Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products.

Parameters of geomorphological characteristics are critical for research on yardangs. However, methods which are low-cost, accurate, and automatic or semi-automatic for extracting these parameters are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R² = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R² = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23% with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138).

It is increasingly recognized that classification accuracy should be characterized locally at the level of individual pixels to depict its spatial variability to better inform users and producers of land-cover information than by conventional error-matrix-based methods. Local or per-pixel accuracy is usually estimated through empirical modelling, such as logistic regression, which often proceeds in a class-aggregated or a class-stratified way, with the latter being generally more accurate due to its accommodation for between-class inhomogeneity in accuracy-context relations. As an extension to class-stratified modelling, class-heterogeneity-stratified modelling, in which logistic models are built separately for contextually heterogeneous vs. homogeneous sub-strata in individual strata of map classes, is proposed in this paper for proper handling of within-class inhomogeneity in accuracy-context relations to increase accuracy of estimation. Unlike in existing literature where sampling is usually approached separately, the double-stratification method is also adopted in sampling design so that more sample data are likely allocated to heterogeneous sub-strata (which are more prone to misclassifications than homogeneous ones). This class-heterogeneity-stratified method furnished for sampling and modelling jointly thus constitutes an integrative framework for accuracy estimation and information refinement. As the first step in building up such a framework, this paper investigates the proposed double-stratification method’s performance and sensitivity to sample size regarding local accuracy estimation in comparison with those of existing methods through a case study concerning Globeland30 2010 land cover over Wuhan, China. A detailed review of existing methods for analyses, estimation, and use of local accuracy was provided, helping to put the proposed research in a broader context. Candidate explanatory variables for logistic regression included sample pixels’ map classes, positions, and contextual features that were computed in different-sized moving windows. Relative performances of these methods were evaluated based on an independent reference sample, with all methods found reliable. It was confirmed that the proposed method is in general the most accurate, as observed with varying sample sizes. The proposed method’s competitive performance is thus proved, reinforcing its potential for information refinement. Extensions to and uncertainty aspects of the proposed method were discussed, with further research proposed.

Cyanobacterial blooms, which are increasingly exacerbated by eutrophication and climate change, pose threats to ecosystems and public health. This paper systematically reviews recent advances in microbial intervention strategies for controlling cyanobacterial blooms. Current approaches primarily comprise direct lysis methods, indirect suppression methods, and integrated strategies. Direct algicide methods rapidly lyse cyanobacterial cells and degrade toxins, although their application is constrained by environmental sensitivity and host specificity. Indirect approaches offer sustainable preventive strategies by inhibiting cyanobacterial growth, yet require careful environmental management. Integrated methods combine microbial strategies with other technologies, enhancing both the efficiency and ecological safety of managing cyanobacterial blooms. While microbial strategies demonstrate significant potential, practical implementation faces challenges, including environmental adaptability, ecological safety, and regulatory frameworks. Future research should focus on integrating synthetic biology, intelligent delivery systems, and multi-omics technologies to achieve more effective and environmentally friendly management of cyanobacterial blooms.

There are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for research and other applications. While it is worth developing advanced pattern classifiers and utilizing the images of finer spatial, temporal, and/or spectral resolution for increased classification accuracy, it is also sensible to increase map classification accuracy through effective map fusion by exploiting complementarity among multi-source products over a study area. This paper presents a novel fusion method that works by weighting multiple source products based on their map-reference cover type transition probabilities, which are predicted using random forest for individual map pixels. The proposed method was tested and compared with three alternatives: consensus-based weighting, random forest, and locally modified Dempster–Shafer evidential reasoning, in a case study, over Shaanxi province, China. For this case study, three types of land cover products (GlobeLand30, FROM-GLC, and GLC_FCS30) of two nominal years (2010 and 2020) were used as the base maps for fusion. Reference sample data for model training and testing were collected following a robust stratified random sampling design that allows for augmenting reference data flexibly. Accuracy assessments show that overall accuracies (OAs) of fused land cover maps have been improved (1~9% in OAs), with the proposed method outperforming other methods by 2~8% in OAs. The proposed method does not need to have the base products’ classification systems harmonized beforehand, thus being robust and highly recommendable for fusing land cover products.