Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2
| Autor: | Vladyslav Khramtsov, Oleg Seliverstov, Mykhailo Yushchuk, Kostiantyn Isaienkov |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Geophysics. Cosmic physics 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Training (civil) logging Deforestation Forest ecology deforestation Computers in Earth Sciences Baseline (configuration management) TC1501-1800 021101 geological & geomatics engineering 0105 earth and related environmental sciences convolutional neural network (CNN) QC801-809 business.industry Logging Environmental resource management deep learning Ocean engineering Remote sensing (archaeology) Change detection Environmental science LSTM Scale (map) business |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 364-376 (2021) |
| ISSN: | 2151-1535 1939-1404 |
| DOI: | 10.1109/jstars.2020.3034186 |
| Popis: | The logging is the leading cause for the reduction in the forest area in the world. At the same time, the number of forest clearcuts continues to grow. However, despite the massive scale, such incidents are difficult to track in time. As a result, huge areas of forests are gradually being cut down. Therefore, there is a need for regular and effective monitoring of changes in forest cover. The multitemporal data sources like Copernicus Sentinel-2 allow enhancing the potential of monitoring the Earth's surface and environmental dynamics including forest plantations. In this article, we present a baseline U-Net model for deforestation detection in the forest-steppe zone. Training and evaluation are conducted on our own dataset created on Sentinel-2 imagery for the Kharkiv region of Ukraine (31 400 km2). As a part of the research, we present several models with the ability to work with time-dependent imagery. The main contribution of this article is to provide a baseline model for the forest change detection inside Ukraine and improve it adding the ability to use several sequential images as an input of the segmentation model. |
| Databáze: | OpenAIRE |
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