Changes in land use and landscape caused by human activities, rapid socioeconomic development and climate change disturb the water cycle process and impact the runoff. This study analyzed the runoff responses to different driving factors in a typical basin in the Beijing-Tianjin-Hebei region of North China combined with methods such as geographically and temporally weighted regression, landscape pattern indexes and Budyko theory. The results indicated that the runoff and runoff depth were higher in the central and south part and were lower in the northwest of the basin. Furthermore, the average runoff increased at the later stage of the study period. Artificial surface and land use intensity exerted positive impacts on runoff and runoff depth in most areas. The complex and diverse landscape with a high shape index blocked runoff to some extent. Moreover, runoff depth would increase by 0.724 mm or decrease by 0.069 mm when the rainfall or potential evaporation increased by 1 mm. In addition, population density and the economic development in both rural as well as urban areas put a heavy burden on runoff and water resource in this basin. From above it could be concluded that the impacts on runoff due to environmental change brought by human activities could not be neglected though the runoff was also greatly affected by climate change. This study reflected the runoff responses to driving factors in a typical basin of North China, which will provide reference for water resource protection and give enlightenment to water management.

Land use/cover change (LUCC) can impact the provision of ecosystem service values (ESVs), particularly in wetland regions that are subject to frequent and unsustainable land conversions. Exploring the past and future trajectory of LUCC and its effects on ESV has a great significance for wetland management and habitat stability. This study tried to reveal the patterns and magnitude of LUCC on ESV under varying land development scenarios in the Yellow River Delta region, which is a typical region undergoing serious degradation in China. In this study, a combined approach utilizing equivalent coefficients of ecosystem services was employed to determine the ESV of the wetland in relation to the major land use types (LUTs). The Markov–FLUS model was then used to simulate LUTs across multiple scenarios in 2030 and to clarify the relationship of ESV between wetland and other LUTs. The results indicated that the wetland was severely degraded, with a loss in area of 6679.89 ha between 2000 and 2020. Cropland and water body were the main sources of diversion and turnover for the wetland, respectively. Despite the multiple scenario projections revealed, the wetland area exhibited a similar growth rate and a homogeneity in ESV under the natural development (ND), urban construction and development (UCD), and the ecological development (ED) scenarios. The ED scenario was deemed the optimal development strategy for the wetland ecosystem. Our research will improve the comprehension of land development decisions and promote sustainable development in estuarine wetland areas.

Net primary production (NPP) serves as a critical proxy for monitoring changes in the global capacity for vegetation carbon sequestration. The assessment of the factors (i.e., human activities and climate changes) influencing NPP is of great value for the study of terrestrial systems. To investigate the influence of factors on grassland NPP, the ecologically vulnerable Qinghai–Tibet Plateau region was considered an appropriate study area for the period from 2000 to 2020. We innovated the use of the RICI index to quantitatively represent human activities and analyzed the effects of RICI and climatic factors on grassland NPP using the geographical detector. In addition, the future NPP was predicted through the integration of two modeling approaches: The Patch-Generating Land Use Simulation (PLUS) model and the Carnegie–Ames–Stanford Approach (CASA) model. The assessment revealed that the expanded grassland contributed 7.55 × 10⁴ Gg C (Gg = 10⁹ g) to the total NPP, whereas the deterioration of grassland resulted in a decline of 1.06 × 10⁵ Gg C. The climatic factor was identified as the dominant factor in grassland restoration, representing 70.85% of the total NPP, as well as the dominant factor in grassland degradation, representing 92.54% of the total NPP. By subdividing the climate change and human activity factors into sub-factors and detecting them with a geographical detector, the results show that climate change and anthropogenic factors have significant ability to explain geographic variation in NPP to a considerable extent, and the effect on NPP is greater when the factors interact. The q-values of the Relative Impact Contribution Index (RICI) and the RICI of the land use change NPP are consistently greater than 0.6, with the RICI of the human management practices NPP and the evapotranspiration remaining at approximately 0.5. The analysis of the interaction between climate and human activity factors reveals an average impact of greater than 0.8. By 2030, the NPP of the natural development scenario, economic development scenario (ED), and ecological protection scenario (EP) show a decreasing trend due to climate change, the dominant factor, causing them to decrease. Human activities play a role in the improvement. The EP indicates a positive expansion in the growth rate of forests, water, and wetlands, while the ED reveals rapid urbanization. It is notable that this is accompanied by a temporary suspension of urban greening.

The processes of urbanization and climate change have exerted a marked influence on net primary productivity (NPP). However, the underlying mechanisms that drive these effects remain intricate and insufficiently understood. The processes of urbanization and climate change both have an adverse effect on habitat quality (HQ) and biodiversity loss. The HQ has a direct influence on the health and stability of ecosystems, which regulate the level of NPP. A higher HQ is associated with stronger NPP. Now, the quantification and assessment of the impacts of climate change and urbanization on NPP are still challenging because of the various driving factors and the intricate mechanisms influencing the production of terrestrial vegetation. Therefore, a new perspective was adopted to study the effects of urbanization and climate change on NPP in the Qinghai–Tibet Plateau in China during 2000–2020. The spatiotemporal analysis method was employed to investigate the impact of the night light urbanization index and climate factors on NPP in different HQ regions (the HQ is divided into five levels, with each area type corresponding to a specific HQ level). Then, the coupled coordination model (CCD) was used to analyze the coupling coordination relationship between NPP and HQ. Finally, the relative contribution of urbanization and climate change to NPP was studied using scenario simulation. The results showed that (1) NPP in the whole Tibetan Plateau increased very little, with an average growth rate of 0.42 g C m⁻² per year. (2) It was surprising to find that NPP in urban areas did not decline significantly as a result of urbanization. However, there was a notable decline in NPP in higher HQ areas. (3) The mean contribution of urbanization to NPP change was found to be 17%, while the mean contribution of climate change and other factors to NPP change was 69% and 14%, respectively. These findings provide valuable insights into the interactions between human development and environmental factors, enhancing our comprehension of their role in the Tibetan Plateau’s carbon cycle.

The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as $Hv$ (high valley value of the 14 May, 26 May, and 7 June SAR data), $Tb$ (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and $Lo$ (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.