LASmoons: Alen Berta

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

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 of the most important things during the creation of the NIR is to know how many forested areas there are and their wood stock and increment. This is needed to calculate the size of the existing carbon pool and its potential for sequestration. Since in Croatia, according to legislative, it is not mandatory to calculate the wood stock and yield of the degraded forest areas (shrubbery and thickets) during the creation of the usual forest management plans, this data is missing. So far, only a rough approximation of the wood stock and increment is used during the creation of NIR. However, these areas are expanding every year due to depopulation of the rural areas and the cessation of traditional farming.

very diverse stand structure of degraded forest areas (shrubbery and thickets)

This study will focus on two things: (1) Developing regression models for biomass volume estimation in continental shrubberies and thickets based on airborne LiDAR data. To correlate LiDAR data with biomass volume, over 70 field plots with a radius of 12 meters have been established in more than 550 ha of the hilly and lowland shrubberies in Central Croatia and all trees and shrubberies above 1 cm Diameter at Breast Height (DBH) were recorded with information about tree species, DBH and height. Precise locations of the field plots are measured with survey GNNS and biomass is calculated with parameters from literature. For regression modeling, various statistics from the point clouds matching the field plots will be used (i.e. height percentiles, standard deviation, skewness, kurtosis, …). 2) Testing the developed models for different laser pulse densities to find out if there is a significant deviation from results if the LiDAR point cloud is thinner. This will be helpful for planning of the later scanning for the change detection (increment or degradation).

641 square km of discrete returns LiDAR data around the City of Zagreb, the capitol of Croatia (but since it is highly populated area, only the outskirts of the area will be used)
+ raw geo-referenced LAS files with up to 3 returns and an average last return point density of 1 pts/m².

LAStools processing:
extract area of interest [lasclip or las2las]
2) create differently dense versions (for goal no. 2) [lasthin]
3) remove isolated noise points [lasnoise]
4) classify point clouds into ground and non-ground [lasground]
5) create a Digital Terrain Model (DTM) [las2dem]
6) compute height of points above the ground [lasheight]
7) classify point clouds into vegetation and other [lasclassify]
8) normalize height of the vegetation points [lasheight]
9) extract the areas of the field plots [lasclip]
10) compute various metrics for each plot [lascanopy]
11) convert LAZ to TXT for regression modeling in R [las2txt]

Rapidlasso receives “Green Asia Award” at ACRS 2015

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 technology. This year’s award commends rapidlasso GmbH on advancing the area of LiDAR processing through their PulseWaves effort. PulseWaves is a vendor-neutral full waveform LiDAR data exchange format and API that simplifies access to full waveform data and allows researchers to focus on algorithms and share results. In the future this technology may prove valuable to improve biomass estimates for carbon credit programs such as the TREEMAPS project of WWF.

Prof. Kohei Cho and Prof. Peter T. Y. Shih present the award

Prof. Kohei Cho and Prof. Peter T. Y. Shih present the Green Asia Award

The society communicated to Dr. Martin Isenburg, CEO of rapidlasso GmbH, that this award was also meant to honor his many years of teaching and capacity building across the Asian region. Since the beginning of 2013 rapidlasso GmbH has conducted well over 50 seminars, training events, and hands-on workshops at universities, research institutes, and government agencies in Thailand, Malaysia, Myanmar, Vietnam, Indonesia, Singapore, Taiwan, Japan, and the Philippines. The on-going LiDAR teaching efforts of rapidlasso GmbH in Asia and elsewhere can be followed via their event page.

Green Asia Award for CEO of rapidlasso GmbH

Green Asia Award given to the CEO of rapidlasso GmbH

The award certificate that was presented to Dr. Martin Isenburg by Prof Kohei Cho and Prof Peter Shih during the closing ceremony of ACRS 2015 came with a cash reward of USD 300. The award money was donated to the ISPRS summer school that followed the ACRS conference to top off the pre-existing “green sponsorship” by rapidlasso GmbH that was already supporting a “green catering” of summer school lunches and dinners to avoid single-use cups, plastic cutlery and styrofoam containers. The additional award money was used for hosting the main summer school dinner at a sustainable family-run restaurant serving “happy chickens” and “happy pigs” raised organically on a local farm.

during the closing ceremony of ACRS 2015

Award Ceremony held during Closing of ACRS 2015

About rapidlasso GmbH:
Technology powerhouse rapidlasso GmbH specializes in efficient LiDAR processing tools that are widely known for their high productivity. They combine robust algorithms with efficient I/O and clever memory management to achieve high throughput for data sets containing billions of points. The company’s flagship product – the LAStools software suite – has deep market penetration and is heavily used in industry, government agencies, research labs, and educational institutions. Visit for more information.