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Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review
2021
Remote Sensing
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Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high ...
The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. ...
The long time series and high revisiting times of these low-resolution remote sensing data is effective to limit gaps in the time series as a result of cloud cover issues [120] . ...
doi:10.3390/rs13193870
fatcat:55i4rx5lhjgo5bzkmkbgttqq6u
Comparison of Canopy Closure Estimation of Plantations Using Parametric, Semi-Parametric, and Non-Parametric Models Based on GF-1 Remote Sensing Images
2020
Forests
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Canopy closure estimation models, using a combination of measured data and remote sensing data, can largely replace traditional survey methods for CC. ...
The RMSE and rRMSE values for the MLR, GAM, and RF established using high spatial resolution GF-1 remote sensing images in the two test areas were within the scope of existing studies, indicating the three ...
Acknowledgments: We thank Leonie Seabrook, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript. ...
doi:10.3390/f11050597
fatcat:brex6tsu2fdn7kyhwfoxiaze6a
Editorial for the Special Issue "Remote Sensing of Water Quality"
2019
Remote Sensing
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The importance of monitoring, preserving, and, where needed, improving the quality of water resources in the open ocean, coastal regions, estuaries, and inland water bodies cannot be overstated [...] ...
Conflicts of Interest: The authors declare no conflict of interest. ...
Acknowledgments: The authors wish to thank the authors and reviewers who made this special issue possible. This is contribution NRL/JA/7230-19-0461. ...
doi:10.3390/rs11182178
fatcat:r63vtyx3qbebpeezjdtowq5f3e
A statistical framework for the analysis of long image time series
2005
International Journal of Remote Sensing
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A contribution of the University of Nebraska Agricultural Research Division, Lincoln, NE, Journal Series No. 14454. ...
Acknowledgments We thank Eric Boer for careful readings of an earlier version and for feedback from two anonymous reviewers. ...
First, we have discussed the importance of the proper use of parametric and non-parametric statistical methods for long image time series analyses. ...
doi:10.1080/01431160512331326657
fatcat:oiccbesgrzca5b3jvqfolbktue
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
2015
Remote Sensing
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Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. ...
) from remote sensing data using machine learning methods. ...
hand, the latter used the multi-temporal remote sensing time series; in this case, the estimation bias will be more local. ...
doi:10.3390/rs71215841
fatcat:jiktp2klrzfffbzdf2ozbbk2ke
A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization
2022
Remote Sensing
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In particular, researchers have investigated the potential of various analytical methods to determine a range of crop parameters using remote sensing data and methods. ...
This paper reviews and analyzes publications from the past 30 years to identify trends in crop monitoring research using remote sensing data and tools. ...
analysis of R 2 in various non-parametric algorithms. ...
doi:10.3390/rs14225633
fatcat:pf54xei3x5d27ae7bed3r3agci
Detection of Change Points in Pseudo-Invariant Calibration Sites Time Series Using Multi-Sensor Satellite Imagery
2021
Remote Sensing
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A single time series was formed using the virtual constellation approach in which multiple sensors data were combined for each site to achieve denser temporal coverage and overcome the limitation of dependence ...
Hence, it can be stated that despite minor changes in all evaluated PICS, they can be used for radiometric calibration of optical remote sensing sensors. ...
Acknowledgments: We would like to thank all the members of the Image Processing Laboratory, SDSU for their constant help and support. ...
doi:10.3390/rs13112079
fatcat:hriihkovfrdj7cbiv4jimz5rua
Editorial Summary, Remote Sensing Special Issue "Advances in Remote Sensing for Global Forest Monitoring"
2021
Remote Sensing
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The need for timely, spatially, and thematically accurate information regarding forests is increasing because of the key role of forests in the global carbon balance and sustainable social, economic, ecological ...
The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental ...
The system introduced combined time-series analysis of small objects that were identified in Sentinel-1 data, which is, segments containing linear features and apparent small-scale disturbances. ...
doi:10.3390/rs13040597
fatcat:5gqmfqf3ojagtid7pd7eyi2m7q
Advancements in satellite remote sensing for mapping and monitoring of alien invasive plant species (AIPs)
2018
Physics and Chemistry of the Earth
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Therefore, to address the inevitable challenges arising with satellite sensor development technology it is necessary to explore the use of robust and advanced non-parametric image classifiers with data ...
This work, therefore, provides an overview of advancements in satellite remote sensing for mapping and monitoring of AIPs and associated challenges and opportunities. ...
This technique can be tested both on single date and on time-series analysis to improve the detection of AIPs. ...
doi:10.1016/j.pce.2018.12.004
fatcat:n7wymqa37jdyhjqrx5ebf5e3gy
Research Progress on Remote Sensing Classification Methods for Farmland Vegetation
2021
AgriEngineering
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How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of ...
Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. ...
Data from different sensors vary in space, time, spectrum, direction, and polarization. Therefore, for the same area, multiple sources of remote sensing data can be obtained. ...
doi:10.3390/agriengineering3040061
fatcat:55boz33pybhahihfb23onp7in4
Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting
2019
Sensors
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Longer term, the results of this work will support finite element model updating for predictive analysis of structural capacity. ...
The results were then validated on experimental data from fatigue testing with high accuracy. ...
Acknowledgments: The authors would like to thank Hugh Bruck and Paul Lara (University of Maryland) for providing access to experimental data used in this work. ...
doi:10.3390/s19204571
pmid:31640152
pmcid:PMC6832978
fatcat:2ushiprlxvf3vnqgffvskfxv6i
A Perspective on Gaussian Processes for Earth Observation
2019
National Science Review
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GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions ...
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. ...
In [10] a GP-based conditional independence test is combined with a scalable causal discovery algorithm allowing high-dimensional graphical models to be inferred from time-series data. ...
doi:10.1093/nsr/nwz028
pmid:34691913
pmcid:PMC8291600
fatcat:o2hsdgspajb6hgak572nhb4ree
Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review
2018
Remote Sensing
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In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps. ...
Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to ...
The authors also thank and acknowledge Siu-Ming Tam for comments and guidance on the report, as Chair of the Task Team at the time the report was written. ...
doi:10.3390/rs10091365
fatcat:dnc5d73szjgk3lfn2c2kzsmbay
The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis
2021
Forests
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A myriad of methods based upon remotely sensed data have been developed, tailored to the different definitions of forest health considered, and covering a broad range of spatial and temporal scales. ...
Forests are increasingly subject to a number of disturbances that can adversely influence their health. Remote sensing offers an efficient alternative for assessing and monitoring forest health. ...
Despite UAV being far less scalable and with higher unitary cost than using satellite data, hence limiting the possibility of gathering data over time, the use of UAVs to collect remote sensing data has ...
doi:10.3390/f12081134
fatcat:7q66eewvunbhxeis3q7cq6ngju
Relating Time Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics
[chapter]
2012
Signal and Image Processing for Remote Sensing, Second Edition
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A better understanding of the relationship between remote sensing and in situ observations at regular time intervals will contribute to the use of remotely sensed spectral data in the development of an ...
This chapter therefore focuses on the extraction of non-serially correlated seasonal metrics from time-series to evaluate quantitative relationships between remote sensing and meteorological time-series ...
Govender from the Scientific Services of Kruger National Park for scientific input.
IX. References ...
doi:10.1201/b11656-10
fatcat:tjdqukw54fht3crkw4t7lkfxre
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