Mapping tree mortality rate in a tropical moist forest using multi-temporal LiDAR

Mora-Huertas, Claudia Elizabeth; Sabatier, Daniel; Derroire, Géraldine; Ferry, Bruno; Jackson, Toby; Pélissier, Raphaël; Vincent, Grégoire

doi:10.1016/j.jag.2022.102780

Cited by 8 publications

(8 citation statements)

References 58 publications

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“…Tall trees are also particularly sensitive to the effects of climate change, such as increased wind speeds and drought (Gora & Esquivel‐Muelbert, 2021), and as such tracking their dynamics over time is increasingly important. Recent remote sensing studies are bringing new insights into disturbance patterns by mapping the gaps left in the forest canopy after a tree (or multiple trees or branches) have fallen (Araujo et al., 2021; Cushman et al., 2022; Huertas et al., 2022). Tracking individual trees over time instead of gaps will make it easier to interpret our results in an ecological context and also to compare the results more directly with the available field inventory data.…”

Section: Discussionmentioning

confidence: 99%

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Ball

Hickman

Jackson

et al. 2023

Remote Sens Ecol Conserv

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Tropical forests are a major component of the global carbon cycle and home to two‐thirds of terrestrial species. Upper‐canopy trees store the majority of forest carbon and can be vulnerable to drought events and storms. Monitoring their growth and mortality is essential to understanding forest resilience to climate change, but in the context of forest carbon storage, large trees are underrepresented in traditional field surveys, so estimates are poorly constrained. Aerial photographs provide spectral and textural information to discriminate between tree crowns in diverse, complex tropical canopies, potentially opening the door to landscape monitoring of large trees. Here we describe a new deep convolutional neural network method, Detectree2, which builds on the Mask R‐CNN computer vision framework to recognize the irregular edges of individual tree crowns from airborne RGB imagery. We trained and evaluated this model with 3797 manually delineated tree crowns at three sites in Malaysian Borneo and one site in French Guiana. As an example application, we combined the delineations with repeat lidar surveys (taken between 3 and 6 years apart) of the four sites to estimate the growth and mortality of upper‐canopy trees. Detectree2 delineated 65 000 upper‐canopy trees across 14 km2 of aerial images. The skill of the automatic method in delineating unseen test trees was good (F1 score = 0.64) and for the tallest category of trees was excellent (F1 score = 0.74). As predicted from previous field studies, we found that growth rate declined with tree height and tall trees had higher mortality rates than intermediate‐size trees. Our approach demonstrates that deep learning methods can automatically segment trees in widely accessible RGB imagery. This tool (provided as an open‐source Python package) has many potential applications in forest ecology and conservation, from estimating carbon stocks to monitoring forest phenology and restoration.Python package available to install at https://github.com/PatBall1/Detectree2.

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Section: Discussionmentioning

confidence: 99%

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Ball

Hickman

Jackson

et al. 2023

Remote Sens Ecol Conserv

Self Cite

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“…Trees that die standing may not immediately create a gap in lidar surveys and may never lead to gap opening if regrowth around the dead tree and decomposition of the snag follow similar rates. Nevertheless, recent studies found that field‐based mortality was related to lidar measures of canopy turnover across the Brazilian Amazon (Dalagnol et al, 2021) and in French Guiana (Huertas et al, 2022). We believe that relations between the two quantities (formation and turnover) may be improved by considering regional variations in the modes of tree death (Esquivel‐Muelbert et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Out of steady state: Tracking canopy gap dynamics across Brazilian Amazon

et al. 2023

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Canopy gaps are evidence of disturbances on forest landscapes. A forest stand is in constant flux, with long stretches of biomass accumulation punctuated by episodic disturbances. We used multitemporal airborne laser scanning data to compare the gap dynamics of four Amazon forest sites. We assessed gap dynamics over 1.9–3.8 years between 2017 and 2020 at sites in the central, central eastern, southeastern, and northeastern regions of the Brazilian Amazon, over areas ranging from 590 to 1205 ha at each site. Gap size ranged from a minimum of 10 m2 to a maximum of about 10,000 m2. We analyzed four stages of gap dynamics: formation, expansion, persistence, and recovery based on two consecutive airborne laser scanning surveys. The gap fraction at our study sites varied between 1.26% and 7.84%. All the sites have similar proportion of gaps among gap size classes. What notably differed among sites was not the gap size‐distribution, but the relative importance of stages of gap dynamics. Expansion and persistence rates ranged from 12 to 118 m2 ha−1. The gap formation rate (formation + expansion) was lower than the recovery rate for three of the four study sites. In contrast, the southeastern site has 1.44 times more area in formation and expansion compared to gap recovery. Over the 2–4 years interval of our study, no site was close to steady state. Multitemporal analyses of large areas over many years are needed to improve our understanding of tropical forest dynamics.

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“…Trees that die standing may not immediately create a gap in lidar surveys and may never lead to gap opening if regrowth around the dead tree and decomposition of the snag follow similar rates. Nevertheless, recent studies found that field based mortality was related to lidar measures of canopy turnover across the Brazilian Amazon (Dalagnol et al 2021) and in French Guiana (Huertas et al 2022). We believe that relations between the two quantities (formation and turnover) may be improved by considering regional variations in the modes of tree death (Esquivel-Muelbert et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Tracking canopy gap dynamics across four sites in the Brazilian Amazon

Görgens

Keller

Jackson³

et al. 2022

Preprint

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Background information to give context to the study: Canopy gaps are the most evident manifestation of how disturbances disrupt forest landscapes. The size distribution and return frequency of gaps, and subsequent recovery processes, determine whether the old-growth state can be reached. The aim or research question: We used remote sensing metrics to compare the disturbance regime of four Amazon regions based on the size distribution of gaps, their dynamics and geometric characteristics. A brief summary of the methodology used: We assessed gap dynamics at four sites in the central, central eastern, southeastern, and northeastern regions of the Brazilian Amazon using repeated airborne laser scanning surveys. We developed a novel analysis to quantify four possible stages of gap dynamics: formation, expansion, persisting and recovering. For that, we overlapped layers of gap locations from two consecutive airborne laser scanning surveys. Key results with some significance measures: The gap fraction in our study sites varied between 1.26% to 7.84%. All the sites have similar proportion of gaps among size classes. What notably changed between sites was not the gap size-distribution, but the relative importance of stages of gap dynamics. Growing and persisting rates were greatest in the site with the stronger seasonal variation in climate, lower annual precipitation, higher mean wind speed and higher solar radiation. The conclusions, which address the main aims: The concept of stability reflects the tendency of a system to quickly return to a position of equilibrium when disturbed. We showed that gap dynamics varied among sites, with one example of low recovery rate contrasted to three other sites with faster recovery. Our results support that such as assessing the size distribution of gaps, investigating their return frequency and severity is crucial for understanding forest dynamics at the landscape and regional scales.

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Mapping tree mortality rate in a tropical moist forest using multi-temporal LiDAR

Cited by 8 publications

References 58 publications

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Out of steady state: Tracking canopy gap dynamics across Brazilian Amazon

Tracking canopy gap dynamics across four sites in the Brazilian Amazon

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