Assessment of Multispectral Vegetation Features for Digital Terrain Modeling in Forested Regions

Arevalo-Ramirez, Tito; Guevara, Javier; Rivera, Robert; Villacrés, Juan; Menéndez, Oswaldo; Fuentes, A.; Cheein, Fernando Auat

doi:10.1109/tgrs.2021.3109601

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…We hypothesize that vegetation indices could add valuable knowledge about urban trees, which might not be decoded in the training stage of the segmentation models due to lacking training samples. The belief that vegetation indices could boost urban tree segmentation is supported by a previous work, which shows that multispectral indices improve the classification of ground points in forested regions [19].…”

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confidence: 77%

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Exploring the Potential of Vegetation Indices for Urban Tree Segmentation in Street View Images

Arevalo-Ramirez¹,

Alfaro²,

Saavedra³

et al. 2023

Preprint

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<p>Urban forests play a crucial role in the development of cities because of the urban ecosystem services they provide. Previous works have alleviated urban forest monitoring by discriminating tree species and performing tree inventories using street view images and convolutional neural networks. However, the characterization of trees from street-view images remains a challenging task. Determining tree structural parameters has been limited because of inaccurate tree segmentation caused by combined, occluded, or leaf-off trees. Therefore, the current work evaluates the potential of vegetation indices derived from red, green, blue, and synthesized near-infrared and red-edge spectral bands for urban tree segmentation. In particular, we attempt to show whether or not vegetation indices add relevant information to deep neural segmentation networks when there are low fine-tuning training samples. A conditional adversarial network generates red-edge and near-infrared images in urban environments, which retrieve an average structural similarity index of 0.86 and 0.81, respectively. Furthermore, we note that by using appropriate multispectral vegetation indices, one can boost the average intersection over the union between 5.07 % to 13.7 %. Specifically, we suggest the SegFormer segmentation network pre-trained with the CityScapes dataset and Red Edge Modified Simple Ration index for improving urban tree segmentation. However, if no multispectral data is available, the DeepLabV3 network pre-trained with the ADE20k dataset is suggested because it could achieve the best RGB outcomes average IoU value of 0.671. </p>

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confidence: 77%

“…The red, green, blue, red-edge, and near-infrared channels are represented by R, G, B, RE, and N IR, respectively. References of each vegetation index equation can be found in [19], [25], [26] Vegetation Index Equation…”

mentioning

confidence: 99%

Exploring the Potential of Vegetation Indices for Urban Tree Segmentation in Street View Images

Arevalo-Ramirez¹,

Alfaro²,

Saavedra³

et al. 2023

Preprint

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“…This work includes techniques to detect and classify terrain surfaces [92] and to identify tree stems using Light Detection and Ranging (LiDAR) [93]. The Autonomous and Industrial Robotics Research Group (GRAI) from the Technical University of Federico Santa María in Chile has also been very active in both agricultural robotics with work on fleets of N-trailer vehicles for harvesting operations [94] and forestry robotics, with work on UAV multispectral imagery in forested areas [95] and multispectral vegetation features for digital terrain modelling [96].…”

Section: Research and Development Throughout The Rest Of The Worldmentioning

confidence: 99%

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Ferreira,

Portugal,

Andrada

et al. 2023

Robotics

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Artificial perception for robots operating in outdoor natural environments, including forest scenarios, has been the object of a substantial amount of research for decades. Regardless, this has proven to be one of the most difficult research areas in robotics and has yet to be robustly solved. This happens namely due to difficulties in dealing with environmental conditions (trees and relief, weather conditions, dust, smoke, etc.), the visual homogeneity of natural landscapes as opposed to the diversity of natural obstacles to be avoided, and the effect of vibrations or external forces such as wind, among other technical challenges. Consequently, we propose a new survey, describing the current state of the art in artificial perception and sensing for robots in precision forestry. Our goal is to provide a detailed literature review of the past few decades of active research in this field. With this review, we attempted to provide valuable insights into the current scientific outlook and identify necessary advancements in the area. We have found that the introduction of robotics in precision forestry imposes very significant scientific and technological problems in artificial sensing and perception, making this a particularly challenging field with an impact on economics, society, technology, and standards. Based on this analysis, we put forward a roadmap to address the outstanding challenges in its respective scientific and technological landscape, namely the lack of training data for perception models, open software frameworks, robust solutions for multi-robot teams, end-user involvement, use case scenarios, computational resource planning, management solutions to satisfy real-time operation constraints, and systematic field testing. We argue that following this roadmap will allow for robotics in precision forestry to fulfil its considerable potential.

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“…Recently, these indices have been used as input data for prediction and classification purposes alike, the spectrum of tree canopies can be considered a distinctive feature of the specific vegetation, thus making VIs useful for both vegetation identification in aerial photographs and for tree classification (Abdollahnejad and Panagiotidis, 2020;Imangholiloo et al, 2020;Yang and Kan, 2020;Guo et al, 2021;Arevalo-Ramirez et al, 2022;Cabrera-Ariza et al, 2022;Shovon et al, 2022). Photosynthetic pigments have a distinctive reflectance in some bands, thus the prediction of chlorophyll content and other pigments is suitable with the appropriate VI (Watt et al, 2020;Kopackova-Strnadováet al, 2021;Lou et al, 2021;Lu et al, 2021;Raddi et al, 2021;Raj et al, 2021;Zhuo et al, 2022).…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…The generation of digital terrain models was explored with the aid of machine learning (Arevalo-Ramirez et al, 2022), using conditional random field (CRF) to extract ground points; this approach generated smoother terrain models than other approaches not based on machine learning methods.…”

Section: Other Algorithmsmentioning

confidence: 99%

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

et al. 2023

Self Cite

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Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.

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Assessment of Multispectral Vegetation Features for Digital Terrain Modeling in Forested Regions

Cited by 6 publications

References 24 publications

Exploring the Potential of Vegetation Indices for Urban Tree Segmentation in Street View Images

Exploring the Potential of Vegetation Indices for Urban Tree Segmentation in Street View Images

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

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