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

Doblas, Juan; Reis, Mariane Souza; Belluzzo, Amanda P.; Quadros, Camila Barata; Moraes, Douglas Rafael Vidal de; Almeida, Cláudio; Maurano, L. E. P.; Carvalho, André F. Araújo de; Sant’Anna, Sidnei J. S.; Shimabukuro, Yosio Edemir

doi:10.3390/rs14153658

Cited by 27 publications

(11 citation statements)

References 39 publications

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“…However, event detection may be delayed by months due to persistent cloud cover. Hence, there has recently been an increasing use of synthetic aperture radar (SAR) data for landslide detection [21][22][23] and continuous monitoring systems for deforestation [6,24,25]. SAR data is also useful for change detection in areas where there are strong seasonal variations including snow, seasonal darkness, and lack of vegetative biomass (e.g., in temperate and cold climates).…”

Section: Of 21mentioning

confidence: 99%

Globally- vs Locally-trained Machine Learning Models for Land-slide Detection: A Case Study of a Glacial Landscape

Jarna¹,

Lindsay²,

Fredin³

et al. 2023

Preprint

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Landslide risk mitigation is limited by data scarcity. This could be improved using continuous landslide detection systems. In order to investigate which image types and machine learning (ML) models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different ML models, for the Jølster case study (30-July-2019), in Western Norway. These included three globally pre-trained models; i) the Continuous Change Detection and Classification (CCDC) algorithm, ii) a combined k-means clustering and Random Forest classification model, and iii) a convolutional neural network (CNN), and two locally-trained models, including; iv) Classification and Regression Trees and v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, digital elevation model (DEM) and slope. The globally-trained models performed poorly in shadowed areas, and were all outperformed by the locally-trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with model v, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep-learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional ML model. These findings contribute towards developing a national continuous monitoring system for landslides.

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Section: Of 21mentioning

confidence: 99%

Globally- vs Locally-trained Machine Learning Models for Land-slide Detection: A Case Study of a Glacial Landscape

Jarna¹,

Lindsay²,

Fredin³

et al. 2023

Preprint

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“…The increasing availability of high spatial and temporal resolution data allows for the detection of strong intra-annual variability in logging patterns (figure 5), which contributes towards the traceability of logging activities over time. However, no operational national and continental-scale satellite forest disturbance alerts have been tuned specifically for selective logging yet (Reiche et al 2018, Hoekman et al 2020, Weisse et al 2021, Doblas et al 2022. Tuning satellite-based forest disturbance alerts for selective logging will benefit detection and monitoring of low intensity tree cover loss and small canopy gaps.…”

Section: All Samplesmentioning

confidence: 99%

Towards the use of satellite-based tropical forest disturbance alerts to assess selective logging intensities

Welsink

Reiche

Carter

et al. 2023

Environ. Res. Lett.

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Illegal logging is an important driver of tropical forest loss. A wide range of organizations and interested parties wish to track selective logging activities and verify logging intensities as reported by timber companies. Recently, free availability of 10 m scale optical and radar Sentinel data has resulted in several satellite-based alert systems that can detect increasingly small-scale forest disturbances in near-real time. This paper provides insight in the usability of satellite-based forest disturbance alerts to track selective logging in tropical forests. We derive the area of tree cover loss from expert interpretations of monthly PlanetScope mosaics and assess the relationship with the RAdar for Detecting Deforestation (RADD) alerts across 50 logging sites in the Congo Basin. We do this separately for various aggregation levels, and for tree cover loss from felling and skidding, and logging roads. A strong linear relationship between the alerts and visually identified tree cover loss indicates that with dense time series satellite data at 10 m scale, the area of tree cover loss in logging concessions can be accurately estimated. We demonstrate how the observed relationship can be used to improve near-real time tree cover loss estimates based on the RADD alerts. However, users should be aware that the reliability of estimations is relatively low in areas with few disturbances. In addition, a trade-off between aggregation level and accuracy requires careful consideration. An important challenge regarding remote verification of logging activities remains: as opposed to tree cover loss area, logging volumes cannot yet be directly observed by satellites. We discuss ways forward towards satellite-based assessment of logging volumes at high spatial and temporal detail, which would allow for better remote sensing based verification of reported logging intensities and tracking of illegal activities.

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“…In addition to these openly available systems covering larger geographies, numerous regional and national systems have been developed using various optical and radar satellite data streams (Diniz et al 2015, Vargas et al 2019, Ballère et al 2021, Doblas et al 2022, Doblas Prieto et al 2023. These include the Peruvian Landsat-based Geobosques system (Vargas et al 2019), the Brazilian Real-Time System for Detection of Deforestation (DETER), which utilizes data from the Advanced Wide Field Sensor onboard the Indian Remote Sensing satellites to provide monthly forest disturbance information at a 56 m spatial scale (Diniz et al 2015), and the Sentinel-1-based Brazilian DETER-R system (Doblas et al 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Studies that introduced alert systems (Hansen et al 2016, Ballère et al 2021, Watanabe et al 2021, Doblas et al 2022 typically report a single accuracy metric summarizing the detection accuracy and timeliness across the entire study area, often at a country or continental scale. This approach falls short in providing a nuanced understanding of the distinct advantages and limitations of the systems associated with different change types (e.g.…”

Section: Introductionmentioning

confidence: 99%

Integrating satellite-based forest disturbance alerts improves detection timeliness and confidence

Reiche,

Balling,

Pickens

et al. 2024

Environ. Res. Lett.

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Satellite-based near-real-time forest disturbance alerting systems have been widely used to support law enforcement actions against illegal and unsustainable human activities in tropical forests. The availability of multiple optical and radar-based forest disturbance alerts, each with varying detection capabilities depending mainly on the satellite sensor used, poses a challenge for users in selecting the most suitable system for their monitoring needs and workflow. Integrating multiple alerts holds the potential to address the limitations of individual systems. We integrated radar-based RAdar for Detecting Deforestation (RADD) (Sentinel-1), and optical-based Global Land Analysis and Discovery Sentinel-2 (GLAD-S2) and GLAD-Landsat alerts using two confidence rulesets at ten 1° sites across the Amazon Basin. Alert integration resulted in faster detection of new disturbances by days to months, and also shortened the delay to increased confidence. An increased detection rate to an average of 97% when combining alerts highlights the complementary capabilities of the optical and cloud-penetrating radar sensors in detecting largely varying drivers and environmental conditions, such as fires, selective logging, and cloudy circumstances. The most improvement was observed when integrating RADD and GLAD-S2, capitalizing on the high temporal observation density and spatially detailed 10 m Sentinel-1 and 2 data. We introduced the highest confidence class as an addition to the low and high confidence classes of the individual systems, and showed that this displayed no false detection. Considering spatial neighborhood during alert integration enhanced the overall labeled alert confidence level, as nearby alerts mutually reinforced their confidence, but it also led to an increased rate of false detections. We discuss implications of this study for the integration of multiple alert systems. We demonstrate that alert integration is an important data preparation step to make use of multiple alerts more user-friendly, providing stakeholders with reliable and consistent information on new forest disturbances in a timely manner. Google Earth Engine code to integrate various alert datesets is made openly available.

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

Cited by 27 publications

References 39 publications

Globally- vs Locally-trained Machine Learning Models for Land-slide Detection: A Case Study of a Glacial Landscape

Globally- vs Locally-trained Machine Learning Models for Land-slide Detection: A Case Study of a Glacial Landscape

Towards the use of satellite-based tropical forest disturbance alerts to assess selective logging intensities

Integrating satellite-based forest disturbance alerts improves detection timeliness and confidence

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