Assessment of forest structure with airborne LiDAR and the effects of platform altitude

Goodwin, Nicholas; Coops, Nicholas C.; Culvenor, Darius

doi:10.1016/j.rse.2006.03.003

Cited by 205 publications

(142 citation statements)

References 28 publications

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“…The results from this study corroborate the finding of Goodwin et al (2006) and suggest that for forest studies based on plot-level canopy data modelling, a higher laser sampling point density does not necessarily produce data that are "superior" with respect to information content (i.e., canopy height and density metrics). It is postulated that the primary advantage of using a higher laser sampling point density lies with a more precise characterization of the upper and lower forest canopy components and potentially for terrain surface modeling.…”

Section: Discussionsupporting

confidence: 85%

“…Second, while Naesset (2004a) applied Bonferroni corrections to control for Type I errors, the experiment-wise error rate was not adjusted to control for Type II errors as suggested by Chandler (1995), and without the p-values reported, direct comparisons of results from the 2 studies are not possible. Goodwin et al (2006) compared normalized canopy height profiles obtained from different flying altitudes for a Eucalyptus forest in Australia. The authors found that at the plot level, there was no significant difference between the relative distribution of LiDAR returns for canopy height profiles derived from 3 different altitudes (1000 m, 2000 m and 300 m), indicating that flying altitude and footprint size do not appear to affect canopy height profile estimation.…”

Section: Discussionmentioning

confidence: 99%

“…This paper does not treat the topic of how these variables are used for the estimation of forest biophysical properties, but instead focuses on the types of variables typically employed and how sampling point density impacts those variables. Note that we do not attempt to treat how individual input survey parameters, such as PRF, scan frequency, scan angle, or beam divergence, affects laser canopy height and density metrics as in the case of Naesset (2004b) or Goodwin et al (2006), but instead focus on the effects of sampling point density on these metrics. It is extremely important to understand the impact that sampling density has on estimating forest structural variables, in order that cost-effective surveys can be designed to maximize information content while minimizing acquisition costs.…”

Section: Introductionmentioning

confidence: 99%

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Examining the effects of sampling point densities on laser canopy height and density metrics

Lim¹,

Hopkinson²,

Treitz³

2008

The Forestry Chronicle

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Forest resource managers rely on the information extracted from forest resource inventories to manage forests sustainably and efficiently, thereby supporting more precise decision-making. Light detection and ranging (LiDAR) is a relatively new technology that has proven to enhance forest resource inventories. However, the relationship between LiDAR sampling point density (which is directly related to acquisition and processing costs) and accuracy and precision of forest variable estimation has not yet been established across a range of forest ecosystems. In this study, 2 airborne LiDAR surveys using the same sensor, but configured with disparate parameters, were carried out over the York Regional Forest near Toronto, Canada producing 2 data sets characterized by different sampling point densities. The effects of 2 sampling point densities on 23 laser canopy height and density metrics typically used in forest studies at the plot level were examined with comparisons grouped by first and last return data. The minimum (h min ) and maximum (h max ) laser canopy heights were statistically different for first and last returns. The proportion of laser returns (i.e., canopy density) in the upper (d 1 ) and lower (d 10 ) range of laser canopy heights was statistically different for the first returns, whereas only a single canopy density metric was different for the last returns (d 9 ). These results suggest that changes in sampling point density (due to changes in scan angle and altitude) only affect laser canopy height and density metrics that are characterized by the small percentage of returns from the very top (h max ; d 1 ) and base of the canopy (h min ; d 10 ) (i.e., those metrics that characterize the tail ends of the distributions of laser canopy heights). Consequently, higher sampling point densities may add little value to current LiDAR forest research or operations at the stand level, as metrics derived from the canopy profile can be implemented for biophysical variable estimation. Implications for forest management are in terms of identifying which aspects of LiDAR project design: a) impact the quality and cost-effectiveness of derived FRI information; b) should be specified within a LiDAR request for proposals; or c) scrutinized within LiDAR project tender documentation.Key words: LiDAR, airborne laser scanning, FRI, canopy profile, biophysical variables, sampling density RÉSUMÉLes gestionnaires des ressources forestières doivent référer aux informations obtenues des inventaires forestiers pour pouvoir aménager les forêts de façon durable et efficace ce qui de fait nécessite une prise de décision plus précise. La détection de la lumière et le calcul des distances (LiDAR) constituent une technologie relativement nouvelle qui s'avère capable d'améliorer les inventaires des ressources forestières. Cependant, la relation entre la densité des points d' échantillonnage du LiDAR (qui a un impact direct sur les coûts d'acquisition et de traitement des données) et l' exactitude et la précision de l' estimatio...

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Examining the effects of sampling point densities on laser canopy height and density metrics

Lim¹,

Hopkinson²,

Treitz³

2008

The Forestry Chronicle

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“…A number of researchers have examined the impacts of different sensor and survey parameters on estimating forest inventory variables [23][24][25][26][27][28][29][30][31][32][33]. It has been shown that the plot-level vertical distribution of LiDAR pulse returns remains relatively consistent with flying altitude, albeit with some subtle differences [27,28].…”

Section: Introductionmentioning

confidence: 99%

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

et al. 2012

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Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m −2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m −2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m −2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.

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“…Depending on the nature of the surface, a portion of the reflected pulse may be returned to the instrument where, if the pulse's magnitude exceeds a predefined threshold, the time elapsed between emittance and reflectance is recorded (Goodwin et al 2006). Based on our knowledge of the speed at which light travels, the time required for the emitted pulse to return to the sensor is converted to a distance.…”

Section: Lidar Technical Characteristicsmentioning

confidence: 99%

The role of LiDAR in sustainable forest management

Wulder¹,

Bater²,

Coops³

et al. 2008

The Forestry Chronicle

309

186

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Forest characterization with light detection and ranging (LiDAR) data has recently garnered much scientific and operational attention. The number of forest inventory attributes that may be directly measured with LiDAR is limited; however, when considered within the context of all the measured and derived attributes required to complete a forest inventory, LiDAR can be a valuable tool in the inventory process. In this paper, we present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. The information needs of sustainable forest management provide the context within which we consider future opportunities for LiDAR and automated data processing.Key words: LiDAR, airborne laser altimetry, forest inventory, height, volume, biomass, update, remote sensing RÉSUMÉ La représentation des forêts à partir de données LiDAR (détection de la lumière et calcul de la distance) a attiré dernière-ment beaucoup d'attention tant scientifique qu' opérationnelle. Le nombre de variables d'inventaire forestier qui peuvent être mesurées directement par LiDAR est limité, mais lorsqu' on considère le contexte de toutes les variables mesurées et dérivées requises pour compléter un inventaire forestier, le LiDAR peut constituer un outil précieux du processus d'inventaire. Nous présentons dans cet article un portrait de la télédétection des forêts par LiDAR, ainsi que les questions portant sur l'appareillage, la collecte des données, les coûts et l' estimation des variables. Les besoins d'information en matière d'aménagement forestier durable constituent le contexte que nous retenons pour les possibilités d'application future du LiDAR et du traitement automatisé des données.

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Assessment of forest structure with airborne LiDAR and the effects of platform altitude

Cited by 205 publications

References 28 publications

Examining the effects of sampling point densities on laser canopy height and density metrics

Examining the effects of sampling point densities on laser canopy height and density metrics

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

The role of LiDAR in sustainable forest management

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