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Mapping Understory Vegetation Density in Mediterranean Forests: Insights from Airborne and Terrestrial Laser Scanning Integration
Ferrara , Carlotta , Puletti , Nicola , Guasti , Matteo , Scotti , Roberto
forest biodiversity lidar terrestrial laser scanner forest structure spatial prediction voxelization
Mostra abstract
The understory is an essential ecological and structural component of forest ecosystems. The lack of efficient, accurate, and objective methods for evaluating and quantifying the spatial spread of understory characteristics over large areas is a challenge for forest planning and management, with specific regard to biodiversity and habitat governance. In this study, we used terrestrial and airborne laser scanning (TLS and ALS) data to characterize understory in a European beech and black pine forest in Italy. First, we linked understory structural features derived from traditional field measurements with TLS metrics, then, we related such metrics to the ones derived from ALS. Results indicate that (i) the upper understory density (5–10 m above ground) is significantly associated with two ALS metrics, specifically the mean height of points belonging to the lower third of the ALS point cloud within the voxel (HM<inf>1/3</inf>) and the corresponding standard deviation (SD<inf>1/3</inf>), while (ii) for the lower understory layer (2–5 m above ground), the most related metric is HM<inf>1/3</inf> alone. As an example application, we have produced a map of forest understory for each layer, extending over the entire study region covered by ALS data, based on the developed spatial prediction models. With this study, we also demonstrated the power of hand-held mobile-TLS as a fast and high-resolution tool for measuring forest structural attributes and obtaining relevant ecological data. © 2023 by the authors.
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2023
Rivista
Sensors
Dettagli
Vol. 23 (1)
DOI
Where are we now with European forest multi-taxon biodiversity and where can we head to?
Burrascano , Sabina , Chianucci , Francesco , Trentanovi , Giovanni , Kepfer-Rojas , Sebastian , Sitzia , Tommaso , Tinya , Flóra , Doerfler , Inken , Paillet , Yoan , Nagel , Thomas A. , Mitić , Božena , Morillas , Lourdes , Munzi , Silvana , Van Der Sluis , Theo , Alterio , Edoardo , Balducci , Lorenzo , de Andrade , Rafael Barreto , Bouget , Christophe , Giordani , P. , Lachat , Thibault , Matošević , Dinka , Napoleone , Francesca , Nascimbene , Juri , Paniccia , Chiara , Roth , Nicolas , Aszalós , Réka , Brazaitis , Gediminas , Cutini , Andrea , D'Andrea , Ettore , de Smedt , Pallieter , Heilmann-Clausen , Jacob , Janssen , Philippe , Kozák , Daniel , Mårell , Anders , Mikoláš , Martin , Nordén , Björn , Matula , Radim , Schall , Peter , Svoboda , Miroslav , Ujházyová , Mariana , Vandekerkhove , Kris , Wohlwend , Michael Rudolf , Xystrakis , Fotios , Aleffi , Michele , Ammer , Christian , Archaux , Frédéric , Asbeck , Thomas , N Avtzis , Dimitrios N. , Ayasse , Manfred , Bagella , Simonetta , Balestrieri , Rosario , Barbati , Anna , Basile , Marco , Bergamini , Ariel , Bertini , Giada , Biscaccianti , Alessandro Bruno , Boch , Steffen , Bölöni , János , Bombi , Pierluigi , Boscardin , Yves , Brunialti , Giorgio , Bruun , Hans Henrik , Buscot , François , Byriel , David Bille , Campagnaro , Thomas , Campanaro , Alessandro , Chauvat , Matthieu , Ciach , Michał , Čiliak , Marek , Cistrone , Luca , Pereira , Joaò Manuel Cordeiro , Daniel , Rolf , de Cinti , Bruno , de Filippo , Gabriele , Dekoninck , Wouter , Di Salvatore , Umberto , Dumas , Yann , Elek , Zoltán , Ferretti , Fabrizio , Fotakis , Dimitrios G. , Frank , Tamás , Frey , Julian , Giancola , Carmen , Gömöryová , Erika , Gosselin , Marion , Gosselin , Frédéric , Goßner , Martin M. , Götmark , Frank , Haeler , Elena , Hansen , Aslak Kappel , Hertzog , Lionel R. , Hofmeister , Jeňýk , Hošek , Jan , Johannsen , Vivian Kvist , Justensen , Mathias Just , Korboulewsky , Nathalie , Kovács , Bence , Lakatos , Ferenc , Landivar , Carlos Miguel , Lens , Luc , Lingua , Emanuele
forest biodiversity biodiversity conservation forest stand structure multi-taxon sustainable management
Mostra abstract
The European biodiversity and forest strategies rely on forest sustainable management (SFM) to conserve forest biodiversity. However, current sustainability assessments hardly account for direct biodiversity indicators. We focused on forest multi-taxon biodiversity to: i) gather and map the existing information; ii) identify knowledge and research gaps; iii) discuss its research potential. We established a research network to fit data on species, standing trees, lying deadwood and sampling unit description from 34 local datasets across 3591 sampling units. A total of 8724 species were represented, with the share of common and rare species varying across taxonomic classes: some included many species with several rare ones (e.g., Insecta); others (e.g., Bryopsida) were represented by few common species. Tree-related structural attributes were sampled in a subset of sampling units (2889; 2356; 2309 and 1388 respectively for diameter, height, deadwood and microhabitats). Overall, multi-taxon studies are biased towards mature forests and may underrepresent the species related to other developmental phases. European forest compositional categories were all represented, but beech forests were over-represented as compared to thermophilous and boreal forests. Most sampling units (94%) were referred to a habitat type of conservation concern. Existing information may support European conservation and SFM strategies in: (i) methodological harmonization and coordinated monitoring; (ii) definition and testing of SFM indicators and thresholds; (iii) data-driven assessment of the effects of environmental and management drivers on multi-taxon forest biological and functional diversity, (iv) multi-scale forest monitoring integrating in-situ and remotely sensed information. © 2023 The Authors
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2023
Rivista
Biological Conservation
Dettagli
Vol. 284
DOI
On the temporal mismatch between in-situ and satellite-derived spring phenology of European beech forests
Ferrara , Carlotta , Chianucci , Francesco , Bajocco , Sofia
beech forest remote sensing phenology bioclimatic variables modis evi pep725
Mostra abstract
Forest phenology plays a key role in the global terrestrial ecosystem influencing a range of ecosystem processes such as the annual carbon uptake period, and many food webs and changes in their timing and progression. The timing of the start of the phenology season has been successfully determined at a range of scales, from the individual tree by in situ observations to landscape and continental scales by using remotely sensed vegetation indices (VIs). The spatial resolution of satellites is much coarser than traditional methods, creating a gap between space-borne and actual field observations, which brings limitations to phenological research at the ecosystem level. Several unconsidered methodological and observational-related limitations may lead to misinterpretation of the timing of the satellite-derived signals. The aim of this study is therefore to clarify the meaning of a set of spring phenology metrics derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series in beech forests distributed across Europe with respect to PEP725 in situ observations, from 2003 to 2020. To this aim, we (i) tested the differences between remotely sensed and in situ start-of-season (SOS) metrics and (ii) quantified the influence of latitude, elevation, temperature, and precipitation on such differences. Results demonstrated that there is a clear temporal gradient among the different SOS metrics, all of them occurring prior to the in situ observations. Furthermore, latitude and temperatures proved to be the main factors guiding the differences between remotely sensed and in situ SOS metrics. Evidence from this study may help in recognizing the actual meaning of what we see by means of remotely sensed phenology metrics. In this perspective, field observations are crucial in understanding phenology events and provide a reference base. Satellite data, on the other hand, complement field observations by filling in gaps in spatial and temporal coverage, thus enhancing the overall understanding. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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2023
Rivista
International Journal of Remote Sensing
Dettagli
Vol. 44 (5) pp. 1684 - 1701
DOI