Multiscale image segmentation plays an important role in object-based image analysis (OBIA) applications, and the evaluation of segmentation quality is a hot topic for OBIA community. Recently, with the increasing use of multiscale and hierarchical strategy in OBIA works, region-based image hierarchies have attracted increasing attentions. A region-based image hierarchy (i.e., binary partition tree and scale-sets hierarchy) is a tree-like structure, and it can be used to record multiscale segmentation results. Although many methods have been employed to evaluate multiscale segmentation, they cannot be applied to evaluate a region-based image hierarchy directly. In this study, a hierarchical segmentation evaluation approach was proposed to evaluate the upper-bound accuracy of a region-based image hierarchy. In our study, an image hierarchy is implemented using a region merging process, and organized as a scale-indexed binary partition tree; the best matching segment of each reference polygon is then selected by scanning whole hierarchy; and finally, the similarity between reference polygons and corresponding best matching segments are used to evaluate the overall performance of the hierarchy. Two high spatial resolution images (GaoFen-2 and QuickBird) and three region merging criteria were used to evaluate the effectiveness of the proposed approach. Moreover, the proposed method was compared with traditional multiscale evaluation strategies. The experimental results have demonstrated the effectiveness of the proposed approach, as well as the advantages compared to existing approaches.

For optical remote sensing images with high spatial resolution and low spectral number, the complexity of ground objects poses great challenges to cloud detection algorithms, such as the differentiation of clouds from objects with similar features as clouds and the identification of thin clouds. In this paper, a novel cloud detection method is proposed for Gaofen-2 remote sensing imagery. The radiation transmittance is estimated based on the dark channel prior, and the overestimated radiation transmittance is corrected using spectral features. A three-step post-processing strategy is adopted to eliminate misidentification introduced by the highlighted surfaces based on object geometric, textural, and boundary features. In the experiments, Gaofen-2 multispectral images with different cloud categories and cloud thicknesses are involved to evaluate the performance of the proposed method. The results show that the proposed method can obtain an average cloud detection accuracy of 0.9573 on six different clouds. The proposed algorithm can also effectively detect both thick and thin clouds with an average accuracy of more than 0.9517. The advantages of the method for thin cloud detection are further demonstrated by comparison with existing algorithms.

Nitrogen dioxide (NO₂) is an important contributor to the formation of acid rain, photochemical smog and aerosol particles, which seriously endangers public health. At present, remote sensing of polar-orbiting satellites is a conventional means to obtain large-scale NO₂ distribution, but it cannot capture the rapid change because of long revisit periods. The Advanced Himawari Imager (AHI) on the Himawari-8 geostationary satellite has the advantage of high time resolution, which makes it possible to realize near-real-time atmospheric monitoring. Here, based on the absorption characteristics of NO₂ in infrared radiation, hourly near-surface NO₂ concentrations are retrieved based on the brightness temperature from AHI and auxiliary information such as meteorology and aerosol. The results of 10-fold cross-validation show that NO₂ estimations from satellite are in good agreement with in-situ measurements, and their determination coefficient (R²) can reach 0.79. Due to different emission and atmospheric diffusion conditions at different time, the model performance presents a diurnal variation of high accuracy in the noon and afternoon but low accuracy in the morning. Based on the retrieval dataset, it is found that high NO₂ concentrations are mainly concentrated in the densely populated and industrial areas such as the North China area. In addition, NO₂ pollution mainly occurs in autumn and winter, and the average NO₂ concentration in winter is about 1.63 times that in summer in 2021. This study provides a new insight for satellite retrieval of NO₂, which is of great significance for real-time pollution monitoring and public health protection.

Wuhan experienced a noticeable enhancement in air quality from January to April 2020 due to the epidemic lockdown. The improvement was a combined result of anthropogenic emission reduction and meteorological variability. Environmental policymakers are often concerned about the impact of industrial production and human activities on improvements in environmental sustainability. This study split and quantified the impact of anthropogenic emissions on the pollution level changes of six major air pollutants (CO, SO₂, NO₂, O₃, PM₁₀, and PM_2.5) for the first half year of 2019 to 2021 in Wuhan with an improved meteorological normalization algorithm. The results show sharp decreases in anthropogenic pollutant loads during 2020, except for O₃, with the ranking of NO₂ > PM₁₀ > SO₂ > CO > PM_2.5. The decrease in NO₂ emissions caused by humans was more than 50% compared to 2019. The low NO₂ led to a decrease in O₃ consumption, resulting in high O₃ concentrations from February to April 2020 during the city lockdown. Moreover, except O₃, the impact of anthropogenic and weather influences on air pollution exhibited opposing effects; that is, meteorology tended to aggravate pollution, while human intervention was conducive to improving air quality, and human factors played the dominant role. Of all six pollutants, O₃ is the one that is relatively least subject to anthropogenic emissions. Although concentrations of SO₂, NO₂, PM₁₀, and PM_2.5 rebounded in 2021, none of them were able to return to their pre-lockdown levels, suggesting the epidemic’s continuous inhibition of people’s activities. Compared with 2019 and 2021, the atmospheric oxidation capacity and secondary aerosol formation showed an overall decreasing trend during 2020. This study provides a reference for assessing the effectiveness of anthropogenic emission reduction policies.