Individual Tree Structure and Growth in Mixed Compared with Monospecific Stands

2017 ◽  
pp. 271-336 ◽  
Author(s):  
Hans Pretzsch
Keyword(s):  
Tree Structure ◽  
Individual Tree

scholarly journals Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

2020 ◽  
Author(s):  
Ahmad Hojatimalekshah ◽  
Zach Uhlmann ◽  
Nancy F. Glenn ◽  
Christopher A. Hiemstra ◽  
Christopher J. Tennant ◽  
...  
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Local Scale ◽  
Snow Accumulation ◽  
Tree Structure ◽  
Scale Model ◽  
Fine Scale ◽  
Individual Tree ◽  
The Impact

Abstract. Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts, and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA, to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We apply the Marker-controlled watershed algorithm for individual tree segmentation and measure the snow depth using the Multi-scale Model to Model Cloud Comparison algorithm. Canopy, topography and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity) along with local scale processes such as wind are highly influential on snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results emphasize the importance of tree species and distribution on snow depth patterns. Fine scale analysis from TLS provides information on local scale controls, and provides an opportunity to be readily coupled with airborne or spaceborne lidar to investigate larger-scale controls on snow depth.

scholarly journals Mango Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV

2018 ◽  
Vol 10 (12) ◽  
pp. 1900 ◽  
Author(s):  
Julien Sarron ◽  
Éric Malézieux ◽  
Cheikh Sané ◽  
Émile Faye
Keyword(s):  
Value Chain ◽  
Tree Species ◽  
Cropping Systems ◽  
Structural Parameters ◽  
Tree Structure ◽  
Species Structure ◽  
Structure Parameters ◽  
Individual Tree ◽  
Production Models ◽  
Tree Production

In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R2 greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards.

An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems

2014 ◽  
Vol 154 ◽  
pp. 378-386 ◽  
Author(s):  
L.I. Duncanson ◽  
B.D. Cook ◽  
G.C. Hurtt ◽  
R.O. Dubayah
Keyword(s):  
Tree Structure ◽  
Individual Tree ◽  
Crown Delineation

Man-Computer Derivations of Tree Structure from Multivariate Data

1968 ◽  
Author(s):  
Gerald H. Shure ◽  
Laurence I. Press ◽  
Miles S. Rogers
Keyword(s):  
Multivariate Data ◽  
Tree Structure

Building a tree structure

1976 ◽  
Author(s):  
Patricia Marks Greenfield ◽  
Leslie Schneider
Keyword(s):  
Tree Structure

A tree-structure model for analyzing laboratory test costs

Pathology ◽  
1991 ◽  
Vol 23 (2) ◽  
pp. 172-176 ◽  
Author(s):  
Leslie Burnett
Keyword(s):  
Laboratory Test ◽  
Tree Structure ◽  
Structure Model

Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

2017 ◽  
Vol 168 (3) ◽  
pp. 127-133
Author(s):  
Matthew Parkan
Keyword(s):  
Cross Sections ◽  
Forest Edge ◽  
Airborne Lidar ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Small Areas ◽  
Interactive 3D ◽  
The Individual ◽  
Airborne Lidar Data ◽  
Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

Sign in / Sign up

Export Citation Format

Share Document