Automation aspects for the georeferencing of photogrammetric aerial image archives in forested scenes

Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D environmental model time series when using small-scale airborne image archives, especially in forested scenes. Furthermore, we investigated the usability of dense digital surface models (DSMs) generated using these data sets as well as the uncertainty propagation of the DSMs. A key element in the automation is georeferencing. It is obvious that for images captured years apart, it is essential to find ground reference locations that have changed as little as possible. We studied a 68-year-long aerial image time series in a Finnish Karelian forestland. The quality of candidate ground locations was evaluated by comparing digital DSMs created from the images to an airborne laser scanning (ALS)-originated reference DSM. The quality statistics of DSMs were consistent with the expectations; the estimated median root mean squared error for height varied between 0.3 and 2 m, indicating a photogrammetric modelling error of 0.1 parts per thousand with respect to flying height for data sets collected since the 1980s, and 0.2 parts per thousand for older data sets. The results show that of the studied land cover classes, "peatland without trees" changed the least over time and is one of the most promising candidates to serve as a location for automatic ground control measurement. Our results also highlight some potential challenges in the process as well as possible solutions. Our results indicate that using modern photogrammetric techniques, it is possible to reconstruct 3D environmental model time series using photogrammetric image archives in a highly automated way.Peer reviewe

Mapping the risk of forest wind damage using airborne scanning LiDAR

Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts. The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage. To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. The probability of wind-induced forest damage (PDAM) in southern Finland (61°N, 23°E) was modelled for a 173 km2 study area of mainly managed boreal forests (dominated by Norway spruce and Scots pine) and agricultural fields. Wind damage occurred in the study area in December 2011. LiDAR data were acquired prior to the damage in 2008. High spatial resolution aerial imagery, acquired after the damage event (January, 2012) provided a source of model calibration via expert interpretation. A systematic grid (16 m x 16 m) was established and 430 sample grid cells were identified systematically and classified as damaged or undamaged based on visual interpretation using the aerial images. Potential drivers associated with PDAM were examined using a multivariate logistic regression model. Risk model predictors were extracted from the LiDAR-derived surface models. Geographic information systems (GIS) supported spatial mapping and identification of areas of high PDAM across the study area. The risk model based on LiDAR data provided good agreement with detected risk areas (73 % with kappa-value 0,47). The strongest predictors in the risk model were mean canopy height and mean elevation. Our results indicate that open-access LiDAR data sets can be used to map the probability of wind damage risk without field data, providing valuable information for forest management planning

AUTOMATIC DETECTION OF STORM DAMAGES USING HIGH-ALTITUDE PHOTOGRAMMETRIC IMAGING

The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters

MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED 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

Test field for airborne laser scanning in Finland

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

FEASIBILITY OF MULTISPECTRAL AIRBORNE LASER SCANNING FOR LAND COVER CLASSIFICATION, ROAD MAPPING AND MAP UPDATING

This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015–2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection

THE CHALLENGE OF AUTOMATED CHANGE DETECTION: DEVELOPING A METHOD FOR THE UPDATING OF LAND PARCELS

Development of change detection methods that are functional and reliable enough for operational work is still a demanding task. This article discusses automated change detection from the viewpoint of one case study: the Finnish Land Parcel Identification System (FLPIS). The objective of the study is to develop a change detection method that could be used as an aid in the updating of the FLPIS. The method is based on object-based interpretation, and it uses existing parcel boundaries and new aerial ortho images as input data. Rules for classifying field and non-field objects are defined automatically by using the classification tree method and training data. Additional, manually created rules are used to improve the results. Classification tests carried out during the development work suggest that real changes can be detected relatively well. According to a recent visual evaluation, 96% of changes larger than 100 m2 were detected, at least partly. The overall accuracy of the change detection results was 93% when compared with reference data pixel-by-pixel. On the other hand, there are also missing changes and numerous false alarms. The main challenges encountered in the method development include the wide diversity of agricultural fields and other land cover objects locally, across the country, and at different times of the spring and summer, variability in the digital numbers (DNs) of the aerial images, the different nature of visual and automatic interpretation, and the small percentage of the total field area that has really changed. These challenges and possible solutions are discussed in the article