scholarly journals Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50 ha plot

2021 ◽  
Vol 18 (24) ◽  
pp. 6517-6531
Author(s):  
Raquel Fernandes Araujo ◽  
Samuel Grubinger ◽  
Carlos Henrique Souza Celes ◽  
Robinson I. Negrón-Juárez ◽  
Milton Garcia ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Temporal Variation ◽  
Forest Canopy ◽  
Tree Mortality ◽  
Extreme Rainfall ◽  
Barro Colorado Island ◽  
Weibull Function ◽  
Wet Season ◽  
Wind Speeds ◽  
Canopy Disturbance

Abstract. A mechanistic understanding of how tropical-tree mortality responds to climate variation is urgently needed to predict how tropical-forest carbon pools will respond to anthropogenic global change, which is altering the frequency and intensity of storms, droughts, and other climate extremes in tropical forests. We used 5 years of approximately monthly drone-acquired RGB (red–green–blue) imagery for 50 ha of mature tropical forest on Barro Colorado Island, Panama, to quantify spatial structure; temporal variation; and climate correlates of canopy disturbances, i.e., sudden and major drops in canopy height due to treefalls, branchfalls, or the collapse of standing dead trees. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though wind speeds were lower in the wet season. The strongest correlate of monthly variation in canopy disturbance rates was the frequency of extreme rainfall events. The size distribution of canopy disturbances was best fit by a Weibull function and was close to a power function for sizes above 25 m2. Treefalls accounted for 74 % of the total area and 52 % of the total number of canopy disturbances in treefalls and branchfalls combined. We hypothesize that extremely high rainfall is a good predictor because it is an indicator of storms having high wind speeds, as well as saturated soils that increase uprooting risk. These results demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates at fine temporal and spatial resolutions over large areas, thereby enabling robust tests of how temporal variation in disturbance relates to climate drivers. Further insights could be gained by integrating these canopy observations with high-frequency measurements of wind speed and soil moisture in mechanistic models to better evaluate proximate drivers and with focal tree observations to quantify the links to tree mortality and woody turnover.

scholarly journals Strong temporal variation in treefall and branchfall rates in a tropical forest is explained by rainfall: results from five years of monthly drone data for a 50-ha plot

2021 ◽  
Author(s):  
Raquel Fernandes Araujo ◽  
Samuel Grubinger ◽  
Carlos Henrique Souza Celes ◽  
Robinson I. Negrón-Juárez ◽  
Milton Garcia ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Temporal Variation ◽  
Forest Canopy ◽  
Barro Colorado Island ◽  
Weibull Function ◽  
Wet Season ◽  
Canopy Disturbance ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees

Abstract. A mechanistic understanding of how tropical tree mortality responds to climate variation is urgently needed to predict how tropical forest carbon pools will respond to anthropogenic global change, which is altering the frequency and intensity of storms, droughts, and other climate extremes in tropical forests. We used five years of approximately monthly drone-acquired RGB imagery for 50 ha of mature tropical forest on Barro Colorado Island, Panama, to quantify spatial structure, temporal variation, and climate correlates of canopy disturbances, i.e., sudden and major drops in canopy height due to treefalls, branchfalls, or collapse of standing dead trees. Treefalls accounted for 77 % of the total area and 60 % of the total number of canopy disturbances in treefalls and branchfalls combined. The size distribution of canopy disturbances was close to a power function for sizes above 25 m2, and best fit by a Weibull function overall. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though windspeeds were lower in the wet season.  The strongest correlate of temporal variation in canopy disturbance rates was the frequency of 1-hour rainfall events above the 99.4th percentile (here 35.7 mm hour−1, r = 0.67). We hypothesize that extreme high rainfall is associated with both saturated soils, increasing risk of uprooting, and with gusts having high horizontal and vertical windspeeds that increase stresses on tree crowns. These results demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates over large spatial scales at fine temporal and spatial resolution, thereby enabling strong tests of linkages to drivers. Future studies should include high frequency measurements of vertical and horizontal windspeeds and soil moisture to better capture proximate drivers, and incorporate additional image analyses to quantify standing dead trees in addition to treefalls.

Seedling responses to forest canopy disturbance following a spruce budworm outbreak in Maine

1994 ◽  
Vol 24 (4) ◽  
pp. 850-859 ◽  
Author(s):  
Akira Osawa
Keyword(s):  
Forest Canopy ◽  
Tree Mortality ◽  
Basal Area ◽  
Patch Dynamics ◽  
Spruce Budworm ◽  
Distribution Patterns ◽  
Balsam Fir ◽  
White Birch ◽  
Canopy Disturbance ◽  
Long Run

Patterns of tree mortality and seedling responses to canopy disturbance were investigated in northern Maine, where an outbreak of the spruce budworm (Choristoneurafumiferana (Clem.)) affected the forests of balsam fir (Abiesbalsamea (L.) Mill.) and spruce (Picea spp.) continuously between 1972 and 1984. The outbreak created a gradient of canopy tree mortality that ranged between 8.5 and 100% of the cumulative basal area in 1984. This was a result of the difference in vulnerability among the host species (balsam fir > spruce) and of their spatial distribution patterns along the site drainage gradient. Two groups of plant species responded differently to the gradient of canopy disturbance: balsam fir, spruce, and white birch (Betulapapyrifera Marsh.) regenerated mostly at the intermediate levels of mortality (≈20%by basal area) of the canopy balsam fir; raspberry (Rubusidaeus L.) and pin cherry (Prunuspensylvanica L.) were most abundant at ≈100% fir mortality. Overall, the observed responses in space and time of the seedlings to budworm-caused canopy disturbance could be mostly explained by the concept of patch dynamics. Long-term changes in species composition of the spruce–fir forests cannot be predicted with precision with the present knowledge. However, I hypothesize, based on the species-specific vulnerability to budworm damage and patterns of regeneration, that the proportion of spruce to fir trees would not differ very much in the long run regardless of extensive tree mortality by the spruce budworm.

Forest canopy disturbance geospatial data for Lake Superior basin, USA, 1985-2008

2014 ◽  
Author(s):  
Ian W. Housman ◽  
Mark D. Nelson ◽  
Charles H. Perry ◽  
Kirk M. Stueve ◽  
Chengquan Huang
Keyword(s):  
Forest Canopy ◽  
Lake Superior ◽  
Geospatial Data ◽  
Canopy Disturbance

Forest canopy disturbance geospatial data for Lake Michigan basin, USA, 1985-2008

2014 ◽  
Author(s):  
Ian W. Housman ◽  
Mark D. Nelson ◽  
Charles H. Perry ◽  
Kirk M. Stueve ◽  
Chengquan Huang
Keyword(s):  
Forest Canopy ◽  
Lake Michigan ◽  
Geospatial Data ◽  
Michigan Basin ◽  
Canopy Disturbance

scholarly journals Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning

2021 ◽  
Vol 172 ◽  
pp. 79-94
Author(s):  
Maryam Pourshamsi ◽  
Junshi Xia ◽  
Naoto Yokoya ◽  
Mariano Garcia ◽  
Marco Lavalle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tropical Forest ◽  
Forest Canopy ◽  
Canopy Height ◽  
Polarimetric Sar ◽  
Height Estimation ◽  
Tropical Forest Canopy

scholarly journals Comparison of Weibull Estimation Methods for Diverse Winds

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ferhat Bingöl
Keyword(s):  
Wind Farm ◽  
Study Data ◽  
Distribution Functions ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Weibull Function ◽  
Wind Speeds ◽  
Low Wind Speed ◽  
Low Wind Speeds ◽  
Wind Distribution

Wind farm siting relies on in situ measurements and statistical analysis of the wind distribution. The current statistical methods include distribution functions. The one that is known to provide the best fit to the nature of the wind is the Weibull distribution function. It is relatively straightforward to parameterize wind resources with the Weibull function if the distribution fits what the function represents but the estimation process gets complicated if the distribution of the wind is diverse in terms of speed and direction. In this study, data from a 101 m meteorological mast were used to test several estimation methods. The available data display seasonal variations, with low wind speeds in different seasons and effects of a moderately complex surrounding. The results show that the maximum likelihood method is much more successful than industry standard WAsP method when the diverse winds with high percentile of low wind speed occur.

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