Absolute Radiometric Calibration of ALS Intensity Data: Effects on Accuracy and Target Classification

Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Geometric Calibration and Radiometric Correction of LiDAR Data and Their Impact on the Quality of Derived Products

LiDAR (Light Detection And Ranging) systems are capable of providing 3D positional and spectral information (in the utilized spectrum range) of the mapped surface. Due to systematic errors in the system parameters and measurements, LiDAR systems require geometric calibration and radiometric correction of the intensity data in order to maximize the benefit from the collected positional and spectral information. This paper presents a practical approach for the geometric calibration of LiDAR systems and radiometric correction of collected intensity data while investigating their impact on the quality of the derived products. The proposed approach includes the use of a quasi-rigorous geometric calibration and the radar equation for the radiometric correction of intensity data. The proposed quasi-rigorous calibration procedure requires time-tagged point cloud and trajectory position data, which are available to most of the data users. The paper presents a methodology for evaluating the impact of the geometric calibration on the relative and absolute accuracy of the LiDAR point cloud. Furthermore, the impact of the geometric calibration and radiometric correction on land cover classification accuracy is investigated. The feasibility of the proposed methods and their impact on the derived products are demonstrated through experimental results using real data

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.Keywords: calibration, normalization, radiometric, correction, intensity, LIDAR, laser scanningThis is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by MDPI. The published article can be found at: http://www.mdpi.com/journal/sensor

Extraction of Digital Terrain Models from Airborne Laser Scanning Data based on Transfer-Learning

With the rapid urbanization, timely and comprehensive urban thematic and topographic information is highly needed. Digital Terrain Models (DTMs), as one of unique urban topographic information, directly affect subsequent urban applications such as smart cities, urban microclimate studies, emergency and disaster management. Therefore, both the accuracy and resolution of DTMs define the quality of consequent tasks. Current workflows for DTM extraction vary in accuracy and resolution due to the complexity of terrain and off-terrain objects. Traditional filters, which rely on certain assumptions of surface morphology, insufficiently generalize complex terrain. Recent development in semantic labeling of point clouds has shed light on this problem. Under the semantic labeling context, DTM extraction can be viewed as a binary classification task. This study aims at developing a workflow for automated point-wise DTM extraction from Airborne Laser Scanning (ALS) point clouds using a transfer-learning approach on ResNet. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and accuracy assessment. First, each point is transformed into a feature image based on its elevation differences with neighbouring points. Then, the feature images are classified into ground and non-ground using ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Lastly, the proposed workflow is compared with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progress TIN Densification (PTD). Results show that the proposed workflow establishes an advantageous accuracy of DTM extraction, which yields only 0.522% Type I error, 4.84% Type II error and 2.43% total error. In comparison, Type I, Type II and total error for PMF are 7.82%, 11.6%, and 9.48%, for PTD are 1.55%, 5.37%, and 3.22%, respectively. The root mean squared error of interpolated DTM of 1 m resolution is only 7.3 cm. Moreover, the use of pre-trained weights largely accelerated the training process and enabled the network to reach unprecedented accuracy even on a small amount of training set. Qualitative analysis is further conducted to investigate the reliability and limitations of the proposed workflow

Aspects of Accuracy, Scanning Angle Optimization, and Intensity Calibration Related to Nationwide Laser Scanning

Osajulkaisut: Publication 1: Ahokas, E., Kaartinen, H., Hyyppä, J. 2004. A quality assessment of repeated airborne laser scanner observations. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, Turkey, Vol. XXXV, part B3, pp. 237-242. ISSN 1682-1750. Publication 2: Ahokas, E., Hyyppä, J., Kaartinen, H., Kukko, A., Kaasalainen, S., Krooks, A. 2010. The effect of biomass and scanning angle on laser beam transmittance. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vienna, Austria, Vol. XXXVIII(7A), pp. 1-6. ISSN 1682-1777. http://www.isprs.org/proceedings/XXXVIII/part7/a/pdf/1_XXXVIII-part7A.pdf Publication 3: Ahokas, E., Hyyppä, J., Yu, X., Holopainen, M. 2011. Transmittance of Airborne Laser Scanning Pulses for Boreal Forest Elevation Modeling. Remote Sensing. 3, 1365-1379. ISSN 2072-4292. http://www.mdpi.com/2072-4292/3/7/1365/ Publication 4: Kaasalainen, S., Ahokas, E., Hyyppä, J., Suomalainen, J. 2005. Study of surface brightness from backscattered laser intensity: Calibration of laser data. IEEE Geoscience and remote sensing letters, Vol. 2, No. 3, pp. 255-259, ISSN 1545-598X. Publication 5: Ahokas, E., Kaasalainen, S., Hyyppä, J., Suomalainen, J. 2006. Calibration of the Optech ALTM 3100 laser scanner intensity data using brightness targets. ISPRS Commission I Symposium, Paris Marne-la-Vallee, 4-6 July 2006, ISPRS Volume XXXVI Part 1/A. pp. 14-20. CD-ROM publication. Also in Revue Française de Photogrammétrie et de Télédétection, No. 182, (2006-2), pp. 10-16. Publication 6: Honkavaara, E., Peltoniemi J., Ahokas, E., Kuittinen R., Hyyppä, J., Jaakkola, J., Kaartinen, H., Markelin, L., Nurminen, K., Suomalainen, J. 2008. A Permanent Test Field for Digital Photogrammetric Systems. Photogrammetric Engineering and Remote Sensing. Vol. 74, No. 1, pp. 95-106.Airborne laser scanning is a technique that produces three-dimensional coordinates of the Earth’s surface as well as generating intensity values. Nationwide airborne laser scanning was launched in Finland in 2008 and some 180 000 km2 had been scanned by the end of 2012. While the main goal in this endeavour is to produce an accurate digital elevation/terrain model (2 x 2 m2 grid size) of the whole of the country, other applications, e.g. forestry, will benefit from the data as well. This study deals with the accuracy of airborne laser scanning, the optimization of the scanning angle, and the calibration of intensity. Accuracy assessments of airborne laser scanning have shown that the geometric accuracy of the method can fulfill the accuracy requirements for producing a nationwide digital elevation model with a grid of 2 x 2 m2. When studying the effect of scanning angle and biomass on elevation modeling capability, it was found that it would be possible to increase the scanning angle applied in Finland’s nationwide laser scanning. Even though the accuracy of the elevation model in the conditions prevailing in Finland allows increasing of the scanning angle, other applications would most probably not benefit from this. For example, these same data are sometimes used in nationwide forest inventory in Finland. A method for relative and absolute calibration of airborne laser scanning intensity was developed. The portable reference targets have proved their usefulness for calibration purposes. An intensity correction method should be used in pre-processing the airborne laser data. As a result of this, the usability of the intensity values may increase in practical applications, such as in classification. The studies constituting this dissertation have already impacted on the practical aspects of the nationwide airborne laser scanning dealing with accuracy assessment, the work done in the field of intensity calibration, and scanning angle analysis may have a further impact on nationwide laser scanning in the coming years. The optimization of airborne laser scanning flight parameters for multi-use nationwide laser scanning is a topic deserving further research.Ilmasta tehtävä laserkeilaus tuottaa 3D-koordinaatteja maan pinnalta sekä intensiteettiarvoja. Suomen valtakunnallinen laserkeilaus aloitettiin vuonna 2008 ja noin 180000 km2 oli keilattu vuoden 2012 loppuun mennessä. Vaikka päätarkoituksena on tuottaa tarkka digitaalinen korkeus/maastomalli (2 x 2 m2 ruutukoko) koko maasta, muutkin sovellukset, kuten metsätalous, hyötyvät tästä aineistosta. Tämä tutkimus käsittelee ilmasta tehtävän laserkeilauksen tarkkuutta, keilauskulman optimointia sekä intensiteetin kalibrointia. Laserkeilauksen tarkkuusarviointi on osoittanut, että menetelmän geometrinen tarkkuus täyttää valtakunnallisen digitaalisen korkeusmallin tuottamisen tarkkuusvaatimukset. Kun tutkittiin keilauskulman ja biomassan vaikutusta korkeusmallin tuottamiseen, huomattiin että olisi mahdollista kasvattaa valtakunnallisen laserkeilauksen havaintokulmaa. Vaikka korkeusmallin tarkkuus mahdollistaisi Suomen oloissa keilauskulman kasvattamisen, muut sovellukset eivät luultavasti hyötyisi tästä. Esimerkiksi tätä samaa aineistoa käytetään Suomen valtakunnallisessa metsien inventoinnissa. Laserkeilauksen intensiteetin suhteellista ja absoluuttista kalibrointia varten kehitettiin menetelmä. Siirrettävät referenssikohteet osoittivat käyttökelpoisuutensa intensiteetin kalibroinnissa. Intensiteetin kalibrointimenetelmää tulisi käyttää laserkeilausaineiston esikäsittelyssä. Tämän tuloksena intensiteettiarvojen käyttökelpoisuus kasvaisi käytännön sovelluksissa, kuten luokittelussa. Tämän väitöskirjan muodostaneet tutkimukset ovat jo käytännössä vaikuttaneet valtakunnallisen laserkeilauksen tarkkuusarvioinnissa. Intensiteetin kalibrointityö ja keilauskulman analysointi vaikuttanevat valtakunnalliseen laserkeilaukseen tulevina vuosina. Lisätutkimusta tarvitaan ilmasta tehtävän laserkeilauksen lentoparametrien optimoimiseksi monikäyttöistä valtakunnallista laserkeilausta varten

Applications of multi-spectral lidar: river channel bathymetry and canopy vegetation indices

This thesis investigates the potential of new state-of-the-art multi-spectral (ms) lidar technology and develops methodologies for applications in water, wetlands, and forest resource monitoring. The scope of the thesis is split into two parts – lidar bathymetry and lidar radiometry. The first topic addresses the need for urban river environment bathymetry and refining of ms lidar data processing routines in complex riparian environments. The second topic presents a framework of experiments to investigate ms lidar radiometry. As a result, a new routine for bathymetric correction was developed. The consistency of spectral vegetation indices (SVIs) through a variety of altitudes was investigated. Radiometric targets were constructed and, after radiometric calibration, comparability of reflectance values derived from ms lidar data to available spectral libraries was shown. Finally, forest plot-level canopy vertical SVI profiles were developed while attempting to understand attenuation losses due to sub-footprint reflectors within the canopy by means of additional complex radiometric targets

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%