Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland

Maltamo, Matti; Packalén, Petteri; Suvanto, Aki; Korhonen, K.; Mehtätalo, Lauri; Hyvönen, Pekka

doi:10.1007/s10342-009-0266-6

Cited by 71 publications

(44 citation statements)

References 32 publications

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“…Substantial improvement was achieved compared to our earlier study with dual-pol ALOS PALSAR data [39], when even multitemporal aggregation provided relatively moderate accuracy figures (RMSE of 43% and R 2 = 0.61). Comparison with earlier studies relying on satellite optical and other multisource data in [58,59] indicates advantage of suggested L-band PolSAR approach.…”

Section: Relative Performance Of Biomass Estimation Approachmentioning

confidence: 96%

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Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

et al. 2017

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Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by a time series of PolSAR images acquired during several seasons in the years 2006 to 2009 by the ALOS PALSAR sensor. The study area was in central Finland, and represented a managed area in typical boreal mixed forest land. Utility of different PolSAR parameters, their temporal stability and cross-correlations were studied along with reference stand-level stem volume data from forest inventory. Further, two polarimetric parameters, cross-polarization backscatter and co-polarization coherence, were chosen for further investigation and stem volume retrieval. A relationship between forest stem volume and PolSAR parameters was established using the kNN regression approach. Ways of optimally combining PolSAR images were evaluated as well. For a single scene, best results were observed with polarimetric coherence (RMSE ≈ 38.8 m 3 /ha) for scene acquired in frozen conditions. An RMSE of 40.8 m 3 /ha (42.9%, R 2 = 0.66) was achieved for cross-polarization backscatter in the best case. Cross-polarization backscatter was a better predictor than polarimetric coherence for few summer scenes. Multitemporal aggregation of selected PolSAR scenes improved estimates for both studied PolSAR parameters. Stronger improvement was observed for coherence with RMSE down to 34 m 3 /ha (35.8%, R 2 = 0.77) compared to 38.8-51.6 m 3 /ha (40.8-54.3%) from separate scenes. Finally, the accuracy statistics reached RMSE of 32.2 m 3 /ha (34%, R 2 = 0.79) when multitemporal HHVV coherence was combined with multitemporal HV-backscatter.

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Section: Relative Performance Of Biomass Estimation Approachmentioning

confidence: 96%

“…Different nearest neighbor regression approaches became popular tools in estimating forest parameters from remotely sensed data [58][59][60][61][62]. Particularly, a multisource national forest inventory (NFI) in Finland is based on k nearest neighbor (kNN) approach [60,61].…”

Section: Sar Based Estimation Of Stem Volumementioning

confidence: 99%

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

et al. 2017

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“…For example, tree or stand diameters are estimated based on tree height, crown size or stem density using allometric models, geometrically weighted regression methods (Salas et al 2010) or various nonparametric approaches (Packalén & Maltamo 2008). The precision of estimates of tree and stand volume, which are the primary variables of forestry interest, ultimately depends on the accuracy of the underlying characteristics, and is affected by the accumulation of both detection and estimation errors (Maltamo et al 2009). Although numerous tree-level algorithms have been reported in the literature, their accuracy is still inadequate for ITD methods to be applied in forest inventories .…”

Section: Introductionmentioning

confidence: 99%

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Sačkov¹,

Hlásny²,

Bucha³

et al. 2017

iForest

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Airborne laser scanning (ALS) has recently gained increasing attention in forestry, as ALS data may facilitate the efficient assessment of forest inventory attributes and ecological indicators related to forest stand structure. This paper presents a novel workflow for individual tree detection and tree crown delineation using ALS data. The developed point-based approach included several tree allometry rules on permissible tree heights and crown dimensions to increase the likelihood of detecting the actual tree profiles. The accuracy of the method was assessed in a heterogeneous forest with a complex stand structure in Slovakia (Central Europe). ALS measurements were taken using a RIEGL Q680i scanner at 700 m of height with a point density of 20 echoes per m 2 . The ground reference data included the measured positions and dimensions of 1332 trees in nine plots distributed across the region. We found that the number of individual trees detected by the algorithm using ALS data was systematically underestimated by 34 ± 15% relative to the reference data. The delineated crown coverage was underestimated by 2 ± 6% as well, but the latter difference was not statistically significant (p>0.05).

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“…This enables the use of NFI sample plot data as field reference data with the remote sensing material of high spatial resolution, such as aerial imagery and ALS data. Also the change from angle-count tree sampling to fixed size sample plots in 2012 improved the applicability of NFI plots as reference data for ALS, though the effect of plot configurations on the error of ALS-based estimates was found to be small (Maltamo et al 2009;Tuominen et al 2014;Tomppo et al 2016). However, image acquisitions or ALS over large areas in various conditions, at different times of the growing season, or even under leaf-off conditions and with different devices complicate the use of NFI sample plots, because the field data should cover the entire variation of forest attributes in the area of interest, that is, within an image.…”

Section: Further Aspects Of Availability Of Forest Data For Scenario mentioning

confidence: 99%

Multi-source forest inventory data for forest production and utilization analyses at different levels

Haakana¹

2017

Diss. For.

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National forest inventory (NFI) data are commonly used in national and regional scenario analyses on forest production and utilization possibilities. There is an increased demand for similar analyses at the sub-regional level, and further, to incorporate spatially explicit data into the analyses. However, the fairly sparse network of NFI sample plots allows analyses only for large areas. The present dissertation explored whether satellite imagery, NFI sample plot data and the k nearest neighbour estimation method can be employed in generating spatial forest data for scenario analyses at the local level. The method was first applied in the area of two villages in Eastern Finland to quantify the effects of administrative land use and technical land-form constraints on timber production. Secondly, the impacts of three alternative regional felling strategies on suitable habitat for the Siberian flying squirrel (Pteromys volans) were assessed.As a scenario analysis tool, the Finnish forestry dynamics model MELA was used. Management units for simulations of forest development and management activities were delineated by means of image segmentation and digital maps on restriction areas, and new weights for NFI sample plots, that is, the representativeness in these units, were estimated by means of satellite image data. The performance of different segmentation methods and different spectral features in the estimation were examined. Image segments corresponding to forest stands enabled the use of patch-and landscape-level models in the prediction of suitable habitat.Satellite image-based estimation of new NFI sample plot weights was found to be a feasible method for generating forest data for scenario analyses in areas smaller than is possible with the plot data only, for example, for municipalities. Satellite imagery with large geographic coverage and continuous NFI field measurements provide cost-efficient data sources for versatile impact and scenario analyses at the local level.

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Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland

Cited by 71 publications

References 32 publications

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Multi-source forest inventory data for forest production and utilization analyses at different levels

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