Wetlands play a critical role in the environment. With the impacts of climate change and human activities, wetlands have suffered severe droughts and the area declined. For the wetland restoration and management, it is necessary to conduct a comprehensive analysis of wetland loss. In this study, the Xiong’an New Area was selected as the study area. For this site, we built a new method to identify the patterns of wetland loss integrated the landscape variation and wetland elements loss based on seven land use maps and Landsat series images from the 1980s to 2015. The calculated results revealed the following: (1) From the 1980s to 2015, wetland area decreased by 40.94 km², with a reduction of 13.84%. The wetland loss was divided into three sub stages: the wet stage from 1980s to 2000, the reduction stage from 2000 to 2019 and the recovering stage from 2009 to 2015. The wetland area was mainly replaced by cropland and built-up land, accounting for 98.22% in the overall loss. The maximum wetland area was 369.43 km² in the Xiong’an New Area. (2) From 1989 to 2015, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil moisture monitoring index (SMMI) showed a degradation, a slight improvement and degradation trend, respectively. The significantly degraded areas were 80.40 km², 20.71 km² and 80.05 km² by the detection of the remote sensing indices, respectively. The wetland loss was mainly dominated by different elements in different periods. The water area (NDWI), soil moisture (SMMI) and vegetation (NDVI) caused the wetland loss in the three sub-periods (1980s–2000, 2000–2009 and 2009–2015). (3) According to the analysis in the landscape and elements, the wetland loss was summarized with three patterns. In the pattern 1, as water became scarce, the plants changed from aquatic to terrestrial species in sub-region G, which caused the wetland vegetation loss. In the pattern 2, due to the water area decrease in sub-regions B, C, D and E, the soil moisture decreased and then the aquatic plants grew up, which caused the wetland loss. In the pattern 3, in sub-region A, due to the reduction in water, terrestrial plants covered the region. The three patterns indicated the wetland loss process in the sub region scale. (4) The research integrated the landscape variation and element loss appears potential in the identification of the loss of wetland areas.

Lakes have an important role in human life and the ecological environment, but they are easily affected by human activity and climate change, especially around urban areas. Hence, it is critical to extract water with a high precision method and monitor long-term sequence dynamic changes in lakes. As the greatest natural lake of the Beijing-Tianjin-Hebei region, Baiyangdian Lake has a significant function in human life, socio-economic development, and regional ecological balance. This lake area has shown large changes due to human activity and climate change. The change monitoring process of the water surface is of great significance in providing support for the management and protection of the lake. The Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method is a new, robust, and low-cost method for water extraction, that has obvious advantages in extracting complex water surfaces. In this paper, the SMDPSO method was used to extract the water surface of Baiyangdian Lake by Landsat images from 1984 to 2018. This method has a good effect on complex water surface extraction with vegetation, shadows, and so forth, and the Landsat images have higher resolution and longer time series. The main contents and results of this paper are as follows: (1) We verified the applicability of the SMDPSO method in the Baiyangdian Lake using visual interpretation and correlation analysis. The relative errors between observed and extracted results were all less than 5% in spring, summer, and fall, and the correlation coefficient between the water area and water level was 0.96. (2) According to seasonal verification and comparison of the extraction results, the SMDPSO method was used to extract the water surface area of Baiyangdian Lake during spring of the years 1984–2018. Water area changes of Baiyangdian Lake can be divided into four periods: Dry period (1984–1988), degraded period (1989–2000), stable period (2000–2008), and recovery period (2008–2018). The water area reached a maximum of 280 km² in 1989 and a minimum of 44 km² in 2002. (3) The possible causes of the changes in the water area of Baiyangdian Lake were also analyzed. The changes were caused by climate and human activities during the first and second periods, but mainly human activities during the third and fourth periods. In fact, effective policies combined with water conservancy projects were directly conducive to improving or even recovering the water and ecological environment of Baiyangdian Lake. Considering its importance for the benign development of the Beijing-Tianjin-Hebei Region and the construction of the Xiong’an New Area, a policy is necessary to ensure that the lake’s ecological environment will not be destroyed under the premise of economic development.

Vegetation is an essential component of terrestrial ecosystems and supplies multiple ecosystem benefits and services. Several indices have been used to monitor changes in vegetation communities using remotely-sensed data. However, only a few studies have conducted a comparative analysis of different indices concerning vegetation greenness variation. Additionally, there have been oversights in assessing the change in greenness of evergreen woody species. In this study, we used the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of terrestrial vegetation (NIRv), and the leaf area index (LAI) data derived from MODIS data to examine spatial and temporal change in vegetation greenness in the growing season (May–September) and then evaluated the evergreen vegetation greenness change using winter (December–February) greenness using trend analysis and consistency assessment methods between 2000 and 2022 on the Tibetan Plateau, China. The results found that vegetation greenness increased in 80% of pixels during the growing season (northeastern, central-eastern, and northwestern regions). Nevertheless, a decline in the southwestern and central-southern areas was identified. Similar trends in greenness were also observed in winter in about 80% of pixels. Consistency analyses based on the four indexes showed that vegetation growth was enhanced by 29% and 30% of pixels in the growing season and winter, respectively. Further, there was relatively strong consistency among the different vegetation indexes, particularly between the NIRv and EVI. The LAI was less consistent with the other indexes. These findings emphasize the importance of selecting an appropriate index when monitoring long-term temporal trends over large spatial scales.

The match relationship between urbanization and ecosystem services (ESs) is a cornerstone of achieving sustainable development. However, the evolution patterns of urbanization/ecosystem service (UES) synergies under economic polarization in the rapid urbanization process remain poorly understood. This study integrates bivariate local Moran’s index and correlation analysis methods to examine the match relationship between urbanization and three key ESs (water yield, carbon sequestration, and food production) from 2000 to 2020 and explores the impact of intra-city disparities on the match relationship of urbanization and ESs. The findings revealed that urbanization and three ecosystem services showed increasing trends during 2000–2020 simultaneously. The spatial aggregation pattern of urbanization and ecosystem services showed smaller variations from 2000 to 2020. There was a High-High aggregation between urbanization and water yield in urban built-up areas and primarily High-Low aggregations between urbanization, carbon sequestration, and food production. Furthermore, the impact of urbanization on ESs decreased with increasing urban polarization. In particular, the Beijing–Tianjin–Tangshan region still demonstrated pronounced economic polarization, suggesting disparities in economic development within its urban core. This study highlights the importance of mitigating the adverse effects of urban polarization on ESs and fostering resilient and sustainable urban ecosystems in rapidly developing regions.