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Species dominance and above ground biomass in the Białowieża Forest, Poland, described by airborne hyperspectral and lidar data
Vaglio Laurin , Gaia , Puletti , Nicola , Grotti , Mirko , Stereńczak , Krzysztof Jan , Modzelewska , Aneta , Lisiewicz , Maciej , Sadkowski , Rafał , Kuberski , Łukasz , Chirici , Gherardo , Papale , Dario
climate change lidar aboveground biomass algorithm data set deciduous tree species diversity species richness vegetation dynamics bialowieza forest scolytinae
Mostra abstract
The objective of this research is to test and evaluate hyperspectral and lidar data to derive information on tree species dominance and above ground biomass in the Białowieża Forest in Poland. This forest is threatened by climate change, fire, bark beetles attacks, and logging, with changes in species composition and dominance. In this conservation valuable area, the monitoring of forest resources is thus critical. Results indicate that vegetation indices from hyperspectral data can support species dominance detection: using a Classification and Regression Trees algorithm the three main plot types (dominated by Deciduous, Spruce, and Pines species) were classified with an Overall Accuracy > 0.9. The accuracy decreased when a ‘Mixed’ group was added to account for very heterogeneous plots, and plots dominated by Spruce were not correctly detected. Hyperspectral vegetation indices were also used to estimate the level of species dominance in the forest plots, using a Multivariate Multiple Linear Regression model; the obtained accuracy varied according to groups, being higher for Deciduous (R<sup>2</sup> = 0.87), compared to Pines (R<sup>2</sup> = 0.61), and to Spruce-dominated plots (R<sup>2</sup> = 0.37). Lidar data were employed to estimate above ground biomass, using an exponential regression model; overall the R<sup>2</sup> resulted equal to 0.66 but ranged from 0.57 to 0.78 when considering subgroups according to species dominance; the addition of hyperspectral vegetation indices improved the result only for Pines. The illustrated methods provide a reliable description of important forest characteristics and simplify resource monitoring, supporting local authorities to address the challenges imposed by climate change and other forest threats. © 2020 The Authors
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2020
Rivista
International Journal of Applied Earth Observation and Geoinformation
Dettagli
Vol. 92
DOI
Global airborne laser scanning data providers database (GlobALS)-A new tool for monitoring ecosystems and biodiversity
Stereńczak , Krzysztof Jan , Vaglio Laurin , Gaia , Chirici , Gherardo , Coomes , David Anthony , Dalponte , Michele , Latifi , Hooman , Puletti , Nicola
lidar forest database globals networking
Mostra abstract
Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS. © 2020 by the authors.
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2020
Rivista
Remote Sensing
Dettagli
Vol. 12 (11)
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