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
Environmental Sciences
Marcin Kluczek, Bogdan Zagajewski, Tomasz Zwijacz-Kozica
Summary: Europe's mountain forests, valuable for their biodiversity and natural characteristics, are undergoing significant changes. Monitoring these forests requires up-to-date information on species composition, extent, and location, as well as the selection of appropriate remote sensing data.
Article
Biodiversity Conservation
Daniel L. Perret, Dov F. Sax
Summary: Using conifers as a model system, it was found that niche-based sampling better represents species' niches compared to geographic sampling, with the size of this difference depending on study design and sample size. Gridded niche-based study designs achieved the most complete sampling at larger sample sizes, covering 15-25% more of a species' niche compared to similar geographical designs. However, all study designs performed poorly with fewer than 10 samples, and niche-based transects achieved slightly higher niche coverage in such cases.
Article
Environmental Sciences
Kathryn Elmer, Margaret Kalacska, J. Pablo Arroyo-Mora
Summary: Invasive species are a major threat to global biodiversity, and early detection is key to prevent their spread. Optical remote sensing technology, particularly airborne hyperspectral imagery and target detection algorithms, can be effectively used for early detection and mapping of invasive vegetation populations. This study successfully utilized these technologies to identify and map invasive reed Phragmites in a national park, covering approximately 7.26% of the park's total area.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2021)
Article
Environmental Studies
Levente Papp, Boudewijn van Leeuwen, Peter Szilassi, Zalan Tobak, Jozsef Szatmari, Matyas Arvai, Janos Meszaros, Laszlo Pasztor
Summary: The study focused on mapping and monitoring the spread of the common milkweed, an invasive plant species in Europe, in Hungary. By analyzing hyperspectral remote sensing data and applying classification algorithms, the researchers successfully distinguished common milkweed individuals with high accuracy, providing a new method for invasive species monitoring.
Article
Environmental Sciences
Janne Mayra, Sarita Keski-Saari, Sonja Kivinen, Topi Tanhuanpaa, Pekka Hurskainen, Peter Kullberg, Laura Poikolainen, Arto Viinikka, Sakari Tuominen, Timo Kumpula, Petteri Vihervaara
Summary: Over the past two decades, forest monitoring and inventory systems have shifted from field surveys to remote sensing-based methods. Current analysis methods have limitations, with deep learning methods showing greater potential. This study focused on the classification of major tree species and achieved high accuracy using 3D convolutional neural networks in hyperspectral data.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Geochemistry & Geophysics
Long Chen, Yuxin Wei, Zongqi Yao, Erxue Chen, Xiaoli Zhang
Summary: The study proposed a novel data augmentation strategy and feature extraction backbone to achieve accurate classification of multiple tree species in forests. The robustness and effectiveness of the strategy were validated on multiple datasets, showing significant improvements in classification accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Chein- Chang
Summary: Band sampling (BSam) is an innovative concept for hyperspectral imaging derived from signal sampling in communications/signal processing and sampling theory in information theory. It differs from band selection (BSel) in that it samples bands with a fixed rate, has no specific means of band sampling, and requires no prior band knowledge compared to BSel. Two strategies for BSam have been developed, namely uniform band sampling (UBSam) and random band sampling (RBSam), which generally perform better than custom-designed BSel methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Fei Tong, Yun Zhang
Summary: This article proposes a spectral-spatial and cascaded multilayer random forests (SSCMRF) method for classifying tree species in high-spatial-resolution hyperspectral images. The method achieves superior classification results by fully utilizing spatial information from shape-adaptive superpixels and shape-fixed patches, integrating two different types of spatial information.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Pavel A. Dmitriev, Boris L. Kozlovsky, Denis P. Kupriushkin, Anastasia A. Dmitrieva, Vishnu D. Rajput, Vasily A. Chokheli, Ekaterina P. Tarik, Olga A. Kapralova, Valeriy K. Tokhtar, Tatiana M. Minkina, Tatiana V. Varduni
Summary: The present study investigated the possibility of identifying invasive and weed species in agrocenoses ecosystem using hyperspectral imaging data. The study found that using multiple vegetation indices in combination can differentiate between species more deliberately and precisely. The decision tree and random forest methods can effectively group the weeds into different categories.
Article
Environmental Sciences
Linghan Gao, Guoqi Chai, Xiaoli Zhang
Summary: This study constructs AGB estimation models for different tree species by combining airborne LiDAR and hyperspectral features. The optimal AGB models are obtained by extracting and selecting the most important features. The results show that feature-fusion-based data can greatly improve the accuracy of the AGB models.
Article
Environmental Sciences
Long Chen, Xiaomin Tian, Guoqi Chai, Xiaoli Zhang, Erxue Chen
Summary: The embedding of a convolutional block attention module (CBAM) in between the convolution blocks of P-Net to construct CBAM-P-Net significantly enhances the feature extraction efficiency of the model. Testing in different sample windows shows that CBAM-P-Net has increased accuracy and kappa coefficient.
Article
Environmental Sciences
Anita Sabat-Tomala, Edwin Raczko, Bogdan Zagajewski
Summary: Recent developments in computer hardware have enabled the assessment of permutation-based approaches in image classification, which involve sampling a reference dataset multiple times to train machine learning models and evaluate accuracy. The study applied support vector machine algorithm to classify invasive plant species with high accuracy, ranging from F1-scores of 0.87 to 0.99 for different species.
Article
Environmental Sciences
Long Chen, Jing Wu, Yifan Xie, Erxue Chen, Xiaoli Zhang
Summary: Studying few-shot learning algorithms is vital for supervised multiple tree species classification. This study introduces a supervised contrastive learning method that combines data augmentation and feature enhancement to improve classification accuracy and reduce overfitting. Experimental results show that supervised contrastive learning enhances sample distinguishability.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Remote Sensing
Phuong D. Dao, Alexander Axiotis, Yuhong He
Summary: This study used airborne high-resolution narrow-band hyperspectral imagery to map invasive species in a heterogeneous grassland ecosystem in southern Ontario, Canada. The results showed high spectral and textural separability between invasive and native plants, with seasonality being the dominant factor for the distribution of invasive species at the landscape level, while small-scale topographic variations partially explain local patches of invasive species.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Dominik Kopec, Anita Sabat-Tomala, Dorota Michalska-Hejduk, Anna Jarocinska, Jan Niedzielko
WETLANDS ECOLOGY AND MANAGEMENT
(2020)
Article
Environmental Sciences
Anna Jarocinska, Dominik Kopec, Barbara Tokarska-Guzik, Edwin Raczko
Summary: The study focused on identifying the main functional traits crucial for differentiating the European dewberry Rubus caesius L. from non-Rubus using hyperspectral and LiDAR data. Differentiation was successful using Optical data but ALS data was less useful. Spectral ranges and indices such as ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR were found to be useful for classification. Lower spectral resolution images were also effective for classifying R. caesius.
Article
Environmental Sciences
Sylwia Szporak-Wasilewska, Hubert Piorkowski, Wojciech Ciezkowski, Filip Jarzombkowski, Lukasz Slawik, Dominik Kopec
Summary: This study evaluated the effectiveness of identifying Natura 2000 wetland habitats using various remotely sensed data, and provided recommended classification scenarios and data acquisition terms. The results showed that combining hyperspectral products with ALS topographical and statistical products can improve the accuracy of wetland habitat classification.
Article
Geography, Physical
Anna Jarocinska, Dominik Kopec, Marlena Kycko, Hubert Piorkowsk, Agnieszka Blonska
Summary: Identification of Natura 2000 habitats using remote sensing techniques is a significant challenge in nature conservation. This study examined the potential for differentiating non-forest Natura 2000 habitats from other habitats using hyperspectral and multispectral data. The research identified the most informative spectral ranges and concluded that hyperspectral data from May to September was useful for differentiation with efficiency over 90%. Multispectral data showed varied potential in distinguishing habitats, with heaths and mires performing better than meadows and grasslands.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Remote Sensing
Agata Zakrzewska, Dominik Kopec, Karol Krajewski, Jakub Charyton
Summary: Recent studies have shown that thermal remote sensing data has high potential for application in environmental analyses. The main objective of this study was to determine the variability of tree canopy temperatures using a new sensor that acquires data in the thermal spectral range of 3.6-4.9 μm, which is rarely used. The findings of the study showed that trees growing in the forest are generally cooler than trees outside the forest, and different tree species have statistically different canopy temperatures. The study also demonstrated that a thermal spectral range of 3.6-4.9 μm can accurately measure the canopy temperature of tree species, supporting its use in remote sensing vegetation studies.
EUROPEAN JOURNAL OF REMOTE SENSING
(2022)
Article
Plant Sciences
Agata Zakrzewska, Dominik Kopec, Adrian Ochtyra, Marketa Potuckova
Summary: Nowadays, it is necessary to gather information about the health conditions of trees in cities. Trees are crucial in regulating urban microclimate and mitigating the urban heat island effect. This research aims to evaluate the possibility of using thermal infrared data to assess the health condition of selected deciduous trees in an urban environment.
URBAN FORESTRY & URBAN GREENING
(2023)
Article
Geography, Physical
Dominik Kopec, Agata Zakrzewska, Anna Halladin-Dabrowska, Justyna Wylazlowska, Lukasz Slawik
Summary: Invasive alien species pose a major threat to biodiversity, and remote sensing combined with machine learning has proven to be effective in monitoring them. However, mapping annual vine species using remote sensing is challenging due to their dynamic growth. This research found that the phenological phase and synchronization between remote sensing and on-ground data are key factors for accurate classification. Multitemporal image fusion did not significantly improve the classification accuracy. Therefore, strict synchronization and appropriate phenological phase are crucial for mapping annual vine species.
GISCIENCE & REMOTE SENSING
(2023)
Article
Multidisciplinary Sciences
Anna Jarocinska, Dominik Kopec, Jan Niedzielko, Justyna Wylazlowska, Anna Halladin-Dabrowska, Jakub Charyton, Agnieszka Piernik, Dariusz Kaminski
Summary: Based on analysis of five study areas in Poland, the use of hyperspectral data resulted in higher classification accuracy compared to multispectral data, particularly for salt, peat, and lowland hay meadows. These findings are important for the management of protected areas.
SCIENTIFIC REPORTS
(2023)
Article
Environmental Sciences
Adriana Marcinkowska-Ochtyra, Adrian Ochtyra, Edwin Raczko, Dominik Kopec
Summary: This study used multitemporal Sentinel-2 data to map three grassland Natura 2000 habitats in Poland. Convolutional Neural Networks (CNNs) were used for classification, and Support Vector Machines (SVMs) resulted in the best accuracy.
Article
Environmental Sciences
Anna Jarocinska, Jan Niedzielko, Dominik Kopec, Justyna Wylazlowska, Bozhena Omelianska, Jakub Charyton
Summary: Mapping vegetation is a key issue in wetland monitoring. This study found that the mean and entropy features of the Gray Level Co-occurrence Matrix have the highest potential for identifying wetland communities. Adding these features improved the accuracy of wetland communities mapping, especially for areas with forest and scrub vegetation.
Article
Forestry
Agata Zakrzewska, Dominik Kopec
Summary: This study develops an automatic workflow for detecting dead trees and trees in poor condition of Picea abies using Middle Wave Infrared spectral range obtained from the aircraft. By analyzing temperature data, different health conditions of trees can be accurately distinguished. The results confirm the effectiveness of fusing thermal and laser scanning data.