scholarly journals Die Baumbedeckung in der Schweiz

2011 ◽  
Vol 162 (9) ◽  
pp. 344-349 ◽  
Author(s):  
Christian Ginzler ◽  
Lukas Mathys ◽  
Esther Thürig
Keyword(s):  
Forest Inventory ◽  
Urban Areas ◽  
Sampling Error ◽  
A Priori ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
Tree Cover ◽  
National Forest ◽  
Aerial Photo ◽  
Small Areas

Tree cover in Switzerland The requirements for national forest inventories have changed in recent decades, as have the issues involved. Initially, the focus was mainly information on timber resources, but today social and environmental functions are also of interest. An a priori separation of the surveyed areas into forest and non-forest during data collection limits the interpretation of the tree resources. Not all trees are located in the forest and not all forests are fully stocked. In the aerial photo interpretation of the 3rd National Forest Inventory, land cover on a regular sampling grid was determined regardless of the land use. This allowed, for the first time, nationwide information on tree resources to be obtained, independent of forest definitions. The tree cover of Switzerland is 27.0% regardless of whether the trees are in the forest or outside. The area covered with forest (proportionally 29.4%) is larger than that covered with trees. The location of the trees outside the forest tends to be mostly either very close to forest or in urban areas. The most densely stocked areas, after forests, are urban areas. The data from aerial photo interpretations of the 3rd National Forest Inventory allow a more nuanced picture of the stocking over the whole country, but the sampling error is still too large to draw conclusions for small areas. The existing and ongoing surveys, however, provide a calibration and reference dataset so that, with remote sensing data and methods, it should be possible to generate more comprehensive spatial datasets to help to fill this gap.

Monitoring Canada's forests: The National Forest Inventory

2005 ◽  
Vol 81 (2) ◽  
pp. 214-221 ◽  
Author(s):  
M D Gillis ◽  
A Y Omule ◽  
T. Brierley
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Forest Inventory ◽  
Forest Cover ◽  
Sustainable Forest Management ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
National Forest ◽  
Inventory Data ◽  
International Reporting

A new national forest inventory is being installed in Canada. For the last 20 years, Canada's forest inventory has been a compilation of inventory data from across the country. Although this method has a number of advantages, it lacks information about the nature and rate of changes to the resource, and does not permit projections or forecasts. To address these limitations a new National Forest Inventory (NFI) was developed to monitor Canada's progress in meeting a commitment towards sustainable forest management, and to satisfy requirements for national and international reporting. The purpose of the new inventory is to "assess and monitor the extent, state and sustainable development of Canada's forests in a timely and accurate manner." The NFI consists of a plot-based system of permanent observational units located on a national grid. A combination of ground plot, photo plot and remote sensing data are used to capture a set of basic attributes that are used to derive indicators of sustainability. To meet the monitoring needs a re-measurement strategy and framework to guide the development of change estimation procedures has been worked out. A plan for implementation has been drafted. The proposed plan is presented and discussed in this paper. Key words: Canada, forest cover, inventory, monitoring, National Forest Inventory, re-measurement, panel

scholarly journals Das Schweizerische Landesforstinventar – eine Bilanz (Essay)

2011 ◽  
Vol 162 (9) ◽  
pp. 274-281
Author(s):  
Peter Brassel
Keyword(s):  
Forest Inventory ◽  
National Level ◽  
National Forest Inventory ◽  
National Forest ◽  
Estimation Errors ◽  
Joint Project ◽  
Small Areas ◽  
Federal Research ◽  
Survey Results ◽  
New Applications

Switzerland's National Forest Inventory – review and outlook (essay) The idea of carrying out a national forest inventory in Switzerland was first raised in the 1950s. It had become increasingly evident that such an inventory was lacking as a basis for evaluating the state of the forest in the whole country. But it was not until 1970 that this idea took concrete shape. And it took another ten years for the Swiss government to agree to the first inventory taking place. From the start, the National Forest Inventory (NFI) was a joint project of the Swiss federal administration and the Swiss Federal Research Institute WSL. It has now established itself as an objective source of information about the Swiss forest. On the national level, it is today the most important instrument for monitoring the sustainability of the management of Swiss forests, and it yields the main data needed for international reports on forests. The success of the NFI is at least partly due to the fact that it has met with widespread support from both the Swiss authorities and scientists. The NFI has, over the past 30 years, continuously developed both methodologically and in terms of content. Thus the first NFI's data catalogue was expanded to include, for example, numerous ecological parameters. Currently the fourth NFI is underway. It will mark the transition from a periodic to a continuous inventory. In the future, the survey results will be published roughly every three years. Reducing the intervals between inventories will, however, be associated with higher estimation errors. On the other hand, interesting new applications can be expected in remote sensing, as well as improved models of future forest development. Some new methods seem promising, like the so-called “small area estimation”, which enables conclusions to be drawn about relatively small areas. However, if the NFI is to continue to perform its tasks adequately in future, it must receive sufficient funding.

Combining national forest inventory field plots and remote sensing data for forest databases

2008 ◽  
Vol 112 (5) ◽  
pp. 1982-1999 ◽  
Author(s):  
Erkki Tomppo ◽  
Håkan Olsson ◽  
Göran Ståhl ◽  
Mats Nilsson ◽  
Olle Hagner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
National Forest ◽  
Sensing Data ◽  
Field Plots

New estimate of growing stock volume and carbon sequestration of Russian forests based on national forest inventory and remote sensing data

2021 ◽  
Author(s):  
Dmitry Schepaschenko ◽  
Elena Moltchanova ◽  
Stanislav Fedorov ◽  
Victor Karminov ◽  
Petr Ontikov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soviet Union ◽  
Forest Inventory ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
The State ◽  
National Forest ◽  
Sensing Data ◽  
Growing Stock ◽  
Russian Forests

<p>Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported (FAO, 2014) almost no changes in growing stock (+1.8%) and biomass (+0.6%). Yet remote sensing products indicate increased vegetation productivity (Guay et al., 2014), tree cover (Song et al., 2018) and above-ground biomass (Liu et al., 2015). Here, we challenge the official national statistics with a combination of recent National Forest Inventory and remote sensing data products to provide an alternative estimate of the growing stock of Russian forests and assess the relative changes in the post-Soviet era. Our estimate for the year 2014 is 118.29±1.3 10<sup>9</sup> m<sup>3</sup>, which is 48% higher than the official value reported for the same year in the State Forest Register. The difference is explained by increased biomass density in forested areas (+39%) and larger forest area estimates (+9%). Using the last Soviet Union report (1988) as a reference, Russian forests have accumulated 1163×10<sup>6</sup> m<sup>3</sup> yr<sup>-1</sup> of growing stock between 1988–2014, which compensates for forest growing stock losses in tropical countries (FAO FRA, 2015). Our estimate of the growing stock of managed forests is 94.2 10<sup>9</sup> m<sup>3</sup>, which corresponds to sequestration of 354 Tg C yr<sup>-1</sup> in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory (National Inventory Report, 2020).</p><p>Acknowledgement: The research plots data collection was performed within the framework of the state assignment of the Center for Forest Ecology and Productivity of the Russian Academy of Sciences (no. АААА-А18-118052590019-7), and the ground data pre-processing were financially supported by the Russian Science Foundation (project no. 19-77-30015).</p>

scholarly journals Russian forest sequesters substantially more carbon than previously reported

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dmitry Schepaschenko ◽  
Elena Moltchanova ◽  
Stanislav Fedorov ◽  
Victor Karminov ◽  
Petr Ontikov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soviet Union ◽  
Forest Inventory ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
Tree Cover ◽  
Growing Stock ◽  
The Soviet Union ◽  
Russian Forests ◽  
Soviet Russia

AbstractSince the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported almost no change in growing stock (+ 1.8%) and biomass (+ 0.6%). Yet remote sensing products indicate increased vegetation productivity, tree cover and above-ground biomass. Here, we challenge these statistics with a combination of recent National Forest Inventory and remote sensing data to provide an alternative estimate of the growing stock of Russian forests and to assess the relative changes in post-Soviet Russia. Our estimate for the year 2014 is 111 ± 1.3 × 109 m3, or 39% higher than the value in the State Forest Register. Using the last Soviet Union report as a reference, Russian forests have accumulated 1163 × 106 m3 yr-1 of growing stock between 1988–2014, which balances the net forest stock losses in tropical countries. Our estimate of the growing stock of managed forests is 94.2 × 109 m3, which corresponds to sequestration of 354 Tg C yr-1 in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory.

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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