Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis

Torres‐Sánchez, Jorge; Castro, Ana Isabel de; Peña, José M.; Jiménez-Brenes, Francisco Manuel; Arquero, Octavio; Lovera, M.; López‐Granados, Francisca

doi:10.1016/j.biosystemseng.2018.10.018

Cited by 90 publications

(95 citation statements)

References 57 publications

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“…Species distribution (evergreen and deciduous) at the canopy level, as well as image resolution, might be the reason for these unconstructed areas. Contradictory to our findings, some previous studies [68,73,74] could reconstruct the vegetation and estimate forest structural attributes (e.g., height and DBH), perhaps due to a higher aerial image resolution than that used in our study. As expected, leaf-on DAP performed better in comparison to leaf-off DAP in terms of total and broadleaf biomass estimations over the AOI (Tables 6 and 7), particularly when using SLR and MLR modeling.…”

Section: Leaf-off Dap For Biomass Estimation Over Uneven-aged Forestsmentioning

confidence: 49%

Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

2019

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Scientifically robust yet economical and efficient methods are required to gather information about larger areas of uneven-aged forest resources, particularly at the landscape level, to reduce deforestation and forest degradation and to support the sustainable management of forest resources. In this study, we examined the potential of digital aerial photogrammetry (DAP) for assessing uneven-aged forest resources. Specifically, we tested the performance of biomass estimation by varying the conditions of several factors, e.g., image downscaling, vegetation metric extraction (point cloud- and canopy height model (CHM)-derived), modeling method ((simple linear regression (SLR), multiple linear regression (MLR), and random forest (RF)), and season (leaf-on and leaf-off). We built dense point clouds and CHMs using high-resolution aerial imagery collected in leaf-on and leaf-off conditions of an uneven-aged mixed conifer–broadleaf forest. DAP-derived vegetation metrics were then used to predict the dominant height and living biomass (total, conifer, and broadleaf) at the plot level. Our results demonstrated that image downscaling had a negative impact on the accuracy of the dominant height and biomass estimation in leaf-on conditions. In comparison to CHM-derived vegetation metrics, point cloud-derived metrics performed better in dominant height and biomass (total and conifer) estimations. Although the SLR (%RMSE = 21.1) and MLR (%RMSE = 18.1) modeling methods produced acceptable results for total biomass estimations, RF modeling significantly improved the plot-level total biomass estimation accuracy (%RMSE of 12.0 for leaf-on data). Overall, leaf-on DAP performed better in total biomass estimation compared to leaf-off DAP (%RMSE of 15.0 using RF modeling). Nevertheless, conifer biomass estimation accuracy improved when leaf-off data were used (from a %RMSE of 32.1 leaf-on to 23.8 leaf-off using RF modeling). Leaf-off DAP had a negative impact on the broadleaf biomass estimation (%RMSE > 35% for SLR, MLR, and RF modeling). Our results demonstrated that the performance of forest biomass estimation for uneven-aged forests varied with statistical representations as well as data sources. Thus, it would be appropriate to explore different statistical approaches (e.g., parametric and nonparametric) and data sources (e.g., different image resolutions, vegetation metrics, and leaf-on and leaf-off data) to inform the interpretation of remotely sensed data for biomass estimation for uneven-aged forest resources.

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Section: Leaf-off Dap For Biomass Estimation Over Uneven-aged Forestsmentioning

confidence: 49%

Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

2019

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“…To construct the 3D models, photogrammetry requires at least two overlapping images of the same scene and/or object(s), captured from different points of view. These kind of techniques can be used for extracting three-dimensional digital surface or terrain models [37,40,43] and/or orthophotos [50,55]. UAV low-altitude data acquisition enables the construction of 3D models with a much higher spatial resolution compared to other remote sensing technologies (such as satellites).…”

Section: Crop Featuresmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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“…All this has led to the spread of both integrated pest management and organic agriculture. Moreover, recently, precision agriculture has been used in other agronomic practices, such as estimating height [12], tree biomass [13][14][15][16][17], fruit tree crop, pollination, and pruning and harvesting amount, as well as for disease detection [18]. The use of these very new technologies has aroused greater environmental awareness and attention to human health for both consumers and fruit growers, along with awareness of the economic costs of treatment for farmers.…”

Section: Introductionmentioning

confidence: 99%

Comparison of UAV Photogrammetry and 3D Modeling Techniques with Other Currently Used Methods for Estimation of the Tree Row Volume of a Super-High-Density Olive Orchard

et al. 2019

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A comparison of three different methods to evaluate the tree row volume (TRV) of a super-high-density olive orchard is presented in this article. The purpose was to validate the suitability of unmanned aerial vehicle (UAV) photogrammetry and 3D modeling techniques with respect to manual and traditional methods of TRV detection. The use of UAV photogrammetry can reduce the amount of estimated biomass and, therefore, reduce the volume of pesticides to be used in the field by means of more accurate prescription maps. The presented comparison of methodologies was performed on an adult super-high-density olive orchard, planted with a density of 1660 trees per hectare. The first method (TRV1) was based on close-range photogrammetry from UAVs, the second (TRV2) was based on manual in situ measurements, and the third (TRV3) was based on a formula from the literature. The comparisons of TRV2-TRV1 and TRV3-TRV1 showed an average value of the difference equal to +13% (max: +65%; min: −11%) and +24% (max: +58%; min: +5%), respectively. The results show that the TRV1 method has high accuracy in predicting TRV with minor working time expenditure, and the only limitation is that professionally skilled personnel is required.

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Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis

Cited by 90 publications

References 57 publications

Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass

A Review on UAV-Based Applications for Precision Agriculture

Comparison of UAV Photogrammetry and 3D Modeling Techniques with Other Currently Used Methods for Estimation of the Tree Row Volume of a Super-High-Density Olive Orchard

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