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Quantitative Estimates of Biomass and Forest Structure in Quantitative Estimates of Biomass and Forest Structure in Coastal Temperate Rainforests Derived from Multi-return Coastal Temperate Rainforests Derived from Multi-return
Airborne LidarAirborne LidarMarc G. Kramer1 and Michael Lefsky2
1NASA-Ames Research Center, Ecosystem Science and Technology Branch, Mail Stop 242-4,Moffett Field, CA 94035 Email: [email protected] 2Colorado State University, Department of Forest Science Forestry Building #131, Fort Collins, CO 80523-1470
•Quantitative estimates of forest structure (stem size, stand density) and biomass were achieved using relatively lidar-derived variables.
•Fine scale characterization of forest structure and stand attributes can be captured over large forested areas with lidar.
•Lower sampling effort (<1 pts / 5 m2) results in a considerable decrease in the R2 of lidar-based regression equations..
•Dense floor vegetation and complex topography still present difficulties in obtaining accurate estimates.
Methods
Obtaining accurate measures of forest structure and biomass in primary temperate rainforests of southeast Alaska from traditional remotely sensed data has been hampered by the complex stand structure of these productive rainforests and difficulties with canopy illumination associated with steep topography occurring at high latitudes. In southeast Alaska, complex natural forest structures develop in concert with disturbances from maritime windstorms and landslides on diverse soils across the region. In spite of their global ecological importance, and rarity, little information exists on the distribution of temperate rainforest structures and biomass dynamics. This paucity of maps or other datasets that adequately capture the complex three-dimensional forest structure has made research on these forests difficult. Our objective in this study was to use a state-of-the-art lidar system, and to apply a wide set of measurements from these devices to make more detailed measurements of natural forest structure than are currently available.
Motivation
Vicinity map
Old-growth forests
Inertial Navigation System
Global Positioning System (GPS)
500 ft
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•12 field plots (0.1-.8 ha in size) were selected. Each represents a range of natural forest conditions from a recently naturally disturbed, young 70 year-old forest to an all-aged old-growth forest with no evidence of recent (<200 yr) disturbance from maritime windstorm activity.
•Forest characteristics (stem size, density, height) were measured at each plot location.
•A multi-return small-footprint light detection and ranging (lidar) system, with a maximum pulse rate of 50 Khz (75 Khz) was used in this study. DATIS-2, owned by 3-Di Corp. was flown in a Cesna 206 in May 2001, and a high density (>2 pts/sq m) 3-dimensional distribution of points was achieved.
•Variables that could be used to predict forest structure attributes were then developed from the lidar data within pixels of varying resolution.
•Lidar measurements were then compared with field-derived measures of biomass, density and stem size.
•The influence of sampling effort (lidar point density) on prediction was evaluated by randomly removing data (to produce a lower point density) and constructing regression equations each time.
Field plot locations
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Mature orest (200 years old)
Young forests (70 years old)
Lidar-derived measures of canopy architectureSite description
Canopy textureCanopy height (1 m resolution)
Frequency distribution of canopy height derived from lidar in select plots (0.1 ha and greater).
Multi-modal distribution
Uni-modal distribution Uni-modal distributiion
Multi-modal distribution
Less-disturbed plots contain multimodal distributions.
Multi-return Light Detection and Ranging (lidar)
The sensor is capable of retrieving multiple (up to 5) returns of elevation and intensity for every shot as they pass through forested canopies. The laser fires at a rate in excess of 4000 pulses per second and is scanned across the aircraft flight path. High point density (>1 pt / m2) can be achieved by multiple flight paths over the same area.
Three dimensional visualization of raw lidar data. High depth of penetrationvalues (>20m) are shown on the left, and low depth of penetration values (<9 m) are shown on the right, superimposed on top of the ground surface. The resolution of these images is 5 m.
Plot 11
