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Special Issue "Image-Based Point Clouds for Forest Inventory Applications"

A special issue of Forests (ISSN 1999-4907).

Deadline for manuscript submissions: closed (15 September 2015)

Special Issue Editor

Guest Editor
Ms. Joanne C. White

Research Scientist, Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, Victoria, British Columbia V8Z 1M5, Canada
Website | E-Mail
Interests: remote sensing; forest inventory; multi-temporal; forest dynamics; disturbance; Landsat; lidar; image-based DSM; large-area

Special Issue Information

Dear Colleagues;

The capacity to acquire information characterizing three-dimensional (3D) forest vertical structure has revolutionized forest inventories around the globe. While Airborne Laser Scanning (ALS) has been the primary data source for this three-dimensional information, there is growing interest in the use of high spatial resolution digital aerial imagery to generate information analogous to ALS data to support forest inventory and monitoring. This interest in alternative technologies for three-dimensional information can be attributed in part to a need to control costs, but also relates to the traditional role that imagery has played in forest inventory, the technical capacity of many forest management agencies, and current regulatory requirements.

This Special Issue, on "Image-Based Point Clouds for Forest Inventory Applications", is broadly targeting research papers that explore the use of image-based point clouds for forest inventory applications. This includes case studies that provide a rigorous comparison and documentation of forest inventory outcomes achievable with ALS data and image-based DSMs across a range of forest environments. Papers should contribute to advancing our understanding of the issues and opportunities associated with the use of image-based point clouds for forest inventory applications.

Joanne C. White
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital aerial images
  • dense image matching
  • forest inventory
  • airborne laser scanning, ALS
  • LiDAR
  • area-based approach
  • digital photogrammetry
  • point cloud
  • semi-global matching (SGM)
  • digital surface model (DSM)
  • canopy height model (CHM)

Published Papers (7 papers)

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Research

Open AccessArticle Data Assimilation in Forest Inventory: First Empirical Results
Forests 2015, 6(12), 4540-4557; https://doi.org/10.3390/f6124384
Received: 15 September 2015 / Revised: 1 December 2015 / Accepted: 4 December 2015 / Published: 11 December 2015
Cited by 10 | PDF Full-text (3154 KB) | HTML Full-text | XML Full-text
Abstract
Data assimilation techniques were used to estimate forest stand data in 2011 by sequentially combining remote sensing based estimates of forest variables with predictions from growth models. Estimates of stand data, based on canopy height models obtained from image matching of digital aerial [...] Read more.
Data assimilation techniques were used to estimate forest stand data in 2011 by sequentially combining remote sensing based estimates of forest variables with predictions from growth models. Estimates of stand data, based on canopy height models obtained from image matching of digital aerial images at six different time-points between 2003 and 2011, served as input to the data assimilation. The assimilation routines were built on the extended Kalman filter. The study was conducted in hemi-boreal forest at the Remningstorp test site in southern Sweden (lat. 13°37′ N; long. 58°28′ E). The assimilation results were compared with two other methods used in practice for estimation of forest variables: the first was to use only the most recent estimate obtained from remotely sensed data (2011) and the second was to forecast the first estimate (2003) to the endpoint (2011). All three approaches were validated using nine 40 m radius validation plots, which were carefully measured in the field. The results showed that the data assimilation approach provided better results than the two alternative methods. Data assimilation of remote sensing time series has been used previously for calibrating forest ecosystem models, but, to our knowledge, this is the first study with real data where data assimilation has been used for estimating forest inventory data. The study constitutes a starting point for the development of a framework useful for sequentially utilizing all types of remote sensing data in order to provide precise and up-to-date estimates of forest stand parameters. Full article
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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Open AccessArticle Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition
Forests 2015, 6(12), 4510-4528; https://doi.org/10.3390/f6124386
Received: 16 September 2015 / Revised: 30 November 2015 / Accepted: 4 December 2015 / Published: 11 December 2015
Cited by 13 | PDF Full-text (5799 KB) | HTML Full-text | XML Full-text
Abstract
Forest mapping is an important source of information for assessing woodland resources and a key issue for any National Forest Inventory (NFI). In the present study, a detailed wall-to-wall forest cover map was generated for all of Switzerland, which meets the requirement of [...] Read more.
Forest mapping is an important source of information for assessing woodland resources and a key issue for any National Forest Inventory (NFI). In the present study, a detailed wall-to-wall forest cover map was generated for all of Switzerland, which meets the requirement of the Swiss NFI forest definition. The workflow is highly automated and based on digital surface models from image-based point clouds of airborne digital sensor data. It fully takes into account the four key criteria of minimum tree height, crown coverage, width, and land use. The forest cover map was validated using almost 10,000 terrestrial and stereo-interpreted NFI plots, which verified 97% agreement overall. This validation implies different categories such as five production regions, altitude, tree type, and distance to the forest border. Overall accuracy was lower at forest borders but increased with increasing distance from the forest border. Commission errors remained stable at around 10%, but increased to 17.6% at the upper tree line. Omission errors were low at 1%–10%, but also increased with altitude and mainly occurred at the upper tree line (19.7%). The main reasons for this are the lower image quality and the NFI height definition for forest which apparently excludes shrub forest from the mask. The presented forest mapping approach is superior to existing products due to its national coverage, high level of detail, regular updating, and implementation of the land use criteria. Full article
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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Open AccessArticle Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA
Forests 2015, 6(11), 4059-4071; https://doi.org/10.3390/f6114059
Received: 26 June 2015 / Revised: 13 October 2015 / Accepted: 28 October 2015 / Published: 10 November 2015
Cited by 15 | PDF Full-text (1114 KB) | HTML Full-text | XML Full-text
Abstract
Image-based point clouds obtained using aerial photogrammetry share many characteristics with point clouds obtained by airborne laser scanning (ALS). Two approaches have been used to predict forest parameters from ALS: the area-based approach (ABA) and the individual tree crown (ITC) approach. In this [...] Read more.
Image-based point clouds obtained using aerial photogrammetry share many characteristics with point clouds obtained by airborne laser scanning (ALS). Two approaches have been used to predict forest parameters from ALS: the area-based approach (ABA) and the individual tree crown (ITC) approach. In this article, we apply the semi-ITC approach, a variety of the ITC approach, on an image-based point cloud to predict forest parameters and compare the performance to the ABA. Norwegian National Forest Inventory sample plots on a site in southeastern Norway were used as the reference data. Tree crown objects were delineated using a watershed segmentation algorithm, and explanatory variables were calculated for each tree crown segment. A multivariate kNN model for timber volume, stem density, basal area and quadratic mean diameter with the semi-ITC approach produced RMSEs of 30%, 46%, 25%, 26%, respectively. The corresponding measures for the ABA were 30%, 51%, 26%, 35%, respectively. Univariate kNN models resulted in timber volume RMSEs of 25% for the semi-ITC approach and 22% for the ABA. A non-linear logistic regression model with the ABA produced an RMSE of 23%. Both approaches predicted timber volume with comparable precision and accuracy at the plot level. The multivariate kNN model was slightly more precise with the semi-ITC approach, while biases were larger Full article
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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Open AccessArticle A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario
Forests 2015, 6(11), 4034-4054; https://doi.org/10.3390/f6114034
Received: 24 August 2015 / Revised: 29 October 2015 / Accepted: 30 October 2015 / Published: 9 November 2015
Cited by 14 | PDF Full-text (1886 KB) | HTML Full-text | XML Full-text
Abstract
Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within [...] Read more.
Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within a stand. Adding size class distribution information to area-based inventories helps to bridge the gap between area- and tree-based inventories. This study examines the potential of ALS and stereo-imagery point clouds to predict size class distributions in a boreal forest. With an accurate digital terrain model, both ALS and imagery point clouds can be used to estimate size class distributions with comparable accuracy. Nonparametric imputations were generally superior to parametric imputations; this may be related to the limitation of using a unimodal Weibull function on a relatively small prediction unit (e.g., 400 m2). Full article
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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Open AccessArticle Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds
Forests 2015, 6(11), 3899-3922; https://doi.org/10.3390/f6113899
Received: 15 September 2015 / Revised: 22 October 2015 / Accepted: 26 October 2015 / Published: 30 October 2015
Cited by 25 | PDF Full-text (2573 KB) | HTML Full-text | XML Full-text
Abstract
Photogrammetric point clouds (PPC) obtained by stereomatching of aerial photographs now have a resolution sufficient to discern individual trees. We have produced such PPCs of a boreal forest and delineated individual tree crowns using a segmentation algorithm applied to the canopy height model [...] Read more.
Photogrammetric point clouds (PPC) obtained by stereomatching of aerial photographs now have a resolution sufficient to discern individual trees. We have produced such PPCs of a boreal forest and delineated individual tree crowns using a segmentation algorithm applied to the canopy height model derived from the PPC and a lidar terrain model. The crowns were characterized in terms of height and species (spruce, fir, and deciduous). Species classification used the 3D shape of the single crowns and their reflectance properties. The same was performed on a lidar dataset. Results show that the quality of PPC data generally approaches that of airborne lidar. For pixel-based canopy height models, viewing geometry in aerial images, forest structure (dense vs. open canopies), and composition (deciduous vs. conifers) influenced the quality of the 3D reconstruction of PPCs relative to lidar. Nevertheless, when individual tree height distributions were analyzed, PPC-based results were very similar to those extracted from lidar. The random forest classification (RF) of individual trees performed better in the lidar case when only 3D metrics were used (83% accuracy for lidar, 79% for PPC). However, when 3D and intensity or multispectral data were used together, the accuracy of PPCs (89%) surpassed that of lidar (86%). Full article
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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Open AccessArticle Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest
Forests 2015, 6(11), 3882-3898; https://doi.org/10.3390/f6113882
Received: 11 September 2015 / Revised: 18 October 2015 / Accepted: 26 October 2015 / Published: 30 October 2015
Cited by 30 | PDF Full-text (850 KB) | HTML Full-text | XML Full-text
Abstract
We investigated the capabilities of a canopy height model (CHM) derived from aerial photographs using the Structure from Motion (SfM) approach to estimate aboveground biomass (AGB) in a tropical forest. Aerial photographs and airborne Light Detection and Ranging (LiDAR) data were simultaneously acquired [...] Read more.
We investigated the capabilities of a canopy height model (CHM) derived from aerial photographs using the Structure from Motion (SfM) approach to estimate aboveground biomass (AGB) in a tropical forest. Aerial photographs and airborne Light Detection and Ranging (LiDAR) data were simultaneously acquired under leaf-on canopy conditions. A 3D point cloud was generated from aerial photographs using the SfM approach and converted to a digital surface model (DSMP). We also created a DSM from airborne LiDAR data (DSML). From each of DSMP and DSML, we constructed digital terrain models (DTM), which are DTMP and DTML, respectively. We created four CHMs, which were calculated from (1) DSMP and DTMP (CHMPP); (2) DSMP and DTML (CHMPL); (3) DSML and DTMP (CHMLP); and (4) DSML and DTML (CHMLL). Then, we estimated AGB using these CHMs. The model using CHMLL yielded the highest accuracy in four CHMs (R2 = 0.94) and was comparable to the model using CHMPL (R2 = 0.93). The model using CHMPP yielded the lowest accuracy (R2 = 0.79). In conclusion, AGB can be estimated from CHM derived from aerial photographs using the SfM approach in the tropics. However, to accurately estimate AGB, we need a more accurate DTM than the DTM derived from aerial photographs using the SfM approach. Full article
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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Open AccessArticle Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment
Forests 2015, 6(10), 3704-3732; https://doi.org/10.3390/f6103704
Received: 4 August 2015 / Revised: 29 September 2015 / Accepted: 8 October 2015 / Published: 15 October 2015
Cited by 47 | PDF Full-text (2917 KB) | HTML Full-text | XML Full-text
Abstract
Digital aerial photogrammetry (DAP) is emerging as an alternate data source to airborne laser scanning (ALS) data for three-dimensional characterization of forest structure. In this study we compare point cloud metrics and plot-level model estimates derived from ALS data and an image-based point [...] Read more.
Digital aerial photogrammetry (DAP) is emerging as an alternate data source to airborne laser scanning (ALS) data for three-dimensional characterization of forest structure. In this study we compare point cloud metrics and plot-level model estimates derived from ALS data and an image-based point cloud generated using semi-global matching (SGM) for a complex, coastal forest in western Canada. Plot-level estimates of Lorey’s mean height (H), basal area (G), and gross volume (V) were modelled using an area-based approach. Metrics and model outcomes were evaluated across a series of strata defined by slope and canopy cover, as well as by image acquisition date. We found statistically significant differences between ALS and SGM metrics for all strata for five of the eight metrics we used for model development. We also found that the similarity between metrics from the two data sources generally increased with increasing canopy cover, particularly for upper canopy metrics, whereas trends across slope classes were less consistent. Model outcomes from ALS and SGM were comparable. We found the greatest difference in model outcomes was for H (ΔRMSE% = 5.04%). By comparison, ΔRMSE% was 2.33% for G and 3.63% for V. We did not discern any corresponding trends in model outcomes across slope and canopy cover strata, or associated with different image acquisition dates. Full article
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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