Retrieval of forest structural parameters using LiDAR remote sensing

Leeuwen, Martin van; Nieuwenhuis, Maarten

doi:10.1007/s10342-010-0381-4

Cited by 333 publications

(199 citation statements)

References 102 publications

(161 reference statements)

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“…Airborne Light Detection and Ranging (LiDAR) is an active remote sensing system which directly estimates the vertical structure of objects by measuring the time of flight between the emitted laser pulses and their received reflectance [10]. This system is well suited for measuring forest structural parameters [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest

Ota

Ogawa

Shimizu

et al. 2015

Forests

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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 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 (R 2 = 0.94) and was comparable to the model using CHMPL (R 2 = 0.93). The model using CHMPP yielded the lowest accuracy (R 2 = 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.

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Section: Introductionmentioning

confidence: 99%

Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest

Ota

Ogawa

Shimizu

et al. 2015

Forests

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“…The capacity to acquire information characterizing the three-dimensional structure of forest canopies has revolutionized forest inventories around the globe [1]. Airborne Laser Scanning (ALS), also referred to as Light Detection and Ranging (LiDAR), has been the primary data source for three-dimensional information on forest vertical structure [2][3][4][5][6], however, there is an increasing interest in the use of high spatial resolution digital aerial imagery to generate information analogous to ALS data [7][8][9] to support forest inventory and monitoring.…”

Section: Introductionmentioning

confidence: 99%

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

White

Wulder

Vastaranta

et al. 2013

Forests

276

217

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Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including height, basal area, and volume). Recently, there has been increasing interest in the advanced processing of high spatial resolution digital airborne imagery to generate image-based point clouds, from which vertical information with similarities to ALS can be produced. Digital airborne imagery is typically less costly to acquire than ALS, is well understood by inventory practitioners, and in addition to enabling the derivation of height information, allows for visual interpretation of attributes that are currently problematic to estimate from ALS (such as species, health status, and maturity). At present, there are two limiting factors associated OPEN ACCESSForests 2013, 4 519 with the use of image-based point clouds. First, a DEM is required to normalize the image-based point cloud heights to aboveground heights; however DEMs with sufficient spatial resolution and vertical accuracy, particularly in forested areas, are usually only available from ALS data. The use of image-based point clouds may therefore be limited to those forest areas that already have an ALS-derived DEM. Second, image-based point clouds primarily characterize the outer envelope of the forest canopy, whereas ALS pulses penetrate the canopy and provide information on sub-canopy forest structure. The impact of these limiting factors on the estimation of forest inventory attributes has not been extensively researched and is not yet well understood. In this paper, we review the key similarities and differences between ALS data and image-based point clouds, summarize the results of current research related to the comparative use of these data for forest inventory attribute estimation, and highlight some outstanding research questions that should be addressed before any definitive recommendation can be made regarding the use of image-based point clouds for this application.

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“…The flexibility of airborne LiDAR, coupled with a high level of positional accuracy and point density, makes airborne LiDAR systems an attractive data acquisition tool for estimating a wide range of tree and forest parameters (Laes et al 2011) such as tree height (Andersen et al 2006;Detto et al 2013), stem volume (Heurich and Thoma 2008), tree biomass (Li et al 2008), and leaf area index (Morsdorf et al 2006). The use of airborne LiDAR for estimating forest inventory parameters and structural characteristics is reviewed by van Leeuwen and Nieuwenhuis (2010), and a meta-analysis of 70 articles has been conducted by Zolkos et al (2013). Airborne LiDAR is already being used to support the national carbon stock estimates, such as in New Zealand (Stephens et al 2012) or in Panama .…”

Section: Light Detection and Ranging Systemsmentioning

confidence: 99%

An overview of existing and promising technologies for national forest monitoring

Henry

Réjou‐Méchain

Cifuentes-Jara

et al. 2015

Annals of Forest Science

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Retrieval of forest structural parameters using LiDAR remote sensing

Cited by 333 publications

References 102 publications

Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest

Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

An overview of existing and promising technologies for national forest monitoring

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