Using airborne LiDAR to assess spatial heterogeneity in forest structure on Mount Kilimanjaro

Abstract. The complex three-dimensional (3-D) structure of tropical forests generates a diversity of light environments for canopy and understory trees. Understanding diurnal and seasonal changes in light availability is critical for interpreting measurements of net ecosystem exchange and improving ecosystem models. Here, we used the Discrete Anisotropic Radiative Transfer (DART) model to simulate leaf absorption of photosynthetically active radiation (lAPAR) for an Amazon forest. The 3-D model scene was developed from airborne lidar data, and local measurements of leaf reflectance, aerosols, and PAR were used to model lAPAR under direct and diffuse illumination conditions. Simulated lAPAR under clear sky and cloudy conditions was corrected for light saturation effects to estimate light utilization, the fraction of lAPAR available for photosynthesis. Although the fraction of incoming PAR absorbed by leaves was consistent throughout the year (0.80–0.82), light utilization varied seasonally (0.67–0.74), with minimum values during the Amazon dry season. Shadowing and light saturation effects moderated potential gains in forest productivity from increasing PAR during dry season months when the diffuse fraction from clouds and aerosols was low. Comparisons between DART and other models highlighted the role of 3-D forest structure to account for seasonal changes in light utilization. Our findings highlight how directional illumination and forest 3-D structure combine to influence diurnal and seasonal variability in light utilization, independent of further changes in leaf area, leaf age, or environmental controls on canopy photosynthesis. Changing illumination geometry constitutes an alternative biophysical explanation for observed seasonality in Amazon forest productivity without changes in canopy phenology.

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Assessment of forest structure with airborne LiDAR and the effects of platform altitude

Remote Sensing of Environment ◽

10.1016/j.rse.2006.03.003 ◽

2006 ◽

Vol 103 (2) ◽

pp. 140-152 ◽

Cited By ~ 142

Author(s):

Nicholas R. Goodwin ◽

Nicholas C. Coops ◽

Darius S. Culvenor

Keyword(s):

Forest Structure ◽

Airborne Lidar

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Characterizing vertical forest structure using small-footprint airborne LiDAR

Remote Sensing of Environment ◽

10.1016/s0034-4257(03)00139-1 ◽

2003 ◽

Vol 87 (2-3) ◽

pp. 171-182 ◽

Cited By ~ 187

Author(s):

Daniel A. Zimble ◽

David L. Evans ◽

George C. Carlson ◽

Robert C. Parker ◽

Stephen C. Grado ◽

...

Keyword(s):

Forest Structure ◽

Airborne Lidar ◽

Small Footprint

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Application of the Conservation Planning Tool Zonation to Inform Retention Planning in the Boreal Forest of Western Canada

Frontiers in Ecology and Evolution ◽

10.3389/fevo.2020.584291 ◽

2020 ◽

Vol 8 ◽

Author(s):

François-Nicolas Robinne ◽

J. John Stadt ◽

Christopher W. Bater ◽

Charles A. Nock ◽

S. Ellen Macdonald ◽

...

Keyword(s):

Forest Structure ◽

Conservation Planning ◽

Management Practices ◽

Sustainable Forest Management ◽

Dispersion Analysis ◽

Primary Objective ◽

Airborne Lidar ◽

Post Harvest ◽

Trade Offs ◽

Retention Forestry

Retention forestry is an approach in which live trees and other components of forest structure are retained within harvested areas. A primary objective of retention forestry is to maintain biodiversity and to hasten post-harvest recovery of forest structure and function. Retention is now a key element in sustainable forest management practices in many regions of the world. However, locating where retention should be placed to best achieve management objectives is a challenging problem, and evidence-based approaches to operational applications are rare. We suggest here that harvest planners could benefit from the use of systematic conservation planning principles and methods to inform retention design. Specifically, we used a conservation planning—or prioritization—tool, Zonation, to create spatially-explicit scenarios of retention harvesting in a boreal mixedwood forest in northwestern Alberta, Canada. Scenarios were informed by several environmental variables related to site productivity; in particular, we used a metric of wetness (depth-to-water from the Wet Areas Mapping algorithm) that is based on airborne lidar-derived terrain models previously shown to correlate with patterns in post-harvest forest regeneration and biodiversity. The nine retention scenarios examined here related to the placement of retention focused to drier, mesic, or wetter sites in combination with other prioritization constraints. Results were compared with an existing harvest plan to assess differences in the spatial pattern of retention (e.g., percent overlapping area, number of patches, size of the patches). We also tested for the homogeneity of forest attributes (e.g., tree species, deciduous density) between scenarios and the existing harvest plan using multivariate dispersion analysis. Our results showed limited commonalities among scenarios compared to the existing harvest plan; they were characterized as having limited spatial overlap, and more and smaller patches with the use of a timber-cost constraint further affecting retention patterns. While modeling results significantly differed from current retention practices, the approach presented here offers flexibility in testing different scenarios and assessing trade-offs between timber production and conservation goals using a standardized conservation planning toolkit.

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Influence of Natural and Anthropogenic Linear Canopy Openings on Forest Structural Patterns Investigated Using LiDAR

Forests ◽

10.3390/f9090540 ◽

2018 ◽

Vol 9 (9) ◽

pp. 540 ◽

Cited By ~ 6

Author(s):

Udayalakshmi Vepakomma ◽

Daniel Kneeshaw ◽

Louis De Grandpré

Keyword(s):

Forest Structure ◽

Black Spruce ◽

Forest Biomass ◽

High Density ◽

Airborne Lidar ◽

Power Lines ◽

Gap Fraction ◽

Spruce Stands ◽

Edge Influence ◽

Forest Conditions

In much of the commercial boreal forest, dense road networks and energy corridors have been developed to access natural resources with unintended and poorly understood effects on surrounding forest structure. In this study, we compare the effects of anthropogenic and natural linear openings on surrounding forest conditions in black spruce stands (gap fraction, tree and sapling height, and density). Forest structure within a 100 m band around the edges of anthropogenic (roads and power lines), natural linear openings (streams), and a reference black spruce forest was measured by identifying individual stems and canopy gaps on recent high density airborne LiDAR canopy height models. CUSUM curves were used to assess the distance of edge influence. Forests surrounding anthropogenic openings were found to be gappier, less dense, and have smaller trees than those around natural openings. Forests were denser around natural and anthropogenic linear openings than in the reference forest with edge effects observed up to 24–75 m and 18–54 m, respectively, into the forest. A high density of saplings in the gappier forests surrounding anthropogenic openings may eventually lead to a higher forest biomass in the zone area surrounding roads as is currently observed around natural openings.

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Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR

Ecological Applications ◽

10.1890/15-0017 ◽

2016 ◽

Vol 26 (2) ◽

pp. 587-601 ◽

Cited By ~ 4

Author(s):

Ha T. Nguyen ◽

Lucy R. Hutyra ◽

Brady S. Hardiman ◽

Steve M. Raciti

Keyword(s):

Forest Structure ◽

Airborne Lidar ◽

Tropical Peat

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Uncertainty in Parameterizing Floodplain Forest Friction for Natural Flood Management, Using Remote Sensing

Remote Sensing ◽

10.3390/rs12111799 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1799

Author(s):

Alexander S. Antonarakis ◽

David J. Milan

Keyword(s):

Remote Sensing ◽

Forest Structure ◽

Vegetation Structure ◽

Flood Management ◽

Riparian Forests ◽

Airborne Lidar ◽

Pine Plantations ◽

Stem Density ◽

Flow Depth ◽

Trunk Diameter

One potential Natural Flood Management (NFM) option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterized for hydraulic models. Remote sensing offers high-resolution datasets capable of characterizing vegetation structure from a variety of platforms, but they contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood, and leaf area indices (20, 10, 30, 20%, respectively) resulted in a combined Manning’s n uncertainty from 11–13% to 11–17% at 2 m to 8 m flow depths. This equates to 7–8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, especially for flow though Pine plantations. For deeper flows, leaf and woody areas become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4–6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterization and flood modeling for NFM.

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Mixtures of airborne lidar-based approaches improve predictions of forest structure

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0506 ◽

2021 ◽

Author(s):

Ryan Christopher Blackburn ◽

Robert Buscaglia ◽

Andrew Sanchez Meador

Keyword(s):

Feature Selection ◽

New Mexico ◽

Random Forests ◽

Forest Structure ◽

Point Cloud ◽

Basal Area ◽

Airborne Lidar ◽

Forest Attributes ◽

Field Plots ◽

Paired T Test

The most common method for modeling forest attributes with airborne lidar, the area-based approach, involves summarizing the point cloud of individual plots and relating this to attributes of interest. Tree- and voxel-based approaches have been considered as alternatives to the area-based approach but are rarely considered in an area-based context. We estimated three forest attributes: basal area, overstory biomass, and volume, across 1,680 field plots in Arizona and New Mexico. Variables from the three lidar approaches (area, tree, voxel) were created for each plot. Random forests were estimated using subsets of variables based on each individual lidar approach and mixtures of each approach. Boruta feature selection was performed on variable subsets, including the mixture of all lidar-approach predictors (KS-Boruta). A corrected paired t-test was utilized to compare six validated models (area-Boruta, tree-Boruta, voxel-Boruta, KS-Boruta, KS-all, ridge-all) for each forest attribute. Based on significant reductions in error (SMdAPE), basal area and biomass were best modeled with KS-Boruta while volume was best modeled with KS-all. Analysis of variable importance shows voxel-based predictors are critical for the prediction of the three forest attributes. This study highlights the importance of multi-resolution voxel-based variables for modeling forest attributes in an area-based context.

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The impacts of Amazon forest degradation and fragmentation on energy, water, and carbon cycles

10.5194/egusphere-egu2020-4801 ◽

2020 ◽

Author(s):

Marcos Longo ◽

Sassan Saatchi ◽

Michael Keller ◽

Kevin Bowman ◽

António Ferraz ◽

...

Keyword(s):

Water Stress ◽

Land Surface Temperature ◽

Forest Structure ◽

Land Surface ◽

Forest Degradation ◽

Dry Season ◽

Airborne Lidar ◽

Carbon Cycles ◽

The Impact

<p>Tropical forest degradation through selective logging, fragmentation, and understory fires substantially changes forest structure and composition.&#160; In the Amazon, degradation is as widespread as deforestation; however, studies addressing the effects of forest degradation on tropical ecosystem functions are scarce. Here, we integrate small-footprint airborne lidar over the Brazilian Amazon (> 250,000 ha), collected between 2016&#8211;2018, with recent ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) land surface temperature and evapotranspiration products (70-m resolution, data acquired in 2018&#8211;2019) to investigate the role of forest structure, forest fragmentation, and disturbance history on dry-season land surface temperature and evapotranspiration.&#160; During the dry season, degraded forests, especially those affected by multiple degradation events, are significantly warmer (up to 9.3&#176;C) and show reduced evapotranspiration (10% less than intact forests). Likewise, forest near the edges (< 350m) experience the greatest warming (up to 6.5&#176;C) and the greatest reduction (9%) in evapotranspiration. We also used the airborne lidar dataset to initialize the Ecosystem Demography Model (ED-2.2) to investigate the impact of degradation on the gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H) under a broader range of climate conditions, including severe droughts. Consistent with ECOSTRESS, the simulations during the dry season in typical years showed that severely degraded forests experienced water-stress with declines in ET (34% reduction), GPP (35% reduction), and increases of H (43% increases) and daily mean ground temperatures (up to 6.5&#176;C) relative to intact forests.&#160; In the model, the simulated changes are mostly driven by increased below-ground water stress, which can be attributed to the shallower rooting profile of degraded forests. However, relative to intact forest, the impact of degradation on energy, water, and carbon cycles markedly diminishes under extreme droughts such as 2015&#8211;2016, when all forests experience severe stress. Our results indicate the potentially important role of tropical forest degradation changing the carbon, water, and energy cycles in the Amazon, and consequently a much broader influence of land use activities on functioning of tropical ecosystems.</p>

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