Update of forest inventory data with lidar and high spatial resolution satellite imagery

2008 ◽  
Vol 34 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Thomas Hilker ◽  
Michael A Wulder ◽  
Nicholas C Coops
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Forest Inventory ◽  
High Spatial Resolution ◽  
Inventory Data ◽  
Forest Inventory Data

Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots

2013 ◽  
Vol 34 (12) ◽  
pp. 4406-4424 ◽  
Author(s):  
Brice Mora ◽  
Michael A. Wulder ◽  
Geordie W. Hobart ◽  
Joanne C. White ◽  
Christopher W. Bater ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Forest Inventory ◽  
High Spatial Resolution ◽  
Height Estimates ◽  
Very High Spatial Resolution ◽  
Stand Height ◽  
Very High

Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery

2008 ◽  
Vol 84 (2) ◽  
pp. 221-230 ◽  
Author(s):  
Michael A Wulder ◽  
Joanne C White ◽  
Geoffrey J Hay ◽  
Guillermo Castilla
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Forest Inventory ◽  
High Spatial Resolution ◽  
Cost Savings ◽  
Computer Assisted ◽  
Automated Segmentation ◽  
Forest Stands ◽  
Segmentation Approach ◽  
Time Required

High spatial resolution satellite imagery, with pixel sizes of one metre or less, are increasingly available. These data provide an accessible and flexible source of information for forest inventory purposes. In addition, the digital nature of these data provides an opportunity for automated and computer-assisted approaches for forest stand delineation to be considered. Specifically, automation has the potential to realize cost savings by minimizing the time required for manual delineation of forest stands; however, inappropriate automation could result in increased costs due to time-consuming revisions of automated delineations. The aim of this research is to present, through example, investigations of an automated segmentation approach for delineating homogeneous forest stands on high spatial resolution satellite imagery. An evaluation of the suitability of IKONOS 1-m panchromatic data for this application is also presented, along with several key issues that must be considered regarding automated segmentation approaches. Key words: forest inventory, segmentation, automation, IKONOS, high spatial resolution satellite, photo interpretation, stand delineation, object

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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