Canopy Temperature Differences between Liana-Infested and Non-Liana Infested Areas in a Neotropical Dry Forest

Yuan, Xu; Laakso, Kati; Marzahn, Philip; Sánchez‐Azofeifa, Arturo

doi:10.3390/f10100890

Cited by 10 publications

(5 citation statements)

References 69 publications

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“…Our results (Figure 4 in particular) also suggest that multispectral sensors (alone or in combination with thermal cameras (Guzmán et al 2018; Yuan et al 2019) or LiDAR) should be able to detect forest stands characterized by high liana coverage. In a related study, Visser et al (2021) show how radiative transfer models can assist in the estimation of liana traits from hyperspectral images of liana-infested canopies just as it is currently achieved for trees (Gong et al 2003; Meroni et al 2004; Serbin et al 2015).…”

Section: Discussionsupporting

confidence: 62%

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Meunier

Visser

Shiklomanov

et al. 2021

Preprint

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Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated tree. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas particularly efficient at light interception and completely modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was dramatically reduced in the understory (-30% of the PAR radiation) and soil temperature decreased by 0.5 degree Celsius. Those liana-specific traits were also responsible for a significant reduction of tree (-19%) and ecosystem (-7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of structural parasitism on forest functioning, and paves the way for the evaluation of the large-scale impacts of woody vines on forest biogeochemical cycles.

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Section: Discussionsupporting

confidence: 62%

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Meunier

Visser

Shiklomanov

et al. 2021

Preprint

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“…Lianas have been successfully detected in forest canopies and gaps by using UAVs fitted with standard cameras because of the ultra-fine resolution, down to centimetre, of the imagery obtained (Waite et al, 2019). Using visible to NIR (Li et al, 2018) and thermal sensors (Yuan et al, 2019) has also proved successful. Waite et al (2019) went beyond detecting liana presence in tree canopies, also assessing the degree of liana infestation in tree canopies.…”

Section: Current Remote Sensing Progressmentioning

confidence: 99%

“…RotorKonzept® RK-8x multicopter UAV Yuan, Laakso, Marzahn, and Sanchez-Azofeifa (2019) 1. Leica ALS50-II -8 W class 4 laser with radiation at 1064 nm recording up to four discrete returns for each emitted pulse.…”

Section: Sensor Platform Citationmentioning

confidence: 99%

Making (remote) sense of lianas

et al. 2022

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Lianas (woody vines) are abundant and diverse, particularly in tropical ecosystems. Lianas use trees for structural support to reach the forest canopy, often putting leaves above their host tree. Thus they are major parts of many forest canopies. Yet, relatively little is known about distributions of lianas in tropical forest canopies, because studying those canopies is challenging. This knowledge gap is urgent to address because lianas compete strongly with trees, reduce forest carbon uptake and are thought to be increasing, at least in the Neotropics. Lianas can be difficult to study using traditional field methods. Their pliable stems often twist and loop through the understorey, making it difficult to assess their structure and biomass, and the sizes and locations of their crowns. Furthermore, liana stems are commonly omitted from standard field surveys. Remote sensing of lianas can help overcome some of these obstacles and can provide critical insights into liana ecology, but to date there has been no systematic assessment of that contribution. We review progress in studying liana ecology using ground‐based, airborne and space‐borne remote sensing in four key areas: (i) spatial and temporal distributions, (ii) structure and biomass, (iii) responses to environmental conditions and (iv) diversity. This demonstrates the great potential of remote sensing for rapid advances in our knowledge and understanding of liana ecology. We then look ahead, to the possibilities offered by new and future advances. We specifically consider the data requirements, the role of technological advances and the types of methods and experimental designs that should be prioritised. Synthesis. The particular characteristics of the liana growth form make lianas difficult to study by ground‐based field methods. However, remote sensing is well suited to collecting data on lianas. Our review shows that remote sensing is an emerging tool for the study of lianas, and will continue to improve with recent developments in sensor and platform technology. It is surprising, therefore, how little liana ecology research has utilised remote sensing to date—this should rapidly change if urgent knowledge gaps are to be addressed. In short, liana ecology needs remote sensing.

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“…Many studies have shown that lianas, as a plant group, can be distinguished from trees based on their spectral reflectance, particularly in the visible (400-690 nm) and Near Infrared (NIR)-region (700-1340 nm) [17][18][19][20][21], as well as thermal properties [22,23]. Subsequently, recent research has successfully detected lianas using data acquired from; UAVs, fitted with RGB [24,25] and thermal [26] sensors, satellite imagery [27] and airborne hyperspectral imagery in seasonal [28] and aseasonal forests [29]. While airborne sensors have the potential to provide high spatial and spectral resolution imagery which can be used to detect liana infestation at landscape-scales, satellite-based sensors can typically afford more frequent measurements across much larger geographical extents.…”

Section: Introductionmentioning

confidence: 99%

Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery

et al. 2021

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Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally.

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Canopy Temperature Differences between Liana-Infested and Non-Liana Infested Areas in a Neotropical Dry Forest

Cited by 10 publications

References 69 publications

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Making (remote) sense of lianas

Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery

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