4.7 Article

DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis

Related references

Note: Only part of the references are listed.
Article Multidisciplinary Sciences

Forest fragmentation impacts the seasonality of Amazonian evergreen canopies

Matheus Henrique Nunes et al.

Summary: The magnitude and timing of leaf phenology in Amazonian forests are controversial. This study shows that plant phenology varies across vertical strata, but is sensitive to disturbances from forest fragmentation. Edge effects have a strong influence on phenological controls in wet forests of Central Amazonia.

NATURE COMMUNICATIONS (2022)

Article Remote Sensing

Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks

Claudia Arantes Silva et al.

Summary: The study utilized a Neural Network algorithm on Sentinel-1 images to identify clear-cut deforested areas in the Brazilian Amazon, revealing the inefficiency of optical-based near-real time deforestation alert systems during the rainy season. A Multi-Layer Perceptron network was used to detect forest disturbances larger than 2 hectares, with the NN algorithm using the best performance with alternative parameters for detection.

EUROPEAN JOURNAL OF REMOTE SENSING (2022)

Article Environmental Sciences

Combining Sentinel-1 and Landsat 8 Does Not Improve Classification Accuracy of Tropical Selective Logging

Matthew G. Hethcoat et al.

Summary: Tropical forests are important for carbon and hydrological cycles, biodiversity, climate change mitigation, and the global economy. However, rapid population growth has led to degradation of these forests. The UN's REDD+ program aims to address this, but effective monitoring systems for forest degradation are still needed.

REMOTE SENSING (2022)

Article Computer Science, Information Systems

Dynamic detection of offshore wind turbines by spatial machine learning from spaceborne synthetic aperture radar imagery

Zhihuo Xu et al.

Summary: This paper proposes an effective approach for dynamic detection of offshore wind turbines by machine learning from spaceborne synthetic aperture radar (SAR) satellite data. The approach includes preprocessing radar images, selecting representative data for training, and utilizing mathematical morphology-based time series spatial data differentiation for monitoring wind turbine changes. The proposed approach demonstrates high accuracy and has the potential for global dynamic detection of offshore wind turbines.

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES (2022)

Article Environmental Sciences

Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks

Mabel Ortega Adarme et al.

Summary: Detecting early deforestation is crucial for reducing forest degradation and carbon emissions. Optical imagery-based monitoring projects are commonly used, but they are limited by cloud coverage, especially in tropical environments. This study evaluated and compared a traditional time series-based method and a Fully Convolutional Network (FCN) with bi-temporal Synthetic Aperture Radar (SAR) images. The results showed that the DL approach outperformed the time series-based method in detecting deforestation in the Brazilian Amazon.

REMOTE SENSING (2022)

Article Environmental Sciences

Forest disturbance alerts for the Congo Basin using Sentinel-1

Johannes Reiche et al.

Summary: The study presents a disturbance alert for humid tropical forests in the Congo Basin using Sentinel-1 radar data, which offers advantages for rapid detection of small-scale forest disturbances. Results suggest that disturbance rates in the Congo Basin may be higher than previously presented in the study.

ENVIRONMENTAL RESEARCH LETTERS (2021)

Article Environmental Sciences

SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery

Marie Ballere et al.

Summary: The forests of French Guiana cover a vast area, with high proportions of forest cover but facing threats from human activity. The local government is committed to protecting biodiversity and real-time monitoring of forest conditions using remote sensing data to prevent illegal deforestation.

REMOTE SENSING OF ENVIRONMENT (2021)

Article Environmental Sciences

Global maps of twenty-first century forest carbon fluxes

Nancy L. Harris et al.

Summary: Global forests have been a net carbon sink over the past two decades, with an estimated annual net carbon uptake ranging from -7.6 +/- 49 GtCO(2)e. The study integrates ground and Earth observation data to map forest-related greenhouse gas emissions and removals globally, aiming to support the development of climate policies. This geospatial monitoring framework introduced in the research aims to promote consistency and transparency in climate policy development.

NATURE CLIMATE CHANGE (2021)

Article Environmental Sciences

Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes

Edson E. Sano et al.

Summary: Comparing forest maps derived from optical and SAR data in the Amazon and Cerrado biomes, the study found statistically significant differences between optical and SAR-derived forest maps, except for the Cerrado biome where the estimations were similar. Additionally, the percentage of pixels classified as forest by both SAR sensors were above 80%, indicating a good overall agreement in forest classification.

REMOTE SENSING (2021)

Article Environmental Sciences

Refined algorithm for forest early warning system with ALOS-2/PALSAR-2 ScanSAR data in tropical forest regions

Manabu Watanabe et al.

Summary: The study presents an automatic change detection method for forest monitoring in tropical regions, utilizing radar technology to detect different deforestation stages and enhancing detection accuracy with multi-temporal data and normalization techniques. The results show the algorithm's effectiveness in providing forest change information in near real-time, with accuracies depending on temporal sequences and types of land cover transitions.

REMOTE SENSING OF ENVIRONMENT (2021)

Article Environmental Sciences

Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data

Stephane Mermoz et al.

Summary: This study demonstrates the accuracy and timeliness of a new operational system in detecting forest loss at a large scale, using Sentinel-1 data and a forest loss detection method based on shadow detection. High accuracy was achieved in detecting forest disturbances and intact forest areas. The study highlights the inefficiency in protecting and conserving natural resources in protected areas, with about half of forest disturbances in Cambodia occurring in these areas. The method also provides reliable detections that can be used to calculate weekly, monthly, or annual forest loss statistics at a national scale, with results comparable to those from Global Forest Watch.

REMOTE SENSING (2021)

Article Geochemistry & Geophysics

Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives

Xiao Xiang Zhu et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2021)

Article Multidisciplinary Sciences

Amazonia as a carbon source linked to deforestation and climate change

Luciana Gatti et al.

Summary: Studies show that total carbon emissions are higher in eastern Amazonia compared to the western part, mainly due to fire emissions. Climate change and deforestation have led to increased ecosystem stress, more frequent fires, and higher carbon emissions in eastern Amazon.

NATURE (2021)

Article Environmental Sciences

Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine

Andreas Vollrath et al.

REMOTE SENSING (2020)

Proceedings Paper Engineering, Electrical & Electronic

ASSESSMENT OF RAINFALL INFLUENCE ON SENTINEL-1 TIME SERIES ON AMAZONIAN TROPICAL FORESTS AIMING DEFORESTATION DETECTION IMPROVEMENT

J. Doblas et al.

2020 IEEE LATIN AMERICAN GRSS & ISPRS REMOTE SENSING CONFERENCE (LAGIRS) (2020)

Article Engineering, Electrical & Electronic

Early-Stage Deforestation Detection in the Tropics With L-band SAR

Manabu Watanabe et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2018)

Article Environmental Sciences

The global forest/non-forest map from TanDEM-X interferometric SAR data

Michele Martone et al.

REMOTE SENSING OF ENVIRONMENT (2018)

Editorial Material Multidisciplinary Sciences

3 Combating deforestation: From satellite to intervention

Matt Finer et al.

SCIENCE (2018)

Article Environmental Sciences

Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts

Johannes Reiche et al.

REMOTE SENSING (2018)

Article Environmental Sciences

Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series

Alexandre Bouvet et al.

REMOTE SENSING (2018)

Article Environmental Sciences

Google Earth Engine: Planetary-scale geospatial analysis for everyone

Noel Gorelick et al.

REMOTE SENSING OF ENVIRONMENT (2017)

Article Environmental Sciences

The SUMO Ship Detector Algorithm for Satellite Radar Images

Harm Greidanus et al.

REMOTE SENSING (2017)

Article Environmental Sciences

Humid tropical forest disturbance alerts using Landsat data

Matthew C. Hansen et al.

ENVIRONMENTAL RESEARCH LETTERS (2016)

Article Engineering, Electrical & Electronic

DETER-B: The New Amazon Near Real-Time Deforestation Detection System

Cesar Guerreiro Diniz et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2015)

Article Computer Science, Interdisciplinary Applications

fitdistrplus: An R Package for Fitting Distributions

Marie Laure Delignette-Muller et al.

JOURNAL OF STATISTICAL SOFTWARE (2015)

Article Geochemistry & Geophysics

Assessment of Atmospheric Propagation Effects in SAR Images

Andreas Danklmayer et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2009)

Article Geochemistry & Geophysics

Filtering of multichannel SAR images

S Quegan et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2001)