Superterrestrial - Adriano Zarosinki by RMIT Landscape Architecture

Improving ‘Betweenness’ with ‘Greenness’ Opportunities for Remotely Sensed Geospatial Data and Spatial Modelling in Improving the Economic Viability of Surabaya’s MRT Program Through Enhanced Ridership

Adriano Zarosinski s3813507

superterrestrial

Acknowledgement

References

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business.

de Abreu-Harbich, LV, Labaki, LC & Matzarakis, A 2015, ‘Effect of tree planting design and tree species on human thermal comfort in the tropics’, Landscape and Urban Planning, vol. 138, pp. 99109. Berawi, MA, Saroji, G, Iskandar, FA, Ibrahim, BE, Miraj, P & Sari, M 2020, ‘Optimizing land use allocation of transit-oriented development (TOD) to generate maximum ridership’, Sustainability, vol. 12, article no: 3798. Buchhorn, M, Lesiv, M, Tsendbazar, NE, Herold, M, Bertels, L & Smets, B 2020, Copernicus Global Land Cover Layers—Collection 2, Remote Sensing 2020, no. 12, vol. 108, 1044. doi:10.3390/rs12061044 Center for International Earth Science Information Network (CIESIN) 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, Columbia University, Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/ H49C6VHW. Chen, KW 2021, ‘Algorithm-assisted design: supporting integrative design in the built environment’, lecture, ARCH1366, RMIT University, viewed 15 March 2021. Czaja, M, Kolton, A & Muras, P 2020, ‘The complex issue of urban trees – stress factor accumulation and ecological service possibilities’, Forests, vol. 11, no. 932, article no: f11090932. Donovan, GH, Prestemon, JP, Butry, DT, Kaminski, AR & Monleon, VJ 2021, ‘The politics of urban trees: tree planting is associated with gentrification in Portland, Oregon’, Forest Policy and Economics, vol. 24, article no: 02387. Esri & Earthstar Geographics 2009, World Imagery, [Updated 8 May 2021], retrieved from https:// services.arcgisonline.com/ArcGIS/rest/services/ World_Imagery/MapServer. Global Future Cities Programme 2018, Surabaya: City context report, viewed, 15 March 2020, <https://www.globalfuturecities.org/node/348>. Hansen Partnership & City Form Lab 2014, Surabaya urban corridor development program, final report, Hansen Partnership & City Form Lab. Hengl, T 2018, Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m (Version v02) [Data set]. Zenodo. 10.5281/ zenodo.1475451 Holling, CS & Goldberg, MA 1971, ‘Ecology and planning’, Journal of the American Institute of Planners, vol. 37, no. 4, pp. 221-230.

Knowles, R & Ferbrache, F 2019, ‘Introduction to transit oriented development and sustainable cities: economics, community and methods’, in R Knowles & F Ferbrache (eds.), Transit oriented development and sustainable cities: economics, community and methods, Edward Elgar Publishing, ProQuest Ebook Central database, pp. 1-10. Lin, J Wang, Q & Li, X 2021, ‘Socio economic and spatial inequalities of street tree abundance, species, diversity, and size structure in New York City’, Landscape and Urban Planning, vol. 206, pp. 1-11. Maxar Technologies & Google Earth Pro 2021, Surabaya, Indonesia, 7°16’11.54”S, 112°44’29.33”E, 2D Map, viewed April 2021, Google Earth Pro Program. Muñoz Sabater, J 2019, ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), doi:10.24381/cds.e2161bac. OpenStreetMap contributor 2021, Planet dump [Data file from April 2021]. Retrieved from https:// planet.openstreetmap.org Planet Labs Inc 2016, Planet SkySat Public Ortho Imagery [Data set]. Planet Labs Inc. Google Earth Engine Data catalog https://developers.google.com/ earth-engine/datasets/catalog/SKYSAT_GEN-A_ PUBLIC_ORTHO_RGB Reed, C & Lister, N 2014, ‘Ecology and design: parallel genealogies’, Places Journal, viewed 04 March 2021, <https://placesjournal.org/article/ ecology-and-design-parallel-genealogies/#0>. Sevtsuk, A 2021, ‘Integrating urban design and analytics for walkable cities’, lecture, ARCH1366, RMIT University, viewed 03 March 2021. U.S. Geological Survey n.d., Landsat-8 Collection 1 Tier 1 (32-day composite), 2020, U.S. Geological Survey. (NDBI) Vailshery, LS, Jaganmohan, M & Nagendra, H 2013, ‘Effect of street trees on microclimate and air pollution in a tropical city’, Urban Forestry & Urban Greening, vol. 12, pp. 408-415. Waty, G 2021, ‘Transit oriented development & streets for people revolution’, lecture, ARCH1366, RMIT University, viewed 03 March 2021.

Abstract

he Surabaya Urban Corridor Development Program (UCDP) is funded by the World Bank to address demand in response to Indonesia’s recent rapid growth and period of de-industrialisation. In establishing the UCDP, the project offers a holistic review of Surabaya’s proposed Mass Rapid Transit (MRT) system, containing a north-south aligned tram line which passes through Central Surabaya between Jembatan Merah in the north and the Wonokromo River and Joyoboyo in the south. Using key principles of Transit Oriented Development (TOD) and Pedestrian Oriented Development (POD), the UCDP offers a series of recommendations to add value to the city’s mass transit system. Ridership is an integral component in the economic justification of the running costs of mass transport systems, with most global transport companies requiring subsidies to sustain their operations (Berawi et al. 2020). Thus, project feasibility can be justified through strategies aimed at facilitating access and, in turn, increasing ridership and boosting project revenue. Established research suggests a ‘6Ds’ model for TOD — density of development, diversity of land use, design of urban grid, distance to transit, destination accessibility and demand management (Knowles & Ferbrache 2019). The UCDP echoes this ‘6Ds’ model by building on the TOD and POD foundations of integrated land use and transit through the implementation of compact, mixed use communities around transit corridors and nodes (Waty 2021). I propose that there should be a seventh ‘D’ – di disstin incct arb arbo oreal pr pres esen ence ce – in the TOD model. There is an opportunity for remotely sensed spatial data to analyse vegetation health, contrasted against socio-economic statistical data, to identify and propose targeted streetscape improvements through vegetation planting and, in so doing, improve ridership, increasing revenue and enhancing project economic feasibility.

A heterogeneous ecological structure is vital for ecosystem health and ecological integrity (Reed & Lister 2014). Similarly, this ecosystems thinking – that is, acknowledging, respecting and responding to the complex interrelationship between agents within a system – should be applied to the planning and design of urban environments (Holling & Goldberg 1971). In the case of the MRT project in Surabaya, the UCDP identifies eight key framework directions, one of which being jalur hijau (‘the green axes’) (Hansen Partnership & City Form Lab 2014). The jalur hijau reinforces Surabaya’s low-carbon green agenda, through enhancing and extending the network of green spaces and boulevards along the MRT route (Hansen Partnership & City Form Lab 2014; Global Future Cities Programme 2018). There is an opportunity, however, for the UCDP to build on the existing jalur hijau and, through remotely sensed spatial vegetation data such as the Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI), identify gaps in the existing urban green corridor network. Integrating these vegetation health data with network analysis modelling (e.g., Urban Network Analysis (UNA)), a stronger understanding of spatial accessibility along the MRT route can potentially be established (Sevtsuk 2021). In other words, this thesis posits that by assessing the health and quality of urban trees, one can establish a model for improving canopy cover along and around Surabaya’s MRT corridor to: a) improve spatial accessibility and increase ridership, b) ensure productive tree planting that contributes to offsetting the carbon emissions resulting from the MRT and within Surabaya more broadly, and c) identify and address existing gaps in the structure of the existing urban tree-planting. It is generally accepted that urban trees provide a diversity of environmental, social and economic services and benefits (Donovan et al. 2021; Czaja, Kolton & Muras 2020; Vailshery, Jaganmohan & Nagendra 2013; Lin, Wang & Li 2021). Noting the benefits of urban tree planting along transit routes and connector streets through Surabaya’s kampungs, it is pertinent to also acknowledge the potential negative implications of urban greenery. Czaja, Kolton & Muras (2020) note that urban conditions can pose a breadth of risks to the health and quality of urban trees. Indeed, when optimal temperatures for productive photosynthesis are exceeded, respiration rates elevate and result in higher net CO2 emissions (Czaja, Kolton & Muras 2020). This phenomenon can be exacerbated in tropical cities such as Surabaya (Vailshery, Jaganmohan & Nagendra 2013). So, while the intention of increased urban tree planting may be to act as a ‘carbon sink’ and to address microclimate change, if ambient conditions are not ideal, urban trees can actually become contributors to a city’s net CO2 emissions (Chen 2021). Additionally, Donovan et al. (2021) explain that while urban trees can provide a number of benefits including improved air quality, reduced stormwater

runoff, lower crime and improved public health there is also the possible consequence of gentrification, or green gentrification. The authors found that “any effect of tree planting on gentrification may take over a decade to fully manifest” (Donovan et al. 2021, p. 6). Further, TOD itself has also been linked to unintentional socioeconomic disparity through increasing property values along transport corridors, ultimately gentrifying the area and displacing the very minority groups they were implemented to service (Knowles & Ferbrache 2019). Can spatial data offer any insight into improving urban greenery and access to public transit within the urban environment while ensuring equity is maintained and elevated? The abovementioned socioeconomic implications of MRT systems and urban greening requires a delicate balancing act, reinforcing the notion of ecological thinking in the implementation of urban policy agendas. In implementing the UCDP, decisionmakers, planners and designers alike must be able to recognise the complexity of the system and predetermine the inevitable unexpected consequences (Holling & Goldberg 1971; Reed & Lister 2014). Remotely sensed spatial data, localised economic data and urban systems modelling can offer an insight. While there are substantial benefits to TOD/POD programs in increasing equity of access to public transit for vulnerable demographic groups who do not have access to private motor-vehicles, care must be taken in implementing urban enhancements which may, in-turn, displace the population it was intending to aid. Acknowledging the paradoxical nature of MRT and urban greening, I contend that through remotely sensed spatial data, urban designers and landscape architects alike can identify those areas currently experiencing economic and ecological disparity, and design accordingly. There are substantial economic, environmental and social benefits that can be enjoyed through a carefully contemplated and holistically considered urban transit intervention. Lin, Wang & Li (2021) argue that the distribution of tree resources can be linked to quantifiable socio-spatial characteristics. “Distribution of tree resources is often uneven across geographical areas and different social groups, leading to inequality in accessibility of greenspace and many tree-derived ecosystem services” (Lin, Wang & Li 2021, p. 1). As a result of this environmental inequity, vulnerable populations are exposed to higher levels of atmospheric air pollution and reduced thermal comfort due to higher microclimate change and localised physiologically equivalent temperature (PET) (Vailshery, Jaganmohan & Nagendra 2013; Abreu-Harbich, Labaki & Matzarakis 2015). Therefore, the Surabaya UCDP must also be able to consider the consequences of infrastructural interventions and investment on the exacerbation of the inequity in urban tree density and diversity, and, in doing so, act as a catalyst for change. The implications of carefully implemented urban greening strategies on social equity I contend, are twofold: increasing urban tree planting would firstly help to lower PET and increase effective sequestration of air pollutants (prevalent attributes of low-income neighbourhoods) and, secondly, enhance the pedestrian experience between transit nodes and, as a result, facilitate improved access to services, jobs and amenities afforded by the TOD infrastructure. Returning to the health of urban trees, Czaja, Kolton & Muras (2020) identify three categories of sources for stress factors in urban conditions being dense building, artificial building materials and heavy traffic. Anthropogenic soils have poorer carbon absorption, water retention and microbial quality than unmodified soils (Czaja, Kolton & Muras 2020). Therefore, in implementing a strategy which increases urban tree coverage along and around the MRT route, decision makers must also consider the broader environmental condition above and below ground. Remotely sensed spatial data, again, can also provide insight into soil quality, enabling high-level early intervention throughout the master planning process. Remediation works in problem areas and appropriately considered planting can help to ensure minimal economic obligations in the maintenance of urban trees. Key questions to be answered throughout the research project are: a) What comparisons can be drawn between statistical demographic data and remotely sensed spatial data? b) Can geospatial census data provide any insight into the quality, health and viability of urban vegetation planting?

c) What insights can the health of vegetation provide in determining pedestrian comfort and mass transit ridership ? d) Are urban green spaces in Surabaya currently equitably distributed? What existing connections can be drawn between spatial network modelling (in terms of access to transit nodes) and remotely sense aerial imagery observations? e) Can predictions on localised physiologically equivalent temperatures be modelled based on speculative planting scenarios? Can this offer any insight into improving accessibility for vulnerable communities? To conclude, this thesis intends to demonstrate the opportunity for analysis of remotely sensed spatial data against geospatial demographic and economic data in implementing an effective urban tree planting framework. Through use of data sets which detail existing environmental, temporal, political and economic trends in the context of Surabaya, urban design can pre-empt potential consequences of urban tree planting and implement strategies which may mitigate negative sideeffects. Ultimately, through a comprehensive urban tree planting initiative the thesis contends that hindrances to spatial accessibility, namely destination characteristics and travel costs, can be ameliorated, in-turn increasing ridership and consequently project economic feasibility and revenue.

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The adjacent ‘conceptual map’ utilises available data collated from the UCDP, as an initial investigation into the spatial relationships between the proposed MRT and the urban fabric of Surabaya (Hansen Partnership & City Form Lab 2014). The purpose of this initial map is to frame the research project, looking at the current network of green spaces, the proposed layout of the MRT and key districts along the transit route. The 800m walkable catchment around the proposed MRT route also provides a high-level indication of the accessibility of the transit intervention. The city of Surabaya and the proposed MRT runs parallel to the Kali Mas river, extending southward from the port district. The green space network appears to be focused along the central axis of the CBD, with dispersed outliers of open space scattered outside the study area.

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While Surabaya contains a number of Kampung (established neighbourhoods which pre-date urbanisation and characterised by high population density and informal settlement and high poverty rates), the ‘Kampung Network’ identified in the UCDP is the focus of this research (Das & King 2019). This is the Kebangsren kampung, comprising the villages of Kedungdoro, Wonorejo and Tegalsari (Shirleyana, Hawken & Sunindijo 2018).

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Population data from 2020 is mapped at a resolution of 1km (gridded cells) globally (CIESIN 2018). This map is intended to frame the global context of Surabaya, looking at population density per square-kilometre on a global scale. Capital cities for each country have also been included for reference.

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Data Sources Center for International Earth Science Information Network (CIESIN) 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, Columbia University, Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. OpenStreetMap contributors 2021, Planet dump [Data file from April 2021], retrieved from https://planet.openstreetmap. org. Esri & Earthstar Geographics 2009, World Imagery, [Updated 8 May 2021], retrieved from https://services.arcgisonline. com/ArcGIS/rest/services/World_Imagery/MapServer.

Java is densely populated in comparison to most countries across the globe. Countries demonstrating a similar level of population density (indicated in purple) include India, the east of China, Nigeria, Ethiopia, South Sudan, West Europe and parts of the United States. This population data is important in considering the location of the project within the global context. Additionally, it is pertinent to note that Java is more densely populated than other parts of Indonesia including Sumatra, Kalimantan and Sulawesi. Areas indicated in yellow feature a population density of 100 people or less per square kilometre, in comparison to the 1000+ people per square kilometre observable in Java.

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Lands under snow or ice cover throughout the year Lakes, reservoirs and rivers. Can be either fresh or salt-water bodies. Does not include oceans. Lands with a permanent mLxture of water and herbaceous or woody vegetation. The vegetation can present in either salt, brackish or fresh water. Lands covered by moss and lichen Tree canopy> 70%, almost all needle leaf trees remain green all year. Canopy is never without reen folia e. �-----------------------Tree canopy> 70%, almost all broad leaf trees remain green year round. Canopy is never without reen folia =-e. ------�---� �--� � � � � Tree canopy> 70%, consists of seasonal needle leaf tree communities with an annual ds.'--�-��- - - - - - f l=e�•�• f�o�n�•=n== d Ie�•�• f�o= ff :oe,cr1•"o°"' �cCL.Cc�Ie�o= �- - �- Tree canopy> 70%, consists of seasonal broad leaf tree communities with an annual c cle of leaf-on and leaf-off eriods. Does not match any of the above definitions Top layer trees 15-70% and second layer mLxed shrubs and grasslands, almost all nee dle leaf trees remain green year round. Canopy is never without green foliage. Top layer trees 15-70% and second layer mLxed of shrubs and grasslands, almost all broad leaf trees remain green year round. Canopy is never without green foliage. Top layer trees 15-70% and second layer mLxed of shrubs and grassland, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off pe riods. Top layer trees 15-70% and second layer mLxed of shrubs and grasslands, consists of seasonal broad leaf tree communities with an annual cycle of leaf-on and leaf-off peri ods. Does not match any of the above definitions.

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Using Collection 2 of the Copernicus Global Land Cover dataset, this map demonstrates the main land cover types at a resolution of 100m (Buchhorn et al. 2020). The data was collected from Google Earth Engine and mapped using QGIS, and is overlayed on Esri world imagery.

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Shn1bs (20) Herbaceous vegetation (30) Cultivated and n1anaged vegetation/agriculture (40) Urban/built up (50) Bare/sparse vegetation (60) Snow and ice (70) Pern1anent water bodies (So) Herbaceous wetlands (90) Moss and lichen (100) Closed forest, evergreen needle leaf (111) Closed forest, evergreen broad leaf (112)

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In responding to the thesis, this map intends to interrogate land cover patterns on a global scale, to better understand the context of Surabaya and Java more broadly. Through visualising land cover data, one can better understand the condition which planning and design must respond to in appropriately addressing canopy cover at a micro scale. This map also enables a comparative analysis of the interest area with neighbouring countries and prevalent land cover patterns.

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Closed forest, deciduous needle leaf (113) Closed forest, deciduous broad leaf (114) Closed forest, n1ixed (115) Closed forest, other (116) Open forest, evergreen needle leaf (121) Open forest, evergreen broad leaf (122) Open forest, deciduous needle leaf (123) Open forest, deciduous broad leaf (124) Open forest, n1ixed (125) Open forest, other (126)

Data Sources Buchhorn, M, Lesiv, M, Tsendbazar, NE, Herold, M, Bertels, L & Smets, B 2020, Copernicus Global Land Cover Layers— Collection 2, Remote Sensing 2020, no. 12, vol. 108, 1044. doi:10.3390/rs12061044 OpenStreetMap contributors 2021, Planet dump [Data file from April 2021], retrieved from https://planet.openstreetmap. org. Esri & Earthstar Geographics 2009, World Imagery, [Updated 8 May 2021], retrieved from https://services.arcgisonline. com/ArcGIS/rest/services/World_Imagery/MapServer.

At this scale, it can be observed that Java features a large portion of cultivated/agricultural land (40) with areas of evergreen broad lead closed forest (112) 11 uchhorn et al. 2020). This observable level of managed vegetation is an indication of the extent of modification which the island of Java has experienced, and something to be considered across further analysis.

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Projecting the same data at a smaller scale, further observations can be made in relation to land cover patterns across Indonesia. Urban/built-up areas (50) 5 ere not immediately identifiable at the larger scale, however in this map a better understanding of how the land has been affected by anthropogenic land use can be developed. Surabaa is predominantly urban land, spreading out toward the south and the southwest, with coastal herbaceous wetlands to both the north and south-east of the city centre. Surabaya, Indonesia

Another interesting observation is the ‘corridors’ of urban/agricultural land spanning between the cities of Surabaya, Yogyakarta, and Semarang. While at the larger scale there appeard to be large portions of closed forest land, at this scale these areas appear to be scattered patches with encroaching human-centred land uses.

Acknowledging and recognising this in the design response of the proposed MRT would be an important consideration.

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Enhanced Vegetation Index (EVI) is mapped at a global scale at a resolution of 30 kilometres, using median Landsat 8 32-day composite for the year of 2020. Unlike NDVI, EVI is responsive to structural variation of tree canopy, taking into consideration canopy architecture, plant and canopy type and structural variation (Huete, Dida, Miura, Rodriguez, Gao & Ferreira 2002). The median was taken to compensate for cloud cover, and ensure clarity in the projection of data. Although Landsat imagery is taken at a resolution of 30 metres, given the scale of the global projection, a coarser resolution was adopted.

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At this scale, comparisons can be drawn to better understand the quality of vegetation in Indonesia in comparison to other parts of the world. Those areas indicated in orange or yellow represent a lower EVI value and as such poorer quality vegetation/canopy health (-1.0). Conversely, those areas in green represent high EVI values (+1.0) and comparatively healthier (or “greener”) canopy.

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Data Sources Landsat-8 (32-day composite), 2020, U.S. Geological Survey. (EVI) OpenStreetMap contributors 2021, Planet dump [Data file from April 2021], retrieved from https://planet.openstreetmap. org. Esri & Earthstar Geographics 2009, World Imagery, [Updated 8 May 2021], retrieved from https://services.arcgisonline. com/ArcGIS/rest/services/World_Imagery/MapServer.

This is an important insight in considering the broader thesis, noting the quality of vegetation observable at this resolution as compared to larger resolution imagery in the proceeding maps.

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Data Sources Muñoz Sabater, J 2019, ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), doi:10.24381/cds.e2161bac. OpenStreetMap contributors 2021, Planet dump [Data file from April 2021], retrieved from https://planet.openstreetmap. org. Esri & Earthstar Geographics 2009, World Imagery, [Updated 8 May 2021], retrieved from https://services.arcgisonline. com/ArcGIS/rest/services/World_Imagery/MapServer.

The adjacent climate map utilises ERA5-Land (Copernicus) post-processed by ECMWF, calculating monthly-mean average for January 2020 (Munoz Sabater 2019). Skin temperature has been projected in the current map which is the theoretical temperature of surface of the earth in the uppermost surface layers (Munoz Sabater 2019). This is particularly relevant in evaluating the impact of optimal temperature and tree health, and consequently, the risk of higher respiration rates and higher net CO2 emissions (Czaja, Kolton & Muras 2020). The mean average temperature for January 2020 in Indonesia is at the higher end of the temperature spectrum (orange), reflective of its location within the tropics. Notably, the skin temperatures appear to be quite consistent across the islands of Indonesia, as compared to the high temperatures evident across Australia and the below-freezing temperatures to the global south and global north, as would be expected. Again, given the scale of the map, data is displayed at a coarse resolution (100km).

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LEGEND Capital City

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Projected at the maximum available resolution of approximately 10km (0.1 arc minutes), the above skin-temperature maps give a more detailed overview of median surface temperature throughout the year of 2020 (Munoz Sabater 2019). What these maps illustrate is a consistent average surface temperature across Indonesia throughout the year.

Again, this is an important consideration in evaluating tree health and in proposing an appropriate response to vegetation/canopy cover. While neighbouring countries display a fluctuation in surface temperature throughout the year (particularly noticeable in Australia and across China), Indonesia and the broader network of islands tend to display a consistent high median surface temperature.

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Overlaying building footprints over Enhanced Vegetation Index (EVI) data (Landsat 8) provides a sense of the current condition of Surabaya and the correlation between vegetation health and building density. EVI in this map is projected at a resolution of 30 metres, providing a good sense of the vegetation quality across Surabaya (US Geological Survey n.d.). Building footprints include residential, commercial, industrial and public buildings (OpenStreetMaps 2021). Limitations in data availability have restricted opportunities for in depth analysis of building typology and use, and further research and data would be required in order to deepen this assessment.

Data Sources U.S. Geological Survey n.d., Landsat-8 Collection 1 Tier 1 (32-day composite), 2020, U.S. Geological Survey. (EVI) OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org

While a correlation between density of building footprint and quality of vegetation can be drawn, there are several gaps where vegetation health does not appear to correlate

with the presence of buildings. Additionally, poor vegetation quality can also be observed along the road networks. Additionally, visually assessing EVI map, there a few evident ‘patches’ of high-quality tree coverage, however,there are equally areas which demonstrate very poor EVI qualities.

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Scale 1:10,000 @ A0 Data Sources U.S. Geological Survey n.d., Landsat-8 Collection 1 Tier 1 (32-day composite), 2020, U.S. Geological Survey. (NDBI) OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet.openstreetmap.org

The Normalized Difference Builtup Index (NDBI) for Surabaya is shown in this series of maps. NDBI is calculated using near infrared (NIR) and shortwave infrared (SWIR) derived from Landsat 8 imagery [NDBI = (SWIR – NIR) / (SWIR + NIR)]. The data is projected at 30m resolution (US Geological Survey n.d.). The more built-up areas are indicated by lighter pixels. Designated open space areas have been mapped and are indicated in green in the map opposite over NDBI 2020 projected median. The above maps show the NDBI of Surabaya in 2015 and again in 2020, for comparison. This data comparison provides insight into the pattern of development and change within Surabaya over a fiveyear period. To further understand this change, using the raster calculator, the adjacent map indicates those areas which have experienced the most change in NDBI between 2015 and 2020, highlighted by those darker purple patches. What these map indicates is the level of development or hard surface across the study area. Development along the Mas River displays a mixed reflectance indicating a mix of use (built-up and non-builtup areas). Similar to the EVI map, it can be seen that areas west of the Mas River display a high NDBI, including the neighbourhoods of Kupang Krajan, Banyu Urip, Putat Jaya and Pakis.

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In an attempt to get a better understanding of the development patterns of East Java, this map projects NDBI also at a resolution of 30-metres, 30however at a scale 1:100,000 to indicate the broader context. As compared to districts to the south such as Sidoarjo, Mojokerto and Pasuruan, Surabaya presents a high NDBI demonstrating the level of development within the city. A ‘corridor’ of built-up areas is observable oriented from southward from Surabaya through to Pasuruan, with less built up areas buffering to the east and west.

LEGEND

In considering the potential for the proposed MRT project, these data provide an insight of the broader context of development and built up areas within the East Java region.

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Data Sources U.S. Geological Survey n.d., Landsat-8 Collection 1 Tier 1 (32-day composite), 2020, U.S. Geological Survey. (NDBI) OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org

Unlike Surabaya, the Bangkalan district opposite the Teluk Lamong bay, displays a mix of highly built up areas with equally non-built-up areas. Ideally, the MRT intervention would be able to help generate a better balance between NDBI values across the Surabaya district.

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Using available data, this map attempts to understand the spatial density of residential development within the study area. Using OSM data, the heat-map (Kernel Map) assesses the proximity of residential buildings within an 800 metre radius (OpenStreetMap 2021). Residential buildings include apartments, houses, dormitories and hotels.

LEGEND Kampung Network (Surabaya UCDP)

It is apparent from this map that density increases as one moves south of the port district. The greatest density of residential buildings are apparent to the west of Mas River (Kali Mas). This is consistent with the location of the Kampung District, identified in the UCDP. Patches of high residential density are also apparent in areas to the east of the port district, as well as in Mojo and Pacar Kembang east of the Mas River.

Train line Rail station Low NDBI

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Scale 1:10,000 @ A0

Data Sources Landsat-8 (Tier 1), 2020, U.S. Geological Survey. (NDBI) OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org

This residential density map provides further insight into the NDBI projection, and pattern of development across Surabaya. It also broadens understanding of why the NDBI data presents areas of Low NDBI versus High NDBI.

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To better understand the relationship between natural systems, the built environment, public transport routes and green open space areas, this map collates this information. Data collected from Open Street Maps (2021) has been manipulated to highlight the relationship between built form and natural waterways such as rivers and streams across the city. This data is useful in understanding the relationship between vegetation and building indices, and areas of potential improvement, particularly in the context of the proposed UCDP and MRT intervention.

LEGEND

The layout of the existing rail system appears to respond to and follow the network of streams and rivers in the city. There does, however, also appear to be a disconnect between the rail system and existing public open space areas. This is something to be considered and addressed in the MRT.

Kampung Network (Surabaya UCDP)

Building Footprint Waterbody Train line Rail station Green open space Data Sources OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org

Scale 1:10,000 @ A0

An interesting insight of this map is that the Kampung Network area features only one small open space area in the north east, and a small portion of a watercourse within the south-east.

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Surabaya Soil Texture

Function and Use Ecosystem Services

In an attempt to better understand the distribution of green space around the study area this map explores soil texture across Surabaya. Soil texture appears to be predominantly clay and clay loam, with evidence of sandy clay and sandy clay loam soils around the periphery of the city (Hengl 2018).

Soil Texture at 100m depth

Soil Texture at 200m depth

Read in conjunction with the EVI maps, NDBI maps and land-uses around Surabaya, observations and correlations between soil type and distribution of built-up areas can begin to be observed.

LEGEND Kampung Network (Surabaya UCDP)

Rail station Rail line

Of relevance to the research thesis, soil type and quality can have a major impact on quality of vegetation health. Using remotely sensed spatial data, soil type can be mapped and policy/strategic interventions can be appropriately addressed. Limitations exist, however, given the resolution of the data available (250m).

SOIL TEXTURE Clay Silty Clay Sandy Clay Clay Loam Silty Clay Loam Sandy Clay Loam Loam Silty Loam Sandy Loam Silt Loamy Sand Sand

Scale 1:10,000 @ A0

The main map assess soil types at a depth of 0-metres. The series of smaller, accompanying maps show soil types at 10, 30, 60, 100 and 200 metre depths.

Data Sources Hengl, T 2018, Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m (Version v02) [Data set]. Zenodo. 10.5281/zenodo.1475451 OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org

Depending on future data availability or further analysis of soil quality, this could be developed further in other research to map correlations between vegetation health, soil quality, and statutory interventions.

Surabaya Open Space Area Density

Engaging Mobility Function and Use

High-resolution aerial imagery (0.8m) is overlayed with a kernel density analysis of open space areas (Planet Labs Inc 2016). This provides a snapshot of the current condition of Surabaya within the real-world as-built context. Using OSM open space data, a Kernel Density analysis has been carried out to determine density of proximity between public open space areas (OpenStreetMap 2021). A radius of 400m was used to generate the heat-map. This radius was selected as, at least within the Australian context, a 400m catchment is considered as the walkable catchment.

LEGEND Rail station Rail line Public Transport Route Low Proximity between Open Space Areas

High Proximity between Open Space Areas

Scale 1:6,000 @ A0

Within the broader study area, there is generally poor proximity between public open space areas. Available public transport data has also been placed into this map to see if there is any correlation between public open space proximity and public transport. There does not appear to be any correlation. Data Sources Planet Labs Inc 2016. Planet SkySat Public Ortho Imagery [Data set]. Planet Labs Inc. Google Earth Engine Data catalog https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org

Given the resolution of the data, the extent of raster data able to be exported for the given area was limited, without compromising the resolution.

Surabaya Street Network & MRT Route Engaging Mobility

This image is a high-level analysis of the existing and proposed network of vehicle and public transit networks. The street network has been mapped from data collected by OpenStreetMap, with any data regarding footpaths, service roads or bicycle paths removed from the dataset. The proposed MRT route and stations taken from the UCDP has also been mapped. This data was traced using a polyline on QGIS, as the data is not publicly available. The train route and stations have also been mapped.

LEGEND Train line Rail station Proposed MRT route Proposed MRT station Waterbody Street network Data Sources Hansen Partnership & City Form Lab 2014, Surabaya urban corridor development program, final report, Hansen Partnership & City Form Lab. OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org

Scale 1:10,000 @ A0

The purpose of this map is to better understand where the ‘Kampung Network’ sits within the broader street and public transport network. As can be seen at this scale, the Kampung Network area comprises a number of east-west connector roads throughout the precinct which generally align with the MRT stations.

Kampung Network Area

Scale 1:5,000 @ Ao

2016

Proposed MRT route Proposed MRT station

Scale 1:2,500 @ A0

High resolution aerial imagery is used here to understand the context of the Kampung Network area from a visual perspective (Maxar Technologies & Google Earth Pro 2021; Planet Labs Inc 2016). The base map is high-resolution imagery exported via Google Earth Engine, at a resolution of 0.8 metres. This shows the condition of the Kampung District as of the year 2016. Satelite imagery by Planet Labs Inc (2016) is collected via RGB (8-bit) bands. In order to compensate for cloud coverage, the median value of each band was exported and projected using QGIS. While the base map shows the condition of the Kampung Network during 2016, 1-kilometre grids of aerial imagery from the years of 2004, 2010 and 2019 have been overlayed across the map for a visual comparison. What this reveals is that there has been very little change over the 15 year period between 2004 and 2019 within the subject area. Canopy coverage is limited, given the small street network and densley packed buildings. The portion to the south of the Kampung Network area is a stark contrast in terms of street layout, canopy coverage and distance between buildings.

LEGEND -

Urban Form Typology and Density

Data Sources Maxar Technologies & Google Earth Pro 2021, Surabaya, Indonesia, 7°16’11.54”S, 112°44’29.33”E, 2D Map, viewed April 2021, Google Earth Pro Program. OpenStreetMap contributor 2021, Planet dump [Data file from April 2021]. Retrieved from https://planet.openstreetmap. org Planet Labs Inc 2016, Planet SkySat Public Ortho Imagery [Data set]. Planet Labs Inc. Google Earth Engine Data catalog https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB

Further analysis will attempt to interrogate the potential for the UCDP to provide improved canopy coverage throughout the Kampung Network, to improve access along arterial bouldevards to the MRT stations.

LEGEND Road network �) )

Building Footprint Proposed MRT route

Proposed MRT station NoNDVI Lo,vNDVI

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Kampung Network EVI and NDVI

Urban Form Ecosystem Services

While EVI has been analysed in previous maps, here focus is on the Kampung Network area specifically. Very minimal green pixels are evident, demonstrative of the site’s poor canopy cover. High EVI values are recorded to the north-east and south-west of the Kampung Network.

LEGEND

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This data is effective in identifying gaps in existing canopy cover and for determining opportunities for improving the tree canopy and green corridor. Building footprints have also been projected to clearly identify those areas between buildings which lack quality vegetation coverage.

Building Footprint Proposed MRT route

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Landsat-8 (32-day composite), 2020, U.S. Geological Survey. (EV!) OpenStreet.\fap contributors. (2021) Planet dump [Data file from April 2021 ]. Retrieved from https://planet. openstreetmap.org Hansen Partnership & City Form Lab 2014, Surabaya urban corridor development program, final report, Hansen Partnership & City Form Lab.

For comparison, the map above projects the same vector data, over NDVI data. This has been provided to demonstrate the discreet variation in the quality of data between the two indices. Discrepancies in the NDVI data, are the result of differences in canopy architecture, plant and canopy type and structural variation which are not accurately accounted for when compared to EVI (Huete, Dida, Miura, Rodriguez, Gao & Ferreira 2002).

Kampung Network NDBI

LEGEND Urban Form Typology and Density

Road network

The adjacent NDBI data projected at a resolution of 30-metres confirms the conclusions previously addressed in the EVI map. The intensity of hard-surface (built-up) areas in the Kampung Network area becomes particularly clear in this map. While surrounding areas display a variation of NDBI values, the Kampung Network area is predominantly yellow/orange, showing that there is a very high level of development here and a clear absence of vegetation coverage.

Proposed MRT route Proposed MRT station Low NDBI

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Scale 1:2,500 @ A0

While there is relatively well balanced built-up areas along and around the MRT route, NDBI values intensify further from the tram line into the Kampung.

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Limited publicly accessible spatial data is available for Surabaya. Using the high-resolution aerial imagery, approximate tree locations were mapped as points using GIS. These points, once mapped, were then processed through a 50m radius kernel density analysis. This data indicates where there are currently “hot spots” of trees, and echoes the findings from the EVI and NBDI maps.

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A 400m buffer was also added to the proposed MRT route indicating a 400 metre catchment on either side of the proposed route. Most of the tree density is within this 400m tram line buffer, with small patches evident across the rest of the subject site. High tree density

Scale 1:2,500 @ A0

Source Hansen Partnership & City Form Lab 2014, Surabaya urban corridor development program, final report, Hansen Partnership & City Form Lab. OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org Planet Labs Inc 2016. Planet SkySat Public Ortho Imagery [Data set]. Planet Labs Inc. Google Earth Engine Data catalog https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB

Another observable pattern emerging from this analysis is that existing canopy coverage tends also to be along arterial roads, with secondary and residential-level roads being generally absent of meaningful tree coverage.

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LEGEND Street network

Proposed MRT route

Proposed MRT station I

Urban Form Ecosystem Services Function and Use

400111 Buffer

Further utilising the kernel density mapping of observed tree coverage within the Kampung Network area, this map intends to compare tree density with the EVI data. Areas of the heat-map in red and orange demonstrate the highest concentration of trees, whereas those areas in blue are more isolated occurrences of trees.

NoEVI Low EV!

The EVI data shown here in contrast, indicates poor EVI in those areas between the heat-map.

HighEVI

This data is useful in analysing the potential for future strategic work that can work toward enhancing canopy cover to improve the broader green axes, as well as pedestrian access to the MRT stations.

Low tree density

High tree density

Scale 1:2,500 @ A0

Source Hansen Partnership & City Form Lab 2014, Surabaya urban corridor development program, final report, Hansen Partnership & City Form Lab. Landsat-8 (32-day composite), 2020, U.S. Geological Survey. (EVI) OpenStreetMap contributors. (2021) Planet dump [Data file from April 2021]. Retrieved from https://planet. openstreetmap.org Planet Labs Inc 2016. Planet SkySat Public Ortho Imagery [Data set]. Planet Labs Inc. Google Earth Engine Data catalog https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB

This data could be improved by higher resolution analysis of vegetation quality and the pedestrian pathway network however this data is currently unavailable.

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This final map collates all data previously collected, and includes a betweeness analysis run using the Urban Network Analysis tool in Rhino. OpenStreetMap data of the existing street network was projected into Rhino 7 running an Elk/Docofossor script through Grasshopper. It takes into account slope/topography, collected via SRTM data for the Surabaya region (Shuttle Radar Topography Mission).

LEGEND Tree Density Low tree density

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Once having projected the OSM and SRTM data, this was baked and gaps in the network were repaired to facilitate a betweenness analysis using the UNA tool. High tree density /4

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To run the analysis, four randomly selected points were chosen and given a resident value of 20. This was chosen to be a very simple analysis of the existing condition of the Kampung Network in relation to the proposed MRT. These four points were set as the origin with each MRT station set as the destination. No observers were selected in running the UNA analysis. Contrasting this network analysis against the tree density kernel mapping, we can begin to speculate on ac-

cessibility between parts of the kampung area and the proposed MRT routes. While the route to the north of the kampung area returns a low betweeness result, this area has a high density of canopy. Conversely, other paths return high betweenness results, but low canopy density (for example, the central east-west origin/destination network). For further research, these data could continue to be interrogated and analysed. Currently the UNA tool does not enable paths/routes to be assigned values based on spatial qualities (i.e. vegetation/canopy coverage). There is an opportunity, however, for this to be used in proposing appropriate tree planting strategies that can help to improve betweenness and access to MRT stations. This would expand on the potential for spatial accessibility analysis via the UNA tool to expand on travel costs/destination characteristics and determining the impact of ‘greenness’ on ‘betweenness’.