Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data

Leite, Rodrigo Vieira; Silva, Carlos Alberto; Broadbent, Eben N.; Amaral, Cibele Hummel do; Liesenberg, Veraldo; Almeida, Danilo Roberti Alves de; Mohan, Midhun; Godinho, Sérgio; Cardíl, Adrián; Hamamura, Caio; Faria, Bruno Lopes de; Brancalion, Pedro Henrique Santin; Hirsch, André; Marcatti, Gustavo Eduardo; Côrte, Ana Paula Dalla; Zambrano, Angélica M. Almeyda; Costa, Máira Beatriz Teixeira da; Matricardi, Eraldo Aparecido Trondoli; Silva, Anne Laura da; Goya, Lucas Ruggeri Ré Y.; Valbuena, Rubén; Mendonça, Bruno Araújo Furtado de; Silva, Celso; Aragão, Luiz E. O. C.; Garcı́a, Mariano; Liang, Jingjing; Merrick, Trina; Hudak, Andrew T.; Xiao, Jingfeng; Hancock, Steven; Duncason, Laura; Ferreira, Matheus Pinheiro; Valle, Denis; Saatchi, Sassan; Klauberg, Carine

doi:10.1016/j.rse.2021.112764

Cited by 42 publications

(27 citation statements)

References 91 publications

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“…Rishmawi et al [31] integrated GEDI measurements with optical satellite observations, to produce maps at a 1 km resolution of US canopy cover (r 2 = 0.79; RMSE = 0.09), plant area index (r 2 = 0.76; RMSE = 0.41), foliage height diversity (r 2 = 0.83; RMSE = 0.25), and tree height (r 2 = 0.8; RMSE = 3.35 m). Leite et al [32] presented a different usage of the GEDI data by predicting multi-layer fuel load in the Brazilian tropical savanna. On the other hand, most of the research focuses on simulated data despite the few studies we mentioned using GEDI data.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…Thus, these alternative methods can be completed at a high temporal resolution, and might be beneficial for land managers to describe changes in ladder fuel density over time. In addition, it is possible that since there was a strong, significant correlation between TLS, HMLS, and ALS based density estimates, these approaches may be able to assist in the calibration and validation of spaceborne optical, LiDAR, and SAR missions (e.g., GEDI, ICESAT-2, NISAR, BIOMASS; Levick et al, 2021;Leite et al, 2022), or a state-wide forest monitoring system such as the California Forest Observatory, which estimates ladder fuels and CBH from multispectral satellite imagery calibrated with ALS.…”

Section: Comparing Ladder Fuel Densities Across Methodsmentioning

confidence: 99%

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

Forbes

Reilly

Clark

et al. 2022

Front. For. Glob. Change

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While fire is an important ecological process, wildfire size and severity have increased as a result of climate change, historical fire suppression, and lack of adequate fuels management. Ladder fuels, which bridge the gap between the surface and canopy leading to more severe canopy fires, can inform management to reduce wildfire risk. Here, we compared remote sensing and field-based approaches to estimate ladder fuel density. We also determined if densities from different approaches could predict wildfire burn severity (Landsat-based Relativized delta Normalized Burn Ratio; RdNBR). Ladder fuel densities at 1-m strata and 4-m bins (1–4 m and 1–8 m) were collected remotely using a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with a multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS) in 35 plots in oak woodlands in Sonoma County, California, United States prior to natural wildfires. Ladder fuels were also measured in the same plots using a photo banner. Linear relationships among ladder fuel densities estimated at broad strata (1–4 m, 1–8 m) were evaluated using Pearson’s correlation (r). From 1 to 4 m, most densities were significantly correlated across approaches. From 1 to 8 m, TLS densities were significantly correlated with HMLS, UAS-SfM and ALS densities and UAS-SfM and HMLS densities were moderately correlated with ALS densities. Including field-measured plot-level canopy base height (CBH) improved most correlations at medium and high CBH, especially those including UAS-SfM data. The most significant generalized linear model to predict RdNBR included interactions between CBH and ladder fuel densities at specific 1-m stratum collected using TLS, ALS, and HMLS approaches (R2 = 0.67, 0.66, and 0.44, respectively). Results imply that remote sensing approaches for ladder fuel density can be used interchangeably in oak woodlands, except UAS-SfM combined with the photo banner. Additionally, TLS, HMLS and ALS approaches can be used with CBH from 1 to 8 m to predict RdNBR. Future work should investigate how ladder fuel densities using our techniques can be validated with destructive sampling and incorporated into predictive models of wildfire severity and fire behavior at varying spatial scales.

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“…Spaceborne lidar is particularly promising for large area mapping fuel load because of its ability to estimate canopy height, which can be used to identify different fuel types. This potential of lidar is exemplified in another Brazilian study [154] where the authors used a GEDI-based modeling framework to predict fuel loads of multiple vegetation layers in a savanna at accuracies of 88% for woody fuels and 71% for the total fuel load.…”

Section: Journal Of Remote Sensingmentioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.

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Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data

Cited by 42 publications

References 91 publications

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

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

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

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