matching of Repeat High Resolution Satellite and Aerial Images. Remote Sensing. 4(1), s. 43–67. doi: 10.3390/rs4010043. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2011). Locally adaptive template sizes for matching repeat images of mass movements. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. s. 4281–4284. Fulltekst i vitenarkiv Vis sammendrag

  Source: Geoscience and Remote Sensing Symposium (IGARSS), 2011. ISBN: 978-1-4577-1005-6

• Debella-Gilo, Misganu & Kääb, Andreas (2011). Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation. Remote Sensing of Environment. ISSN 0034-4257. 115(1), s. 130–142. doi: 10.1016/j.rse.2010.08.012. Fulltekst i vitenarkiv
• Bryn, Anders & Debella-Gilo, Misganu (2011). GIS-based prognosis of potential forest regeneration affecting tourism locations and cultural landscapes in South Norway. Scandinavian Journal of Hospitality and Tourism. ISSN 1502-2250. 11(2), s. 166–189. doi: 10.1080/15022250.2011.576827. Fulltekst i vitenarkiv Vis sammendrag

  The Norwegian landscape is changing as a result of forest regeneration within the cultural landscape, and forest expansion has impacts on accessibility, visibility, and landscape aesthetics, thereby affecting the country's tourism industry. This study aimed at identifying the potential areas of forest regeneration and anticipated subsequent landscape effects on different categories of tourist locations in southern Norway. Deforested areas with a potential for forest regeneration were identified from several map sources by GIS-analyses, and 180 tourist locations were randomly selected from the Norwegian national tourism database (Reiselivsbasen), and then buffered by 2 km radius for land cover classes. The findings revealed that approximately 15% of southern Norway has the climatic potential for future forest regeneration, in addition to 5% of cultivated land. Future forest regeneration will affect the landscapes surrounding the tourist locations of rural south Norway, and while the potential is nationwide, it is not uniformly distributed. Two important tourist landscape regions seem especially exposed to forest regeneration: the coastal heath region and the mountain landscapes. Large parts of these areas do not have sufficient numbers of domestic grazing animals necessary to maintain the present character of the landscape.

• Haug, Torborg; Debella-Gilo, Misganu; Karstensen, Jonas & Kääb, Andreas (2010). PERFORMANCE AND APPLICATION OF DIFFERENT IMAGE MATCHING ALGORITHMS FOR INVESTIGATING GLACIER AND ICE-SHELF FLOW, PERMAFROST CREEP AND LANDSLIDES. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. s. 3636–3639. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Etzelmuller, Bernd (2009). Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County, Norway. CATENA. ISSN 0341-8162. 77(1), s. 8–18. doi: 10.1016/j.catena.2008.12.001. Fulltekst i vitenarkiv
• Gilo, Misganu Debella & Etzelmuller, Bernd (2009). Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County, Norway. CATENA. ISSN 0341-8162. 77(1), s. 8–18. doi: 10.1016/j.catena.2008.12.001. Fulltekst i vitenarkiv

Se alle arbeider i NVA

• Debella-Gilo, Misganu (2024). Relative performance of super-resolved Sentinel-2 images for built-up area mapping using deep learning. Fulltekst i vitenarkiv
• Krøgli, Svein Olav; Debella-Gilo, Misganu; Aune-Lundberg, Linda & Dramstad, Wenche (2023). Mer og samlet areal gir mer areal i drift. NIBIO POP. ISSN 2464-1170. 9(31). Fulltekst i vitenarkiv Vis sammendrag

  I 2021 var det cirka 122 000 landbrukseiendommer i Norge som hadde mer enn 10 dekar maskinelt høstbart jordbruksareal. De fleste av disse, nesten hundre tusen, hadde så og si alt jordbruksareal i drift. Det betyr imidlertid også at mer enn 20 000 landbrukseiendommer har noe eller mesteparten av eiendommens jordbruksareal tilsynelatende ute av drift. Vi har delt landbrukseiendommer inn i fire grupper basert på mengde areal som kan være ute av drift, og vurdert om det er forskjell mellom gruppene med tanke på størrelse og fordeling av jordstykker.

• Debella-Gilo, Misganu; Gjertsen, Arnt Kristian; Borchsenius, Bjørn & Strand, Geir-Harald (2023). Veksttyper på jordbruksareal fra satellitt. NIBIO POP. ISSN 2464-1170. 9(7). Fulltekst i vitenarkiv Vis sammendrag

  JordbrukSat er et nasjonalt vektorkart som viser den geografiske fordelingen av ulike veksttyper på jordbruksareal, samt jordbruksareal som er nedbygd. Kartet framstilles fra satellittbilder og offentlig kartdata med bruk av maskinlæring og deler jordbruksarealet i seks klasser.

• Debella-Gilo, Misganu; Rizzi, Jonathan & Munsterhjelm, Nicolai (2022). Mapping and updating land area types using semantic segmentation on the pan-European very high-resolution satellite image mosaics. Fulltekst i vitenarkiv
• Breidenbach, Johannes; Waser, Lars T.; Debella-Gilo, Misganu; Schumacher, Johannes; Rahlf, Johannes & Hauglin, Marius [Vis alle 8 forfattere av denne artikkelen] (2020). National mapping and estimation of forest area by dominant tree species using Sentinel-2 data. arXiv. Fulltekst i vitenarkiv
• Krøgli, Svein Olav; Debella-Gilo, Misganu & Dramstad, Wenche (2020). Målkonflikter i bioøkonomien fra et geografisk perspektiv: jordbruk, skogbruk, artsmangfold. Fulltekst i vitenarkiv
• Rizzi, Jonathan; Nystuen, Ingvild; Debella-Gilo, Misganu & Søvde, Nils Egil (2020). From remote sensing to bioeconomy: how big data can improve automatic map generation. Fulltekst i vitenarkiv
• Dramstad, Wenche; Krøgli, Svein Olav & Debella-Gilo, Misganu (2019). Multiple aims - one landscape. Fulltekst i vitenarkiv
• Rizzi, Jonathan; Nystuen, Ingvild; Debella-Gilo, Misganu; Søvde, Nils Egil & Heggem, Eva Solbjørg Flo (2019). Big data and machine learning in the bioeconomy sector: preliminaryresults from the Norwegian case. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Gjertsen, Arnt Kristian (2019). Automated Detection of Unregistered Built-up Agricultural Areas from Sentinel-2 Images Using Machine Learning. Fulltekst i vitenarkiv
• Hauglin, Marius; Rahlf, Johannes; Debella-Gilo, Misganu; Astrup, Rasmus Andreas & Breidenbach, Johannes (2019). SR16 – A forest resource map for Norway. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu (2019). The use of remote sensing in resource inventory and monitoring at NIBIO. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu; Krøgli, Svein Olav; Nørstebø, Vibeke Stærkebye; Dramstad, Wenche & Valdes, Gerardo Alfredo Perez (2018). Exploring Scenarios for Sustainable Bioeconomic Resource Use and Industrial Cluster Locations. Fulltekst i vitenarkiv
• Nørstebø, Vibeke Stærkebye; Valdes, Gerardo Alfredo Perez; Krøgli, Svein Olav; Dramstad, Wenche; Debella-Gilo, Misganu & Uggen, Kristin Tolstad (2018). Identifying Suitable Bioeconomic Cluster Sites by Integrating GIS-MCDA and Operational Research Methods. Fulltekst i vitenarkiv
• Valdes, Gerardo Alfredo Perez; Nørstebø, Vibeke Stærkebye; Krøgli, Svein Olav; Dramstad, Wenche; Debella-Gilo, Misganu & Uggen, Kristin Tolstad (2018). GIS-MCDA-OR-I/O Framework for Biocluster Localization. Fulltekst i vitenarkiv
• Dramstad, Wenche; Debella-Gilo, Misganu; Krøgli, Svein Olav & Nørstebø, Vibeke Stærkebye (2016). Bioøkonomi: Optimal bruk av stedfestede ressurser, hvor og hva? Fulltekst i vitenarkiv
• Dramstad, Wenche; Nørstebø, Vibeke Stærkebye; Krøgli, Svein Olav; Debella-Gilo, Misganu & Pérez-Valdés, Gerardo A (2016). Locating resources and using them wisely in the transition to a "smart" bioeconomy. Fulltekst i vitenarkiv Vis sammendrag

  Aim: Identify where bioeconomic development would best be located to maximise both local resources and the reusable waste from potentially collaborating sectors. We seek to answer the questions like Where are the best locations for bioeconomic clusters and how should this be assessed? What are the tradeoffs, how can they be mapped and described, and are there any general major obstacles? What are the conditions that would aid in developing a smart bioeconomy and what are the spatial implications of different developments?

• Debella-Gilo, Misganu (2016). BARE-EARTH EXTRACTION AND DTM GENERATION FROM PHOTOGRAMMETRIC POINT CLOUDS WITH A PARTIAL USE OF AN EXISTING LOWER RESOLUTION DTM. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Archives). ISSN 1682-1750. 41, s. 201–206. doi: 10.5194/isprsarchives-XLI-B3-201-2016. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu; Bjørkelo, Knut; Breidenbach, Johannes & Rahlf, Johannes (2013). OBJECT-BASED ANALYSIS OF AERIAL PHOTOGRAMMETRIC POINT CLOUD AND SPECTRAL DATA FOR LAND COVER MAPPING. Fulltekst i vitenarkiv
• Kääb, Andreas; Heid, Torborg & Debella-Gilo, Misganu (2012). Recent advances in measurement of glacier flow using repeat optical images. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2011). Locally adaptive template sizes for matching repeat images of mass movements. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2010). SUB-PIXEL PRECISION IMAGE MATCHING FOR DISPLACEMENT MEASUREMENT OF MASS MOVEMENTS USING NORMALISED CROSS-CORRELATION. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Archives). ISSN 1682-1750. XXXVIII, s. 181–186. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2010). SUB-PIXEL PRECISION IMAGE MATCHING FOR DISPLACEMENT MEASUREMENT OF MASS MOVEMENTS USING NORMALISED CROSS-CORRELATION. Fulltekst i vitenarkiv
• Haug, Torborg; Debella-Gilo, Misganu; Karstensen, Jonas & Kääb, Andreas (2010). Performance and application of different image matching algortihms for investigating glacier and ice-shelf flow, permafrost creep and landslides. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2010). Sub-pixel precision algorithms for normalised cross-correlation based image matching of mass movements. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu & Kääb, Andreas (2010). Sub-pixel Precision Image Matching for Displacement Measurement of Mass Movements Using Normalised Cross-Correlation. Fulltekst i vitenarkiv
• Kääb, Andreas Max; Strozzi, T; Delaloye, R; Ambrosi, C & Debella-Gilo, Misganu (2009). Paraglacial rock mass movements in response to glacier retreat. Examples from Aletsch Glacier, Swiss Alps. Fulltekst i vitenarkiv
• Kääb, Andreas Max; Strozzi, T; Delaloye, R; Ambrosi, C & Debella-Gilo, Misganu (2009). Paraglacial rock mass movements in response to glacier retreat. Examples from Aletsch Glacier, Swiss Alps. Fulltekst i vitenarkiv
• Kääb, Andreas Max; Strozzi, T; Delaloye, R; Ambrosi, C & Debella-Gilo, Misganu (2009). Comparison and combination of airborne digital photogrammetry, satellite SAR interferometry, GPS and terrestrial surveying for monitoring rock mass movements. Fulltekst i vitenarkiv
• Kääb, Andreas Max; Debella-Gilo, Misganu; Haug, Torborg & Karstensen, Jonas (2009). Measurement of surface displacements on rockglaciers. Fulltekst i vitenarkiv
• Bryn, Anders & Debella-Gilo, Misganu (2008). Reiseliv og gjengroing av kulturlandskap. Kulturarven : kysten, innlandet, byen. ISSN 0806-7023. 44, s. 10–13. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu; Etzelmuller, Bernd & Klakegg, Ove (2007). Digital Soil Mapping Using Digital Terrain Analysis and Statistical Modeling Integrated into GIS: Examples from Vestfold County of Norway. Fulltekst i vitenarkiv
• Debella-Gilo, Misganu; Gizachew, Belachew; Rizzi, Jonathan; Systad, Jarle & Husebråten, Johan (2025). Bruk av Sentinel 2 og GEDI-satellittdata for å kartlegge mangroveskogens utbredelse og biomasse. Norges miljø- og biovitenskapelige universitet (NMBU). Fulltekst i vitenarkiv Vis sammendrag

  Mangrove forests are important ecosystems that store large amounts of carbon, protect coastlines, and provide habitats for a variety of marine species. Despite their ecological significance, mangrove forests are highly threatened. It is therefore crucial to map and monitor these areas to protect them. The purpose of this master's thesis is to map the distribution of mangrove forests and estimate their above-ground biomass in Ghana. Optical satellite images from Sentinel-2 were used for the classification. The biomass estimates were based on a combination of satellite images and LiDAR data from GEDI (Global Ecosystem Dynamics Investigation). In addition, fieldwork was conducted in Ghana to collect training and test data for the models. The global dataset Global Mangrove Watch (GMW) was used as a reference to assess the quality of the classification results. The distribution of the mangrove forest was mapped using Object-Based Image Analysis (OBIA), where the satellite images are divided into segments based on reflectance values in different spectral bands. The segments were then classified using the machine learning methods Support Vector Machine (SVM) and Random Forest (RF). To calculate the biomass, regression models were developed combining GEDI data with bands and indices from Sentinel-2. The models were evaluated based on the coefficient of determination (R²) and root mean square error (RMSE). The results show that the combination of Sentinel-2 data and local field measurements provides more accurate maps than existing global datasets. Among the two classification methods, SVM performed best on the validation data, with an F1-score of 96.5%, compared to 75.4% for GMW. The SVM model estimated the total area of mangrove forest in Ghana to be 143.54 km². The above-ground biomass in Ghana’s mangrove forest was estimated to be approximately 313,000 tons, with an average of about 21.8 tons per hectare. The best model for biomass estimation was the Gradient Boosting model, with a multiple regression analysis based on all attributes achieving an R² value of 0.38 and an RMSE of 24.5.

• Wenberg, Sivert Leander Engan; Semb, Tobias Horn; Debella-Gilo, Misganu & Rizzi, Jonathan (2024). Grønnstrukturkartlegging utenfor tettbebygde områder ved bruk av høyoppløselige satellittbilder : en objektbasert tilnærming. Norges miljø- og biovitenskapelige universitet (NMBU). Fulltekst i vitenarkiv Vis sammendrag

  Over the past few years, high-resolution satellite images have become more accessible. Borchsenius et al. (2023) have used high-resolution satellite images together with digital elevation models to map green structures in urban areas. In this study, a method is developed for mapping green structures outside urban areas. The four study areas mapped in the thesis are the norwegian municipalities of Rakkestad, Kinn, Ringsaker, and Eigersund. The approach for mapping green structures is based on geographic object-based image analysis. In geographic object-based image analysis, there are two key processes: segmentation and classification. The segmentation method incorporated is region-based. The study shows that a smaller segmentation scale is advantageous for achieving high accuracy in mapping green structures outside urban areas. The green structures mapped are divided into three vertical layers: tree, shrub, and field layers. The classification of green structures is performed using three methods: one rule-based and two machine learning models. The machine learning models used are support vector machine and random forest. The random forest model, in particular, shows promising results. The model performs better than the rule-based classification for the training areas of random forest but gives a few percent lower accuracy for the test areas. Mapping with random forest provides accuracies between 79-94%, while the rule-based classification provides accuracies between 74-85%. The random forest model performs well for tree and shrub layers, while the biggest challenges lie in distinguishing the field layer and non-green structures. This is mainly because vegetation fields within agricultural areas are mapped. It is complicated to distinguish between agricultural land and the field layer since they have similar properties in height and color. The study recommends using random forest with training data from several different areas to increase geographical variation. Using a method for distinguishing green structures before classification could also provide better accuracy.

• Nordby, Lars Michaloff; Debella-Gilo, Misganu & Puliti, Stefano (2024). Automatisk klassifisering av naturlandskap ved bruk av maskinlæring og terrestriske bilder. Norges miljø- og biovitenskapelige universitet (NMBU). Fulltekst i vitenarkiv Vis sammendrag

  In this master thesis, the aim is to find out if it is possible to identify and segment out Juniper from terrestrial pictures taken during the fieldwork of NIBIO’s National Forest Inventory (NFI) project. The goal of the thesis has been to develop a machine learning model that can identify and localize juniper in the pictures. It is also studied if the model training could benefit from the addition of more data from an external dataset. Segmentation and classification are used to try to achieve these goals. The models are trained on datasets containing images from NIBIO and iNaturalist. The models have then been tested on pictures from NIBIO, and the validation parameters compared to find the best model. The model architectures used are from the YOLOv8 models, and this architecture has been used in both classification and segmentation tasks. The results show that all the segmentation models generally perform poorly, while the classification model with an adjustment in the confidence level delivers the best results. However, the results show that the segmentation models still have some potential to perform better by training on a sufficient dataset. The model trained on a combination dataset shows that using an external dataset could result in a model that is more accurate and confident in its predictions and shows signs of delivering a more robust model. The thesis concludes, therefore, that it is possible to develop a machine learning algorithm that can detect juniper in terrestrial pictures. Further work should be focused on gathering a sufficient dataset and trying out different model architectures.

• Amameo, Jude Michael Bihay; Debella-Gilo, Misganu & Bjugstad, Nils (2024). Komparativ evaluering i avlingsvariasjon mellom Sentinel-2, droner og høyoppløsningdata av KI for norsk presisjonslandbruk. Norges miljø- og biovitenskapelige universitet (NMBU). Fulltekst i vitenarkiv Vis sammendrag

  Sustainable crop production, crop yield increase and reducing ecological footprint are important themes within Norwegian agriculture. Precision agriculture is a new technique that combines agricultural tools with newer technology to better manage and allocate resources effectively. In particular applying variable rate application of nitrogenous fertilizers. Norwegian farmers use Sentinel-2 satellite data and drones to assess crop variability. AI-based super-resolution image is a viable solution to address the challenges/limitations around Sentinel-2 and drones. In the context of scalability and profitability. In this research paper, we are comparing Sentinel-2 satellite imagery, drones and super-resolution imagery from Digifarm to evaluate whether spatial resolution impacts within-field detection of soil and crop variability. The test area conducted was at Vollebekk in Ås and consisted of two areas named polygon A [2.4 daa] and polygon B [9.3 daa]. With 4 measurements used, we found a statistical difference in the detection of soil and crop variability between groups. The evaluation of which of the groups that scored best in the geolocation accuracy was found to be inconclusive. This was due to missing geolocation error data of Sentinel-2 and drone. Further analysis tells us that the data from Digifarm performs the same or better than drones, assuming high geolocation accuracy. Furthermore, the results indicate that the data from Digifarm estimated -30.3% and -32.0% in reduction of required fertilization compared to drones, based on the area of polygon A and B respectively. These two findings suggest that super-resolution imagery could be an alternative for farmers who wants to save time and resources.Additionally, solving challenges regarding scalability and profitability from drone measurements. We hope that Norwegian farmers are open to using this technology as an improvement of today’s precision agriculture.

• Overrein, Julie; Shafiee, Sahameh & Debella-Gilo, Misganu (2024). Predicting Within-Field Yield Variation: an Evaluation Between UAV and Satellite Platforms. Norges miljø- og biovitenskapelige universitet (NMBU). Fulltekst i vitenarkiv Vis sammendrag

  This study explores the use of multispectral images from both Unmanned Aerial Vehicles (UAVs) and satellite platforms to build prediction models for within-field yield variations. Satellite images from the early stages and UAV images from the middle and late stages of crop growth were collected from two spring wheat fields in Norway. From the images, reflectance values were extracted. Various features, such as vegetation indices, simple ratios, and a temporal Normalized Difference Vegetation Index (NDVI), were added to the datasets to assess their impacts on model performance. Harvesters from the fields provided detailed yield data. The analysis includes regression and classification techniques, including random forest models, multilayer perceptron (MLP), and 1-dimensional Convolutional Neural Network (CNN). Correlation analysis of the data features against grain yield indicates strong correlations between the temporal NDVI and yield for both platforms, ranging from 0.18 to 0.47. The findings from the model analyses reveal that random forest models integrating UAV reflectance data with additional non-linear features in simple ratios and vegetation indices have increased prediction scores compared to alternative models. These models outperformed the deep learning models in this context. The best-performing regression model had an R2 score of 0.638 and an MSE score of 0.292, and the best-performing classification model achieved an accuracy of 59.4%. These models correspond to the crops’ filling stage and arise from datasets with reflectance values and added vegetation indices. Models only utilizing reflectance values achieved an R2 score of 0.588, MSE score of 0.332, and accuracy of 56.5%. Results from the satellite classification models indicate that augmenting the datasets to include non-linear features (simple ratios or vegetation indices) occasionally decreased the models’ performance.

• Hagen, Håvard Ullebust; Debella-Gilo, Misganu & Munsterhjelm, Nicolai (2023). Automatisk deteksjon av myr med bruk av hyperspektrale bilder og maskinlæring. Norges miljø- og biovitenskapelige universitet (NMBU). Fulltekst i vitenarkiv Vis sammendrag

  Myra i Norge kan sees på dens utbredte karbonlager og det biologiske mangfoldet den inneholder. Myra over våre skoggrenser er dårlig kartlagt, og i denne oppgaven undersøkes det om det er mulig å detektere myra automatisk. Undersøkelsen benytter seg av en metode som klassifiserer egenskaper i hyperspektrale data. Metoden baserer seg på å utnytte diverse maskinlæringsmodeller til å kunne klassifisere myra. Maskinlæringsmodellene blir trent ved hjelp av et arealressurskart, der det er tilstrekkelig mengde klassifisert myr. Maskinlæringsmodellene som testes er klassifiseringsalgoritmene Support Vector Machine (SVM), Random Forest Classifier (RFC), Logistic Regression (LogReg) og K-Nearest Neighbors (KNN). Denne oppgaven ser på diverse evalueringsmål for modellene for å veie dem opp mot hverandre og se hvilke av de som presterer best. De ulike evalueringsmålene som ble brukt i oppgaven var Matthews korrelasjonskoeffisient (MCC), kappa-koeffisient, klassifiseringsrapporten til modellene, modellenes forvirringsmatrise, AUC-verdi og modellenes klassifiseringsnøyaktighet. Ved å undersøke klassifiseringsalgoritmene sine evalueringsmål, viser det seg at det er mulig å gjennomføre en vellykket, aksepterbar og tilfredsstillende klassifisering av myr. SVM-modellen presterte best i de fleste evalueringsmålene. Den presterte med klassifiseringsnøyaktighet på 0.811, en MCC på 0.632, kappa-koeffisient på 0.632 og en AUC score på 0.87. Disse verdiene forteller at myrarealene som klassifiseres av modellene samsvarer godt med myrarealene i treningsdataene.

• Rizzi, Jonathan; Debella-Gilo, Misganu; Mathisen, Åsmund Ertshus & Skog, Tor-Einar (2021). Spatial Big Data. Application examples from NIBIO. NIBIO. ISSN 9788217029175. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Rizzi, Jonathan & Debella-Gilo, Misganu (2021). Spatial Big Data tools and methods within NIBIO. NIBIO. ISSN 9788217029182. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Åström, Jens; Dramstad, Wenche; Debella-Gilo, Misganu; Hovstad, Knut Anders; Åström, Sandra Charlotte Helene & Rusch, Graciela (2014). Assessing Norwegian pollination deficits. Capacity building towards IPBES - implementation and methodological evaluation of the "Protocol to Detect and Assess Pollination Deficits in crops". Norsk institutt for naturforskning (NINA). ISSN 9788242627216. Fulltekst i vitenarkiv Vis sammendrag

  In 2012, the Norwegian Environmental Agency funded an extension to the Global Pollination Project, coordinated by the FAO (Food and Agriculture Organization of the United Nations) to expand the number of connected countries from 7 fully participating to in total 13 countries. This international effort seeks to build capacity for pollination studies and add to the knowledge base for the Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES). IPBES is currently conducting its first fast track case study on pollination. Specifically, the Global Pollination Project implements the “Protocol to detect and assess pollination deficits in crops: a handbook for its use” (Vaissière et al. 2011), developed through the FAO. The proto-col outlines a unified method to investigate pollination and measure pollination deficits in vari-ous agricultural systems around the world. NINA (the Norwegian Institute for Nature Research) was tasked with setting up a Norwegian collaboration to implement the protocol in Norway, to analyse its applicability to Nordic conditions and evaluate its strength in relation to alternative research strategies. The present report is the result of this effort. This report does not communicate the final results of the analyses, as they will be conducted in the two larger “host-projects” that made the implementation of the protocol possible. Instead, it outlines the rationale of the protocol, and evaluates its potential for providing management rel-evant information on pollination deficits, with particular emphasis on Norway. We discuss the state and trends of pollination dependent crops in Norway, as a background for the need for pollination in Norwegian Agriculture. The protocol is general enough to be applied to a wide variety of settings, and we did not expe-rience any fundamental problems of implementing it in a Nordic setting. We did however notice challenges to an effective implementation, which might be especially pronounced in a Norwe-gian or Scandinavian setting. First, it can be difficult to find a wide enough range of factors that influence pollinators to efficiently analyse the importance of pollination without resorting to ma-nipulative treatments. For example, the amount of flower resources and fragmentation of habi-tat are factors known to influence pollinators. But many crops are spatially aggregated to rela-tively narrow valleys and therefore experience similar surroundings. Secondly, it can be chal-lenging to find enough replicate farms since Norway is a relatively small agricultural nation. Thirdly, pollinators in Norway (as in many other parts of the world) are intractably linked to ag-ricultural and animal husbandry practices that provide a diversity of flowering resources neces-sary for pollinating insects, yet these practices and resulting resources in the surrounding land-scape is not sufficiently captured by the survey protocol. The protocol is designed to estimate differences in yield given differences in pollination, and various methods are available to approach optimal pollination, that acts as benchmark. Esti-mating the effect of pollination on yield is the foundation to understanding the status of pollina-tion deficits for any crop. The protocol appears to be a successful effort to create a unified standard of measuring pollination and pollination deficits by this definition. It thus marks a great improvement for pollination research in agriculture internationally. Pollination, Ecosystem services, Bees, Bumble bees, Pollination deficit Protocol, FAO, IPBES, Policy, apple, red clover, Norway, pollinering, økosystemtjenester, bier, humler, protokoll for polline-ringsunderskudd, FAO, IPBES, eple, rødkløver

• Bryn, Anders & Debella-Gilo, Misganu (2008). Gjengroing i reiselivets landskap. NIBIO. Fulltekst i vitenarkiv Vis sammendrag

  Reiselivet i Norge har de siste tiåra vært oppmerksomme på landskapsendringene som skjer i Norge. I følge reiselivsnæringa truer gjengroing av kulturlandskapet viktige segmenter innen det norske reiselivet. Samtidig legges det årlig ned et sted mellom 1500 - 2000 gardsbruk i Norge.

• Klakegg, Ove Mindor; Hofmeister, Frauke & Debella-Gilo, Misganu (2008). Jordsmonnstatistikk 012 Drammensvassdraget. NIBIO. ISSN 9788231100454. Fulltekst i vitenarkiv Vis sammendrag

  En produksjonslinje for publisering av jordsmonndata og annen relevant jordbruksstatistikk per vassdragsenhet (REGINE-enhet) er etablert. Her presenteres statistikken for Drammensvassdraget. Informasjonskilden for denne rapporten er først og fremst jordsmonndatabasen, men det er også lagt inn noe informasjon fra produksjonsregisteret (temaene planteproduksjon og dyretetthet). Jordsmonndatabasen dekker kun dyrket mark og bare den sydlige halvdelen av vassdraget. Jordsmonnstatistikken er utarbeidet for vassdragsenheter på overordnet nivå som har mer enn 80 % dekning av jordsmonndata. Kartlagt areal er ikke representative for vassdraget som helhet. Det er også utarbeidet en mer detaljert statistikk for følgende enheter: Vigga-Randsfjorden i Oppland, Simoa i Buskerud og deler av Vestfossenelvas nedslagsfelt i Vestfold. Her er det også laget statistikk som viser hvordan egenskapene varierer med avstanden fra vannstrengen.

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