Assessment of ICESat-2 ATL08 Canopy Height Estimates for Tropical Forests in the Americas

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations.

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A New Method for Incorporating Hillslope Effects to Improve Canopy-Height Estimates From Large-Footprint LIDAR Waveforms

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2011.2179635 ◽

2012 ◽

Vol 9 (4) ◽

pp. 730-734 ◽

Cited By ~ 5

Author(s):

T. Allouis ◽

S. Durrieu ◽

P. Couteron

Keyword(s):

Canopy Height ◽

New Method ◽

Height Estimates

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Analyzing canopy height variations in secondary tropical forests of Malaysia using NASA GEDI

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/880/1/012031 ◽

2021 ◽

Vol 880 (1) ◽

pp. 012031

Author(s):

E Adrah ◽

W S Wan Mohd Jaafar ◽

S Bajaj ◽

H Omar ◽

R V Leite ◽

...

Keyword(s):

Tropical Forests ◽

Large Scale ◽

Sustainable Forest Management ◽

Secondary Forest ◽

Age Groups ◽

Rapid Change ◽

Structural Data ◽

Peninsular Malaysia ◽

Canopy Height ◽

Geographical Regions

Abstract Tropical forests play a significant role in regulating the average global atmospheric temperature encompassing 25 % of the carbon present in the terrestrial biosphere. However, the rapid change in climate, arising from unsustainable human practices, can significantly affect their carbon uptake capability in the future. For understanding these deviations, it is important to identify and quantify the large-scale canopy height variations arising from previous anthropogenic disturbances. With the advent of NASA GEDI spaceborne LiDAR (light detection and ranging), it is now possible to acquire three-dimensional vertical structural data of forests globally. In this study, we evaluate the applicability of GEDI for analyzing relative canopy height variations of secondary tropical forests of different age groups located across multiple geographical regions of peninsular Malaysia. The results for RH98 GEDI metric trends for the lowland and hill forests category across 4 different disturbance groups show a positive correlation between mean relative height and secondary forest ages. The consistency of these findings with previous studies in the region indicate the usefulness of GEDI to provide valuable insights into the patterns and drivers of forest height variation. Thus, this study contributes toward the operationalization of spaceborne LiDAR technology for monitoring forest disturbances and measuring biomass recovery rates and should help support large-scale sustainable forest management initiatives with respect to the tropical forests of Malaysia.

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Investigating the agreement between global canopy height maps and airborne Lidar derived height estimates over Canada

Canadian Journal of Remote Sensing ◽

10.5589/m13-036 ◽

2013 ◽

Vol 39 (sup1) ◽

pp. S139-S151 ◽

Cited By ~ 18

Author(s):

Douglas K. Bolton ◽

Nicholas C. Coops ◽

Michael A. Wulder

Keyword(s):

Airborne Lidar ◽

Canopy Height ◽

Height Estimates

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Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery

Remote Sensing ◽

10.3390/rs12071160 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1160 ◽

Cited By ~ 4

Author(s):

Ovidiu Csillik ◽

Pramukta Kumar ◽

Gregory P. Asner

Keyword(s):

Tropical Forests ◽

Spatial Resolution ◽

Large Scale ◽

Forest Canopy ◽

Satellite Image ◽

Spectral Projection ◽

Forest Degradation ◽

Canopy Height ◽

Textural Features ◽

Conservation Actions

Monitoring tropical forests using spaceborne and airborne remote sensing capabilities is important for informing environmental policies and conservation actions. Developing large-scale machine learning estimation models of forest structure is instrumental in bridging the gap between retrospective analysis and near-real-time monitoring. However, most approaches use moderate spatial resolution satellite data with limited capabilities of frequent updating. Here, we take advantage of the high spatial and temporal resolutions of Planet Dove images and aim to automatically estimate top-of-canopy height (TCH) for the biologically diverse country of Peru from satellite imagery at 1 ha spatial resolution by building a model that associates Planet Dove textural features with airborne light detection and ranging (LiDAR) measurements of TCH. We use and modify features derived from Fourier textural ordination (FOTO) of Planet Dove images using spectral projection and train a gradient boosted regression for TCH estimation. We discuss the technical and scientific challenges involved in the generation of reliable mechanisms for estimating TCH from Planet Dove satellite image spectral and textural features. Our developed software toolchain is a robust and generalizable regression model that provides a root mean square error (RMSE) of 4.36 m for Peru. This represents a helpful advancement towards better monitoring of tropical forests and improves efforts in reducing emissions from deforestation and forest degradation (REDD+), an important climate change mitigation approach.

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