Tree crown complementarity links positive functional diversity and aboveground biomass along large-scale ecological gradients in tropical forests

2019 ◽  
Vol 656 ◽  
pp. 45-54 ◽  
Author(s):  
Arshad Ali ◽  
Si-Liang Lin ◽  
Jie-Kun He ◽  
Fan-Mao Kong ◽  
Jie-Hua Yu ◽  
...  
2020 ◽  
Vol 12 (8) ◽  
pp. 1288 ◽  
Author(s):  
José R. G. Braga ◽  
Vinícius Peripato ◽  
Ricardo Dalagnol ◽  
Matheus P. Ferreira ◽  
Yuliya Tarabalka ◽  
...  

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.


Forests ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 759 ◽  
Author(s):  
Wan Wan Mohd Jaafar ◽  
Iain Woodhouse ◽  
Carlos Silva ◽  
Hamdan Omar ◽  
Khairul Abdul Maulud ◽  
...  

Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying “problematic” segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe.


Author(s):  
Nidhi Jha ◽  
Nitin Kumar Tripathi ◽  
Nicolas Barbier ◽  
Salvatore G. P. Virdis ◽  
Wirong Chanthorn ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mulalo M. Muluvhahothe ◽  
Grant S. Joseph ◽  
Colleen L. Seymour ◽  
Thinandavha C. Munyai ◽  
Stefan H. Foord

AbstractHigh-altitude-adapted ectotherms can escape competition from dominant species by tolerating low temperatures at cooler elevations, but climate change is eroding such advantages. Studies evaluating broad-scale impacts of global change for high-altitude organisms often overlook the mitigating role of biotic factors. Yet, at fine spatial-scales, vegetation-associated microclimates provide refuges from climatic extremes. Using one of the largest standardised data sets collected to date, we tested how ant species composition and functional diversity (i.e., the range and value of species traits found within assemblages) respond to large-scale abiotic factors (altitude, aspect), and fine-scale factors (vegetation, soil structure) along an elevational gradient in tropical Africa. Altitude emerged as the principal factor explaining species composition. Analysis of nestedness and turnover components of beta diversity indicated that ant assemblages are specific to each elevation, so species are not filtered out but replaced with new species as elevation increases. Similarity of assemblages over time (assessed using beta decay) did not change significantly at low and mid elevations but declined at the highest elevations. Assemblages also differed between northern and southern mountain aspects, although at highest elevations, composition was restricted to a set of species found on both aspects. Functional diversity was not explained by large scale variables like elevation, but by factors associated with elevation that operate at fine scales (i.e., temperature and habitat structure). Our findings highlight the significance of fine-scale variables in predicting organisms’ responses to changing temperature, offering management possibilities that might dilute climate change impacts, and caution when predicting assemblage responses using climate models, alone.


2016 ◽  
Vol 46 (9) ◽  
pp. 1138-1144 ◽  
Author(s):  
M. Maltamo ◽  
O.M. Bollandsås ◽  
T. Gobakken ◽  
E. Næsset

This study considered airborne laser scanning (ALS) based aboveground biomass (AGB) prediction in mountain forests. The study area consisted of a long transect from southern Norway to northern parts of the country with wide ranges of elevation along a long latitudinal gradient (58°N–69°N). This transect was covered by ALS data and field data from 238 plots. AGB was modeled using different types of predictor variables, namely ALS metrics, variables related to growing conditions (elevation, latitude, and climatic variables), and tree species information. Modelling of AGB in the long transect covering diverse mountainous forest conditions was challenging: the RMSE values were rather large (37%–70%). The effects of growing conditions on model predictions were minor. However, species information was essential to improve accuracy. The analysis revealed that when doing inventories of spruce-dominated areas, all plots should be pooled together when the models are developed, whereas if pine or deciduous species dominate the area in question, separate dominant species-wise models should be constructed.


2015 ◽  
Vol 112 (43) ◽  
pp. 13267-13271 ◽  
Author(s):  
Geertje M. F. van der Heijden ◽  
Jennifer S. Powers ◽  
Stefan A. Schnitzer

Tropical forests store vast quantities of carbon, account for one-third of the carbon fixed by photosynthesis, and are a major sink in the global carbon cycle. Recent evidence suggests that competition between lianas (woody vines) and trees may reduce forest-wide carbon uptake; however, estimates of the impact of lianas on carbon dynamics of tropical forests are crucially lacking. Here we used a large-scale liana removal experiment and found that, at 3 y after liana removal, lianas reduced net above-ground carbon uptake (growth and recruitment minus mortality) by ∼76% per year, mostly by reducing tree growth. The loss of carbon uptake due to liana-induced mortality was four times greater in the control plots in which lianas were present, but high variation among plots prevented a significant difference among the treatments. Lianas altered how aboveground carbon was stored. In forests where lianas were present, the partitioning of forest aboveground net primary production was dominated by leaves (53.2%, compared with 39.2% in liana-free forests) at the expense of woody stems (from 28.9%, compared with 43.9%), resulting in a more rapid return of fixed carbon to the atmosphere. After 3 y of experimental liana removal, our results clearly demonstrate large differences in carbon cycling between forests with and without lianas. Combined with the recently reported increases in liana abundance, these results indicate that lianas are an important and increasing agent of change in the carbon dynamics of tropical forests.


1997 ◽  
Vol 13 (5) ◽  
pp. 697-708 ◽  
Author(s):  
M. Delaney ◽  
S. Brown ◽  
A. E. Lugo ◽  
A. Torres-Lezama ◽  
N. Bello Quintero

ABSTRACTOne of the major uncertainties concerning the role of tropical forests in the global carbon cycle is the lack of adequate data on the carbon content of all their components. The goal of this study was to contribute to filling this data gap by estimating the quantity of carbon in the biomass, soil and necromass for 23 long-term permanent forest plots in five life zones of Venezuela to determine how C was partitioned among these components across a range of environments. Aboveground biomass C ranged from 70 to 179 Mg ha−1 and soil C from 125 to 257 Mg ha−1, and they represented the two largest C components in all plots. The C in fine litter (2.4 to 5.2 Mg ha−1), dead wood (2.4 to 21.2 Mg ha−1) and roots (23.6 to 38.0 Mg ha−1) accounted for less than 13% of the total C. The total amount of C among life zones ranged from 302 to 488 Mg ha−1, and showed no clear trend with life zone. In three of the five life zones, more C was found in the dead (soil, litter, dead wood) than in the live (biomass) components (dead to live ratios of 1.3 to 2.3); the lowland moist and moist transition to dry life zones had dead to live ratios of less than one. Results from this research suggest that for most life zones, an amount equivalent to between 20 and 58% of the aboveground biomass is located in necromass and roots. These percentages coupled with reliable estimates of aboveground biomass from forest inventories enable a more complete estimation of the C content of tropical forests to be made.


Author(s):  
Zhao Sun ◽  
Yifu Wang ◽  
Lei Pan ◽  
Yunhong Xie ◽  
Bo Zhang ◽  
...  

AbstractPine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.


2021 ◽  
Author(s):  
Xin Rui Ong ◽  
David Hemprich‐Bennett ◽  
Claudia L. Gray ◽  
Victoria Kemp ◽  
Arthur Y. C. Chung ◽  
...  

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