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In recent years, non-point source pollution (NPSP) has become prominent in the Yellow River Basin (YRB) of China and seriously affects human survival and sustainable development. The Henan section was selected as an example region of the YRB in China, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model has been used to quantitatively evaluate nitrogen (N) and phosphorus (P). The results show that (1) the cultivated land (CL), grassland (GL), and unused land (UL) decreased, while forest land (FL), water areas (WAs), and built-up areas (BAs) increased in 2000–2020, and precipitation increased from the northeast to the southwest in 2000–2020; (2) the N export was much more than P export in 2000, 2010, and 2020, and the N and P exports increased in 2000–2020; the spatial distribution of N and P export changes was different in the whole basin in 2000–2010, 2010–2020, and 2000–2020; (3) the N and P exports decreased in 2000–2010, 2010–2020, and 2000–2020 with increasing precipitation; and (4) the N and P exports on CL and BAs were more than those on the other land-use types, and the overall change trends of N and P exports on all land-use types increased from 2000 to 2020.

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.

Introduction: Water is an important ecological factor, and non-point source pollution (NPSP) seriously affects water security. In recent years, NPSP has affected water security and human survival in China.Methods: The Luhun Lake Basin has been selected as an example region of Yellow River Basin (YRB) in China, and the trends and impacts of nitrogen (N) and phosphorus (P) has been quantitatively evaluated by using geographic information system (GIS), remote sensing (RS) and InVEST model.Results: The results show that (1) the arable land and water bodies decreased, while forest land, grass land, construction land and unutilized land increased in the 20 years. The average annual precipitation increased from the northeast to the southwest in the 20 years; (2) the N and P exports first increased in 2000–2010 and then decreased in 2010–2020, and finally increased in 2000–2020; (3) with the increase of precipitation, the N and P exports decreased in 2000–2010, 2010–2020 and 2000–2020; (4) the amount of N and P exports on arable land and construction land were more than that on the other land use types. The N and P exports on each land use type all first increased and then decreased, and the overall change trend all increased in 2000–2020.Discussion: This study demonstrates how geographic information system (GIS), remote sensing (RS) and InVEST model can be used to assess nitrogen and phosphorus pollution in data scarce regions.

Tropical regions experience a diverse range of dense clouds, posing challenges for the daily reconstruction of chlorophyll-a concentration data. This underscores the pressing need for a practical method to reconstruct daily-scale chlorophyll-a concentrations in such regions. While traditional data reconstruction methods focus on single variables and rely on specific factors to infer missing data at specific locations, these single-variable methods may falter when applied to tropical oceans due to the scarcity of available data. Fortunately, all oceanographic variables undergo similar atmospheric and marine dynamic processes, creating internal relationships between them. This allows for the reconstruction of missing data through correlations between variables. Thus, this study introduces a multivariate reconstruction approach using the extended data interpolating empirical orthogonal function (ExDINEOF) method to reconstruct missing daily-scale chlorophyll-a concentration data. The ExDINEOF method considers the simultaneous relationships among multiple variables for data reconstruction in tropical oceans. To verify the method’s robustness, missing data were reconstructed during the formation and passage of severe tropical cyclone Hudhud through the Bay of Bengal. The results demonstrate that ExDINEOF outperforms traditional data reconstruction methods, exhibiting favorable spatial distribution and enhanced accuracy within the dynamic tropical marine environment. Furthermore, an assessment of marine physical environmental factors associated with chlorophyll-a concentration data provides additional evidence for the ExDINEOF method’s accuracy. Notably, the ExDINEOF method offers comprehensive spatial distribution aligned with underlying physical mechanisms governing phytoplankton distribution patterns, detailed phytoplankton growth, bloom, extinction variations in time series, satisfactory accuracy, and comprehensive local-level details.

Analyzing runoff variation characteristics and quantifying the impact of different factors on the runoff variation in the source area of Lancang River, are of significance for scientific response to the ecological protection of the region and Lancang River. The Budyko method is adopted to quantitatively calculate the contribution rate of human factors and climate factors to runoff change in the source area of Lancang River. The results show that: 1) the annual runoff at Qamdo hydrological station decreased significantly during 1961–2018. 2) 1966 is the mutation year of runoff at Qamdo hydrological station from 1961 to 2018. 3) At the Qamdo hydrological station, the contribution rates of precipitation P) to the runoff change are 16.92–72.8% before 1990 and -1.91∼-53.61% after 1990, the contribution rates of potential evaporation (ET0) to the runoff changes are 0.14–39.19%. The contribution rates of human factors ω) to the runoff changes are between 26.9% and 114.42%. This study has important theoretical reference and practical significant for maintaining the integrity and sustainable development of the ecosystems of the Lancang River.

Nighttime light (NTL) images obtained by the Visible Infrared Imaging Radiometer (VIIRS) mounted on the National Polar-orbiting Partnership (NPP) could objectively represent human activities and instantly identify urban shapes on a temporal and spatial scale. From 2013 to 2020, the built-up areas of eight provincial capital cities were extracted using NPP/VIIRS NTL data to examine the dynamic changes in city expansion and socioeconomic development in the Yellow River Basin during the urbanization process. The spatial characteristics of urban built-up area expansion were generated using the eight-quadrant analysis method and combined with the statistical data of population and (gross domestic product) GDP to analyze the correlations between the light intensity of built-up areas, population and GDP; this enables an understanding of the changes in population and economy in the development of urban built-up area expansion. The findings show that: (1) unbalanced city development existed in the Yellow River Basin’s upper, middle, and lower reaches, and the expansion and light intensity of cities in the upper reaches were slower than those in the middle and lower reaches; (2) the spatial differentiation of urban expansion was significant between each of the reaches in the Yellow River Basin, and greatly influenced by natural geographical elements; and (3) positive correlation exists between light intensity, population, and GDP in the built-up areas of the middle and lower reaches, while the correlations in the upper reaches were not stable. In conclusion, light data indirectly reflects urban development and could be used as a substitute variable for socioeconomic development indicators under certain conditions.