LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert

Author:

Mullissa Adugna12ORCID,Saatchi Sassan123,Dalagnol Ricardo123ORCID,Erickson Tyler4ORCID,Provost Naomi1,Osborn Fiona1,Ashary Aleena1,Moon Violet1,Melling Daniel1

Affiliation:

1. Ctrees.org, Pasadena, CA 91105, USA

2. Institute of Environment and Sustainability, University of California, Los Angeles, CA 90095, USA

3. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA

4. VorGeo, Los Altos, CA 94022, USA

Abstract

The Land Use Change Alert (LUCA) dataset was developed for effective and timely monitoring of global forest changes that are mostly associated with human activities. Near- real-time changes of forest land use are mapped at 0.05 ha minimum mapping unit for all forest types across the Earth’s ecoregions, every two weeks. LUCA is based on Sentinel-1 cloud penetrating synthetic aperture radar (SAR) observations to circumvent limitations of optical imagery from pervasive cloud cover over forested areas globally, and especially in the tropics. The methodology is based on a combination of time-series change detection and machine learning analytics to achieve high accuracy of alerts across all ecoregions and landscapes globally with an average area-adjusted users and producers accuracy of 83% and 63%, respectively. The bi-weekly global alert maps capture forest clearing associated with deforestation and industrial timber harvesting, along with forest degradation associated with selective logging, fragmentation, fire, and roads. The product was developed and released publicly through Google Earth Engine to allow for the rapid assessment of land use change activities, quantifying patterns and processes driving forest change and dynamics across forest ecoregions. LUCA is designed to help monitor a variety of emission reduction programs at the local to regional scales and play a key role in implementing regulations on deforestation-free products.

Publisher

MDPI AG

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sentinel-1 SAR Based Weakly Supervised Learning for Tropical Forest Mapping;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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