A primeira edição do Seminars in Agriculture and Geospatial Information contou com a presença do Dr. Roope Näsi do Finnish Geospatial Research Institute, com a palestra “Utilizing drones and remote sensing in boreal forests and fields”.

O evento ocorreu na próxima terça-feira (22/agosto) às 9 h no Canal do PPGAIG no Youtube: https://www.youtube.com/watch?v=3OHuvLXIP3k .

Referências da Apresentação

1. Forest health - bark beetle damage monitoring

Scientific publications

Junttila, S., Näsi, R., Koivumäki, N., Imangholiloo, M., Saarinen, N., Raisio, J., ... & Honkavaara, E. (2022). Multispectral imagery provides benefits for mapping spruce tree decline due to bark beetle infestation when acquired late in the season. Remote Sensing, 14(4), 909.

Honkavaara, E., Näsi, R., Oliveira, R., Viljanen, N., Suomalainen, J., Khoramshahi, E., Hakala, T., Nevalainen, O., Markelin, L., Vuorinen, M., Kankaanhuhta, V., Lyytikäinen-Saarenmaa, P., and Haataja, L.: USING MULTITEMPORAL HYPER- AND MULTISPECTRAL UAV IMAGING FOR DETECTING BARK BEETLE INFESTATION ON NORWAY SPRUCE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 429–434, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-429-2020, 2020. 

Näsi, R., Honkavaara, E., Lyytikäinen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., ... & Holopainen, M. (2015). Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sensing, 7(11), 15467-15493.

Näsi, R., Honkavaara, E., Blomqvist, M., Lyytikäinen-Saarenmaa, P., Hakala, T., Viljanen, N., ... & Holopainen, M. (2018). Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban Forestry & Urban Greening, 30, 72-83.

Kanerva, H., Honkavaara, E., Näsi, R., Hakala, T., Junttila, S., Karila, K., ... & Lyytikäinen-Saarenmaa, P. (2022). Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network. Remote Sensing, 14(24), 6257.

Thesis

Madeleine Östersund, 2022. Monitoring bark beetle infestation using remote sensing. M.Sc Thesis, Aalto university 2022

Roope Näsi, 2021 Drone-based spectral and 3D remote sensing applications for forestry and agriculture, PhD Thesis, Aalto University 2021

2. Grass yield and quality estimation

Viljanen N, Honkavaara E, Näsi R, Hakala T, Niemeläinen O, Kaivosoja J. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture. 2018; 8(5):70. https://doi.org/10.3390/agriculture8050070

Oliveira, R. A., Näsi, R., Niemeläinen, O., Nyholm, L., Alhonoja, K., Kaivosoja, J., ... & Honkavaara, E. (2020). Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry. Remote Sensing of Environment, 246, 111830.

Oliveira et 2023. Accepted manuscript: High-precision estimation of grass quality and quantity by using UAS-based VNIR and SWIR hyperspectral cameras and machine learning

Karila K, Alves Oliveira R, Ek J, Kaivosoja J, Koivumäki N, Korhonen P, Niemeläinen O, Nyholm L, Näsi R, Pölönen I, Honkavaara E. Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks. Remote Sensing. 2022; 14(11):2692. https://doi.org/10.3390/rs14112692

Alves Oliveira, R., Marcato Junior, J., Soares Costa, C., Näsi, R., Koivumäki, N., Niemeläinen, O., ... & Honkavaara, E. (2022). Silage grass sward nitrogen concentration and dry matter yield estimation using deep regression and RGB images captured by UAV. Agronomy, 12(6), 1352.

3. Detection of alien crops using deep learning (gluten-free oat production)

Khoramshahi, E., Näsi, R., Rua, S., Oliveira, R. A., Päivänsalo, A., Niemeläinen, O., ... & Honkavaara, E. (2023). A Novel Deep Multi-Image Object Detection Approach for Detecting Alien Barleys in Oat Fields Using RGB UAV Images. Remote Sensing, 15(14), 3582.