Mapping Chestnut Stands Using Bi-Temporal VHR Data

Marchetti, Francesca; Waske, Björn; Arbeló, Manuel; Moreno-Ruiz, José A.; Alonso-Benito, Alfonso

doi:10.3390/rs11212560

Cited by 4 publications

(1 citation statement)

References 63 publications

(91 reference statements)

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“…Moreover, satellite imagery is also the basis to classify chestnut stands within a heterogeneous land plot [56] using high-resolution multispectral satellite data (WorldView 2 and 3). Regarding the use of UAVs, studies encompassing chestnut trees include the use of orthorectified UAV-based data to monitor the decline of chestnut trees [57,58], the automatic monitoring of chestnut trees and the estimation of tree geometrical parameters [59] for the multi-temporal analysis of chestnut stands, the estimation of pruning wood biomass, the extraction of the tree height and crown diameter from different UAV flight parameters [60], the estimation of phytosanitary issues [61], and the mapping of chestnut blight severity [62].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Leaf Area Index of Castanea sativa Miller Using UAV-Based Multispectral and Geometrical Data

Pádua

Chiroque-Solano

Marques

et al. 2022

Drones

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Remote-sensing processes based on unmanned aerial vehicles (UAV) have opened up new possibilities to both map and extract individual plant parameters. This is mainly due to the high spatial data resolution and acquisition flexibility of UAVs. Among the possible plant-related metrics is the leaf area index (LAI), which has already been successfully estimated in agronomy and forestry studies using the traditional normalized difference vegetation index from multispectral data or using hyperspectral data. However, the LAI has not been estimated in chestnut trees, and few studies have explored the use of multiple vegetation indices to improve LAI estimation from aerial imagery acquired by UAVs. This study uses multispectral UAV-based data from a chestnut grove to estimate the LAI for each tree by combining vegetation indices computed from different segments of the electromagnetic spectrum with geometrical parameters. Machine-learning techniques were evaluated to predict LAI with robust algorithms that consider dimensionality reduction, avoiding over-fitting, and reduce bias and excess variability. The best achieved coefficient of determination (R2) value of 85%, which shows that the biophysical and geometrical parameters can explain the LAI variability. This result proves that LAI estimation is improved when using multiple variables instead of a single vegetation index. Furthermore, another significant contribution is a simple, reliable, and precise model that relies on only two variables to estimate the LAI in individual chestnut trees.

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Section: Introductionmentioning

confidence: 99%

Mapping the Leaf Area Index of Castanea sativa Miller Using UAV-Based Multispectral and Geometrical Data

Pádua

Chiroque-Solano

Marques

et al. 2022

Drones

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Very high-resolution true color leaf-off imagery for mapping Taxus baccata L. and Ilex aquifolium L. understory population

et al. 2020

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Chestnut Burr Segmentation for Yield Estimation Using UAV-Based Imagery and Deep Learning

Carneiro,

Santos,

Sousa

et al. 2024

Drones

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Precision agriculture (PA) has advanced agricultural practices, offering new opportunities for crop management and yield optimization. The use of unmanned aerial vehicles (UAVs) in PA enables high-resolution data acquisition, which has been adopted across different agricultural sectors. However, its application for decision support in chestnut plantations remains under-represented. This study presents the initial development of a methodology for segmenting chestnut burrs from UAV-based imagery to estimate its productivity in point cloud data. Deep learning (DL) architectures, including U-Net, LinkNet, and PSPNet, were employed for chestnut burr segmentation in UAV images captured at a 30 m flight height, with YOLOv8m trained for comparison. Two datasets were used for training and to evaluate the models: one newly introduced in this study and an existing dataset. U-Net demonstrated the best performance, achieving an F1-score of 0.56 and a counting accuracy of 0.71 on the proposed dataset, using a combination of both datasets during training. The primary challenge encountered was that burrs often tend to grow in clusters, leading to unified regions in segmentation, making object detection potentially more suitable for counting. Nevertheless, the results show that DL architectures can generate masks for point cloud segmentation, supporting precise chestnut tree production estimation in future studies.

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Mapping Chestnut Stands Using Bi-Temporal VHR Data

Cited by 4 publications

References 63 publications

Mapping the Leaf Area Index of Castanea sativa Miller Using UAV-Based Multispectral and Geometrical Data

Mapping the Leaf Area Index of Castanea sativa Miller Using UAV-Based Multispectral and Geometrical Data

Very high-resolution true color leaf-off imagery for mapping Taxus baccata L. and Ilex aquifolium L. understory population

Chestnut Burr Segmentation for Yield Estimation Using UAV-Based Imagery and Deep Learning

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