Forest structure and solar-induced fluorescence across intact and degraded forests in the Amazon

https://doi.org/10.1016/j.rse.2022.112998Get rights and content

Highlights

  • Structure and function relationships in degraded tropical forests are investigated.

  • Degradation by fire causes stronger impacts on structure than selective logging.

  • SIF signal is lower in recently burned than in older-burned and logged forests.

  • Older-burned forests (> 4 years) show higher wet season SIF than intact forests.

  • Recovery of forest structure and function in degraded forests is decoupled.

Abstract

Tropical forest degradation (e.g., anthropogenic disturbances such as selective logging and fires) alters forest structure and function and influences the forest's carbon sink. In this study, we explored structure-function relationships across a variety of degradation levels in the southern Brazilian Amazon by 1) investigating how forest structural properties vary as a function of degradation history using airborne lidar data; 2) assessing the effects of degradation on solar-induced chlorophyll fluorescence (SIF) seasonality using TROPOMI data; and 3) quantifying the contribution of structural variables to SIF using multiple regression models with stepwise selection of lidar metrics. Forest degradation history was obtained through Landsat time-series classification. We found that fire, logging, and time since disturbance were major determinants of forest structure, and that forests affected by fires experienced larger variability in leaf area index (LAI), canopy height and vertical structure relative to logged and intact forests. Moreover, only recently burned forests showed significantly depressed SIF during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Unexpectedly, we found that wet-season SIF was higher in active regenerating forests (~ 4 years after fires or logging) compared with intact forests, despite lower LAI. Our findings help to elucidate the mechanisms of carbon accumulation in anthropogenically disturbed tropical forests and indicate that they can capture large amounts of carbon while recovering.

Introduction

Forest degradation by selective logging, fires and fragmentation affects large regions of the tropics (Bullock et al., 2020; Souza Jr et al., 2013; Tyukavina et al., 2017). In the Amazon region, the drivers of forest degradation are part of a complex socio-economic system that includes forest clearing for pastures and crops, usually preceded by the selective extraction of marketable wood (Broadbent et al., 2008; Lima et al., 2012; Moran, 1993). Fire is used extensively for forest clearing and the maintenance of pastures (Aragão et al., 2014; Cochrane, 2003). Fires frequently penetrate managed and unmanaged forests so that within the Amazon annually region large areas of forest burn, especially during drought years (Morton et al., 2013).

Forest degradation leads to changes in forest composition, carbon stocks, and forest functions. Logging, fragmentation, and fires promote crown damage and tree mortality resulting in persistent alterations to gap-phase dynamics, potentially leading to species composition shifts toward early-successional, light-demanding species over time (Ordway and Asner, 2020). Carbon stocks in degraded forests are highly variable at the local scale, with lightly disturbed forests (e.g., reduced-impact logging) storing as much carbon as intact forests, while forests impacted by multiple fires may lose most of their original carbon stocks (Berenguer et al., 2014; Longo et al., 2016; Rappaport et al., 2018; Silva et al., 2018). Productivity may decline when forests are damaged but may even exceed old-growth forest productivity when forests do not experience further disturbances (Odum, 1969). Shifts in degraded forest productivity are driven by changes to forest structure and species composition resulting from increased plant community turnover, disrupted seedling recruitment patterns, and altered nutrient cycling (Bomfim et al., 2020; Dantas de Paula et al., 2015; Prestes et al., 2020; Silva et al., 2018). Water cycling can also be affected by forest degradation. Brief disturbance in evapotranspiration (ET) has been measured in selectively logged forest (Miller et al., 2011). In contrast, ET declined significantly for 3 years after fires and took 7–8 years to recover fully, according to flux tower estimates from the Southern Amazon (Brando et al., 2019).

Forest degradation has also been recognized as a major driver of forest structure changes worldwide. In tropical forests, forest degradation affects live and dead biomass distribution (Longo et al., 2016; Rappaport et al., 2018; Scaranello et al., 2019), the vertical distribution of foliage (Brando et al., 2019; Rangel Pinagé et al., 2019), and canopy gap distribution (Rangel Pinagé et al., 2019; Vaughn et al., 2015). Lidar data can capture both the vertical and horizontal dimensions of forest structure (Drake et al., 2002; Lefsky et al., 2002), hence, it offers an excellent tool to investigate structural changes from degradation processes such as selective logging and fires.

Multilayered, heterogeneously arranged canopies contain a complement of sun and shade leaves functioning optimally under a range of light conditions (Gough et al., 2019). Within temperate intact forests, widespread positive relationships between canopy structural complexity and production were found, suggesting underlying mechanisms of improved canopy light absorption and light-use efficiency (Atkins et al., 2018; Gough et al., 2019; Hardiman et al., 2011). In the Amazon, recent studies have suggested an important role of canopy structural arrangement on phenology (Smith et al., 2019; Tang and Dubayah, 2017), but how canopy structural complexity affects the functioning of tropical forests is still a largely uncharted territory. Forest degradation can both enhance or reduce structural complexity (e.g., gaps caused by the removal of large canopy trees increase canopy height variability, or on the other hand, intensive forest fires cause widespread tree mortality and stimulate the regrowth of a uniform understory). Further investigation is needed to clarify the structure-function linkages controlling forest productivity, especially considering the high diversity of species, functional and forest types, as well as disturbance and recovery pathways of tropical forests.

Solar-induced chlorophyll fluorescence (SIF), the natural emission of photons from the light-harvesting structures of plants (Zuromski et al., 2018), is a biophysical consequence of light absorption. SIF may show linear (Sun et al., 2018) or non-linear (Kim et al., 2021) correlation to photosynthesis, depending on many factors such as vegetation type, light regime, averaging period of observations, and plant physiological status. Empirical evidence suggests that SIF is sensitive to canopy properties such as chlorophyll content, leaf area index (LAI) and leaf angle distributions (Koffi et al., 2015; Verrelst et al., 2015). SIF also reflects dynamic photosynthetic responses to heat and water stress (Parazoo et al., 2014).

Until recently, estimation of vegetation productivity from space depended on estimates based on vegetation near-infrared reflectance. SIF appears promising as a physiologically meaningful proxy to photosynthesis at the canopy scale and may be able to capture differences in photosynthesis between intact forests and forests regenerating from anthropogenic or natural disturbances. Another key advantage of SIF is that it is not as much affected by atmospheric scattering due to aerosols and cloud cover (Sun et al., 2018) as traditional vegetation indices such as the Normalized Difference Vegetation Index (NDVI, Rouse Jr et al., 1974) and Enhanced Vegetation Index (EVI, Huete et al., 2002), an aspect that gains even more relevance in the tropics. However, there are many complications of interpreting SIF in complex canopies and illumination conditions such as those found in tropical forests. Recent advances in SIF retrieval techniques and satellite sensors such as the Global Ozone Monitoring Experiment–2 (GOME-2), the Orbiting Carbon Observatory-2 (OCO-2) and the TROPOspheric Monitoring Instrument (TROPOMI) have enabled remote sensing of SIF in unprecedented spatial and temporal scales (Köhler et al., 2018a). TROPOMI SIF data in particular, despite having a coarse spatial resolution (~5.5 km) in relation to the scale of forest disturbances in the Amazon, have fine temporal resolution that allows tracking rapid vegetation changes.

In this study, we use a novel combination of airborne lidar and spaceborne SIF data to investigate solar-induced fluorescence emissions and forest structure variability in intact and degraded forests, also taking into consideration the time since last disturbance events. We address the following questions: 1) How does forest structure change as a function of disturbance history? 2) How does the disturbance history affect SIF emissions and their seasonal patterns? 3) How are forest structural attributes related to SIF across intact and degraded forests?

Section snippets

Site description

The study area covers approximately 100,000 km2 at the southern portion of closed-canopy Amazon forests in the Brazilian state of Mato Grosso (Fig. 1) and includes a rectangle around the municipality of Feliz Natal. The area is fairly homogeneous in regard to topography, soil and vegetation (Fig. S1 of the Supplemental Material) and is covered mostly by ecotonal broadleaf seasonal forests and agricultural/ pastoral managed lands originally covered by forests (IBGE, 2021; MapBiomas Project, 2019

Structural properties of intact and degraded forests

The structural properties of degraded forests, estimated using high-density airborne lidar data, demonstrated greater changes for burned versus logged areas, compared to intact forests (an example showing eight-hectare samples for each disturbance class is presented in Fig. 4). The most obvious effect of disturbance on forest structure is the decrease of canopy heights of the disturbance classes, as depicted by the examples in Fig. 4.

Logging was associated with a greater frequency of canopy

Discussion

In this study, we used multi-source remote sensing data to assess the effects of tropical forest degradation (fires and selective logging) on canopy structural attributes and SIF. Moreover, we modelled the contribution of lidar-derived structural variables to SIF variability. Our results highlighted disturbance type and recovery time as important drivers of forest structure and SIF. We also showed that forest regeneration can result in higher SIF, presumably due to structural, compositional and

Conclusion

Our study employed a combination of airborne lidar and SIF data and highlighted differences in ecosystem structure and function in a broad array of degraded sites. SIF showed positive or negative changes in degraded forests based on recent degradation and recovery history. By using spaceborne assets of forest function, our results show that combined observations improve our ability to detect the regional effects of forest degradation and indicate that anthropogenically disturbed forests can

Author contributions

E.R.P., D.M.B., and A.H. designed the study. E.R.P. and C.A.S. processed the lidar data. E.R.P., S.G., P.K., and C.F. processed the SIF data. E.R.P., D.M.B., M.L., M.K., C.A.S., and A.H. contributed with the methodological framework and data analysis. E.R.P. wrote the manuscript with contributions from all authors to the interpretation, quality control and revisions of the manuscript. All authors read and approved the final version of the manuscript.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Lidar data were provided by the Sustainable Landscapes Brazil project, a collaboration of the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, USAID, and the US Department of State; and by the Estimativa de Biomassa da Amazonia project (EBA_BNDES-Amazon Fund [Grant 14.2.0929.1]; the NAS and USAID [Grant AID-OAA-A-11-00012]). Funding source for TROPOMI: NASA Earth Science U.S. Participating Investigator [Grant Number NNX15AH95G]. The research carried out at the Jet

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