Estimating tree growth from complex forest monitoring data

2013 ◽  
Vol 23 (6) ◽  
pp. 1288-1296 ◽  
Author(s):  
Melissa Eitzel ◽  
John Battles ◽  
Robert York ◽  
Jonas Knape ◽  
Perry de Valpine
2020 ◽  
Author(s):  
Katarina Merganicova ◽  
Roland Hollos ◽  
Zoltan Barcza ◽  
Jan Merganic ◽  
Zuzana Sitkova ◽  
...  

<p>Carbon cycling in forest ecosystems is affected by a number of interacting environmental factors. Here we analyse carbon sequestration in temperate forests composed of three common Central European species: Norway spruce, European beech and oak along an extended environmental gradient across Central Europe using long-term monitoring data and process-based modelling of forest dynamics. For the analyses we used selected ICP forest monitoring plots, long-term forest research plots from thinning trials, and highly-equipped intensively monitored plots from five central European countries: Croatia, Hungary, Slovakia, Poland and the Czech Republic. Their temporal development was simulated using a process-based model Biome-BGCMuSo, which is sensitive to soil and climate conditions. Since such models of forest growth dynamics implicitly describe relationships between forest productivity and environmental conditions, their implementation can reveal the main factors affecting carbon cycling in forests along the gradients of latitude, altitude, or other environmental factors as long as they are included in the models. The study indicates that by linking long-term monitoring data and forest growth modelling we can not only test the model capacity to simulate forest dynamics, but above all we can increase our capacity to address main challenges faced by the central European forestry with respect to the global climate change.  </p>


2019 ◽  
Vol 10 ◽  
Author(s):  
Sophia Etzold ◽  
Kasia Ziemińska ◽  
Brigitte Rohner ◽  
Alessandra Bottero ◽  
Arun K. Bose ◽  
...  

2020 ◽  
Vol 698 ◽  
pp. 134129 ◽  
Author(s):  
Albert Ciceu ◽  
Ionel Popa ◽  
Stefan Leca ◽  
Diana Pitar ◽  
Serban Chivulescu ◽  
...  

2013 ◽  
Vol 39 (6) ◽  
Author(s):  
Lara Roman ◽  
E. Gregory McPherson ◽  
Bryant Scharenbroch ◽  
Julia Bartens

Urban forest monitoring data are essential to assess the impacts of tree planting campaigns and management programs. Local practitioners have monitoring projects that have not been well documented in the urban forestry literature. To learn more about practitioner-driven monitoring efforts, the authors surveyed 32 local urban forestry organizations across the United States about the goals, challenges, methods, and uses of their monitoring programs, using an e-mailed questionnaire. Non-profit organizations, municipal agencies, state agencies, and utilities participated. One-half of the organizations had six or fewer urban forestry staff. Common goals for monitoring included evaluating the success of tree planting and management, taking a proactive approach towards tree care, and engaging communities. The most commonly recorded data were species, condition rating, mortality status, and diameter at breast height. Challenges included limited staff and funding, difficulties with data management and technology, and field crew training. Programs used monitoring results to inform tree planting and maintenance practices, provide feedback to individuals responsible for tree care, and manage tree risk. Participants emphasized the importance of planning ahead: carefully considering what data to collect, setting clear goals, developing an appropriate database, and planning for funding and staff time. To improve the quality and consistency of monitoring data across cities, researchers can develop standardized protocols and be responsive to practitioner needs and organizational capacities.


2019 ◽  
Vol 11 (7) ◽  
pp. 758 ◽  
Author(s):  
Stuart Krause ◽  
Tanja G.M. Sanders ◽  
Jan-Peter Mund ◽  
Klaus Greve

The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.


Geoderma ◽  
2008 ◽  
Vol 146 (3-4) ◽  
pp. 475-488 ◽  
Author(s):  
Gert Jan Reinds ◽  
Marcel van Oijen ◽  
Gerard B.M. Heuvelink ◽  
Hans Kros

1901 ◽  
Vol 22 (1) ◽  
pp. 71-88
Author(s):  
C. E. Hall
Keyword(s):  

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