Leaf area index and above ground biomass estimation fromunmanned aerial vehicle and terrestrial lidar data usingmachine learning approaches
Başlık çevirisi mevcut değil.
- Tez No: 771875
- Danışmanlar: DR. BENJAMİN BREDE, DR. HARM BARTHOLOMEUS
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Bilim ve Teknoloji, Havacılık ve Uzay Mühendisliği, Computer Engineering and Computer Science and Control, Science and Technology, Aeronautical Engineering
- Anahtar Kelimeler: Terrestrial LiDAR, UAV LiDAR, Above Ground Biomass, Leaf Area Index, Machine learning, Precision Agriculture
- Yıl: 2021
- Dil: İngilizce
- Üniversite: Wagenıngen Unıversıteıt
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
Above-ground biomass (AGB) and leaf area index (LAI) are two critical crop parameters for evaluating crop growth, health, and productivity. Various techniques have been used in the past for the estimation of crop parameters. Development and accessibility of remote sensing technologies by airborne, ground devices, satellites opened a new era for estimating the crop parameter. In this paper, the crop parameters LAI and AGB were estimated using Unmanned Aerial Vehicle LiDAR and ground-based Terrestrial Laser Scanning (TLS) data applying three machine learning methods over four crop types with different canopy properties; wheat, sugarbeet, soybean and maize. The study compared how the predictive performance of three modelling techniques multiple linear regression (MLR), random forest regression (RFR), support vector machine regression (SVR) differ, how the predictability performance differ between crop parameters, crop types and LiDAR sensors. The analysis was carried out using all the crop samples obtained and the crop-specific. Two statistical criteria were used as evaluation metrics, the coefficient of determination (R2 ) and Root Mean Square Error (RMSE). The results obtained from the models applied with all crop samples indicated that the predictability performance of AGB was over 0.80 𝑅 2 and the LAI was predicted with the 𝑅 2 of 0.76. In crop-specific modelling, the estimation of soybean and maize AGB and LAI parameters with models applied with features obtained from both UAV-LS and TLS data was satisfactory. The prediction was failed on sugar beet AGB estimation (𝑅 2 in 0.54–0.76 range) and wheat LAI prediction (𝑅 2 in 0.46–0.86 range). Based on the model performances, the RFR and SVR models performed better than the linear model MLR in most of the analyses. The models compared to the data of the sensors used in this study, the models applied with the features obtained from UAV-LS and TLS gave similar 𝑅 2 and 𝑅𝑀𝑆𝐸 when made with all crop samples. However, in crop-specific analyses, the models applied with the features obtained from UAV-LS showed a powerful performance in all LAI estimation models. Although the results obtained from models applied using TLS and UAV-LS features to estimate AGB were very close, only sugarbeet AGB estimation gave better results with TLS data than UAV-LS. Given these results, the lacking application in crop studied with LiDAR remote sensing will be a step to close to fill the gap using both TLS and UAV-LS derived data for several crop types.
Özet (Çeviri)
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