TOWARDS AUTOMATIC SINGLE-SENSOR MAPPING BY MULTISPECTRAL AIRBORNE LASER SCANNING

This paper describes the possibilities of the Optech Titan multispectral airborne laser scanner in the fields of mapping and forestry. Investigation was targeted to six land cover classes. Multispectral laser scanner data can be used to distinguish land cover classes of the ground surface, including the roads and separate road surface classes. For forest inventory using point cloud metrics and intensity features combined, total accuracy of 93.5% was achieved for classification of three main boreal tree species (pine, spruce and birch).When using intensity features – without point height metrics - a classification accuracy of 91% was achieved for these three tree species. It was also shown that deciduous trees can be further classified into more species. We propose that intensity-related features and waveform-type features are combined with point height metrics for forest attribute derivation in area-based prediction, which is an operatively applied forest inventory process in Scandinavia. It is expected that multispectral airborne laser scanning can provide highly valuable data for city and forest mapping and is a highly relevant data asset for national and local mapping agencies in the near future.

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TOWARDS AUTOMATIC SINGLE-SENSOR MAPPING BY MULTISPECTRAL AIRBORNE LASER SCANNING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-155-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 155-162 ◽

Cited By ~ 8

Author(s):

E. Ahokas ◽

J. Hyyppä ◽

X. Yu ◽

X. Liang ◽

L. Matikainen ◽

...

Keyword(s):

Land Cover ◽

Tree Species ◽

Forest Inventory ◽

Laser Scanning ◽

Laser Scanner ◽

Ground Surface ◽

Airborne Laser Scanning ◽

Forest Mapping ◽

Airborne Laser Scanner ◽

Airborne Laser

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Estimation of Vegetation-Induced Flow Resistance for Hydraulic Computations Using Airborne Laser Scanning Data

Water ◽

10.3390/w13131864 ◽

2021 ◽

Vol 13 (13) ◽

pp. 1864

Author(s):

Peter Mewis

Keyword(s):

Laser Scanning ◽

Flow Resistance ◽

Laser Scanner ◽

Airborne Laser Scanning ◽

Density Parameter ◽

Vegetation Density ◽

Airborne Laser Scanner ◽

Airborne Laser ◽

Bed Roughness ◽

Available Information

The effect of vegetation in hydraulic computations can be significant. This effect is important for flood computations. Today, the necessary terrain information for flood computations is obtained by airborne laser scanning techniques. The quality and density of the airborne laser scanning information allows for more extensive use of these data in flow computations. In this paper, known methods are improved and combined into a new simple and objective procedure to estimate the hydraulic resistance of vegetation on the flow in the field. State-of-the-art airborne laser scanner information is explored to estimate the vegetation density. The laser scanning information provides the base for the calculation of the vegetation density parameter ωp using the Beer–Lambert law. In a second step, the vegetation density is employed in a flow model to appropriately account for vegetation resistance. The use of this vegetation parameter is superior to the common method of accounting for the vegetation resistance in the bed resistance parameter for bed roughness. The proposed procedure utilizes newly available information and is demonstrated in an example. The obtained values fit very well with the values obtained in the literature. Moreover, the obtained information is very detailed. In the results, the effect of vegetation is estimated objectively without the assignment of typical values. Moreover, a more structured flow field is computed with the flood around denser vegetation, such as groups of bushes. A further thorough study based on observed flow resistance is needed.

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How much can airborne laser scanning based forest inventory by tree species benefit from auxiliary optical data?

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.06.017 ◽

2018 ◽

Vol 72 ◽

pp. 91-98 ◽

Cited By ~ 4

Author(s):

Mikael Kukkonen ◽

Lauri Korhonen ◽

Matti Maltamo ◽

Aki Suvanto ◽

Petteri Packalen

Keyword(s):

Tree Species ◽

Forest Inventory ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Optical Data ◽

Airborne Laser

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EVALUATING THE POSSIBILITY OF TREE SPECIES CLASSIFICATION WITH DUAL-WAVELENGTH ALS DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1097-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1097-1103

Author(s):

M. Pilarska ◽

W. Ostrowski

Keyword(s):

Spatial Data ◽

Tree Species ◽

Laser Scanning ◽

Laser Scanner ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Tree Canopy ◽

Species Classification ◽

Airborne Laser ◽

Simultaneous Acquisition

<p><strong>Abstract.</strong> Airborne laser scanning (ALS) plays an important role in spatial data acquisition. One of the advantages of this technique is laser beam penetration through vegetation, which makes it possible to not only obtain data on the tree canopy but also within and under the canopy. In recent years, multi-wavelength airborne laser scanning has been developed. This technique consists of simultaneous acquisition of point clouds in more than one band. The aim of this experiment was to examine and assess the possibilities of tree segmentation and species classification in an urban area. In this experiment, point clouds registered in two wavelengths (532 and 1064&thinsp;nm) were used for tree segmentation and species classification. The data were acquired with a Riegl VQ-1560i-DW laser scanner over Elblag, Poland, during August 2018. Tree species collected by a botanist team within terrain measurements were used as a reference in the classification process. Within the experiment segmentation and classification process were performed. Regarding the segmentation, TerraScan software and Li et al.’s algorithm, implemented in LidR package were used. Results from both methods are clearly over-segmented in comparison to the manual segments. In Terrasolid segmentation, single reference segments are over-segmented in 28% of cases, whereas, for LidR, over-segmentation occurred in 73% of the segments. According the classification results, Thuja, Salix and Betula were the species, for which the highest classification accuracy was achieved.</p>

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Prediction and model-assisted estimation of diameter distributions using Norwegian national forest inventory and airborne laser scanning data

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0440 ◽

2021 ◽

Author(s):

Janne Räty ◽

Rasmus Astrup ◽

Johannes Breidenbach

Keyword(s):

Tree Species ◽

Forest Inventory ◽

Laser Scanning ◽

National Forest Inventory ◽

Airborne Laser Scanning ◽

Systematic Sampling ◽

National Forest ◽

Prediction Errors ◽

K Nearest Neighbor ◽

Airborne Laser

Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed- effects (PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95–1.47 and 0.96–1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%.

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Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data

Forest Ecosystems ◽

10.1186/s40663-021-00338-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Marius Hauglin ◽

Johannes Rahlf ◽

Johannes Schumacher ◽

Rasmus Astrup ◽

Johannes Breidenbach

Keyword(s):

Tree Species ◽

Forest Inventory ◽

Laser Scanning ◽

National Forest Inventory ◽

Mixed Effects ◽

Airborne Laser Scanning ◽

National Forest ◽

Airborne Laser ◽

Maturity Class ◽

Species Specific

Abstract Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.

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Test field for airborne laser scanning in Finland

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-9-2014 ◽

2014 ◽

Vol XL-1 ◽

pp. 9-12 ◽

Cited By ~ 1

Author(s):

E. Ahokas ◽

H. Kaartinen ◽

A. Kukko ◽

P. Litkey

Keyword(s):

Laser Scanning ◽

Laser Scanner ◽

Ground Surface ◽

Ground Level ◽

Reference Points ◽

Airborne Laser Scanning ◽

Measurement Tool ◽

Test Field ◽

Airborne Laser ◽

Spatial Coverage

Airborne laser scanning (ALS) is a widely spread operational measurement tool for obtaining 3D coordinates of the ground surface. There is a need for calibrating the ALS system and a test field for ALS was established at the end of 2013. The test field is situated in the city of Lahti, about 100 km to the north of Helsinki. The size of the area is approximately 3.5 km × 3.2 km. Reference data was collected with a mobile laser scanning (MLS) system assembled on a car roof. Some streets were measured both ways and most of them in one driving direction only. The MLS system of the Finnish Geodetic Institute (FGI) consists of a navigation system (NovAtel SPAN GNSS-IMU) and a laser scanner (FARO Focus3D 120). In addition to the MLS measurements more than 800 reference points were measured using a Trimble R8 VRS-GNSS system. Reference points are along the streets, on parking lots, and white pedestrian crossing line corners which can be used as reference targets. The National Land Survey of Finland has already used this test field this spring for calibrating their Leica ALS-70 scanner. Especially it was easier to determine the encoder scale factor parameter using this test field. Accuracy analysis of the MLS points showed that the point height RMSE is 2.8 cm and standard deviation is 2.6 cm. Our purpose is to measure both more MLS data and more reference points in the test field area to get a better spatial coverage. Calibration flight heights are planned to be 1000 m and 2500 m above ground level. A cross pattern, southwest–northeast and northwest–southeast, will be flown both in opposite directions.

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Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data

Canadian Journal of Forest Research ◽

10.1139/x05-246 ◽

2006 ◽

Vol 36 (2) ◽

pp. 426-436 ◽

Cited By ~ 93

Author(s):

M Maltamo ◽

J Malinen ◽

P Packalén ◽

A Suvanto ◽

J Kangas

Keyword(s):

Laser Scanning ◽

Laser Scanner ◽

Airborne Laser Scanning ◽

Data Sources ◽

Aerial Images ◽

Stem Volume ◽

Scanner Data ◽

Airborne Laser Scanner ◽

Airborne Laser ◽

Other Information

In forest management planning and forestry decision-making there is a continuous need for higher quality information on forest resources. The aim of this study was to improve the quality of forest resource information acquired by airborne laser scanning by combining it with aerial images and current stand-register data. A k-MSN (most similar neighbor) application was constructed for the prediction of the plot and stand volumes of standing trees. The application constructed used various data sources, including laser scanner data, aerial digital photographs, class variables describing a stand, and updated old stand volumes. The ability of these data sources to predict stem volume was tested together and separately. In the airborne laser scanner data based k-MSN application, characteristics of canopy quantiles were used as independent variables. The results show that with respect to individual plot and stand volume estimation approaches, the laser-based technique is a superior one. The results were improved further when other information sources were used together with the laser scanner data. Using a combination of laser scanner data, aerial images, and class variables (on the grounds of the current forest database) improved the root mean square error (RMSE) of the estimated plot volume by 15% (from 16% to 13%) as compared to using laser scanner data on their own. When the results were averaged at the stand level, the accuracy improved considerably, but the use of other information sources together with airborne laser scanner data did not further improve the results as it did at the plot level. The RMSE of stand volume was about 6% in all data combinations where airborne laser scanning information was used. One conclusion is that making use of additional available data sources together with laser material improves the reliability of plot volume estimates. As these additional data typically mean no extra material costs (since they are available in any case), making combined use of these data and laser scanner data improves the cost efficiency of a forest inventory.

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

Forest Ecosystems ◽

10.1186/s40663-020-00274-9 ◽

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.

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