Consequence of DTM Precision for Flood Hazard Mapping: A Case Study in SW Finland

Spatial information on floods, which includes inundation maps and estimations of flood damage are essential tools for the creation of effective plans for both flood protection and mitigation. In flood modelling, the accuracy of the model geometry used has a remarkable impact upon flood mapping. Therefore, in this study, flood hazard mapping was undertaken with two existing DTM (digital terrain model) products and a high-precision LiDAR-based DTM. Their characteristics were evaluated with respect to flood hazard mapping. An accuracy assessment of these digital terrain models and their applicability for one-dimensional flood inundation mapping clearly showed that LiDAR DTM topography was the most applicable. Although the 10×10 m DTM from the Finnish National Land Survey could be utilised to show where flooding might occur for very coarse flood mapping surveys, these were not suitable for more exact estimations of flood boundaries. Nevertheless, some inaccuracies in riverbank topography were also found using the LiDAR-DTM. Hence, to study detailed hydrological processes such as short-term channel dynamics, this particular DTM could be further improved by additional data

Laser scanning applications in fluvial studies

Peer reviewe

Yksityiskohtaisen metsävaratiedon tuottaminen – kohti täsmämetsätaloutta?

Tieteen tori: Yksityiskohtainen metsävaratiet

3D-tiedosta lisäarvoa kaupunkien vihersuunnitteluun sekä katu- ja puistopuiden hoitoon

Tieteen tori: Yksityiskohtainen metsävaratiet

A Review: Remote Sensing Sensors

The cost of launching satellites is getting lower and lower due to the reusability of rockets (NASA, 2015) and using single missions to launch multiple satellites (up to 37, Russia, 2014). In addition, low-orbit satellite constellations have been employed in recent years. These trends indicate that satellite remote sensing has a promising future in acquiring high-resolution data with a low cost and in integrating high-resolution satellite imagery with ground-based sensor data for new applications. These facts have motivated us to develop a comprehensive survey of remote sensing sensor development, including the characteristics of sensors with respect to electromagnetic spectrums (EMSs), imaging and non-imaging sensors, potential research areas, current practices, and the future development of remote sensors.Peer reviewe

Comparing accuracy of airborne laser scanning and TerraSAR-X radar images in the estimation of plot-level forest variables

Peer reviewe

Multisource Point Clouds, Point Simplification and Surface Reconstruction

As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for surface reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for surface reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for surface reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known surface reconstruction methods, i.e., Alpha shapes, Screened Poisson reconstruction (SPR), the Crust, and Algebraic point set surfaces (APSS Marching Cubes), were utilized for object reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for surface reconstruction. These reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that (i) the capacity of surface reconstruction in dealing with diverse objects needs to be improved; (ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the reconstruction methods might fail; (iii) for some reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; (iv) all reconstruction methods are beneficial from the reduction of running time; and (v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics

Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)

Special Issue Microwave Remote Sensing.Abstract: During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats). Keywords: Composite multispectral modeling; SAR; classification; SatPhenClass algorithm; minimum dataset; cereal yield; phenology; LAI-bridge; CAP; IACS; FLPISPeer reviewe

Puustobiomassan kartoituksen ja seurannan kehittäminen

Tieteen tori: Luonnonvarariskien hallint