Industrial parks are key contributors to localized urban heat intensification, forming sub-industrial heat islands (IHIs) that influence the urban thermal environment. This study investigates the industrial heat island effect (IHIE) in the Guangzhou–Foshan metropolitan area (GFMA), with a generalized additive model (GAM) to explore the influence of park spatial patterns and land cover characteristics, using indicators such as industrial heat island intensity (IHII), industry warming area (IWA), industry warming efficiency (IWE), and industry warming gradient (IWG). The results show that (1) industrial land significantly contributes to industrial heat islands (IHIs), with heat extending up to 200 m into surrounding areas. (2) IHIE intensity varies notably across park types, with each dominated by different factors: manufacturing parks by landscape shape index (LSI); comprehensive parks by impervious surfaces (IWS) and internal building land (IB); and special parks primarily by IB. (3) In most industrial parks, park area (S), IWS, and LSI are the key factors affecting IHIE. As IWS increases, IHIE strengthens, though this trend can be mitigated by expanding park area. Conversely, a higher LSI weakens IHIE. (4) Several variables, including arable land (AL) and water body (WB), exhibited nonlinear or threshold effects, suggesting that IHIE is shaped by complex mechanisms. These findings offer valuable insights for optimizing land use in urban and industrial planning to reduce IHIE and promote sustainable urban development.

UAV-based topographic change detection is widely used in geoscience communities. The change detection involves comparison of two digital elevation models (DEMs) produced by UAV surveys, which are affected by the DEM resolution. Coarse resolution DEMs introduce errors in change detection, but the DEM resolution effect remains poorly understood. Moreover, effective strategies for mitigating the resolution effect have yet to be investigated. This study generated UAV-based DEMs at resolutions ranging from 0.1 m to 10 m with various resampling methods. The impact of DEM resolution on topographic change detection was then evaluated by analyzing the difference of DEM (DoD) and volume budget errors with indices such as mean error (ME), standard deviation (STD), and Moran’s I. The results from two human-altered landscapes showed that the random errors of DoD increase rapidly with the DEM resolution coarsening, while DoD systematic errors (spatial distribution of errors) become stable after 4 m resolution. The volume budget errors also increase with DEM coarsening. Coarser resolution DEMs tend to underestimate the volume budget (gross erosion, gross deposition, and net changes). Moreover, selecting an appropriate method for generating DEM is beneficial in decreasing the errors caused by the resolution effect. Among the seven methods (MAX, MIN, MEAN, BIL, NEAR, NEB, and TIN), the BIL method is optimum for mitigating both DoD and volume errors. The NEAR, NEB, and TIN methods are equivalent, and they are superior to the aggregation methods (MAX, MIN, MEAN). The slope of DoD (SDoD) should be considered when selecting a resolution for change detection. Large errors tend to appear in areas with large SDoD and vice versa. Coarse resolution DEMs are tolerable in areas with low SDoD, while high resolution DEMs are necessary in areas with large SDoD.

Riverscapes are coupled social-ecological systems (SESs), in which the differences between the scales and functioning of interacting social and ecological components ( “mismatch”) impose challenges for global arid basin sustainability. Here, we defined riverscape SESs as networks of connected ecological and social components (nodes) to disentangle the structure and effects of SESs in Heihe River Basin (HRB) in arid regions of northwest China. Results showed the ecological network in HRB has low network density and high vertex strength. Heihe River Basin Bureau, as an emerging bridging organization, changed the SES structure and increased the matching degree of SES from 0.33 to 0.53, which has caused an obvious improvement in the downstream ecology. However, the characteristics of the ecological network demonstrated that cross-boundary management actions restricted to only the river would exacerbate local environmental pressures, such as the continued decline of groundwater in midstream regions and the potential appropriation of water for ecology by the expanding farmland in the downstream region. Our study demonstrated that network analysis could be one promising direction to untangle the complex SES and understand the relationship between SES structure and outcomes. We suggest comanaging the cross-boundary river and lands to further match the SES for basin sustainability.