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A century of national forest inventories – informing past, present and future decisions

A century of national forest inventories – informing past, present and future decisions In 2019, 100 years had elapsed since the first National Forest Inventory (NFI) was established in Norway. Motivated by a fear of over-exploitation of timber resources, NFIs today enable informed policy making by providing data vital to decision support at international, national, regional, and local scales. This Collection of articles celebrates the 100th anniversary of NFIs with a description of past, present, and future research aiming at improving the monitoring of forest and other terrestrial ecosystems. Introduction sequestration as well as biodiversity indicators and many The establishment of the Norwegian National Forest In- other ecosystem services in general. Today, NFIs enable ventory (NFI) in 1919 was motivated by a fear of over- informed policy making by providing data vital to deci- exploitation of timber resources. Just a few years later – sion support at international, national, regional and even in the 1920’s – similar monitoring programs were to fol- local scales. For example, NFIs provide data to inter- low in Finland, Sweden and the USA (Tomppo et al. national reporting under the United Nations Framework 2010). In the 1960’s, during the World War II recon- Convention on Climate Change, and to international struction phase, the NFIs of France, Austria, Spain, forest health monitoring programs. In line with the wid- Portugal and Greece, were initiated (Vidal et al. 2016). ening of objectives during the past century, techniques Concerns regarding acid rain in the 1980’s were a trigger and sampling designs in NFIs have evolved to provide for initiating NFIs in central Europe. In recent years, cli- relevant answers for societal problems. mate change (REDD+) has prompted the establishment From May 19th to 23rd 2019 the Norwegian NFI team of new NFIs, especially in developing countries, while took the opportunity to celebrate the first 100 years of most developed countries now have regular NFI NFI history by bringing together researchers and practi- programs. tioners with an interest in forest monitoring in Sundvol- One hundred years ago, the primary motivations for len, Norway. Approximately 200 participants from more establishing NFIs were to obtain an overview of timber than 20 countries discussed past challenges, lessons resources and to guide the sustainable use of the forest learned, and methods for improving future large-scale resources. Since then, NFIs have gradually evolved to forest and landscape inventory programs via more than provide answers for a much broader range of issues. 100 presentations and posters. Exhibitors presented their While monitoring timber resources and sustainability is measurement devices and services in the poster hall, and still a major component, NFIs today also monitor forest during a field excursion the five Nordic NFIs explained damage and diseases, forestry management, carbon their plot setups in the forest. Six keynote speakers gave far-sighted presentations that introduced session topics and were live-streamed for those who could not partici- * Correspondence: johannes.breidenbach@nibio.no Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy pate in person. Research (NIBIO), 1431 Ås, Norway Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Breidenbach et al. Forest Ecosystems (2021) 8:36 Page 2 of 4 While the program and abstracts of the conference are the Natural Forests Inventory of New Zealand (Paul available at NIBIO (2019), this Collection (special issue) et al. 2021). combines multiple scientific articles of which most were Breidenbach et al. (2020) describe the development of the presented at the conference (Fig. 1). A total of 14 papers Norwegian NFI since 1919 and how present challenges likely passed the review process coordinated by the Guest Edi- will influence future developments. The Norwegian NFI started tors who are the authors of this Editorial. We are grate- as aline-basedtransectsurveyand todayusesmorethan22, ful for 58 expert reviewers who ensured high scientific 000 permanent sample plots to survey land-use and land-use quality. The published articles can be categorized into change, primarily based on remotely sensed data. More than three topics: i) descriptions of NFI programs and their half of the sample plots are located in 12 Mha of forest and fa- histories, ii) new estimators and methods including the cilitate monitoring of more than 100 variables. The develop- use of remotely sensed data, and iii) use of NFI data for ment of the Norwegian Forest Resource Map, SR16,which is greenhouse-gas reporting and monitoring ecosystem based on a combination of NFI field plots, airborne laser scan- services. ning, image matching, and satellite data, addresses the current The challenges of the currently on-going Corona pan- increasing demand of policy makers and other stakeholders for demic imposed on societies in general affect NFI pro- forest information in the form of fine resolution maps. Forest grams as well. Travel restrictions within and between resource maps can, however, also be used to improve estimates countries reduce the availability of qualified field of population parameters which is of special interest for small workers, and training courses essential to calibrate field areas in NFIs (Breidenbach et al. 2020). assessments are difficult to conduct. Even though these Henry et al. (2021)documentmethods fordesigning challenges have developed after publication of most pa- and conducting the NFI of Bangladesh which was im- pers in this Collection, we expect that the Collection will plemented 2016–2019. It is designed as a permanent provide relevant information for ongoing and future for- program and consists of a resource and socio- est monitoring programs. economic survey. Information is obtained from a combination of more than 1700 field plots with re- motely sensed data and interviews of 6400 house- NFI programs and their history holds. The full development of the inventory, from This Collection contains descriptions of the first NFI the creation of a land-use map to the optimization of which was conducted in Norway (Breidenbach et al. the sampling design and governance measures ensur- 2020) and two new members of the NFI family, namely ing a continuous commitment to forest monitoring, is the Bangladesh Forest Inventory (Henry et al. 2021) and described (Henry et al. 2021). Fig. 1 Number of abstracts submitted by country to the conference ‘A century of national forest inventories – informing past, present and future decisions’ in Sundvollen, Norway, 19–23 May 2019 Breidenbach et al. Forest Ecosystems (2021) 8:36 Page 3 of 4 Paul et al. (2021) present methods for estimating car- scale of their study area, the use of HMB revealed that bon stocks and stock changes in New Zealand’s pre- 75% of the uncertainty in biomass estimates was caused 1990 natural forests using field measurements from two by uncertainties in tree-level biomass model parameter inventory cycles. Estimates of above-ground biomass estimates. used individual tree measurements, although a small Kleinn et al. (2020) describe how a new perspective on proportion of the plots was measured only at the first continuous landscape variables, such as full-tree bio- time point and a regression model was constructed to mass, can reduce uncertainty in estimates using com- predict the carbon stocks of those plots at the second mon field sampling. With their continuous approach, the time point. The estimates show that New Zealand’s nat- spatial, 2-dimensional biomass distribution of trees is ural forests are in carbon balance; they are neither a car- modelled, instead of aggregating all biomass to the point bon sink nor a carbon source. of the stem position as with the traditional approach. The surface that is surveyed using sample plots is New estimators and methods smoother in the continuous approach relative to the NFIs are typically based on approximate systematic grids traditional approach and, thereby, reduces the sample of sample plots which generally produce conservative variance. New measurement methods such as terrestrial (i.e., too large) estimates of uncertainty if design-based laser scanning (TLS) make the continuous approach an estimators assuming simple random sampling (SRS) are interesting option. used. Magnussen et al. (2020) document the 100-year The age of forest stands is critical information for for- long quest of improving variance estimation in system- est management and decision-making. However, this in- atic sampling using model-based methods and add pre- formation is usually not available at fine resolution for viously untested estimators to the set of alternatives to large geographic scales. Two studies in this Collection using simple expansion estimators with SRS. Of import- describe the development of regression models for large- ance for NFIs, they conclude “In large populations, and area mapping of forest age using a combination of NFI, a low sampling intensity, the performance of the investi- ALS, and other data (Maltamo et al. 2020; Schumacher gated estimators becomes more similar” (Magnussen et al. 2020). Using Norwegian NFI data, Schumacher et al. 2020). et al. (2020) model stand age by exploiting tree height The local pivotal method (LPM) is a form of balanced predicted from ALS, a site index prediction map, and sampling method that produces small uncertainties with Sentinel-2 data as predictor variables. Satisfactory results a minimum number of sample plots which, of course, is were obtained, especially for stands with large site indi- of considerable relevance for NFI programs. In the con- ces. Using Finnish NFI data, Maltamo et al. (2020) ex- text of the Finnish NFI, Räty et al. (2020) found, how- ploit ALS and geographical data to model stand age. ever, that LPM-sampling could not markedly improve They highlight that the utility of age predictions varies estimates based on systematic sampling when consider- according to applications. In contract to Schumacher ing several variables of interest as is typical in NFIs. et al. (2020), Maltamo et al. (2020) focus on managed Complementing the study by Magnussen et al. (2020), forests younger than 100 years of age. Räty et al. (2020) identify a variance estimator originally developed for LPM that is well-suited for systematic Greenhouse-gas reporting and monitoring sampling. ecosystem services In a simulation study, Kangas et al. (2020) show that The current importance of information on carbon stock old measurements on permanent sample plots can con- changes and ecosystem services is underlined by four stitute valuable source of auxiliary information for aug- studies based on the Swiss NFI. menting and complementing high-quality airborne laser Traub and Wüest (2020) analyze the quality field data scanning (ALS) data. The study highlights data-fusion of the woody species composition and provide several opportunities with model-assisted and model-based approaches for doing so. They find that data quality was estimators. as great as 30% less than the expected data quality limit, The hierarchical model based approach (HMB) is a and the percentage of omitted species was as great as method for propagating uncertainties from multiple re- 20% less. These results show the relevance of consider- gression models when combining multiple remotely ing information on NFI data quality in terms of the re- sensed data layers. Saarela et al. (2020) advanced an ana- producibility of collected data. lytical HMB method for the important class of non- Hararuk et al. (2020) developed deadwood decay linear models. In an ALS-based application, they show models from repeated Swiss NFI measurements which the close connection between fine resolution mapping show-cases the contribution of long-term NFI monitor- and model-based inference for estimators for areas that ing programs to understanding deadwood dynamics. aggregate arbitrary numbers of mapped pixels. At the The inclusion of decay drivers allows the application of Breidenbach et al. Forest Ecosystems (2021) 8:36 Page 4 of 4 the models in carbon budget simulations that integrate Didion M (2020) Extending harmonized national forest inventory herb layer vegetation cover observations to derive comprehensive biomass estimates. above-ground and soil carbon pools. Forest Ecosyst 7(1):16. https://doi.org/10.1186/s40663-020-00230-7 Temperli et al. (2020) study the trade-offs between ecosys- Hararuk O, Kurz WA, Didion M (2020) Dynamics of dead wood decay in Swiss tem service provision and the predisposition to disturbances forests. Forest Ecosyst 7(1):36. https://doi.org/10.1186/s40663-020-00248-x Henry M, Iqbal Z, Johnson K, Akhter M, Costello L, Scott C, Jalal R, Hossain MA, using the Swiss NFI for five different scenarios: a business- Chakma N, Kuegler O, Mahmood H, Mahamud R, Siddique MRH, as-usual (BAU) scenario and four scenarios with increased Misbahuzzaman K, Uddin MM, Al Amin M, Ahmed FU, Sola G, Siddiqui MB, timber harvesting. The results were evaluated using indica- Birigazzi L, Rahman M, Animon I, Ritu S, Rahman LM, Islam A, Hayden H, Sidik F, Kumar MF, Mukul RH, Nishad H, Belal AH, Anik AR, Khaleque A, tors for forest ecosystem services and biodiversity, including Shaheduzzaman M, Hossain SS, Aziz T, Rahaman MT, Mohaiman R, Meyer P, timber provision and predisposition to disturbances. In- Chakma P, Rashid AZMM, Das S, Hira S, Jashimuddin M, Rahman MM, creased timber production without increasing the proportion Wurster K, Uddin SN, Azad AK, Islam SMZ, Saint-André L (2021) A multi- purpose National Forest Inventory in Bangladesh: design, operationalisation of conifers generally reduced predisposition to disturbances and key results. Forest Ecosyst 8(1):12. https://doi.org/10.1186/s40663-021-002 but was in a trade-off with biodiversity indicators. 84-1 Didion (2020) describes a method for obtaining com- Kangas A, Gobakken T, Næsset E (2020) Benefits of past inventory data as prior information for the current inventory. Forest Ecosyst 7(1):20. https://doi.org/1 prehensive and consistent estimates of herb layer bio- 0.1186/s40663-020-00231-6 mass on NFI plots to complement biomass estimates for Kleinn C, Magnussen S, Nölke N, Magdon P, Álvarez-González JG, Fehrmann L, the tree and tall shrub layer. The estimates are based on Pérez-Cruzado C (2020) Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass. Forest a model requiring elevation and vegetation cover as pre- Ecosyst 7(1):57. https://doi.org/10.1186/s40663-020-00268-7 dictor variables. Magnussen S, McRoberts RE, Breidenbach J, Nord-Larsen T, Ståhl G, Fehrmann L, Schnell S (2020) Comparison of estimators of variance for forest inventories Acknowledgements with systematic sampling-results from artificial populations. Forest Ecosyst The Beijing Forestry University waived the Open Access fees of all studies 7(1):17. https://doi.org/10.1186/s40663-020-00223-6 published in this Collection. Maltamo M, Kinnunen H, Kangas A, Korhonen L (2020) Predicting stand age in managed forests using National Forest Inventory field data and airborne laser Authors’ contributions scanning. Forest Ecosyst 7(1):44. https://doi.org/10.1186/s40663-020-00254-z J.B. wrote the first draft to which all authors contributed. The authors read NIBIO (2019) A century of national forest inventories – informing past, present and approved the final manuscript. and future decisions. https://nibio.pameldingssystem.no/nfi100years. Accessing 25 Apr 2020 Funding Paul T, Kimberley MO, Beets PN (2021) Natural Forests in New Zealand – a large J.B. acknowledges NIBIO, the FACCE ERA-GAS project INVENT (NRC 276398), terrestrial carbon pool in a national state of equilibrium. Forest Ecosyst 8(1). and the SNS-funded network project CARISMA for supporting this Editorial Räty M, Kuronen M, Myllymäki M, Kangas A, Mäkisara K, Heikkinen J (2020) and the conference “A century of national forest inventories – informing Comparison of the local pivotal method and systematic sampling for past, present and future decisions”, May 19th to 23rd 2019, in Sundvollen, national forest inventories. Forest Ecosyst 7(1):54. https://doi.org/10.1186/s4 Norway. 0663-020-00266-9 Saarela S, Wästlund A, Holmström E, Mensah AA, Holm S, Nilsson M, Fridman J, Ståhl G (2020) Mapping aboveground biomass and its prediction uncertainty Availability of data and materials using LiDAR and field data, accounting for tree-level allometric and LiDAR Not applicable. model errors. Forest Ecosyst 7(1):43. https://doi.org/10.1186/s40663-020-0024 5-0 Declarations Schumacher J, Hauglin M, Astrup R, Breidenbach J (2020) Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data. Ethics approval and consent to participate Forest Ecosyst 7(1):60. https://doi.org/10.1186/s40663-020-00274-9 Not applicable. Temperli C, Blattert C, Stadelmann G, Brändli U-B, Thürig E (2020) Trade-offs between ecosystem service provision and the predisposition to disturbances: Consent for publication a NFI-based scenario analysis. Forest Ecosyst 7(1):27. https://doi.org/10.1186/ Not applicable. s40663-020-00236-1 Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (2010) National forest Competing interests inventories: Pathways for Common Reporting. Springer, Berlin, Germany. We declare no competing interests. https://doi.org/10.1007/978-90-481-3233-1 Traub B, Wüest RO (2020) Analysing the quality of Swiss National Forest Author details Inventory measurements of woody species richness. Forest Ecosyst 7(1):37. Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy https://doi.org/10.1186/s40663-020-00252-1 Research (NIBIO), 1431 Ås, Norway. Department of Forest Resources, Vidal C, Alberdi I, Hernández L, Redmond J (2016) National forest inventories - University of Minnesota, Saint Paul, Minnesota 55108, USA. Centro de Assessment of Wood Availability and Use. Springer International Publishing, Investigación Forestal, Centro Superior de Investigaciones científicas, Instituto Switzerland. https://doi.org/10.1007/978-3-319-44015-6 Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Ctra. A Coruña, Km. 7.5, 28040 Madrid, Spain. Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, FI-00014 Helsinki, Finland. Department of Electronics and Nanoengineering, P.O. Box 15500, FI-00076 Aalto, Finland. References Breidenbach J, Granhus A, Hylen G, Eriksen R, Astrup R (2020) A century of National Forest Inventory in Norway – informing past, present, and future decisions. Forest Ecosyst 7(1):46. https://doi.org/10.1186/s40663-020-00261-0 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Forest Ecosystems" Springer Journals

A century of national forest inventories – informing past, present and future decisions

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Abstract

In 2019, 100 years had elapsed since the first National Forest Inventory (NFI) was established in Norway. Motivated by a fear of over-exploitation of timber resources, NFIs today enable informed policy making by providing data vital to decision support at international, national, regional, and local scales. This Collection of articles celebrates the 100th anniversary of NFIs with a description of past, present, and future research aiming at improving the monitoring of forest and other terrestrial ecosystems. Introduction sequestration as well as biodiversity indicators and many The establishment of the Norwegian National Forest In- other ecosystem services in general. Today, NFIs enable ventory (NFI) in 1919 was motivated by a fear of over- informed policy making by providing data vital to deci- exploitation of timber resources. Just a few years later – sion support at international, national, regional and even in the 1920’s – similar monitoring programs were to fol- local scales. For example, NFIs provide data to inter- low in Finland, Sweden and the USA (Tomppo et al. national reporting under the United Nations Framework 2010). In the 1960’s, during the World War II recon- Convention on Climate Change, and to international struction phase, the NFIs of France, Austria, Spain, forest health monitoring programs. In line with the wid- Portugal and Greece, were initiated (Vidal et al. 2016). ening of objectives during the past century, techniques Concerns regarding acid rain in the 1980’s were a trigger and sampling designs in NFIs have evolved to provide for initiating NFIs in central Europe. In recent years, cli- relevant answers for societal problems. mate change (REDD+) has prompted the establishment From May 19th to 23rd 2019 the Norwegian NFI team of new NFIs, especially in developing countries, while took the opportunity to celebrate the first 100 years of most developed countries now have regular NFI NFI history by bringing together researchers and practi- programs. tioners with an interest in forest monitoring in Sundvol- One hundred years ago, the primary motivations for len, Norway. Approximately 200 participants from more establishing NFIs were to obtain an overview of timber than 20 countries discussed past challenges, lessons resources and to guide the sustainable use of the forest learned, and methods for improving future large-scale resources. Since then, NFIs have gradually evolved to forest and landscape inventory programs via more than provide answers for a much broader range of issues. 100 presentations and posters. Exhibitors presented their While monitoring timber resources and sustainability is measurement devices and services in the poster hall, and still a major component, NFIs today also monitor forest during a field excursion the five Nordic NFIs explained damage and diseases, forestry management, carbon their plot setups in the forest. Six keynote speakers gave far-sighted presentations that introduced session topics and were live-streamed for those who could not partici- * Correspondence: johannes.breidenbach@nibio.no Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy pate in person. Research (NIBIO), 1431 Ås, Norway Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Breidenbach et al. Forest Ecosystems (2021) 8:36 Page 2 of 4 While the program and abstracts of the conference are the Natural Forests Inventory of New Zealand (Paul available at NIBIO (2019), this Collection (special issue) et al. 2021). combines multiple scientific articles of which most were Breidenbach et al. (2020) describe the development of the presented at the conference (Fig. 1). A total of 14 papers Norwegian NFI since 1919 and how present challenges likely passed the review process coordinated by the Guest Edi- will influence future developments. The Norwegian NFI started tors who are the authors of this Editorial. We are grate- as aline-basedtransectsurveyand todayusesmorethan22, ful for 58 expert reviewers who ensured high scientific 000 permanent sample plots to survey land-use and land-use quality. The published articles can be categorized into change, primarily based on remotely sensed data. More than three topics: i) descriptions of NFI programs and their half of the sample plots are located in 12 Mha of forest and fa- histories, ii) new estimators and methods including the cilitate monitoring of more than 100 variables. The develop- use of remotely sensed data, and iii) use of NFI data for ment of the Norwegian Forest Resource Map, SR16,which is greenhouse-gas reporting and monitoring ecosystem based on a combination of NFI field plots, airborne laser scan- services. ning, image matching, and satellite data, addresses the current The challenges of the currently on-going Corona pan- increasing demand of policy makers and other stakeholders for demic imposed on societies in general affect NFI pro- forest information in the form of fine resolution maps. Forest grams as well. Travel restrictions within and between resource maps can, however, also be used to improve estimates countries reduce the availability of qualified field of population parameters which is of special interest for small workers, and training courses essential to calibrate field areas in NFIs (Breidenbach et al. 2020). assessments are difficult to conduct. Even though these Henry et al. (2021)documentmethods fordesigning challenges have developed after publication of most pa- and conducting the NFI of Bangladesh which was im- pers in this Collection, we expect that the Collection will plemented 2016–2019. It is designed as a permanent provide relevant information for ongoing and future for- program and consists of a resource and socio- est monitoring programs. economic survey. Information is obtained from a combination of more than 1700 field plots with re- motely sensed data and interviews of 6400 house- NFI programs and their history holds. The full development of the inventory, from This Collection contains descriptions of the first NFI the creation of a land-use map to the optimization of which was conducted in Norway (Breidenbach et al. the sampling design and governance measures ensur- 2020) and two new members of the NFI family, namely ing a continuous commitment to forest monitoring, is the Bangladesh Forest Inventory (Henry et al. 2021) and described (Henry et al. 2021). Fig. 1 Number of abstracts submitted by country to the conference ‘A century of national forest inventories – informing past, present and future decisions’ in Sundvollen, Norway, 19–23 May 2019 Breidenbach et al. Forest Ecosystems (2021) 8:36 Page 3 of 4 Paul et al. (2021) present methods for estimating car- scale of their study area, the use of HMB revealed that bon stocks and stock changes in New Zealand’s pre- 75% of the uncertainty in biomass estimates was caused 1990 natural forests using field measurements from two by uncertainties in tree-level biomass model parameter inventory cycles. Estimates of above-ground biomass estimates. used individual tree measurements, although a small Kleinn et al. (2020) describe how a new perspective on proportion of the plots was measured only at the first continuous landscape variables, such as full-tree bio- time point and a regression model was constructed to mass, can reduce uncertainty in estimates using com- predict the carbon stocks of those plots at the second mon field sampling. With their continuous approach, the time point. The estimates show that New Zealand’s nat- spatial, 2-dimensional biomass distribution of trees is ural forests are in carbon balance; they are neither a car- modelled, instead of aggregating all biomass to the point bon sink nor a carbon source. of the stem position as with the traditional approach. The surface that is surveyed using sample plots is New estimators and methods smoother in the continuous approach relative to the NFIs are typically based on approximate systematic grids traditional approach and, thereby, reduces the sample of sample plots which generally produce conservative variance. New measurement methods such as terrestrial (i.e., too large) estimates of uncertainty if design-based laser scanning (TLS) make the continuous approach an estimators assuming simple random sampling (SRS) are interesting option. used. Magnussen et al. (2020) document the 100-year The age of forest stands is critical information for for- long quest of improving variance estimation in system- est management and decision-making. However, this in- atic sampling using model-based methods and add pre- formation is usually not available at fine resolution for viously untested estimators to the set of alternatives to large geographic scales. Two studies in this Collection using simple expansion estimators with SRS. Of import- describe the development of regression models for large- ance for NFIs, they conclude “In large populations, and area mapping of forest age using a combination of NFI, a low sampling intensity, the performance of the investi- ALS, and other data (Maltamo et al. 2020; Schumacher gated estimators becomes more similar” (Magnussen et al. 2020). Using Norwegian NFI data, Schumacher et al. 2020). et al. (2020) model stand age by exploiting tree height The local pivotal method (LPM) is a form of balanced predicted from ALS, a site index prediction map, and sampling method that produces small uncertainties with Sentinel-2 data as predictor variables. Satisfactory results a minimum number of sample plots which, of course, is were obtained, especially for stands with large site indi- of considerable relevance for NFI programs. In the con- ces. Using Finnish NFI data, Maltamo et al. (2020) ex- text of the Finnish NFI, Räty et al. (2020) found, how- ploit ALS and geographical data to model stand age. ever, that LPM-sampling could not markedly improve They highlight that the utility of age predictions varies estimates based on systematic sampling when consider- according to applications. In contract to Schumacher ing several variables of interest as is typical in NFIs. et al. (2020), Maltamo et al. (2020) focus on managed Complementing the study by Magnussen et al. (2020), forests younger than 100 years of age. Räty et al. (2020) identify a variance estimator originally developed for LPM that is well-suited for systematic Greenhouse-gas reporting and monitoring sampling. ecosystem services In a simulation study, Kangas et al. (2020) show that The current importance of information on carbon stock old measurements on permanent sample plots can con- changes and ecosystem services is underlined by four stitute valuable source of auxiliary information for aug- studies based on the Swiss NFI. menting and complementing high-quality airborne laser Traub and Wüest (2020) analyze the quality field data scanning (ALS) data. The study highlights data-fusion of the woody species composition and provide several opportunities with model-assisted and model-based approaches for doing so. They find that data quality was estimators. as great as 30% less than the expected data quality limit, The hierarchical model based approach (HMB) is a and the percentage of omitted species was as great as method for propagating uncertainties from multiple re- 20% less. These results show the relevance of consider- gression models when combining multiple remotely ing information on NFI data quality in terms of the re- sensed data layers. Saarela et al. (2020) advanced an ana- producibility of collected data. lytical HMB method for the important class of non- Hararuk et al. (2020) developed deadwood decay linear models. In an ALS-based application, they show models from repeated Swiss NFI measurements which the close connection between fine resolution mapping show-cases the contribution of long-term NFI monitor- and model-based inference for estimators for areas that ing programs to understanding deadwood dynamics. aggregate arbitrary numbers of mapped pixels. At the The inclusion of decay drivers allows the application of Breidenbach et al. Forest Ecosystems (2021) 8:36 Page 4 of 4 the models in carbon budget simulations that integrate Didion M (2020) Extending harmonized national forest inventory herb layer vegetation cover observations to derive comprehensive biomass estimates. above-ground and soil carbon pools. Forest Ecosyst 7(1):16. https://doi.org/10.1186/s40663-020-00230-7 Temperli et al. (2020) study the trade-offs between ecosys- Hararuk O, Kurz WA, Didion M (2020) Dynamics of dead wood decay in Swiss tem service provision and the predisposition to disturbances forests. Forest Ecosyst 7(1):36. https://doi.org/10.1186/s40663-020-00248-x Henry M, Iqbal Z, Johnson K, Akhter M, Costello L, Scott C, Jalal R, Hossain MA, using the Swiss NFI for five different scenarios: a business- Chakma N, Kuegler O, Mahmood H, Mahamud R, Siddique MRH, as-usual (BAU) scenario and four scenarios with increased Misbahuzzaman K, Uddin MM, Al Amin M, Ahmed FU, Sola G, Siddiqui MB, timber harvesting. The results were evaluated using indica- Birigazzi L, Rahman M, Animon I, Ritu S, Rahman LM, Islam A, Hayden H, Sidik F, Kumar MF, Mukul RH, Nishad H, Belal AH, Anik AR, Khaleque A, tors for forest ecosystem services and biodiversity, including Shaheduzzaman M, Hossain SS, Aziz T, Rahaman MT, Mohaiman R, Meyer P, timber provision and predisposition to disturbances. In- Chakma P, Rashid AZMM, Das S, Hira S, Jashimuddin M, Rahman MM, creased timber production without increasing the proportion Wurster K, Uddin SN, Azad AK, Islam SMZ, Saint-André L (2021) A multi- purpose National Forest Inventory in Bangladesh: design, operationalisation of conifers generally reduced predisposition to disturbances and key results. Forest Ecosyst 8(1):12. https://doi.org/10.1186/s40663-021-002 but was in a trade-off with biodiversity indicators. 84-1 Didion (2020) describes a method for obtaining com- Kangas A, Gobakken T, Næsset E (2020) Benefits of past inventory data as prior information for the current inventory. Forest Ecosyst 7(1):20. https://doi.org/1 prehensive and consistent estimates of herb layer bio- 0.1186/s40663-020-00231-6 mass on NFI plots to complement biomass estimates for Kleinn C, Magnussen S, Nölke N, Magdon P, Álvarez-González JG, Fehrmann L, the tree and tall shrub layer. The estimates are based on Pérez-Cruzado C (2020) Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass. Forest a model requiring elevation and vegetation cover as pre- Ecosyst 7(1):57. https://doi.org/10.1186/s40663-020-00268-7 dictor variables. Magnussen S, McRoberts RE, Breidenbach J, Nord-Larsen T, Ståhl G, Fehrmann L, Schnell S (2020) Comparison of estimators of variance for forest inventories Acknowledgements with systematic sampling-results from artificial populations. Forest Ecosyst The Beijing Forestry University waived the Open Access fees of all studies 7(1):17. https://doi.org/10.1186/s40663-020-00223-6 published in this Collection. Maltamo M, Kinnunen H, Kangas A, Korhonen L (2020) Predicting stand age in managed forests using National Forest Inventory field data and airborne laser Authors’ contributions scanning. Forest Ecosyst 7(1):44. https://doi.org/10.1186/s40663-020-00254-z J.B. wrote the first draft to which all authors contributed. The authors read NIBIO (2019) A century of national forest inventories – informing past, present and approved the final manuscript. and future decisions. https://nibio.pameldingssystem.no/nfi100years. Accessing 25 Apr 2020 Funding Paul T, Kimberley MO, Beets PN (2021) Natural Forests in New Zealand – a large J.B. acknowledges NIBIO, the FACCE ERA-GAS project INVENT (NRC 276398), terrestrial carbon pool in a national state of equilibrium. Forest Ecosyst 8(1). and the SNS-funded network project CARISMA for supporting this Editorial Räty M, Kuronen M, Myllymäki M, Kangas A, Mäkisara K, Heikkinen J (2020) and the conference “A century of national forest inventories – informing Comparison of the local pivotal method and systematic sampling for past, present and future decisions”, May 19th to 23rd 2019, in Sundvollen, national forest inventories. Forest Ecosyst 7(1):54. https://doi.org/10.1186/s4 Norway. 0663-020-00266-9 Saarela S, Wästlund A, Holmström E, Mensah AA, Holm S, Nilsson M, Fridman J, Ståhl G (2020) Mapping aboveground biomass and its prediction uncertainty Availability of data and materials using LiDAR and field data, accounting for tree-level allometric and LiDAR Not applicable. model errors. Forest Ecosyst 7(1):43. https://doi.org/10.1186/s40663-020-0024 5-0 Declarations Schumacher J, Hauglin M, Astrup R, Breidenbach J (2020) Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data. Ethics approval and consent to participate Forest Ecosyst 7(1):60. https://doi.org/10.1186/s40663-020-00274-9 Not applicable. Temperli C, Blattert C, Stadelmann G, Brändli U-B, Thürig E (2020) Trade-offs between ecosystem service provision and the predisposition to disturbances: Consent for publication a NFI-based scenario analysis. Forest Ecosyst 7(1):27. https://doi.org/10.1186/ Not applicable. s40663-020-00236-1 Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (2010) National forest Competing interests inventories: Pathways for Common Reporting. Springer, Berlin, Germany. We declare no competing interests. https://doi.org/10.1007/978-90-481-3233-1 Traub B, Wüest RO (2020) Analysing the quality of Swiss National Forest Author details Inventory measurements of woody species richness. Forest Ecosyst 7(1):37. Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy https://doi.org/10.1186/s40663-020-00252-1 Research (NIBIO), 1431 Ås, Norway. Department of Forest Resources, Vidal C, Alberdi I, Hernández L, Redmond J (2016) National forest inventories - University of Minnesota, Saint Paul, Minnesota 55108, USA. Centro de Assessment of Wood Availability and Use. Springer International Publishing, Investigación Forestal, Centro Superior de Investigaciones científicas, Instituto Switzerland. https://doi.org/10.1007/978-3-319-44015-6 Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Ctra. A Coruña, Km. 7.5, 28040 Madrid, Spain. Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, FI-00014 Helsinki, Finland. Department of Electronics and Nanoengineering, P.O. Box 15500, FI-00076 Aalto, Finland. References Breidenbach J, Granhus A, Hylen G, Eriksen R, Astrup R (2020) A century of National Forest Inventory in Norway – informing past, present, and future decisions. Forest Ecosyst 7(1):46. https://doi.org/10.1186/s40663-020-00261-0

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"Forest Ecosystems" – Springer Journals

Published: Jun 4, 2021

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