Kati Anttila

Change Detection of Tree Biomass with Terrestrial Laser Scanning and Quantitative Structure Modelling

We present a new application of terrestrial laser scanning and mathematical modelling for the quantitative change detection of tree biomass, volume, and structure. We investigate the feasibility of the approach with two case studies on trees, assess the accuracy with laboratory reference measurements, and identify the main sources of error, and the ways to mitigate their effect on the results. We show that the changes in the tree branching structure can be reproduced with about ±10% accuracy. As the current biomass detection is based on destructive sampling, and the change detection is based on empirical models, our approach provides a non-destructive tool for monitoring important forest characteristics without laborious biomass sampling. The efficiency of the approach enables the repeating of these measurements over time for a large number of samples, providing a fast and effective means for monitoring forest growth, mortality, and biomass in 3D.

Artificial target detection with a hyperspectral LiDAR over 26-h measurement

Abstract. Laser scanning systems that simultaneously measure multiple wavelength reflectances integrate the strengths of active spectral imaging and accurate range measuring. The Finnish Geodetic Institute hyperspectral lidar system is one of these. The system was tested in an outdoor experiment for detecting man-made targets from natural ones based on their spectral response. The targets were three camouflage nets with different structures and coloring. Their spectral responses were compared against those of a Silver birch (Betula pendula), Scots pine shoots (Pinus sylvestris L.), and a goat willow (Salix caprea). Responses from an aggregate clay block and a plastic chair were used as man-made comparison targets. The novelty component of the experiment was the 26-h-long measurement that covered both day and night times. The targets were classified with 80.9% overall accuracy in a dataset collected during dark. Reflectances of four wavelengths located around the 700 nm, the so-called red edge, were used as classification features. The addition of spatial aggregation within a 5-cm neighborhood improved the accuracy to 92.3%. Similar results were obtained using a set of four vegetation indices (78.9% and 91.0%, respectively). The temporal variation of vegetation classes was detected to differ from those in man-made classes.

The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009–2010

Seasonal snow surface roughness is an important parameter for remote sensing data analysis since it affects the scattering properties of the snow surface. To understand the phenomenon, snow surface roughness was measured near the town of Sodankyla, in Finnish Lapland, during winters 2009 and 2010 using a photogrammetry-based plate method. The images were automatically processed so that an approximately 1 m long horizontal profile was extracted from each image. The data set consists of 669 plate profiles from different times and canopy types. This large data set was used to study the temporal and spatial variability of seasonal snow surface roughness. The profiles were analyzed using parameters derived from the root mean square height (σ) and correlation length (L) as functions of measured length. Also, the autocorrelation functions were calculated and analyzed. The (σ) and (L) were found to be so strongly correlated (R2 ~ 0.97) that a more detailed analysis was made using only the scaling parameters derived from σ. These parameters are related to the distance dependence of the rms height. The results show that they react to different characteristics of the profiles and are therefore well able to distinguish between different types of snow. They also show a clear difference between midwinter snow and melting snow, and the effects of snowfall events and slower melting in forested areas are evident in the data.

Detection of snow surface roughness and hoar at Summit, Greenland, using RADARSAT data

The RASCALS (Radiation, Snow Characteristics and Albedo at Summit) campaign was carried out at the Greenland Summit camp research station during June–July 2010. The collection of surface roughness, dielectric constant, and density profiles values of snow were gathered. Polarimetric interferometry of RADARSAT-2 quad pol fine beam images is used to study the snow surface anisotropy at Summit, Greenland. Various methods of determining the polarimetric coherence are tested and the results are compared with the in situ surface roughness results, which show a clear anisotropy varying with time. In addition, backscattering modelling is used to check the fraction of the surface backscattering. The circularly polarized coherence and backscattering coefficient can be used for surface roughness variation and surface hoar formation detection.

The change of seasonal snow surface roughness in Sodankylä finnish lapland during winters 2009 and 2010

This article presents some results from the snow surface roughness measurements that were made during the Snow Reflectance Transition Experiment -campaign in Sodankylä, Finnish Lapland, in 2009 and 2010. The surface roughness was measured with a photogrammetry-based method where a black plate was inserted into the snow and photographed. After field work a 2D profile was automatically extracted from the image. The profiles extracted from the photographs were analyzed using root mean square height and correlation length as function of measured length. 777 profiles were measured during the SORTEX -campaign in different locations with different canopy type, including marshland, pine, birch and mixed forest and lake ice. In addition to this some profiles were measured in February 2010. The results show a clear shift from midwinter to melting season conditions as well as the effect of fresh snow on the surface.