Understanding ecological transitions under recurrent wildfire: A case study in the seasonally dry tropical forests of the Chiquitania, Bolivia

Devisscher, Tahia; Malhi, Yadvinder; Landívar, Víctor Diego Rojas; Oliveras, Immaculada

doi:10.1016/j.foreco.2015.10.033

Cited by 29 publications

(30 citation statements)

References 44 publications

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“…Similar to other studies [22,23,75], we showed a significant biomass reduction in forest affected by fire. More interestingly, our LIDAR analysis allowed the detection of subtle changes in the canopy of burned sites in subsequent years after fire.…”

Section: Discussionsupporting

confidence: 91%

“…Based on previous studies [15,[21][22][23], we first hypothesize that (1) forest biomass and height should be lower in areas affected by fire four to five years prior the measurements (fire occurrence in 2010) in relation to unburned forests and that (2) forest biomass and height should be similar in areas affected by fire eight to ten years prior the measurements (fire occurrence in 2005) than in unburned forests, assuming that these areas already had time to recover their fire-affected forest structure.…”

Section: Introductionmentioning

confidence: 99%

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Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia

Sato

Gomes²,

Shimabukuro

et al. 2016

Remote Sensing

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Abstract:Fire is one of the main factors directly impacting Amazonian forest biomass and dynamics. Because of Amazonia's large geographical extent, remote sensing techniques are required for comprehensively assessing forest fire impacts at the landscape level. In this context, Light Detection and Ranging (LiDAR) stands out as a technology capable of retrieving direct measurements of vegetation vertical arrangement, which can be directly associated with aboveground biomass. This work aims, for the first time, to quantify post-fire changes in forest canopy height and biomass using airborne LiDAR in western Amazonia. For this, the present study evaluated four areas located in the state of Acre, called Rio Branco, Humaitá, Bonal and Talismã. Rio Branco and Humaitá burned in 2005 and Bonal and Talismã burned in 2010. In these areas, we inventoried a total of 25 plots (0.25 ha each) in 2014. Humaitá and Talismã are located in an open forest with bamboo and Bonal and Rio Branco are located in a dense forest. Our results showed that even ten years after the fire event, there was no complete recovery of the height and biomass of the burned areas (p < 0.05). The percentage difference in height between control and burned sites was 2.23% for Rio Branco, 9.26% for Humaitá, 10.03% for Talismã and 20.25% for Bonal. All burned sites had significantly lower biomass values than control sites. In Rio Branco (ten years after fire), Humaitá (nine years after fire), Bonal (four years after fire) and Talismã (five years after fire) biomass was 6.71%, 13.66%, 17.89% and 22.69% lower than control sites, respectively. The total amount of biomass lost for the studied sites was 16,706.3 Mg, with an average loss of 4176.6 Mg for sites burned in 2005 and 2890 Mg for sites burned in 2010, with an average loss of 3615 Mg. Fire impact associated with tree mortality was clearly detected using LiDAR data up to ten years after the fire event. This study indicates that fire disturbance in the Amazon region can cause persistent above-ground biomass loss and subsequent reduction of forest carbon stocks. Continuous monitoring of burned forests is required for depicting the long-term recovery trajectory of fire-affected Amazonian forests.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia

Sato

Gomes²,

Shimabukuro

et al. 2016

Remote Sensing

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“…Se seleccionaron tres zonas con alta ocurrencia del fuego (Figura 1): Bosques Amazónicos de Colombia en proceso de fragmentación y cambio en el uso del suelo con alta ocurrencia del fuego (Armenteras et al, 2017); Bosque Amazónico en transición a Bosque Chiquitano de Bolivia, notablemente afectado por el fuego (Rodríguez-Montellano et al, 2015) y sometido a procesos de fragmentación por el cambio en el uso del suelo (Devisscher et al, 2015); y un área de El Chaco seco de Argentina, región que está entre las más afectadas por el avance de la frontera agrícola a escala global (Sofía et al, 2017). En general el aumento de la demanda global de productos agrícolas implica también un avance en el proceso de intensificación y expansión de las fronteras agrícolas.…”

Section: áRea De Estudiounclassified

Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube

2018

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<p>There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina.</p>

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“…In the Amazon, forest fires can alter tree composition by selecting pioneer trees (Barlow and Peres 2008), trees with higher tolerance to fire (Veldman and Putz 2011), or with resprouting ability (Jakovac et al 2015). Nonetheless, the persistence of forest tree species allows the system to recover canopy closure in a short time (Mesquita et al 2001;Jakovac et al 2015;Devisscher et al 2016). Due to the apparently high resilience of Amazonian forests (Poorter et al 2016), the mechanisms that could drive forest savannization remain uncertain.…”

Section: Evidence Of Past Shifts In Tropical Vegetationmentioning

confidence: 99%

“…The best documented cases of savannization were the result of centuries of intensive land-use, fire and cattle ranching leading to soil erosion and the replacement of upland tropical forests by true savanna vegetation (Borhidi 1988;Cavelier et al 1998). More often, tropical forests tend to recover canopy closure fast after smallscale perturbations (Jakovac et al 2015;Devisscher et al 2016;Poorter et al 2016), revealing that the transition to savanna does not occur so easily (Barlow and Peres 2008;Veldman and Putz 2011).…”

Section: Significancementioning

confidence: 99%

Resilience of Amazonian forests : the roles of fire, flooding and climate

Flores¹

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Understanding ecological transitions under recurrent wildfire: A case study in the seasonally dry tropical forests of the Chiquitania, Bolivia

Cited by 29 publications

References 44 publications

Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia

Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia

Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube

Resilience of Amazonian forests : the roles of fire, flooding and climate

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