scholarly journals Assessing single photon LiDAR for operational implementation of an enhanced forest inventory in diverse mixedwood forests

2021 ◽  
Vol 97 (01) ◽  
pp. 78-96
Author(s):  
Joanne C. White ◽  
Margaret Penner ◽  
Murray Woods
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Forest Type ◽  
Single Photon ◽  
Basal Area ◽  
Stem Volume ◽  
Lidar Data ◽  
Large Area ◽  
Mixedwood Forests ◽  
Forest Inventories

Airborne laser scanning (ALS; LiDAR) data are an increasingly common data source for forest inventories, and approaches integrating ALS data with field plot measurements have become operational in several jurisdictions. As technology continues to evolve, different LiDAR sensors can provide new opportunities to incorporate LiDAR data into forest inventory workflows. Single photon LiDAR (SPL) enables efficient, large area data acquisition and merits further investigation for forest inventory applications. Herein, we investigated the capacity of leaf-on SPL data, combined with 269 field plots, for estimating forest inventory attributes in the Great Lakes–St. Lawrence mixedwood forests of southern Ontario, Canada. Inventory attribute estimates were validated at the stand level using independent reference data acquired for 27 intensively sampled stands. Top height, Lorey’s height, gross total volume for merchantable stems, merchantable stem volume, basal area, quadratic mean diameter, and total aboveground biomass were estimated with a relative RMSE of 13.52%, 7.24%, 14.61%, 16.27%, 14.42%, 12.25%, and 11.72%, respectively. Relative bias was < 1% for all attributes except top height (10.34%), merchantable volume (3.37%), and basal area (1.68%). Accuracy and bias varied by forest type and stand-level validation was important for assessing model performance in different stand conditions. SPL data can be used to generate accurate, area-based forest inventories in mixedwood forests that have a multitude of tree species and complex forest management histories.

scholarly journals Reuse of field data in ALS-assisted forest inventory

Silva Fennica ◽  
2020 ◽  
Vol 54 (5) ◽  
Author(s):  
Ana de Lera Garrido ◽  
Terje Gobakken ◽  
Hans Ørka ◽  
Erik Næsset ◽  
Ole Bollandsås
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Field Data ◽  
Reference Data ◽  
Growth Models ◽  
Basal Area ◽  
Tree Height ◽  
Cost Component ◽  
Forest Inventories ◽  
Forest Attributes

Forest inventories assisted by wall-to-wall airborne laser scanning (ALS), have become common practice in many countries. One major cost component in these inventories is the measurement of field sample plots used for constructing models relating biophysical forest attributes to metrics derived from ALS data. In areas where ALS-assisted forest inventories are planned, and in which the previous inventories were performed with the same method, reusing previously acquired field data can potentially reduce costs, either by (1) temporally transferring previously constructed models or (2) projecting field reference data using growth models that can serve as field reference data for model construction with up-to-date ALS data. In this study, we analyzed these two approaches of reusing field data acquired 15 years prior to the current ALS acquisition to estimate six up-to-date forest attributes (dominant tree height, mean tree height, stem number, stand basal area, volume, and aboveground biomass). Both approaches were evaluated within small stands with sizes of approximately 0.37 ha, assessing differences between estimates and ground reference values. The estimates were also compared to results from an up-to-date forest inventory relying on concurrent field- and ALS data. The results showed that even though the reuse of historical information has some potential and could be beneficial for forest inventories, systematic errors may appear prominent and need to be overcome to use it operationally. Our study showed systematic trends towards the overestimation of lower-range ground references and underestimation of the upper-range ground references.

scholarly journals Comparison of photogrammetric canopy models from archived and made-to-order aerial imagery in forest inventory

Silva Fennica ◽  
2020 ◽  
Vol 54 (5) ◽  
Author(s):  
Sakari Tuominen ◽  
Andras Balazs ◽  
Annika Kangas
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Species Recognition ◽  
Aerial Imagery ◽  
Large Area ◽  
Growing Stock ◽  
Forest Inventories ◽  
Optical Imagery ◽  
Canopy Model

In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0–20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.

Temporal transferability of LiDAR-based imputation of forest inventory attributes

2015 ◽  
Vol 45 (4) ◽  
pp. 422-435 ◽  
Author(s):  
Patrick A. Fekety ◽  
Michael J. Falkowski ◽  
Andrew T. Hudak
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Stand Density ◽  
Stem Volume ◽  
Planning Decisions ◽  
Forest Inventories ◽  
Resource Managers ◽  
Stand Density Index ◽  
Northern Idaho ◽  
Response Variables

Forest inventory and planning decisions are frequently informed by LiDAR data. Repeated LiDAR acquisitions offer an opportunity to update forest inventories and potentially improve forest inventory estimates through time. We leveraged repeated LiDAR and ground measures for a study area in northern Idaho, U.S.A., to predict (via imputation) — across both space and time — four forest inventory attributes: aboveground carbon (AGC), basal area (BA), stand density index (SDI), and total stem volume (Vol). Models were independently developed from 2003 and 2009 LiDAR datasets to spatially predict response variables at both times. Annual rates of change were calculated by comparing response variables between the two collections. Additionally, a pooled model was built by combining reference observations from both years to test if imputation can be performed across measurement dates. The R2 values for the pooled model were 0.87, 0.90, 0.89, and 0.87 for AGC, BA, SDI, and Vol, respectively. Mapping response variables at the landscape level demonstrates that the relationship between field data and LiDAR metrics holds true even though the data were collected in different years. Pooling data across time increases the number of reference observations available to resource managers and may ultimately improve inventory predictions.

scholarly journals Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Caileigh Shoot ◽  
Hans-Erik Andersen ◽  
L. Monika Moskal ◽  
Chad Babcock ◽  
Bruce D. Cook ◽  
...  
Keyword(s):  
Random Forest ◽  
Forest Inventory ◽  
Forest Type ◽  
Vegetation Indices ◽  
National Forest Inventory ◽  
Classification Algorithm ◽  
Lidar Data ◽  
National Forest ◽  
Interior Alaska ◽  
Type Information

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

scholarly journals Zur Auswirkung der Hangneigungskorrektur auf Schätzwerte im Schweizerischen Landesforstinventar (LFI) | Investigation of the effect of the slope correction method as applied in the Swiss National Forest Inventory of estimates

2001 ◽  
Vol 152 (6) ◽  
pp. 215-225 ◽  
Author(s):  
Michael Köhl ◽  
Peter Brassel
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Correction Method ◽  
National Forest Inventory ◽  
National Forest ◽  
Average Deviation ◽  
Forest Inventories ◽  
Slope Correction ◽  
Number Of Stems ◽  
Elliptical Area

For forest inventories on slopes, it is necessary to correct the test areas, because the circular areas, when projected, become elliptical. Based on 93 samples from the Swiss National Forest Inventory (FNI), it was determined whether the simplified method, which increases the radius to match that of the elliptical area, leads to a distortion of the results. An average deviation of 2% was found between the FNI estimated values and the actual values for the basal area and the number of stems. For estimations of smaller units, greater distortions of the results are expected.

Presenting the GeForse approach to create synthetic LiDAR data from simulated forest stands to optimize forest inventories

2021 ◽  
Author(s):  
Fabian E. Fassnacht ◽  
Jannika Schäfer ◽  
Hannah Weiser ◽  
Lukas Winiwarter ◽  
Nina Krašovec ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Forest Growth ◽  
Sensitivity Analyses ◽  
Future Application ◽  
Lidar Data ◽  
Real Point ◽  
Entire Area ◽  
Single Tree ◽  
Forest Inventories ◽  
Tree Models

&lt;p&gt;LiDAR-based forest inventories focusing on estimating and mapping structure-related forest inventory variables across large areas have reached operationality. In the commonly applied area-based approach, a set of field-measured inventory plots is combined with spatially co-located airborne laserscanning data to train empirical models that can then be used to predict the target metric over the entire area covered by LiDAR data.&lt;/p&gt;&lt;p&gt;The area-based approach was found to produce reliable estimates for structure-related forest inventory metrics such as wood volume and biomass across many forest types. However, the current workflows still leave space for improvement that may result in cost-reduction with respect to data acquisition or improved accuracies. This is particularly relevant as the area-based approach is increasingly used in operational forestry settings. To further optimize existing workflows, experiments are required that need large amounts of forest inventory data (e.g., to examine the effect of sample size or the field inventory design on the model performances) or multiple LiDAR acquisitions (e.g., to identify optimal/cost-efficient acquisition settings). The acquisition of these types of data is cost-intensive and is hence often limited to small extents within scientific experiments.&lt;/p&gt;&lt;p&gt;Here, we present the &amp;#8221;GeForse - Generating Synthetic Forest Remote Sensing Data&amp;#8221; approach to create synthetic LiDAR datasets suitable for such optimization studies. GeForse combines a database of single-tree models consisting of point clouds extracted from real LiDAR data with the outputs of a spatially explicit, single tree-based forest growth simulator (in this case SILVA). For each simulated tree, we insert a real point-cloud tree with properties (species, crown diameter, height) matching the properties of the simulated tree. This results in a synthetic 3D forest with a realistic 3D-structure where the inventory metrics of each tree are known. This 3D forest then serves as input to the &amp;#8220;Heidelberg LiDAR Operations Simulator&amp;#8221; (HELIOS++, https://github.com/3dgeo-heidelberg/helios) and thereby enables the simulation of LiDAR acquisition flights with varying acquisition settings and flight trajectories. In combination with the &amp;#8220;full inventory&amp;#8221; of all trees in the simulated forest, this enables a wide variety of sensitivity analyses.&lt;/p&gt;&lt;p&gt;In this contribution, we give an overview of the complete GeForse approach from extracting the tree models, to generating the 3D forest and simulating LiDAR flights over the 3D forest using HELIOS++. Further, we present a brief case-study where this approach was applied to optimize certain aspects of area-based forest inventory approaches using LiDAR data from a forest area in central Europe. Finally, we provide an outlook on future application fields of the GeForse approach.&lt;/p&gt;

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.

Estimating Basal Area and Stem Volume for Individual Trees from Lidar Data

2007 ◽  
Vol 73 (12) ◽  
pp. 1355-1365 ◽  
Author(s):  
Qi Chen ◽  
Peng Gong ◽  
Dennis Baldocchi ◽  
Yong Q. Tian
Keyword(s):  
Basal Area ◽  
Stem Volume ◽  
Lidar Data ◽  
Individual Trees

Supporting large-area, sample-based forest inventories with very high spatial resolution satellite imagery

2009 ◽  
Vol 33 (3) ◽  
pp. 403-423 ◽  
Author(s):  
Michael J. Falkowski ◽  
Michael A. Wulder ◽  
Joanne C. White ◽  
Mark D. Gillis
Keyword(s):  
Forest Inventory ◽  
Information Needs ◽  
High Spatial Resolution ◽  
Temporal Frequency ◽  
Forest Monitoring ◽  
Large Area ◽  
Remotely Sensed Imagery ◽  
Forest Inventories ◽  
Very High Spatial Resolution ◽  
Very High

Information needs associated with forest management and reporting requires data with a steadily increasing level of detail and temporal frequency. Remote sensing satellites commonly used for forest monitoring (eg, Landsat, SPOT) typically collect imagery with sufficient temporal frequency, but lack the requisite spatial and categorical detail for some forest inventory information needs. Aerial photography remains a principal data source for forest inventory; however, information extraction is primarily accomplished through manual processes. The spatial, categorical, and temporal information requirements of large-area forest inventories can be met through sample-based data collection. Opportunities exist for very high spatial resolution (VHSR; ie, <1 m) remotely sensed imagery to augment traditional data sources for large-area, sample-based forest inventories, especially for inventory update. In this paper, we synthesize the state-of-the-art in the use of VHSR remotely sensed imagery for forest inventory and monitoring. Based upon this review, we develop a framework for updating a sample-based, large-area forest inventory that incorporates VHSR imagery. Using the information needs of the Canadian National Forest Inventory (NFI) for context, we demonstrate the potential capabilities of VHSR imagery in four phases of the forest inventory update process: stand delineation, automated attribution, manual interpretation, and indirect attribute modelling. Although designed to support the information needs of the Canadian NFI, the framework presented herein could be adapted to support other sample-based, large-area forest monitoring initiatives.

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