Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

Hamunyela, Eliakim; Rosca, Sabina; Mirt, Andrei; Engle, Eric; Herold, Martin; Gieseke, Fabian; Verbesselt, Jan

doi:10.3390/rs12182953

Cited by 40 publications

(22 citation statements)

References 49 publications

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“…The BFAST Monitor algorithm was applied through the bfastspatial package [37] (https://github.com/loicdtx/bfastSpatial, accessed on 20 May 2021) using R [64]. It is worth mentioning that BFAST implementation in GEE is now available, which might be of interest for future users [65].…”

Section: Forest Disturbance Detectionmentioning

confidence: 99%

How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest

et al. 2021

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Time series analysis has gained popularity in forest disturbance monitoring thanks to the availability of satellite and airborne remote sensing images and the development of different time series methods for change detection. Previous research has focused on time series data noise reduction, the magnitude of breakpoints, and accuracy assessment; however, few have looked in detail at how the trend and seasonal model components contribute to disturbance detection in different forest types. Here, we use Landsat time series images spanning 1994–2018 to map forest disturbance in a western Pacific area of Mexico, where both temperate and tropical dry forests have been subject to severe deforestation and forest degradation processes. Since these two forest types have distinct seasonal characteristics, we investigate how trend and seasonal model components, such as the goodness-of-fit (R2), magnitude of change, amplitude, and model length in a stable historical period, affect forest disturbance detection. We applied the Breaks For Additive Season and Trend Monitor (BFAST) algorithm and after accuracy assessment by stratified random sample points, and we obtained 68% and 86% of user accuracy and 75.6% and 86% of producer’s accuracy in disturbance detection, in tropical dry forests and temperate forests, respectively. We extracted the noncorrelated trend and seasonal model components R2, magnitude, amplitude, length of the stable historical period, and percentage of pixels with NA and tested their effects on disturbance detection employing forest-type specific logistic regression. Our results showed that, for all forests combined, the amplitude and stable historical period length contributed to disturbance detection. While for tropical dry forest alone, amplitude was the main predictor, and for the temperate forest alone, the stable historical period length contributed most to the prediction, although it was not statistically significant. These findings provide insights for improving the results of forest disturbance detection in different forest types.

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Section: Forest Disturbance Detectionmentioning

confidence: 99%

How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest

et al. 2021

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“…It was feasible to process the global time series data and produce a map of detected changes in approximately a week, running the algorithm on the Terrascope cluster, which provides approximately 1000 processor cores and 1.5 terrabytes of memory. To build upon these large-scale processing capabilities, BFAST Lite can further be ported to Google Earth Engine, similarly to BFAST Monitor [9]. This would allow running the algorithm over higher spatial resolution time series, such as Landsat, and would further ease the algorithm accessibility for a wider user audience.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…Some of these algorithms have been well established and widely used, e.g., the BFAST family, LandTrendr and CCDC. Recent developments on these algorithms include improvements to upscaling and ease of use, such as implementing them on big data platforms such as Google Earth Engine [8,9]. In contrast, many other change detection algorithms are at the proof-of-concept stage.…”

Section: Introductionmentioning

confidence: 99%

BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

et al. 2021

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BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas.

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“…Dense time series integrating images from different sensors [76] pose an opportunity to increase the accuracy of the detected changes [23]. Additionally, the BFAST monitoring algorithm has been recently implemented in the Google Earth Engine, supporting the replication of the methodology over large areas and alleviating the users from downloading and processing bulky files [77].…”

Section: Discussionmentioning

confidence: 99%

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

Esteban¹,

Fernández-Landa²,

Tomé³

et al. 2021

Remote Sensing

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Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six Landsat-derived vegetation indexes (VIs) were analyzed with the BFAST (Breaks for Additive Season and Trend) algorithm aiming to characterize the changes resulting from harvesting practices of different intensities (clear-cutting, cutting with seed-trees, and thinning) in a Mediterranean forest area of Spain. To assess the contribution of airborne laser scanner (ALS) data and the potential implications of it being after or before the detected changes, two scenarios were defined (based on the year in which ALS data were acquired (2010), and thereby detecting changes from 2005 to 2010 (before ALS data) and from 2011 to 2016 (after ALS data). Pixels identified as change by BFAST were attributed with change in VI intensity and ALS-derived statistics (99th height percentile and forest canopy cover) for classification with random forests, and derivation of change maps. Fusion techniques were applied to leverage the potential of each individual VI change map and to reduce mapping errors. The Tasseled Cap Brightness (TCB) and Normalized Burn Ratio (NBR) indexes provided the most accurate results, the latter being more precise for thinning detection. Our results demonstrate the suitability of Landsat time series and ALS data to characterize forest stand changes caused by harvesting practices of different intensity, with improved accuracy when ALS data is acquired after the change occurs. Clear-cuttings were more readily detectable compared to cutting with seed-trees and thinning, detection of which required fusion approaches. This methodology could be implemented to produce annual cartography of harvesting practices, enabling more accurate statistics and spatially explicit identification of forest operations.

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Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

Cited by 40 publications

References 49 publications

How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest

How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest

BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

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