Effects of leaf litter depth on the emergence and seedling growth of deciduous forest tree species in relation toseed size1

The distribution of forest tree species provides crucial data for regional forest management and ecological research. Although medium-high spatial resolution remote sensing images are widely used for dynamic monitoring of forest vegetation phenology and species identification, the use of multiresolution images for similar applications remains highly uncertain. Moreover, it is necessary to explore to what extent spectral variation is responsible for the discrepancies in the estimation of forest phenology and classification of various tree species when using up-scaled images. To clarify this situation, we studied the forest area in Harqin Banner in northeast China by using year-round multiple-resolution time-series images (at four spatial resolutions: 4, 10, 16, and 30 m) and eight phenological metrics of four deciduous forest tree species in 2018, to explore potential impacts of relevant results caused by various resolutions. We also investigated the effect of using up-scaled time-series images by comparing the corresponding results that use pixel-aggregation algorithms with the four spatial resolutions. The results indicate that both phenology and classification accuracy of the dominant forest tree species are markedly affected by the spatial resolution of time-series remote sensing data (p < 0.05): the spring phenology of four deciduous forest tree species first rises and then falls as the image resolution varies from 4 to 30 m; similarly, the accuracy of tree species classification increases as the image resolution varies from 4 to 10 m, and then decreases as the image resolution gradually falls to 30 m (p < 0.05). Therefore, there remains a profound discrepancy between the results obtained by up-scaled and actual remote sensing data at the given spatial resolutions (p < 0.05). The results also suggest that combining phenological metrics and time-series NDVI data can be applied to identify the regional dominant tree species across different spatial resolutions, which would help advance the use of multiscale time-series satellite data for forest resource management.

Download Full-text

Dry-forest tree species with large seeds and low stem specific density show greater survival under drought

Journal of Tropical Ecology ◽

10.1017/s0266467418000421 ◽

2019 ◽

Vol 35 (1) ◽

pp. 26-33 ◽

Cited By ~ 1

Author(s):

Lalitha Krishnan ◽

Deepak Barua ◽

Mahesh Sankaran

Keyword(s):

Drought Tolerance ◽

Seed Size ◽

Seedling Growth ◽

Tree Species ◽

Growing Season ◽

Forest Tree ◽

Dry Forest ◽

Dry Matter Content ◽

Specific Density ◽

Forest Tree Species

AbstractTree establishment in tropical dry forests is constrained by drought-related seedling mortality during early stages of recruitment. Predicted increases in the duration of growing-season droughts in the future pose a significant threat to these ecosystems that could significantly alter their vegetation structure and composition. Here, we examined drought tolerance in seedlings of seven common dry-forest tree species from the Indian subcontinent. We conducted a dry-down experiment on 3-wk-old seedlings, and asked whether the key plant functional traits, specific leaf area (SLA), leaf dry matter content (LDMC), seed size and stem specific density (SSD) were good predictors of seedling growth under well-watered conditions, and survival during drought. Seedlings displayed substantial drought tolerance with most seedlings surviving for more than 2 wk under protracted drought. Seed size in combination with SLA predicted seedling growth under well-watered conditions and seed size predicted survival under drought. In contrast to our expectations, seedlings with lower SSD survived for longer without water. Our results suggest that dry-forest species will be differentially affected by the predicted increases in the duration of growing-season droughts, and detrimental effects will be more severe for species with smaller seeds.

Download Full-text