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
Forestry
Olga Cholewinska, Andrzej Keczynski, Barbara Kusinska, Bogdan Jaroszewicz
Summary: The study found that the species of large trees have a significant impact on the diversity, distance, and frequency of adjacent trees, and as the diameter of the large tree increases, the distance between neighboring trees and the large tree also increases.
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
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
Environmental Sciences
Jordi Cristobal, Patrick Graham, Anupma Prakash, Marcel Buchhorn, Rudi Gens, Nikki Guldager, Mark Bertram
Summary: A pilot study for mapping Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge in Alaska, using hyperspectral images and various classification methods to achieve the best classification performance. Recommendations for future work include the acquisition of LiDAR or RGB photo-derived digital surface models to improve classification efforts.
Article
Geosciences, Multidisciplinary
Shanshan Wang, Kefa Zhou, Jinlin Wang, Jie Zhao
Summary: Airborne hyperspectral remote sensing data provide a rapid and high-quality method for mineral mapping and lithological discrimination. In this study, NEO HySpex cameras were used to acquire high-resolution hyperspectral images, and a data processing workflow and random forest algorithm were applied for geological survey and exploration. The results confirm the feasibility and effectiveness of this method.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Computer Science, Information Systems
Simranjit Singh, Singara Singh Kasana
Summary: A novel framework for hyperspectral image classification is proposed in this paper, utilizing interpolation to address noisy band losses, and extracting hybrid features using PCA and LPP to preserve spatial information before passing them to machine learning models. Comparative analysis with standard datasets showed a significant increase in classification accuracy when the proposed framework was combined with state-of-the-art classifiers.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Environmental Sciences
Weihua Chen, Jie Pan, Yulin Sun
Summary: This study explores the fusion method of GF-5 and Sentinel-2A images and achieves tree species classification using a random forest classifier. The results show that the fused image has higher spatial integration and spectral fidelity, and the classification accuracy is significantly improved.
Review
Computer Science, Information Systems
Reaya Grewal, Singara Singh Kasana, Geeta Kasana
Summary: The growth of HSI analysis is due to advancements that enable cameras to collect continuous spectral information. The classification of HSI is challenging due to redundant spectral bands and limited training samples. Traditional Machine Learning techniques and Deep Learning techniques have been compared and it is observed that DL-based techniques outperform ML-based techniques. Spectral-spatial classification is found to be more effective than pixel-by-pixel classification. The performance of ML and DL-based techniques has been evaluated on commonly used land cover datasets.
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
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
Forestry
Els Dhiedt, Lander Baeten, Pallieter De Smedt, Bogdan Jaroszewicz, Kris Verheyen
Summary: Trees have a significant impact on the chemistry of topsoil, with the degree and direction of this impact depending on the tree species. Nutrient-poor trees have the potential to degrade soil fertility, while nutrient-rich trees can improve soil quality. In this study conducted in the Bialowieza Forest in Poland, the effects of tree species on topsoil chemistry were investigated on a small scale. The results showed that the concentration of total carbon, availability of phosphorus and base cations, and carbon-to-nitrogen ratio were higher near the trees. However, the pH was not affected by distance. Different tree species had varying effects on the proximity of trees, with nutrient-poor trees having a more negative impact on pH and base cations compared to nutrient-rich trees. The study suggests the importance of mixing nutrient-rich and nutrient-poor species and the choice of tree species in terms of topsoil chemical composition at a small scale within a forest stand.
EUROPEAN JOURNAL OF FOREST RESEARCH
(2022)
Article
Environmental Sciences
Bin Wang, Jianyang Liu, Jianing Li, Mingze Li
Summary: Based on UAV LiDAR and hyperspectral data, this study designed different classification schemes to explore the effects of different data sources, classifiers, and canopy morphological features on the classification of single tree species. The results showed that multisource remote sensing data had higher classification accuracy than single data source. Random forest and support vector machine classifiers had similar classification accuracies, with overall accuracies above 78%. The BP neural network classifier had the lowest classification accuracy of 75.8%. The addition of UAV LiDAR-extracted canopy morphological features slightly improved the classification accuracy of all three classifiers for tree species.
Article
Environmental Sciences
Nontembeko Dudeni-Tlhone, Onisimo Mutanga, Pravesh Debba, Moses Azong Cho
Summary: Hyperspectral sensors capture and analyze the spectral reflectance of objects in multiple wavelength bands, which results in a high-dimensional space. Handling and analyzing high spectral dimensionality can be challenging and require advanced techniques. This research examines ensemble-based techniques for high-dimensional data and evaluates the impact of time-induced variability when modeling tree species using hyperspectral measurements taken at different time periods. The classification errors for the stochastic gradient boosting (SGB) and random forest (RF) methods ranged from 5.6% to 13.5%. The study finds that measurement time is important in improving discrimination between tree species due to variations in optical leaf characteristics throughout the year.
Rating: 8/1
Article
Geography, Physical
Sawaid Abbas, Qian Peng, Man Sing Wong, Zhilin Li, Jicheng Wang, Kathy Tze Kwun Ng, Coco Yin Tung Kwok, Karena Ka Wai Hui
Summary: This study established a hyperspectral library for urban tree species in Hong Kong, developed a Deep Neural Network classification model for accurate identification of tree species, and analyzed the seasonal patterns of urban tree species using hyperspectral imaging. The research provides a unique baseline for understanding hyperspectral characteristics and seasonality of urban tree species.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Forestry
Aneta Modzelewska, Agnieszka Kaminska, Fabian Ewald Fassnacht, Krzysztof Sterenczak
Summary: Tree species composition maps derived from hyperspectral data are accurate, but the optimal time window for image acquisition remains unclear. Our study in the Polish part of the Biatowieza Forest used multitemporal hyperspectral data to classify tree species, with early summer acquisition achieving the highest accuracies. Comparison of different data acquisitions showed slightly better results for the stacked multitemporal dataset, indicating the potential benefits of using multiple acquisitions for classification.
Article
Remote Sensing
Fabian Ewald Fassnacht, Ephraim Schmidt-Riese, Teja Kattenborn, Jaime Hernandez
Summary: The study utilizes very high resolution Unmanned Aerial System (UAS) orthoimages to analyze landscape structure before and after wildfires, and finds that a sparse set of predictors can explain over 80% of variability in dNBR and 75% in RdNBR values observed by Sentinel-2. The fraction of consumed canopy cover is identified as a major factor influencing the variability, while prefire vegetation composition does not have a large influence on dNBR and RdNBR values. The influence of cast shadows from snags and standing dead trees with remaining crown structure on the dNBR signal is noted as potentially underestimated.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Remote Sensing
Fabian Ewald Fassnacht, Javiera Poblete-Olivares, Lucas Rivero, Javier Lopatin, Andres Ceballos-Comisso, Mauricio Galleguillos
Summary: The study presents a workflow for deriving landscape-level biomass estimates in a large watershed, combining various remote sensing data, feature selection, and machine learning techniques to estimate biomass for different vegetation types. The results show that the workflow yields biomass predictions comparable to studies focusing on individual vegetation types.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Ecology
Li Li, Fabian Ewald Fassnacht, Matthias Burgi
Summary: The study used the Tibetan pastoral landscape as an example to explore SES regime shifts in black-soil formation. It found that land-use intensification in the 1990s led to increased landscape-scale degradation risks, eventually scaling up to the landscape level in the 2010s. The findings suggest that unfavorable SES regime shifts are strongly linked across spatial scales.
Article
Remote Sensing
Tobias Graenzig, Fabian Ewald Fassnacht, Birgit Kleinschmit, Michael Foerster
Summary: Mapping invasive plant species and understanding their dynamics is crucial. A new method using UAV and Sentinel-2 data was introduced to estimate the coverage of the invasive shrub Ulex europaeus in Chile. By focusing on Sentinel-2 acquisition in November, the distribution of Ulex europaeus was significantly improved and distinguishable from other plant species.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Andreas Ch Braun, Fabian Fassnacht, Diego Valencia, Maximiliano Sepulveda
Summary: Central Chile is an important biodiversity hotspot in Latin America where the pressure of land-use change and large forest fires may threaten local plant species richness hotspots. While land-use change can destroy habitat integrity through loss and fragmentation, wildfires may destroy the last remnants of native forests. Our study shows that land-use change has a greater impact on biodiverse habitats compared to wildfires, but the combination of the two may pose a significant threat to biodiversity hotspots.
REGIONAL ENVIRONMENTAL CHANGE
(2021)
Article
Plant Sciences
Fabian Ewald Fassnacht, Jana Mullerova, Luisa Conti, Marco Malavasi, Sebastian Schmidtlein
Summary: There is a potential link between spectral variation and plant biodiversity, but further research and refinement are needed to establish a clear relationship.
APPLIED VEGETATION SCIENCE
(2022)
Article
Remote Sensing
Elham Shafeian, Fabian Ewald Fassnacht, Hooman Latifi
Summary: Woody canopy cover is crucial for understanding vegetation health, carbon accumulation, and land-atmosphere exchange processes. Remote sensing data combined with machine learning algorithms can provide accurate estimates of woody cover over large areas, as demonstrated in the Zagros Mountains region. The approach shows stable performance with 40 m spatial grain models, which can potentially be applied to other arid and semi-arid regions for improving global woody cover products.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Hannah Weiser, Lukas Winiwarter, Katharina Anders, Fabian Ewald Fassnacht, Bernhard Hoefle
Summary: The study analyzed the performance of virtual laser scanning technology in simulating individual tree scans in forests, finding that using scaled voxel models can effectively reduce the discrepancies between simulated and real data, improving the realism and accuracy of the simulations.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Forestry
Tomasz Hycza, Agnieszka Kaminska, Krzysztof Sterenczak
Summary: This study compared methods for determining the area for which canopy cover is calculated using data from ALS, discussing the differences in accuracy and complexity. The most accurate method was Method 2, while Method 1 was found to be the least accurate option. Accuracy was better in the case of the Kyoto Protocol definition.
Article
Environmental Sciences
Agnieszka Kaminska, Maciej Lisiewicz, Krzysztof Sterenczak
Summary: Tree species classification using a fusion of ALS and CIR imagery datasets showed that classification based on both point clouds and image spectral information had the highest accuracy. Coniferous tree species were consistently classified better than deciduous tree species, with intensity features being more important in the classification process.
Article
Environmental Sciences
Maciej Lisiewicz, Agnieszka Kaminska, Bartlomiej Kraszewski, Krzysztof Sterenczak
Summary: This study aims to develop a method to correct the results of individual tree detection algorithms (ITD) for more reliable tree identification. The proposed three-step approach improves segmentation accuracy by correcting and merging segmentation errors. The study demonstrates the effectiveness of the method in complex and diverse forest communities.
Article
Environmental Sciences
Pawel Zbigniew Banasiak, Piotr Leszek Berezowski, Rafal Zaplata, Milosz Mielcarek, Konrad Duraj, Krzysztof Sterenczak
Summary: Airborne Laser Scanning technology, used in forested areas, can help identify terrain relief features and potentially discover unknown archaeological monuments. Archaeologists face challenges in interpreting spatial relationships of objects of different shapes and sizes. Deep learning neural networks, such as the U-Net model, can be used for automatic recognition of archaeological monuments by performing image segmentation based on ALS data, with performance evaluated using metrics like IoU and Dice-Sorensen coefficient.
Article
Environmental Sciences
Yousef Erfanifard, Mohsen Lotfi Nasirabad, Krzysztof Sterenczak
Summary: This study accurately mapped and detected the spatiotemporal changes of mangrove forests along the southern coast of Iran using Landsat imagery. Various indices and metrics were employed to analyze the changes in mangrove area, connectivity, and complexity. The results showed an overall increase in mangrove area and improvements in connectivity and complexity, but also highlighted fragmentation and weaker connectivity in some locations.
Article
Biodiversity Conservation
Tobias Graenzig, Anne Clasen, Fabian Ewald Fassnacht, Anna Cord, Michael Foerster
Summary: This study proposes a workflow that combines habitat suitability maps, remote sensing data, and cellular automata models to reconstruct the spreading patterns and predict the future spread of the invasive shrub Ulex europaeus on Chiloe Island, Chile. The workflow produced satisfactory results and predicted a continuous expansion of the species' potential range.
BIOLOGICAL INVASIONS
(2023)