Toward utilizing multitemporal multispectral airborne laser scanning, Sentinel-2, and mobile laser scanning in map updating

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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MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING

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

10.5194/isprsarchives-xli-b3-323-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 323-330 ◽

Cited By ~ 6

Author(s):

Leena Matikainen ◽

Juha Hyyppä ◽

Paula Litkey

Keyword(s):

Change Detection ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Aerial Images ◽

Sensor Technology ◽

Classification Error ◽

Surface Model ◽

Airborne Laser ◽

Map Updating

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

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Comparison of Airborne Laser Scanning (3D) with Sentinel-2 (2D) for Estimating Above Ground Biomass/Carbon Stock in a Subtropical Chir Pine Forest of Balakot, Pakistan

10.21203/rs.3.rs-173795/v1 ◽

2021 ◽

Author(s):

Sami Ullah ◽

Tahir Saeed ◽

Muhammad Shafique ◽

Muhammad Saad ◽

Adnan Khan

Keyword(s):

Carbon Stock ◽

Laser Scanning ◽

Forest Ecosystems ◽

Pine Forest ◽

Airborne Laser Scanning ◽

Biomass Carbon ◽

Above Ground Biomass ◽

Airborne Laser ◽

Chir Pine ◽

Sentinel 2

Abstract Forest ecosystems act as a sink of atmospheric carbon dioxide in the form of biomass, and plays one of the crucial role for carbon sequestration and in regulating the global carbon cycle. Few studies based on ground sample plots were conducted for estimating forest biomass/carbon stock across Pakistan. This study comparing the first time the potential of three dimensional (3D) airborne laser scanning (ALS) with two dimensional (2D) Sentinel-2 to estimate above-ground biomass/carbon stock (AGB/C) in a Subtropical Chir Pine forest of Balakot, Pakistan. We derived height and density metrics from the ALS canopy height model (CHM), and different metrics from Sentinel-2 images, and were regressed with field measured AGB/C at sample plots locations. We found R2 = 0.86 with RMSE% = 25.70, and R2 = 0.62 with RMSE% = 43.92 for ALS and for Sentinel-2 respectively with ground measured AGB/C at sample plots locations. Our study demonstrated that 3D ALS technology has greater potential and is the most accurate option as compared to 2D Sentinel-2 for regular planning and monitoring of AGB/C in the context of the national forest inventory of Pakistan. Our study will be useful for the accomplishment of the REDD+ in measuring, reporting, and verification of forest resources, and future sustainable utilization of forest, safeguarding the livelihoods of forest-dependent people, and reducing pressure on forest ecosystems.

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MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING

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

10.5194/isprs-archives-xli-b3-323-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 323-330 ◽

Cited By ~ 1

Author(s):

Leena Matikainen ◽

Juha Hyyppä ◽

Paula Litkey

Keyword(s):

Change Detection ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Aerial Images ◽

Sensor Technology ◽

Classification Error ◽

Surface Model ◽

Airborne Laser ◽

Map Updating

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

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Wavelet based buildings segmentation in airborne laser scanning data set

Geodesy and Cartography ◽

10.2478/v10277-012-0010-0 ◽

2011 ◽

Vol 60 (2) ◽

Cited By ~ 1

Author(s):

Wolfgang Keller ◽

Andrzej Borkowski

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Data Set ◽

Airborne Laser

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A STUDY ON SNOW DEPTH DISTRIBUTION ON WINDWARD SLOPE AND LEEWARD SLOPE USING AIRBORNE LASER SCANNING

Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering) ◽

10.2208/jscejhe.74.i_883 ◽

2018 ◽

Vol 74 (4) ◽

pp. I_883-I_888

Author(s):

Terumasa NISHIHARA ◽

Atsushi TANISE

Keyword(s):

Snow Depth ◽

Laser Scanning ◽

Depth Distribution ◽

Airborne Laser Scanning ◽

Airborne Laser ◽

Windward Slope

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New Advances in Automated Urban Modelling from Airborne Laser Scanning Data

Recent Patents on Engineering ◽

10.2174/187221211797636890 ◽

2011 ◽

Vol 5 (3) ◽

pp. 196-208 ◽

Cited By ~ 8

Author(s):

D. F. Laefer ◽

T. Hinks ◽

H. Carr ◽

L. Truong-Hong

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Airborne Laser ◽

Urban Modelling

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Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

Sustainability ◽

10.3390/su13041917 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1917

Author(s):

Alma Elizabeth Thuestad ◽

Ole Risbøl ◽

Jan Ingolf Kleppe ◽

Stine Barlindhaug ◽

Elin Rose Myrvoll

Keyword(s):

Remote Sensing ◽

Laser Scanning ◽

Digital Terrain Model ◽

Image Data ◽

Remote Sensing Image ◽

Airborne Laser Scanning ◽

Aerial Images ◽

Detection Rates ◽

Airborne Laser ◽

Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

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Airborne laser scanning reveals large tree trunks on forest floor

Forest Ecology and Management ◽

10.1016/j.foreco.2021.119225 ◽

2021 ◽

Vol 491 ◽

pp. 119225

Author(s):

Einari Heinaro ◽

Topi Tanhuanpää ◽

Tuomas Yrttimaa ◽

Markus Holopainen ◽

Mikko Vastaranta

Keyword(s):

Forest Floor ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Large Tree ◽

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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