Using aerial canopy data from UAVs to measure the effects of neighbourhood competition on individual tree growth

Vanderwel, Mark C.; Lopez, Eva L.; Sprott, Adam H.; Khayyatkhoshnevis, Pedram; Shovon, Tanvir Ahmed

doi:10.1016/j.foreco.2020.117949

Cited by 18 publications

(9 citation statements)

References 29 publications

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“…Conventionally, different germplasms (genotypes) were classified according to their working phenotypes by designated biologists, where "manual" images were used in terms of canopy architecture, leaf area, and other functions [5][6][7][8] .…”

Section: Introduction mentioning

confidence: 99%

Development of a mobile application for identification of grapevine (Vitis vinifera L.) cultivars via deep learning

Liu

Shen

et al. 2021

International Journal of Agricultural and Biological Engineering

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Traditional vine variety identification methods usually rely on the sampling of vine leaves followed by physical, physiological, biochemical and molecular measurement, which are destructive, time-consuming, labor-intensive and require experienced grape phenotype analysts. To mitigate these problems, this study aimed to develop an application (App) running on Android client to identify the wine grape automatically and in real-time, which can help the growers to quickly obtain the variety information. Experimental results showed that all Convolutional Neural Network (CNN) classification algorithms could achieve an accuracy of over 94% for twenty-one categories on validation data, which proves the feasibility of using transfer deep learning to identify grape species in field environments. In particular, the classification model with the highest average accuracy was GoogLeNet (99.91%) with a learning rate of 0.001, mini-batch size of 32, and maximum number of epochs in 80. Testing results of the App on Android devices also confirmed these results.

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Section: Introduction mentioning

confidence: 99%

Development of a mobile application for identification of grapevine (Vitis vinifera L.) cultivars via deep learning

Liu

Shen

et al. 2021

International Journal of Agricultural and Biological Engineering

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“…UAV‐mounted sensors can quantify structural interactions at much larger scales than is possible through TLS. For example, LiDAR‐derived crown properties can predict how neighbourhood competition drives tree growth (Vanderwel et al, 2020), and UAV photogrammetry‐derived crowns can be used to determine the relative strength of interspecific vs intraspecific competition on growth (Erfanifard et al, 2021). New insights into how individual shape and competitive interactions affect whole‐canopy properties are also emerging; for example, TLS data have shown how crown shape, branching topology and shade tolerance influence crown position and shading interactions (Martin‐Ducup et al, 2021).…”

Section: Sensing the Three‐dimensional Canopy: Competition For Light ...mentioning

confidence: 99%

The shape of trees: Reimagining forest ecology in three dimensions with remote sensing

Lines¹,

Fischer

Owen³

et al. 2022

Journal of Ecology

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Forest ecology has long relied on a simple measure-the diameter of the trunk of a tree at breast height (dbh)-as a way to summarise individual tree status and performance. This measurement, with its origins in forestry, is non-destructive and quick to take, requiring no special equipment or extensive training, and this ease of use has led dbh to be widely adopted as the key measure of an individual tree in forest ecology. It is used to quantify and predict individual tree demography (Lines et al., 2010;Ruiz-Benito et al., 2013), as a proxy for tree age (Stephenson et al., 2014), to estimate key properties including leaf area, height, crown shape and biomass (Chave et al., 2014;

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“…Temuan ini menggarisbawahi peran kunci yang dapat dimainkan UAV untuk penilaian hutan berbasis penginderaan jauh, mengingat bahwa data penginderaan jauh udara dan satelit saat ini tidak memberikan resolusi spasial yang sebanding (Saliu et al, 2021). Data gambar yang dikumpulkan dengan UAV dapat diproses untuk menghasilkan informasi rinci tentang struktur kanopi lokal di sekitar pohon individu pada kawasan hutan, yang mungkin menjadi proksi yang berguna untuk jumlah persaingan yang dialami pohon dari tetangganya (Vanderwel et al, 2020). kendaraan udara tak berawak (UAV), selama 5 tahun terakhir telah berfungsi untuk menemukan kembali potensi yang dimiliki platform dan sensor kecil untuk akuisisi berbiaya rendah dari berbagai data dan citra udara dan ditambah dengan pengembangan siap untuk teknologi fly (RTF), kamera digital berbiaya rendah, GPS, pemrosesan gambar, perangkat lunak fotogrametri soft-copy, dan sensor multispektral, hiperspektral, termal, dan LiDAR, UAV kini menawarkan sarana canggih untuk memperoleh banyak fotografi resolusi dan kumpulan data video 4K untuk studi cakupan area kecil (Green et al, 2019).…”

Section: Pendahuluanunclassified

Pemanfaatan Wahana Drone Untuk Pemetaan Kawasan Mangrove Di Kabupaten Pangkep

Amal

2022

JES

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ABSTRAKPenggunaan UAV di masa kini sangat membantu dalam pemantauan kawasan mangrove karena resolusinya yang tinggi. Penelitian ini bertujuan untuk melakukan pemetaan hutan mangrove pada 4 jenis peruntukan kawasan di kabupaten Pangkep. Metode penelitian ini adalah deskriptif kuantitatif dengan pengambilan data secara purposive sampling. Data hasil UAV berupa foto dianalisis menggunakan metode PJ dan GIS dengan bantuan Software Agisoft 17.0 untuk menghasilkan ortophoto. Hasil penelitian menunjukan bahwa pada keempat lokasi pengamatan terdapat beberapa objek dasar yang dapat dikenali yaitu hutan mangrove, tambak, kawasan terbangun, tubuh air dan jaringan jalan. Sedangkan objek yang dapat dikenali lebih lanjut karena asosiasinya terhadap objek lain diantara kawasan ekowisata, kawasan hutan konservasi, kawasan tambak sebagai budidaya yang bagian depanya terdapat mangrove penghalang dan kawasan silvofishery yang ditandai dengan tambak dengan beberapa individu mangrove di dalamnya atau di sisi-sisinya.

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Using aerial canopy data from UAVs to measure the effects of neighbourhood competition on individual tree growth

Cited by 18 publications

References 29 publications

Development of a mobile application for identification of grapevine (Vitis vinifera L.) cultivars via deep learning

Development of a mobile application for identification of grapevine (Vitis vinifera L.) cultivars via deep learning

The shape of trees: Reimagining forest ecology in three dimensions with remote sensing

Pemanfaatan Wahana Drone Untuk Pemetaan Kawasan Mangrove Di Kabupaten Pangkep

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