The role of stand structure and palm abundance in predicting above-ground biomass at local scale in southern Amazonia

Scaranello, M. A.; Castro, Bruno; Farias, Renato; Vieira, Simone A.; Alves, Luciana F.; Robortella, Henrique Simionato; Aragão, Luiz E. O. C.

doi:10.1080/17550874.2016.1264015

Cited by 4 publications

(7 citation statements)

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“…, Scaranello et al . ). Whatever the scale, the extent and intensity of human land use are also likely important factors in determining rates of biomass recovery in secondary forests (Aide et al .…”

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confidence: 97%

“…Variation in soils affects productivity and biomass density at multiple sites (Davidson et al 2004Becknell & Powers 2014, Waring et al 2015a, but soil properties also affect species composition and vice versa (John et al 2007, Waring et al 2015b. Species diversity and composition can play a role in biomass distributions , as can the abundance of functional groups such as lianas or palms (Ledo et al 2016, Scaranello et al 2016. Whatever the scale, the extent and intensity of human land use are also likely important factors in determining rates of biomass recovery in secondary forests (Aide et al 2000, Guariguata & Ostertag 2001.…”

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Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

et al. 2018

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Secondary forests account for more than half of tropical forests and represent a growing carbon sink, but rates of biomass accumulation vary by a factor of two or more even among plots in the same landscape. To better understand the drivers of this variability, we used airborne lidar to measure forest canopy height and estimate biomass over 4529 ha at Serra do Conduru Park in Southern Bahia, Brazil. We measured trees in 30 georeferenced field plots (0.25‐ha each) to estimate biomass using allometry. Then we estimated aboveground biomass density (ABD) across the lidar study area using a statistical model developed from our field plots. This model related the 95th percentile of the distribution of lidar return heights to ABD. We overlaid this map of ABD on a Landsat‐derived forest age map to determine rates of biomass accumulation. We found rapid initial biomass regeneration (~6 Mg/ha yr), which slowed as forests aged. We also observed high variability in both height and biomass across the landscape within forests of similar age. Nevertheless, a regression model that accounted for spatial autocorrelation and included forest age, slope, and distance to roads or open areas explained 62 and 77 percent of the landscape variation in ABD and canopy height, respectively. Thus, while there is high spatial heterogeneity in forest recovery, and the drivers of this heterogeneity warrant further investigation, we suggest that a relatively simple set of predictor variables is sufficient to explain the majority of variance in both height and ABD in this landscape.

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Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

et al. 2018

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“…Palmeiras reúnem um conjunto de caracteres estruturais que limitam a sua colonização por lianas, tais como crescimento rápido, caule flexível monopodial, folhas grandes e decíduas (Putz, 1984;Rich et al, 1987;Mascaro et al, 2004;Pérez-Salicrup & Meijere, 2005;Van der Heijden & Phillips, 2008;Sfair et al, 2016;Cox et al, 2019). O domínio de palmeiras reduz drasticamente a biomassa de lianas nas florestas sul-amazônicas, frequentemente referidas como formações altamente dinâmicas (Emilio et al, 2014;Scaranello et al, 2016). Isso pode resultar de processos como competição por recursos (e.g.…”

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“…Isso pode resultar de processos como competição por recursos (e.g. água e luz) e interferência mecânica devido ao sufocamento das lianas causado pela queda das folhas de palmeiras (Scaranello et al, 2016;Van der Heijden et al, 2015).…”

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“…Diante do exposto, o nosso objetivo foi avaliar se a presença de palmeiras influencia a colonização da comunidade vegetal por lianas em três parcelas florestais da Estação Ecológica da Serra das Araras. Trabalhamos com a hipótese de que o alto povoamento de palmeiras limita a proliferação de lianas porque, com base em informações de literatura, as palmeiras reúnem caracteres morfofuncionais que impedem a invasão massiva de lianas nas áreas onde elas ocorrem (Putz, 1984;Scaranello et al, 2016;Cox et al, 2019).…”

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Palmeiras limitam a proliferação de lianas em florestas do ecótono Amazônia-Cerrado-Pantanal

Moraes

Cruz

Olivo-Neto

et al. 2021

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As palmeiras reúnem caracteres estruturais que limitam a sua colonização por lianas (e.g., crescimento rápido, caule flexível monopodial e folhas decíduas). O objetivo do nosso estudo foi avaliar se a presença de palmeiras pode influenciar na infestação das plantas por lianas em três comunidades florestais (FG, FS1 e FS2) da Estação Ecológica da Serra das Araras (EESA), o único espaço de transição entre três biomas em território brasileiro. Em cada floresta alocamos uma parcela permanente de 1 ha, amostramos árvores e palmeiras com DAP ≥ 10 cm, atribuímos uma classificação binária quanto à presença ou a ausência das trepadeiras nos indivíduos, bem como calculamos as taxas médias de infestação por lianas e de ocupação por palmeiras. Ao todo, registramos 1.557 indivíduos, distribuídos em 41 famílias e 147 espécies. Contabilizamos 682 plantas infestadas e 875 livres de lianas em um total de 1.063 árvores e 494 palmeiras, sendo que, 73% das plantas infestadas eram árvores. A presença de palmeiras limitou a proliferação de lianas nas nossas áreas. As árvores foram mais vulneráveis à infestação por lianas do que as palmeiras e a condição favorável para a colonização massiva das mesmas foi aquela em que a taxa de ocupação por palmeiras atingiu 23% por hectare. Descobrimos que a forma de vida influencia substancialmente a infestação das plantas do ecótono Amazônia-Cerrado-Pantanal, sendo a maior infestação observada entre as árvores baixas. Concluímos que a taxa de infestação tende a reduzir em função do aumento na densidade palmeiras.

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Canopy palm cover across the Brazilian Amazon forests mapped with airborneLiDARdata and deep learning

Dalagnol

Wagner

Emilio

et al. 2022

Remote Sens Ecol Conserv

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The Amazon region in Brazil contains c. 5% of the palm species of the world. However, palm cover at macroecological scales has not yet been quantified in this biome. Here, we used high spatial resolution LiDAR data, acquired from 610 flightlines over the Brazilian Amazon, to map canopy palm cover for the first time using a deep learning approach. The image segmentation model from U-Net deep learning was selected for mapping palm segments using the LiDAR canopy height model (CHM) at 0.5-m spatial resolution. To train and validate the model, we manually delineated 6971 canopy palm segments over 931.43 ha of forests on four training sites by inspecting their unique star-shaped crown architecture in the CHM. The results indicated an accuracy of 80% to automatically map canopy palm area. The approach detected >1.1 million palm segments over the 480 000 ha sampled by LiDAR and roughly estimated 1.05 billion palm segments for the Brazilian Amazon. Palm cover was not evenly distributed over the Amazon, revealing undocumented hotspots of high cover (>5%) in eastern Amazon (Pará state), and confirming documented hotspots in southwest (Acre state) and north of the region (Roraima state). Palm segment height was strongly and positively correlated with forest height, where palm segments showed overall lower height. A higher canopy palm cover was observed over shorter forests, while the opposite was found over taller forests, where palms may not be visible from the canopy. Palm segments occurred more frequently at valleys but they were also observed in other landscapes, depending on site location and forest height. Our findings highlight the disproportional occurrence of palm cover in some Amazonian canopies. This fact should be taken into account to improve regional carbon cycle representation and promote initiatives of biodiversity conservation and bioeconomic use of these forests.

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The role of stand structure and palm abundance in predicting above-ground biomass at local scale in southern Amazonia

Cited by 4 publications

References 44 publications

Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

Palmeiras limitam a proliferação de lianas em florestas do ecótono Amazônia-Cerrado-Pantanal

Canopy palm cover across the Brazilian Amazon forests mapped with airborneLiDARdata and deep learning

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