Effects of Temporal and Interspecific Variation of Specific Leaf Area on Leaf Area Index Estimation of Temperate Broadleaved Forests in Korea

Abstract

This study investigated the effects of interspecific and temporal variation of specific leaf area (SLA, cm 2 ·g −1 ) on leaf area index (LAI) estimation for three deciduous broadleaved forests (Gwangneung (GN), Taehwa (TH), and Gariwang (GRW)) in Korea with varying ages and composition of tree species. In fall of 2014, fallen leaves were periodically collected using litter traps and classified by species. LAI was estimated by obtaining SLAs using four calculation methods (A: including both interspecific and temporal variation in SLA; B: species specific mean SLA; C: period-specific mean SLA; and D: overall mean), then multiplying the SLAs by the amount of leaves. SLA varied across different species in all plots, and SLAs of upper canopy species were less than those of lower canopy species. The LAIs calculated using method A, the reference method, were GN 6.09, TH 5.42, and GRW 4.33. LAIs calculated using method B showed a difference of up to 3% from the LAI of method A, but LAIs calculated using methods C and D were overestimated. Therefore, species specific SLA must be considered for precise LAI estimation for broadleaved forests that include multiple species.

Practical LAI Estimation with DHP Images in Complex Forest Structure with Rugged Terrain

“…The Gwangneung site (Figure 5a), a deciduous broadleaf forest, reveals seasonal patterns whose L is the highest in summer (June to August) and lowest in winter (December to February). The estimated L falls within the range of previous results, which are 5.25 [28] and 6.09 ± 0.72 [30]. Wando and Jeju sites (Figure 5b,c) also showed near constant and weak seasonal patterns, reflecting forest types such as evergreen broadleaved forest and mixed forest, respectively.…”

Optimizing shrub parameters to estimate gross primary production of the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model

“…each PFT. Moreover, several studies suggest that the parameters 0 and SLA vary considerably across seasons (Groenendijk et al, 2011;Kwon et al, 2016;Olsoy et al, 2016;Zhang et al, 2014). The mismatch in daily GPP pattern between simulated and flux tower data for specific seasons could be partly attributed to the lack of the model's ability to address these seasonal deviations correctly.…”

Developing and optimizing shrub parameters representing sagebrush (<i>Artemisia</i> spp.) ecosystems in the northern Great Basin using the Ecosystem Demography (EDv2.2) model

“…Such an effort would also increase the number of parameters required, potentially complicating the process of optimization and validation unique to each PFT. Moreover, several studies suggest that the parameters V m0 and SLA vary considerably across seasons (Groenendijk et al, 2011;Kwon et al, 2016;Olsoy et al, 2016;Zhang et al, 2014). The mismatch in daily GPP patterns between simulated and flux tower data for specific seasons could be partly attributed to the lack of the model's ability to address these seasonal deviations correctly.…”