Can Uav Lidar Derive Vertical Structure of Herbaceous Vegetation on Riverdike?

Miura, N.; Koyanagi, Tomoyo F.; Yokota, Shigeru; Yamada, S.

doi:10.5194/isprs-annals-iv-2-w5-127-2019

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…It is a non-destructive method to derive plant parameters in large area, compared to traditional destructive field measurement techniques (Catchpole and Wheeler, 1992). Using UAV LiDAR which is an active sensor, Miura et al (2019; estimated herbaceous vegetation height and its vertical structure on riverdike. With a combination of structure from motion (SfM) and multi-view stereo (MVS) technique, passively acquired UAV imagery can also generate 3D information.…”

Section: Figure 1 Miscanthus Sinensismentioning

confidence: 99%

ESTIMATION OF CANOPY HEIGHT AND BIOMASS OF MISCANTHUS SINENSIS IN SEMI-NATURAL GRASSLAND USING TIME-SERIES UAV DATA

Miura¹,

Yamada²,

Niwa³

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Grasslands are important ecosystems to provide various economic and ecological services. In Japan, grassland of Miscanthus sinensis, which is a tall, perennial grass species, has been one of the symbolic landscape and require efficient monitoring system for better management. In this study, canopy height and biomass of M. sinensis in semi-natural grassland are estimated using time-series UAV imagery and structure from motion (SfM) and multi-view stereo (MVS) technique. The effect of complex topography on estimation of the canopy height and biomass is analysed as well as monitoring growth of M. sinensis. The results showed that UAV derived maximum canopy height and biomass have significant correlation with vegetation survey data producing R2 value of 0.92 and 0.78, respectively. The effect of topographic landforms was found to be smallest on top of the hill, followed by slope. Valley-like sunken place was affected worst. Analysis using time-series UAV data revealed that growth of M. sinensis is different between the landforms, and the best time to estimate its biomass was different between them. In order to accurately estimate canopy height and biomass of tall grass species such as M. sinensis, it is important to take plant growth stage into consideration as well as topographic landforms.

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Section: Figure 1 Miscanthus Sinensismentioning

confidence: 99%

ESTIMATION OF CANOPY HEIGHT AND BIOMASS OF MISCANTHUS SINENSIS IN SEMI-NATURAL GRASSLAND USING TIME-SERIES UAV DATA

Miura¹,

Yamada²,

Niwa³

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Several studies, which explored the potential of UAV LiDAR to estimate canopy height in grasslands, reported height underestimation ( Miura et al., 2019 ; Zhao et al., 2022 ). The underestimation would affect the estimation of grassland ecosystem functions related to canopy height and its heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…Although grasslands may have a higher canopy density than forests, the gaps between grass individuals combined with high point cloud density allow UAV LiDAR to obtain reliable ground elevation information ( Getzin et al., 2021 ; Zhao et al., 2022 ). The height loss at canopy was proved to be the main cause which greatly reduced the accuracy of grassland height-related structural traits ( Miura et al., 2019 ). Zhao et al.…”

Section: Introductionmentioning

confidence: 99%

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

Zhao

Zheng

et al. 2023

Front. Plant Sci.

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Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25–65cm) and segmented (25–40cm, 40–50cm and 50–65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing.

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“…Whilst unmanned aerial vehicle (UAV) technologies are becoming more popular for fine‐scale vegetation surveys over larger areas (e.g., Tay et al, 2018) many species still require ground‐survey by botanists to be accurately identified. Similarly, whilst LIDAR has considerable potential to measure general vegetation structure (Miura et al, 2019) identification to species level would still require more conventional methods such as point quadrats or pin frames. One obvious limitation of recording dominant or subdominant species is that it is less able to detect rare species or those with sparse cover abundance.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, whilst LIDAR has considerable potential to measure general vegetation structure (Miura et al, 2019) identification to species level would still require more conventional methods such as point quadrats or pin frames. One obvious limitation of recording dominant or subdominant species is that it is less able to detect rare species or those with sparse cover abundance.…”

Section: Introductionmentioning

confidence: 99%

A rapid method for quantifying small‐scale vegetation patch structure to complement conventional quadrat surveys

Butler

Sanderson

2021

Applied Vegetation Science

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Aims: Vegetation sampling typically involves the use of quadrats to estimate species cover abundance. Such surveys do not generally record small-scale vegetation patch structure at sub-quadrat scales. Here we test a simple method to quantify patch structure that complements conventional techniques. We compare the two methods, and analyse metrics derived from small-scale patch surveys with environment/management data.Location: Northumberland, United Kingdom. Methods:We recorded cover abundance of all species in an upland moor with 1-m 2 quadrats. These were divided into 100 'sub-quadrats', 10 cm × 10 cm, and the dominant and subdominant species identified. Patch metrics (number, area and shape) for individual species recorded as dominant or subdominant in the subquadrat survey were analysed using multi-variate generalised linear models with environmental and management data. Sub-quadrat data were also aggregated for each quadrat, to create species composition data. The two sets of compositional data, from whole-quadrat and sub-quadrat aggregations, were compared via Procrustes rotation of ordination scores.Results: Patch number, area and shape for dominant and subdominant species were all significantly affected by soil pH, soil water content, slope and elevation. Effects of proximity to sheep tracks and drainage ditches were less consistent amongst species. Ordinations of vegetation data from conventional and sub-quadrats were similar, with significant Procrustes R 2 of 67% and 70% for dominant and subdominant species respectively.Conclusions: Sub-quadrat surveys can easily be used to complement existing wholequadrat surveys at little cost in time or resources. Their patch metrics can provide additional insights into the environmental and management drivers that may affect the growth of individual plants or clumps, potentially in relation to plant traits, and thus alter the overall community composition. The methods we describe can readily be adapted to other sizes of quadrats and sub-quadrats in a wide range of vegetation communities.

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Can Uav Lidar Derive Vertical Structure of Herbaceous Vegetation on Riverdike?

Cited by 7 publications

References 9 publications

ESTIMATION OF CANOPY HEIGHT AND BIOMASS OF MISCANTHUS SINENSIS IN SEMI-NATURAL GRASSLAND USING TIME-SERIES UAV DATA

ESTIMATION OF CANOPY HEIGHT AND BIOMASS OF MISCANTHUS SINENSIS IN SEMI-NATURAL GRASSLAND USING TIME-SERIES UAV DATA

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

A rapid method for quantifying small‐scale vegetation patch structure to complement conventional quadrat surveys

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