Estimating forest timber volume by means of “low-cost” LiDAR data

Clementel, F.; Colle, Giacomo; Farruggia, Claudia; Floris, Antonio; Scrinzi, G.; Torresan, Chiara; Nicolini, Piazza

doi:10.5721/itjrs201244110

Cited by 18 publications

(9 citation statements)

References 11 publications

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“…Thus, it is important to be aware of this when using this obtained forest stand height to assess other forest stand parameters, because of error accumulation in the calculation. There is the possibility to improve the input data for the calculation of other forest stand characteristics, e.g., by using forest inventory data and statistical models (Andersen et al 2005, Naesset et al 2005, Hollaus et al 2009, Clementel et al 2012. Although some individual results of the present study are similar to those of other authors (Heurich 2008, Wezyk et al 2008, most are relatively poor.…”

Section: Resultscontrasting

confidence: 43%

Forest stand height determination from low point density airborne laser scanning data in Roznava Forest enterprise zone (Slovakia)

Smreček¹,

Danihelová²

2013

iForest

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The presented paper discusses the potential of low point density airborne laser scanning (ALS) data for use in forestry management. Scanning was carried out in the Rožnava Forest enterprise zone, Slovakia, with a mean laser point density of 1 point per 3 m2. Data were processed in SCOP++ using the hierarchic robust filtering technique. Two DTMs were created from airborne laser scanning (ALS) and contour data and one DSM was created using ALS data. For forest stand height, two normalised DSMs (nDSMs) were created by subtraction of the DSM and DTM. The forest stand heights derived from these nDSMs and the application of maximum and mean zonal functions were compared with those contained in the current Forest Management Plan (FMP). The forest stand heights derived from these data and the application of maxima and mean zonal functions were compared with those contained in the current Forest management plan. The use of the mean function and the contour-derived DTM resulted in forest stand height being underestimated by approximately 3% for stands of densities 0.9 and 1.0, and overestimated by 6% for a stand density of 0.8. Overestimation was significantly greater for lower forest stand densities: 81% for a stand density of 0.0 and 37% for a density of 0.4, with other discrepancies ranging between 15 and 30%. Although low point density ALS should be used carefully in the determination of other forest stand parameters, this low-cost method makes it useful as a control tool for felling, measurement of disaster areas and the detection of gross errors in the FMP data. Through determination of forest stand height, tree felling in three forest stands was identified. Because of big differences between the determined forest stand height and the heights obtained from the FMP, tree felling was verified on orthoimages

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Section: Resultscontrasting

confidence: 43%

Forest stand height determination from low point density airborne laser scanning data in Roznava Forest enterprise zone (Slovakia)

Smreček¹,

Danihelová²

2013

iForest

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“…This representation has commonly been used by various research studies producing maps of forest parameters, such as basal area or timber volume [15,18,45], in order to facilitate the interpretability, as well as the readability of a map. We propose to treat this map representation as a classification procedure and, consequently, adopted the concept of confusion matrices to provide a differentiated assessment for each particular class as well as the complete mapping system.…”

Section: Classification Accuracymentioning

confidence: 99%

Accuracy Assessment of Timber Volume Maps Using Forest Inventory Data and LiDAR Canopy Height Models

Hill

Breschan

Mandallaz

2014

Forests

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Maps of standing timber volume provide valuable decision support for forest managers and have therefore been the subject of recent studies. For map production, field observations are commonly combined with area-wide remote sensing data in order to formulate prediction models, which are then applied over the entire inventory area. The accuracy of such maps has frequently been described by parameters such as the root mean square error of the prediction model. The aim of this study was to additionally address the accuracy of timber volume classes, which are used to better represent the map predictions. However, the use of constant class intervals neglects the possibility that the precision of the underlying prediction model may not be constant across the entire volume range, resulting in pronounced gradients between class accuracies. This study proposes an optimization technique that automatically identifies a classification scheme which accounts for the properties of the underlying model and the implied properties of the remote sensing support information. We demonstrate the approach in a mountainous study site in Eastern Switzerland covering a forest area of 2000 hectares using a multiple linear regression model approach. A LiDAR-based canopy height model (CHM) provided the auxiliary information; timber volume observations from the latest forest inventory were used for model calibration and map validation. classes, the optimization model successfully identified those classification schemes that achieved the highest possible accuracies for each class.

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“…without green foliage during winter), which are those mostly frequent under temperate and Mediterranean forest environments, these rates can significantly be raised by winter surveys [Ackermann et al, 1994]: thus, commercial ALS flights for DTM production are almost always made between November and March under temperate and Mediterranean environments and even under alpine environments, at least in those areas not permanently covered by snow in that period. On the contrary the most favorable season to obtain ALS data specifically suitable for forestry applications under temperate and Mediterranean environments is summer, since in winter only the wooden part of the prevalently deciduous canopies generates LiDARsignificant returns [Clementel et al, 2012]. Summer and winter CHMs have been compared by Clementel et al [2010] in an alpine site, showing strong correlation but with a systematic, significant stand height underestimation by the winter CHM.…”

Section: Data Availabilitymentioning

confidence: 99%

“…ALS data most commonly used for forestry applications under alpine, temperate and Mediterranean environments are not produced by dedicated flights: forest technicians simply exploit the raster CHM available at low [Clementel et al, 2012] or even no cost from ALS surveys carried out for purposes other than forest applications. In Italy around 30% of the land has been covered so far for accurate hydraulic modeling.…”

Section: Data Availabilitymentioning

confidence: 99%

Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

Corona

Cartisano

Salvati

et al. 2012

European Journal of Remote Sensing

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This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates.

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Estimating forest timber volume by means of “low-cost” LiDAR data

Cited by 18 publications

References 11 publications

Forest stand height determination from low point density airborne laser scanning data in Roznava Forest enterprise zone (Slovakia)

Forest stand height determination from low point density airborne laser scanning data in Roznava Forest enterprise zone (Slovakia)

Accuracy Assessment of Timber Volume Maps Using Forest Inventory Data and LiDAR Canopy Height Models

Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

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