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Accuracy Assessment and Validation of Remotely Sensed Data and Product II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 10335

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Special Issue Editors

Dr. Giorgos Mallinis

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Guest Editor

Laboratory of Photogrammetry and Remote Sensing (PERS Lab), School of Rural and Surveying Engineering, The Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
Interests: land use/land cover (LULC) mapping; forests; classification development and comparison; geographic object-based image analysis; natural disasters; UAS; ecosystem services
Special Issues, Collections and Topics in MDPI journals

Dr. Charalampos Georgiadis

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Guest Editor

School of Civil Engineering, Laboratory of Photogrammetry and Remote Sensing Unit (PERS Lab), School of Rural and Surveying Engineering, The Aristotle University of Thessaloniki, Univ. Box 465, GR-54124 Thessaloniki, Greece
Interests: remote sensing; land use/land cover (LULC) mapping; photogrammetry; unmanned aerial systems (UAS); LiDAR; GIS; 3D modelling; mobile mapping systems; image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of sensor technology, automation of the processing chains and the increased availability of free or low-cost, nearly continuous measurements of Earth’s surface, a growing interest is noted for remote sensing applications in various scientific domains and themes. This interest has been also fueled by the need for accurate spatially-explicit information on Earth’s physical cover to address pressuring demands for policies and actions for sustainable development in terms of efficiency in resources use, disaster risk reduction, ecosystems monitoring and protection. However, such usage of remotely sensed data, requires reliable and quantitative accuracy reports to support the confidence in the information generated. Accuracy assessment and validation is essential in remote sensing-based projects since decision making or scientific analysis with data of unknown or little accuracy will result in information with low reliability, error propagation effects and subsequently, of limited value.

The aim of this special issue is to explore new challenges and new insights related to the assessment of the thematic and positional accuracy of remotely sensed data and derived products.

Research contributions, as well as surveys are welcome. In particular, novel contributions covering, but not limited to, the following subtopics are welcome

Accuracy assessment of approaches focusing in time-series analysis of remotely-sensed data
Benchmarking and evaluation of different classification approaches
Accuracy metrics for object extraction from remote sensing images
Error and uncertainty analysis within the accuracy assesment process
Design and protocols for the validation of large‐area remote sensing products
Accuracy assesment of novel platforms and sensors i.e. Unmanned Aerial Systems (UAS), LiDAR data etc.

Dr. Georgios Mallinis
Dr. Charalampos Georgiadis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Positional accuracy
Horizontal accuracy
Thematic accuracy
Sampling design
Accuracy metrics

Published Papers (5 papers)

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Research

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19 pages, 4863 KiB

Open AccessArticle

Consistency Analysis and Accuracy Evaluation of Multi-Source Land Cover Data Products in the Eastern European Plain

by Guangmao Jiang, Juanle Wang, Kai Li, Chen Xu, Heng Li, Zongyi Jin and Jingxuan Liu

Remote Sens. 2023, 15(17), 4254; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15174254 - 30 Aug 2023

Cited by 2 | Viewed by 1011

Abstract

Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were analyzed and evaluated for component similarity, type confusion degree, spatial consistency, and accuracy verification. The research found that the three products provided consistent descriptions of land-cover types in the East European Plain. There was a strong correlation in the type area between the different products, with a correlation coefficient >0.85. Medium-to-high-consistency areas represented 92.31% of the total plains area. The low-consistency areas were mainly concentrated on Yuzhny Island, Kola Peninsula, and Pechora River Basin. The comparison revealed high consistency among the three products in identifying forest, cropland, water, and permanent ice/snow types. However, the consistency was poor for shrubs, wetlands, and bare land. Using the GLCVSS_V1 validation dataset, the highest overall accuracy among the assessed land cover data products was observed in the FROM_GLC (73.96%), followed by GlobeLand30 (69.80%) and GLC_FCS30 (67.29%). The FROM_GLC dataset is suitable for studying forests, tundra, water, and providing an overall representation of the region’s land cover. The GLC_FCS30 dataset is more suitable for agricultural research. The differences between products arise from the differences in classification systems, algorithms, and data correction. In the future, it will be necessary to utilize the advantages of different products for data fusion, focusing on areas with high heterogeneity and easily confused types, and improving the reliability of land-cover data products. Full article

(This article belongs to the Special Issue Accuracy Assessment and Validation of Remotely Sensed Data and Product II)

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25 pages, 2855 KiB

Open AccessArticle

A Framework of Generating Land Surface Reflectance of China Early Landsat MSS Images by Visibility Data and Its Evaluation

by Cong Zhao, Zihua Wu, Qiming Qin and Xin Ye

Remote Sens. 2022, 14(8), 1802; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081802 - 08 Apr 2022

Cited by 3 | Viewed by 1606

Abstract

The Landsat time-series dataset is one of the most widely used datasets for land surface research due to its long time-series and Land Surface Reflectance (LSR) product. Though the United States Geological Survey (USGS) provides Landsat LSR products for later Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI), no early Landsat 1–5 Multispectral Scanner System (MSS) LSR product is generated currently, limiting the research traced back to the 1970s. Atmospheric correction is one of the necessary preprocesses for generating LSR products. However, it is challenging for MSS images, not only because the image quality is lower and bands are different compared with the current sensors, but also because of the multiple effects of other preprocesses, such as radiometric calibration. Based on the Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) model, we propose a novel framework for generating Landsat 1–5 MSS LSR data of China. Ground-based visibility records are introduced to replace the images-based aerosol optical depth (AOD) to effectively generate MSS LSR data of the 1970s. We evaluate the generated MSS LSR data by the cross-validation of the simultaneous observation of MSS and TM sensors in Landsat 4 and Landsat 5 using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) surface reflectance product as the truth value. The evaluation result shows that the generated MSS LSR data is comparable with the later Landsat TM LSR product, with slightly larger uncertainties. In addition, it shows that the non-atmospheric factors (e.g., the difference of relative spectral responses of TM and MSS, the georegistration errors, the radiometric calibration uncertainty, and image noises) bring larger uncertainties than the atmospheric factors (e.g., the AOD retrieval method by visibility) to the cross-validation results. We apply the MSS LSR data generated by the proposed framework on time series analysis in the regions of interest (ROIs) of the spectral-stable land cover in China for all the MSS sensors. The application demonstrates the potential and promise of the MSS LSR data generated by the proposed framework. Full article

(This article belongs to the Special Issue Accuracy Assessment and Validation of Remotely Sensed Data and Product II)

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26 pages, 9306 KiB

Open AccessArticle

Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

by Xiaocheng Zhou, Xueping Liu, Xiaoqin Wang, Guojin He, Youshui Zhang, Guizhou Wang and Zhaoming Zhang

Remote Sens. 2022, 14(1), 83; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010083 - 24 Dec 2021

Cited by 3 | Viewed by 3201

Abstract

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R² values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible. Full article

(This article belongs to the Special Issue Accuracy Assessment and Validation of Remotely Sensed Data and Product II)

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18 pages, 4807 KiB

Open AccessArticle

Improving Estimates of Natural Resources Using Model-Based Estimators: Impacts of Sample Design, Estimation Technique, and Strengths of Association

by John Hogland and David L. R. Affleck

Remote Sens. 2021, 13(19), 3893; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193893 - 29 Sep 2021

Viewed by 1564

Abstract

Natural resource managers need accurate depictions of existing resources to make informed decisions. The classical approach to describing resources for a given area in a quantitative manner uses probabilistic sampling and design-based inference to estimate population parameters. While probabilistic designs are accepted as being necessary for design-based inference, many recent studies have adopted non-probabilistic designs that do not include elements of random selection or balance and have relied on models to justify inferences. While common, model-based inference alone assumes that a given model accurately depicts the relationship between response and predictors across all populations. Within complex systems, this assumption can be difficult to justify. Alternatively, models can be trained to a given population by adopting design-based principles such as balance and spread. Through simulation, we compare estimates of population totals and pixel-level values using linear and nonlinear model-based estimators for multiple sample designs that balance and spread sample units. The findings indicate that model-based estimators derived from samples spread and balanced across predictor variable space reduce the variability of population and unit-level estimators. Moreover, if samples achieve approximate balance over feature space, then model-based estimates of population totals approached simple expansion-based estimates of totals. Finally, in all comparisons made, improvements in estimation were achieved using model-based estimation over design-based estimation alone. Our simulations suggest that samples drawn from a probabilistic design, that are spread and balanced across predictor variable space, improve estimation accuracy. Full article

(This article belongs to the Special Issue Accuracy Assessment and Validation of Remotely Sensed Data and Product II)

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Other

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10 pages, 1913 KiB

Open AccessTechnical Note

Reference Data Accuracy Impacts Burned Area Product Validation: The Role of the Expert Analyst

by Magí Franquesa, Armando M. Rodriguez-Montellano, Emilio Chuvieco and Inmaculada Aguado

Remote Sens. 2022, 14(17), 4354; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174354 - 02 Sep 2022

Cited by 2 | Viewed by 1594

Abstract

Accurate reference data to validate burned area (BA) products are crucial to obtaining reliable accuracy metrics for such products. However, the accuracy of reference data can be affected by numerous factors; hence, we can expect some degree of deviation with respect to real ground conditions. Since reference data are usually produced by semi-automatic methods, where human-based image interpretation is an important part of the process, in this study, we analyze the impact of the interpreter on the accuracy of the reference data. Here, we compare the accuracy metrics of the FireCCI51 BA product obtained from reference datasets that were produced by different analysts over 60 sites located in tropical regions of South America. Additionally, fire severity, tree cover percentage, and canopy height were selected as explanatory sources of discrepancies between interpreters’ reference BA classifications. We found significant differences between the FireCCI51 accuracy metrics obtained with the different reference datasets. The highest accuracies (highest Dice coefficient) were obtained with the reference dataset produced by the most experienced interpreter. The results indicated that fire severity is the main source of discrepancy between interpreters. Disagreement between interpreters was more likely to occur in areas with low fire severity. We conclude that the training and experience of the interpreter play a crucial role in guaranteeing the quality of the reference data. Full article

(This article belongs to the Special Issue Accuracy Assessment and Validation of Remotely Sensed Data and Product II)

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