scholarly journals High-resolution mapping of time since disturbance and forest carbon flux from remote sensing and inventory data to assess harvest, fire, and beetle disturbance legacies in the Pacific Northwest

2016 ◽  
Vol 13 (22) ◽  
pp. 6321-6337 ◽  
Author(s):  
Huan Gu ◽  
Christopher A. Williams ◽  
Bardan Ghimire ◽  
Feng Zhao ◽  
Chengquan Huang
Keyword(s):  
Pacific Northwest ◽  
Forest Inventory ◽  
Carbon Flux ◽  
Forest Carbon ◽  
Stand Age ◽  
Net Ecosystem Productivity ◽  
Ecosystem Productivity ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
The Pacific

Abstract. Accurate assessment of forest carbon storage and uptake is central to policymaking aimed at mitigating climate change and understanding the role forests play in the global carbon cycle. Disturbances have highly diverse impacts on forest carbon dynamics, making them a challenge to quantify and report. Time since disturbance is a key intermediate determinant that aids the assessment of disturbance-driven carbon emissions and removals legacies. We propose a new methodology of quantifying time since disturbance and carbon flux across forested landscapes in the Pacific Northwest (PNW) at a fine scale (30 m) by combining remote sensing (RS)-based disturbance year, disturbance type, and above-ground biomass with forest inventory data. When a recent disturbance is detected, time since disturbance can be directly determined by combining three RS-derived disturbance products, or time since the last stand clearing can be inferred from a RS-derived 30 m biomass map and field inventory-derived species-specific biomass accumulation curves. Net ecosystem productivity (NEP) is further mapped based on carbon stock and flux trajectories derived from the Carnegie-Ames-Stanford Approach (CASA) model in our prior work that described how NEP changes with time following harvest, fire, or bark beetle disturbances of varying severity. Uncertainties from biomass map and forest inventory data were propagated by probabilistic sampling to provide a statistical distribution of stand age and NEP for each forest pixel. We mapped mean, standard deviation, and statistical distribution of stand age and NEP at 30 m in the PNW region. Our map indicated a net ecosystem productivity of 5.9 Tg C yr−1 for forestlands circa 2010 in the study area, with net uptake in relatively mature (> 24 years old) forests (13.6 Tg C yr−1) overwhelming net negative NEP from tracts that had recent harvests (−6.4 Tg C yr−1), fires (−0.5 Tg C yr−1), and bark beetle outbreaks (−0.8 Tg C yr−1). The approach will be applied to forestlands in other regions of the conterminous US to advance a more comprehensive monitoring, mapping, and reporting of the carbon consequences of forest change across the US.

scholarly journals High-resolution forest carbon flux mapping and monitoring in the Pacific Northwest with time since disturbance and disturbance legacies inferred from remote sensing and inventory data

2016 ◽  
Author(s):  
Huan Gu ◽  
Christopher A. Williams ◽  
Bardan Ghimire ◽  
Feng Zhao ◽  
Chengquan Huang
Keyword(s):  
Remote Sensing ◽  
Pacific Northwest ◽  
Forest Inventory ◽  
Stand Age ◽  
Net Ecosystem Productivity ◽  
Fine Scale ◽  
Ecosystem Productivity ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
The Pacific

Abstract. Assessment of forest carbon storage and uptake is central to understanding the role forests play in the global carbon cycle and policy-making aimed at mitigating climate change. Current U.S. carbon stocks and fluxes are monitored and reported at fine-scale regionally, or coarse-scale nationally. We proposed a new methodology of quantifying carbon uptake and release across forested landscapes in the Pacific Northwest (PNW) at a fine scale (30 m) by combining remote-sensing based disturbance year, disturbance type, and aboveground biomass with forest inventory data in a carbon modelling framework. Time since disturbance is a key intermediate determinant that aided the assessment of disturbance-driven carbon emissions and removals legacies. When a recent disturbance was detected, time since disturbance can be directly determined by remote sensing-derived disturbance products; and if not, time since last stand-clearing was inferred from remote sensing-derived 30 m biomass map and field inventory-derived species-specific biomass regrowth curves. Net ecosystem productivity (NEP) was further mapped based on carbon stock and flux trajectories that described how NEP changes with time following harvest, fire, or bark beetle disturbances of varying severity. Uncertainties from biomass map and forest inventory data were propagated by probabilistic sampling to provide a probabilistic, statistical distribution of stand age and NEP for each forest pixel. We mapped mean, standard deviation and statistical distribution of stand age and NEP at 30 m in the PNW region. Our map indicated a net ecosystem productivity of 5.2 Tg C y−1 for forestlands circa 2010 in the study area, with net uptake in relatively mature (> 24 year old) forests (13.6 Tg C y−1) overwhelming net negative NEP from tracts that have seen recent harvest (−6.4 Tg C y−1), fires (−0.5 Tg C y−1), and bark beetle outbreaks (−1.4 Tg C y−1). The approach will be applied to forestlands in other regions of the conterminous U.S. to advance a more comprehensive monitoring, mapping and reporting the carbon consequences of forest change across the U.S.

scholarly journals Forest Type Definitions by Cluster Analysis

2001 ◽  
Vol 16 (3) ◽  
pp. 101-105 ◽  
Author(s):  
Willem W.S. van Hees ◽  
Kevin Dobelbower ◽  
Kenneth Winterberger
Keyword(s):  
Cluster Analysis ◽  
Pacific Northwest ◽  
Forest Inventory ◽  
Forest Type ◽  
Research Station ◽  
Inventory Data ◽  
Southeast Alaska ◽  
Cluster Development ◽  
Forest Inventory Data

Abstract A method is presented to develop forest type definitions by using cluster analysis of forest inventory data collected in southeast Alaska from 1995 through 1998. Species stocking levels were used as variables for cluster development. Pacific Northwest Research Station forest inventory staff could not compute forest type for some forested conditions in southeast Alaska using then existing forest type definitions. Forest type definitions developed by cluster analysis improved computed assignment of forest type. West. J. Appl. For. 16(3):101–105.

A statistical power analysis of woody carbon flux from forest inventory data

2013 ◽  
Vol 118 (3-4) ◽  
pp. 919-931 ◽  
Author(s):  
James A. Westfall ◽  
Christopher W. Woodall ◽  
Mark A. Hatfield
Keyword(s):  
Forest Inventory ◽  
Carbon Flux ◽  
Power Analysis ◽  
Statistical Power ◽  
Inventory Data ◽  
Statistical Power Analysis ◽  
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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