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MASTREE+: Time-series of plant reproductive effort from six continents
Hacket-Pain , Andrew J. , Foest , Jessie J. , Pearse , Ian S. , LaMontagne , Jalene M. , Koenig , Walter D. , Vacchiano , Giorgio , Bogdziewicz , Michał , Caignard , Thomas , Celebias , Paulina , van Dormolen , Joep , Fernández-Martínez , Marcos , Moris , Jose V. , Palaghianu , Ciprian , Pesendorfer , Mario B. , Satake , Akiko , Schermer , Éliane , Tanentzap , Andrew J. , Thomas , Peter A. , Vecchio , Davide , Wion , Andreas P. , Wohlgemuth , Thomas , Xue , Tingting , Abernethy , Katharine A. , Aravena Acuña , Marie Claire , Barrera , Marcelo Daniel , Barton , Jessica H. , Boutin , Stan A. , Bush , Emma R. , Donoso Calderón , Sergio R. , Carevic , Felipe S. , Castilho , Carolina V. , Manuel Cellini , Juan , Chapman , Colin A. , Chapman , H. M. , Chianucci , Francesco , Costa , Patricia Da , Croisé , Luc , Cutini , Andrea , Dantzer , Ben J. , DeRose , Robert Justin , Dikangadissi , Jean Thoussaint , Dimoto , Edmond , da Fonseca , Fernanda Lopes , Gallo , Leonardo Ariel , Gratzer , Georg , Greene , David F. , Hadad , Martín Ariel , Huertas Herrera , Alejandro , Jeffery , Kathryn J. , Johnstone , Jill F. , Kalbitzer , Urs , Kantorowicz , Władysław , Klimas , Christie Ann , Lageard , Jonathan G.A. , Lane , Jeffrey E. , Lapin , Katharina , Ledwoń , Mateusz , Leeper , Abigail C. , Lencinas , María Vanessa , Lira-Guedes , Ana Cláudia , Lordon , Michael C. , Marchelli , Paula , Marino , Shealyn , Schmidt van Marle , Harald , McAdam , Andrew G. , Momont , Ludovic R.W. , Nicolas , Manuel , de Oliveira Wadt , Lúcia Helena , Panahi , Parisa , Martínez Pastur , Guillermo J. , Patterson , Thomas W. , Luis Peri , Pablo , Piechnik , Łukasz , Pourhashemi , Mehdi , Espinoza Quezada , Claudia , Roig , Fidel Alejandro , Peña-Rojas , Karen A. , Rosas , Yamina Micaela , Schueler , Silvio , Seget , Barbara , Soler , Rosina M. , Steele , Michael A. , Toro Manríquez , Mónica Del Rosario , Tutin , Caroline E.G. , Ukizintambara , Tharcisse , White , Lee J.T. , Yadok , Biplang Godwill , Willis , John L. , Zolles , Anita , Żywiec , Magdalena , Ascoli , Davide
plant reproduction masting demography flowering general flowering recruitment regeneration
Mostra abstract
Significant gaps remain in understanding the response of plant reproduction to environmental change. This is partly because measuring reproduction in long-lived plants requires direct observation over many years and such datasets have rarely been made publicly available. Here we introduce MASTREE+, a data set that collates reproductive time-series data from across the globe and makes these data freely available to the community. MASTREE+ includes 73,828 georeferenced observations of annual reproduction (e.g. seed and fruit counts) in perennial plant populations worldwide. These observations consist of 5971 population-level time-series from 974 species in 66 countries. The mean and median time-series length is 12.4 and 10 years respectively, and the data set includes 1122 series that extend over at least two decades (≥20 years of observations). For a subset of well-studied species, MASTREE+ includes extensive replication of time-series across geographical and climatic gradients. Here we describe the open-access data set, available as a.csv file, and we introduce an associated web-based app for data exploration. MASTREE+ will provide the basis for improved understanding of the response of long-lived plant reproduction to environmental change. Additionally, MASTREE+ will enable investigation of the ecology and evolution of reproductive strategies in perennial plants, and the role of plant reproduction as a driver of ecosystem dynamics. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
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2022
Rivista
Global Change Biology
Dettagli
Vol. 28 (9) pp. 3066 - 3082
DOI
Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach
Li , Linyuan , Mu , Xihan , Chianucci , Francesco , Qi , Jianbo , Jiang , Jingyi , Zhou , Jiaxin , Chen , Ling , Huang , Huaguo , Yan , Guangjian , Liu , Shouyang
crown cover canopy mapping image overlap self-supervised deep learning sfm point cloud uav imagery
Mostra abstract
Accurate wall-to-wall estimation of forest crown cover is critical for a wide range of ecological studies. Notwithstanding the increasing use of UAVs in forest canopy mapping, the ultrahigh-resolution UAV imagery requires an appropriate procedure to separate the contribution of understorey from overstorey vegetation, which is complicated by the spectral similarity between the two forest components and the illumination environment. In this study, we investigated the integration of deep learning and the combined data of imagery and photogrammetric point clouds for boreal forest canopy mapping. The procedure enables the automatic creation of training sets of tree crown (overstorey) and background (understorey) data via the combination of UAV images and their associated photogrammetric point clouds and expands the applicability of deep learning models with self-supervision. Based on the UAV images with different overlap levels of 12 conifer forest plots that are categorized into “I”, “II” and “III” complexity levels according to illumination environment, we compared the self-supervised deep learning-predicted canopy maps from original images with manual delineation data and found an average intersection of union (IoU) larger than 0.9 for “complexity I” and “complexity II” plots and larger than 0.75 for “complexity III” plots. The proposed method was then compared with three classical image segmentation methods (i.e., maximum likelihood, Kmeans, and Otsu) in the plot-level crown cover estimation, showing outperformance in overstorey canopy extraction against other methods. The proposed method was also validated against wall-to-wall and pointwise crown cover estimates using UAV LiDAR and in situ digital cover photography (DCP) benchmarking methods. The results showed that the model-predicted crown cover was in line with the UAV LiDAR method (RMSE of 0.06) and deviate from the DCP method (RMSE of 0.18). We subsequently compared the new method and the commonly used UAV structure-from-motion (SfM) method at varying forward and lateral overlaps over all plots and a rugged terrain region, yielding results showing that the method-predicted crown cover was relatively insensitive to varying overlap (largest bias of less than 0.15), whereas the UAV SfM-estimated crown cover was seriously affected by overlap and decreased with decreasing overlap. In addition, canopy mapping over rugged terrain verified the merits of the new method, with no need for a detailed digital terrain model (DTM). The new method is recommended to be used in various image overlaps, illuminations, and terrains due to its robustness and high accuracy. This study offers opportunities to promote forest ecological applications (e.g., leaf area index estimation) and sustainable management (e.g., deforestation). © 2022 The Author(s)
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2022
Rivista
International Journal of Applied Earth Observation and Geoinformation
Dettagli
Vol. 107
DOI