Correlating the Spatial Distribution of Snow Depth to Forest Canopy Parameters Derived from Terrestrial Laser Scans

2018 ◽  
Author(s):  
Zachary Uhlmann
Keyword(s):  
Spatial Distribution ◽  
Snow Depth ◽  
Forest Canopy

scholarly journals Characterizing Tree Spatial Distribution Patterns Using Discrete Aerial Lidar Data

2020 ◽  
Vol 12 (4) ◽  
pp. 712 ◽  
Author(s):  
Xiaofei Wang ◽  
Guang Zheng ◽  
Zengxin Yun ◽  
L. Monika Moskal
Keyword(s):  
Spatial Distribution ◽  
Point Processes ◽  
Forest Canopy ◽  
Forest Ecosystems ◽  
Urban Forest ◽  
Pair Correlation ◽  
Distribution Patterns ◽  
Individual Tree ◽  
Spatial Distribution Patterns ◽  
Water Cycles

Tree spatial distribution patterns such as random, regular, and clustered play a crucial role in numerical simulations of carbon and water cycles and energy exchanges between forest ecosystems and the atmosphere. An efficient approach is needed to characterize tree spatial distribution patterns quantitatively. This study aims to employ increasingly available aerial laser scanning (ALS) data to capture individual tree locations and further characterize their spatial distribution patterns at the landscape or regional levels. First, we use the pair correlation function to identify the categories (i.e., random, regular, and clustered) of tree spatial distribution patterns, and then determine the unknown parameters of statistical models used for approximating each tree spatial distribution pattern using ALS-based metrics. After applying the proposed method in both natural and urban forest sites, our results show that ALS-based tree crown radii can capture 58%–77% (p < 0.001) variations of visual-based measurements depending on forest types and densities. The root mean squared errors (RMSEs) of ALS-based tree locations increase from 1.46 m to 2.51 m as the forest densities increasing. The Poisson, soft-core, and hybrid-Gibbs point processes are determined as the optimal models to approximate random, regular, and clustered tree spatial distribution patterns, respectively. This work provides a solid foundation for improving the simulation accuracy of forest canopy bidirectional reflectance distribution function (BRDF) and further obtain a better understanding of the processes of carbon and water cycles of forest ecosystems.

The influence of forest stand characteristics on snow interception in the coastal forests of British Columbia

1988 ◽  
Vol 18 (5) ◽  
pp. 566-573 ◽  
Author(s):  
R. Scott McNay ◽  
Leslie D. Peterson ◽  
J. Brian Nyberg
Keyword(s):  
Snow Depth ◽  
Forest Canopy ◽  
Basal Area ◽  
Related Phenomenon ◽  
Linear Regression Models ◽  
Coastal Forests ◽  
Winter Habitat ◽  
Independent Variables ◽  
Fresh Snow ◽  
Stand Characteristics

The capability of forest stands to intercept snow is an important factor in determining management prescriptions for such hydrologically related phenomenon as avalanches, floods, and water supply as well as suitability for ungulate winter habitat. This study tested the hypothesis that snow interception can be predicted as a function of various stand characteristics and storm sizes. The dependent variable was fresh snow depth under the forest canopy; the independent variables were crown completeness, crown length, crown width, basal area per hectare, tree height, tree density, and storm size. Ten stands were selected for study from two locations on Vancouver Island. Snow depth was monitored over 24 storms ranging from 1.4 to 38.0 cm. The best simple linear regression models that incorporated forest variables were those for individual storms, with fresh snow expressed as a function of mean crown completeness. The best assessments of a particular stand's capability to intercept snow were made using an equation with both storm size and mean crown completeness as independent variables.

Measurement of snow cover depth using 100 × 100 meters sampling plot and Structure from Motion method in Adventdalen, Svalbard

2016 ◽  
Vol 6 (2) ◽  
pp. 155-168
Author(s):  
Radim Stuchlík ◽  
Jan Russnák ◽  
Tomáš Plojhar ◽  
Zdeněk Stachoň
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Structure From Motion ◽  
Snow Depth ◽  
Surface Model ◽  
Snow Surface ◽  
Reference Volume ◽  
Snow Cover Depth ◽  
Sampling Plot ◽  
Motion Method

We tried to verify the concept of Structure from Motion method for measuring the volume of snow cover in a grid of 100×100 m located in Adventdalen, Central Svalbard. As referencing method we utilized 121 depth measurements in one hectare area. Using avalanche probe a snow depth was measured in mentioned 121 nodes of the grid. We detected maximum snow depth of 2.05 m but snowless parts as well. From gathered depths’ data we geostatistically (ordinary kriging) interpolated snow surface model which we used to determine reference volume of snow at research plot (5 569 m3). As a result, we were able to calculate important metrics and analyze topography and spatial distribution of snow cover at the plot. For taking photos for Structure from Motion method, bare pole in hands with a camera mounted was used. We constructed orthomosaic of research plot.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals Population Dynamics of Acromyrmex crassispinus (Forel) (Hymenoptera: Formicidae) and Attacks on Pinus taeda Linnaeus (Pinaceae) plantations

Sociobiology ◽  
2015 ◽  
Vol 62 (3) ◽  
pp. 340 ◽  
Author(s):  
Mariane Aparecida Nickele ◽  
Wilson Reis Filho
Keyword(s):  
Population Dynamics ◽  
Spatial Distribution ◽  
Pinus Taeda ◽  
Forest Canopy ◽  
First Year ◽  
Nest Density ◽  
Nuptial Flight ◽  
The Third ◽  
Leaf Cutting ◽  
Pinus Taeda L

This work aimed to study the population dynamics of Acromyrmex crassispinus (Forel) in Pinus taeda L. plantations, evaluating the density and spatial distribution of nests over time, inferring about the period of the first nuptial flight of A. crassispinus colonies, and evaluating the levels of attack of this leaf-cutting ant on P. taeda plants. Assessments were performed monthly in the first year after planting, every three months until the third year and every six months until the plantation was six years old. The presence of nests was observed only after 15 months after planting. The nest density gradually increased until the planting completed 30 months, and decreased when the forest canopy began to close (after 54 months). Spatial distribution of A. crassispinus nests was random. Probably, the first nuptial flight of an A. crassispinus colony occurs after the third year of the colony foundation. Pinus taeda plants were not attacked by A. crassispinus throughout the evaluation period. Then, when dealing with a replanting area of Pinus plantation, where the previous forest has not been subject to pruning nor thinning, the problem with A. crassispinus is almost null if the clearcutting and the new planting occur during the winter. In this case, leaf-cutting ants control can be alleviated and it is not necessary to carry out systematic control of ants where A. crassispinus is the predominant leaf cutting ant species. Acromyrmex crassispinus control should be done only if nests are located or if attacked plants by ants are detected.

scholarly journals Spatial Distribution and Scaling Properties of Lidar‐Derived Snow Depth in the Extratropical Andes

2020 ◽  
Vol 56 (12) ◽  
Author(s):  
Pablo A. Mendoza ◽  
Thomas E. Shaw ◽  
James McPhee ◽  
Keith N. Musselman ◽  
Jesús Revuelto ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Snow Depth ◽  
Scaling Properties

scholarly journals Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

2012 ◽  
Vol 13 (1) ◽  
pp. 204-222 ◽  
Author(s):  
Maheswor Shrestha ◽  
Lei Wang ◽  
Toshio Koike ◽  
Yongkang Xue ◽  
Yukiko Hirabayashi
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Hydrological Model ◽  
Lapse Rate ◽  
Distributed Hydrological Model ◽  
Snow Cover Area ◽  
Temperature Lapse Rate ◽  
Snow Cover Extent

Abstract In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923 ◽  
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

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