Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase

Unsustainable imbalances in urbanization and ecological quality in the old industrial base province of China

The benign interaction between ecological quality and urbanization is a crucial guarantee for regional sustainability, especially for the old industrial bases in China. However, the linkage and interplay between these two aspects have not yet been well revealed. By using the compound night light index (CNLI) and the remote sensing ecological index (RSEI), we conducted a long-term quantitative assessment of Liaoning, which is a typical old industrial base province in China. Both CNLI and RSEI have been proven to be effective indicators for monitoring and evaluating regional urbanization and ecological quality (EQ) respectively. Trend analysis methods were performed to detect the spatiotemporal dynamics of EQ. The coupling coordination degree (CCD) model is employed to estimate the linkage between urbanization and EQ. Results show that the mean RSEI of Liaoning province generally fluctuated within the range of moderate grade and displayed an oscillating upward trend from 2001 to 2020. The gaps of EQ among different cities were narrowing, and obvious clustering characteristics can be seen. According to the trend analysis findings, the area exhibiting EQ improvement over the past 20 years was larger than that showing degradation, in which forest land and cropland were the main contributors. The results from the CCD model showed that the majority of cities in the case area have not fully entered the stage of transformation development. The imbalance between urbanization and EQ is very severe and common in Liaoning province, which is mainly attributed to the relatively sluggish spatial urbanization. Additionally, the negative impact of spatial urbanization on ecological land use is not obvious in Liaoning, which indicates the relationship between urbanization and ecological quality is not always a simple negative correlation. Our results have an important implication for further understanding urbanization-induced impacts on the ecological environment and promoting sustainable development

Damage and hardening evolution characteristics of sandstone under multilevel creep–fatigue loading

Abstract During the operation of artificial underground structures, the surrounding rock experiences fatigue and creep damage caused by several types of disturbances under long-term constant loading. To quantify the mechanical response of sandstone under creep–fatigue loading, a damage–hardening evolution model based on the linear superposition concept is proposed. In the model, coupling is applied to represent the synergistic effect of creep and fatigue. Creep–fatigue tests of sandstone specimens are conducted under multilevel loading. The damage and hardening effects of sandstone under creep–fatigue loading are complex. Hardening is the dominant effect under low creep–fatigue loads, and damage is the dominant effect under high creep–fatigue loads. The strength of the rock specimens undergoes increasing and decreasing trends under this loading path, and the evolution of the Mohr–Coulomb envelope is discussed. The proposed model can be used to describe the test data and the evolution of the creep–fatigue process. With increasing creep–fatigue number, the acoustic emission amplitude, energy, and cumulative counts increase. However, the amplitude is more sensitive than the energy, indicating that it is more suitable for describing creep–fatigue loading. Furthermore, the peak frequencies of the AE signals are mostly distributed in the 0–15 kHz, 15–30 kHz, 30–45 kHz, and 45–55 kHz regions. The signal proportion in the 45–55 kHz zone decreases with the creep–fatigue number. However, other frequency zones increase with the creep–fatigue number. This phenomenon illustrates that the crack scale of the specimens increases with the creep–fatigue number

SUST and RUST: Two Datasets for Uyghur Scene Text Recognition

The main objective of scene text recognition is to recognize text in complex images and convert it into editable text. However, scene text recognition research has long been focused on English, and there is a lack of research on other small languages, such as Uyghur language. This paper conducts a series of studies on Uyghur text recognition in natural scenes, with the following main contributions: 1) To address the lack of Uyghur scene text recognition datasets, we established the Synthetic Uyghur Scene Text dataset SUST and the Real Uyghur Scene Text dataset RUST, and we augmented RUST with STR-Aug; 2) The contemporary existing STR models are selected to conduct experiments on the dataset proposed in this paper, through which we search for the model structure suitable for Uyghur scene text recognition; 3) According to the characteristics of Uyghur text, this paper designs a lightweight Uyghur text recognition model named LUSTR, which takes into account both lightweight and accuracy, and achieves good performance in the Uyghur scene text recognition dataset proposed in this paper. The SUST and RUST are now publicly available at https://github.com/kongfnajie/SUST-and-RUST-datasets-for-Uyghur-STR

Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase

A Unified Scalable Equivalent Formulation for Schatten Quasi-Norms

The Schatten quasi-norm is an approximation of the rank, which is tighter than the nuclear norm. However, most Schatten quasi-norm minimization (SQNM) algorithms suffer from high computational cost to compute the singular value decomposition (SVD) of large matrices at each iteration. In this paper, we prove that for any p, p1, p2>0 satisfying 1/p=1/p1+1/p2, the Schatten p-(quasi-)norm of any matrix is equivalent to minimizing the product of the Schatten p1-(quasi-)norm and Schatten p2-(quasi-)norm of its two much smaller factor matrices. Then, we present and prove the equivalence between the product and its weighted sum formulations for two cases: p1=p2 and p1≠p2. In particular, when p>1/2, there is an equivalence between the Schatten p-quasi-norm of any matrix and the Schatten 2p-norms of its two factor matrices. We further extend the theoretical results of two factor matrices to the cases of three and more factor matrices, from which we can see that for any 0<p<1, the Schatten p-quasi-norm of any matrix is the minimization of the mean of the Schatten (⌊1/p⌋+1)p-norms of ⌊1/p⌋+1 factor matrices, where ⌊1/p⌋ denotes the largest integer not exceeding 1/p