Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty

AbstractBetter understanding and prediction of tree growth is important because of the many ecosystem services provided by forests and the uncertainty surrounding how forests will respond to anthropogenic climate change. With the ultimate goal of improving models of forest dynamics, here we construct a statistical model that combines complementary data sources – tree-ring and forest inventory data. A Bayesian hierarchical model is used to gain inference on the effects of many factors on tree growth – individual tree size, climate, biophysical conditions, stand-level competitive environment, tree-level canopy status, and forest management treatments – using both diameter at breast height (DBH) and tree-ring data. The model consists of two multiple regression models, one each for the two data sources, linked via a constant of proportionality between coefficients that are found in parallel in the two regressions. The model was applied to a dataset developed at a single, well-studied site in the Jemez Mountains of north-central New Mexico, U. S. A. Inferences from the model included positive effects of seasonal precipitation, wetness index, and height ratio, and negative effects of seasonal temperature, southerly aspect and radiation, and plot basal area. Climatic effects inferred by the model compared well to results from a dendroclimatic analysis. Combining the two data sources did not lead to higher predictive accuracy (using the leave-one-out information criterion, LOOIC), either when there was a large number of increment cores (129) or under a reduced data scenario of 15 increment cores. However, there was a clear advantage, in terms of parameter estimates, to the use of both data sources under the reduced data scenario: DBH remeasurement data for ~500 trees substantially reduced uncertainty about non-climate fixed effects on radial increments. We discuss the kinds of research questions that might be addressed when the high-resolution information on climate effects contained in tree rings are combined with the rich metadata on tree- and stand-level conditions found in forest inventories, including carbon accounting and projection of tree growth and forest dynamics under future climate scenarios.

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Ectomycorrhizal fungal composition and function predict tree growth across Europe

10.5194/egusphere-egu21-7875 ◽

2021 ◽

Author(s):

Mark Anthony ◽

Thomas Crowther ◽

Sietse van der Linde ◽

Laura Suz ◽

Martin Bidartondo ◽

...

Keyword(s):

Ectomycorrhizal Fungi ◽

Tree Growth ◽

Fungal Community ◽

Forest Inventory ◽

Inorganic Nitrogen ◽

Inventory Data ◽

Community Surveys ◽

Forest Inventory Data ◽

Ectomycorrhizal Fungal Community ◽

Slow Growing

<p>Ectomycorrhizal fungi are central members of the forest fungal community, forming symbiosis with most trees in temperate and boreal forests, enhancing plant access to limiting soil nutrients. Decades of greenhouse studies have shown that specific mycorrhizal fungi enhance tree seedlings growth and nutrient uptake rates, and that these effects are sustained when seedlings are out-planted into regenerating forests. Whether these relationships scale up to affect the growth of mature trees and entire forests harboring diverse fungal communities remains unknown. In this study, we combined a continental set of European forest inventory data from the ICP forest network with molecular ectomycorrhizal fungal community surveys to identify features of the mycorrhizal mycobiome linked to forest productivity. We found that ectomycorrhizal fungal community composition was a key predictor of tree growth, and this effect was robust to statistically accounting for climate, nitrogen deposition, soil inorganic nitrogen availability, soil pH, and forest stand characteristics. Furthermore, ectomycorrhizal fungi with greater genomic investment in energy production and inorganic nitrogen metabolism, but lower investment in organic nitrogen acquisition, were linked to faster tree growth. Lastly, we sampled soils from fast and slow growing forests and introduced their microbiomes into a sterilized growth medium to experimentally isolate microbiome effects on tree development. Consistent with our observational analysis, tree seedling growth was accelerated when inoculated with microbiomes from fast vs. slow growing forests. &#160;By linking molecular community surveys and long-term forest inventory data in the field, and then pairing this with a microbiome manipulation study under controlled conditions, this work demonstrates an emerging link between the forest microbiome and dominant European tree growth rates.</p>

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Extracting coherent tree-ring climatic signals across spatial scales from extensive forest inventory data

PLoS ONE ◽

10.1371/journal.pone.0189444 ◽

2017 ◽

Vol 12 (12) ◽

pp. e0189444 ◽

Cited By ~ 8

Author(s):

Louis Duchesne ◽

Loïc D’Orangeville ◽

Rock Ouimet ◽

Daniel Houle ◽

Daniel Kneeshaw

Keyword(s):

Tree Ring ◽

Forest Inventory ◽

Spatial Scales ◽

Inventory Data ◽

Forest Inventory Data ◽

Climatic Signals

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Reconstructing past forest status using inventory and tree ring data to support uneven-aged forest management

Central European Forestry Journal ◽

10.1515/forj-2017-0022 ◽

2017 ◽

Vol 63 (4) ◽

pp. 203-211 ◽

Cited By ~ 1

Author(s):

Ladislav Kulla ◽

Michal Bošeľa ◽

Vlastimil Murgaš ◽

Joerg Roessiger ◽

Vladimír Šebeň

Keyword(s):

Forest Management ◽

Tree Ring ◽

Forest Inventory ◽

Management System ◽

Inventory Data ◽

Diameter Increment ◽

Forest Inventory Data ◽

Tree Ring Data ◽

Species Specific ◽

Forest Status

Abstract The decision to change forest management system from the traditional even-aged to the selection one based on statistical inventory is often limited by a missing previous inventory. To avoid this issue, we used available forest inventory data from ca 2 000 ha of mixed uneven-aged beech-fir-spruce-pine forest and tree ring data from 831 trees to reconstruct forest status from one decade ago. For this purpose, we have created three sets of species-specific models: 1) diameter-stump models to reconstruct the diameter of missing trees, 2) diameter-increment models based on tree ring data to estimate past diameters, and 3) height-diameter models to estimate past tree heights. This approach has allowed us to completely reconstruct the state of the forest as it was ten years ago and use the results as a substitution for a previously missing inventory.

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Rapid Assessment of Tree Damage Resulting from a 2020 Windstorm in Iowa, USA

Forests ◽

10.3390/f12050555 ◽

2021 ◽

Vol 12 (5) ◽

pp. 555

Author(s):

Thomas C. Goff ◽

Mark D. Nelson ◽

Greg C. Liknes ◽

Tivon E. Feeley ◽

Scott A. Pugh ◽

...

Keyword(s):

Forest Inventory ◽

Damage Zone ◽

Satellite Image ◽

Rapid Assessment ◽

Aerial Survey ◽

Inventory Data ◽

Tree Damage ◽

Forest Inventory Data ◽

Damage Susceptibility ◽

The Impact

A need to quantify the impact of a particular wind disturbance on forest resources may require rapid yet reliable estimates of damage. We present an approach for combining pre-disturbance forest inventory data with post-disturbance aerial survey data to produce design-based estimates of affected forest area and number and volume of trees damaged or killed. The approach borrows strength from an indirect estimator to adjust estimates from a direct estimator when post-disturbance remeasurement data are unavailable. We demonstrate this approach with an example application from a recent windstorm, known as the 2020 Midwest Derecho, which struck Iowa, USA, and adjacent states on 10–11 August 2020, delivering catastrophic damage to structures, crops, and trees. We estimate that 2.67 million trees and 1.67 million m3 of sound bole volume were damaged or killed on 23 thousand ha of Iowa forest land affected by the 2020 derecho. Damage rates for volume were slightly higher than for number of trees, and damage on live trees due to stem breakage was more prevalent than branch breakage, both likely due to higher damage probability in the dominant canopy of larger trees. The absence of post-storm observations in the damage zone limited direct estimation of storm impacts. Further analysis of forest inventory data will improve understanding of tree damage susceptibility under varying levels of storm severity. We recommend approaches for improving estimates, including increasing spatial or temporal extents of reference data used for indirect estimation, and incorporating ancillary satellite image-based products.

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Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

Remote Sensing ◽

10.3390/rs13081592 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1592

Author(s):

Nikolai Knapp ◽

Andreas Huth ◽

Rico Fischer

Keyword(s):

Remote Sensing ◽

Forest Inventory ◽

Canopy Height ◽

Biomass Estimation ◽

Inventory Data ◽

Border Effects ◽

Tree Crowns ◽

Forest Inventory Data ◽

Estimation Uncertainty ◽

Voxel Space

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

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Evaluating the Condition of Selectively Logged Production Forests in Myanmar: An Analysis Using Large-scale Forest Inventory Data for Yedashe Township

Journal of Forest Planning ◽

10.20659/jfp.23.1_1 ◽

2018 ◽

Vol 23 (1) ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Zar Chi Win ◽

Nobuya Mizoue ◽

Tetsuji Ota ◽

Tsuyoshi Kajisa ◽

Shigejiro Yoshida ◽

...

Keyword(s):

Forest Inventory ◽

Large Scale ◽

Inventory Data ◽

Forest Inventory Data ◽

Production Forests

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Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.183-185.220 ◽

2011 ◽

Vol 183-185 ◽

pp. 220-224

Author(s):

Ming Ze Li ◽

Wen Yi Fan ◽

Ying Yu

Keyword(s):

Tree Species ◽

Forest Inventory ◽

Forest Biomass ◽

Biomass Estimation ◽

Total Biomass ◽

Inventory Data ◽

Biomass Equation ◽

Tree Species Composition ◽

Forest Inventory Data ◽

Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

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Use of artificial neural networks and geographic objects for classifying remote sensing imagery

CERNE ◽

10.1590/01047760.201420021615 ◽

2014 ◽

Vol 20 (2) ◽

pp. 267-276 ◽

Cited By ~ 4

Author(s):

Pedro Resende Silva ◽

Fausto Weimar Acerbi Júnior ◽

Luis Marcelo Tavares de Carvalho ◽

José Roberto Soares Scolforo

Keyword(s):

Neural Networks ◽

Time Series ◽

Image Segmentation ◽

Artificial Neural Networks ◽

Forest Inventory ◽

Landsat Tm ◽

Inventory Data ◽

Forest Inventory Data ◽

Artificial Neural ◽

Test Use

The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

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