Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

Shade, Charlotte; Kremer, Peleg

doi:10.3390/land8020028

Cited by 48 publications

(31 citation statements)

References 35 publications

(76 reference statements)

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“…Green infrastructure that incorporates urban nature is important for physical and mental health benefits [5]. Several authors have provided papers on green infrastructure and spatial planning [5][6][7], and they all agree that green urban infrastructure has an important role in the quality of a city's lifestyle. Satellite imagery is the fastest method for data collection for urban planning.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

et al. 2019

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The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

et al. 2019

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“…The CN raster file was derived from the USDA's TR-55 (1986) [3] based on soil type, land use, and land cover. The CN of a few specific features in the urban landscape (i.e., community gardens and GSI installations) were derived from the published literature [35,36]. The curve numbers were assumed to remain constant through the event.…”

Section: Nrcs Methodsmentioning

confidence: 99%

“…The city is highly urbanized, with paved areas accounting for more than 61% of its land cover. Table 1 shows land cover types and the percentage of each class in the study area, based on the one-meter land-cover data [35,36]. The mean annual precipitation in the city exceeds 1000 mm (40 inches) [37], causing major flooding in the city at least 12 days per year [27,38].…”

Section: Study Areamentioning

confidence: 99%

“…The city has a cutting-edge stormwater-management program that promotes research, design, implementation, and maintenance of GSI in urban areas. In addition, there are strict design requirements for stormwater mitigation projects in the city [35]. Nevertheless, urban areas in the city have increased by 11% from 2008 to 2015, while natural areas have decreased by 15%.…”

Section: Study Areamentioning

confidence: 99%

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A Generalized Automated Framework for Urban Runoff Modeling and Its Application at a Citywide Landscape

Hosseiny

Crimmins

Smith

et al. 2020

Water

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This research presents a fully automated framework for runoff estimation, applied to Philadelphia, Pennsylvania, a major urban area. Trends in global urbanization are exacerbating stormwater runoff, making it an increasingly critical challenge in urban areas. Understanding the fine-scale spatial distribution of local flooding is difficult due to the complexity of the urban landscape and lack of measured data, but it is critical for urban management and development. A one-meter resolution Digital Elevation Model (DEM) was used in conjunction with a model developed by using ArcGIS Pro software to create urban micro-subbasins. The DEM was manipulated to account for roof drainage and stormwater infrastructure, such as inlets. The generated micro-subbasins paired with 24-h storm data with a 10-year return period taken from the National Resources Conservation Service (NRCS) for the Philadelphia area was used to estimate runoff. One-meter land-cover and land-use data were used to estimate pervious and impervious areas and the runoff coefficients for each subbasin. Peak runoff discharge and runoff depth for each subbasin were then estimated by the rational and modified rational methods and the NRCS method. The inundation depths from the NRCS method and the modified rational method models were compared and used to generate percent agreement, maximum, and average of inundation maps of Philadelphia. The outcome of this research provides a clear picture of the spatial likelihood of experiencing negative effects of excessive precipitation, useful for stormwater management agencies, city managers, and citizen.

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“…While several studies have already successfully applied predictive land change modelling to support land use management and decision-making processes [9][10][11][12], a thorough literature review indicates that such an approach has been applied in just a few case studies and hence it is necessary to explore further cases in order to assure its applicability across different landscapes. Therefore, a remote area is targeted in Africa covering the Virunga NP and its immediate vicinity.…”

Section: Problem Statement and Research Questionsmentioning

confidence: 99%

Stimulating Implementation of Sustainable Development Goals and Conservation Action: Predicting Future Land Use/Cover Change in Virunga National Park, Congo

Christensen

Arsanjani

2020

Sustainability

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The United Nations 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDG's) presents a roadmap and a concerted platform of action towards achieving sustainable and inclusive development, leaving no one behind, while preventing environmental degradation and loss of natural resources. However, population growth, increased urbanisation, deforestation, and rapid economic development has decidedly modified the surface of the earth, resulting in dramatic land cover changes, which continue to cause significant degradation of environmental attributes. In order to reshape policies and management frameworks conforming to the objectives of the SDG's, it is paramount to understand the driving mechanisms of land use changes and determine future patterns of change. This study aims to assess and quantify future land cover changes in Virunga National Park in the Democratic Republic of the Congo by simulating a future landscape for the SDG target year of 2030 in order to provide evidence to support data-driven decision-making processes conforming to the requirements of the SDG's. The study follows six sequential steps: (a) creation of three land cover maps from 2010, 2015 and 2019 derived from satellite images; (b) land change analysis by cross-tabulation of land cover maps; (c) submodel creation and identification of explanatory variables and dataset creation for each variable; (d) calculation of transition potentials of major transitions within the case study area using machine learning algorithms; (e) change quantification and prediction using Markov chain analysis; and (f) prediction of a 2030 land cover. The model was successfully able to simulate future land cover and land use changes and the dynamics conclude that agricultural expansion and urban development is expected to significantly reduce Virunga's forest and open land areas in the next 11 years. Accessibility in terms of landscape topography and proximity to existing human activities are concluded to be primary drivers of these changes. Drawing on these conclusions, the discussion provides recommendations and reflections on how the predicted future land cover changes can be used to support and underpin policy frameworks towards achieving the SDG's and the 2030 Agenda for Sustainable Development.Sustainability 2020, 12, 1570 2 of 28 protect the critically endangered mountain gorilla (Gorilla Beringei Beringei) [2]. The park covers an area of 790,000 ha [3], and hosts the majority of the fragments of the last remaining habitat suitable for the mountain gorilla. Furthermore, the multitude of variety in nature and climate variables, including large lakes, open land savannah, vast forest areas, snow-covered mountain tops and erupting volcanoes, provides critical habitats for a great variety of the other large species of mammals we associate with Africa [1]. For this reason, the park was inscribed as a United Nations (UN) Educational, Scientific and Cultural Organization (UNESCO) World Heritage site in 1979. However, the NP is located in one of...

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Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

Cited by 48 publications

References 35 publications

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

A Generalized Automated Framework for Urban Runoff Modeling and Its Application at a Citywide Landscape

Stimulating Implementation of Sustainable Development Goals and Conservation Action: Predicting Future Land Use/Cover Change in Virunga National Park, Congo

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