Tree stress estimation with deep learning


Ebfea2e0 ffe5 4583 9ba2 6efe3c5e9375.jpg 500 500

Starting Date: earliest start: (2017-11-01) latest end: (2018-07-31)

Organization: Photogrammetry and Remote Sensing (Prof. Schindler)

Involved Host(s): Berger Monique

Abstract: A healthy canopy prevents soil erosion, slows rainwater runoff, and is key to clean and ample water supply. However, the amount of trees, their stress level (e.g., defoliation), species, biomass, and age are often unknown because no up-to-date database exists due to the high cost of in-situ surveys.

Description: A healthy canopy prevents soil erosion, slows rainwater runoff, and is key to clean and ample water supply. However, the amount of trees, their stress level (e.g., defoliation), species, biomass, and age are often unknown because no up-to-date database exists due to the high cost of in-situ surveys. With this project, a collaboration between ETH and WSL, we aim at developing an automated, image-based system to track changes of trees' states (e.g., stress level, pests, re-planting events) over time at country-scale. The long-term objective is to measure the impact of climate change on Swiss trees. The system centers on state-of-the-art supervised deep convolutional neural networks where sparse very high-resolution ground-level images from in-situ surveys are combined with lower resolution aerial and satellite images that cover entire Switzerland. The general idea is to use sparse, but very accurate tree measurements acquired by WSL for training deep machine learning approaches that can then predict tree stress at any other location in Switzerland using satellite images of the Sentinel 2 satellites.

Goal: With this project, a collaboration between ETH and WSL, we aim at developing an automated, image-based system to track changes of trees' states (e.g., stress level, pests, re-planting events) over time at country-scale. The long-term objective is to measure the impact of climate change on Swiss trees.

Contact Details: Dr. Jan Dirk Wegner (jan.wegner@geod.baug.ethz.ch)

Satellite images deep machine learning


Labels: Bachelor Thesis Master Thesis CLS Student Project (MPG ETH CLS)
Topics: Information, Computing and Communication Sciences Engineering and Technology

© 2017, Copyright Max-Planck-Gesellschaft - Imprint