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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
Evolutionary ecology of masting: mechanisms, models, and climate change
Bogdziewicz , Michał , Kelly , Dave J. , Ascoli , Davide , Caignard , Thomas , Chianucci , Francesco , Crone , Elizabeth E. , Fleurot , Emilie , Foest , Jessie J. , Gratzer , Georg , Hagiwara , Tomika , Han , Qingmin , Journé , Valentin , Keurinck , Léa , Kondrat , Katarzyna , McClory , Ryan W. , LaMontagne , Jalene M. , Mundo , Ignacio A. , Nussbaumer , Anita , Oberklammer , Iris , Ohno , Misuzu , Pearse , Ian S. , Pesendorfer , Mario B. , Resente , Giulia , Satake , Akiko , Shibata , Mitsue , Snell , Rebecca S. , Szymkowiak , Jakub , Touzot , Laura , Zwolak , Rafał , Żywiec , Magdalena , Hacket-Pain , Andrew J.
climate change economies of scale environmental prediction plant demography plant reproduction
Mostra abstract
Many perennial plants show mast seeding, characterized by synchronous and highly variable reproduction across years. We propose a general model of masting, integrating proximate factors (environmental variation, weather cues, and resource budgets) with ultimate drivers (predator satiation and pollination efficiency). This general model shows how the relationships between masting and weather shape the diverse responses of species to climate warming, ranging from no change to lower interannual variation or reproductive failure. The role of environmental prediction as a masting driver is being reassessed; future studies need to estimate prediction accuracy and the benefits acquired. Since reproduction is central to plant adaptation to climate change, understanding how masting adapts to shifting environmental conditions is now a central question. © 2024 The Authors
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2024
Rivista
Trends in Ecology and Evolution
Dettagli
Vol. 39 (9) pp. 851 - 862
DOI