scholarly journals Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

2015 ◽  
Vol 7 (4) ◽  
pp. 4343-4370 ◽  
Author(s):  
Christian Ginzler ◽  
Martina Hobi
Keyword(s):  
Forest Inventory ◽  
Image Matching ◽  
National Forest Inventory ◽  
Stereo Image ◽  
National Forest ◽  
Surface Models ◽  
Stereo Image Matching

Countrywide surface models from historical panchromatic and true color stereo imagery – a retrospective analysis of forest structures in Switzerland

2020 ◽  
Author(s):  
Christian Ginzler ◽  
Mauro Marty ◽  
Lars T. Waser
Keyword(s):  
Forest Inventory ◽  
Digital Terrain Model ◽  
National Forest Inventory ◽  
Aerial Images ◽  
National Forest ◽  
Terrain Model ◽  
Digital Terrain ◽  
Surface Models ◽  
Stereo Imagery ◽  
True Color

<p><strong>Countrywide surface models from historical panchromatic and true color stereo imagery – a retrospective analysis of forest structures in Switzerland</strong></p><p><strong>Mauro Marty<sup>1</sup>, Lars T. Waser<sup>1</sup>, Christian Ginzler<sup>1</sup></strong></p><p><sup>1</sup> Swiss Federal Institute for Forest, Snow and Landscape Research WSL, <br>Zürcherstrasse 111, CH - 8903 Birmensdorf, Switzerland</p><p>Remote sensing methods allow the acquisition of 3D structures of forests over large areas. Active systems, such as Airborne Laser Scanning (ALS) and Synthetic Aperture Radar (SAR) and passive systems, such as multispectral sensors, have been established to acquire 3D and 2.5D data of the earth's surface. Nationwide calculations of surface models with photogrammetric methods from digital stereo aerial images or ALS data are already in operation in some countries (e.g. Switzerland, Austria, some German states).</p><p>The availability of historical stereo aerial images allows the calculation of digital surface models from the past using photogrammetric methods. We present a workflow with which we have calculated nationwide surface models for Switzerland for the 1980s, 1990s and 2000s. Current surface models are available from the National Forest Inventory (LFI) Switzerland.</p><p>In the context of the Swiss land use and land cover statistics, the Federal Office of Topography (swisstopo) scanned and oriented the analogue black and white stereo aerial photographs with a mean scale of ~1:30'000 of the nationwide flights of 1979 - 84 and1993 - 1997 with 14 µm. The true colour image data from 1998 – 2007 were scanned for the production of the orthoimages swissimage by swisstopo. All these data – the scanned images and the orientation parameters - are also available to the National Forest Inventory (NFI). Within the framework of the NFI, we developed a highly automated workflow to generate digital surface models (DSMs) from many thousands of overlapping frame images covering the whole country. In total, more than 25'000 individual stereo models were processed to nationwide surface models. For their normalization, the digital terrain model of Switzerland 'swissAlti3D' was used. As the image orientation in some areas showed high vertical inaccuracies, corrections had to be made. Data from the Swiss land use and land cover statistics were used for this purpose. At places with constant surface cover since the 1980s (e.g. grassland), correction grids were calculated using the digital terrain model and applied to the surface models.</p><p>The results are new data sets on the 2.5D surface of Switzerland from the 1980s, 1990s and 2000s with a high spatial resolution of 1 m. It can be stated that the completeness of the image correlation in forested areas was quite satisfactory. In open areas with agricultural land, however, the matching points were often reduced to the road network, as the meadows and fields in the scanned SW stereo aerial images had very little texture.</p><p>This new historical, nationwide data on the horizontal and vertical structure in forests now allows their analysis of changes over the last 40 years.</p>

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.

Assessing forest resource damage following natural disasters using national forest inventory plots

2022 ◽  
Author(s):  
Tom Brandeis ◽  
Jeffery Turner ◽  
Andrés Baeza Motes ◽  
Mark Brown ◽  
Samuel Lambert
Keyword(s):  
Natural Disasters ◽  
Forest Inventory ◽  
National Forest Inventory ◽  
National Forest ◽  
Forest Resource

scholarly journals Comparison of Kalman filters in combining panel data from the annual inventory system of the South Korea National Forest Inventory

2013 ◽  
Author(s):  
Tzeng Yih Lam ◽  
Raymond L. Czaplewski ◽  
Jong Su Yim ◽  
Kyeong Hak Lee ◽  
Sung Ho Kim ◽  
...  
Keyword(s):  
Panel Data ◽  
South Korea ◽  
Forest Inventory ◽  
Kalman Filters ◽  
National Forest Inventory ◽  
Inventory System ◽  
National Forest ◽  
The South
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