Automatic mapping of forest density from airborne LIDAR data

This paper presents new methods for the automatic mapping of vegetation from airborne lidar data. The methods are developed specifically for orienteering maps, which are detailed maps in scale 1:15,000 or 1:10,000 of forested areas. However, the methods may be modified to be used for automatic mapping of vegetation for national topographic map series in various scales, e.g., 1:25,000 or 1:50,000. We introduce the normalized difference vegetation density (NDVD) as an indicator of vegetation density in airborne lidar data. A modified version of NDVD is used for reduced runability mapping. By comparing pixel-by-pixel the automatic mapping with the manual survey in four different forest areas in Oslo, Norway, the correct classification rate varies from 71% to 75%. However, close investigation reveals that the automatic mapping is better than manual survey for open areas. On the other hand, the automatic mapping of reduced runability remains a difficult problem. In many cases, the automatic method is able to identify the major areas of reduced runability, while in other areas the correspondence is low between the automatic mapping and manual survey of reduced runability. Still, the automatic method may be used to quickly produce an initial mapping of reduced runability, or in the production of orienteering maps in remote areas where a full manual survey cannot be afforded

Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks

Detailed and complete mapping of forest roads is important for the forest industry since they are used for timber transport by trucks with long trailers. This paper proposes a new automatic method for large-scale mapping forest roads from airborne laser scanning data. The method is based on a fully convolutional neural network that performs end-to-end segmentation. To train the network, a large set of image patches with corresponding road label information are applied. The final network is then applied to detect and map forest roads from lidar data covering the Etnedal municipality in Norway. The results show that we are able to map the forest roads with an overall accuracy of 97.2%. We conclude that the method has a strong potential for large-scale operational mapping of forest roads

Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania

Abstract Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. The maps contributed nothing or even negatively to the precision of mean height and mean AGB estimates. However, after being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions

Application of satellite data in management of cultural heritage. Project report 2009

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Evaluation of binarization methods for document images

This paper presents an evaluation of locally adaptive binarization methods for gray scale images with low contrast, variable background intensity, and noise. Such low quality images occur frequently in the domain of document image analysis. For such images, global binarization methods cannot be used. Eleven locally adaptive binarization methods were tested on seven different map images. The postprocessing step (PS) of the Yanowitz and Bruckstein method improved all the other best binarization methods. The method of Niblack with PS gave the best performance. The methods of Eikvil, Taxt and Moen with PS and of Bernsen with PS did almost as well. Comparison was also made on the CPU requirement