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When applying remote sensing in forestry, there is often a need for reference sample plot data as a link between what is seen in the image and the actual ground conditions. In many cases such data exist, although it may be old and need to be updated. In this study, the effects on the estimation accuracy of using forecasted reference sample plot data when estimating stem volume in forests are investigated. The k nearest neighbour (kNN) estimation method is applied, using spectral information in a satellite image and sample plot reference data forecasted up to 25 years. The study is based on simulation. The basic components of the simulation model are, however, derived from empirical data. The mean square error (MSE) of volume estimates was found to increase with increasing length of the forecasts, although after excluding disturbed plots (e.g. due to thinnings) from the reference material, use of old reference data led to a very modest decrease of the accuracy. Thus, if disturbed plots can be identified, the results point towards the possibility of using updated old reference data as a means to increase the cost-efficiency in this kind of inventory. However, the results are shown to depend on the correlation between ground state and satellite spectral data. When this correlation is higher, the relative increase in MSE with forecasting length was higher, as when disturbed plots had been removed from the reference material. [ABSTRACT FROM AUTHOR]

Hair lichens are strongly influenced by forest structure at local scales, but their broad-scale distributions are less understood. We compared the occurrence and length of Alectoria sarmentosa (Ach.) Ach., Bryoria spp., and Usnea spp. in the lower canopy of > 5000 Picea abies (L.) Karst. trees within the National Forest Inventory across all productive forest in Sweden. We used logistic regression to analyse how climate, nitrogen deposition, and forest variables influence lichen occurrence. Distributions overlapped, but the distribution of Bryoria was more northern and that of Usnea was more southern, with Alectoria's distribution being intermediate. Lichen length increased towards northern regions, indicating better conditions for biomass accumulation. Logistic regression models had the highest pseudo R2 value for Bryoria, followed by Alectoria. Temperature and nitrogen deposition had higher explanatory power than precipitation and forest variables. Multiple logistic regressions suggest that lichen genera respond differently to increases in several variables. Warming decreased the odds for Bryoria occurrence at all temperatures. Corresponding odds for Alectoria and Usnea decreased in warmer climates, but in colder climates, they increased. Nitrogen addition decreased the odds for Alectoria and Usnea occurrence under high deposition, but under low deposition, the odds increased. Our analyses suggest major shifts in the broad-scale distribution of hair lichens with changes in climate, nitrogen deposition, and forest management. [ABSTRACT FROM AUTHOR]

Abstract: An airborne profiling lidar was used to estimate total aboveground dry biomass, a surrogate for aboveground carbon stocks, for all land cover types in Hedmark County, Norway. One-hundred-five parallel profiling flight lines systematically spaced three km apart, totaling 8309km, were acquired to measure forest canopy height and density across the 27,390km2 County. Two sampling/estimation strategies were investigated; both use profiling lidar data to augment field data collected as part of a systematic network of Norwegian National Forest Inventory (NFI) ground plots. The two strategies include (1) a model-dependent (MD), two-phase approach which permits calculation of sampling and model error, and (2) a model-assisted (MA), probability-based, two-stage approach which utilizes asymptotically design-unbiased estimators. The lidar-augmented survey results from these two sampling strategies were compared to NFI ground estimates for the County. The MD estimate (38.88±1.07Mgha−1, one σ) differs from the NFI ground estimate (37.64±0.94Mgha−1) by +1.24Mgha−1 (+3.3%) and the MA (36.09±1.56Mgha−1) versus NFI difference is −1.55Mgha−1 (−4.1%). As expected, as smaller geographical areas are considered, lidar–NFI differences increase, and at the individual cover class level, MD lidar–ground absolute differences average 3.1Mgha−1 for the eight individual classes (8.2% of the County mean). The comparable MA lidar–ground average absolute difference for the individual cover classes is 4.3Mgha−1 (11.4% of the County mean). For all of Hedmark, in general, the MD approach agreed most closely with NFI estimates. The standard errors (SE) generated using the MA approach were, in general, 2–3 times larger than the MD SEs for productive forest classes at the County level, though neither lidar sample was consistently more precise than the NFI ground estimates alone. For Hedmark County, the results indicate that profiling lidar surveys can provide estimates comparable to ground surveys but, at this particular juncture, the MD and MA estimators did not improve the precision of adequately designed (in terms of sample size), probability-based ground samples. However when smaller political units are considered, i.e., as estimates are made on smaller and smaller subunits within Hedmark County, in general, MD-SE < NFI-SE ≪ MA-SE. Of the two designs considered in this study, the MD approach is most amenable to satellite lidar surveys since it does not require that profiling lidar satellite pulses intercept ground plots already established for a regional or national forest inventory. Direct comparisons of lidar and ground SEs are complicated by two factors: (1) the influence of treating both lidar and ground surveys as random samples when, in fact, they’re systematic, and (2) by the two stage/phase structure of the lidar surveys versus the single stage structure of the NFI. These results should be viewed as preliminary, subject to validation using a Monte Carlo simulator. [Copyright &y& Elsevier]