Article
Remote Sensing
Lei Deng, Yong Chen, Yun Zhao, Lin Zhu, Hui-Li Gong, Li-Jie Guo, Han-Yue Zou
Summary: This study utilized UAV-based oblique photography technology to obtain high-spatial resolution and high-accuracy continuous RA data, optimizing the selection of multi-angle observation data using the Monte Carlo method. The accuracy and applicability of two BRDF inversion models were thoroughly analyzed and compared, expanding the research and application of RA measurement.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Plant Sciences
Christian Nansen, Hyoseok Lee, Anil Mantri
Summary: This study investigates the temporal radiometric repeatability of airborne remote sensing data. The results show that the spectral bands from 900-970 nm have significantly lower repeatability compared to the bands from 416-900 nm. Among the four calibration methods, the ARTM calibrations outperform the ELM calibration, especially ARTM2+. It is concluded that at least a 5% radiometric error should be expected when acquiring airborne remote sensing data at multiple time points, and objects for classification should have at least a 5% difference in optical traits for accurate and consistent classification.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Environmental Sciences
Mengmeng Yang, Yong Hu, Hongzhen Tian, Faisal Ahmed Khan, Qinping Liu, Joaquim Goes, Helga do R. Gomes, Wonkook Kim
Summary: Airborne hyperspectral data are important for remote sensing of coastal waters, requiring atmospheric correction to reduce atmospheric effects. Polymer showed superior performance in deriving R-rs spectral shape compared to other correction approaches, but differences in magnitude were observed when compared to MODIS-Aqua data, possibly due to time difference, land adjacency effects, or errors in MODIS R-rs from Polymer.
Article
Geochemistry & Geophysics
Yan Mo, Xudong Kang, Puhong Duan, Shutao Li
Summary: Unmanned aerial vehicle (UAV) hyperspectral imaging is widely used in various fields. However, due to the limited imaging width, the captured hyperspectral images (HSIs) by UAVs need to be stitched together to effectively cover the study area. This article proposes an effective seamless stitching method using deep feature matching and elastic warp, achieving superior results compared to six representative image stitching approaches.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Hailiang Gao, Qianqian Wang, Xingfa Gu, Jian Yang, Qiyue Liu, Zui Tao, Xingchen Qiu, Wei Zhang, Xinda Shi, Xiaofei Zhao
Summary: This study investigates the uncertainty of point-to-pixel-scale conversion generated via different ground sampling methods in the upscaling process. The research findings demonstrate that airborne hyperspectral images can accurately simulate ground measurement spectra and serve as an effective means of ground spectral sampling and uncertainty analysis.
Article
Agronomy
Faxu Guo, Quan Feng, Sen Yang, Wanxia Yang
Summary: Potato canopy nitrogen content (CNC) is an important metric for assessing potato growth status and guiding field management. Hyperspectral indices (HIs) optimization was used to estimate CNC, and the FD-MLR model demonstrated the highest accuracy in predicting CNC. The FD-MLR model can be used to map the CNC distribution map of monitored potato planting plots to guide precision fertilization.
Article
Environmental Sciences
You Li, Qi Han, Xiang Peng, Qiong Li, Xiaojun Tong
Summary: This paper proposes a multi-source two-channel linear time-invariant (MTLI) correlation model, considering the maneuvering magnetic interference and airborne equipment magnetic interference in UAV magnetic surveys. The method provides stable compensation effects in maneuvers and smooth flight, and the workflow is simple and fast. Results from actual flight experiments show significant suppression of two kinds of UAV interference fields in both smooth flight and maneuvering flight.
Article
Environmental Sciences
Wiktor R. Zelazny, Krzysztof Kusnierek, Jakob Geipel
Summary: This study evaluates the accuracy of using remote sensing to predict crop parameters and compares different algorithms and methods. The results show that using the Gaussian process regression model and techniques such as data fusion can improve prediction accuracy, making it suitable for precision agriculture applications.
Article
Environmental Sciences
Maria D. Raya-Sereno, J. Ivan Ortiz-Monasterio, Maria Alonso-Ayuso, Francelino A. Rodrigues, Arlet A. Rodriguez, Lorena Gonzalez-Perez, Miguel Quemada
Summary: This study optimized remote sensing spectral information for the assessment of crop parameters in spring wheat, showing potential for evaluating grain yield and nitrogen output. Different combinations of growth stages and spectral bands were found to be effective in assessing agronomic variables, but there was still significant variability in estimating grain nitrogen concentration, posing challenges in providing reliable fertilizer recommendations.
Article
Spectroscopy
Sun Lin, Bi Wei-hong, Liu Tong, Wu Jia-qing, Zhang Bao-jun, Fu Guang-wei, Jin Wa, Wang Bing, Fu Xing-hu
Summary: The study utilized airborne hyperspectral remote sensing to accurately monitor the coverage area of green tide. Through data collection, preprocessing, and model construction, the hyperspectral green tide inversion model was established. The experimental results showed that the model achieved high accuracy in predicting the green algae coverage area, indicating its significance and potential in the field of marine monitoring.
SPECTROSCOPY AND SPECTRAL ANALYSIS
(2023)
Article
Environmental Sciences
Jan Hanus, Lukas Slezak, Tomas Fabianek, Lukas Fajmon, Tomas Hanousek, Ruzena Janoutova, Daniel Kopkane, Jan Novotny, Karel Pavelka, Miroslav Pikl, Frantisek Zemek, Lucie Homolova
Summary: FLIS is a multi-sensor platform that integrates optical, thermal, and laser scanning remotely sensed data to study terrestrial ecosystems. It provides spectral data, landscape orography, and 3D structure information, allowing for the assessment of vegetation ecosystems and the study of thermal behavior in urban systems.
Article
Environmental Sciences
Floris Hermanns, Felix Pohl, Corinna Rebmann, Gundula Schulz, Ulrike Werban, Angela Lausch
Summary: The study utilized unsupervised learning to analyze hyperspectral imagery for ecosystem monitoring and understanding grassland drought responses. The application of SiVM for grassland stress detection at the ecosystem canopy scale was successful, with carotenoid-related variables playing a significant role in the interannual stress model. The study highlights the potential of combining imaging spectrometry and unsupervised learning for vegetation stress monitoring and remote estimation of photosynthetic efficiency.
Article
Remote Sensing
Jinuk Kim, Wonjin Jang, Jin Hwi Kim, Jiwan Lee, Kyung Hwa Cho, Yong-Gu Lee, Kangmin Chon, Sanghyun Park, JongCheol Pyo, Yongeun Park, Seongjoon Kim
Summary: In this study, a CDOM retrieval model was constructed using airborne hyperspectral reflectance data and a machine learning model. The best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths were evaluated. The results showed that the model had a high performance in estimating CDOM concentration and evaluating its spatiotemporal variation.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Geosciences, Multidisciplinary
Cheng Liu, Chengzhi Xing, Qihou Hu, Shanshan Wang, Shaohua Zhao, Meng Gao
Summary: This article reviews the recent advances in hyperspectral remote sensing techniques and discusses the future application prospects in air pollution monitoring. It recommends the use of a multi-means joint hyperspectral stereoscopic remote sensing monitoring mode for effective monitoring and regulation of air pollution.
EARTH-SCIENCE REVIEWS
(2022)
Article
Geography, Physical
Qu Zhou, Sheng Wang, Nanfeng Liu, Philip A. Townsend, Chongya Jiang, Bin Peng, Wouter Verhoef, Kaiyu Guan
Summary: This study proposed an operational atmospheric correction pipeline for obtaining surface reflectance from AHIS data. The research focused on selecting a suitable model for atmospheric lookup tables, identifying key parameters for atmospheric correction, and testing the performance of machine learning emulators. The proposed method improved the accuracy and efficiency of atmospheric correction.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Engineering, Environmental
Terhikki Manninen, Kati Anttila, Roberta Pirazzini, Petri Raisanen, Leena Leppanen, Anna Kontu, Jouni Peltoniemi
Summary: This paper presents an approach using a monochromatic camera to observe the principal plane bidirectional reflectance characteristics of snow. Photos are taken continuously at nadir view with a 1-minute interval. The mean intensity of the photos is used to observe the relative dependence of snow reflectance on the solar zenith angle (SZA). The decrease in snow bidirectional reflectance and its diurnal asymmetry with respect to SZA during the melting season are demonstrated.
COLD REGIONS SCIENCE AND TECHNOLOGY
(2022)
Article
Geochemistry & Geophysics
Anxin Ding, Shunlin Liang, Ziti Jiao, Han Ma, Alexander A. Kokhanovsky, Jouni Peltoniemi
Summary: This study proposes an improved model, ARTF, by multiplying by a correction term in the asymptotic radiative transfer (ART) model to enhance the accuracy in characterizing snow bidirectional reflectance. Compared to the ART and ARTS models, the ARTF model exhibits higher accuracy and is more effective in representing snow hyperspectral reflectance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Kirsi Karila, Raquel Alves Oliveira, Johannes Ek, Jere Kaivosoja, Niko Koivumaki, Panu Korhonen, Oiva Niemelainen, Laura Nyholm, Roope Nasi, Ilkka Polonen, Eija Honkavaara
Summary: The objective of this study was to explore the potential of using drone images and various neural network architectures for measuring the parameters of silage grass, and comparing the results with other methods. The findings showed that neural networks outperformed random forest in most cases, and RGB data performed well in certain parameters, while hyperspectral images showed advantages in other parameters.
Article
Environmental Sciences
Lei Yan, Yanfei Li, Wei Chen, Yi Lin, Feizhou Zhang, Taixia Wu, Jouni Peltoniemi, Hongying Zhao, Siyuan Liu, Zihan Zhang
Summary: This study aims to explore the distribution pattern of global polarized sunlight radiation. A Rayleigh scattering model and a polarized fisheye camera were used to obtain experimental results and analyze the distribution and daily variation of polarization. The results demonstrate the physical basis, characteristics, and usability of the polarization field.
Article
Agronomy
Raquel Alves Oliveira, Jose Marcato Junior, Celso Soares Costa, Roope Nasi, Niko Koivumaki, Oiva Niemelainen, Jere Kaivosoja, Laura Nyholm, Hemerson Pistori, Eija Honkavaara
Summary: In this study, low-cost RGB images captured by a UAV were used along with convolutional neural networks to estimate dry matter yield and nitrogen concentration of grass swards. The results demonstrate that this approach is a promising and effective tool for practical applications.
Article
Environmental Sciences
Einari Heinaro, Topi Tanhuanpaa, Mikko Vastaranta, Tuomas Yrttimaa, Antero Kukko, Teemu Hakala, Teppo Mattsson, Markus Holopainen
Summary: Fallen tree mapping is important for assessing the ecological value of boreal forests. This study compared the performance of line-detection-based fallen tree detection using moderate and high-point-density laser scanning data. The results showed that increasing point density improved detection rates, but noise sensitivity caused false detections, which could be reduced using filters at the cost of true detections. A less noise-sensitive method utilizing high-density point clouds is recommended for high-point-density data.
Article
Remote Sensing
Anand George, Niko Koivumaeki, Teemu Hakala, Juha Suomalainen, Eija Honkavaara
Summary: This study implemented and assessed a redundant positioning system for high flying altitude drones based on visual-inertial odometry (VIO). The performance of various implementations was studied, and stereo-VIO provided the best results. The stereo baseline of 30 cm was most optimal for flight altitudes of 40-60 m, with a positioning accuracy of 2.186 m for an 800 m-long trajectory. The research results are important for the increasing use of autonomous drones and beyond visual line-of-sight flying.
Article
Forestry
Rorai Pereira Martins-Neto, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Eija Honkavaara, Milto Miltiadou, Erika Akemi Saito Moriya, Hassan Camil David
Summary: This study explores the use of different combinations of UAV hyperspectral data and LiDAR metrics to classify tree species in a degraded Brazilian Atlantic Forest remnant. By combining spectral data with geometric information from LiDAR, the classification accuracy was improved in a complex tropical forest.
Article
Environmental Sciences
Jaakko Oivukkamaki, Jon Atherton, Shan Xu, Anu Riikonen, Chao Zhang, Teemu Hakala, Eija Honkavaara, Albert Porcar-Castell
Summary: The potential of chlorophyll fluorescence and photoprotection-based indices for the detection of a wide range of nutrient contents in vegetation was investigated. Leaf-level observations showed that the relationships between these indices and foliar nutrient contents were influenced by leaf chlorophyll contents and leaf morphology. Canopy-level observations further revealed that spectral indices were also influenced by canopy structure, affecting their capacity to detect foliar nutrient contents.
Article
Agronomy
Roope Nasi, Hannu Mikkola, Eija Honkavaara, Niko Koivumaki, Raquel A. Oliveira, Pirjo Peltonen-Sainio, Niila-Sakari Keijala, Mikael Anakkala, Laura Alakukku, Laura Alakukku
Summary: Crop growth within agricultural parcels can be uneven, even with even management. Aerial images can determine vegetation presence and variability, but the reasons for uneven growth are less studied. This study evaluated the relationship between drone image data and field/soil quality indicators. The results showed that soil/field indicators can effectively explain spatial variability in drone images, which can be utilized for cultivation planning and field parcel evaluation.
Article
Agronomy
Erika Akemi Saito Moriya, Nilton Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Eija Honkavaara, David Luciano Rosalen
Summary: This study used vegetation indices and hyperspectral remote sensing technology to successfully detect the areas affected by sugarcane mosaic disease, providing an effective tool for crop disease monitoring.
Article
Environmental Sciences
Mohammad Imangholiloo, Ville Luoma, Markus Holopainen, Mikko Vastaranta, Antti Makelainen, Niko Koivumaki, Eija Honkavaara, Ehsan Khoramshahi
Summary: Tree species information is crucial for forest management, especially in seedling stands. This study proposes a pre-processing technique based on canopy threshold to improve seedling classification, and compares the accuracy of convolutional neural network (CNN) and random forest (RF) methods. It also demonstrates that fusing vegetation indices with multispectral data enhances the classification accuracy.
Article
Environmental Sciences
Ehsan Khoramshahi, Roope Naesi, Stefan Rua, Raquel A. A. Oliveira, Axel Paivansalo, Oiva Niemelainen, Markku Niskanen, Eija Honkavaara
Summary: This article explores the use of drone techniques to identify alien barleys in oat fields. By employing a machine learning approach and drone images, the study successfully detects and localizes barley plants, providing a useful method for modern grain production industries.
