Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables

Strunk, Jacob L.; Temesgen, Hailemariam; Andersen, Hans‐Erik; Flewelling, James; Madsen, Lisa

doi:10.5589/m12-052

Cited by 50 publications

(39 citation statements)

References 23 publications

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“…Previous studies have evaluated the impact of lidar pulse density on forest attribute estimation from lidar data (e.g., [31][32][33]), yet few studies have evaluated the impacts of lidar pulse density on forest AGB stock estimates in tropical forest [19,20,34]. To our knowledge, this is the first study to assess the impact of airborne lidar pulse density on AGB stocks and AGB change estimations in tropical forest, and in the context of using an airborne lidar system in selective logging for monitoring forest AGB change for REDD+ and emission reduction programs.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we mapped AGB change at the landscape level at a spatial resolution of 50 m. Therefore, unlike most previous studies (e.g., [31][32][33]), we not only evaluated the impact of pulse density on AGB stocks estimation at plot the level, but also at the landscape scale. Andersen et al [8] used repeated lidar flights to monitor selective logging in western Amazonia, and AGB stock and changes maps were accurately derived from lidar data acquired in 2010 and 2011 with pulse densities of 25 pulses·m −2 and 14 pulses·m −2 , respectively.…”

Section: Discussionmentioning

confidence: 99%

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Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

et al. 2017

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Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m −2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m −2 . For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha −1 when pulse density decreased from 12 to 0.2 pulses·m −2 . The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha −1 . The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

et al. 2017

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“…<1 pulse·m −2 ) affect the quality of the biomass estimates, controlled reduction of the ALS data has been used to assess the effect on model prediction accuracy [12][13][14][15][16][17][18][19][20][21][22][23]. Findings from such controlled experiments have resulted in reduced pulse density and lowered cost for ALS acquisitions for operational forest inventory purposes, making the technology more widely applied while providing biomass estimates at acceptable precision levels.…”

Section: Introductionmentioning

confidence: 99%

Effects of Pulse Density on Digital Terrain Models and Canopy Metrics Using Airborne Laser Scanning in a Tropical Rainforest

Hansen¹,

Gobakken²,

Næsset³

2015

Remote Sensing

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Airborne laser scanning (ALS) is increasingly being used to enhance the accuracy of biomass estimates in tropical forests. Although the technological development of ALS instruments has resulted in ever-greater pulse densities, studies in boreal and sub-boreal forests have shown consistent results even at relatively small pulse densities. The objective of the present study was to assess the effects of reduced pulse density on (1) the digital terrain model (DTM), and (2) canopy metrics derived from ALS data collected in a tropical rainforest in Tanzania. We used a total of 612 coordinates measured with a differential dual frequency Global Navigation Satellite System receiver to analyze the effects on DTMs at pulse densities of 8, 4, 2, 1, 0.5, and 0.025 pulses·m . Furthermore, canopy metrics derived for each pulse density and from four different field plot sizes (0.07, 0.14, 0.21, and 0.28 ha) were analyzed. Random variation in DTMs and canopy metrics increased with reduced pulse density. Similarly, increased plot size reduced variation in canopy metrics. A reliability ratio, quantifying replication effects in the canopy metrics, indicated that most of the common metrics assessed were reliable at pulse densities >0.5 pulses·m −2 at a plot size of 0.07 ha.

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“…Plots have different size because the field surveys were made with various aims, but many studies aimed at analysing the influence of plot size (and LiDAR density in some cases) on forest structure attribute estimates (i.e. Strunk et al 2012, Watt et al 2013, Ruiz et al 2014 have demonstrated that minimum plot areas of 500-600 m 2 are needed for basal area estimates and that larger plot sizes do not significantly increase the accuracy of the estimates, but they do, however, increase the cost of fieldwork. We want point out that the assessment of the influence of plot size on forest structure attribute by means of LiDAR data was not a goal of this study.…”

Section: Discussionmentioning

confidence: 99%

Using classification trees to predict forest structure types from LiDAR data

Torresan

Corona

Scrinzi³

et al. 2016

Ann. For. Res.

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Torresan C., Corona P., Scrinzi G., Marsal J.V., 2016. Using classification trees to predict forest structure types from LiDAR data. Ann. For. Res. 59(1): 281-298.Abstract. This study assesses whether metrics extracted from airborne Li-DAR (Light Detection and Ranging) raw point cloud can be exploited to predict different forest structure types by means of classification trees. Preliminarily, a bivariate analysis by means of Pearson statistical test was developed to find associations between LiDAR metrics and the proportion of basal area into three stem diameter classes (understory, mid-story, and over-story trees) of 243 random distributed plots surveyed from 2007 to 2012 in Trento Province (Northern Italy). An unsupervised clustering approach was adopted to determine forest structural patterns on the basis of basal area proportion in the three stem diameter classes, using a k-means procedure combined with a previous hierarchical classification algorithm. A comparison among the identified clusters centroids was performed by the Kruskall-Wallis test. A classification tree model to predict forest structural patterns originating from the cluster analysis was developed and validated. Between 18 potential LiDAR metrics, 11 were significantly correlated with the proportion of basal area of understory, mid-story, and overstory trees. The results coming from the agglomerative hierarchical clustering allowed identification of 5 clusters of forest structure: pole-stage (70% of the considered cases), young (15%), adult (24.3%), mature (24.3%), and old forests (30%). Five LiDAR metrics were selected by the classification tree to predict the forest structural types: standard deviation and mode of canopy heights, height at which 95% and 99% of canopy heights fall below, difference between height at which 90% and 10% of canopy heights fall below. The validation tree model process showed a misclassification error of 45.9% and a level of user's accuracy ranging between 100% and 33.3% in the validation data set. The highest level of user's accuracy was reached in the classification of pole-stage forests (100%), in which more than 82% of basal area is due to the understory-trees, follow by the classification of old forests types (63.5% of basal area due to the overstory-trees) achieved 76.5% of user's accuracy. The model has provided moderately satisfactory results in term of classification performance: substantial room for improvement might be established by multi-or hyperspectral imaging that allow detailed characterization of the spectral behaviour of the forest structure types.

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Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables

Cited by 50 publications

References 23 publications

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

Effects of Pulse Density on Digital Terrain Models and Canopy Metrics Using Airborne Laser Scanning in a Tropical Rainforest

Using classification trees to predict forest structure types from LiDAR data

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