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

2018 ◽  
Vol 23 (1) ◽  
pp. 1-8 ◽  
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

Potentials and limitations of using large-scale forest inventory data for evaluating forest succession models

2009 ◽  
Vol 220 (2) ◽  
pp. 133-147 ◽  
Author(s):  
M. Didion ◽  
A.D. Kupferschmid ◽  
M.J. Lexer ◽  
W. Rammer ◽  
R. Seidl ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Large Scale ◽  
Forest Succession ◽  
Inventory Data ◽  
Forest Inventory Data

scholarly journals A large-scale forest landscape model incorporating multi-scale processes and utilizing forest inventory data

Ecosphere ◽  
2013 ◽  
Vol 4 (9) ◽  
pp. art106 ◽  
Author(s):  
Wen J. Wang ◽  
Hong S. He ◽  
Martin A. Spetich ◽  
Stephen R. Shifley ◽  
Frank R. Thompson III ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Large Scale ◽  
Forest Landscape ◽  
Landscape Model ◽  
Inventory Data ◽  
Multi Scale ◽  
Forest Inventory Data ◽  
Forest Landscape Model

Large-scale estimation of aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data

2016 ◽  
Vol 186 ◽  
pp. 626-636 ◽  
Author(s):  
Liviu Theodor Ene ◽  
Erik Næsset ◽  
Terje Gobakken ◽  
Ernest William Mauya ◽  
Ole Martin Bollandsås ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Large Scale ◽  
National Forest Inventory ◽  
National Forest ◽  
Miombo Woodlands ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Airborne Laser

Large-scale estimation of change in aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data

2017 ◽  
Vol 188 ◽  
pp. 106-117 ◽  
Author(s):  
Liviu Theodor Ene ◽  
Erik Næsset ◽  
Terje Gobakken ◽  
Ole Martin Bollandsås ◽  
Ernest William Mauya ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Large Scale ◽  
National Forest Inventory ◽  
National Forest ◽  
Miombo Woodlands ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Airborne Laser

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.

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

2022 ◽  
Author(s):  
Kelly A. Heilman ◽  
Michael C. Dietze ◽  
Alexis A. Arizpe ◽  
Jacob Aragon ◽  
Andrew Gray ◽  
...  
Keyword(s):  
Tree Ring ◽  
Tree Growth ◽  
Forest Inventory ◽  
Inventory Data ◽  
Ecological Forecasting ◽  
Forest Inventory Data

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

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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