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Potential of ALOS2 Polarimetric Imagery to Support Management of Poplar Plantations in Northern Italy
Vaglio Laurin , Gaia , Mattioli , Walter , Innocenti , Simone , Lombardo , Emanuela , Valentini , Riccardo , Puletti , Nicola
sar age classes alos2 forest machine learning plantation polarimetric decomposition poplar
Mostra abstract
Poplar is one of the most widespread fast-growing forest species. In Northern Italy, plantations are characterized by large interannual fluctuations, requiring frequent monitoring to inform on wood supply and to manage the stands. The use of radar satellite data is proving useful for forest monitoring, being weather independent and sensitive to the changes in forest canopy structure, but it has been scarcely tested in the case of poplar. Here, L-band ALOS2 (Advanced Land Observing Satellite-2) dual-pol data were tested to detect clear-cut plantations in consecutive years. ALOS2 quad-pol data were used to discriminate among different age classes, a much complex task than detecting poplar plantations extent. Results from different machine learning algorithms indicate that with dual-pol data, poplar forest can be discriminated from clear-cut areas with 80% overall accuracy, similar to what is usually obtained with optical data. With quad-pol data, four age classes were classified with moderate overall accuracy (73%) based on polarimetric decompositions, three 3 age classes with higher accuracy (87%) based on HV band. Sources of error are represented by poplar areas of intermediate age when stems, branches and leaves were not developed enough to detect by scattering mechanisms. This study demonstrates the feasibility of monitoring poplar plantations with satellite radar, which represents a growing source of information thanks to already-planned future satellite missions. © 2022 by the authors.
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2022
Rivista
Remote Sensing
Dettagli
Vol. 14 (20)
DOI
A deep learning approach for automatic mapping of poplar plantations using Sentinel-2 imagery
D'Amico , Giovanni , Francini , Saverio , Giannetti , Francesca , Vangi , Elia , Travaglini , Davide , Chianucci , Francesco , Mattioli , Walter , Grotti , Mirko , Puletti , Nicola , Corona , P. , Chirici , Gherardo
deep learning big data forest tree crops fully connected neural networks multitemporal classification tree species mapping
Mostra abstract
Poplars are one of the most widespread fast-growing tree species used for forest plantations. Owing to their distinct features (fast growth and short rotation) and the dependency on the timber price market, poplar plantations are characterized by large inter-annual fluctuations in their extent and distribution. Therefore, monitoring poplar plantations requires a frequent update of information–not feasible by National Forest Inventories due to their periodicity–achievable by remote sensing systems applications. In particular, the new Sentinel-2 mission, with a revisiting period of 5 days, represents a potentially efficient tool for meeting this need. In this paper, we present a deep learning approach for mapping poplar plantations using Sentinel-2 time series. A reference dataset of poplar plantations was available for a large study area of more than 46,000 km<sup>2</sup> in Northern Italy and served as training and testing data. Two classification methods were compared: (1) a fully connected neural network (also called multilayer perceptron), and (2) a traditional logistic regression. The performance of the two approaches was estimated through bootstrapping procedure with a confidence interval of 99%. Results indicated for deep learning an omission error rate of 2.77%±2.76%, showing improvements compared to logistic regression, omission error rate = 8.91%±4.79%. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
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2021
Rivista
GIScience and Remote Sensing
Dettagli
Vol. 58 (8) pp. 1352 - 1368
DOI
Influence of image pixel resolution on canopy cover estimation in poplar plantations from field, aerial and satellite optical imagery
Chianucci , Francesco , Puletti , Nicola , Grotti , Mirko , Bisaglia , Carlo , Giannetti , Francesca , Romano , Elio , Brambilla , Massimo , Mattioli , Walter , Cabassi , Giovanni , Bajocco , Sofia , Li , Linyuan , Chirici , Gherardo , Corona , P. , Tattoni , Clara
canopy photography crown cover foliage cover sentinel-2 unmanned aerial vehicles
Mostra abstract
Accurate estimates of canopy cover (CC) are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used to estimate CC from the complement of gap fraction measurements obtained with restricted-view sensors. In this short note we evaluated the influence of the image pixel resolution (ground sampling distance; GSD) on CC estimation in poplar plantations obtained from field (cover photography; GSD < 1 cm), unmanned aerial (UAV; GSD <10 cm) and satellite (Sentinel-2; GSD = 10 m) imagery. The trial was conducted in poplar tree plantations in Northern Italy, with varying age and canopy cover. Results indicated that the coarser resolution available from satellite data is suitable to obtain estimates of canopy cover, as compared with field measurements obtained from cover photography; therefore, S2 is recommended for larger scale monitoring and routine assessment of canopy cover in poplar plantations. The higher resolution of UAV compared with Sentinel-2 allows finer assessment of canopy structure, which could also be used for calibrating metrics obtained from coarser-scale remote sensing products, avoiding the need of ground measurements. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.
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2021
Rivista
Annals of Silvicultural Research
Dettagli
Vol. 46 (1) pp. 8 - 13
DOI
Probabilistic sampling and estimation for large-scale assessment of poplar plantations in Northern Italy
Corona , P. , Chianucci , Francesco , Marcelli , Agnese , Gianelle , Damiano , Fattorini , Lorenzo , Grotti , Mirko , Puletti , Nicola , Mattioli , Walter
forest inventory hybrid poplar populus spp tessellation stratified sampling two-phase sampling
Mostra abstract
In the recent decades, growing demand for wood products, combined with efforts to conserve natural forests, has supported a steady increase in the global extent of planted forests. In this paper, a two-phase sampling strategy for large-scale assessment of hybrid poplar plantations in Northern Italy was implemented. The first phase was performed by means of tessellation stratified sampling on high-resolution remotely sensed imagery, covering the survey area by a grid of regular polygons of equal size and randomly and independently selecting one point per quadrat. All the plantations spotted by at least one sample point were selected. In the second phase, we randomly chosen a subset of plantations by stratified sampling that were visited on the ground to collect qualitative and quantitative attributes. The resulting estimates were reliable, and the survey demonstrated relatively easy to be implemented and replicated. These considerations support the use of the proposed sampling strategy to frequently update information on fast-growing forest plantations within agricultural farms, like hybrid poplar crops. Moreover, the results of the case study here presented highlight the relevance of hybrid poplar plantations in Italy, in the context of sustainable development strategies under a green economy perspective. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
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2020
Rivista
European Journal of Forest Research
Dettagli
Vol. 139 (6) pp. 981 - 988
DOI
Large-scale two-phase estimation of wood production by poplar plantations exploiting sentinel-2 data as auxiliary information
Marcelli , Agnese , Mattioli , Walter , Puletti , Nicola , Chianucci , Francesco , Gianelle , Damiano , Grotti , Mirko , Chirici , Gherardo , D'Amico , Giovanni , Francini , Saverio , Travaglini , Davide , Fattorini , Lorenzo , Corona , P.
national forest inventories regression estimator sentinel-2 design-based inference first-phase tessellation stratified sampling second-phase stratified sampling simulation study
Mostra abstract
Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed. © 2020, Finnish Society of Forest Science. All rights reserved.
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2020
Rivista
Silva Fennica
Dettagli
Vol. 54 (2)
DOI
A dataset of forest volume deadwood estimates for Europe
Puletti , Nicola , Canullo , R. , Mattioli , Walter , Gawryś , Radosław , Corona , P. , Czerepko , Janusz
deadwood decay classes european forest types icp forests monitoring programme stand age stand management
Mostra abstract
Key message: ICP Forests relies on a representative pan-European network based on a 16 × 16 km grid-net covering around 6000 plots. Dead wood volumes for 3243 plots, related to 19 European Countries, are presented in this data paper as a result of harmonised sampling procedure, and under compliance with FAIR Data Principles. Dataset access is at https://zenodo.org/record/1467784. Associated metadata are available athttps://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/a27d2a8f-1a2d-4a1c-b932-86ec5f4bd8a6(link to geo-network provided after acceptance). Context: ICP-Forests dataset represents unique opportunity for the assessment of forest resources sustainability and biodiversity in Europe because it monitors the status of forests under a coordinated Pan-European umbrella by standardised methods. Aims: The main goal of this paper is to provide standardized estimates of deadwood volume at European scale for a broader use among forest scientists. Methods: After quality checks, calculations of deadwood volumes distinguished by deadwood types (standing and lying dead trees, snags, coarse woody debris, stumps) have been performed. The obtained plot level data have been joined to available forest stand information (namely: forest type, forest management, and stand age) over 3,243 plots among Europe. Results: The database provides a basis for the evaluation of combined relationships between deadwood volume and forest type, deadwood type, decay status, forest management, and stand age classes at European level. Conclusion: Deadwood volume and quality is recognized as one of the most important source of information for forest biodiversity. Here, first results of a systematic and standardized European survey scheme for assessing deadwood volume are presented. This ICP Forests datasets analysis represents the base for further analysis and relationships. © 2019, INRA and Springer-Verlag France SAS, part of Springer Nature.
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2019
Rivista
Annals of Forest Science
Dettagli
Vol. 76 (3)
DOI
Monitoring the effects of extreme drought events on forest health by Sentinel-2 imagery
Puletti , Nicola , Mattioli , Walter , Bussotti , Filippo , Pollastrini , M.
copernicus program crown dieback dry spell forest health mediterranean forest stands
Mostra abstract
Global climate change is expected to result in more frequent and intense drought events, especially during the warm season. In such perspective, it is crucial to assess the forest stands vulnerability to extreme climatic events, such as drought, even for Mediterranean forest tree species, commonly considered resistant to dry spell. To test the capability of multitemporal imagery derived by Sentinel-2 (S2) in detecting the impacts of extreme drought events on forest health assessed as crown dieback, some forest stands in Tuscany (central Italy) were analyzed. Vegetation indices (VIs) and ancillary digital terrain model-derived data have been collected in 118 observational samples distributed along an ecological gradient. VIs detected a reduction of trees of photosynthetic activity in August 2017. S2 data have allowed the observation of the different response strategies of the tree species considered in this study to the extreme climatic event that occurred. The case study presented shows that S2 can be applied for monitoring climate-related processes providing a synthetic overview of forest conditions at regional scale. © 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).
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2019
Rivista
Journal of Applied Remote Sensing
Dettagli
Vol. 13 (2)
DOI
A PLOT SAMPLING STRATEGY FOR ESTIMATING THE AREA OF OLIVE TREE CROPS AND OLIVE TREE ABUNDANCE IN A MEDITERRANEAN ENVIRONMENT
Grotti , Mirko , Puletti , Nicola , Chianucci , Francesco , Mattioli , Walter , Floris , Antonio , Clementel , Fabrizio , Torresan , C. , Marchi , Maurizio , Gentile , Alessandra , Pisante , Michele , Marcelli , Agnese , Corona , P.
tessellation stratified sampling olea europaea olive crops inventory
Mostra abstract
Accurate inventory and mapping of olive (Olea europaea L.) tree attributes represents a central issue to support the olive production system. With reference to the cultivation, there is a high heterogeneity and complexity in the cultivation of olive trees, which is reflected in the large variability in olive grove surfaces. This poses some challenge in accurately estimating olive tree attributes via traditional inventory approaches, as commonly adopted in national forest inventory. From a methodological point of view, the complexity and heterogeneity of olive tree groves can be comparable to the problem of accurately estimating tree outside forests (TOF) attributes. In this study, we tested whether a plot sampling approach formerly developed for TOF is suitable for estimating olive tree attributes at large scale. We tested this approach in a case study where the census of the olive crop area and the number of olive groves was conducted from photo-interpretation of high resolution aerial orthoimagery, used as benchmark to test the effectiveness of the plot sampling approach. The main result of this study is that the plot sampling method can be applied for estimating olive tree attributes. Our obtained RSEs were below 20%, with a limited sampling effort of about 6% of the studied population; the obtained RSEs were below 6% when increasing sampling up to about 21% the studied population. Using robust statistical procedures among countries, should allow obtaining harmonized and comparable information, which can increase the knowledge of olive geographical distribution and structure at its relevant Mediterranean scale. © 2019, Italian Society of Remote Sensing. All rights reserved.
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2019
Rivista
Trends in Earth Observation
Dettagli
Vol. 1 pp. 11 - 14
Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data
Vaglio Laurin , Gaia , Balling , Johannes , Corona , P. , Mattioli , Walter , Papale , Dario , Puletti , Nicola , Rizzo , Maria , Truckenbrodt , John , Urban , Marcel
sentinel-1 alos2 forest above-ground biomass
Mostra abstract
The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction (R2=0.7) using integrated sensors when an upper bound of 400Mg ha-1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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2018
Rivista
Journal of Applied Remote Sensing
Dettagli
Vol. 12 (1)
DOI
Trends of ungulate species in Europe: not all stories are equal
Cerri , Jacopo , Chirichella , Roberta , Arnold , Walter , Bartoš , Luděk , Borowik , Tomasz , Carranza , Juan , Chianucci , Francesco , Csányi , Sándor , Ericsson , Göran , Heurich , Marco , Kojola , Ilpo , Mysterud , Atle , Pokorny , Boštjan , Schmidt , Krzysztof , Šprem , Nikica , Vicente , Joaquín , Alagić , Ajša , Balčiauskas , Linas , Casaer , Jim , Cellina , Sandra , Done , Gundega , Find’o , Slavomír , Fonseca , Carlos M.M.S. , Gačić , Dragan P. , Melovski , Dime , Ozoliņš , Jânis , Papaioannou , Haritakis I. , Pusenius , Jyrki , Randveer , Tiit , Ruusila , Vesa , Saint-Andrieux , Christine , Veeroja , Rauno , Apollonio , Marco
hunting bags reforestation rural abandonment time-series analysis wild ungulates wildlife management
Mostra abstract
Wild ungulates have deep impacts on socio-ecological systems, and analyzing large-scale population trends in a multispecies set can identify their environmental and socio-economic drivers. We collected annual hunting bags (n = 11,046, period 1975–2018) of European roe deer, red deer, wild boar, fallow deer, mouflon, northern chamois and moose, across Europe. We identified different temporal trends in their hunting bags and evaluated the social and environmental drivers of their relative abundances. The number of harvested red deer and fallow deer, increased steadily across Europe, with minor differences among countries, despite variations in land use and climate. On the contrary, European roe deer harvests have decreased in six European countries since the late 1990s, probably due to landscape changes and locally also due to predation, interspecific competition, and/or increasing temperatures. Northern chamois harvests in Austria and Switzerland have decreased markedly, probably due to increasing temperatures, which decrease the survival of kids at high altitudes. Wild boar harvests have decreased in Poland, Estonia, Latvia, and Lithuania since the African Swine Fever outbreak in 2013–2014. Minor differences emerged between countries adopting different management regimes for wild ungulates. While many studies pointed out landscape changes as the cornerstone for the increase in wild ungulates across Europe, our research emphasizes important species-specific differences. There is a need to predict how landscape dynamics, climate change and recovering large carnivores will affect populations of species already showing signs of decline, like the European roe deer or the northern chamois. © The Author(s), under exclusive licence to Mammal Research Institute Polish Academy of Sciences 2026.
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2026
Rivista
Mammal Research
Dettagli
Vol. 71 (2)
DOI
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