Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2020, Volume and Issue: 89, P. 102091 - 102091

Published: Feb. 29, 2020

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements.However, few studies have compared accuracy different technologies for estimation height.In this study, we evaluated accuracy, flexibility, aerial coverage limitations five techniques measure height two types mango avocado trees.Canopy estimates from Terrestrial Laser Scanning (TLS) were used a reference dataset against Airborne (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB multi-spectral field measurements.Overall, imagery obtained UAV platform found provide measurement comparable that TLS (R 2 = 0.89, RMSE 0.19 m rRMSE 5.37 % trees; R 0.81, 0.42 4.75 trees), although area is limited 1-10 km due battery life line-of-sight flight regulations.The ALS data also achieved reasonable both trees 0.67, 0.24 7.39 0.63, 0.43 5.04 providing optimal point density altitude, therefore offers an effective large areas (10 -100 ).However, cost availability consideration.WV-3 produced lowest accuracies crops 0.50, 0.84 32.64 0.45, 0.74 8.51 trees) when other platforms, but may still present viable option commercial required.This provides industries growers with valuable information on how select most appropriate approach parameters each assess trees.

Language: Английский

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Estimating Tree Height and Volume Using Unmanned Aerial Vehicle Photography and SfM Technology, with Verification of Result Accuracy

Drones, Journal Year: 2020, Volume and Issue: 4(2), P. 19 - 19

Published: May 11, 2020

This study aimed to investigate the effects of differences in shooting and flight conditions for an unmanned aerial vehicle (UAV) on processing method estimated results images. Forest images were acquired under 80 different conditions, combining various photography methods conditions. We verified errors values measured by UAV measurement accuracy with respect tree height volume. Our showed that could be processed all studied However, although crown decipherable created 3D model 64 they undecipherable 16. The standard deviation (SD) area each target was 0.08 0.68 m2. measurements tended lower than actual values, RMSE (root mean square error) high (5.2 7.1 m) through modeled With volume being volume, condition from 0.31 0.4 m3. Therefore, irrespective measurements, low values.

Language: Английский

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An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(24), P. 4039 - 4039

Published: Dec. 10, 2020

The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost time efficiencies. However, little research has examined accuracies these technologies for measuring young trees. This study compared data captured by UAV laser scanning system (ULS), structure from motion photogrammetry (SfM), with traditional field-measured heights in series central North Island New Zealand. Data were UAVs, then processed into point clouds, which derived to field measurements. results show predictions both ULS SfM very strongly correlated tree (R2 = 0.99, RMSE 5.91%, R2 0.94, 18.5%, respectively) but height underprediction was markedly lower than (Mean Bias Error 0.05 vs. 0.38 m). Integration DTM made minor improvement precision 0.95, 16.5%). Through plotting error against height, we identified minimum threshold 1 m, under accuracy measurements using significantly declines. Our collected remote sensing can be used accurately measure trials. It hoped this will give foresters breeders confidence start operationalise technology monitoring

Language: Английский

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Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(24), P. 4144 - 4144

Published: Dec. 18, 2020

Modern forestry poses new challenges that space technologies can solve thanks to the advent of unmanned aerial vehicles (UAVs). This study proposes a methodology extract tree-level characteristics using UAVs in spatially distributed area pine trees on regular basis. Analysis included different vegetation indices estimated with high-resolution orthomosaic. Statistically reliable results were found through three-phase workflow consisting image acquisition, canopy analysis, and validation field measurements. Of 117 field, 112 (95%) detected by algorithm, while height, area, crown diameter underestimated 1.78 m, 7.58 m2, 1.21 respectively. Individual tree attributes obtained from UAV, such as total height (H) (CD), made it possible generate good allometric equations infer basal (BD) at breast (DBH), R2 0.76 0.79, Multispectral useful vigor parameters, although normalized-difference index (NDVI) was highlighted best proxy monitor phytosanitary condition orchard. Spatial variation individual productivity suggests differential management ramets. The consistency allows for its application including complementation spectral information be generated; increase accuracy efficiency path modern inventories. However, limitation forests more complex structures is identified; therefore, further research recommended.

Language: Английский

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Assessing tree height and density of a young forest using a consumer unmanned aerial vehicle (UAV)

New Forests, Journal Year: 2021, Volume and Issue: 52(5), P. 843 - 862

Published: Jan. 4, 2021

Language: Английский

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