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948 results on '"Tong, Xiaohua"'

37. Validation of Multi-Temporal Land-Cover Products Considering Classification Error Propagation.

Author

Liao, Shicheng, Xie, Huan, Gong, Yali, Jin, Yanmin, Xu, Xiong, Chen, Peng, and Tong, Xiaohua

Subjects
LAND cover, SAMPLE size (Statistics), PRODUCT attributes, EVALUATION methodology, CLASSIFICATION
Abstract

Reducing the lag in the accuracy assessment of multi-temporal land-cover products has been a hot research topic. By identifying the changed strata, the annual accuracy in multi-temporal products can be quickly evaluated. However, there are still two limitations in the accuracy assessment of multi-temporal products. Firstly, the setting of the parameters (e.g., the total sample size, allocation of samples in the changed strata, etc.) in the fundamental sampling design is not based on specific setting criteria. Therefore, this evaluation method is not always applicable when the product or research area changes. Secondly, the accuracy evaluation of multi-temporal products does not consider the influence of misclassification. This can lead to an overestimation of the accuracy of changed strata in single-year evaluations. In this paper, we describe how the total sample and the assignment of samples in every stratum can be adjusted according to the characteristics of the land-cover product, which improves the applicability of the evaluation. The samples in the changed strata that propagate misclassification are essentially pixels that have not undergone any land-cover change. Therefore, in order to eliminate the propagation of this inter-annual classification error, the misclassified samples are reclassified as unchanged strata. This method was used in the multi-temporal ESA CCI land-cover product. The experimental results indicate that the single-year accuracy, considering classification error, is closer to the traditional evaluation accuracy of single-temporal data. For the categories with a small ratio of unchanged strata samples to changed strata samples, the accuracy improvement, after eliminating the classification errors, is more obvious. For the urban class, in particular, the misclassification affects its estimated accuracy by 9.72%. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Global DEM Product Generation by Correcting ASTER GDEM Elevation with ICESat-2 Altimeter Data.

Author

Li, Binbin, Xie, Huan, Liu, Shijie, Ye, Zhen, Hong, Zhonghua, Weng, Qihao, Sun, Yuan, Xu, Qi, and Tong, Xiaohua

Subjects
LAND cover, SYNTHETIC aperture radar, LASER altimeters, SCIENTIFIC method, DIGITAL elevation models
Abstract

Advancements in scientific inquiry and practical applications have put forward a higher demand for the accuracy of global digital elevation models (GDEMs), especially for GDEMs whose main data source is optical imagery. To address this challenge, integrating GDEM and satellite laser altimeter data (global coverage and high-accuracy ranging) is one of the important research directions, in addition to the technological enhancement of the main data source. In this paper, we describe the datasets and algorithms used to generate a GDEM product (IC2-GDEM) by correcting ASTER GDEM elevation data with ICESat-2 altimeter data. The algorithm scheme presents the details of the strategies used for the various challenges, such as the processing of DEM boundaries, the fusion of the different data, the geographical layout of the satellite laser altimeter data, etc. We used a high-accuracy global elevation control point dataset and multiple high-accuracy local DEMs as the validation data for a comprehensive assessment at a global scale. The results from the validation comparison present that the elevation accuracy of IC2-GDEM is evidently superior to that of the ASTER GDEM product. The root-mean-square error (RMSE) reduction ratio of the corrected GDEM elevation is between 16 % and 82 %, and the average reduction ratio is about 47 %. From the analysis of the different topographies and land covers, it was also found that this error reduction is effective even in areas with high topographic relief (>15°) and high vegetation cover (>60 %). ASTER GDEM has been in use for more than a decade, and many historical datasets and models are based on its elevation data. IC2-GDEM facilitates seamless integration with these historical datasets, which is essential for longitudinal studies examining long-term environmental change, land use dynamics, and climate impacts. Meanwhile, IC2-GDEM can serve as a new complementary data source to existing DEMs (Copernicus DEM, etc.) mainly sourced from synthetic aperture radar (SAR) observation. By cross-validating qualities, filling data gaps, conducting multi-scale analyses,etc., it can lead to more reliable and comprehensive scientific discoveries, thereby improving the overall quality and reliability of earth science research. IC2-GDEM product is openly released via https://doi.org/10.11888/RemoteSen.tpdc.301229 (Xie et al., 2024). [ABSTRACT FROM AUTHOR]

Published
2024
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50. Generation of 100 m, Hourly Land Surface Temperature Based on Spatio-Temporal Fusion

Author

Tang, Yijie, Wang, Qunming, Tong, Xiaohua, Atkinson, Peter M., Tang, Yijie, Wang, Qunming, Tong, Xiaohua, and Atkinson, Peter M.

Abstract

Landsat surface temperature (LST) is an important physical quantity for global climate change monitoring. Over the past decades, several LST products have been produced by satellite thermal infrared (TIR) bands or land surface models (LSMs). Recent research has increased the spatio-temporal resolution of LST products to 2 km, hourly based on Geostationary Operational Environmental Satellites (GOES)-R Advanced Baseline Imager (ABI) LST data. The spatial resolution of 2 km, however, is insufficient for monitoring at the regional scale. This paper investigates the feasibility of applying spatio-temporal fusion to generate reliable 100 m, hourly LST data based on fusion of the newly released 2 km, hourly GOES-16 ABI LST and 100 m Landsat LST data. The most accurate fusion method was identified through a comparison between several popular methods. Furthermore, a comprehensive comparison was performed between fusion (with Landsat LST) involving satellite-derived LST (i.e., GOES) and model-derived LSMs (i.e., European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis v .5 (ERA5)-Land). The spatial and temporal adaptive reflectance fusion model (STARFM) method was demonstrated to be an appropriate method to generate 100 m, hourly data, which produced an average root mean square error (RMSE) of 2.640 K, mean absolute error (MAE) of 2.159 K and average coefficient of determination ( R 2 ) of 0.982 referring to the in situ time-series. Furthermore, inheriting the advantages of direct observation, and the fusion of Landsat and GOES for the generation of 100 m, hourly LST produced greater accuracy compared to the fusion of Landsat and ERA5-Land LST in the experiments. The generated 100 m, hourly LST can provide important diurnal data with fine spatial resolution for various monitoring applications.

Published
2024

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