Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes

Hyyppä, Juha; Hyyppä, Hannu; Inkinen, M.; Engdahl, Marcus; Linko, Susan; Zhu, Y.-H.

doi:10.1016/s0378-1127(99)00278-9

Cited by 339 publications

(205 citation statements)

References 8 publications

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“…Increasing the LiDAR hit density, by either flying at a lower altitude or at a slower speed, or by aggregating the data from multiple overpasses, will reduce this underestimation. For example, Hyyppä et al (2000) increased sampling rate from 9 to 24 hits / m 2 and found a reduction in the error and bias in tree height estimation. In addition positional errors associated with both the plot placement using GPS (potentially up to 1 -2 m) as well as possible locational errors with the LiDAR of up to 1 m can both contribute to the lack of a perfect fit between the observed maximum height and the LiDAR observed heights.…”

Section: Discussionmentioning

confidence: 99%

Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR

Coops

Hilker

Wulder

et al. 2007

Trees

311

223

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Section: Discussionmentioning

confidence: 99%

Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR

Coops

Hilker

Wulder

et al. 2007

Trees

311

223

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“…Subsequent studies of ERS-1/2 tandem data demonstrated in particular that the one-day repeat pass coherence is useful in land use mapping (Strozzi et al, 2000) and estimation of stem volume in forests (Koskinen, Pulliainen, Hyyppä, Engdahl, & Hallikainen, 2001;Santoro, Askne, Smith, & Fransson, 2002;Smith et al, 1998). Hyyppä et al (2000) found that, compared to the JERS and ERS intensity images, the ERS tandem coherence was best suited to predicting height, basal area and stem volume over a 600-ha boreal forest site in southern Finland. This paper showed, however, that airborne measurements (profiling radar, aerial photographs, imaging spectrometer) and even optical satellite images (SPOT and Landsat) included more information than the ERS interferometric data for their test area.…”

Section: Ers and Jers Sar In Forestry Applicationsmentioning

confidence: 99%

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Wagner

Luckman

Vietmeier

et al. 2003

Remote Sensing of Environment

127

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Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km 2 in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m 3 /ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m. D

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“…Reason for decreased uncertainties related to forest inventory data and growth modeling errors is that if inventory method and growth models are unbiased, the larger and more homogeneous the inventory unit, the smaller the relative standard error achievable. Several remote-sensing related forest inventory studies have revealed that the relative root-mean squared error (rmse) value describing the accuracy of the inventory method is strongly dependent on the size of inventory unit examined (tree, plot, stand, property) and its degree of internal variation (homogeneity) (e.g., [50]). …”

Section: Forest Property Dmentioning

confidence: 99%

Uncertainty in Forest Net Present Value Estimations

Holopainen

Mäkinen

Rasinmäki³

et al. 2010

Forests

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Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV). The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS) inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%). Timber price fluctuation was portrayed with geometric mean-reverting (GMR) price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A) a property having an even development class distribution, (B) sapling stands, (C) young thinning stands, and (D) mature stands. Simulations resulted in predicted yield value (predicted NPV) distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1-7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate). ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant OPEN ACCESSForests 2010, 1 178 in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate). The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the output results with levels of confidence.

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Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes

Cited by 339 publications

References 8 publications

Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR

Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Uncertainty in Forest Net Present Value Estimations

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