Geir-Harald Strand
Head of Research
(+47) 415 01 640
geir.harald.strand@nibio.no
Place
Ås O43
Visiting address
Oluf Thesens vei 43, 1433 Ås
Abstract
This is the final report for the NIBIO assignment in E2SOILAGRI. The report is a final project implementation review (PIR) assessing implementation results according to the planned results for E2SOILAGRI and the recommendations provided in the mid-term PIR is also be assessed. The review is sub-activity 4.1 in E2SOILAGRI project.
Authors
Geir-Harald StrandAbstract
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The objective of this study is to identify the needs related to geospatial LC, LU, and LCLUC information for spatial planning in Poland and Norway, and examine the usefulness of CLMS products in the context of these planning systems. The research has conducted based on a comparative analysis of two planning systems, to indicate areas where CLMS can improve or supplement national spatial data. The study shows that CLMS can provide information on up-to-date spatial data showing actual LC/LU/LCLUC, but that the degree of detail and the accuracy may be insufficient. CLMS data is harmonised across Europe and thus meets the need expressed by international organisations, for data that are consistent at a continental level. This is not a requirement in national planning systems in Poland and Norway, where the needs are regulated by national legislation. The thematic and geometric accuracy of national data sources are usually better than the data provided by CLMS, but CLMS might fill gaps when specific topics are missing in national mapping programs.
Authors
Geir-Harald StrandAbstract
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Geir-Harald StrandAbstract
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Geir-Harald StrandAbstract
The Copernicus high-resolution layer imperviousness density (HRL IMD) for 2018 is a 10 m resolution raster showing the degree of soil sealing across Europe. The imperviousness gradation (0–100%) per pixel is determined by semi-automated classification of remote sensing imagery and based on calibrated NDVI. The product was assessed using a within-pixel point sample of ground truth examined on very high-resolution orthophoto for the section of the product covering Norway. The results show a high overall accuracy, due to the large tracts of natural surfaces correctly portrayed as permeable (0% imperviousness). The total sealed area in Norway is underestimated by approximately 33% by HRL IMD. Point sampling within pixels was found to be suitable for verification of remote sensing products where the measurement is a binomial proportion (e.g., soil sealing or canopy coverage) when high-resolution aerial imagery is available as ground truth. The method is, however, vulnerable to inaccuracies due to geometrical inconsistency, sampling errors and mistaken interpretation of the ground truth. Systematic sampling inside each pixel is easy to work with and is known to produce more accurate estimates than a simple random sample when spatial autocorrelation is present, but this improvement goes unnoticed unless the status and location of each sample point inside the pixel is recorded and an appropriate method is applied to estimate the within-pixel sampling accuracy.
Authors
Geir-Harald StrandAbstract
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This is a mid-term report for the NIBIO assignment in “E2SOILAGRI”. The report gives information on the process and the deliveries for the Latvian partners in the project and includes remarks from the review team. This task is defined as sub-activity 4.1 in the Terms of Reference for the NIBIO assignment.
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Dette er en oppstartrapport for NIBIOs bidrag i prosjektet “E2SOILAGRI”. Rapporten sammenfatter informasjon om det latviske jordinformasjonssystemet som framkom gjennom intervjuer med part-nere og interessenter i prosjektet. Arbeidet er definert som underaktivitet 4.1 i Terms of Reference for NIBIOs rolle i prosjektet.
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The CORINE Land Cover dataset for Norway for the reference year 2018 (CLC2018) was compared to detailed national land cover and land use data. This allowed us to describe the thematic composition of the CLC-polygons and aggregate the information into statistical profiles for each CLC-class. We compared the results to the class definitions found in the CLC mapping instructions, while considering the generalization and minimal mapping units required for CLC. The study showed that CLC2018 in general complied with the definitions. Non-conformities were mainly found for detailed and (in a Norwegian context) marginal classes. The classification can still be improved by complementing visual interpretation with classification based on the statistical profile of each polygon when detailed land use and land cover information is available. The use of auxiliary information at the polygon level can thus provide a better, thematically more accurate CLC dataset for use in European land monitoring.
Authors
Auvikki de Boon Camilla Sandström Ugo Arbieu Inger Hansen Lisa Lehnen Agnese Marino Mari Pohja-Mykrä Camilla Risvoll Geir-Harald Strand Katrina RønningenAbstract
Policy mixes (i.e. the total structure of policy processes, strategies, and instruments) are complex constructs that can quickly become incoherent, inconsistent, and incomprehensive. This is amplified when the policy mix strives to meet multiple objectives simultaneously, such as in the case of large carnivore policy mixes. Building on Rogge and Reichardt's analytical framework for the analysis of policy mixes, we compare the policy mixes of Norway, Sweden, Finland, the Netherlands, Germany (specifically Saxony and Bavaria), and Spain (specifically Castilla y León). The study shows that the large carnivore policy mixes in the case countries show signs of lacking vertical and horizontal coherence in the design of policy processes, weak consistency between objectives and designated policy instruments, and, as a consequence, lacking comprehensiveness. We conclude that creating consistent, coherent, and comprehensive policy mixes that build on multiple objectives requires stepping away from sectorized policy development, toward a holistic, systemic approach, strong collaborative structures across policy boundaries and regions, the inclusion of diverse stakeholders, and constant care and attention to address all objectives simultaneously rather than in isolation.
Authors
Geir-Harald StrandAbstract
Sheep farmers in areas with large carnivores experience economic loss, psychological stress, and perceived alienation from political processes. This can result in decisions that differ from those made by farmers in areas without large carnivores, possibly influencing the whole farming system. We used applications for farming subsidies to examine changes in sheep farming in Norway 1999 to 2017. Along the urbanrural dimension, we found a stronger decline in increasingly rural areas. The decline was furthermore larger inside regions used for the reintroduction of large carnivores than outside these regions. The observed decline in some regions was compensated by growth in central regions, outside carnivore prone areas, and on managed land where the sheep was protected from carnivores. The result complements studies of mental dispositions and decision processes aiming to explain how large carnivores and the carnivore management policy influence the farmers' attitudes and decisions, resulting in behaviors that effect larger social systems.
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Advances in techniques for automated classification of point cloud data introduce great opportunities for many new and existing applications. However, with a limited number of labelled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 85.0% in term of overall accuracy, and 71.1% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score. Additionally, transfer learning using the Bergen 2018 dataset, without model retraining, was also performed. Even though our proposal provides a consistent 3% improvement in term of accuracy, more work still needs to be done to alleviate the generalization problem on the domain adaptation and the transfer learning field.
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We investigated the impact of Norway’s current zonal carnivore management system for four large carnivore species on sheep farming. Sheep losses increased when the large carnivores were reintroduced, but has declined again after the introduction of the zoning management system. The total number of sheep increased outside, but declined slightly inside the management zones. The total sheep production increased, but sheep farming was still lost as a source of income for many farmers. The use of the grazing resources became more extensive. Losses decreased because sheep were removed from the open outfield pastures and many farmers gave up sheep farming. While wolves expel sheep farming from the outfield grazing areas, small herds can still be kept in fenced enclosures. Bears are in every respect incompatible with sheep farming. Farmers adjust to the seasonal and more predictable behavior of lynx and wolverine, although these species also may cause serious losses when present. The mitigating efforts are costly and lead to reduced animal welfare and lower income for the farmers, although farmers in peri-urban areas increasingly are keeping sheep as an avocation. There is a spillover effect of the zoning strategy in the sense that there is substantial loss of livestock to carnivores outside, but geographically near the management zones. The carnivore management policy used in Norway is a reasonably successful management strategy when the goal is to separate livestock from carnivores and decrease the losses, but the burdens are unequally distributed and farmers inside the management zones are at an economic disadvantage.
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Increasing populations of large carnivores are leading to tension and conflicts with livestock production, a situation that potentially might escalate. In Norway the objective of the large carnivore policy is two-folded: to ensure viable carnivore populations and to secure a sustainable grazing industry. The main instrument is zonation, with carnivore management zones (CMZs) prioritized for reproduction of the large carnivore species separated from other areas prioritized for grazing livestock. The objective of this paper is to describe current knowledge about the impact of the zoning management strategy on the grazing industry. This is done by documenting status and changes in sheep production, losses of livestock to predating carnivores, and the use of grazing areas inside and outside the CMZs. CMZs offering protection for lynx, wolverine, bear and wolf cover 55% of the Norwegian mainland. 30% of the sheep and 50% of the Sami reindeer grazing areas are found inside the CMZs. Livestock (semi-domestic reindeer excluded) is using 59% of the available natural pasture areas outside the CMZs, but only 26% inside the CMZs. The lowest use of available grazing areas was found inside zones for wolves (12%) and brown bears (6%). Livestock in these zones are confined to fenced enclosures, mostly on the farm itself, or moved to pastures outside the management zone for summer grazing. Livestock losses increased in the affected regions during the period when carnivores were reestablished. Later, losses declined when CMZs were established and mitigation efforts were implemented in these zones. The bulk of sheep and reindeer killed by carnivores are now found in boundary areas within 50 km off the CMZs, where sheep are still grazing on open mountain and forest ranges. Therefore, instruments to protect livestock in areas close to the CMZs are also needed. The number of sheep declined inside the CMZs from 1999 to 2014, but increased outside the zones. The reduction in the absolute number of sheep in the CMZs is balanced by a similar increase outside, thus the total sheep production in Norway is maintained. We conclude that although of little consequence for the total food production in Norway, the economic and social impact of the large carnivore management strategy can be serious for local communities and individual farmers who are affected. There is a need for more exact carnivore population monitoring to quantify the carnivore pressure, better documentation of reindeer losses, and a clearer and stricter practicing of the zoning strategy. Increased involvement of social sciences is important in order to understand the human dimension of the carnivore conflicts.
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Lecture – Impact of large carnivores on Norwegian grazing industry.
Inger Hansen, Geir-Harald Strand
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The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km2 at 18 km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric–vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.
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Inspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR) data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep learning approach. Our investigation concludes with the proposition of some novel deep learning architectures for generating detailed land resource maps by employing a semantic segmentation approach. The contribution of our work is threefold. (1) First, we implement the multiclass version of the intersection-over-union (IoU) loss function that contributes to handling highly imbalanced datasets and preventing overfitting. (2) Thereafter, we propose a novel deep learning architecture integrating the deep atrous network architecture with the stochastic depth approach for speeding up the learning process, and impose a regularization effect. (3) Finally, we introduce an early fusion deep layer that combines image-based and LiDAR-derived features. In a benchmark study carried out using the Follo 2014 LiDAR data and the NIBIO AR5 land resources dataset, we compare our proposals to other deep learning architectures. A quantitative comparison shows that our best proposal provides more than 5% relative improvement in terms of mean intersection-over-union over the atrous network, providing a basis for a more frequent and improved use of LiDAR data for automatic land cover segmentation.
Authors
Geir-Harald StrandAbstract
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Academic – A study of variance estimation methods for systematic spatial sampling
Geir-Harald Strand
Authors
Geir-Harald StrandAbstract
An undesirable property of systematic spatial sampling is that there is no known method allowing unbiased estimation of the uncertainty of statistical estimates from these surveys. A number of alternative variance estimation methods have been tested and reported by various authors. Studies comparing these estimators are inconclusive, partly because the studies compare different sets of estimators. In this paper, three estimators recommended in recent studies are compared using a single test dataset with known properties. The first estimator compared in this study (ST4) is based on post-stratification of the data. The second estimator (V08) is using a predetermined correction factor calculated from the spatial autocorrelation. The third estimator (MB) is a model based prediction calculated using values from the semivariogram. MB and ST4 were both found to be fairly accurate, while V08 consistently underestimated the variance in this study. V08 relies on the assumption that the autocorrelation structure in the dataset can be described using a particular exponential function. The most likely explanation of the weak result for V08 is that this assumption is violated by the empirical data used in the experiment. A better correction factor can be calculated, but the safe approach is to use MB or ST4.
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Detailed descriptions of individual vegetation types shown on vegetation maps can improve the ways in which the composition and spatial structure within the types are understood. The authors therefore examined dwarf shrub heath, a vegetation type covering large areas and found in many parts of the Norwegian mountains. They used data from point samples obtained in a wall-to-wall area frame survey. The point sampling method provided data that gave a good understanding of the composition and structure of the vegetation type, but also revealed a difference between variation within the vegetation type itself (intra-class variation) and variation resulting from the inclusion of other types of vegetation inside the map polygons (landscape variation). Intra-class variation reflected differences in the botanical composition of the vegetation type itself, whereas landscape variation represented differences in the land-cover composition of the broader landscape in which the vegetation type was found. Both types of variation were related to environmental gradients. The authors conclude that integrated point sampling method is an efficient way to achieve increased understanding of the content of a vegetation map and can be implemented as a supporting activity during a survey.
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Geir-Harald StrandAbstract
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Geir-Harald StrandAbstract
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Stephan Arnold Geoffrey Smith Gerard Hazeu Barbara Kosztra Christoph Perger Gebhard Banko Tomas Soukup Geir-Harald Strand Nuria V. Sanz Michael BockAbstract
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Atle Mysterud Yngve Rekdal Leif Egil Loe Michael Angeloff Ragnhild Mobæk Øystein Holand Geir-Harald StrandAbstract
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Geir-Harald StrandAbstract
Four chapters follow in this book: Background, Challenges, Foresight, and Conclusion – What’s Next. The first chapter, Background, takes stock of land monitoring practices in European countries. The second chapter, Challenges, relates a range of issues encountered with land monitoring as it is currently practised and how such matters can be better resolved through improved collaboration. Building upon these findings, the third chapter, Foresight, outlines the HELM (Harmonised European Land Monitoring) roadmap towards a mature, integrated pan-European land monitoring system based upon aggregated national data which are supplemented by centrally produced base data. The concluding chapter, What’s Next, sets the HELM project and its recommendations in context.
Authors
Geir-Harald StrandAbstract
There is a growing demand for reliable information about land cover and land resources. The Norwegian area frame survey of land cover and outfield land resources (AR18X18) is a response to this demand. AR18X18 provides unbiased land cover and land resource statistics and constitutes a baseline for studying changes in outfield land resources in Norway and a framework for a national land resource accounting system for the outfields. The area frame survey uses a systematic sampling technique with 0.9 km2 sample plots at 18 km intervals. A complete wall-to-wall land cover map of an entire plot surveyed is obtained in situ by a team of fieldworkers equipped with aerial photographs. The use of sample plots with extended coverage (0.9 km2) ensures that the survey also deals with local variation, thus strengthening the estimates well beyond simple point sampling. The article documents the methodology used in the survey, followed by a discussion of issues raised by the choice of methodology. These issues include the problem of calculating uncertainty and a confidence interval for the estimates, the focus on common rather than rare land cover categories, and the prospect of downscaling the results in order to obtain statistics for subnational regions.
Editors
Geir-Harald StrandAbstract
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The objective of this paper is to examine a method for estimation of land cover statistics for local environments from available area frame surveys of larger, surrounding areas. The method is a simple version of the small-area estimation methodology. The starting point is a national area frame survey of land cover. This survey is post-stratified using a coarse land cover map based on topographic maps and segmentation of satellite images. The approach is to describe the land cover composition of each stratum and subsequently use the results to calculate land cover statistics for a smaller area where the relative distribution of the strata is known. The method was applied to a mountain environment in Gausdal in Eastern Norway and the result was compared to reference data from a complete in situ land cover map of the study area. The overall correlation (Pearson’s rho) between the observed and the estimated land cover figures was r = 0.95. The method does not produce a map of the target area and the estimation error was large for a few of the land cover classes. The overall conclusion is, however, that the method is applicable when the objective is to produce land cover statistics and the interest is the general composition of land cover classes – not the precise estimate of each class. The method will be applied in outfield pasture management in Norway, where it offers a cost-efficient way to screen the management units and identify local areas with a land cover composition suitable for grazing. The limited resources available for in situ land cover mapping can then be allocated efficiently to in-depth studies of the areas with the highest grazing potential. It is also expected that the method can be used to compile land cover statistics for other purposes as well, provided that the motivation is to describe the overall land cover composition and not to provide exact estimates for the individual land cover classes.
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AR-FJELL is the Norwegian land resource database for the mountain areas. AR-FJELL is not distributed as a separate product from Skog og landskap, but does – together with topographic data (series N50) from the Norwegian Mapping Authority (Statens kartverk) form the basis for the classification of mountain areas in the national land resource maps AR50 and AR250. The five Norwegian AR-FJELL classes are documented through descriptive statistical “profiles” of the actual content of each class. Profiles of the AR-FJELL classes were obtained through a GIS overlay operation between AR-FJELL and the available AR18X18 (Land resource accounting for the Norwegian outfields) survey plots. The distribution of vegetation classes for each AR-FJELL class was compiled from this overlay. The report also consider the distribution of the AR-FJELL classes by elevation asl and the distribution of the vegetation types in the AR18X18 sample. AR18X18 is (2011) only available for parts of Norway. The study should be repeated when a full national coverage is available. This is expected in 2015. The study was carried out with funding from the Norwegian Space Centre.
Authors
Geir-Harald StrandAbstract
A landscape region can be drawn on a map as a geographic feature with distinct boundaries. Reality is, however, that the change from one landscape to another usually is gradual and that landscapes therefore have uncertain or undetermined boundaries. A thematic map of landscape regions is therefore a too simple model of the landscape. An alternative approach is to consider landscape categories as purely theoretical concepts. With this perspective, a particular geographical location can be more or less affiliated with a number of different landscape categories. Such a conception of landscape does not lead to a traditional thematic map of uniform, non-overlapping regions, but to a landscape model composed of multiple overlapping probability surfaces. This article shows how such a landscape model can be established using binary logistic regression. The method is tested and the result is assessed against an existing landscape map of Norway much used in policy impact analysis in this country. The overall objective is to develop a data driven landscape model that can supplement, elucidate and for some purposes maybe even replace, the qualitative landscape description represented by the traditional landscape map.
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CORINE Land Cover (CLC) is a seamless European land cover vector database. The Norwegian CLC2000 was completed by the Norwegian Forest and Landscape Institute (Skog og landskap) in 2008 and was produced from existing national land cover datasets wherever available. CLC has a standardized nomenclature with 44 classes. 31 classes are represented in the Norwegian dataset. CLC is a small scale map showing built up areas, agriculture, forest and semi-natural areas, wetlands and water bodies. CLC has a minimum mapping unit of 25 ha. CLC2000 can be used for visualization of the general land cover patterns in Norway at a scale 1:250 000 or smaller. CLC2000 is representing the land cover situation close to year 20001. This report presents the Norwegian CLC2000 project and the methods and automatic generalization processes that were used in the project. CORINE Land Cover is one of four land cover maps (AR5, AR50, AR250 and CLC) published by Skog og landskap. CLC2000 was produced with support from the European Environmental Agency (EEA) who has joint ownership to the product....
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CORINE Land Cover (CLC) is a seamless European land cover vector database. The Norwegian CLC for the reference year 2006 (CLC2006) was completed by the Norwegian Forest and Landscape Institute (Skog og landskap) in 2009 and was produced according to CLC2006 technical guidelines (EEA 2007). CLC has a common nomenclature with 44 classes that is used throughout Europe. 31 of these classes are found in the Norwegian dataset. A coordinating Technical Team from the European Topic Centre on Land Use and Spatial Information (ETC-LUSI) is coordinating the mapping efforts ensuring that the classification is applied in a similar fashion in each country....
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All the Norwegian CLC2006 classes are documented through descriptive statistical “profiles” of the actual contents in each class. The CLC2006 profiles are worked through based on an overlay operation between CLC2006 and AR5 (under the timberline) and AR50 (above the timberline). Based on this dataset statistics are generated, that shows the percent distribution of AR5 and AR50 classes in each CLC2006 class. The study was carried out with funding from the Norwegian Space Centre.
Authors
Geir-Harald StrandAbstract
The change from one landscape to another is gradual. Landscape classes can therefore be considered as theoretical concepts and a particular location or area can be affiliated with a number of different landscape classes. Landscape classification thus becomes a statistical and probabilistic exercise. Such a probabilistic approach to landscape regions can be made operational using a grid model combined with binary logistic regression. This approach was tested on a landscape region in Norway.
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AR18×18 is an area frame survey of land resources in Norway, methodologically linked to the Lucas survey carried out by Eurostat (Eurostat 2003). The purpose of the survey is to establish an unbiased and accurate land cover and land use statistic providing a description of the state of land resources in Norway. The survey will also provide a baseline for future reports regarding changes in land resources – a national land resource accounting system. AR18×18 is based on Lucas (Land use/cover agricultural survey), a European area frame survey carried out in the EU countries by Eurostat. The sampling units of Lucas are points located on the intersections of an 18 × 18 kilometer grid mesh throughout Europe. Each of these points is the centre of a Primary Statistical Unit (PSU) of 1500 × 600 meters. The Lucas survey is carried out on ten sample points scattered within each PSU. The Norwegian modification of Lucas is to add a land cover survey of the whole PSU following the Norwegian system for vegetation and land cover mapping at intermediate scale (1: 20,000). [...]
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AR18×18 is an area frame survey of land resources in Norway, methodologically linked to the Lucas survey carried out by Eurostat. The method has been adapted to Norwegian conditions. Data accessible through existing mapping systems and public registers are not collected. On the other hand, the survey is strengthened with a land cover mapping component. The purpose of the survey is to establish an unbiased and accurate land cover and land use statistic providing a description of the state of land resources in Norway. The study will also provide a baseline for future reports regarding changes in land resources
Editors
Geir-Harald StrandAbstract
This report describes a methodology for Norwegian reporting under Article 3.3 and Article 3.4 (if elected) of the Kyoto protocol. The proposal is to report Afforestation/Reforestation (AR) and Deforestation (D) under Article 3.3 (mandatory) and Forest management (FM) under Article 3.4 (if elected). The reporting requirements can probably not be fulfilled if Norway also elects Cropland management (CM), Grazing land management (GM) or Revegetation (RV) under Article 3.4 (all electives) because the necessary data are unavailable and probably also unobtainable. The reason is that change in carbon pools in 1990 is needed as part of the report for these three electives. Such data, with the required quality, are not available in Norway today. Regional stratification is recommended in order to use two different approaches in two different parts of the country (here called “Lowlands” and “Highlands”). It is not recommended to stratify the Norwegian reports because it is not realistic to provide the additional statistical support (in terms of additional sampling units) needed to break the results down to meaningful regional reporting units (eg County). […]
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Svein Solberg Kjell Andreassen Nicholas Clarke Kjetil Tørseth Ole Einar Tveito Geir-Harald Strand Stein Michael TomterAbstract
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Wenche Dramstad Wendy Fjellstad Geir-Harald Strand Henrik Forsberg Mathiesen Gunnar Engan Jogeir N. StoklandAbstract
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Change in crown density for Norway spruce (Picea abies) from 1988 to 1993 in three independent forest monitoring projects in southern Norway were compared. An increase in crown density was found in a countywide systematic random sample, whie measurements taken in old-growth forests reported a decline. These contradictory results may be due to: (1) high sensitivity of high-elevation forests to various kinds of environmental impact; (2) differences in stand age and management practice; and (3) different sensitivity to long distance airborne pollutants. The systematic random sample encompassed stans of several age classes from two counties, while the two other studies were restricted to old-growth forest in two smaller are as. A possibe explanation of the differences is thus that the three studies refer to differet popuations as a resut of different sampling strategies.
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Geir-Harald StrandAbstract
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Geir-Harald StrandAbstract
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Geir-Harald StrandAbstract
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Geir-Harald StrandAbstract
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