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An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning
Grotti , Mirko , Calders , Kim , Origo , Niall , Puletti , Nicola , Alivernini , Alessandro , Ferrara , Carlotta , Chianucci , Francesco
hemispherical photography lai librat return intensity terrestrial lidar
Mostra abstract
Accurate in situ estimates of leaf area index (LAI) are essential for a wide range of ecological studies and applications. Due to the destructiveness and impracticality of direct measurements, indirect optical methods have mostly been used in the field to derive estimates of LAI from gap fraction measurements. Terrestrial laser scanning (TLS) is strongly supporting use of this active technology, which possesses several advantages compared to passive sensors. However, edge effects and partial beam interceptions are significantly challenges for the accurate retrieval of gap fraction from 3D point cloud data available from TLS, particularly in phase-shift instruments, which in turns require point cloud filtering to correct erroneous point measurements. As the limitations above influences the point cloud, we proposed a new method which is based only on the laser return intensity (LRI) information derived from raw TLS data, which are used to generate 2D intensity images. The intensity image contains all the unfiltered LRI information captured by TLS, which is used to separate gap from non-gap pixels, using a procedure comparable to the standard image analysis processing of digital hemispherical images. This allows a theoretically consistent comparison between active and passive optical measurements of gap fraction across all the zenith angle range. The method was tested in real and simulated forests. Gap fraction, canopy openness and effective leaf area index derived from real and simulated intensity TLS images were compared with those obtained using digital hemispherical photography (DHP). Results indicated that the intensity, image-based method outperformed DHP, as the higher pixel resolution of the intensity images and the larger distance covered by TLS allowed detection of many small canopy elements, particularly at higher zenith angles (longer optical distance), which are not detected in DHP. The main findings support the reliability of the intensity, image-based method to standardize protocols for TLS phase-shift scan data processing and use of the produced canopy estimates as a benchmark for passive optical measurements. © 2019 Elsevier B.V.
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2020
Rivista
Agricultural and Forest Meteorology
Dettagli
Vol. 280
DOI
TRY plant trait database – enhanced coverage and open access
Kattge , Jens , Bönisch , Gerhard , Díaz , Sandra M. , Lavorel , Sandra , Prentice , Iain Colin , Leadley , Paul W. , Tautenhahn , Susanne , Werner , Gijsbert , Aakala , Tuomas , Abedi , Mehdi , Acosta , Alicia Teresa Rosario , Adamidis , George C. , Adamson , Kairi , Aiba , Masahiro , Albert , Cécile Hélène , Alcántara , Julio M. , Alcázar C , Carolina , Aleixo , Izabela , Ali , Hamada E. , Amiaud , Bernard , Ammer , Christian , Amoroso , Mariano Martín , Anand , Madhur , Anderson , Carolyn G. , Anten , Niels P.R. , Antos , Joseph A. , Apgaua , Deborah Mattos Guimarães , Ashman , Tia Lynn , Asmara , Degi Harja , Asner , Gregory P. , Aspinwall , Michael J. , Atkin , Owen K. , Aubin , Isabelle , Baastrup-Spohr , Lars , Bahalkeh , Khadijeh , Bahn , Michael , Baker , Timothy R. , Baker , William J. , Bakker , Jan P. , Baldocchi , Dennis D. , Baltzer , Jennifer L. , Banerjee , Arindam , Baranger , Anne , Barlow , Jos B. , Barneche , Diego R. , Baruch , Zdravko , Bastianelli , Denis , Battles , John J. , Bauerle , William L. , Bauters , Marijn , Bazzato , Erika , Beckmann , Michael , Beeckman , Hans , Beierkuhnlein , Carl , Bekker , Renée M. , Belfry , Gavin , Belluau , Michaël , Beloiu Schwenke , Mirela , Benavides , Raquel , Benomar , Lahcen , Berdugo-Lattke , Mary Lee , Berenguer , Erika , Bergamin , Rodrigo Scarton , Bergmann , Joana , Carlucci , Marcos B. , Berner , Logan T. , Bernhardt-Römermann , Markus , Bigler , Christof , Bjorkman , Anne D. , Blackman , Chris J. , Blanco , Carolina Casagrande , Blonder , Benjamin Wong , Blumenthal , Dana M. , Bocanegra-González , Kelly Tatiana , Boeckx , Pascal , Bohlman , Stephanie Ann , Böhning-Gaese , Katrin , Boisvert-Marsh , Laura , Bond , William J. , Bond-Lamberty , Ben P. , Boom , Arnoud , Boonman , Coline C.F. , Bordin , Kauane Maiara , Boughton , Elizabeth H. , Boukili , Vanessa K.S. , Bowman , David M.J.S. , Bravo , Sandra Josefina , Brendel , Marco R. , Broadley , Martin R. , Brown , Kerry A. , Bruelheide , Helge , Brumnich , Federico , Bruun , Hans Henrik , Bruy , David , Buchanan , Serra Willow , Bucher , Solveig Franziska , Buchmann , Nina , Buitenwerf , Robert , Bunker , Daniel E. , Bürger , Jana
functional diversity data coverage data integration data representativeness plant traits try plant trait database
Mostra abstract
Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives. © 2019 The Authors. Global Change Biology published by John Wiley & Sons Ltd
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2020
Rivista
Global Change Biology
Dettagli
Vol. 26 (1) pp. 119 - 188
DOI