Life Sciences Commons^™

Evaluating Vascular Plant Composition And Species Richness On Horn Island, Mississippi, Using Passive And Active Remote Sensing In Conjunction With Ground Based Measurements, Kelly Lynn Lucas

Dissertations

Barrier island vegetation is subjected to chronic abiotic stressors combined with periodic storm events that favor species adapted to harsh environments. These islands are the first landforms to be affected by changes in coastal subsidence and sea-level rise. Evaluating changes in vegetation is important for understanding the impact of global climate change on coastal environments.

This study assesses vegetation composition and plant species richness on Horn Island, Mississippi using ground data in conjunction with remotely sensed spectral and LIDAR data. The goals of this research are to: 1) classify and map vegetation composition on Horn Island using hyperspectral and LIDAR …

Plant Succession On Gopher Mounds In Western Cascade Meadows: Consequences For Species Diversity And Heterogeneity, Chad C. Jones, Charles B. Halpern, Jessica Niederer

Botany Faculty Publications

Pocket gophers have the potential to alter the dynamics of grasslands by creating mounds that bury existing vegetation and locally reset succession. Gopher mounds may provide safe sites for less competitive species, potentially increasing both species diversity and vegetation heterogeneity (spatial variation in species composition). We compared species composition, diversity and heterogeneity among gopher mounds of different ages in three montane meadows in the Cascade Range of Oregon. Cover of graminoids and forbs increased with mound age, as did species richness. Contrary to many studies, we found no evidence that mounds provided safe sites for early successional species, despite their …

Seasonal Adaptation Of Vegetation Color In Satellite Images, Srinivas Jakkula, Vamsi K.R. Mantena, Ramu Pedada, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)

Electrical & Computer Engineering Faculty Publications

Remote sensing techniques like NDVI (Normal Difference vegetative Index) when applied to phenological variations in aerial images, ascertained the seasonal rise and decline of photosynthetic activity in different seasons, resulting in different color tones of vegetation. The rise and fall of NDVI values decide the biological response, either the green up or brown down [1]. Vegetation in green up period appears with more vegetative vigor and during brown down period it has a dry appearance. This paper proposes a novel method that identifies vegetative patterns in satellite images and then alters vegetation color to simulate seasonal changes based on training …