Automation aspects for the georeferencing of photogrammetric aerial image archives in forested scenes

Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D environmental model time series when using small-scale airborne image archives, especially in forested scenes. Furthermore, we investigated the usability of dense digital surface models (DSMs) generated using these data sets as well as the uncertainty propagation of the DSMs. A key element in the automation is georeferencing. It is obvious that for images captured years apart, it is essential to find ground reference locations that have changed as little as possible. We studied a 68-year-long aerial image time series in a Finnish Karelian forestland. The quality of candidate ground locations was evaluated by comparing digital DSMs created from the images to an airborne laser scanning (ALS)-originated reference DSM. The quality statistics of DSMs were consistent with the expectations; the estimated median root mean squared error for height varied between 0.3 and 2 m, indicating a photogrammetric modelling error of 0.1 parts per thousand with respect to flying height for data sets collected since the 1980s, and 0.2 parts per thousand for older data sets. The results show that of the studied land cover classes, "peatland without trees" changed the least over time and is one of the most promising candidates to serve as a location for automatic ground control measurement. Our results also highlight some potential challenges in the process as well as possible solutions. Our results indicate that using modern photogrammetric techniques, it is possible to reconstruct 3D environmental model time series using photogrammetric image archives in a highly automated way.Peer reviewe

The potential of dual-wavelength terrestrial lidar in early detection of Ips typographus (L.) infestation – Leaf water content as a proxy

Climate change is causing novel forest stress around the world due to changes in environmental conditions. Forest pest insects, such as Ips typographus (L.), are spreading toward the northern latitudes and are now able to produce more generations in their current range; this has increased forest disturbances. Timely information on tree decline is critical in allowing forest managers to plan effective countermeasures and to forecast potential infestation areas. Field-based infestation surveys of bark beetles have traditionally involved visual estimates of entrance holes, resin flow, and maternal-gallery densities; such estimates are prone to error and bias. Thus, objective and automated methods for estimating tree infestation status are required.In this study, we investigated the feasibility of dual-wavelength terrestrial lidar in the estimation and detection of I. typographus infestation symptoms. In addition, we examined the relationship between leaf water content (measured as gravimetric water content and equivalent water thickness) and infestation severity. Using two terrestrial lidar systems (operating at 905 nm and 1550 nm), we measured 29 mature Norway spruce (Picea abies [L.] Karst.) trees that exhibited low or moderate infestation symptoms. We calculated single and dual-wavelength lidar intensity metrics from stem and crown points to test these metrics' ability to discriminate I. typographus infestation levels using regressions and linear discriminant analyses.Across the various I. typographus infestation levels, we found significant differences (p </p

The potential of dual-wavelength terrestrial lidar in early detection of Ips typographus (L.) infestation – Leaf water content as a proxy

Climate change is causing novel forest stress around the world due to changes in environmental conditions. Forest pest insects, such as Ips typographus (L.), are spreading toward the northern latitudes and are now able to produce more generations in their current range; this has increased forest disturbances. Timely information on tree decline is critical in allowing forest managers to plan effective countermeasures and to forecast potential infestation areas. Field-based infestation surveys of bark beetles have traditionally involved visual estimates of entrance holes, resin flow, and maternal-gallery densities; such estimates are prone to error and bias. Thus, objective and automated methods for estimating tree infestation status are required. In this study, we investigated the feasibility of dual-wavelength terrestrial lidar in the estimation and detection of I. typographus infestation symptoms. In addition, we examined the relationship between leaf water content (measured as gravimetric water content and equivalent water thickness) and infestation severity. Using two terrestrial lidar systems (operating at 905 nm and 1550 nm), we measured 29 mature Norway spruce (Picea abies [L.] Karst.) trees that exhibited low or moderate infestation symptoms. We calculated single and dual-wavelength lidar intensity metrics from stem and crown points to test these metrics' ability to discriminate I. typographus infestation levels using regressions and linear discriminant analyses. Across the various I. typographus infestation levels, we found significant differences (p Peer reviewe

Hemlock woolly adelgid niche models from the invasive eastern North American range with projections to native ranges and future climates

The hemlock woolly adelgid (Adelges tsugae Annand - HWA) is invasive in eastern North America where it causes extensive mortality to hemlock communities. The future of these communities under projected climate change is an issue of landscape ecological interest and speculation. We employed the MaxEnt algorithm with the random subset feature selection algorithm (RSFSA) in creating HWA niche models. Final models were ensembles of 12 statistically best models with six predictors each. Out of 119 climatic, topographic, and soil variables, 42 were used in at least one final model. Soil features, followed by climate and topographic features, were most common in selected models. The three most important variables among all models were November potential evapotranspiration, slope, and percent Ochrepts soil. The potential distributions of HWA within eastern North America were projected under historical and four future climate scenarios for 2050 and 2070 under low and high CO2 emissions. The mean of the minimum values for the minimum temperature of the coldest month from the 12 MaxEnt model projections in eastern North America was -15.8°C. This value was close to -15°C, the extreme minimum temperature found for both HWA occurrence points and previously reported HWA cold temperature limits. These results indicate that HWA may be close to equilibrium distribution in eastern North America under current climate. We also reverse-casted the eastern North American MaxEnt model back onto the HWA native ranges in eastern Asia and western North America. The projections match best with native ranges in Asian islands, such as Japan, and the Cascade Mountains in western North America. Statistically significant HWA range shifts of 221-468 km northwards and 110-164 km eastwards were projected by the 12 models for 2050-2070. The 2070 high CO2 emission scenario models projects HWA suitability throughout most of the northern range of eastern hemlock