Hyperspectral, lidar, and multi-temporal sensing form the core of his efforts, with various ecosystem and forestry projects, e.g., land quality and global change (multi-temporal), forest and savanna ...

LiDAR remote sensing has emerged as a powerful tool for capturing detailed terrain features and three-dimensional vegetation structures. In this figure, we focus on the Mississippi Delta region in the ...

Accurate classification of wetland vegetation is essential for biodiversity conservation and carbon cycle monitoring. This study developed an adaptive ensemble learning (AEL-Stacking) framework that ...

Researchers have developed a machine learning-based ensemble approach to quantify fire-induced thaw settlement across the entire Tanana Flats in Alaska, which encompasses more than 3 million acres.

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A project at the University of Utah has developed a method for predicting rates of travel for on- and off-path walkers, based on analysis of lidar data mapping the terrain they cover. Christened ...