Evaluation of the similarity in tree community composition in a tropical rainforest using airborne LiDAR data

Ioki, Keiko; Tsuyuki, Satoshi; Hirata, Yasumasa; Phua, Mui How; Wong, Wilson Vun Chiong; Ling, Zia Yiing; Johari, Shazrul Azwan; Korom, Alexius; James, Daniel; Saito, Hideki; Takao, Gen

doi:10.1016/j.rse.2015.07.024

Cited by 14 publications

(13 citation statements)

References 72 publications

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“…This suggests that change in community composition would at least partly bring about change in forest (canopy) structure. A previous study in Borneo demonstrated that tree height was a useful indicator to evaluate forest degradation, as tree height was correlated with the similarity in community composition that reflected different degrees of human disturbances [11]. Our results especially at high elevation are in accordance with the previous finding.…”

Section: Discussionsupporting

confidence: 92%

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Variation in Tree Community Composition and Carbon Stock under Natural and Human Disturbances in Andean Forests, Peru

Miyamoto

Satô

Olivos

et al. 2018

Forests

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Deforestation and forest degradation in Andean forests is influenced by natural and social environments including a wide elevation range and anthropogenic disturbance. Tree community composition is receiving attention as a key indicator of forest degradation. However, difference in factors affecting community composition at different elevation zones remains unclear. We aimed at elucidating factors (natural and human disturbances, and forest characteristics) that influence the variations in community composition in Andean forests. We conducted a ground-based survey setting 45 plots across a wide elevation range (ca. 600 to 3500 m a.s.l.) in Cusco region, Peru. Above ground biomass (AGB) decreased with increasing elevation. The generalized linear models for multivariate abundance data suggested that a factor affecting community composition was natural disturbance (erosion) at low elevation (<1000 m), while human disturbance (infrastructure such as sheds and trails) at high elevation (≥2400 m). Within each of the different elevation zones, the AGB affected community composition only at mid elevation (1000-2400 m), whereas mean tree height showed a consistent effect on community composition across the three elevations. Our results suggest that the effects of human disturbance on community composition were more prominent at higher elevation. The results also suggest that mean tree height may have a potential to be a key measure for evaluating variations in community composition in Andean forests.

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Section: Discussionsupporting

confidence: 92%

“…Several studies on natural production forests in Borneo have tackled the challenges to seek adequate biodiversity measures and to verify their applicability [10][11][12]. They focused on tree community composition as an indicator of forest degradation.…”

Section: Introductionmentioning

confidence: 99%

Variation in Tree Community Composition and Carbon Stock under Natural and Human Disturbances in Andean Forests, Peru

Miyamoto

Satô

Olivos

et al. 2018

Forests

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“…In other literatures, variables derived directly from the LiDAR height statistics are often selected as the AGB predictor in tropical forests (Asner et al 2012a;Ota et al 2015). However, in this study, LP variables were the better predictors for AGB, because LP variables are probably linked to the canopy structure difference of forest types (Ioki et al 2016). Our result indicates the potential use of LP variables for AGB estimation in primary tropical forests with different forest types.…”

Section: Discussionmentioning

confidence: 56%

Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests

Phua

Johari

Wong

et al. 2017

Forest Ecology and Management

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Developing a robust and cost-effective method for accurately estimating tropical forest's carbon pool over large area is a fundamental requirement for the implementation of Reducing Emissions from Deforestation and forest Degradation (REDD+). This study aims at examining the independent and combined use of airborne LiDAR and Landsat 8 Operational Land Imager (OLI) data to accurately estimate the above-ground biomass (AGB) of primary tropical rainforests in Sabah, Malaysia. Thirty field plots were established in three types of lowland rainforests: alluvial, sandstone hill and heath forests that represent a wide range of AGB density and stand structure. We derived the height percentile and laser penetration variables from the airborne LiDAR and calculated the vegetation indices, tasseled cap transformation values, and the texture measures from Landsat 8 OLI data. We found that there are moderate correlations between the AGB and laser penetration variables from airborne LiDAR data (r =-0.411 to-0.790). For Landsat 8 OLI data, the 6 vegetation indices and the 46 texture measures also significantly correlated with the AGB (r = 0.366 to 0.519). Stepwise multiple regression analysis was performed to establish the estimation models for independent and combined use of airborne LiDAR and Landsat 8 OLI data. The results showed that the model based on a combination of the two remote sensing data achieved the highest accuracy (R 2 adj = 0.81, RMSE = 17.36%) whereas the models using Landsat 8 OLI data airborne LiDAR data independently obtained the moderate accuracy (R 2 adj = 0.52, RMSE = 24.22% and R 2 adj = 0.63, RMSE = 25.25%, respectively). Our study indicated that texture measures from Landsat 8 OLI data provided useful information for AGB estimation and synergistic use of Landsat 8 OLI and airborne LiDAR data could improve the AGB estimation of primary tropical rainforest.

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“…In addition to recording tree species taxonomy and distribution, the monitoring of forest populations and forest community structures [471] or forest types is also possible using RS, although it is also subject to the same constraints as for the discrimination of tree species. Ioki et al, [472] assessed the similarity between tree community compositions in a tropical rainforest using airborne LiDAR RS data, whereas Laurin et al, [436] recorded tropical forest types, dominant species, and functional guilds in FES by using hyperspectral and simulated multispectral Sentinel-2 data.…”

Section: Monitoring Stress On Vegetation In Fes With Rsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

et al. 2016

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Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH.

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Evaluation of the similarity in tree community composition in a tropical rainforest using airborne LiDAR data

Cited by 14 publications

References 72 publications

Variation in Tree Community Composition and Carbon Stock under Natural and Human Disturbances in Andean Forests, Peru

Variation in Tree Community Composition and Carbon Stock under Natural and Human Disturbances in Andean Forests, Peru

Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

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