scholarly journals Understanding and predicting forest mortality in the western United States using long‐term forest inventory data and modeled hydraulic damage

2020 ◽  
Author(s):  
Martin D. Venturas ◽  
Henry N. Todd ◽  
Anna T. Trugman ◽  
William R. L. Anderegg
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Western United States ◽  
Inventory Data ◽  
Forest Mortality ◽  
Forest Inventory Data

scholarly journals Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

2019 ◽  
Vol 11 (15) ◽  
pp. 1803 ◽  
Author(s):  
John Hogland ◽  
Nathaniel Anderson ◽  
David L. R. Affleck ◽  
Joseph St. Peter
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Forest Type ◽  
Longleaf Pine ◽  
Basal Area ◽  
Pinus Palustris ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Inventory Data ◽  
Forest Inventory Data

This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. Spatial, statistical and machine learning algorithms were used to relate United States Forest Service Forest Inventory and Analysis (FIA) field plot data to relatively normalized Landsat 8 imagery based texture. Modeling algorithms employed include softmax neural networks and multiple hurdle models that combine softmax neural network predictions with linear regression models to estimate key forest characteristics across 2.3 million ha in Georgia, USA. Forest metrics include forest type, basal area and stand density. Results show strong relationships between Landsat 8 imagery based texture and field data (map accuracy > 0.80; square root basal area per ha residual standard errors < 1; natural log transformed trees per ha < 1.081). Model estimates depicting spatially explicit, fine resolution raster surfaces of forest characteristics for multiple coniferous and deciduous species across the study area were created and made available to the public in an online raster database. These products can be integrated with existing tabular, vector and raster databases already being used to guide longleaf pine conservation and restoration in the region.

scholarly journals Assessment of the implementation of the new forest care rules by the example of Tikhvinsk forestry

2021 ◽  
pp. 119-136
Author(s):  
А.В. Сафонов ◽  
М.А. Крестьянова ◽  
С.А. Суворов ◽  
Д.А. Данилов
Keyword(s):  
Forest Inventory ◽  
Inventory Data ◽  
Normative Documents ◽  
Qualitative And Quantitative ◽  
Forest Inventory Data ◽  
New Forest ◽  
Quantitative Indicators ◽  
Qualitative Distinction ◽  
Made In

Рубки ухода за лесом – это комплекс лесохозяйственных мероприятий, направленный на улучшение качественных и количественных показателей древостоя, формирование высокопродуктивных, устойчивых и хозяйственно-ценных насаждений, путем удаления из насаждений больных, поврежденных, фаутных деревьев, а также деревьев нежелательных пород в молодняках, жердняках и средневозрастных дендроцинозах. В работе представлено сравнение нормативных показателей по двум правилам ухода за лесом, основным различием которых является подход к выделению максимально допустимого вырубаемого запаса, основывающегося на анализе абсолютной, для нового норматива, и относительной, для старого, полнот древостоя. Была произведена оценка и сравнение классов товарности, процентов вырубаемого запаса и его распределения по делянкам, с целью выявления различий и особенностей подходов двух рассматриваемых нормативных подходов. По результатам проведенных анализов было выявлено различие данных лесоустройства по реальным качественным и количественным показателям древостоя на большинстве делянок, большой разницей между классами товарности по рассматриваемым нормативам, что в свою очередь ведет к различиям в выходе по запасам деловой и дровяной древесины, а также их качественному различию, и интенсивности изреживания полога, что обуславливается вышеописанными особенностями по выделению максимально допустимого вырубаемого запаса. В связи с вышеизложенным, необходимо разрабатывать региональные нормативы уходов за лесом на базе полученных долговременных наблюдений на постоянных пробных площадях с полным циклом проведённых уходов за лесом и при необходимости вносить коррективы, возможность которых принципиально исключается существующей схемой разработки и введения в действие нормативных документов. Forest thinning is a complex of forestry measures aimed at improving the qualitative and quantitative indicators of the stand, the formation of highly productive, sustainable and economically valuable stands, by removing sick, damaged, fallow trees, as well as trees of undesirable species in young stands, stumps and middle-aged stands. The work presents a comparison of the normative indicators for the two rules of forest maintenance, the main difference of which is the approach to the allocation of the maximum allowable felling stock, based on the analysis of the absolute, for the new standard, and the relative, for the old, completeness of the stand. The evaluation and comparison of classes of marketability, percent of the harvested stock and its distribution across the plots were made in order to identify the differences and peculiarities of the approaches of the two normative approaches under consideration. By results of the carried out analyses it was revealed difference of the forest inventory data to real qualitative and quantitative indicators of a stand on the majority of plots, the big difference between classes of marketability on the considered standards that in turn leads to distinctions in an exit on stocks of business and wood, and as their qualitative distinction, and intensity of thinning of a canopy that is caused by the above-named features on allocation of the maximum allowable cut stock. In connection with the above stated, it is necessary to develop regional norms of forest tending on the basis of received long-term observations on permanent trial areas with a full cycle of conducted forest tending and, if necessary, to make corrections, the possibility of which is fundamentally excluded by the existing scheme of development and introduction of normative documents.

scholarly journals Forecasting Long-Term Acorn Production with and without Oak Decline Using Forest Inventory Data

2014 ◽  
Vol 60 (2) ◽  
pp. 222-230 ◽  
Author(s):  
Cathryn H. Greenberg ◽  
Chad E. Keyser ◽  
Leah C. Rathbun ◽  
Anita K. Rose ◽  
Todd M. Fearer ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Oak Decline ◽  
Inventory Data ◽  
Acorn Production ◽  
Forest Inventory Data

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

Forests ◽  
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.

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

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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