Canada's National Forest Inventory: What can it tell us about old growth?

2003 ◽  
Vol 79 (3) ◽  
pp. 421-428 ◽  
Author(s):  
Mark D Gillis ◽  
Stephen L Gray ◽  
Dennis Clarke ◽  
Katja Power
Keyword(s):  
Forest Inventory ◽  
National Forest Inventory ◽  
Stand Age ◽  
National Forest ◽  
Sources Of Information ◽  
Old Growth ◽  
Management Level ◽  
Industrial Management ◽  
National Inventory ◽  
Growth Indicators

Canada's National Forest Inventory is a periodic compilation of existing inventory information from across the country. The main sources of information are detailed, stand-level descriptions contained in provincial, territorial, and industrial management-level inventories. Typical management inventories do not identify old growth as a specific attribute. Therefore, the National Forest Inventory, based on management-level inventories, does not specifically show old growth. Indicators of old growth, such as stand age and maturity, are contained in the national inventory and are analysed to illustrate the distribution of forest in Canada by age and maturity. A further analysis by selected species is also provided. Finally, a new plot-based national inventory that will provide additional old-growth indicators is discussed. Key words: forest inventory, inventory attributes, old growth, old-growth indicators, NFI, CanFI, EOSD

scholarly journals Wiederholungsaufnahmen als Instrument zur Qualitätssicherung im Schweizerischen Landesforstinventar

2016 ◽  
Vol 167 (3) ◽  
pp. 118-127 ◽  
Author(s):  
Berthold Traub ◽  
Fabrizio Cioldi ◽  
Christoph Düggelin
Keyword(s):  
Forest Inventory ◽  
Stand Structure ◽  
Tree Height ◽  
National Forest Inventory ◽  
Stand Age ◽  
National Forest ◽  
Stage Of Development ◽  
Statistical Measures ◽  
Expected Values ◽  
Stand Attributes

Repeat surveys as a quality assurance tool in the Swiss National Forest Inventory The Swiss National Forest Inventory (NFI) repeats surveys to guarantee the quality of fieldwork. To this end, approximately 10% of sample plots are completely surveyed a second time over a field season. Based on the results of the repeat survey, the current investigation focuses on the assessment precision, i.e. the reproducibility of various tree and stand attributes in NFI4. It also investigates whether the change from periodic (NFI1–NFI3) to continuous (NFI4) fieldwork has had a positive effect on the reproducibility of the attributes. The current results of the repeat surveys for NFI4 (2009/2017) are compared with those for NFI3 (2004/2006) to this end. We used statistical measures as well as measurement quality objectives (MQO) set by the NFI instructor team as a reference for evaluating reproducibility. The results vary for tree attributes which are vital for estimating stock. The result for the diameter at breast height (dbh) corresponds to the expected values, while that for upper stem diameter at seven meters height and tree height were approximately 5% below the expected values. With regard to the seven stand attributes also analyzed, four of them exceeded the quality goals (stand age, stand stability, the degree of cover of secured regeneration, and stage of development). The results for the mixture proportion, the stand structure and crown closure were between 5 and 18% below MQO. The result for presence of woody species shows that the recording of larger plants (above 130 cm) is clearly more reproducible than for smaller plants (40–130 cm). In NFI4, the reproducibility for almost all studied attributes was improved. The results suggest that the modified structure of fieldwork (with only three field teams and continuous fieldwork in NFI4) has a positive influence on the reproducibility of the included attributes.

scholarly journals Using Landsat Imagery to Assess Burn Severity of National Forest Inventory Plots

2021 ◽  
Vol 13 (10) ◽  
pp. 1935
Author(s):  
Flavie Pelletier ◽  
Bianca N.I. Eskelson ◽  
Vicente J. Monleon ◽  
Yi-Chin Tseng
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Landsat Imagery ◽  
The United States ◽  
Remotely Sensed ◽  
National Forest Inventory ◽  
Burn Severity ◽  
National Forest ◽  
Remotely Sensed Data ◽  
National Inventory

As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots.

scholarly journals Damage to spruce stands in the Kysuce and Orava region determined from the National Forest Inventory

Beskydy ◽  
2012 ◽  
Vol 5 (1) ◽  
pp. 9-18
Author(s):  
Vladimír Šebeň ◽  
Michal Bošeľa ◽  
Bohdan Konôpka
Keyword(s):  
Forest Inventory ◽  
Bark Beetles ◽  
Mechanical Damage ◽  
Health Condition ◽  
National Forest Inventory ◽  
Stand Age ◽  
National Forest ◽  
Spruce Forest ◽  
The Status ◽  
Spruce Stands

An analysis of the status of spruce stands in the Kysuce and Orava region was performed and their health condition was compared to the spruce forest constitution in the rest of Slovakia. For this purpose, the inventory plots established within the first circle of National Forest Inventory (NFI) in 2005 and 2006 were used. The Kysuce and Orava regions significantly differ from the rest of Slovak forests by its high proportion of spruce. The health status of the spruce stands in these regions, according to the salvage felling as a result of the damage caused prevailingly by stem rotting and bark beetles appears worse than in the rest of Slovakia. The analysis showed that not only the acute damage of spruce stands (insects) but also the chronic damage of trees (rotting, mechanical damage during logging) is more serious in these regions than in the rest of Slovakia. The browsing caused by deer game (data does not include young stands) in both regions as well as in the rest of Slovakia seems to be low and not so serious compared to the previously mentioned types of damage. Other sorts of damage (stem breakage, standing dead trees, or damage by insect) have also small proportion. More detailed analyses of NFI data with acceptable precision were possible only for most frequent kinds of damages such as stem rotting and damage by logging. The results showed the frequency of rotting tended to be higher with increasing stand age. Influence of altitude on the frequency of the damage was not evident. Since variability of selected types of damage on spruce forest in the target regions was high, all results are presented with the precision at 68 % confidence interval.

scholarly journals Assessing site productivity based on national forest inventory data and its dependence on site conditions for spruce dominated forests in Germany

2019 ◽  
Vol 28 (2) ◽  
pp. e007
Author(s):  
Susanne Brandl ◽  
Wolfgang Falk ◽  
Thomas Rötzer ◽  
Hans Pretzsch
Keyword(s):  
Forest Inventory ◽  
Stand Density ◽  
National Forest Inventory ◽  
Additive Models ◽  
Karst Area ◽  
Stand Age ◽  
National Forest ◽  
Site Productivity ◽  
The North ◽  
Biomass Increment

Aim of study: (i) To estimate site productivity based on German national forest inventory (NFI) data using above-ground wood biomass increment (ΔB) of the stand and (ii) to develop a model that explains site productivity quantified by ΔB in dependence on climate and soil conditions as well as stand characteristics for Norway spruce (Picea abies (L.) Karst.).Area of study: Germany, which ranges from the North Sea to the Bavarian Alps in the south encompassing lowlands in the north, uplands in central Germany and low mountain ranges mainly in southern Germany.Material and methods: Biomass increment of the stand between the 2nd and 3rd NFI was calculated as measure for site productivity. Generalized additive models were fitted to explain biomass increment in dependence on stand age, stand density and environmental variables.Main results: Great part of the variation in biomass increment was due to differences in stand age and stand density. Mean annual temperature and summer precipitation, temperature seasonality, base saturation, C/N ratio and soil texture explained further variation. External validation of the model using data from experimental plots showed good model performance.Research highlights: The study outlines both the potential as well as the restrictions in using biomass increment as a measure for site productivity and as response variable in statistical site-productivity models: biomass increment of the stand is a comprehensive measure of site potential as it incorporates both height and basal area increment as well as stem number. However, it entails the difficulty of how to deal with the influence of management on stand density.Keywords: Site index; site potential; biomass increment; statistical model; climate.

Steigende Bestandszahlen bei Spechten und anderen Vogelarten dank Zunahme von Totholz? | An increase in the population of woodpeckers and other bird species thanks to an increase in the quantities of deadwood?

2009 ◽  
Vol 160 (11) ◽  
pp. 334-340 ◽  
Author(s):  
Pierre Mollet ◽  
Niklaus Zbinden ◽  
Hans Schmid
Keyword(s):  
Forest Inventory ◽  
Bird Species ◽  
National Forest Inventory ◽  
Population Increase ◽  
National Forest ◽  
Forest Species ◽  
Breeding Populations ◽  
Willow Tit ◽  
Middle Spotted Woodpecker ◽  
Important Habitat

Results from the monitoring programs of the Swiss Ornithological Institute show that the breeding populations of several forest species for which deadwood is an important habitat element (black woodpecker, great spotted woodpecker, middle spotted woodpecker, lesser spotted woodpecker, green woodpecker, three-toed woodpecker as well as crested tit, willow tit and Eurasian tree creeper) have increased in the period 1990 to 2008, although not to the same extent in all species. At the same time the white-backed woodpecker extended its range in eastern Switzerland. The Swiss National Forest Inventory shows an increase in the amount of deadwood in forests for the same period. For all the mentioned species, with the exception of green and middle spotted woodpecker, the growing availability of deadwood is likely to be the most important factor explaining this population increase.

scholarly journals A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Matieu Henry ◽  
Zaheer Iqbal ◽  
Kristofer Johnson ◽  
Mariam Akhter ◽  
Liam Costello ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Data Collection ◽  
Land Cover ◽  
Forest Inventory ◽  
Sustainable Forest Management ◽  
National Forest Inventory ◽  
Forest Resources ◽  
National Forest ◽  
Land Cover Map

Abstract Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

scholarly journals Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Johannes Schumacher ◽  
Marius Hauglin ◽  
Rasmus Astrup ◽  
Johannes Breidenbach
Keyword(s):  
Forest Inventory ◽  
Regression Models ◽  
Laser Scanning ◽  
Site Index ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
National Forest ◽  
Practical Applications ◽  
Airborne Laser ◽  
Sentinel 2

Abstract Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.

scholarly journals Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Caileigh Shoot ◽  
Hans-Erik Andersen ◽  
L. Monika Moskal ◽  
Chad Babcock ◽  
Bruce D. Cook ◽  
...  
Keyword(s):  
Random Forest ◽  
Forest Inventory ◽  
Forest Type ◽  
Vegetation Indices ◽  
National Forest Inventory ◽  
Classification Algorithm ◽  
Lidar Data ◽  
National Forest ◽  
Interior Alaska ◽  
Type Information

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

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