Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Guimarães, Nathalie; Pádua, Luís; Marques, Pedro; Silva, Nuno; Peres, Emanuel; Sousa, Joaquim J.

doi:10.3390/rs12061046

Cited by 158 publications

(97 citation statements)

References 156 publications

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“…It is perhaps for this reason that remote sensing has seldom been used in forest surveys to assess vitality. However, recent advances in remote sensing make the development of methods to assess photosynthetic rates easier and more promising for forest-scale studies [28].…”

Section: Tree Physiological Processesmentioning

confidence: 99%

Tree Vitality and Forest Health: Can Tree-Ring Stable Isotopes Be Used as Indicators?

2021

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Purpose of Review Society is concerned about the long-term condition of the forests. Although a clear definition of forest health is still missing, to evaluate forest health, monitoring efforts in the past 40 years have concentrated on the assessment of tree vitality, trying to estimate tree photosynthesis rates and productivity. Used in monitoring forest decline in Central Europe since the 1980s, crown foliage transparency has been commonly believed to be the best indicator of tree condition in relation to air pollution, although annual variations appear more closely related to water stress. Although crown transparency is not a good indicator of tree photosynthesis rates, defoliation is still one of the most used indicators of tree vitality. Tree rings have been often used as indicators of past productivity. However, long-term tree growth trends are difficult to interpret because of sampling bias, and ring width patterns do not provide any information about tree physiological processes. Recent Findings In the past two decades, tree-ring stable isotopes have been used not only to reconstruct the impact of past climatic events, such as drought, but also in the study of forest decline induced by air pollution episodes, and other natural disturbances and environmental stress, such as pest outbreaks and wildfires. They have proven to be useful tools for understanding physiological processes and tree response to such stress factors. Summary Tree-ring stable isotopes integrate crown transpiration rates and photosynthesis rates and may enhance our understanding of tree vitality. They are promising indicators of tree vitality. We call for the use of tree-ring stable isotopes in future monitoring programmes.

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Section: Tree Physiological Processesmentioning

confidence: 99%

Tree Vitality and Forest Health: Can Tree-Ring Stable Isotopes Be Used as Indicators?

2021

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“…Other option is the use of LiDAR scanners which provide dense point clouds of natural environments but these are more expensive than digital cameras [34]. LiDAR data discriminate better plant's canopy since it penetrates vegetation [35,36], and, therefore, potentially making the identification of the grapevine trunks easier. Moreover, photogrammetric techniques tend to estimate erroneous points in the cases where some points from the ground are estimated along a post.…”

Section: Point Cloud Reconstruction and Processingmentioning

confidence: 99%

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

et al. 2020

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The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant’s structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

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“…This is different from RGB sensors, which have a lower cost and are highly available. (34) demonstrated that the majority of recent applications are implementing RGB imagery data in the vegetation detection scenario. The visual inspection of remote sensing imagery is a timeconsuming, labor-intensive, and biased task.…”

Section: P R E P R I N Tmentioning

confidence: 99%

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

Lsd¹,

MdSd²,

Deg³

et al. 2021

Preprint

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Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. State-of-the-art deep learning methods could be capable of identifying tree species with an attractive cost, accuracy, and computational load in RGB images. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment, and considers the likelihood of every pixel in the image to be recognized as a possible tree by implementing a confidence map feature extraction. This study compares the performance of the proposed method against state-of-the-art object detection networks. For this, images from a dataset composed of 1,394 airborne scenes, where 5,334 palm-trees were manually labeled, were used. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than both Faster R-CNN and RetinaNet considering equal experiment conditions. The proposed network provided fast solutions to detect the palm trees, with a delivered image detection of 0.073 seconds and a standard deviation of 0.002 using the GPU. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

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Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Cited by 158 publications

References 156 publications

Tree Vitality and Forest Health: Can Tree-Ring Stable Isotopes Be Used as Indicators?

Tree Vitality and Forest Health: Can Tree-Ring Stable Isotopes Be Used as Indicators?

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

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