An open science and open data approach for the statistically robust estimation of forest disturbance areas

Francini, Saverio; McRoberts, Ronald E.; D’Amico, Giovanni; Coops, Nicholas C.; Hermosilla, Txomin; White, Joanne C.; Wulder, Michael A.; Marchetti, Marco; Mugnozza, Giuseppe Scarascia; Chirici, Gherardo

doi:10.1016/j.jag.2021.102663

Cited by 22 publications

(40 citation statements)

References 56 publications

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“…To overcome these issues, an algorithm specifically conceived for Mediterranean ecosystems was recently developed (3I3D), which resulted particularly accurate in Tuscany [19], compared to more traditional approaches such as the Landrendr [12], the Global Forest Change map [22], and the Two Thresholds Method [21], which are three of the most sophisticated forest disturbance prediction algorithms. 3I3D was also recently used to map forest disturbance across Italy in 2018, and considerable accuracy was confirmed [20] as all the events in the reference dataset were detected -even with slightly smaller areas than the real ones.…”

Section: Introductionmentioning

confidence: 87%

“…Forest disturbance prediction was performed using the BAP composites and the GEE implementation [20] of the Three Indices Three Dimensions (3I3D) algorithm [19]. 3I3D is an unsupervised algorithm that predicts forest changes by analyzing the trends of three photosynthetic activity indices (3I) used as axes of three-dimensional space (3D) over three consecutive years.…”

Section: Forest Disturbance Mappingmentioning

confidence: 99%

“…The photosynthetic activity indices are the Normalized Difference Moisture (NDMI) [63], the Normalized Burn Ratio (NBR) [64], and the Moisture Stress Index (MSI) [65]. For a detailed description of 3I3D, we refer to Francini et al [19,20]. The complete 3I3D code is open access [66], for which further indications are provided on GitHub [67].…”

Section: Forest Disturbance Mappingmentioning

confidence: 99%

“…The accuracy of time-series segmentation methods also decreases for near-past detection applications [19]. Near real-time monitoring using optical remote sensing imagery instead is a relatively novel topic [2,20] and it is useful for quick assessment of relevant/extensive damages [21]. However, the achievable products often result in multiple inaccuracies, as the greater the promptness with which products are provided, the greater the expected number of errors in the maps [14].…”

Section: Introductionmentioning

confidence: 99%

“…First, we compared about four million AGBD data from GEDI pulses with ALS data available across Italy. Second, we mapped forest disturbances in Italy between 1985 and 2019 using Landsat imagery and 3I3D [20]. Subsequently, we analyzed about one million AGBD GEDI pulses for all years following predicted disturbances.…”

Section: Introductionmentioning

confidence: 99%

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Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy

Francini

D’Amico

Vangi

et al. 2022

Sensors

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Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems.

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