A temporal-spectral value and shape change detection method integrating thematic index information and spectral band information.

Satellite imagery time series change detection methods are effective in avoiding pseudochange due to vegetation phenology to a certain extent. Traditional time series change detection methods use thematic indexes (e.g., NDVI, RVI) to obtain time series information for corresponding change detection. However, change detection methods using several thematic index time series may not make full use of other spectral band information in remotely sensed images and may still suffer from over- and under-detections. To address this challenge, a temporal-spectral value and shape change detection method integrating thematic index information and spectral band information (TISB) is proposed. Possible clouds and cloud shadowing phenomena are removed according to the changes in the spectral values of the remotely sensed images to avoid the generation of pseudochanges in clouds. The spectral and time series information is used to obtain change information from the value perspective, and then, further possible enhanced change regions from a shape perspective to obtain the final change detection results through the expectation-maximization (EM) method. Experiments with Landsat images have shown that the TISB method improves detection results by approximately 1-4% compared to the comparison method.

Analysis of carbon emissions from land cover change during 2000 to 2020 in Shandong Province, China.

Land cover change affects the carbon emissions of ecosystems in some way. The qualitative and quantitative understanding of carbon emissions from human activities (e.g., land cover change, industrial production, etc.) is highly significant for realizing the objective of carbon neutrality. Therefore, this paper used GlobeLand30 land cover maps, annual average normalised difference vegetation index (NDVI) data, annual average net ecosystem productivity (NEP) data and statistical yearbook data from 2000 to 2020 to explore the relationship between land cover change and carbon emissions. Specifically, it included land cover change, carbon storage changes influenced by land cover change, spatial and temporal analysis of carbon sources and sinks, land use intensity change and anthropogenic carbon emissions. The results of the study show that the main land cover changes in Shandong province during 2000-2020 was cultivated land conversion to artificial surfaces. Among them, the area of cultivated land converted to artificial surfaces from 2000 to 2010 was 4930.62 km2, and the proportion of cultivated land converted to artificial surfaces from 2010 to 2020 was as high as 78.35%. The total carbon stock of vegetation affected by land cover change decreased by 463.96 × 104 t and 193.50 × 104 t in 2000-2010 and 2010-2020 respectively. The spatial and temporal distribution of carbon sources and sinks differed more markedly from 2000 to 2020, and land use intensity changes in Shandong Province showed an upward trend. Of the total energy production, industry has the largest energy consumption, followed closely by total energy consumption in transportation, storage and postal services.

Spatial and temporal changes analysis of air quality before and after the COVID-19 in Shandong Province, China.

Due to the COVID-19 pandemic outbreak, the home quarantine policy was implemented to control the spread of the pandemic, which may have a positive impact on the improvement of air quality in China. In this study, Google Earth Engine (GEE) cloud computing platform was used to obtain CO, NO2, SO2 and aerosol optical depth (AOD) data from December 2018-March 2019, December 2019-March 2020, and December 2020-March 2021 in Shandong Province. These data were used to study the spatial and temporal distribution of air quality changes in Shandong Province before and after the pandemic and to analyze the reasons for the changes. The results show that: (1) Compared with the same period, CO and NO2 showed a decreasing trend from December 2019 to March 2020, with an average total change of 4082.36 mol/m2 and 167.25 mol/m2, and an average total change rate of 4.80% and 38.11%, respectively. SO2 did not have a significant decrease. This is inextricably linked to the reduction of human travel production activities with the implementation of the home quarantine policy. (2) The spatial and temporal variation of AOD was similar to that of pollutants, but showed a significant increase in January 2020, with an average total amount increase of 1.69 × 107 up about 2.54% from December 2019 to March 2020. This is attributed to urban heating and the reduction of pollutants such as NOx. (3) Pollutants and AOD were significantly correlated with meteorological data (e.g., average temperature, average humidity, average wind speed, average precipitation, etc.). This study provides data support for atmospheric protection and air quality monitoring in Shandong Province, as well as theoretical basis and technical guidance for policy formulation and urban planning.