Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery

Chung, Chi-Jui; Wang, Chao-Huan; Hsieh, Han-Ching; Huang, Chin Wei

doi:10.1080/15481603.2019.1627044

Cited by 23 publications

(13 citation statements)

References 51 publications

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“…We used equation 2 to estimate tree height of sparse broadleaf trees in the litterfall plots. The protocol used for tree height measurements can be found in Chung et al (2019). We then derived the statistics of the plot scale tree height (mean, SD , CV , maximum, and range) using the measured DBH at the 15 litterfall plots.…”

Section: Methodsmentioning

confidence: 99%

“…The base airborne lidar digital elevation model (DEM) data (the altitude of the land surface above sea level excluding aboveground objects such as trees) for the entire study region from 2012 to 2015 was obtained through a national scale geological inventory project conducted by the Taiwan Central Geological Survey (see Lai et al, 2021 for the sensor specifications). Lidar point density was +1.5 points m −2 , and these point cloud data were gridded into a 1‐meter resolution DEM using adaptive kriging (SCOP++, Department of Geodesy and Geoinformation, Vienna, Austria) (Chung et al, 2019). The vertical accuracy of the DEM was ≤22.5 cm, which was determined by comparisons with measurements from a high‐precision global positioning system.…”

Section: Methodsmentioning

confidence: 99%

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Variation of seasonal litterfall in subtropical montane cloud forests to typhoon severity and environmental factors

Huang

Liu

et al. 2022

Biotropica

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Forest litterfall is pivotal for biogeochemical cycles and for assessing the impacts of perturbations on ecosystems. Typhoon occurrence is the primary mechanism for producing litterfall; Taiwan is situated in one of the most frequently disturbed regions. However, no typhoons were recorded in 2018, only occurring three times since 1911. This rare occasion, along with the regular (2017) and extreme typhoon (2016) years, provides an opportunity to investigate the responses of typhoon‐prone forest ecosystems to a future climate scenario: Elevated temperatures amplify the intensity but reduce the frequency of typhoons. We compared three years (2016–2018) of summer typhoon season (July–October) mean monthly litterfall (MML) in the subtropical montane cloud forests of northeastern Taiwan, and investigated the relationships between MML/typhoon‐induced MML (ΔMML) and 17 biophysical, bioclimatic and topographic attributes. More MML was produced in 2016, caused by strong winds and heavy rainfall. However, there was no statistical difference between 2017 and 2018 since forests may also produce substantial amounts of litterfall in summer without typhoons. The relationship between ΔMML and 17 variables was relatively insensitive to typhoon severity. Variables associated with succession and forest management were crucial for modeling MML in the presence of typhoons, but none of them were pivotal for MML without typhoons. The mean air temperature and elevation (related to forest productivity) were crucial for MML without typhoons; surface curvature may form shelters to prevent extreme typhoon with reduced MML. These outcomes may shed light on future ecosystem dynamics in typhoon‐prone forests under a changing climate. Abstract in Mandarin is available with online material.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Variation of seasonal litterfall in subtropical montane cloud forests to typhoon severity and environmental factors

Huang

Liu

et al. 2022

Biotropica

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“…4) and some previous literature (Hsu, Horng & Kuo, 2002;Köhler et al, 2007;Chen, Liu & Wang, 2010), we found that there may be a significant relationship between tree size and the abundance of EB (Gómez González et al, 2017). With the availability of a three-dimensional tree size spatial layer at the regional scale derived from high spatial resolution airborne lidar (light detection and ranging) or aerial photographic point cloud data (Chung et al, 2019;Kellner et al, 2019), we may be able to map wall-to-wall EB biomass over a vast region. This also answers the second part of our third research question (Q 3 ) as to the potential of the proposed EB biomass measuring approach for the regional scale spatial assessment.…”

Section: Limitation and Future Directionsmentioning

confidence: 66%

Epiphytic bryophyte biomass estimation on tree trunks and upscaling in tropical montane cloud forests

Lai

Liu

Kuo

et al. 2020

PeerJ

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Epiphytic bryophytes (EB) are some of the most commonly found plant species in tropical montane cloud forests, and they play a disproportionate role in influencing the terrestrial hydrological and nutrient cycles. However, it is difficult to estimate the abundance of EB due to the nature of their “epiphytic” habitat. This study proposes an allometric scaling approach implemented in twenty-one 30 × 30 m plots across an elevation range in 16,773 ha tropical montane cloud forests of northeastern Taiwan to measure EB biomass, a primary metric for indicating plant abundance and productivity. A general allometry was developed to estimate EB biomass of 100 cm2 circular-shaped mats (n = 131) with their central depths. We developed a new point-intercept instrument to rapidly measure the depths of EB along tree trunks below 300 cm from the ground level (sampled stem surface area (SSA)) (n = 210). Biomass of EB of each point measure was derived using the general allometry and was aggregated across each SSA, and its performance was evaluated. Total EB biomass of a tree was estimated by referring to an in-situ conversion model and was interpolated for all trees in the plots (n = 1451). Finally, we assessed EB biomass density at the plot scale of the study region. The general EB biomass-depth allometry showed that the depth of an EB mat was a salient variable for biomass estimation (R2 = 0.72, p < 0.001). The performance of upscaling from mats to SSA was satisfactory, which allowed us to further estimate mean (±standard deviation) EB biomass of the 21 plots (272 ± 104 kg ha−1). Since a significant relationship between tree size and EB abundance is commonly found, regional EB biomass may be mapped by integrating our method and three-dimensional remotely sensed airborne data.

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“…Although the cost of UAV digital images is low, the accuracy and quality of the acquired point clouds are easily affected by weather conditions [31,32]. Compared with optical sensors, light detection and ranging (LiDAR) is an active remote sensing tool that can obtain the three-dimensional coordinate information of the target, and can provide vegetation height and vertical structure information [33,34]. In addition, it is not affected by light conditions [35].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data

Zhou

Cheng

et al. 2020

Agriculture

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Lodging stress seriously affects the yield, quality, and mechanical harvesting of maize, and is a major natural disaster causing maize yield reduction. The aim of this study was to obtain light detection and ranging (LiDAR) data of lodged maize using an unmanned aerial vehicle (UAV) equipped with a RIEGL VUX-1UAV sensor to analyze changes in the vertical structure of maize plants with different degrees of lodging, and thus to use plant height to quantitatively study maize lodging. Based on the UAV-LiDAR data, the height of the maize canopy was retrieved using a canopy height model to determine the height of the lodged maize canopy at different times. The profiles were analyzed to assess changes in maize plant height with different degrees of lodging. The differences in plant height growth of maize with different degrees of lodging were evaluated to determine the plant height recovery ability of maize with different degrees of lodging. Furthermore, the correlation between plant heights measured on the ground and LiDAR-estimated plant heights was used to verify the accuracy of plant height estimation. The results show that UAV-LiDAR data can be used to achieve maize canopy height estimation, with plant height estimation accuracy parameters of R2 = 0.964, RMSE = 0.127, and nRMSE = 7.449%. Thus, it can reflect changes of plant height of lodging maize and the recovery ability of plant height of different lodging types. Plant height can be used to quantitatively evaluate the lodging degree of maize. Studies have shown that the use of UAV-LiDAR data can effectively estimate plant heights and confirm the feasibility of LiDAR data in crop lodging monitoring.

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Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery

Cited by 23 publications

References 51 publications

Variation of seasonal litterfall in subtropical montane cloud forests to typhoon severity and environmental factors

Variation of seasonal litterfall in subtropical montane cloud forests to typhoon severity and environmental factors

Epiphytic bryophyte biomass estimation on tree trunks and upscaling in tropical montane cloud forests

Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data

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