Comparison of field sampling methods for assessing coarse woody debris and use of airborne laser scanning as auxiliary information

Pesonen, Annukka; Leino, Olli; Maltamo, Matti; Kangas, Annika

doi:10.1016/j.foreco.2009.01.009

Cited by 34 publications

(26 citation statements)

References 27 publications

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“…This hypothesis was tested by simulating the sampling of a reduced number of field reference plots for predicting V (cf. [52][53][54]), i.e., applying Probability Proportional to Size (PPS) sampling for the selection of the first-stage sample units [79].…”

Section: Unsupervised Classification Of the Forested Areamentioning

confidence: 99%

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Niemi

2016

Remote Sensing

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Abstract:Area-based analyses of airborne laser scanning (ALS) data are an established approach to obtain wall-to-wall predictions of forest characteristics for vast areas. The analyses of sparse data in particular are based on the height value distributions, which do not produce optimal information on the horizontal forest structure. We evaluated the complementary potential of features quantifying the textural variation of ALS-based canopy height models (CHMs) for both supervised (linear regression) and unsupervised (k-Means clustering) analyses. Based on a comprehensive literature review, we identified a total of four texture analysis methods that produced rotation-invariant features of different order and scale. The CHMs and the textural features were derived from practical sparse-density, leaf-off ALS data originally acquired for ground elevation modeling. The features were extracted from a circular window of 254 m 2 and related with boreal forest characteristics observed from altogether 155 field sample plots. Features based on gray-level histograms, distribution of forest patches, and gray-level co-occurrence matrices were related with plot volume, basal area, and mean diameter with coefficients of determination (R 2 ) of up to 0.63-0.70, whereas features that measured the uniformity of local binary patterns of the CHMs performed poorer. Overall, the textural features compared favorably with benchmark features based on the point data, indicating that the textural features contain additional information useful for the prediction of forest characteristics. Due to the developed processing routines for raster data, the CHM features may potentially be extracted with a lower computational burden, which promotes their use for applications such as pre-stratification or guiding the field plot sampling based solely on ALS data.

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Section: Unsupervised Classification Of the Forested Areamentioning

confidence: 99%

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Niemi

2016

Remote Sensing

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“…This greater interest in tropical forests has been principally motivated by the need to improve the quantification of aboveground biomass as an important component of the global carbon cycle (Keller et al, 2001). Sampling and spatial variability of course woody debris (CWD)-an important structural component of forest ecosystems that has been recognized as one of the strongest indicators of forest biodiversity (Pesonen et al, 2009), has also received a fair amount of attention world-wide (e.g. Bate et al, 2004;Pesonen et al, 2009;Woldendorp et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Local variability of stand structural features in beech dominated natural forests of Central Europe: Implications for sampling

Král

Janík

Vrška

et al. 2010

Forest Ecology and Management

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“…The times taken to survey the plots were estimated from the actual measurement times recorded during the fieldwork (for details, see Pesonen et al, 2009), so that the measurement of one type of CWD material on a 400 m 2 plot was defined as taking on average 3.4 min, for example. The time needed for transition between plots was calculated as the average walking distance between the plots multiplied by an assumed walking speed in the forest of 2 km h À1 .…”

Section: Simulation and Comparison Of Sampling Efficienciesmentioning

confidence: 99%

Effects of auxiliary data source and inventory unit size on the efficiency of sample-based coarse woody debris inventory

Pesonen

Kangas

Maltamo

et al. 2010

Forest Ecology and Management

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Comparison of field sampling methods for assessing coarse woody debris and use of airborne laser scanning as auxiliary information

Cited by 34 publications

References 27 publications

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Local variability of stand structural features in beech dominated natural forests of Central Europe: Implications for sampling

Effects of auxiliary data source and inventory unit size on the efficiency of sample-based coarse woody debris inventory

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