Tree line

Summary

The tree line is the edge of the habitat at which trees are capable of growing. It is found at high elevations and high latitudes. Beyond the tree line, trees cannot tolerate the environmental conditions (usually low temperatures, extreme snowpack, or associated lack of available moisture). The tree line is sometimes distinguished from a lower timberline, which is the line below which trees form a forest with a closed canopy. At the tree line, tree growth is often sparse, stunted, and deformed by wind and cold. This is sometimes known as krummholz (German for "crooked wood"). The tree line often appears well-defined, but it can be a more gradual transition. Trees grow shorter and often at lower densities as they approach the tree line, above which they are unable to grow at all. Given a certain latitude, the tree line is approximately 300 to 1000 meters below the permanent snow line and roughly parallel to it. Causes Due to their vertical structure,

Official source

https://en.wikipedia.org/wiki/Tree_line

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2010

Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning

Benjamin Alexander Kellenberger, Thiên-Anh Claris Nguyen, Devis Tuia

Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Common methods usually predict a map from remote sensing images without deliberately considering intermediate semantic concepts that are relevant to the final map. This makes the mapping process difficult to interpret, especially when using opaque deep learning models. Moreover, such procedure is entirely agnostic to the definitions of the mapping targets (e.g., forest types depending on variables such as tree height and tree density). Common models can at best learn these rules implicitly from data, which greatly hinders trust in the produced maps. In this work, we aim at building an explainable deep learning model for forest mapping that leverages prior knowledge about forest definitions to provide explanations to its decisions. We propose a model that explicitly quantifies intermediate variables like tree height and tree canopy density involved in the forest definitions, corresponding to those used to create the forest maps for training the model in the first place, and combines them accordingly. We apply our model to mapping forest types using very high resolution aerial imagery and lay particular focus on the treeline ecotone at high altitudes, where forest boundaries are complex and highly dependent on the chosen forest definition. Results show that our rule-informed model is able to quantify intermediate key variables and predict forest maps that reflect forest definitions. Through its interpretable design, it is further able to reveal implicit patterns in the manually-annotated forest labels, which facilitates the analysis of the produced maps and their comparison with other datasets.

Elsevier2022

The uppermost limits of past treelines in the Alps are established using soil type and soil charcoal mass. In all the studied sites, a sharp decrease of soil charcoal mass is correlated with the upper altitudinal limit of podzols. On the basis of this evidence, the uppermost tree limit reached 2500 +/- 100 m a.s.l. in the Valaisan Alps during the Holocene, i.e., it was 250 +/- 100 in higher than today's potential treeline. Consequently, the timberline would have reached 2400 +/- 100 m a.s.l.. From the strong decline of charcoals concentration in soils above 2500m a.s.l., we infer that conifer species were rare or very rare above this altitude during the Holocene. Joint interpretation of charcoal, pollen, soil and macrofossil data suggest that alpine meadows with at most scattered conifers were present throughout the Holocene in the today's middle and upper alpine belt.

2004

Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning

Benjamin Alexander Kellenberger, Thiên-Anh Claris Nguyen, Devis Tuia

Elsevier2022