Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral- and three-dimensional information from UAV-based aerial imagery

The alpine treeline ecotone is expected to move upwards in elevation with global warming. Thus, mapping treeline ecotones is crucial in monitoring potential changes. Previous remote sensing studies have focused on the usage of satellites and aircrafts for mapping the treeline ecotone. However, treeline ecotones can be highly heterogenous, and thus the use of imagery with higher spatial resolution should be investigated. We evaluate the potential of using unmanned aerial vehicles (UAVs) for the collection of ultra-high spatial resolution imagery for mapping treeline ecotone land covers. We acquired imagery and field reference data from 32 treeline ecotone sites along a 1100 km latitudinal gradient in Norway (60–69°N). Before classification, we performed a superpixel segmentation of the UAV-derived orthomosaics and assigned land cover classes to segments: rock, water, snow, shadow, wetland, tree-covered area and five classes within the ridge-snowbed gradient. We calculated features providing spectral, textural, three-dimensional vegetation structure, topographical and shape information for the classification. To evaluate the influence of acquisition time during the growing season and geographical variations, we performed four sets of classifications: global, seasonal-based, geographical regional-based and seasonal-regional-based. We found no differences in overall accuracy (OA) between the different classifications, and the global model with observations irrespective of data acquisition timing and geographical region had an OA of 73%. When accounting for similarities between closely related classes along the ridge-snowbed gradient, the accuracy increased to 92.6%. We found spectral features related to visible, red-edge and near-infrared bands to be the most important to predict treeline ecotone land cover classes. Our results show that the use of UAVs is efficient in mapping treeline ecotones, and that data can be acquired irrespective of timing within a growing season and geographical region to get accurate land cover maps. This can overcome constraints of a short field-season or low-resolution remote sensing data.publishedVersio

Prediction of Timber Quality Parameters of Forest Stands by Means of Small Footprint Airborne Laser Scanner Data

The aim of this study was to explore the capability of airborne laser scanner (ALS) data to explain the variation in field-measured variables representing timber quality within square 0.25 ha grid cells in a mature conifer forest in the southeast of Norway. These variables were the mean ratio between stem diameter at six m above ground and the diameter at breast height (R D6 ), the volume of saw logs (V SL ), the proportion of saw logs relative to the total volume (P SL ), the ratio between tree height and diameter at breast height (HD), mean basal area diameter (D g ), and crown height (CH). Each of these variables was modeled using a mixed modeling approach. Model fit was expressed by the Pseudo-R 2 , and were 0.85, 0.50, 0.78, 0.57, 0.74, and 0.58 for the respective quality variables. Furthermore, much of the residual error could be attributed to the different forest stands from which the grid cells originated even though we used field-observed tree species proportions as auxiliary information. It was concluded that more auxiliary information is needed to estimate models that are general across stands, but that the relationships between ALS-data and the quality variables considered here seem strong enough to be utilized for example to prioritize between stands in relation to harvest when specific quality distributions are sought

Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach

International audienceAbstractKey messageTested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above- and belowground biomass models for three mangrove species were therefore developed. The species-specific models fitted better to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania.ContextMangroves are essential for climate change mitigation through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and carbon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented.AimsThe aims of the study were to develop above- and belowground biomass models and to evaluate the predictive accuracy of existing aboveground biomass models developed for mangroves in other regions and neighboring countries when applied on data from Tanzania.MethodsData was collected through destructive sampling of 120 trees (aboveground biomass), among these 30 trees were sampled for belowground biomass. The data originated from four sites along the Tanzanian coastline covering three dominant species: Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith, and Rhizophora mucronata Lam. The biomass models were developed through mixed modelling leading to fixed effects/common models and random effects/species-specific models.ResultsBoth the above- and belowground biomass models improved when random effects (species) were considered. Inclusion of total tree height as predictor variable, in addition to diameter at breast height alone, further improved the model predictive accuracy. The tests of existing models from other regions on our data generally showed large and significant prediction errors for aboveground tree biomass.ConclusionInclusion of random effects resulted into improved goodness of fit for both above- and belowground biomass models. Species-specific models therefore are recommended for accurate biomass estimation of mangrove forests in Tanzania for both management and ecological applications. For belowground biomass (S. alba) however, the fixed effects/common model is recommended

Accuracy assessment of the nationwide forest attribute map of Norway constructed by using airborne laser scanning data and field data from the national forest inventory

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Legacy effects of herbivory on treeline dynamics along an elevational gradient

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Infective mitral valve myxoma with coronary artery embolization: Surgical intervention followed by prolonged survival

Mauyaetal.CarbonBalanceandManagement (2015) 10:10 DOI 10.1186/s13021-015-0021-x © 2015 Mauya et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Background: Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent re- search in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. Results: The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R 2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m 2 . The variance ratio of field-based esti- mates relative to model-assisted variance ranged from 1.7 to 7.7. Conclusions: This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory

Airborne laser scanning reveals increased growth and complexity of boreal forest canopies across a network of ungulate exclosures in Norway

Large herbivores are often classed as ecosystem engineers, and when they become scarce or overabundant, this can alter ecosystem states and influence climate forcing potentials. This realization has spurred a call to integrate large herbivores in earth system models. However, we lack a good understanding of their net effects on climate forcing, including carbon and energy exchange. A possible solution to this lies in harmonizing data across the myriad of large herbivore exclosure experiments around the world. This is challenging due to differences in experimental designs and field protocols. We used airborne laser scanning (ALS) to describe the effect of herbivore removal across 43 young boreal forest stands in Norway and found that exclusion caused the canopy height to increase from 1.7 0.2 to 2.5 0.2 m (means SE), and also causing a marked increase in vertical complexity and above-ground biomass. We then go on to discuss some of the issues with using ALS; we propose ALS as an approach for studying the effects of multiple large herbivore exclosure experiments simultaneously, and producing area-based estimates on canopy structure and forest biomass in a cheap, efficient, standardized and reproducible way. We suggest that this is a vital next step towards generating biome-wide predictions for the effects of large herbivores on forest ecosystem structure which can both inform both local management goals and earth system models biomass, herbivory, large herbivores, LiDAR, moose, remote sensingpublishedVersio

Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference

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The Relative Role of Climate and Herbivory in Driving Treeline Dynamics along a Latitudinal Gradient, 2020

"The Relative Role of Climate and Herbivory in Driving Treeline Dynamics along a Latitudinal Gradient, 2020" is based on 2 projects: "Effects of changing climate on the alpine tree line and mountain forest carbon pools along 1500 km n-s and elevation gradients "TREELINE"" and "Changing forest area and forest productivity - climatic and human causes, effects, monitoring options, and climate mitigation potential "FORESTPOTENTIAL"". Global studies show a trend of expanding forests into the boreal-alpine ecotone, which are important in Norway. Climate predictions for Norway indicate future changes with a potential for rapid growth in existing forests and dispersion to areas without tree growth. In order to preserve the bio-diversity and fulfill international obligations to reduce greenhouse gas emission, it is important to understand the changes in the boreal-alpine zone will influence the vegetation and potential for carbon sequestration. Grazing has historically been an important factor that has influenced the boreal-alpine ecotones. One of the main goals of FORESTPOTENTIAL was to analyse how the forest in areas near alpine zones can change in the near future. In order to run analyses connected to this, in 2018 and in 36 unique locations, data was collected concerning the establishment, growth, and mortality of pioneer trees in the ecotone between forests and mountains along an 1100 km latitude gradient. At the same time, data on the vegetation on the same localities was collected, and by using a drone, pictures were taken and spectral information registered. The same locations were also previously measured (in 2008 and 2012), funded by the NFR through a research project within the NORKLIMA-programme. In the previous measurements, data were also collected with airborne advanced remote sensing as an uninterrupted transect from the southernmost to the northernmost site - a total of 1,500 km by airline. The data set therefore provides a unique opportunity to follow the dynamics of the tree line over time. Related to each site, we also have information on pasture pressure for a period of more than 50 years, as well as historical climate data over the same period. Based on this, we will also be able to distinguish the contributions of anthropogenic and climatic factors on the tree line dynamic