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

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 13; p. 3172
• Main Authors: Adrah, Esmaeel, Wan Mohd Jaafar, Wan Shafrina, Omar, Hamdan, Bajaj, Shaurya, Leite, Rodrigo Vieira, Mazlan, Siti Munirah, Silva, Carlos Alberto, Chel Gee Ooi, Maggie, Mohd Said, Mohd Nizam, Abdul Maulud, Khairul Nizam, Cardil, Adrián, Mohan, Midhun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: Algorithms; Annual precipitation; Availability; Biodiversity; Bivariate analysis; Canopies; canopy height; Climate change; Climate prediction; Climatic data; Ecosystem models; Ecosystems; Elevation; Evapotranspiration; Forest ecosystems; Forest management; forest remote sensing; Forests; GEDI; Herbivores; Hydraulics; Hypotheses; Investigations; Landscape; Lasers; LiDAR; Machine learning; Montane environments; mountain forest; Mountain forests; Precipitation; Radiation; Remote sensing; Resampling; Terrestrial ecosystems; Topography; Tropical forests; Water availability; Water shortages; Water supply; Selangor Malaysia; Malaysia
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14133172

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.