Species-specific forest variable estimation using non-parametric modeling of multi-spectral photogrammetric point cloud data

The recent development in software for automatic photogrammetric processing of multispectral aerial imagery, and the growing nation-wide availability of Digital Elevation Model (DEM) data, are about to revolutionize data capture for forest management planning in Scandinavia. Using only already available aerial imagery and ALS-assessed DEM data, raster estimates of the forest variables mean tree height, basal area, total stem volume, and species-specific stem volumes were produced and evaluated. The study was conducted at a coniferous hemi-boreal test site in southern Sweden (lat. 58° N, long. 13° E). Digital aerial images from the Zeiss/Intergraph Digital Mapping Camera system were used to produce 3D point-cloud data with spectral information. Metrics were calculated for 696 field plots (10 m radius) from point-cloud data and used in k-MSN to estimate forest variables. For these stands, the tree height ranged from 1.4 to 33.0 m (18.1 m mean), stem volume from 0 to 829 m3 ha-1 (249 m3 ha-1 mean) and basal area from 0 to 62.2 m2 ha-1 (26.1 m2 ha-1 mean), with mean stand size of 2.8 ha. Estimates made using digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet) showed RMSEs (in percent of the surveyed stand mean) of 7.5% for tree height, 11.4% for basal area, 13.2% for total stem volume, 90.6% for pine stem volume, 26.4 for spruce stem volume, and 72.6% for deciduous stem volume. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry

Black Stork Back: Species distribution model predictions of potential habitats for Black Stork Ciconia nigra in Sweden

Increased understanding of the need to save endangered and locally extinct species has led to restoration or preservation of populations through reintroductions. Reintroduction of a species is worthwhile if the prerequisites for existence at the historical location have improved. Thus, background information about the habitat requirements of a target species is important for introduction programmes to be successful. The Black Stork Ciconia nigra was lost as a breeding species in Sweden during the 20th century, but recent observations and reports of potential breeding indicate that habitat conditions for Black Stork in Sweden may have improved. In this study, we used species characteristics and references to identify habitats in Sweden suitable for potential reintroduction of Black Stork. We identified several suitable areas in the former distribution range of this species in southern Sweden. Seven Swedish counties contained more than 18 % suitable habitat within their total area, with highest proportions in Jönköping County (25.8 %), Blekinge County (23.9 %), Västra Götaland County (22.1 %) and Kronoberg County (20.7 %). We suggest these areas to be made the primary targets for Black Stork reintroduction in Sweden

Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions

Detecting disease- or insect-infested forests as early as possible is a classic application of remote sensing. Under conditions of climate change and global warming, outbreaks of the European spruce bark beetle (Ips typographus, L.) are threatening spruce forests and the related timber industry across Europe, and early detection of infestations is important for damage control. Infested trees without visible discoloration (green attack) have been identified using multispectral images, but how early green attacks can be detected is still unknown. This study aimed to determine when infested trees start to show an abnormal spectral response compared with healthy trees, and to quantify the detectability of infested trees during the infestation process. Pheromone bags were used to attract bark beetles in a controlled experiment, and subsequent infestations were assessed in the field on a weekly basis. In total, 977 trees were monitored, including 208 attacked trees. Multispectral drone images were obtained before and during the insect attacks, representing different periods of infestation. Individual tree crowns (ITC) were delineated by marker-controlled watershed segmentation, and the average reflectance of ITCs was analyzed based on the duration of infestation. The detectability of green attacks and driving factors were examined. We propose new Multiple Ratio Disease-Water Stress Indices (MR-DSWIs) as vegetation indices (VI) for detecting infestations. We defined a VI range of 5-95% as a healthy tree, and a VI value outside that range as an infested tree. Detection rates using multispectral images were always higher than discoloration rates observed in the field, and the newly proposed MR-DSWIs detected more infested trees than the established VIs. Infestations were detectable at 5 and 10 weeks after an attack at a rate of 15% and 90%, respectively, from the multispectral drone images. Weeks 5-10 of infestation therefore represent a suitable period for using the proposed methodology to map infestation at an early stage

Potential of mapping forest damage from remotely sensed data

Remote sensing is an efficient tool for mapping, monitoring, and assessing forest damage and the risk of damage. This report presents ongoing research on those topics with preliminary results as well as research planned by the Department of Forest Resource Management, SLU in Umeå, in the near future. The damage types include spruce bark beetle attacks, storm damage, and forest fire. The report also outlines proposed continued research in the area and possible collaborations within and outside SLU

Optimal Robot Placement for Tasks Execution

AbstractAutomotive assembly cells are cluttered environments, including robots, workpieces, and fixtures. Due to high volumes and several product variants assembled in the same cell, robot placement is crucial to increase flexibility and throughput. In this paper, we propose a novel method to optimize the base position of an industrial robot with the objective to reach all predefined tasks and minimize cycle time: robot inverse kinematics and collision avoidance are integrated together with a derivative-free optimization algorithm. This approach is successfully used to find feasible solutions on industrial test cases, showing up to 20% cycle time improvement

Assimilating remote sensing data with forest growth models

As we are entering an era of increased supply of remote sensing data, we believe that dataassimilation that combines growth forecasts of previous estimates with new observations of thecurrent state has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013).The data assimilation will update a forest model e in an optimal way based on the uncertainties inthe forecast and the observations, each time new data becomes available. These forecasting andupdating steps can be repeated with new available observations to get improved estimations. In thisstudy we present the first practical results from data assimilation of mean tree height, basal area andgrowing stock. The remote sensing data used were canopy height models obtained from matching ofdigital aerial photos over the test site Remningstorp in Sweden. The photos were acquired 2003,2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning.The procedure for the data assimilation was as follows: mean tree height, basal area and growingstock were predicted on 18 m × 18 m raster cells using the area based method. Ten meter radiussample plots were used as field calibration data. For each photo year, the field data were adjustedfor growth to have the same state year as each acquisition year of the photos. Growth models wereconstructed from National Forest Inventory plot data. Data assimilation could then be performed onraster cell level by initially start with the estimates from 2003 year´s photos. This prediction was thenforecasted to year 2005 by calculating the growth for the raster cell. This forecasted value is thenblended with the new remote sensing estimation collected 2005. The process was then repeated forthe following years where new measurements were available. In this study, extended Kalmanfiltering was used to blend the forecasted values with the new remote sensing measurements.Validation was done for 40 m radius field plots. Further, the results were also compared with twoalternative approaches: the first was to forecast the first remote sensing estimate to the endpointand the second was to use remote sensing data acquired at the endpoint only.The preliminary results for the eight forest stands show that the variances were lower when usingassimilation of new estimates and there were less fluctuation compared to only using remote sensingdata from the endpoint. However, the mean deviation from the measured value 2011 was lowerwhen only data from the endpoint were used. The assimilated values 2011 were consistently closerto the validation data compared to only forecasting the starting estimate from 2003 to 2011

Dust-correlated cm-wavelength continuum emission on translucent clouds {\zeta} Oph and LDN 1780

The diffuse cm-wave IR-correlated signal, the "anomalous" CMB foreground, is thought to arise in the dust in cirrus clouds. We present Cosmic Background Imager (CBI) cm-wave data of two translucent clouds, {\zeta} Oph and LDN 1780 with the aim of characterising the anomalous emission in the translucent cloud environment. In {\zeta} Oph, the measured brightness at 31 GHz is 2.4{\sigma} higher than an extrapolation from 5 GHz measurements assuming a free-free spectrum on 8 arcmin scales. The SED of this cloud on angular scales of 1{\odot} is dominated by free-free emission in the cm-range. In LDN 1780 we detected a 3 {\sigma} excess in the SED on angular scales of 1{\odot} that can be fitted using a spinning dust model. In this cloud, there is a spatial correlation between the CBI data and IR images, which trace dust. The correlation is better with near-IR templates (IRAS 12 and 25 {\mu}m) than with IRAS 100 {\mu}m, which suggests a very small grain origin for the emission at 31 GHz. We calculated the 31 GHz emissivities in both clouds. They are similar and have intermediate values between that of cirrus clouds and dark clouds. Nevertheless, we found an indication of an inverse relationship between emissivity and column density, which further supports the VSGs origin for the cm-emission since the proportion of big relative to small grains is smaller in diffuse clouds.Comment: 13 pages, 14 figures, 7 tables. Accepted for publication in MNRA

Visualisering av skog och skogslandskap

Allt oftare har allmänheten åsikter om hur skogsbruket skall skötas t.ex. i närheten av byar, eller utmed vägar och sjöar. För skogs- bruket kan detta innebära diskussioner om hur en åtgärd skall gör- as eller vilken hänsyn som skall tas. Vid en sådan intressekonflikt skulle visualisering av olika framtida scenarier kunna användas som diskussionsunderlag för att visa på hur t.ex. en avverkning skulle påverka landskapsvyn. Ett annat användningsområde för visualisering är som pedagogiskt verktyg vid diskussioner mellan fastighetsägare och skogsbruksplanläggare, virkesköpare etc. Detta blir mer aktuellt då andelen ägare som bor på fastigheten minskar och samtidigt som dessa ägare ofta har inkomst från ann- an verksamhet, vilket kan innebära att de har mindre intresse för och kunskap om skogsbruk och dess terminologi. För dessa skulle en visualiserad skogsbruksplan kunna vara ett bra komplement till de konventionella tabeller, grafer och kartor som beskriver fastigheten

Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions

With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous

Data collection for forest management planning using stereo photogrammetry

Forest managers need information about the forest state for planning treatments. The information needs to be sufficient for the purpose and preferably obtained at low cost and with regular updates. In the last decade, development and use of airborne laser scanning (ALS) for forest variable estimation has been a revolution for forest management planning. But it has also created opportunities for other three dimensional (3D) technologies which can describe the forest canopy surface, since it provides an accurate model of the ground elevation. One such technique is stereo photogrammetry using aerial images. Using aerial images from national image surveying programs, high resolution 3D data and spectral data can be acquired regularly with a frequency of about 2-4 years over forest land in Sweden. The aim was to produce forest variable raster maps which can be used at stand level but also as information to describe within stand variation and updating stand boarders after clear-cut. In this thesis aerial images from the National Land Survey’s image acquisition program has been used in all studies, but also high resolution and highly overlapping images have been evaluated. Using field plots, the 3D and spectral data can be linked by models to predict forest variables of interest. In this thesis; tree height, diameter, basal area, stem volume, species-specific stem volume and species proportions have been the variables of interest. Models have been applied and evaluated at Remningstorp in southern Sweden (Lat. 58°N, Long. 13°E), but also scaled up to national level using field plots from the national forest inventory. The included studies show that aerial images can produce forest variable estimates with good accuracy where best results in terms of root mean square error of the mean were 8.8% for tree height, 14.9% for basal area and 13.1% for stem volume, but that species-specific variables did not perform as well. In conclusion, aerial images with 0.5 m resolution and 60% overlap using stereo photogrammetry produce estimates with an acceptable level of accuracy for use as a data source for forest management planning. However, very sparse forests, deciduous forests and mature forests have larger estimation errors. Nevertheless, from a forest management perspective, forest information can be collected at very low costs and with high spatial and temporal resolution