Analyzing Canopy Height Patterns and Environmental Landscape Drivers in Tropical Forests Using NASA’s GEDI Spaceborne LiDAR

Abstract

Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA’s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia’s tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests.

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

“…For example, Dubayah et al [14] note that forests covered by "power beam" GEDI data types are twice as precise as "coverage beams". This is because power beams can penetrate dense canopies more effectively, thus reducing saturation at high tree densities [14,76,77]. Consequently, if the "power beam" GEDI data are unavailable, this can lead to greater uncertainty in the GEDI Rh metrics for broadleaf stands [47].…”

Canopy-Height and Stand-Age Estimation in Northeast China at Sub-Compartment Level Using Multi-Resource Remote Sensing Data

“…The raw GEDI waveform data were a Level-1 product [36 The GEDI Level-2A data contained 2 versions (Version 1 and Version 2). The recently released GEDI V2 significantly improved the precision and the validation of footprints [38], so the GEDI V2 available on GEE was used in this study (Figure 1b). The RH90 was extracted as RH data from GEDIL2A for each study location.…”

“…As compared to other algorithms, the RF algorithm was insensitive to the values of its free parameters [48]. It has been widely used as a machine-learning technique for canopy-height estimation [38,49]. After repeated testing, the random state of the RF algorithm was set at 5, and the number of the regression trees was set at 200 [40,48].…”

Assessing the potential of machine learning and integrated satellite data for canopy height mapping: a case study on the Iberian Peninsula

“…Canopy height serves as a significant parameter for measuring and comprehending forested areas as it provides crucial insights into the maximum height of trees and serves as an indicator of environmental variables (water availability, temperature, precipitation, topography...), forest structure, and diversity 5,6 . Additionally, it partially explains biomass, further underscoring its importance in ecological research 7 .…”

“…Operating as a spaceborne LIDAR system, GEDI is specifically designed to measure on a global scale. By analysing GEDI waveforms, researchers can estimate canopy top height globally and assess the canopy height variation across landscapes 5,9 .…”