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Landscape characteristics modulate heatwave extreme severity at the regional scale in Germany
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10980-009-9402-4openalex-snapshot
Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns
inferredcites →https://doi.org/10.1007/s10980-012-9833-1openalex-snapshot
Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona
inferredcites →https://doi.org/10.1007/s10980-021-01302-xopenalex-snapshot
The importance of spatial configuration of neighbouring land cover for explanation of surface temperature of individual patches in urban landscapes
inferredcites →https://doi.org/10.1007/s10980-022-01439-3openalex-snapshot
A landscape connectivity approach to mitigating the urban heat island effect
inferredcites →https://doi.org/10.1007/s11284-016-1411-6openalex-snapshot
Does forest fragmentation cause an increase in forest temperature?
inferredcites →https://doi.org/10.1007/s11356-021-15763-9openalex-snapshot
Spatiotemporal variability of land surface temperature in north-western Ethiopia
inferredcites →https://doi.org/10.1016/j.apgeog.2013.07.004openalex-snapshot
The impact of tree cover loss on land surface temperature: A case study of central Massachusetts using Landsat Thematic Mapper thermal data
inferredcites →https://doi.org/10.1016/j.enconman.2018.02.087openalex-snapshot
Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China
inferredcites →https://doi.org/10.1016/j.inffus.2021.11.011openalex-snapshot
Tabular data: Deep learning is not all you need
inferredcites →https://doi.org/10.1016/j.isprsjprs.2016.02.008openalex-snapshot
The seasonal carbon and water balances of the Cerrado environment of Brazil: Past, present, and future influences of land cover and land use
inferredcites →https://doi.org/10.1016/j.scitotenv.2016.02.029openalex-snapshot
The impact of built-up surfaces on land surface temperatures in Italian urban areas
inferredcites →https://doi.org/10.1016/j.scitotenv.2016.10.195openalex-snapshot
Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia
inferredcites →https://doi.org/10.1016/j.scitotenv.2018.11.108openalex-snapshot
Quantifying and simulating landscape composition and pattern impacts on land surface temperature: A decadal study of the rapidly urbanizing city of Beijing, China
inferredcites →https://doi.org/10.1016/j.scitotenv.2023.164422openalex-snapshot
How can urban green spaces be planned to mitigate urban heat island effect under different climatic backgrounds? A threshold-based perspective
inferredcites →https://doi.org/10.1016/j.tra.2018.02.009openalex-snapshot
Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo
inferredcites →https://doi.org/10.1038/s44284-024-00091-zopenalex-snapshot
Mitigating urban heat island through neighboring rural land cover
inferredcites →https://doi.org/10.1073/pnas.1315126111openalex-snapshot
Afforestation in China cools local land surface temperature
inferredcites →https://doi.org/10.1088/1757-899x/960/3/032041openalex-snapshot
Transformation of the Lower Lusatia Landscape in the Context of Climate and Energy Change
inferredcites →https://doi.org/10.1111/ecog.04617openalex-snapshot
landscapemetrics : an open‐source R tool to calculate landscape metrics
inferredcites →https://doi.org/10.1111/ecog.05360openalex-snapshot
Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution models
inferredcites →https://doi.org/10.1111/gcb.14415openalex-snapshot
Canopy structure and topography jointly constrain the microclimate of human‐modified tropical landscapes
inferredcites →https://doi.org/10.1177/15501329221106935openalex-snapshot
Research and application of XGBoost in imbalanced data
inferredcites →https://doi.org/10.1186/s12302-023-00738-zopenalex-snapshot
Perspectives of lignite post-mining landscapes under changing environmental conditions: what can we learn from a comparison between the Rhenish and Lusatian region in Germany?
inferredcites →https://doi.org/10.18637/jss.v028.i05openalex-snapshot
Building Predictive Models in R Using the caret Package
inferredcites →https://doi.org/10.3389/fenrg.2023.1275733openalex-snapshot
Biomass and bioenergy perspectives of a coal region: status quo, potential and scenarios in Lusatia
inferredcites →https://doi.org/10.3389/fevo.2019.00064openalex-snapshot
Scaling Contagious Disturbance: A Spatially-Implicit Dynamic Model
inferredcites →https://doi.org/10.3390/mining2020014openalex-snapshot
TRIM4Post-Mining: Transition Information Modelling for Attractive Post-Mining Landscapes—A Conceptual Framework
inferredcites →https://doi.org/10.4141/cjss2010-063openalex-snapshot
A history of lignite coal mining and reclamation practices in Lusatia, eastern Germany
inferredcites →https://doi.org/10.5194/adgeo-32-49-2012openalex-snapshot
Potential climate change impacts on the water balance of subcatchments of the River Spree, Germany
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