Impact of Base-to-Height Ratio on Canopy Height Estimation Accuracy of Hemiboreal Forest Tree Species by Using Satellite and Airborne Stereo Imagery

The present study assessed the large-format airborne (UltraCam) and satellite (GeoEye1 and Pleiades1B) image-based digital surface model (DSM) performance for canopy height estimation in predominantly mature, closed-canopy Latvian hemiboreal forestland. The research performed the direct comparison of calculated image-based DSM models with canopy peaks heights extracted from reference LiDAR data. The study confirmed the tendency for canopy height underestimation for all satellite-based models. The obtained accuracy of the canopy height estimation GeoEye1-based models varied as follows: for a pine (−1.49 median error, 1.52 m normalised median absolute deviation (NMAD)), spruce (−0.94 median, 1.97 m NMAD), birch (−0.26 median, 1.96 m NMAD), and black alder (−0.31 median, 1.52 m NMAD). The canopy detection rates (completeness) using GeoEye1 stereo imagery varied from 98% (pine) to >99% for spruce and deciduous tree species. This research has shown that determining the optimum base-to-height (B/H) ratio is critical for canopy height estimation efficiency and completeness using image-based DSMs. This study found that stereo imagery with a B/H ratio range of 0.2–0.3 (or convergence angle range 10–15°) is optimal for image-based DSMs in closed-canopy hemiboreal forest areas.

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

Agriculture ◽

10.3390/agriculture10050146 ◽

2020 ◽

Vol 10 (5) ◽

pp. 146 ◽

Cited By ~ 2

Author(s):

Longfei Zhou ◽

Xiaohe Gu ◽

Shu Cheng ◽

Guijun Yang ◽

Meiyan Shu ◽

...

Keyword(s):

Plant Height ◽

Maize Yield ◽

Canopy Height ◽

Estimation Accuracy ◽

Yield Reduction ◽

Lidar Data ◽

Height Estimation ◽

Aerial Vehicle ◽

Yield Quality ◽

Maize Plants

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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Canopy height affects the allocation of photosynthetic carbon and nitrogen in two deciduous tree species under elevated CO2

Journal of Plant Physiology ◽

10.1016/j.jplph.2021.153584 ◽

2022 ◽

Vol 268 ◽

pp. 153584

Author(s):

Siyeon Byeon ◽

Wookyung Song ◽

Minjee Park ◽

Sukyung Kim ◽

Seohyun Kim ◽

...

Keyword(s):

Elevated Co2 ◽

Tree Species ◽

Canopy Height ◽

Deciduous Tree ◽

Carbon And Nitrogen ◽

Photosynthetic Carbon

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Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests

Remote Sensing ◽

10.3390/rs11070889 ◽

2019 ◽

Vol 11 (7) ◽

pp. 889 ◽

Cited By ~ 5

Author(s):

Wenjian Ni ◽

Jiachen Dong ◽

Guoqing Sun ◽

Zhiyu Zhang ◽

Yong Pang ◽

...

Keyword(s):

Aboveground Biomass ◽

Point Cloud ◽

Forest Canopy ◽

Deciduous Forest ◽

Ground Surface ◽

Digital Terrain Model ◽

Canopy Height ◽

Surface Model ◽

Lidar Data ◽

Stereo Imagery

Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R2 higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB.

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Purification of plasma membranes from leaves of conifer and deciduous tree species by phase partitioning and free-flow electrophoresis

Physiologia Plantarum ◽

10.1034/j.1399-3054.1995.950310.x ◽

1995 ◽

Vol 95 (3) ◽

pp. 399-408 ◽

Cited By ~ 1

Author(s):

Elena Toll ◽

Federico J. Castillo ◽

Pierre Crespi ◽

Michele Crevecoeur ◽

Hubert Greppin

Keyword(s):

Tree Species ◽

Plasma Membranes ◽

Free Flow ◽

Deciduous Tree ◽

Phase Partitioning

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The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring

Science of Remote Sensing ◽

10.1016/j.srs.2021.100024 ◽

2021 ◽

pp. 100024

Author(s):

David P. Roy ◽

Herve B. Kashongwe ◽

John Armston

Keyword(s):

Tropical Forest ◽

Forest Canopy ◽

Canopy Height ◽

Height Estimation ◽

The Impact ◽

Change Monitoring ◽

Tropical Forest Canopy

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Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

Remote Sensing ◽

10.3390/rs13153042 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3042

Author(s):

Kateřina Gdulová ◽

Jana Marešová ◽

Vojtěch Barták ◽

Marta Szostak ◽

Jaroslav Červenka ◽

...

Keyword(s):

Bark Beetle ◽

Synergistic Effects ◽

Canopy Height ◽

Surface Model ◽

Multiple Time ◽

Central European ◽

Time Points ◽

Bohemian Forest ◽

Multiple Time Points ◽

Surrounding Areas

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.

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