Rainfall partitioning in relation to forest structure in differently managed montane forest stands in Central Sulawesi, Indonesia

2006 ◽  
Vol 237 (1-3) ◽  
pp. 170-178 ◽  
Author(s):  
Johannes Dietz ◽  
Dirk Hölscher ◽  
Christoph Leuschner ◽  
Hendrayanto
Keyword(s):  
Forest Structure ◽  
Montane Forest ◽  
Forest Stands ◽  
Central Sulawesi

scholarly journals Stratifying Forest Overstory for Improving Effective LAI Estimation Based on Aerial Imagery and Discrete Laser Scanning Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2126 ◽  
Author(s):  
Zhaoshang Xu ◽  
Guang Zheng ◽  
L. Monika Moskal
Keyword(s):  
Forest Structure ◽  
Laser Scanning ◽  
Structural Information ◽  
Canopy Cover ◽  
Remotely Sensed ◽  
Background Information ◽  
Estimation Accuracy ◽  
Forest Stands ◽  
Remotely Sensed Data ◽  
Area Index

Accurately mapping forest effective leaf area index (LAIe) at the landscape level is a crucial step to better simulate various ecological and physiological processes such as photosynthesis, respiration, transpiration, and precipitation interception. The LAIe products obtained from two-dimensional (2-D) remotely sensed optical imageries are usually biased due to their inability to identify the vertical forest structure and eliminate the effects of forest background (i.e., shrubs, grass, snow, and bare earth). In this study, we first stratified the forest overstory and background layers and generated a forest background mask layer based on the structural information implicitly contained within the aerial laser scanning (ALS) data. We improved the retrieval accuracy of LAIe by combining light detection and ranging (Lidar)-based three dimensional (3-D) structural and 2-D spectral information. Then, we obtained the improved final LAIe estimation result by masking the forest background pixels from the optical remotely sensed imageries. Our results showed that: (1) Removing forest background information could effectively (R2 increase from 20% to 30%) improve the estimation accuracy of optical-based forest LAIe depending on forest structure characteristics. (2) The forest background in the forest stands with low canopy cover showed more apparent effects on LAIe estimation compared with the forest stands with a high canopy cover. (3) The combination of ALS and optical remotely sensed data could produce the best LAIe retrieval result effectively by removing the forest background information.

scholarly journals The importance of forest structure to biodiversity–productivity relationships

2017 ◽  
Vol 4 (1) ◽  
pp. 160521 ◽  
Author(s):  
Friedrich J. Bohn ◽  
Andreas Huth
Keyword(s):  
Species Distribution ◽  
Forest Structure ◽  
Basal Area ◽  
Tree Height ◽  
National Forest Inventory ◽  
National Forest ◽  
Forest Stands ◽  
Annual Productivity ◽  
Structure Indices ◽  
Modelling Approach

While various relationships between productivity and biodiversity are found in forests, the processes underlying these relationships remain unclear and theory struggles to coherently explain them. In this work, we analyse diversity–productivity relationships through an examination of forest structure (described by basal area and tree height heterogeneity). We use a new modelling approach, called ‘forest factory’, which generates various forest stands and calculates their annual productivity (above-ground wood increment). Analysing approximately 300 000 forest stands, we find that mean forest productivity does not increase with species diversity. Instead forest structure emerges as the key variable. Similar patterns can be observed by analysing 5054 forest plots of the German National Forest Inventory. Furthermore, we group the forest stands into nine forest structure classes, in which we find increasing, decreasing, invariant and even bell-shaped relationships between productivity and diversity. In addition, we introduce a new index, called optimal species distribution, which describes the ratio of realized to the maximal possible productivity (by shuffling species identities). The optimal species distribution and forest structure indices explain the obtained productivity values quite well ( R 2 between 0.7 and 0.95), whereby the influence of these attributes varies within the nine forest structure classes.

Variation in soil biological characteristics on an elevational gradient in the montane forest of north-west Argentina

2008 ◽  
Vol 24 (4) ◽  
pp. 457-461 ◽  
Author(s):  
Adriana B. Abril ◽  
Enrique H. Bucher
Keyword(s):  
Forest Structure ◽  
Cloud Cover ◽  
Elevational Gradient ◽  
Montane Forest ◽  
Temperature Gradients ◽  
Biological Characteristics ◽  
Important Influence ◽  
Rain Forests ◽  
Montane Forests ◽  
North West

Montane tropical and subtropical rain forests are complex ecosystems, characterized by marked rainfall and temperature gradients with altitude, which in turn control the vegetation altitudinal zones (Hueck 1978). Montane forests are often referred to as cloud forests in recognition of the important influence of a dense and frequent cloud cover that conditions forest structure and functioning (Bautista-Cruz & del Castillo 2005, Holder 2004).

scholarly journals Horizontal Visibility in Forests

2021 ◽  
Vol 13 (21) ◽  
pp. 4455
Author(s):  
Mait Lang ◽  
Andres Kuusk ◽  
Kersti Vennik ◽  
Aive Liibusk ◽  
Kristina Türk ◽  
...  
Keyword(s):  
Forest Structure ◽  
Laser Scanning ◽  
Prediction Models ◽  
Important Variable ◽  
Pattern Generation ◽  
Forest Understory ◽  
Arc Length ◽  
Forest Stands ◽  
Horizontal Visibility ◽  
Structure Indices

The important variable of horizontal visibility within forest stands is gaining increasing attention in studies and applications involving terrestrial laser scanning (TLS), photographic measurements of forest structure, and autonomous mobility. We investigated distributions of visibility distance, open arc length, and shaded arc length in three mature forest stands. Our analysis was based (1) on tree position maps and TLS data collected in 2013 and 2019 with three different scanners, and (2) on simulated digital twins of the forest stands, constructed with two pattern-generation models incorporating commonly used indices of tree position clumping. The model simulations were found to yield values for visibility almost identical to those calculated from the corresponding tree location maps. The TLS measurements, however, were found to diverge notably from the simulations. Overall, the probability of free line of sight was found to decrease exponentially with distance to target, and the probabilities of open arc length and shaded arc length were found to decrease and increase, respectively, with distance from the observer. The TLS measurements, which are sensitive to forest understory vegetation, were found to indicate increased visibility after vegetation removal. Our chosen visibility prediction models support practical forest management, being based on common forest inventory parameters and on widely used forest structure indices.

scholarly journals Predicting tree heights for biomass estimates in tropical forests – a test from French Guiana

2014 ◽  
Vol 11 (12) ◽  
pp. 3121-3130 ◽  
Author(s):  
Q. Molto ◽  
B. Hérault ◽  
J.-J. Boreux ◽  
M. Daullet ◽  
A. Rousteau ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Forest Structure ◽  
French Guiana ◽  
Tree Height ◽  
Biomass Estimation ◽  
Forest Plot ◽  
Model Parameters ◽  
Forest Stands ◽  
Forest Stand Structure ◽  
Height Model

Abstract. The recent development of REDD+ mechanisms requires reliable estimation of carbon stocks, especially in tropical forests that are particularly threatened by global changes. Even though tree height is a crucial variable for computing aboveground forest biomass (AGB), it is rarely measured in large-scale forest censuses because it requires extra effort. Therefore, tree height has to be predicted with height models. The height and diameter of all trees over 10 cm in diameter were measured in 33 half-hectare plots and 9 one-hectare plots throughout northern French Guiana, an area with substantial climate and environmental gradients. We compared four different model shapes and found that the Michaelis–Menten shape was most appropriate for the tree biomass prediction. Model parameter values were significantly different from one forest plot to another, and this leads to large errors in biomass estimates. Variables from the forest stand structure explained a sufficient part of plot-to-plot variations of the height model parameters to improve the quality of the AGB predictions. In the forest stands dominated by small trees, the trees were found to have rapid height growth for small diameters. In forest stands dominated by larger trees, the trees were found to have the greatest heights for large diameters. The aboveground biomass estimation uncertainty of the forest plots was reduced by the use of the forest structure-based height model. It demonstrated the feasibility and the importance of height modeling in tropical forests for carbon mapping. When the tree heights are not measured in an inventory, they can be predicted with a height–diameter model and incorporating forest structure descriptors may improve the predictions.

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