Multispectral indices and individual-tree level attributes explain forest productivity in a pine clonal orchard of Northern Mexico

Gallardo-Salazar, José Luis; Carrillo-Aguilar, Daniela M.; Pompa-García, Marín; Aguirre-Salado, Carlos Arturo

doi:10.1080/10106049.2021.1886341

Cited by 6 publications

(7 citation statements)

References 47 publications

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“…On the contrary, the xylogenesis was studied at individual level and at finer temporal scale. We argue that unmanned aerial vehicles may solve some inconsistencies between different spatial scales and provide solutions to ecological questions at finer spatial and temporal resolutions (Gallardo-Salazar et al 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Inter and intra-annual links between climate, tree growth and NDVI: improving the resolution of drought proxies in conifer forests

et al. 2021

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The inter- and intra-annual variability in radial growth reflects responses to climatic variability and water shortage, especially in areas subjected to seasonal drought. However, it is unknown how this variability is related to forest productivity, which can be assessed by measuring changes in canopy greenness and cover through remote sensing products as the Normalized Difference Vegetation Index (NDVI). We combine xylogenesis with measurements of inter-annual changes in seasonal wood production (earlywood width, adjusted latewood width) and NDVI to improve the understanding of climate and drought impacts on growth and forest productivity in a Pinus teocote stand located in northern Mexico. Cambial dynamics accelerated in March and a high production of radially enlarging and thickening tracheids were observed from April to October and from June to October, respectively. Tracheid maturation was very active in October when latewood production peaked. Wet conditions in winter-spring and summer-autumn enhanced earlywood and latewood production, respectively. Earlywood and latewood were constrained by long (4–10 months) and short (2–3 months) droughts, respectively. The earlywood production depended on April soil moisture, which agrees with the peak of radially enlarging tracheid production found during that month. Aligning drought proxies at inter- and intra-annual scales by using growth and productivity measures improves our understanding of conifer forest responses to water shortage.

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Section: Discussionmentioning

confidence: 99%

Inter and intra-annual links between climate, tree growth and NDVI: improving the resolution of drought proxies in conifer forests

et al. 2021

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“…This technology can identify ingredients and determine their content based on the spectral data of a substance [5]. Unmanned aerial vehicle (UAV) multispectral imaging detection technology enables rapid acquisition of orchard spectral data across a broad area [6,7]. These data can be used to infer the fruit tree growth status and soil quality in orchards [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

Ma,

Wang,

Luo

et al. 2024

Applied Sciences

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To enhance the accuracy of multispectral detection using unmanned aerial vehicles (UAVs), multispectral data of jujube fruit with different soluble solids content (SSC) and moisture content (MC) were obtained under different relative azimuth angles. Prediction models for SSC and MC of jujube fruit were established using partial least squares regression (PLSR) and support vector machines (SVM), respectively. The findings revealed that the MC of jujube fruit had the best prediction effect when the relative azimuth angle was 90°, while the SSC of the jujube fruit had the best prediction effect at an azimuth angle of 180°. Then, the spectral reflectance data corresponding to the eight relative azimuth angles were used as input variables to establish a jujube fruit quality detection model. The results showed that the prediction model for MC and SSC, established using the angle fusion method, had higher detection accuracy compared to the prediction model established at a single angle. This research provides a technical reference for improving the accuracy of outdoor jujube fruit quality detection using spectral technology.

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“…Likewise, the use of remotely sensed imagery has contributed to the study of vegetation indices (Becker et al, 2018;Gallardo-Salazar et al, 2021;Rodrıǵuez et al, 2021;Fakhri et al, 2022;Qiu et al, 2022;Talavera et al, 2022;Xu et al, 2022;, forest mapping (Lin Y. Z. et al, 2021;Onishi and Ise, 2021;Fakhri et al, 2022;Nasiri et al, 2022;Trencanováet al, 2022;Xu et al, 2022), evaluation and detection of diseased forests (Lin et al, 2018;Sapes et al, 2022), canopy characterization (Furukawa et al, 2021;Ribas Costa et al, 2022), tree species classification (Liu et al, 2021;Mäyrä et al, 2021;Onishi and Ise, 2021;Hell et al, 2022;Yang and Kan, 2022), identification of fire-prone ecosystems (Trencanováet al, 2022), prediction of chlorophyll and nitrogen content (Yao et al, 2021;Narmilan et al, 2022;Wan et al, 2022), recognition of intrinsic forest factors (Xu et al, 2019;Dainelli et al, 2021), wildfire prevention (Trencanováet al, 2022), and so on.…”

Section: Introductionmentioning

confidence: 99%

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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Multispectral indices and individual-tree level attributes explain forest productivity in a pine clonal orchard of Northern Mexico

Cited by 6 publications

References 47 publications

Inter and intra-annual links between climate, tree growth and NDVI: improving the resolution of drought proxies in conifer forests

Inter and intra-annual links between climate, tree growth and NDVI: improving the resolution of drought proxies in conifer forests

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

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

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