Article
Environmental Sciences
Masoud Mahdianpari, Jean Elizabeth Granger, Fariba Mohammadimanesh, Sherry Warren, Thomas Puestow, Bahram Salehi, Brian Brisco
Summary: This study aims to produce the first high-resolution wetland map of the City of St. John's in Canada using advanced machine learning algorithms, very high-resolution satellite imagery, and airborne LiDAR technology. By applying an object-based random forest algorithm to features extracted from WorldView-4, GeoEye-1, and LiDAR data, the study characterizes five wetland classes within an urban area with an overall accuracy of 91.12% and produces wetland surface water flow connectivity using LiDAR data.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Forestry
Katrina Ariel Henn, Alicia Peduzzi
Summary: The benefits and services of urban forests, especially carbon storage, are well documented. A generalizable individual urban tree model was developed using NAIP aerial imagery and LiDAR data. The model was then used to estimate the total biomass and carbon stored for all the trees in the county. Recommendations include adapting ground inventory techniques to the limitations of remote sensing biomass estimation.
Article
Engineering, Electrical & Electronic
Svetlana Illarionova, Alexey Trekin, Vladimir Ignatiev, Ivan Oseledets
Summary: By utilizing multispectral satellite imagery and neural networks, the forest classification problem was addressed as an image segmentation task, represented as a hierarchical set of binary classification tasks, to achieve better results.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Ecology
Gabriel Guariglia Perez, Vandoir Bourscheidt, Luciano Elsinor Lopes, Juliana Toshie Takata, Patricia Alves Ferreira, Danilo Boscolo
Summary: This study explored the possibility of estimating vegetation height in Atlantic Forest using Sentinel 2 imagery and LiDAR data. The results showed that simple linear models can accurately predict vegetation height, and the model is transferable to new images and locations.
ECOLOGICAL INFORMATICS
(2022)
Article
Environmental Sciences
Hakon Sundt, Knut Alfredsen, Atle Harby
Summary: This study tested the potential of using platform-specific, regionalized linear models for depth retrieval in rivers lacking local bathymetry, finding that these regionalized linear models have the potential to be applied for extensive depth mapping in water bodies where local in-situ depth measurements are lacking.
Article
Agronomy
Hanchao Liu, Yuan Qi, Wenwei Xiao, Haoxin Tian, Dehua Zhao, Ke Zhang, Junqi Xiao, Xiaoyang Lu, Yubin Lan, Yali Zhang
Summary: This study used remote sensing images obtained with a UAV and vegetation indices to accurately identify the male and female parents of hybrid rice using pixel-based supervised classification and sample-based object-oriented classification methods. The ExGR index showed the best performance in classification. This method can provide a reference for determining optimal pollination timing for hybrid rice in large-scale seed production farms.
Article
Environmental Sciences
Qian Guo, Jian Zhang, Shijie Guo, Zhangxi Ye, Hui Deng, Xiaolong Hou, Houxi Zhang
Summary: This study establishes an efficient and practical method for urban tree identification by combining an object-oriented approach and a random forest algorithm using UAV multispectral images. The results show that the random forest classifier performs better than SVM and KNN classifiers. Spectral features are the most important type of features, followed by index features, texture features and geometric features. The accuracy of Camphor and Cinnamomum Japonicum is lower than that of other tree species, suggesting the need to add additional features in the future to improve accuracy.
Article
Ecology
Edmund J. Zlonis, Ram Deo, James B. Berdeen
Summary: This study successfully predicted the locations of suitable nesting cavities for Wood Ducks by using a combination of LiDAR, multispectral imagery, and SAR remote sensing data, providing important insights for conservation actions for cavity-nesting wildlife.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2022)
Article
Computer Science, Artificial Intelligence
Fang Qingyun, Wang Zhaokui
Summary: This study proposes a novel multispectral feature fusion approach, which improves the perception ability of detection algorithms by cross-modality fusing complementary information. It achieves robust and reliable performance in applications such as nighttime detection.
PATTERN RECOGNITION
(2022)
Article
Environmental Sciences
Simone Franceschini, Amelia C. Meier, Aviv Suan, Kaci Stokes, Samapriya Roy, Elizabeth M. P. Madin
Summary: Reef halos are rings of bare sand surrounding coral reef patches, and their formation is likely the result of interactions between healthy predator and herbivore populations. By identifying and measuring these halos, we can monitor their size changes more efficiently and accurately, providing insights into the status of predator and herbivore populations.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Yanchao Zhang, Wen Yang, Ying Sun, Christine Chang, Jiya Yu, Wenbo Zhang
Summary: This study examined the fusion of spectral bands information and vegetation indices for almond plantation classification using different machine learning algorithms. It was found that spectral information can be used for ground classification, with SVM performing the best among the algorithms tested. The combination of multispectral bands and vegetation indices can improve classification accuracy, with specific vegetation indices like NDEGE, NDVIG, and NDVGE showing consistent performance in enhancing accuracy.
Article
Environmental Sciences
Omer Saud Azeez, Biswajeet Pradhan, Ratiranjan Jena
Summary: This paper introduces a novel urban tree extraction modeling approach using laser scanning point clouds and object-based textural analysis. The model, characterized by four sub-models, was successfully applied to classify urban trees based on height and crown widths, showing effective results for practical use.
GEOCARTO INTERNATIONAL
(2021)
Article
Environmental Sciences
Miranda Brooke Rose, Mystyn Mills, Janet Franklin, Loralee Larios
Summary: This study evaluated the potential and accuracy of using UAVs and multispectral data for vegetation monitoring in ecological restoration. The results showed that SVM and RF classifiers performed well in estimating shrub cover, and aggregating data to larger resolutions resulted in more accurate estimates.
Article
Environmental Sciences
Francisco Arguello, Dora B. Heras, Alberto S. Garea, Pablo Quesada-Barriuso
Summary: This article presents a method for automatically monitoring river basins in Galicia, Spain, using multispectral images and image processing algorithms to determine the state of vegetation and detect man-made structures occupying the river basin. By selecting techniques and algorithms for fast execution and efficient use of computational resources, the proposed approach proves to achieve the monitoring goal with speed and precision.
Article
Ecology
Nan Wu, Runhe Shi, Wei Zhuo, Chao Zhang, Zhu Tao
Summary: Biological invasion by Spartina alterniflora poses a threat to native vegetation in coastal wetlands in China. Remote sensing images are valuable for identifying vegetation, but the spatial resolutions of satellite sensors may not be ideal for small-scale communities. A comprehensive classification method incorporating spectral and textural features improved classification accuracy, particularly when considering neighboring pixels.
Article
Remote Sensing
Timothy G. Whiteside, Renee E. Bartolo
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2015)
Article
Remote Sensing
Timothy G. Whiteside, Renee E. Bartolo
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2018)
Article
Environmental Sciences
Kirrilly S. Pfitzner, Andrew J. Harford, Timothy G. Whiteside, Renee E. Bartolo
SCIENCE OF THE TOTAL ENVIRONMENT
(2018)
Article
Remote Sensing
Timothy G. Whiteside, Guy S. Boggs, Stefan W. Maier
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2011)
Article
Remote Sensing
Timothy G. Whiteside, Stefan W. Maier, Guy S. Boggs
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2014)
Article
Geography, Physical
Timothy G. Whiteside, Guy S. Boggs, Stefan W. Maier
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
(2011)
Article
Ecology
Timothy G. Whiteside, Andrew J. Esparon, Renee E. Bartolo
ECOLOGICAL INFORMATICS
(2020)
Article
Ecology
Lynda D. Prior, Timothy G. Whiteside, Grant J. Williamson, Renee E. Bartolo, David M. J. S. Bowman
Article
Ecology
Lorna Hernandez-Santin, Mitchel L. Rudge, Renee E. Bartolo, Timothy G. Whiteside, Peter D. Erskine
Summary: The study presents a detailed approach for selecting reference sites in the context of mining and resource development within a savanna ecosystem. It emphasizes the importance of the reference site selection process to ensure the accuracy and effectiveness of closure criteria and restoration guidelines.
RESTORATION ECOLOGY
(2021)
Article
Environmental Sciences
Shaun R. Levick, Tim Whiteside, David A. Loewensteiner, Mitchel Rudge, Renee Bartolo
Summary: Savanna ecosystems are difficult to map and monitor due to the dynamic nature of their vegetation. UAV-based remote sensing shows potential for providing high-resolution measurements over large areas at regular intervals. However, the suitability of UAV-based LiDAR for mapping and monitoring savanna vegetation structure is not well established.
Article
Environmental Sciences
Barbara D'hont, Kim Calders, Harm Bartholomeus, Tim Whiteside, Renee Bartolo, Shaun Levick, Sruthi M. Krishna Moorthy, Louise Terryn, Hans Verbeeck
Summary: This study investigates the use of UAV-based observations for precise and scalable termite mound detection and morphological characterisation. The results show that UAV-LS data can be rapidly acquired and provide higher spatial detail for monitoring and mapping termite mounds over relatively large areas compared to airborne and spaceborne remote sensing techniques.
Article
Environmental Sciences
Kirrilly Pfitzner, Renee Bartolo, Tim Whiteside, David Loewensteiner, Andrew Esparon
Summary: The study used miniaturisation of hyperspectral sensors on drones to monitor non-native grass species, revealing subtle spectral differences that can be used to distinguish between species. The late dry season and end of the wet season provided the best timeframe for obtaining spectral data on non-native grass species.
Article
Biodiversity Conservation
Sarah E. Fischer, Andrew C. Edwards, Patrice Weber, Stephen T. Garnett, Timothy G. Whiteside
Summary: Despite significant urban development in the Darwin region over the past twenty years, the study found that bird communities remain relatively stable, showing no significant differences in species numbers or primary food types between 1998 and 2018.
Article
Environmental Sciences
Harm Bartholomeus, Kim Calders, Tim Whiteside, Louise Terryn, Sruthi M. Krishna Moorthy, Shaun R. Levick, Renee Bartolo, Hans Verbeeck
Summary: In vegetation monitoring, it is important to understand the changes caused by measurement setup and true representations of vegetation dynamics. This study examines the differences in derived forest metrics using three different UAV-LiDAR systems in a savanna woodland. The findings show that all three systems can accurately derive plot characteristics, but clear differences exist between metrics derived with different sensors, particularly in the lower parts of the canopy. It is crucial to be aware of these differences when comparing UAV-LiDAR data of forest areas through time.