Implementation of the LandTrendr Algorithm on Google Earth Engine

Kennedy, Robert E.; Yang, Zhiqiang; Gorelick, Noel; Braaten, Justin; Cavalcante, Lucas Costa Pereira; Cohen, Warren B.; Healey, Sean P.

doi:10.3390/rs10050691

Cited by 374 publications

(288 citation statements)

References 18 publications

Supporting

Mentioning

218

Contrasting

Unclassified

Order By: Relevance

“…For example, our methodology will allow those who are not remote sensing experts, but have some familiarity with GEE, to quickly produce fire severity datasets ( Figure 3). This benefit is due to the efficiency and speed of the cloud-based GEE platform [37,38] and because no a priori scene selection is necessary. Furthermore, compared to the standard approach in which only one pre-and post-fire scene are used, the GEE mean composite fire severity datasets exhibit higher validation statistics in terms of the correspondence (R 2 ) to CBI and higher classification accuracies for most severity classes.…”

Section: Discussionmentioning

confidence: 99%

Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential

et al. 2018

View full text Add to dashboard Cite

Abstract:Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre-and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: The delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection but instead use a mean compositing approach in which all valid pixels (e.g., cloud-free) over a pre-specified date range (pre-and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared to the standard approach in which one pre-fire and one post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western United States. These validations are compared to Landsat-based fire severity datasets produced using only one pre-and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets generally show improved validation statistics compared to parallel versions in which only one pre-fire and one post-fire scene are used, though some of the improvements in some validations are more or less negligible. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e., fire perimeters) be produced independently and prior to applying our methods, we suggest that our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.

show abstract

Section: Discussionmentioning

confidence: 99%

Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential

et al. 2018

View full text Add to dashboard Cite

show abstract

“…At the core of our mapping workflow we rely on an established time-series segmentation approach called LandTrendr 47 , implemented in the high-performance cloud-computing environment Google Earth Engine 48 . In essence, LandTrendr segments annual Landsat pixel time series into linear features from which a set of metrics can be extracted.…”

Section: Methodsmentioning

confidence: 99%

Mapping the forest disturbance regimes of Europe

Senf

Seidl

2020

Preprint

View full text Add to dashboard Cite

Forest disturbance shape ecosystem composition and structure, and changes in 15 forest disturbances can have strong consequences for carbon storage and biodiversity. Yet we 16 currently lack consistent quantitative data on Europe's forest disturbance regimes and their 17 changes over time. Here we used satellite data to map three decades of forest 18 disturbances across continental Europe, covering 35 countries and a forest area of 210 Mill. 19 ha at a spatial grain of 30 m, and analyzed the patterns and trends in disturbance size, 20 frequency and severity. Between 1986 and 2016, 17% of Europe's forest area was disturbed 21 by anthropogenic or natural causes, totaling to 25 Mill. individual disturbance patches with a 22 mean patch size 1.09 ha (range between 1 st and 99 th percentile 0.18 -10.10 ha). On average 23 0.52 (0.02 -3.01) disturbances occurred per km² every year, removing on average 77% (22 -24 100%) of the canopy. While spatial patterns of disturbance were highly variable, disturbance 25 frequency consistently increased, and disturbance severity decreased since 1986. Both social 26 and ecological factors are needed to explain the observed patterns and trends in forest 27 disturbance. We thus conclude that in order to understand and manage the changes in 28 Europe's forest disturbance regimes a coupled human and natural systems perspective is 29 needed. 30 31 Keywords: Coupled Human and Environmental System; Disturbance regime; Remote 32 sensing; Forest management; Resilience 33 34 3Forests cover 33 % of Europe's total land area and provide important services to society, 35 ranging from carbon sequestration to the filtration of water, protection of soil from erosion, 36 and human infrastructure from natural hazards 1 . Europe's forests have expanded in recent 37 decades 2 and accumulated substantial amounts of biomass due to intensive post-war 38 reforestation programs, changes in management systems, and timber harvesting rates that 39 remained below increment 3 . This success story of 20 th century forestry in Europe, however, 40 also has side effects, as the resultant changes in forest structure have -in combination with 41 climate change -led to an episode of increasing forest disturbances in recent decades 4-7 . 42 Increasing forest disturbances have the potential to erode Europe's carbon storage potential 8,9 43 and also impact other important services provided by Europe's forests 10,11 . Given a predicted 44 increase in the demand for wood 1 and an expected future intensification of forest dieback 45 under climate change 12 , it is fundamental to increase the resilience of Europe's forests to 46 changing disturbances 13-15 . 47Po-Valley, and the Pannonian Basin. In contrast, low disturbance severities were recorded for 130 South-Eastern Europe along the Dinaric mountain range, as well as in the Apennine 131 mountains of Italy. 132

show abstract

“…Landsat Tier 1 Surface Reflectance imagery was cloud masked before producing temporal composites of spectral indices which were used as input for the LandTrendr Algorithm in Google Earth Engine (Kennedy et al 2018). Initial tests revealed a date range of 22 September-22 December best captured coniferous tree canopy cover while minimizing the influence of background vegetation which is predominantly senesced in autumn.…”

Section: Image and Topographic Data Preparationmentioning

confidence: 99%

“…R 2 ) than objectbased approaches with VHR imagery. These approaches can now easily be applied across greater spatiotemporal extents because of the availability of Landsat Surface Reflectance Products in cloud computing platforms like Google Earth Engine (Gorelick et al 2017) which enable the use of trend-fitting algorithms to improve interannual consistency (Kennedy et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Monitoring pinyon-juniper cover and aboveground biomass across the Great Basin

Filippelli

Falkowski

Hudak

et al. 2020

Environ. Res. Lett.

View full text Add to dashboard Cite

Since the mid-1800s pinyon-juniper (PJ) woodlands have been encroaching into sagebrush-steppe shrublands and grasslands such that they now comprise 40% of the total forest and woodland area of the Intermountain West of the United States. More recently, PJ ecosystems in select areas have experienced dramatic reductions in area and biomass due to extreme drought, wildfire, and management. Due to the vast area of PJ ecosystems, tracking these changes in woodland tree cover is essential for understanding their consequences for carbon accounting efforts, as well as ecosystem structure and functioning. Here we present a carbon monitoring, reporting, and verification (MRV) system for characterizing total aboveground biomass stocks and flux of PJ ecosystems across the Great Basin. This is achieved through a two-stage remote sensing approach by first using spatial wavelet analysis to rapidly sample tree cover from very high-resolution imagery (1 m), and then training a Random Forest model which maps tree cover across the region from 2000 to 2016 using temporallysegmented Landsat spectral indices obtained from the LandTrendr algorithm in Google Earth Engine. Estimates of cover were validated against field data from the SageSTEP project (R 2 =0.67, RMSE=10% cover). Biomass estimated from cover-based allometry was higher than estimates from the Forest Inventory and Analysis Program (FIA) at the plot-level (bias=5 Mg ha −1 and RMSE=15.5 Mg ha −1 ) due in part to differences in tree-level biomass allometrics. County-level aggregation of biomass closely matched estimates from the FIA (R 2 =0.97) after correcting for bias at the plot level. Even after many previous decades of encroachment, we find forest area (i.e. areas with 10% cover) increasing at a steady rate of 0.46% per year, but 80% of the 9.86 Tg increase in biomass is attributable to infilling of existing forest. This suggests that the known consequences of encroachment such as reduced water availability, impacts to biodiversity, and risk of severe wildfire may have been increasing across the region in recent years despite the actions of sagebrush steppe restoration initiatives.

show abstract

Implementation of the LandTrendr Algorithm on Google Earth Engine

Cited by 374 publications

References 18 publications

Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential

Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential

Mapping the forest disturbance regimes of Europe

Monitoring pinyon-juniper cover and aboveground biomass across the Great Basin

Contact Info

Product

Resources

About