Jaan Praks

Flood Mapping With TerraSAR-X in Forested Regions in Estonia

In this study, an extensive flood in Estonia during spring 2010 was mapped with TerraSAR-X data acquired over both open and forested areas. This was the first time when a large scale flooding area was mapped in Estonia by means of spaceborne remote sensing. This was also the first time when X-band SAR images were successfully used for flood mapping under the forest canopy in the temperate forest zone. The tree height in the study region was 15-25 meters on average, and main tree species were birch (leaf-off condition), pine and spruce. The results were compared with ALOS PALSAR and Envisat ASAR images of the same flooding event. In the study area, TerraSAR-X provided on average 3.2 dB higher backscatter over mixed forest flooded areas compared to non-flooded areas. In deciduous and coniferous forests the difference in average backscatter between flooded and non-flooded forests was even greater, 6.2 dB and 4.0 dB, respectively. A supervised classification algorithm was developed to produce high resolution maps of the flooded area from the TerraSAR-X images to demonstrate the flood mapping capability at X-band. Our results show, that spaceborne X-band SAR data, which currently has the highest resolution among the SAR instruments in space, can be used to map floods under forest canopy in temperate zone despite its short wavelength and high attenuation.

Land Cover and Soil Type Mapping From Spaceborne PolSAR Data at L-Band With Probabilistic Neural Network

This paper evaluates performance of fully polarimetric SAR (PolSAR) data in several land cover mapping studies in the boreal forest environment, taking advantage of the high canopy penetration capability at L-band. The studies included multiclass land cover mapping, forest-nonforest delineation, and classification of soil type under vegetation. PolSAR data used in the study were collected by the ALOS PALSAR sensor in 2006-2007 over a managed boreal forest site in Finland. A supervised classification approach using selected polarimetric features in the framework of probabilistic neural network (PNN) was adopted in the study. It has no assumptions about statistics of the polarimetric features, using nonparametric estimation of probability distribution functions instead. The PNN-based method improved classification accuracy compared with standard maximum-likelihood approach. The improvement was considerably strong for soil type mapping under vegetation, indicating notable non-Gaussian effects in the PolSAR data even at L-band. The classification performance was strongly dependent on seasonal conditions. The PolSAR feature data set was further modified to include a number of recently proposed polarimetric parameters (surface scattering fraction and scattering diversity), reducing the computational complexity at practically no loss in the classification accuracy. The best obtained accuracies of up to 82.6% in five-class land cover mapping and more than 90% in forest-nonforest mapping in wall-to-wall validation indicate suitability of PolSAR data for wide-area land cover and forest mapping.

Boreal forest tree height estimation from interferometric TanDEM-X images

The paper describes algorithm development for tree height retrieval in the boreal forest zone from TanDEM-X interferometric imagery. A set of 8 TanDEM-X pairs was acquired during summer and autumn 2011 over southern Finland in order to evaluate the potential tree height retrieval performance for this space-borne instrument. Another focus of the study was evaluation of seasonal dependence of interferometric signature of boreal forest. The obtained results are compared to our previous studies on tree height retrieval with the airborne DLR E-SAR instrument in the same area. The obtained results show good potential of TanDEM-X in forest mapping when external terrain elevation model is available, though accuracy seems to be somewhat lower compared to airborne instruments due to increased noise.

Aalto-1 - An experimental nanosatellite for hyperspectral remote sensing

In this paper we describe the Finnish Earth Observation nanosatallite project Aalto-1. The Aalto-1 is a 4 kg student satellite, based on CubeSat standards. The satellite is designed to carry the worlds smallest remote sensing imaging spectrometer for Earth Observation and several other payloads.

Monitoring of Agricultural Grasslands With Time Series of X-Band Repeat-Pass Interferometric SAR

This study evaluates the potential of X-band interferometry for monitoring of agricultural grasslands. Time series of HH-polarization COSMO-SkyMed 1-day repeat-pass interferometric SAR (InSAR) pairs is analyzed in regard to detecting mowing events, and assessing vegetation height and biomass on grasslands. The time series of four InSAR pairs was analyzed in regard to the ground reference data collected during an extensive campaign covering 11 agricultural grasslands. The calculated temporal interferometric coherence was found to be inversely correlated to the vegetation height and wet above-ground biomass. It was found that grass removal increases the coherence magnitude indicating a potential use of this parameter for the detection of mowing. However, precipitation and farming activity between the acquisitions interfere with this effect. Temporal coherence was expressed as a function of the vegetation height through the random motion of scatterers in the vegetation layer. For vegetation height limited to the range between 0 and 1 m, a very strong correlation between the grass height and the linearised temporal coherence was found, with a coefficient r = 0.81. No significant correlation was found between the backscattering coefficient and the wet above-ground biomass as well as the height of grass. However, a strong negative correlation was found between the backscattering coefficient and the measured soil moisture.

Seasonal Differences in Forest Height Estimation From Interferometric TanDEM-X Coherence Data

This paper demonstrates the use of X-band bistatic synthetic aperture radar (SAR) interferometric coherence for retrieving tree height of coniferous and deciduous forests during leaf-off season in early spring and leaf-on period in summer. TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) HH and VV polarization channel coherence images were studied for over 249 ha of forests in Estonia and compared against light detection and ranging (LiDAR) and forest registry field inventory data. Strong correlation was found between interferometric coherence magnitude and LiDAR measured average tree height, especially for winter period. The regression models show the strongest correlation between pine stand heights and single-polarization interferometric coherence, where the correlation coefficients (r2) range between 0.75 and 0.97. The highest correlation for mixed deciduous tree stands was found during leaf-off period with r2 ranging from 0.87 to 0.94, whereas leaf-on period resulted in r2 from 0.58 to 0.75. Strong correlations were also found for spruce trees with r2 between 0.54 and 0.83. Moreover, a simple semiempirical model based on random volume over ground model framework was constructed to describe the relation between the forest height and interferometric coherence. Also, the seasonal variability of the correlation was studied. Our results demonstrate that under Northern-European conditions, seasonal changes have a significant effect for deciduous trees as standard deviations dropped from 1.34-1.78 m during leaf-off conditions to 2.22-3.16 m for leaf-on conditions. Thus, height estimation of deciduous stands requires leaf-off conditions for accurate coherence-based height retrieval. Correlation coefficients for pine stands were unvarying across different weather conditions and least affected by the season. The observed strong sensitivity of interferometric coherence to forest height makes it feasible for estimating canopy height for boreal and deciduous forests in both summer and winter conditions. The estimation algorithm works best for coniferous forests.

Particle Swarm Optimization With Rotation Axis Fitting for Magnetometer Calibration

This paper presents an improved multiobjective particle swarm optimization algorithm for magnetometer calibration in spacecraft. The proposed algorithm combines scalar checking with novel rotation axis fitting objective and avoids the requirement for perfectly aligned measurement axis. The improved approach is designed to solve 12 calibration parameters based on the knowledge of the magnetometer rotation axis direction. The performance of the novel algorithm is demonstrated with simulations and experimental data on Aalto-1 nanosatellite.

Towards detecting mowing of agricultural grasslands from multi-temporal COSMO-SkyMed data

This work investigates applicability of spaceborne repeat-pass interferometric SAR images for detecting mowing events on agricultural fields. Four pairs of one-day repeat-pass COSMO-SkyMed acquisitions were analysed and compared to in situ measurements of 11 agricultural grasslands to study the potential of X-band temporal interferometric coherence for detecting mowing events. Field works covered 11 test plots in Central Estonia with varying species composition and homogeneity. Temporal decorrelation due to changes in the grass height and wet biomass was analysed. A nonlinear relationship was observed between the wet biomass and temporal coherence, as well as between the grass height and the temporal coherence. Our results show that one-day temporal coherence decreases as the grass height and the wet biomass increases, until reaching a noise level at 25 cm and 400 g, respectively. The current study shows that detecting mowing event from multitemporal interferometric SAR images is a feasible technique and could be used for monitoring applications on the European level.

Aalto-1: a hyperspectral Earth observing nanosatellite

This paper introduces the Aalto-1 remote sensing nanosatellite, which is being built under the coordination of The Department of Radio Science and Engineering of Aalto University School of Electrical Engineering. The satellite is a three unit CubeSat, and it will be mostly built by students. The satellite platform is designed to house several payloads, and the main payload of the Aalto-1 mission will be the worlds smallest hyperspectral imager while secondary payloads being a compact radiation monitor and an electrostatic plasma brake for de-orbiting.

Miniaturized spectral imager for Aalto-1 nanosatellite

The Aalto-1 is a 3U-cubesat project coordinated by Aalto University. The satellite, Aalto-1, will be mainly built by students as project assignments and thesis works. VTT Technical Research Centre of Finland will develop the main Earth observation payload, a miniaturized spectral imager, for the satellite. It is a novel highly miniaturized tunable filter type spectral imager. Mass of the spectral imager will be less than 400 grams, and dimensions will be approximately 80 mm x 80 mm x 45 mm. The spectral imager is based on a tunable Fabry-Pérot interferometer (FPI) accompanied by an RGB CMOS image sensor. The FPI consists of two highly reflective surfaces separated by a tunable air gap and it is based either on a microelectromechanical (MEMS) or piezo-actuated structure. The MEMS FPI is a monolithic device, i.e. it is made entirely on one substrate in a batch process, without assembling separate pieces together. The gap is adjusted by moving the upper mirror with electrostatic force. Benefits of the MEMS FPI are low mass and small size. However, large aperture (2-10 mm) MEMS FPIs are currently under development, thus it is not yet known if their performance is adequate. The piezo-actuated FPI uses three piezo-actuators and is controlled in a closed capacitive feedback loop. The drawback of the piezo-actuated FPI is its higher mass. However, it has a large aperture which enables a shorter exposure times. Selection of the FPI type will be done after thorough evaluation. Depending on the selected FPI type, the spectral resolution of the imager will be 5 - 10 nm at full width at half maximum and it will operate in the visible and/or near infrared range.