On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

Spatiotemporal heterogeneity of ecosystem service interactions and their drivers at different spatial scales in the Yellow River Basin

Accurately understanding ecosystem service (ES) interactions and an analysis of the complex, multiscale driving mechanisms are foundational prerequisites for implementing effective multiscale ES management. This study dives into the spatial and temporal variations of ES interactions in the Yellow River Basin across four spatial scales. The eXtreme Gradient Boosting (XGBoost) model is later deployed to pinpoint the key drivers of ecosystem services and their indirect pathways to ESs are illuminated utilizing Partial Least Squares-Structural Equation Modeling (PLS-SEM). The results indicate that (1) The synergistic effect between ES pairs in the Yellow River Basin surpasses that of trade-offs. Various types of ecosystem service bundles have transformed into each other from 2000 to 2020, and the spatial patterns of ES interactions bear resemblances at different scales. (2) The factors driving habitat quality (HQ), carbon sequestration (CS), and landscape aesthetics (LA) are mainly the landscape configuration and biophysical conditions. The factor driving food production (FP) is mainly the level of urbanization, whereas soil conservation (SC) and water yield (WY) are mainly subject to climate. (3) When biophysical conditions and level of urbanization serve as mediating variables in pathways, driving factors invariably have negative indirect effects on ESs. When landscape configuration serves as a mediating variable, biophysical conditions positively influence HQ and CS, and negatively impact FP, WY, and LA. Conversely, the level of urbanization negatively affects all ESs. (4) The combination of XGBoost and PLS-SEM offers a comprehensive and innovative lens for analyzing ESs driving mechanisms. Based on our findings, scientific management of ESs should account not only for the direct impacts of driving elements but also for their scale and indirect effects.

New method for scaling nonpoint source pollution by integrating the SWAT model and IHA-based indicators

Nonpoint source (NPS) pollution shows spatial scaling effects because it is affected by topography, river networks, and many other factors. Currently, the lack of an integrated methodology for quantifying the scaling effect has become a crucial barrier in evaluating NPS pollution. In this study, a new method was proposed for scaling NPS pollution by integrating hydrological model and hydrological alteration indicators. Nested catchments were delineated by eight-direction algorithm, and a semidistributed hydrological model was used to simulate the interannual process within the drainage area and to obtain data series of runoff, sediment, and total phosphorus (TP) at different spatial scales. In addition, the average, the extrema, the change rate and feature variables of each type of indicators were proposed to quantitatively describe the pattern of NPS pollution at different spatial scales. The results show the coefficients of variation (CVs) of most runoff and TP indicators are 0.6-0.8, while those of sediment vary greatly from 0.4 to 1.6 with the threshold of those indicators being 0.33. With the increase in drainage area, the NPS load-related indicators show an increasing trend, while load intensity indicators show a decreasing trend and their changing patterns are affected by the heterogeneity of topographic or hydrological information included. Based on logarithmic variance of the change rate, 825 km² was identified as the turning point for scaling transformation where the slope changes dramatically. The proposed methodology comprehensively describes features of the NPS scaling effect that could be utilized for targeted monitoring and control of NPS pollution in other watersheds.

Response Relationship between the Regional Thermal Environment and Urban Forms during Rapid Urbanization (2000–2010–2020): A Case Study of Three Urban Agglomerations in China

Urban agglomerations are currently facing regional thermal environment deterioration. However, the relationship between thermal environment changes in urban agglomerations in response to urban expansion and the underlying urban morphology-driven mechanisms is not clear. This study utilized data from the three largest urban agglomerations in China for 2000, 2010, and 2020 to explore the response of regional heat island changes to urban morphological variations induced by urban expansion through the quantification of urban landscape form, correlation analysis, and relative importance analysis. The results indicate that the distribution of heat source and built-up areas in urban agglomerations has clear spatial and temporal consistency. Moreover, a high regional heat island intensity (RHII) cluster was shown in a “strip-like” form in Beijing–Tianjin–Hebei and the Yangtze River Delta, while the Pearl River Delta, with the most rapid expansion and contiguity of heat source areas, showed a “ring-like” form. RHII was positively correlated with the area of urban clusters and the proportion of built-up areas. However, configuration metrics, such as patch aggregation, also positively affected RHII. Thus, different landscape structures with the same impervious surface area percentage resulted in different RHII values. The relative importance of urban form metrics varied in different urbanization stages; the impervious layer rate was dominant for low and high urban intensity levels, while the shape complexity of urban patches primarily mitigated the thermal environment at the medium urban development level. These results revealed the response relationship between the regional thermal environment and urban morphology, providing insights into how we can improve the regional thermal environment through targeted strategies for optimizing urban form patterns for areas at different urbanization stages.

Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses

The normalized difference vegetation index (NDVI) contains important data for providing vegetation-cover information and supporting environmental analyses. However, understanding long-term vegetation cover dynamics remains challenging due to data outliers that are found in cloudy regions. In this article, we propose a sliding-window-based tensor stream analysis algorithm (SWTSA) for reconstructing outliers in NDVI from multitemporal optical remote-sensing images. First, we constructed a tensor stream of NDVI that was calculated from clear-sky optical remote-sensing images corresponding to seasons on the basis of the acquired date. Second, we conducted tensor decomposition and reconstruction by SWTSA. Landsat series remote-sensing images were used in experiments to demonstrate the applicability of the SWTSA. Experiments were carried out successfully on the basis of data from the estuary area of Salween River in Southeast Asia. Compared with random forest regression (RFR), SWTSA has higher accuracy and better reconstruction capabilities. Results show that SWTSA is reliable and suitable for reconstructing outliers of NDVI from multitemporal optical remote-sensing images.