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Articles on this Page
- 10/23/14--03:05: _Discriminating Vege...
- 11/04/14--00:40: _Rasterizing Perfect...
- 07/29/15--06:59: _Two ASPRS awards fo...
- 11/15/15--21:32: _Rapidlasso receives...
- 01/26/16--03:29: _LASmoons: Andreas K...
- 02/03/16--05:29: _Generating Spike-Fr...
- 01/02/17--04:42: _LASmoons: Alen Berta
- 02/20/17--04:00: _LASmoons: Elia Palo...
- 03/01/17--15:42: _LASmoons: Chloe Brown
- 03/12/17--11:40: _Leaked: “Classified...
- 03/27/17--12:29: _Plots to Stands: Pr...
- 05/08/17--05:31: _LASmoons: Muriel Lavy
- 05/13/17--17:34: _LASmoons: Gudrun No...
- 06/09/17--22:00: _LASmoons: Marzena W...
- 06/13/17--02:17: _Integrating Externa...
- 07/08/17--05:00: _LASmoons: Huaibo Mu
- 12/09/17--10:26: _LASmoons: Chris J. ...
- 01/10/18--18:39: _LASmoons: Manuel Ju...
- 03/07/18--09:27: _LASmoons: Sebastian...
- 10/23/14--03:05: Discriminating Vegetation from Buildings
- 11/04/14--00:40: Rasterizing Perfect Canopy Height Models from LiDAR
- 07/29/15--06:59: Two ASPRS awards for “pit-free” CHM algorithm
- 11/15/15--21:32: Rapidlasso receives “Green Asia Award” at ACRS 2015
- 01/26/16--03:29: LASmoons: Andreas Konring and Susanne Bjerg Petersen
- 02/03/16--05:29: Generating Spike-Free Digital Surface Models from LiDAR
- 01/02/17--04:42: LASmoons: Alen Berta
- 02/20/17--04:00: LASmoons: Elia Palop-Navarro
- 03/01/17--15:42: LASmoons: Chloe Brown
- 03/12/17--11:40: Leaked: “Classified LiDAR” of Pentagon in LAS 1.4 Format
- 05/08/17--05:31: LASmoons: Muriel Lavy
- 05/13/17--17:34: LASmoons: Gudrun Norstedt
- 06/09/17--22:00: LASmoons: Marzena Wicht
- 07/08/17--05:00: LASmoons: Huaibo Mu
- 12/09/17--10:26: LASmoons: Chris J. Chandler
- 01/10/18--18:39: LASmoons: Manuel Jurado
- 03/07/18--09:27: LASmoons: Sebastian Kasanmascheff
I came across an interesting blog article by Jarlath O’Neil-Dunne from the University of Vermont on how LiDAR return information can be used as a simple way to discriminate vegetated areas from buildings. He first computes a normalized first-return DSM and a normalized last-return DSM that he subtracts from another to highlight the vegetation. He writes “This is because […]
downtownfarmrdDEMfirst-return DSMlast-return DSMAutomated building and vegetation classification with lasclassify.classifiedSHP file generated with lasboundary with polygons describing the vegetation.SHP file generated with lasboundary with polygons describing the buildings.
In literature you sometimes read “we generated a Canopy Height Models (CHM) and then did this and that” without the process that was used to create the CHM being described in detail. One approach computes the CHM as a difference between DSM and DTM: create a DTM from the ground returns and a DSM from the first […]
downtownfarmthe 100 by 100 meter sample plot 'drawno.laz'Gridding the highest point that falls into each 0.33333 meter by 0.33333 meter cell.Gridding the highest z value after turning each point into a circle with 20 cm diameter.Gridding after turning each point into a circle with 30 cm diameter.Gridding after turning each point into a circle with 40 cm diameter.Gridding after turning each point into a circle with 50 cm diameter.Rasterizing the TIN that interpolates all first returns onto a 0.33333 meter grid.Rasterizing the TIN that interpolates only the highest points falling into each 0.33333 meter by 0.33333 meter grid cell.Rasterizing the TIN that interpolates only the highest points of a 0.33333 meter grid after first splatting points into circles with 20 cm in diameter.Rasterizing the TIN that interpolates only the highest points of a 0.16667 meter grid after first splatting points into circles with 20 cm in diameter onto a raster with step size 0.33333.Running the pit-free algorithm on the highest LiDAR returns in a 0.16667 meter grid (after splatting them to circles 20 cm in diameter) and producing a 0.33333 meter raster CHM.Running the pit-free algorithm on the highest LiDAR returns in a 0.16667 meter grid (after splatting them to circles only 10 cm in diameter) and producing a 0.33333 meter raster CHM.
PRESS RELEASE (for immediate release) July 29, 2015 rapidlasso GmbH, Gilching, Germany The paper “Generating Pit-free Canopy Height Models from Airborne LiDAR” co-authored by rapidlasso GmbH and published in the September 2014 issue of PE&RS (the journal of the ASPRS) was awarded twice at the IGTF 2015 – ASPRS Annual Conference in Tampa, Florida last May. The paper took home […]
downtownfarmThe John I. Davidson President’s Award for Practical Papers (2nd Place).Side-by-side comparison of a "standard" CHM and a "pit-free" CHM.
PRESS RELEASE (for immediate release) November 16, 2015 rapidlasso GmbH, Gilching, Germany At the Asian Conference on Remote Sensing 2015 (ACRS 2015) held in Manila, rapidlasso GmbH was honored with the “Green Asia Award” by the Chinese Society of Photogrammetry and Remote Sensing (CSPRS). This award is given to a paper that directs Asia towards a greener future using remote sensing […]
downtownfarmProf. Kohei Cho and Prof. Peter T. Y. Shih present the awardGreen Asia Award for CEO of rapidlasso GmbHduring the closing ceremony of ACRS 2015
Andreas Konring and Susanne Bjerg Petersen (recipients of three LASmoons) Department of Environmental Engineering Technical University of Denmark, Lyngby, DENMARK Background: Copenhagen has in the recent years experienced severe floodings due to cloudbursts which has increased the focus of climate adaption and the implementation of green infrastructure. The use of sustainable urban drainage system (SUDS) solutions […]
downtownfarmExample of a pit-free CHM in an urban environment.
A Digital Surface Model (DSM) represents the elevation of the landscape including all vegetation and man-made objects. An easy way to generate a DSM raster from LiDAR is to use the highest elevation value from all points falling into each grid cell. However, this “binning” approach only works when then the resolution of the LiDAR is higher than the resolution […]
downtownfarmReturns of four fightlines on two trees.interpolating all first returnsinterpolating all relevant returnshillshaded first-return DSM "france_fr.png"hillshaded spike-free DSM "france_sf.png"hillshaded first-return DSM "zurich_fr.png"hillshaded spike-free DSM "zurich_sf.png"first-return TIN of france.lazspike-free TIN of france.lazfirst-return TIN of france.lazspike-free TIN of france.lazfirst-return TIN of france.lazspike-free TIN of france.lazfirst-return TIN of zurich.lazspike-free TIN of zurich.laz
Alen Berta (recipient of three LASmoons) Department of Terrestrial Ecosystems and Landscape, Faculty of Forestry University of Zagreb and Oikon Ltd Institute for Applied Ecology, CROATIA Background: After becoming the EU member state, Croatia is obliged to fulfill the obligation risen from the Kyoto protocol: National Inventory Report (NIR) of the Green House Gasses according to UNFCCC. One […]
Elia Palop-Navarro (recipient of three LASmoons) Research Unit in Biodiversity (UO-PA-CSIC) University of Oviedo, SPAIN. Background: Old-growth forests play an important role in biodiversity conservation. However, long history of human transformation of the landscape has led to the existence of few such forests nowadays. Its structure, characterized by multiple tree species and ages, old trees […]
downtownfarmlasmoons_elia_palopnavarro_0Vegetation profile colored by height in a LiDAR sample of the study area.
Chloe Brown (recipient of three LASmoons) Geosciences, School of Geography University of Nottingham, UK Background: Malaysia’s North Selangor peat swamp forest is experiencing rapid and large scale conversion of peat swampland to oil palm agriculture, contrary to prevailing environmental guidelines. Given the global importance of tropical peat lands, and the uncertainties surrounding historical and future oil […]
downtownfarmsome clever caption
LiDAR leaks have happened! Black helicopters are in the sky! A few days ago a tiny tweet leaked the online location of “classified LiDAR” for Washington, DC. This LiDAR really is “classified” and includes an aerial scan of the Pentagon. For rogue scientists world-wide we offer a secret download link. It links to a file code-named ‘pentagon.laz‘ that contains the 8,044,789 “classified” […]
Some professionals in remote sensing find LAStools a useful tool to extract statistical metrics from LiDAR that are used to make estimations about a larger area of land from a small set of sample plots. Common applications are prediction of the timber volume or the above-ground biomass for entire forests based on a number of representative plots where […]
Muriel Lavy (recipient of three LASmoons) RED (Risk Evaluation Dashboard) project ISE-Net s.r.l, Aosta, ITALY. Background: The Aosta Valley Region is a mountainous area in the heart of the Alps. This region is regularly affected by hazard natural phenomena connected with the terrain geomorphometry and the climate change: snow avalanche, rockfalls and landslide. In July […]
Gudrun Norstedt (recipient of three LASmoons) Forest History, Department of Forest Ecology and Management Swedish University of Agricultural Sciences, Umeå, Sweden Background: Until the end of the 17th century, the vast boreal forests of the interior of northern Sweden were exclusively populated by the indigenous Sami. When settlers of Swedish and Finnish ethnicity started to […]
Marzena Wicht (recipient of three LASmoons) Department of Photogrammetry, Remote Sensing and GIS Warsaw University of Technology, Poland. Background: More than half of human population (Heilig 2012) suffers from many negative effects of living in cities: increased air pollution, limited access to the green areas, Urban Heat Island (UHI) and many more. To mitigate some […]
The biggest problem of generating a Digital Terrain Model (DTM) from the photogrammetric point clouds that are produced from aerial imagery with dense-matching software such as SURE, Pix4D, or Photoscan is dense vegetation: when plants completely cover the terrain not a single point is generated on the ground. This is different for LiDAR point clouds […]
Huaibo Mu (recipient of three LASmoons) Environmental Mapping, Department of Geography University College London (UCL), UK Background: This study is a part of the EU-funded Metrology for Earth Observation and Climate project (MetEOC-2). It aims to combine terrestrial and airborne LiDAR data to estimate biomass and allometry for woodland trees in the UK. Airborne LiDAR can […]
Chris J. Chandler (recipient of three LASmoons) School of Geography University of Nottingham, UNITED KINGDOM Background: Wetlands provide a range of important ecosystem services: they store carbon, regulate greenhouse gas emissions, provide flood protection as well as water storage and purification. Preserving these services is critical to achieve sustainable environmental management. Currently, mangrove forests are protected […]
Manuel Jurado (recipient of three LASmoons) Departamento de Ingeniería Topográfica y Cartografía Universidad Politécnica de Madrid, SPAIN Background: The availability of LiDAR data is creating a lot of innovative possibilities in different fields of science, education, and other field of interests. One of the areas that has been deeply impacted by LiDAR is cartography and in […]
Sebastian Kasanmascheff (recipient of three LASmoons) Forest Inventory and Remote Sensing Georg-August-Universität Göttingen, GERMANY Background: Forest inventories are the backbone of forest management in Germany. In most federal forestry administrations in Germany, they are performed every ten years in order to assure that logging activities are sustainable. The process involves trained foresters who visit each […]