Crown volume estimation

It’s been a long time since my previous post. Meanwhile I have been mostly working with non-canopy problems, especially lidar-based forest inventory. I’ve also had some teaching and supervision responsibilities, and a six-month visit to Norwegian University of Life Sciences, where I (for a change) had plenty of time for research. Thus I finally got out the first paper based on our 2011 field campaign: crown volume estimation using airborne lidar data.

The roots of this study go back to 2005 when I collected data for my master’s thesis at Metla’s Suonenjoki research station. Pola and Miina had employed three students to work there, and one of us, Sanna, made some curious measurements using an instrument called angle measurer (a.k.a. “miinaharava”).  It is a T-shaped stick that can be used to measure crown widths at different heights, enabling estimation of vertical canopy profiles and crown volume.  The data was needed for developing spectral reflectance models – some papers have been published in AFM and Silva Fennica.

The angle measurer 

Example of a crown profile.

Sanna’s work came back to my mind later when I learned what a colleague of mine at the UEF, Jari Vauhkonen, was studying. Jari worked with detecting individual tree crowns from airborne lidar data, and was the first person to test the alpha shape method in the prediction of tree attributes. Alpha shapes are a method that can be used to combine a set of 3D points into one geometrical shape, which is defined by the alpha parameter.  The volume of the shape can be calculated based on the triangulation of its individual points (=lidar echoes). Thus, when the echoes represent a tree crown, its volume can be estimated automatically. Our idea was to validate these estimates using the angle measurer.

Thus Juha and Laura measured the crown volumes of 89 trees during the 2011 field campaign. 77 of these trees were detected from the lidar data and used in the analysis. The echo segmentation phase was somewhat laborious, as my automated algorithm did not always delineate the tree perfectly and plenty of manual work was needed to remove this error source (and actually this had to be done several times because of some personal blunders and errors in the lidar data preprocessing). Based on the delineated echoes, Jari calculated the crown volumes and we simply plotted them against field-measured values. The results showed that the lidar-based volumes were clearly smaller than field values, mainly because there were not enough echoes from the lower part of the crown. Yet the results were better than those obtained using a general model for the crown dimensions  and assuming an ellipsoidal crown shape. Full paper can be read here.

This simple experiment is anyway a step into a direction that is very interesting to me – using features derived directly from the lidar point cloud instead of predictions based on forestry databases. Most of the existing theoretical forest models use traditional forest attributes such as tree density, height, and basal area, which are increasingly estimated using lidar, so why not directly use lidar point cloud features instead? One problem is that the lidar features can be sensitive to scanner settings. Nevertheless, in my opinion more this kind of investigations should be made in the near future.

Needle work

We have been lacking a spectral database on boreal tree species for a long time. Most data available have been measured for North American species or for ‘mats of needles’ i.e. not for single needles. Another problem has been that the previous spectral databases lack a detailed description of the structural and biochemical properties of the tree leaves and needles needed in many ecophysiological applications. 

 Collecting samples in the forest with 16-meter long scissors.

Finally, to fill the large gap in our knowledge, Petr et al. toiled in Hyytiälä last summer for several weeks measuring the optical properties of the most common (and nearly only..) tree species in Finland: Scots pine, Norway spruce and Silver birch. Measuring the reflectance and transmittance of single needles is really tough due to their small size and twisted shape. There are no commercial gear available and in-house solutions have to be developed for holding the small samples. The task was further complicated by our ambitious plans: we wanted (whenever possible) to measure separate the adaxial and abaxial sides of the foliage elements and both for shaded and sunlit crown positions. 

Yes, it does take a lot of patience and a peaceful lab environment to prepare the samples for the spectroradiometer (and other) measurements. Another challenge was acting very quickly: the needles are, after all, alive and their spectra may begin to change if they have been detached from the branch for a long time. Quick fingers (accustomed to needle work or playing a musical instrument) were definitely an asset in operating the tweezers. Bare fingers were not, of course, allowed to touch the samples and contaminate them.

 

Detaching young and very soft spruce needles for spectral measurements.

The hard work paid off, and we are now proud to present a carefully measured data set on the reflectance and transmittance spectra of needles and leaves of boreal tree species. And just a couple of weeks ago, the paper presenting the data was published! The data are now freely available and can be downloaded from the SPECCHIO database.

For more info, see Petr’s paper:

Lukeš, P., Stenberg, P., Rautiainen, M., Mõttus, M. & Vanhatalo, K.M. 2013. Optical properties of leaves and needles for boreal tree species in Europe. Remote Sensing Letters, 4(7): 667-676.

How to estimate LAI in boreal forests?

How should we estimate leaf area index for a stand in a boreal forest? How much do the different LAI estimation methods actually differ? These are questions I’m often asked when people working in other fields need a quick way to get LAI for their study sites.

In an effort to tackle these questions, we pooled together our LAI data for nearly 700 stands measured during the past decade and compared a range of methods such as allometric equations, LAI-2000 measurements and inversion of a canopy radiation model. Even though we did not have the so-called true LAI (from destructive sampling) for these stands, we were still able to get an idea of how the different methods perform. Many ecosystem or growth models need LAI as an input variable, and therefore it is crucial to understand how using LAI data from different sources can affect the modeling results.

 

 Intercalibrating LAI-2000 units in an open area before making measurements in the forest.

 Titta measuring the LAI of a Scots pine stand using the LAI-2000 PCA instrument.

   

Which LAI estimation method should you use then? There is no simple, straight-forward recommendation; the optimal LAI estimation method depends on the spatial and temporal extent of the monitoring activity you will be doing. The optical LAI estimation methods are usually superior if your research focus is on seasonal or abrupt changes (e.g. defoliation or insect outbreaks) in foliage mass. In addition, optical measurements are relatively fast, inexpensive and less laborious than conventional forest inventories – if you have a camera with a hemispherical lens or an LAI-2000 Plant Canopy Analyzer at hand and are familiar with the theory behind them. For large area applications (such as regional or national LAI maps) a more suitable ground reference method is to use allometric equations based forestry databases. However, allometric equations cannot (currently) predict the seasonal development of LAI in a forest. They will only provide you with the peak growing season LAI which corresponds to the maximum foliage mass in a forest.

For more quantitative information, check out Titta’ s new paper:

Majasalmi, T., Rautiainen, M., Stenberg, P. & Lukes, P. 2013. An assessment of ground reference methods for estimating LAI of boreal forests. Forest Ecology and Management, 292: 10-18.

Tiedote: Kasvillisuuden kartoitus avaruudesta käsin muuttuu tarkemmaksi (in Finnish)

Tuoreessa kansainvälisessä tutkimuksessa kehitettiin uusi menetelmä maailmanlaajuiseen metsien kartoitukseen satelliittikuvien avulla. Menetelmä huomioi entistä tarkemmin metsien rakenteen vaikutuksen satelliittien mittaamaan metsien heijastukseen. Tutkimus julkaistaan joulukuun alussa yhdessä maailman arvovaltaisimmista tieteellisistä julkaisusarjoista, Yhdysvaltain tiedeakatemian lehdessä (Proceedings of the National Academy of Sciences of the United States of America, PNAS). Helsingin yliopiston tutkijat ovat olleet keskeisessä roolissa tutkimuksen suunnittelussa ja toteutuksessa.

Tutkimus kumoaa myös vallalla olleen käsityksen siitä, että kasvillisuudessa oleva typpi vaikuttaisi kasvillisuuden heijastamaan auringonsäteilyyn ja sitä kautta maapallon ilmastoon. Tutkimuksessa osoitettiin, että tämä väite perustui aineistojen virheelliseen tulkintaan.

 -Uudet tulokset sen sijaan osoittavat, että metsän lajikoostumus ja latvusrakenne määräävät heijastuneen säteilyn suuruuden, tarkentaa tutkimuksessa mukana ollut Helsingin yliopiston metsänarvioimistieteen professori Pauline Stenberg.

Tutkimuksessa tarkasteltiin kasvillisuuden heijastamaa lyhytaaltoista säteilyä lauhkealla ja havumetsävyöhykkeellä.

 -Metsän heijastama säteily riippui selvästi siitä, kuinka paljon lehti- tai havupuita alueella kasvoi. Lehtimetsät heijastivat huomattavasti enemmän kuin havumetsät, selittää akatemiatutkija Miina Rautiainen Helsingin yliopiston metsätieteiden laitokselta.

-Tuloksillamme on merkittävä rooli uusien hyperspektristen satelliittimissioiden suunnittelussa ja toteutuksessa Yhdysvalloissa ja Euroopassa, kertoo Helsingin yliopiston kaukokartoituksen dosentti Matti Mõttus.

Tulevan vuosikymmenen aikana on tarkoitus laukaista useita uusia satelliitteja, joissa on maapallon kasvillisuuden tilaa seuraavia hyperspektrisiä mittalaitteita. Näiden mittalaitteiden tuottamien aineistojen tulkintaan tarvitaan laskentamenetelmiä, joita on julkaistu tässä artikkelissa.


Artikkeli on luettavissa verkossa: http://www.pnas.org/content/early/2012/12/03/1210196109). Artikkeli on syntynyt Bostonin yliopiston ja Helsingin yliopiston yhteisen, NASA:n rahoittaman tutkimusprojektin puitteissa.

A Paper in PNAS - Hyperspectral Remote Sensing of Foliar Nitrogen Content

Our paper on remote sensing of foliar nitrogen was published in the highly esteemed 'Proceedings of the National Academy of Sciences of the United States of America' (PNAS) on December 4th. We were lucky to celebrate it with many of the co-authors who were with us at the AGU fall meeting in San Francisco. For those interested in what all this is about, please read our paper. It is available online: http://www.pnas.org/content/early/2012/12/03/1210196109

Physically based optical remote sensing: a teaching perspective

My current vacancy at the UEF is "university researcher", but "lecturer" would be more correct as far as early 2012 is concerned. At the moment, I'm running course "advanced remote sensing", which has a strong focus on laser scanning but also other topics such as satellite images, radars and photogrammetry are covered. Visiting experts give most of the lectures, and my job mostly consists of supervising the students in computer labs.

Last week dealt with a topic that was especially important to me: physically based optical remote sensing. In previous years, the physical approach was covered in two lectures, but I wanted to emphasize this part more - after all, the University of Helsinki has an entire course on it (thanks to LAIDetectives!). So first I gave an introductory lecture where the basic concepts from irradiance to LAI were discussed. In the following computer lab, the students analyzed a set of hemispherical canopy images and had to consider different uncertainties of the process.

The highlight of the week was certainly the two lectures given by Miina. The topics included different reflectance model types, global monitoring with MODIS, and finally an introduction to PARAS reflectance model. We restricted the amount of equations on purpose - when the audience consists of forestry students, either the math has to be simple, or plenty of time must be reserved for explaining it. Miina's part ended with a detailed derivation of the PARAS model equation on the whiteboard, so everyone who followed certainly got the main idea!

The physical week ended with a PARAS exercise, where the students used the model to simulate the spectra of two forest stands in Sodankylä with different types of ground reflectance. Finally, the results were compared with BRFs obtained from SPOT satellite. The level of difficulty was OK: first the students really had to study the model equation and see what it means, but finally everyone figured it out and got the exercise done in the given time. Hopefully they now have an understanding why the digital numbers of the forest pixels in a satellite image are what they are - I'll see that soon in the exam!

Looking back

This year has indeed been interesting. The thesis defence was certainly a highlight, but when it comes to personal development, teaching was the most important new challenge I faced. I started by teaching the basics of forest mensuration to our first year students in January. This information was then applied to practice as I was the assistant teacher at our forest mensuration field course. In the autumn, I continued by teaching the same bunch of students the basics of remote sensing. Congratulations to all of you who made it through! Hopefully some became interested in the forest inventory issues: for a teacher that would be the best recognition. Often the students seem to be overwhelmed by the technical stuff that is required in these courses, so few want to study it further.

Research was thus less in focus this year. Most of the time I spent dealing with lidar-based inventory of seedling stands; some working papers have already been delivered and a scientific article will follow later. In the field of canopy research my only output was to organize a new field campaign in Hyytiälä and participate in Ilkka's new field photogrammetry experiment. Now we have some quite unique new data sets; hopefully next year will bring time to squeeze everything out of them. However, before that there is some more (international) teaching to be done...

Happy holidays!