The Shift from Energy to Water Limitation in Local Canopy Height from Temperate to Tropical Forests in China

Abstract

Canopy height greatly affects the biomass stock, carbon dynamics, and maintenance of biodiversity in forests. Previous research reported that the maximum forest canopy height (Hmax) at global and regional scales could be explained by variations in water or energy availability, that is, the water- or energy-related hypothesis. However, fundamental gaps remain in our understanding of how different drivers (i.e., water and energy) contribute to the Hmax at the local scale. In this study, we selected eight dynamic forest plots (20–30 ha) across a latitudinal gradient (from 21.6° N to 48.1° N) in China and measured the canopy structure using airborne light detection and ranging (LiDAR) data. Based on the LiDAR point cloud data, we extracted the maximum tree height (Hmax) in a 20 × 20 m quadrat as a proxy for canopy height, and the topographic wetness index (TWI) and digital terrain model-derived insolation (DTMI) were calculated as proxies for water and energy conditions. We used a linear mixed model and spatial simultaneous autoregressive error model to quantify how TWI and DTMI contributed to variations in Hmax at the local scale. We found that the positive effect of TWI was stronger in subtropical and tropical forests, highlighting that water was the main factor that drives the canopy height pattern in these regions. In contrast, although the effects of DTMI can be both positive and negative, its relative contribution was higher in temperate forest plots than in other forest types, supporting the idea that energy input is more critical for Hmax in temperate forests. Overall, our study revealed the directional change from energy to water limitation from temperate to subtropical and tropical forests. Our findings can offer important insights into forest management, especially under global climate change in the Anthropocene.

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

“…Subsequently, it becomes necessary to understand the drivers influencing canopy height and its spatial variation along with the relationships among them. Among the several factors that influence canopy height, various studies have found the hydraulic limitation hypothesis (water availability) and the energy limitation hypothesis (energy in the form of solar radiation or temperature) to be pivotal [6,[8][9][10][11]. At large spatial scales, climatic attributes along with historical conditions have been found to mediate canopy height, whereas at fine scale, it is the site-specific parameters such as topographical variables (elevation, slope, curvature, aspect), soil parameters and local environmental conditions which drive canopy height [12][13][14][15].…”

“…With advances in technology, remotely sensed LiDAR (satellite and airborne) data have been utilized to analyze canopy heights at global and local scales, with a few utilizing LiDAR for investigating the relationship of canopy height with its environmental drivers. Wang et al [11] investigated the relationships between canopy height, water and energy conditions using UAV-based LiDAR point cloud data. They found that the water limitation hypothesis was able to better explain the variance in canopy heights in tropical forests in China, while the same was true for the energy limitation hypothesis in temperate forests [11].…”

“…Wang et al [11] investigated the relationships between canopy height, water and energy conditions using UAV-based LiDAR point cloud data. They found that the water limitation hypothesis was able to better explain the variance in canopy heights in tropical forests in China, while the same was true for the energy limitation hypothesis in temperate forests [11]. In another study, Fricker et al [14] made use of airborne LiDAR, often referred to as airborne laser scanning (ALS), for studying the association between canopy height and environmental variables (soil bulk density, pH, topographic wetness index, slope curvature and potential solar radiation) at six different spatial scales (25,50,100,250, 500, 1000 m) across an elevational gradient ranging from 200 to 3000 m above sea level (a.s.l.)…”