Detecting phenological transition dates of vegetation based on multiple deep learning models
Z. Cheng (TU Delft - Electrical Engineering, Mathematics and Computer Science)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Vegetation phenology is the interaction between vegetation activities and ecosystem. Accurate monitoring of vegetation phenology is required to build models and enhance the understanding of the relationship between creatures and climate-environment. PhenoCam is a ground-level, webcam based images database recording the growing of various vegetations, PhenoCam and multiple modeling methods have been utilized to study vegetation phenology since 2000s. In this paper, it first time the deep learning models are applied to detect the phenological transition dates of vegetation. Four different deep learning models: Convolution Neural Network (CNN), Siamese Network, 3-D Fully Convolution Neural Network (FCN) and Regression Network are used to study the vegetation phenology, based on these approaches, the transition dates of vegetation activities within annual time can be determined from webcam-based images, some of these deep learning methods are more accurate than traditional modeling method in detecting the transition dates.