Construction of 3D maps of vegetation indices retrieved from UAV multispectral imagery in forested areas

Villacrés, Juan; Cheein, Fernando Auat

doi:10.1016/j.biosystemseng.2021.11.025

Cited by 7 publications

(8 citation statements)

References 35 publications

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“…Researchers focus on these five bands since most of the reviewed works use commercial infrared cameras that capture the radiation at these wavelengths. Other indices take advantage of the full spectrum and not only on specific bands but these indices are also obtained with the aid of a hyper-spectral camera or by a laboratory or hand-held spectrometer (Abdollahnejad and Panagiotidis, 2020;Watt et al, 2020;Yang and Kan, 2020;de Almeida et al, 2021;Raj et al, 2021;Villacreś and Cheein, 2022;Wan et al, 2022;Yang and Kan, 2022). Li et al (2021) of phosphorus and nitrogen, which is related to photosynthetic efficiency (Watt et al, 2020;Raj et al, 2021), the information gathered by hyperspectral indices, allows the processing data models to make more accurate predictions.…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…Comparisons between hyper-spectral information and multispectral indices have been performed to evaluate drought responses in various ecosystems (Raddi et al, 2021). Other studies show that there is the possibility to recreate indices from hyper-spectral bands with the information gathered from multi-spectral indices (Villacreś and Cheein, 2022).…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…The predicted canopy from the random forest was validated with the real value through a linear regression achieving a correlation value of 0, 92. Villacreś and Cheein (2022) used random forests to retrieve spectral VIs from multispectral imagery essential for mapping moisture content, however, the results were unsatisfactory, and other regression methods were needed.…”

Section: Random Forestmentioning

confidence: 99%

“…The results yielded that Neural Networks outperformed the other algorithms based on prediction error and the correlation factor. In the study presented by Villacreś and Cheein (2022), boosting gradient machines were used to reconstruct vegetation indices. Another task accomplished by GBM is the prediction of leaf nitrogen content based on hyperspectral indices, this is done by Raj et al (2021), where the model achieved a correlation factor of 0.63, in areas with water-stressed vegetation; however, the model didn't achieve the same results in well-irrigated areas.…”

Section: Other Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2023

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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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Section: Vegetation Indicesmentioning

confidence: 99%

Section: Vegetation Indicesmentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

Section: Other Algorithmsmentioning

confidence: 99%

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

et al. 2023

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“…It also faces difficulties in capturing accurate DTMs in areas with dense vegetation [18]. Methods for generating DTMs from point clouds can be broadly classified into morphological filtering methods [21][22][23][24][25][26][27][28][29], vegetation index-based methods [30][31][32][33][34], composite methods [35][36][37][38], and machine learning-based methods [39][40][41] [35] effectively removed vegetation in steep and rugged terrains using morphological filters such as Progressive Morphological Filter (PMF), Simple Morphological Filter (SMRF), Cloth Simulation Filter (CSF), Adaptive Triangular Irregular Network (ATIN), and CAractérisation de NUages de POints (CANUPO). [42] achieved vegetation removal using CSF and ATIN.…”

Section: Introductionmentioning

confidence: 99%

Development of a river DTM generation algorithm based on SfM point clouds using vegetation and morphological filters

Lee,

Gou,

Park

et al. 2024

Preprint

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Developing algorithms for generating accurate Digital Terrain Model (DTM) of rivers is necessary due to the limitations of traditional field survey methods, which are time-consuming and costly and do not provide continuous data. The objective of this study was to develop an advanced algorithm for generating high-quality DTM of rivers using Structur from Motion (SfM) data. A leveling survey was conducted on four cross-sections of the Bokha stream in Icheon City, S. Korea, and SfM-based DTM was produced using the Pix4Dmapper program and Phantom 4 multispectral drone. Two vegetation filters (NDVI and ExG) and two morphological filters (ATIN and CSF) were applied to the data, and the best filter combination was identified based on MAE and RMSE analyses. The integration of NDVI and CSF showed the best performance for the vegetation area, while a single application of NDVI showed the lowest MAE for the bare area. The effectiveness of the SfM method in eliminating waterfront vegetation was confirmed, with an overall MAE of 0.299 m RMSE of 0.375 m. These findings suggest that generating DTMs of riparian zones can be achieved efficiently with a limited budget and time using the proposed methodology.

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A modularity total environment network model for sustainable forest transportation

Tsiotas,

Kantartzis,

Kolkos

et al. 2023

Euro-Mediterr J Environ Integr

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Forest road network planning and development is important in providing accessibility to remote forest areas for timber harvesting, transportation to markets, and recreational activities, as well as preventing environmental degradation, soil erosion, water pollution, and an increased risk of forest fires and wildlife habitat fragmentation. Within the context that careful forest road transportation planning and design promotes sustainable forest management and development, this paper develops a multilayer network model for supporting sustainable forest transportation development. The model builds on a total environment conceptualization and the network paradigm, and it is composed of several layers, each containing information from an environmental aspect or a forest road network land use. By using community detection analysis from network science, the model provides insights into the decomposition of the forest network into functional areas, highlights the importance of places that connect different communities to maintain market integration, and provides a list of policies and good practices for the resulting communities. Overall, this paper presents a quantitative methodological framework that can be used for sustainable forest transportation.

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Construction of 3D maps of vegetation indices retrieved from UAV multispectral imagery in forested areas

Cited by 7 publications

References 35 publications

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

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

Development of a river DTM generation algorithm based on SfM point clouds using vegetation and morphological filters

A modularity total environment network model for sustainable forest transportation

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