Intraseasonal interactive effects of successive typhoons characterize canopy damage of forests in Taiwan: A remote sensing-based assessment

Peereman, Jonathan; Hogan, J. Aaron; Lin, Teng-Chiu

doi:10.1016/j.foreco.2022.120430

Cited by 7 publications

(3 citation statements)

References 85 publications

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“…Therefore, with more observations, wind speed may significantly explain the cyclone-induced changes in both VIs (Negrón-Juárez et al 2014). In addition, cyclone variables, such as wind speed, wind direction, and rainfall (Hall et al 2020), can vary locally to modulate disturbance effects on tropical forests (Cortés-Ramos et al 2020, Hogan et al 2020, Taillie et al 2020, Peereman et al 2022.…”

Section: Environmental Soil and Cyclone Moderators Of ∆Lai And ∆Evimentioning

confidence: 99%

“…25.5 m s −1 average wind speeds) (Bomfim et al 2021, Bloom et al 2022. Taiwan is frequently hit by multiple typhoons (Peereman et al 2022), making it possible to observe the effects of more than one cyclone on the Taiwanese forests. Generally, tropical forests are affected differently by both cyclone regimes (less-damaging, frequent cyclones and more-damaging, less-frequent cyclones).…”

Section: Environmental Soil and Cyclone Moderators Of ∆Lai And ∆Evimentioning

confidence: 99%

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Combining field and remote sensing data to estimate forest canopy damage and recovery following tropical cyclones across tropical regions

Bloom,

Bomfim,

Feng

et al. 2023

Environ. Res.: Ecology

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As tropical forests cycle the most water and carbon, it is crucial to understand the short- and long-term effects of intensifying cyclones on these ecosystems. Soil nutrient status has been shown to moderate forest cyclone responses using field litterfall measurements, but litterfall is one metric of multiple cyclone impacts, which may or may not be correlated with one another or with site nutrients. We used remotely sensed vegetation indices to quantify immediate damage and two-year recovery for 42 cases across nine tropical forests in Hawaii, Puerto Rico, Mexico, Australia, and Taiwan affected by 12 cyclones between 2004-2017. We tested whether changes in leaf area index (LAI) and enhanced vegetation index (EVI) with changes in litterfall observations and varied with total soil phosphorus (P) concentrations across regions. We compared cyclone-induced changes and recovery of LAI and EVI to litterfall observations compiled in a pantropical meta-analysis. We found large variation in changes in LAI and EVI across forests, with the greatest reductions in LAI (−77%) and EVI (−77%) in Mexico (Jalisco) and Puerto Rico, respectively. LAI (r = −0.52) and EVI (r = −0.60) changes correlated with those in litterfall across cases. Post-cyclone data showed recovery of LAI by four months, EVI by two months, and litterfall by ten months. We detected larger changes in LAI and EVI in forests with higher soil P, but these relationships were not significant when accounting for cyclone and site as random effects. Principal component analyses indicated a regional clustering of cases related to their contrasting cyclone regimes, with the frequency and intensity of cyclone events negatively correlated. Overall, remote sensing observations complement but do not substitute for ground observations that reveal cyclone damage and post-cyclone recovery in tropical forests, and that soil phosphorus moderates some but not all metrics of stability in response to cyclones.

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Section: Environmental Soil and Cyclone Moderators Of ∆Lai And ∆Evimentioning

confidence: 99%

Section: Environmental Soil and Cyclone Moderators Of ∆Lai And ∆Evimentioning

confidence: 99%

Combining field and remote sensing data to estimate forest canopy damage and recovery following tropical cyclones across tropical regions

Bloom,

Bomfim,

Feng

et al. 2023

Environ. Res.: Ecology

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“…Vegetation types and their wide distribution are important in forestry [1][2][3][4] and agriculture [5,6]. Stand information, including stand structure [7][8][9], species composition [10][11][12][13], and forest damage [14][15][16][17], are important and depend on the results of vegetation classification. Mountain vegetation type classification is important in the field of vegetation type classification.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

Li,

Fei,

Chen

et al. 2023

Remote Sensing

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The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

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Development of a new indicator for identifying vegetation destruction events using remote sensing data

Zhao,

Pan,

Zhang

2024

Ecological Indicators

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Intraseasonal interactive effects of successive typhoons characterize canopy damage of forests in Taiwan: A remote sensing-based assessment

Cited by 7 publications

References 85 publications

Combining field and remote sensing data to estimate forest canopy damage and recovery following tropical cyclones across tropical regions

Combining field and remote sensing data to estimate forest canopy damage and recovery following tropical cyclones across tropical regions

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

Development of a new indicator for identifying vegetation destruction events using remote sensing data

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