Abstract
Urbanization is a rapid global trend, leading to consequences such as urban heat islands and local flooding. Imminent climate change is predicted to intensify these consequences, forcing cities to rethink common infrastructure practices. One popular method of adaptation is green infrastructure implementation, which has been found to reduce local temperatures and alleviate excess runoff when installed effectively. As cities continue to change and adapt, land use/landcover modeling becomes an important tool for city officials in planning future land usage. This study uses a combination of cellular automata, machine learning, and Markov chain analysis to predict high resolution land use/landcover changes in Philadelphia, PA, USA for the year 2036. The 2036 landcover model assumes full implementation of Philadelphia’s green infrastructure program and past temporal trends of urbanization. The methodology used to create the 2036 model was validated by creating an intermediate prediction of a 2015 landcover that was then compared to an existing 2015 landcover. The accuracy of the validation was determined using Kappa statistics and disagreement scores. The 2036 model successfully met Philadelphia’s green infrastructure goals. A variety of landscape metrics demonstrated an overall decrease in fragmentation throughout the landscape due to increases in urban landcover.
Highlights
On a global scale, urbanization is predicted to continually harm important ecosystem services far into the future [1], causing continuous challenges for governments, policymakers, and urban planners in resource reallocation [2]
This study finds that LULCCs from Green infrastructure (GI) policies can successfully be modeled in a heterogeneous intra-urban environment using fine-scale data
As Philadelphia continues to grow, GI will be implemented throughout the city to meet its GI program goals
Summary
Urbanization is predicted to continually harm important ecosystem services far into the future [1], causing continuous challenges for governments, policymakers, and urban planners in resource reallocation [2]. Many communities are combating the consequences of urbanization through policies focused on nature-based solutions (NBS). NBS policies encourage actions that help societies address a variety of environmental, social, and economic challenges in sustainable ways [3], using the science–policy–practice interface [4]. Green infrastructure (GI) is a common example of NBS. Green infrastructure can enhance ecosystem function in fewer, larger areas compared to numerous, small patches [5], but connectivity cannot be achieved by solely enlarging the total area of GI The concept of GI describes the interdependence of land conservation and land development, and refers to a contiguous, interconnected green network consisting of a range of natural environments [5].
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