The Shire of wind power. Jackie Gu by RMIT Landscape Architecture

The Shire of Wind Power: transforming materials between nature and urban in Anthropocene Jackie Gu

RMIT Design Research Seminar : Superterrestrial Tutor: Dr Yazid Ninsalam

Acknowledgement

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.

Reference

Avdan, U. and Jovanovska, G., 2016. Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 2016, pp.1-8. Bélanger, P., 2017. Landscape As Infrastructure. Abingdon, Oxon: Routledge. Berger, A., 2006. Drosscape. New York: Princeton Architectural Press. Climate Active 2020, About us, viewed 1 April 20200, https://www. climateactive.org.au/what-climate-active/about-us Corner, J. and Hirsch, A., 2014. The Landscape Imagination. New York: Princeton Architectural Press. DAWE https://publications.industry.gov.au/publications/climate-change/ system/files/resources/c42/factsheet-australias-2030-climate-changetarget.pdf DELWP, 2005. Rural Residential Development Planning Practice Note | 37. Department of Environment,land water and plannning, p.1. Department of Environment, Land, Water and Planning (DELWP), Victorian Greenhouse Gas Emissions Report 2019, viewed 1 April 2020, < https:// www.climatechange.vic.gov.au/__data/assets/pdf_file/0016/443014/ Victorian-Greenhouse-Gas-Emissions-Report-2019.pdf > Department of Environment, Land, Water and Planning (DELWP), 2019 Victorian’s Renewable Energy Targets (VRET), viewed 2 April 2020, <https://www.energy.vic.gov.au/renewable-energy/victorias-renewableenergy-targets> Glenelg Shire Council, 2009. Green Triangle Region Freight Action Plan(2009). Glenelg Shire Council, p.3. Hawken, P., 2014. The Ecology Of Commerce. [Place of publication not identified]: HarperBusiness. Odum, H., 1970. A Tropical Rain Forest. Washington, DC: U.S. Atomic Energy Comm., Office of Information Services. Wolman, A., 1965. The Metabolism of Cities. Scientific American, 213(3), pp.178-190.

Exacutive Summary

Wind Energy is in demand worldwide and Victoria is in action aiming at carbon net zero in 2050. There are increasing number of wind infrastructure development in Victoria and it is controversial from stakeholders and local communities. The proposed research project have identified the gap between practical ongoing development of windfarm and policy driven development guild lines. This project sets up Multi-Criteria-DecisionMaking (MCDM) model and generate the geospatial suitability map of future wind-farm development. The result is a balance of practicality of project, community interest and most importantly the environmental impact especially the local vegetation health and fauna habitat. The research is taken from the prospective of post Anthropocene and looking deeply into the urbanization, Ecology by analysing the correlation between varies layers of geospatial data.

Abstract

The Shire of Wind Power: System transforming materials between nature and urban in Anthropocene Jackie Gu

, temperature), biodiversity data, also input the top-down planning data such as infrastructure (proximity to VicRoads, urban growth area, PPL), the natural area (national parks, ramsar, public land management), and the demography data (popolation, land price and total family income as well as medium age by LGA). The data below have been imported to the model(as below).

Wind as the earth material: wind energy in demands Australia is taking a strong, creditable and responsible commitment to the Paris Climate Change conferences” – Australian Government Although contributing to 1.3% of the global emissions, Australia is committed to reduce the GHG to 26-28% on 2005 level by the year of 2030 and it is expected to fall 50-52 per cent when it comes to Australia’s emissions per person, which is linked to the population and economic growth. (DAWE) Department of Industry, Science, Energy and Resources (DISER) have release Carbon neutral certification against Climate Active Carbon Neutral Standard which representing Australia joint effort between government, business and community to evaluate, minimize and offset carbon emissions to address climate change (Climate Active) According to the Environment Victoria (2019), the quickest approach to emission deduction is intervening of coal-burning power station. The closure of Hazelwood Power Station leads to 25.2% less emission in the electricity sector. In addition, the use of renewable energy contribute to the deduction of GGE. According to DELWP (2019), the Victorian Renewable Energy Target (VRET) will witness 25% of the Victorian electricity production by renewable sources and the number is expected to increase by 50% in 2030. Ultimately, Victoria aims at net zero emissions in 2050. DELWP Wind Energy (2020) Data reveals that in 2018, Victoria had 25 functioning wind farms, which produced near 9 per cent of the electricity generated in the Victoria.

Understanding the density: Data generates the site As the population surge globally, the density of the population decrease since the increasing popularity of automobility, whereas density based on two main factors, which is footprint calculation and population assessment (Bélanger, 2017). At the age of Anthropocene, the density is measured beyond the terms of population but all the factors operates in both natural and urban systems. The density reveals the hidden interconnections of externalities, which is demonstrated by the process of production, distribution and consumption. (Berger, 2006) Especially, the energy production can be capture geographically across Victoria, the distribution pattern correlates to the human activity footprint and the consumption is continuous in the territory of time. By deconstructing the locality of wind farm, it occupies a specific geographic location in or in the void of a density map. The relationship between different overlays of densities determined the suitability of infrastructure development site. Such as the development site sits in the void of EVC density and the void of population density, but need to sit closer the power grid. Thus, the analytical site selection analysis have been conducted in State of Victoria scale to investigate which part of the state is suitable for further research. Therefore tools such as Geographic Information System (GIS) has been used to processes data in MCDM model. Data are collected form wide range of sources, including bottom-up geomorphological data (landform, hillside, aspect ridge line, flooding area) , geology data (soil type

it is not in the backyard, but it still affects the local community greatly from territorial and biophysical aspects. National Wind Farm Commissioner Recommendations 8.2.1 – 8.2.7 issued by Shire of Moyne on 05 April 2019 have summarized some the criterial of the wind farm development and suitability. To be more specific, it includes the economic development of the power supply infrastructure, the clear zoning and overlay, regulate the developer’s consolation with local communities, the impact of permits adjacent to the wind farm construction, such as the noise, shadow flickering and visual amenity, and the integration of transmission infrastructure. The project will address the current biophysical and territorial argument between stakeholders and local communities by identifying the suitable windfarm development locations(MAP19), and simulate the sight analysis to investigate where the landscape project could be inserted. (MAP20)

The Current Gap and Research: The result of this model indicates the suitability of the windfarm development. As the bellow drawing indicates, the red area is the most suitable site, the orange one is medium, and the green one is least suitable.

When over layering the suitability map with the LGA map in Victoria (the blue line is the boundary of LGA_Shire of Moyne, the red hatch is the most suitable location for Windfarm, the red outlined area is the existing wind farm site). The Shire of Moyne has the highest percentage of land, which is suitable for wind farm development, so it is valuable to take further research into this site. Since the resolution of the analysis is conducted in Victoria State scale in 2km cell size, the fine-grain cell size of 100m*100m have been used in this project (MAP2-18)

Decentralization: the argument communities and stakeholders

between

local

The landscape of decentralization is generated by the”a range of terrestrial and biophysical configurations that together are generating unprecedented synergies and flexibilities” which is highly adapt to dynamics and argument as well as the uncertainty in the current urbanization, such as resource cycling this century. (Bélanger 2017) The development of the windfarm has terrestrial argument with local communities, since the wind farm in most cases in Victoria takes up the private land, which is part of the broader local communities. It is an unprecedented emerging technology in Anthropocene scale, the land profile and local ecosystem of a the windfarm site remains uncertain. What’s more, the infrastructure site have biophysical argument with local dwellings as well. Because of the size of windmills and poles, which in total is over 120m in height, it is a visual focus when viewing from the distance, especially for local dwellings live kilometres away from the site and is not the owner of the land. Same as the shadow flickering and noise. Although

Windfarm, one of the significant anthropogenic structures, harvest and transforms the endless earth material to the basic need of human activities without releasing pollutions to the environment. Currently, in state of Victoria, the Minister of Planning is responsible for the planning permit of future renewable energy infrastructure. The planning department issued “Policy and planning guidelines for development of wind energy facilities in Victoria” (updated 2019) as development guidelines and other statutory approvals are listed such as Environment Effects Act 1978. The framework of the policy from Planning Department have cleared out a certain areas are prohibit for windfarm development in top-down approach. However, in the void of policy-controlled area, projects area struggled since the local community raises the environmental concerns (National Wind Farm Commissioner Recommendations 8.2.1 – 8.2.7, 2019). Although the land where windfarm development commenced are leased out from private owner, it’s not just sits in a larger ecological system with indigenous flora and fauna, with soil and topography formed by past million years ; also it sits in the urbanization progress that the land may be flexible for future use. The suitability guidance of windfarm development in free policycontrolled area are absent from current decision-making. The development site selection should understand the conditions and relationship between local climate including wind speed, vegetation performance, soil conditions, soil and agriculture productivity local community’s impact sits in the primary location. By using the remote sensing technology and data analytics, we can understand the correlation between varies layers and schedule an effective and sustainable development. The research question is: How the interplay of earth data and human settlement data inform the suitability of future low-impact wind farm development in Shire of Moyne? By investigating at the assemblage of landscape layers, several key aspect can be identified: The atmosphere, the settlement, the vegetated environment, the fauna, the earth surface, and most importantly, the time. Consequently, the research question can be discussed in the following Research sub-question: - What is the correlation between environmental degradation and enhancement (indicated by Normalized Difference Vegetation Index variation) with human settlement flexibility (drawn by Land Use

changes)? - How human settlement pattern change spatially over time? - What is the spatial relationship between geological materials and flexible human settlement - What is the correlation between environmental degradation and enhancement (indicated by NDVI variation) with earth surface conditions? - What is the spatial relationship between atmosphere materials and human settlement? - What is the correlation between environmental vegetation with earth surface conditions?

Methodology: The research is delivered in different phrases and varies method have been used to deliver the outcome. Mapping is a effective device to reveal the hidden correlation between layers of information (Corner 2014). By having access to the remote sensing technology and Geographical Information System, the map have ability to find the not just qualitative but also quantitative hidden correlation between layers. Consequently, the research been conducted through the process of data collection, data clean up and processing, spatial analysis, calculation, modelling as well as data visualization. There are two distinctive highlight of this methodology: Firstly, it focused less on the display the data, but dived into revealing hidden relationship by visualizing the result of quantitative data processing, so there is a statics evidence and algorithm backup behind the mapping.(MAP4,5,6,8,9,10,11,12,13,17,18,19). Secondly, it maps out the changes in the territory of time. It has been done by mathematical calculation of data value in different years , so the result is not just described what have been changed , but also how much have been changed. (MAP 3,4,5,6,8,10,11,12 ,15,16,17,18). This research have been conducted in three phrases: First phrases is the Context research which is conducted in Global scale and Victoria State scale (MAP1-2), Second phrase is the analytical phrases(MAP3-18), within this phrase, maps are produced to investigate and respond to research subquestions. The data has been collected from different tiers of sources, for example, using google earth engine to process remote sensing data and using ArcGIS Pro to process the data from government. Then processed by mathematical algorithms or shapes with attribute table. Excel is used to calculate and summarized the quantitative data. Eventually visualize the result of the data processing in Illustrator and Photoshop. Third phrase is decision making with the help of MCDM, the conclusions from phrase2 (MAP2-18) are translated into the scoring system in its designed weight. The result is illustrated in the MAP19. Finally, it is the simulation phrases (MAP20), which took the result of MCDM to the real context and investigate the causality of MCDM output. At the same time, it opens up the questions to the future research.

1. Wind farm and wind from global scale

2. Geographical location and Power connection

Methodology:

This map located the wind farm in Shire of Moyne in global context. Electricity transmission line layer, global population layer, power plant layer as well as the wind speed have been added to the map.

1. there are 5 datasets been used in this map, State of Victoria and Metro Melbourne Region as well as the Shire of Moyne Region is from Locality Boundaries (Property) (polygon) - Vicmap Admin dataset by Department of Environment, Land, Water & Planning. They have been added in ArcGIS Pro and selecting attribute function have been used.

The Global population data is from GPWv411: Population Density (Gridded Population of the World Version 4.11) by NASA SEDAC at the Center for International Earth Science Information Network. the time frame have been selected as average population from 2013 to 2019.

2. the power plant location and electricity transmission line is from Electricity Transmission Lines datasets by Commonwealth of Australia (Geoscience Australia). The shape file have been brought into the ArcGIS Pro.

The windspeed data is from world clim which is processed through ArcGIS Pro.

3. Vicroad declared road is from VicRoads Declared Roads Dataset prepared by Vicroad, and it have been brought into ArcGIS Pro as shape file.

Power stations have been connected by the proximity definition in grasshopper to investigate the relationship between power plants.

Conclusion: -Shire of Moyne are well connected to the electricity grid in Victoria . and 2 main power lines travel throught the region and seleral branch have been extend out to specific power stations(Including 2 wind farm)

Conclusion:

Australia has rich wind resources along itâ&#x20AC;&#x2122;s costal area as well as the innerland, but only serves small ratio of population. So australia have opportunity to export the electricity generated by windfarm.

State of Victoria Boundary Metro Melbourne Region Shire of Moyne Region Power Plant Power Transmission Network Vic road Declared Road

-The Shire of Moyne have easy access to the road system which connect to other parts of Victoria.

Australia Construction Output %

3. Land use difference and Population difference

4. Land use have been changed from farming to non-farming due to urbanization

Global Lumber Prices

Research Sub-Question

How human settlement pattern changed spatially over time? And what is the scope of future change?

Hypothesis:

- Land use are changed adjacent to population growth area - Land use are changed in cluster and the closer to the cluster of population growth area, the higher density the changes are.

Methodology:

The “human settlement pattern change” is divided into two datasets: the land use changed and population growth. 1. Land use change datasets: two datasets have been processed, a) Victorian Land Use Information System 2016-2017 from Department of Jobs, Precincts and Regions b) Victorian Land Use Information System 2006-2007, two datasets have been imported to ArcGIS Pro as shape file, then been joined according to RFI number of each land parcel. Thus, selection tools have been used to select the area if Land use in 2007 is different to 2017’s and exported as the Red area in the map 2. Population growth is processed by Google earth engine (GEE) platform, the dataset GPWv411: Population Density (Gridded Population of the World Version 4.11) has been used provided by NANA SEDAC, then subtract the value (number of people in a grid cell ) in 2007 from 2019, so we got the population change. So the positive value have been mapped

Conclusion:

-The land use change and population growth area are not always correlated. the area close to the exiting town centre are more likely to sit adjacent to(within 100m) each other -Land use changed parcel are denser but smaller in area when coming closer to the large cluster of population growth area. - Large cluster of Land use changed area are sit on the western part of the Shire -Land use are more likely to change along the road system but in small size

Future scope:

- Land use may change in cluster near future population growth cluster. -Land use may change in large cluster in the future based on the change already happened between 2007 and 2019

Data source: investing.com.au 2007

2012

2018

Hypothesis:

- land use changed from farming to non-farming are most likely to happen adjacent to population growth area -Land are most likely to change from farming to other commercial production area. - Land use are changed in cluster and the closer to the cluster of population growth area, the higher density the changes are.

Methodology:

From qualitative perspective, the map is built based on mapping 3.0, the attribute table of which can be visualized in different colours. The cluster of residential area as well as the linear formation have been identified based on the spatial pattern of each land use category From quantitative perspective, the file attribute table with column of “desc”(land use name) “area” has been exported into Excel for further data processing. The area have been calculated in Excel by using SUMIF algorithm. The statistical chart of Australian construction out put and Global Lumber price have been taken from investing.com.au to investigate whether it is a profit-driven change from farming to soft/hard wood plantation.

Conclusion:

-The land use of hardwood / softwood plantation are employed to increasing the land productivity as well as blocking the view/ noise generated from the Macathur Windfarm, which is identical in the area of interest. - Nearly 60% of land transformed from mix farming have been changed to other production with higher economic outcome, such as hardwood plantation and livestock plantation. -About 30% of the land have been changed from farming to residential rural land use due to the population growth and urbanization. -Hardwood / softwood plantation are developed in cluster formation in the western Shire of Moyne. Most of the livestock production are spread along the southern part of the shire , from 5-50km to shoreline. The rural residential land use are set near existing town and in the area where the topography is not steady. -the Hardwood/soft plantation change is not driven by price of timber nor the soar demanding of timber in construction since there is no identical increase for lumber price and construction output index from 2007 to 2017

Future scope:

- Hard/soft wood plantation could be used in the area adjacent to future wind farm site. - there will be more rural residential land tramsformed from mix farming in the future, and it will grow from the centre point of the existing high population area.

5. High Agriculture value land (Soil Structure) and Landuse changed from farming to nonfarming Research Sub-Question

What is the spatial relationship between geological materials and flexible human settlement? And what is the scope of future change?

Hypothesis:

- the landuse changed area are situated at low agricultural suitability area so that the land could be used for other production. - Soil in most area within the Shire of Moyne are well structured since the majority of the land is used for farming. -the existing wind farm areas away from well structued soil area to reduce the damage to agriculture productivity.

Methodology:

1.In terms of soil structure data, The Soil and Landscape Grid of Australia (SLGA) byCSIRO have been used and processed through the Google Earth Engine, the band of “silt”, “clay” and “sand” have been exported to ArcGIS Pro for further modification 2. the research by Geo.msu,edu(2020) found that Loam soil are the most suitable for plant growth since the ratio of clay silt and sand provide desirable environment not just for nutrition and water to travel through but also for roots to grow. Therefore, the soil triangle have been introduced as the criteria of the Soil suitability analysis. 3. As the Silt weight ranged from 2% to 20%, only part of sandy loam, sandy clay loam and loamy sand can be described as the suitable soil for agriculture in this case. According to the soil texture triangle, Areas with sand weight from 45% to 85%, clay from 0-35%. silt from 2% to 20% area have been identified as the suitable area. Based on the fact that loam soil is the most suitable soil structure (Geo.msu.edu, 2020), more close to the loam soil, higher suitability is, so total areas are equality divided by 5 to form the ranking according to the proximity to loam soil centre (each grade has 20% of area equally) 4. The land use changed area is the same methodology as map3.

Conclusion:

- the high agriculture area are situated in the south- eastern and eastern region, as well as the north-west region where area of interest it, it’s presented in diagonal formation. - Macarthur Wind farm in area of interest takes up the most suitable soil structured area for agriculture, it’s a damage to local agriculture industry. -Majority of the Land use change from farming to non-farming happens in the low or least suitability area or the, especially in the area of interest.

Future scope: -Future wind farm site should sit away from most and high suitable soil structure area for agriculture to reduce the impact on the potential agriculture productivity. -Future land use change will most likely to happen away from the most suitable soil structure area for agriculture.

6. Land use have been changed from farming to non-farming and Soil Chemicals including soil depth, pH value and Carbon : Nitrogen Ratio Research Sub-Question

What is the spatial relationship between geological materials and flexible human settlement? And what is the scope of future change?

Hypothesis:

-land use change will take place on the edge of all suitable area -Land use change should not happen on the high soil depth area since it is hard to modify biologically.

Methodology:

1. Data acquired in same methodology as Map5, the band from The Soil and Landscape Grid of Australia (SLGA) by CSIRO is different to map5, this map use “Organic Carbon” (“Mass fraction of carbon by weight in the < 2mm soil material as determined by dry combustion at 900 Celsius”) , “Total Nitrogen” (Mass fraction of total nitrogen in the soil by weight), pH (CaCl2) (pH of 1:5 soil/0.01M calcium chloride extract) and “Depth of Soil” 2. C:N ratio is calculated in Arcgis Pro by raster calculator function, the research by Mooshammer (2014) and Zechmeister– Boltenstern (2015) found that the C:N ratio of 24:1 is most suitable for planting , and if “C:N ratio greater than 24:1 will result in a temporary nitrogen deficit (immobilization), and those with a C:N ratio less than 24:1 will result in a temporary nitrogen surplus (mineralization)” Consequently, the suitability of C:N ratio is determined by the index proximity to 21:1. the algorithm are defined as Suitbility Index(i) = square ( C(cell a)/N(cell a) - 24). Thus, total numbers of cells are divided by 5 so each grading contains equally 20% of total areas. the smaller the Index is, the more suitable it is. Top grading(top 20% of total area) of C:N ratio hasbeen add to the left map 3. the research by Truog E (1946) described in his research Soil reaction influence on availability of plant nutrients that the pH 7 is most suitable for plants growth and his pH diagram have been listed on the left. So the same algorithm as Methodology point2 have been processed. Suitability Index(i) = square ( pH - 7). Total numbers of cells are divided by 5 so each grading contains equally 20% of total areas. The smaller the Index is, the more suitable it is. Top grading(top 20% of total area) of pH value has been add to the left map 4. Soil depth is positively correlates to the suitbility of farming, which could be identified in the research by Hirzel and Matu’s (2013), it argues that “an increase in the soil depth profile positively affected grain yield, plant height, and number of stems m-2 in durum wheat. Grain yield increased 37% and 16% for ‘CorcolénINIA’ and ‘Llareta-INIA’, respectively, in deep soil as compared with shallow soil” Consequently, the total cells are divided by 5 and each grading contains equally 20% of the total area. Top grading(top 20% of total area) of soil depth has been add to the left map

Conclusion:

-Areas of Most suitable soil in C:N ratio and most suitable in Soil Depth are highly correlated. -Macurthur Windfarm doesn’t sit on high agricultre valued and measured by 3 mentioned prameters, although the soil suitable CN ratio area spread around the wind farm site. - Most of the land use change from mix-farming area to nonfarming are situated outside of mentioned 3 suitable areas, except for the north-eastern regions that a large patch sits in the suitable pH area, even some sits in the overlay areas all three categories.

Future scope:

-Future wind farm areas should keep away from overlapping areas in the map

7. Air pollution and Land use in 2019

8. Air Pollution Seasonal move and Industry land use and wind speed

Research Sub-Question

What is the spatial relationship between atmosphere materials and human settlement? And what is the scope of future change?

Hypothesis:

-Air pollution are generated by the humans settlement, the electricity power plant and substation release the pollution to the environment - Livestock production release the green-house gas to the air

What is the spatial relationship between atmosphere materials and human settlement? And what is the scope of future change?

Hypothesis:

-The pollution is less accumulated in high wind speed area -The seasonal move for different pollutant in same area should be the same.

-All purpose factory is the hot spot of air pollution

Methodology: Methodology:

The dataset used in this map are divided into two parts.the remote sensing for atmosphere components and human settlement data which is same process as map3. 1. the atmosphere data is taken from The Sentinel-5 Precursor mission, it provides the systematic measurements particles in the air. the map has select 4 key components, Carbon monoxide (CO), which is the indicator of “fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons”, Nitrogen dioxide(fossil fuel combustion and biomass burning) , sulphur dioxide (SO2),, which “affect human health and air quality and also have an effect on climate through radiative forcing” formaldehyde (HCHO), which is “an intermediate gas in almost all oxidation chains of non-methane volatile organic compounds (NMVOC), leading eventually to CO” 2. the data is filtered from 01/01/2019 to 31/12/2019, and the annual average number have been calculated in Google earth engine. 3. the dataset are processed in Google earth engine and are brought to Arcgis for further process. All cells are sorted and ranked to 5 category according to the value of each cell, top 1/5(20%) areas have been selected and add to the this map.

Conclusion:

- Air pollution pattern is not correlated with any land industrial and agricultural land use. -Air pollution pattern is unpredictable - In area of interest, the wind farm have effect to clear out the pollution since the Macarthur Wind farm sit in the void of polluted area -Void area can be identified as marked and lined for further investigation. -the north western regions within Shire of Moyne are heavily polluted than any other parts.

Future scope: -Wind farm may have ability to clear out the air quality of local areas -It may not be a good parameter of future wind farm site selection unless the local air quality need to be urgently cleaned.

1. the way to acquire and process the data is the same as Map7,but the data of 4 category have been merged into one dataset called the pollution index. the algorithm is that i = Value/max-value then add calculate the average number of i from 4 pollution types. 2. The calculated pollution index are selected quarterly rather than yearly. Q1 is from 01/01/2019 to 31/03.2019, Q2 is from 01/04/19 to 30/06/19, Q3 is filtered from 01/07/19 to 30/09/19. Q4 is from 01/10/19 to 31/12/19. all cells have been sorted and ranked into 5 grades, each grades have same amount of cells. Consequently, top 20% areas have been added to maps 3. A grasshopper definition have been processed to move one a patch of area from Q1 to the closest and largest patch in Q2, then Q3 and Q4, so that’s the moving routine of a patch of pollution.

Conclusion:

- The general factory are releasing CO and SO2 to the atmosphere. - The seasonal movement of the pollution seems random without any regularity. - High wind speed may impact the clearing of the air pollution area, it is identical in the area of interest, but the areas in the north eastern region is not correlated , the reason maybe that the air is too polluted and even the high speed wind can’t clear them.

Future scope:

- it does not provide any constructive suggestions to the wind farm development.

9. Ecology and fauna

10. Environmental degradation and enhancement with Land use change

Research Sub-Question

What is the correlation between environmental ecology (indicated by Normalized Difference Vegetation Index variation) with biodiversity pattern(indicated by the fauna pattern)

Hypothesis:

- Fauna distribution pattern are highly correlated to high NDVI area since more fauna habitat have been identified, the better local environment it is.

What is the correlation between environmental degradation and enhancement (indicated by Normalized Difference Vegetation Index variation) with human settlement flexibility (drawn by Land Use changes)? And what is the scope of future change?

Hypothesis:

- Environmental degradation is the result of human’s settlement , so where the land use have been changed, the NDVI values will change accordingly.

-There is less fauna especially birds in the existing wind farm area - Endangered species are located in the high NDVI area

- The environmental degradation or enhancement happens in cluster formation since the immediate environment are connected.

Methodology:

1. NDVI is an index to measure the plant health conditions, the higher the NDVI is, the better planing condition is which indicates that the local ecological condition is better. NDVI is calculated by near-infrared spectrum (NIR)and red range of spectrum(RED) detected by Land-sat satellite. the formula of NDVI= (NIR-RED)/ (NIR+RED) 2. The NIR and RED data are taken from Landsat 8 satellite and the data is calculated in Google earth engine, in this map, the number o f NIR and RED are taken as the average number between 2013 to 2017. 3. All the cell value have been ranked into 5 grades and each grades have same number of cells, so top 20% of cells have been added to the map. 4. the Fauna is collected in Victorian Biodiversity Atlas fauna - 1 minute grid summary datasets by Department of Environment, Land, Water and Planning. it records the location and actual species of a geo-location with longitude and latitude from 2014 to 2020, and the data is processed in ArcGIS 5. the endangered species have been selected according to the keywords selection and add to the map. Details text on fauna are visualized in 3 key area by excel and data visualization tool. Since the birds is most impacted species by windf arm, so the location of the birds

Conclusion:

- The spread of fauna is not highly correlated to the high NDVI area - Fauna recorded in the atlas are more likely to spread along the shoreline and cluster in certain parts, especailly the north-eastern region as identified on the map(where suffered from heavy air pollution as identified on map7). - Birds is the most common type among all the fauna in this region and they exist in the Macarthur Windfarm region which is identical in the AOI - Some endangered species such as Latham’s Snipe and Brolga lives in Macarthur Wind farm region

Future scope: -Wind farm development should not set near the endangered species’ habitat nor the high density of fauna. - If the development takes over the habitat of endangered fauna, an alternative habitat for the fauna should be considered so that they can migrate to the new habitat.

1. NDVI calculations are conducted by google earth engine and has the same formula as left map (Map9) 2. 2 individual NDVI have been calculated, the anual average NDVI value the year of 2019 and 2013, then subtract NDVI 2013’s from 2019’s, so in each cell, if the value is negative, it means the NDVI is decreasing, so it is in the environment degrading process. If the value is positive, it means the cell size land is in the environment enhancement process. 3. the NDVI changed index have been sorted and ranked to 5 grades and each grades have same amount of cells. Therefore, top 20% (change value from +0.24 to +0.41)and bottom 20%(changing value form -0.1 to +0.14) of cells are extracted and add to the map to compare. Then the land use change have been added to the map, the data process of which is same as Map3.

Conclusion:

- it is identical that areas near shoreline and existing town are suffered from environmental degradation while the north-eastern region witness the environmental enhancement. - In some areas the environmental change are relates to the land use change, especially the western region(marked corss area), the land degradation area sits in the land use change area. However, most of the land use change sit in the void of environmental enhancement in the north-eastern region - Only within 5km distance to shore line, environmental enhancement can be seen. -There is a clear boundary of environmental degradation area, very complex contours can be seen from the overlay.

Future scope: -Wind farm development should take care of environmental degradation area, try to avoid these area so that it has less impact on the plants growth environment. - Land use change relating to land farm development should consider the environmental causality.

11. Environmental degradation with Land-use change

12. Environmental Enhancement with Land-use change

Research Sub-Question

Hypothesis:

- Manufacturing or factory based land use will cause the land degradation - Land-use caused degradation will happened in cluster formation -Residential land use will cause the environmental degradation.

Methodology:

1. The data processing method is the same as Map10, in arcGIS platform, colour code the land use change area by the Landuse in 2017, which is the current land use . 2. The data have been further analysed in Excel. Extracting the types of land use in 2017, and use SUMIF tool to calculate the total area of a land use type. Then calculate the weight percentage of each type so that dashboard can visualize it.

Conclusion:

Hypothesis:

-Some land use change with vegetation ground cover will benefit the environmental enhancement - The land use change type are formed in cluster formation or in large coverage of area.

Methodology:

- It is surprising that the almost 1/3 environmental degradation happens on the Hardwood Plantation area which in an industrial plantation land use. The area is part of Green Triangle region Freight Action Plan(2009), under which the local council decide to boost the plantation industry in order to enhance timber production and increase exports. Although the land is covered by trees (such as Eucalyptus pilularis) , it still cause the environmental degradation. One of the reasons could be that the industrial plantation area are single-species environment which does not create a rich biodiversity habitat for flora and fauna.

Conclusion:

- Furthermore, the total timber production area including Hardwood plantation, softwood plantation and forestry-commercial timber production, accounts for over 45% of land use area. So it is identical that timber production industry is the main cause of environmental degradation (planting health) in Shire of Moyne.

-Protected seascape are contribute to the positive move to the environmental enhancement along the shoreline

- Residential rural area also contribute to the environment degradation by 13.87%. DELWP (2015) explained that â&#x20AC;&#x153;Rural residential development refers to land in a rural setting, used and developed for dwellings that are not primarily associated with agricultureâ&#x20AC;?

-General Cropping significantly contribute to the environmental enhancement, it takes up 1847 hec or 34.55% of landuse change total area in enhancement area. According to the Agriculture Victoria(2020), the general cropping areas have been sued to grow oats, wheat and barley back to 1944. -Spatially the farming areas are clustered and sits along the north eastern edge of the region. while the farming areas are sits parell to the north-eastern edge as well but offset 5km - 20km inside.

-The implementation of nature reserve in Shire of Moyne contribute 9.03% to the environmental enhancement.

Future scope:

-General cropping areas could be used to optimize the environmental degradation area while increasing the economic output of the land.

Spatially, the area are located in linear form around the exsisting town(as red dot line in the map)

-Setting the Nature reserve area is a way to repair the damaged environment lead by wind-farm development.

- The spatial form of Timber production are clustered together and they are located in the western region, and in the AOI near the Macarthur Windfarm. It is reduce the cost of production and transportation.

- Wind farm development should avoid the environment enhancement area where the agriculture farming take place, such as the general cropping area in the enhancement area.

Future scope: -Future timber production industry land use change could be an option in the western region, but the environmental and ecological impact must be taken into consideration. -Residential rural land use need to optimized the planting conditions when building houses on the farm land

13 . Vegetation (NDVI) and Land Surface Temperature(LST)

14. Vegetation performance and Windspeed

Research Sub-Question

Hypothesis:

What is the correlation between environmental vegetation with earth surface conditions?

- Land surface are positively correlated to the NDVI index since the top NDVI area has mature local ecological system where healthy plants have ability to adjust the micro-climate. -Land surface temperature is cooler in the area near the coastal area than inner-land. -The pattern of low LST area are linked to the pattern of high NDVI area -Dramatic topography change may impact the NDVI and LST relationships.

Methodology:

1. the LST temperature are calculated in the Google earth engine through Landsat 8 Dataset. Avdan and Jovanovska(2016) summarised the algorithm to calculate LST from landsat satellite data and it have been used in the Google earth engine calculation . Then the selection process have been ran in ArcGIS Pro, it divided all cells to 5 ranks according to the LST value, and each grades have same number of cells. the lowest LST grades have been added to the map based on the hypothesis. The time for teh LST are taken from 01.01.2013 to 31.12.2019. 2. NDVI calculation method is the same as Map9, this map use filtered the date from 01.01.2013 to 31.12.2019 and top 20% ares have been added to map according to average value ranking. 3. the overlayed areas have been selected by Clipping tool in ArcGIS Pro and added to the map. 4. 5m contour lines are added based on the hypothesis.

Conclusion:

- The spatial pattern of top NDVI and low LST areas are similar, they are situated and denser within 50km distance to the coastal line. - More close to the shoreline, more correlated these 2 figures are . It can be identified by the density of back outline in the map -Dramatic topography change does impact the relationship between LST and NDVI index, which not only can be seen at the mid western area where 2 index become absence when topograhy changed flat to steep, but also the areas in the middle region where marked by the cross, the significant landforms make low LST disconnected to adjacent region. - In the AOI, the implementation of Macrthur Wind farm maybe reduce the LST although the NDVI performance is poor.

Future scope: -Wind farm developmentsâ&#x20AC;&#x2122; impact on the NDVI is critical since the development will also influence the Land surface tempreture which relates to the flora and fauna in the region.

What is the correlation between environmental vegetation with earth surface conditions?

-Wind speed and planting performance are negatively correalted, the higher the wind speed is, the worse vegetationâ&#x20AC;&#x2122;s performance is. -Topography have significant impact on the wind speed

Methodology:

2 datasets have been used in this map, the first is the anually wind speed map and the other one is the anual average NDVI index. 1. The NDVA data acquire process is the same as Map9, the data in this map have been selected from 01.01.2019 to 31.12.2019, the data in this map describes the annual average vegetation performance 2. the wind speed data is from World Clim, which a database of high spatial resolution global weather and climate data. the data resolution. The data downloaded are months based, so Raster Calculator tool hve been used in ArcGIS Pro to get the average wind speed.

Conclusion:

-Topography and NDVI are correlated, it can be identified at the south-eastern region where the high NDVI areas are followed the dense contour lines. - Wind speed are genearally higher along the coastal line, and also the northern areas where AOI are located also have rich wind sources. - NDVI and wind speed are in negative correlation in some areas where can be identified by the boundary mark in the map, but it is not a general conclusion since the correlation is location based.

Future scope: -Wind farm should definitely fully covered by the high wind speed map as the Macarthur Wind farm in the AOL.

15. Hydro-Geomorphological flow and Environmental degradation or enhancement

16. Hydro-Geomorphological flow and Environmental degradation or enhancement

Research Sub-Question

Hypothesis:

-NDVI are usually have the positive change adjacent to waterbody since it has easy water access.

Methodology:

What is the correlation between environmental degradation and enhancement (indicated by NDVI variation) with earth surface conditions? And what is the scope of future change?

-Stream flow is one of the most important factors that influence the NDVI

Two datasets have been used in this datasets, DTM and NDVI. 1. The NDVI change datasets are processed in the same way as Map10, the time have been selected from 01.01.2013 to 31.12. 2019. 2. The Hydro-Geomorphological flow line is simulated in ArcGIS pro. DEM model is generaged from contour line which is form Vicmap Elevation - 1-5 Contours & Relief Dataset by DELWP, the Contour to TOPO function have been used to generate the DTM. 3. the flow line is simulated by linear algorithm of flow accumulation - flow direction - stream order. Through this process we will know the surface water flow.

Conclusion:

- Generally the north-eastern region have more spread of water bodies tan coastal areas although we can see one or several lakes. -Lakes are contributing the environmental positive move, which can be captured in the southern areas where worst NDVI change areas are avoiding the stream line. In another world, worst NDVI change areas are more likely to become absence when meets with the stream line.

Future scope:

-Wind farm should not sits in the dense stream line area since it may cause flooding issue in the future. Also i should minimize the damage to the vegetation health on the stream line.

What is the correlation between environmental degradation and enhancement (indicated by NDVI variation) with earth surface conditions? And what is the scope of future change?

-Stream flow is one of the most important factors that influence the NDVI

Two datasets have been used in this datasets, DTM and NDVI. Step 1.2.3 are the same as Map12. 1. The NDVI change datasets are processed in the same way as Map10, the time have been selected from 01.01.2013 to 31.12. 2019. 2. The Hydro-Geomorphological flow line is simulated in ArcGIS pro. DEM model is generaged from contour line which is form Vicmap Elevation - 1-5 Contours & Relief Dataset by DELWP, the Contour to TOPO function have been used to generate the DTM. 3. the flow line is simulated by linear algorithm of flow accumulation - flow direction - stream order. Through this process we will know the surface water flow. 4. Since the steam path is build based on in 100m*100m cell size, all the environment enhancement or degradation area on the stream path will be selected. Therefore, by looking at the pattern of the overlapping areas, we can conclude the solution that whether the stream is contributing or not correlate with NDVI change.

Conclusion:

- It is evident from the map that the overlapping areas of stream path and environmental enhancement areas are ways more than the one overlapping with environmental degradation area. It suggests that the Stream line helps the plants performance and increase the plants health.

Future scope:

-Wind farm should not sits in the dense stream path area, because it will impact the environmental rehabilitation process.

17. Topography (Terrain Aspect) and Environmental degradation or enhancement

18. Topography (Slope angle) and Environmental degradation or enhancement

Research Sub-Question

What is the correlation between environmental degradation and enhancement (indicated by NDVI variation) with earth surface conditions? And what is the scope of future change?

Hypothesis:

- The direction of the slope affects the environmental behavious, the slopes facing north are most likely to experience environmental enhancement.

- The slope angle affects the environmental behaviours, steep slope surface are most likely to experience environmental degradation.

Methodology:

Two datasets have been used in this datasets, DTM and NDVI. 1. The NDVI change datasets are processed in the same way as Map10, the time have been selected from 01.01.2013 to 31.12. 2019.

2. DEM model is generaged from contour line which is form Vicmap Elevation - 1-5 Contours & Relief Dataset by DELWP, the Contour to TOPO function have been used to generate the DTM.

2. DEM model is generaged from contour line which is form Vicmap Elevation - 1-5 Contours & Relief Dataset by DELWP, the Contour to TOPO function have been used to generate the DTM. Resolution of this DTM is 100m*100m.

3. Aspect function have been ran to analyse the slope facing, it remap facing direction to number from 0-360. 4. The Aspect value have been cliped by NDVI positive and negative change area and the data have been exported to Excel for area calculation and dashboard visualization.

3. slope angle have been ran to analyse the grading, it maps the grading angle of a 100m*100m cell plane. 4. The slope angle value have been cliped by NDVI positive and negative change area and the data have been exported to Excel for area calculation and dashboard visualization.

Conclusion:

- Slope direction and NDVI change are correlated according to the attribute table. Some aspect types contribute more to the NDVI gain and less to the NDVI loss will be defined as the factor that contributing to the environmental enhancement, otherwise, it is defined as the slope-aspect which contribute to the environmental degradation. - Southeast facing(5), Northeast facing(3) and East(4) facing is more likely to contribute to the environmental degradation than any other slope facing, to be more specific, they contribute 14.17%, 12.79% and 12.72% to NDVI Loss and only 10.92%, 9/04% and 8.86% to NDVI gain. - Northwest (9) and North (10) and Southwest(7) facing is more likely to contribute to the environmental enhancement since it is only contribute 9.24% and 9.15% and 10.41% to NDVI Loss but contribute to 11.27% and 10.99% and 13.08% to the NDVI gain.

Conclusion:

- slope angle and NDVI change are correlated according to the attribute table. Some slopes contribute more to the gain and less to the loss will be defined as the anle contributing to the environmental enhancement, otherwise, it is defined as the slope angle contribute to the environmental degradation. -Slope angle from 0.01 to 0.13 degree area and 1.65 to 11.2 degree contribute to the environmental enhancement most, since they contribute only 8.62% and 1.46% to the NDVI loss but contribute to 12.3% and 10.62% NDVI gain. Slope angle from 0.48 - 0.7 degree leads to Environmental loss since it contribute 11.34 to NDVI Loss and only 8.33% NDVI Gain.

Future scope:

-Wind farm should sit away form slope angle 0.01 to 0.13 and 1.65 to 11.2 area since the development will impact the vegetation health.

Future scope:

-Wind farm sit at Southeast , northeast and east facing slopes rather than Northwest, north and southwest slopes to reduce the impact on the vegetation health.

-windfarm is recommanded to sit in the slope angle of 0.48 to 0.7 degree to minimize the impact on the vegetation health.

19.Geospatial Suitability Analysis of Future wind farm development

20. Viewshed Analysis of Proposed Wind farm development and Landscape Opportunities Outlook

Methodology:

The suitability map are generated by the Multi Criteria Decision Making (MCDM) Model based on the observation and conclusion from previous 18 mappings in this Visual Essay. There are 10 input parameters in to the model: 1. Proximity to built up area: wind farm will keep away from the dwellings to reduce the noise, visual and shades impact on the property. (Scoring Milestone: <2km, <4km, <8km, <12km, <16km; Weight 1) 2. Proximity to Existing power transmission line: wind farm need to connect to power grid in order to transmit the electricity. More close to the grid, the less cost of a wind farm will be. (Scoring Milestone: 5km, 10km,15km,20km,20km+ ; Weight 1 ) 3. Proximity to terrain ridge line: wind mills normally sits on the ridge in order to capture the higher speed wind from higher space. The correlation between proximity to ridge and suitability is positive correlation. (Scoring Milestone: <200m, <600m, <1000m, <1400km, <1800km+ ; Weight 1 ) 4. Wind speed: the wind farm will only work where the average wind speed is over 3.5m/s. The correlation between wind speed and suitability is positive correlation. (Score rank: <7.65m/s <7.84m/s, <7.98m/s, <8.18m/s <9.55m/s; Weight 5) 5. Soil pH: Wind farm will stay away from the soil pH of 7 to minimize the impact on the agriculture productivity. (MAP5)The correlation between proximity to soil pH 7 and suitability is negative correlation. (Scoring Milestone according to proximity to 7 index: <2, <2.6, <3, <3.4, <5.8 ; Weight 1) 6. Soil Structure: Wind farm will stay away from the C:N ratio of 1:24 to minimize the impact on the agriculture productivity. (MAP4) The correlation between proximity to C:N ratio 1:24 and suitability is negative correlation (Scoring Milestone according to proximity to1:24 index: <2, <2.6, <3, <3.4, <5.8; Weight 1) 7. Soi Depth: Wind farm will stay away from the deep soil depth area to minimize the impact on the agriculture productivity. (MAP5) The correlation between soil depth and suitability is negative correlation (Scoring Milestone: <0.91, <0.96, <0.99, <1.02, <1.26; Weight 1 ) 8.Biodiversity (Particularly fauna) value: Windfarm development need to stay away from the fauna habitat area since the wind mill impact the faunaâ&#x20AC;&#x2122;s migration path. (MAP9) The correlation between proximity to recorded faunaâ&#x20AC;&#x2122;s habitat and suitability is negative correlation. (Scoring Milestone: <300m, <600m, <900m, <1200m, >1200m; Weight 1 )

Research Sub-Question

What is the role of landscape architecture in future renewable energy (wind) development

Methodology:

-The hypothetical wind mills (point 120m in height above ground) have been placed along the edge of the top 20% suitability area as well as randomly spread within the area. - The DTM have been imported to the Viewshed analysis in ArcGIS Pro. The GreenBlue hatched area is the wind mill visible area. Noting that the visible area is based on the analysis of DTM rather than DEM, the surface landscape such as tree buffering is not considered. - Dewelling areas have been added to the map. which is processed on Google earth engine (GEE) platform, the dataset GPWv411: Population Density (Gridded Population of the World Version 4.11) has been used which is provided by NANA SEDAC, the average value have been taken out from date range is 01-01-2019 to 31-12-2019. - Contours information have been added to the map to investigate the relationship between visibility and topography.

Conclusion:

- It is evident that the wind mills in in selected areas are visible to over 70% of the land based on the view-shed analysis. In addition, the majority of dwelling areas are also in the visible area which raised the issue that how landscape project can buffer the view of windmills if needed. -The areas in the south-western region are situated outside of the visible area, and the topography is relevantly flat, it could be an idea rural residential housing development region. -the proposed development are situated in northern and north-eastern and southwestern these 3 main region in cluster formation, which corresponding to the 3 existing wind farm locations.

9.Vegetation Performance area (NDVI): Wind farm development need to stay away from the high planting performance area (MAP13), so the correlation between NDVI and suitability is negative correlation. (Scoring Milestone: <0.163, <0.180, <0.193, <0.207, >0.207 ; Weight 2)

Future scope:

10. Terrain Aspect: Wind farm development need to stay away from Northwest, north and southwest, but more suitable in Southeast , northeast and east facing. (MAP17) (Scoring Milestone: 5/9,3/4,10/2, 0, 8/6/7; Weight 1)

-Landscape Buffering: Having Visual break trees such as Eastern Red Cedar or Red pine(the species used for windbreak in on the edge of mix farming and grazing region) in on the ridge lines adjacent to dwelling areas are also important.

11.Terrain Grading: Wind farm development need to stay away from slope angle 0.01 to 0.13 and 1.65 to 11.2 but more suitable at 0.48 to 0.7 degree. (MAP18) (Scoring Milestone: 5/6 ,3/7, 8, 4/1, 2/9; Weight 1) In addition, according to the Development of Wind Energy Facilities in Victoria - Policy and Planning Guidelines 2019 by Victorian Government logo and the Department of Environment, Land, Water and Planning (DELWP), below areas are prohibited from the wind farm development. The prohibited areas are: - National Parks - Ramsar wetlands - Yarra Valley and Dandenong ranges, Bellarine and Mornington Peninsulas, - the Great Ocean Road area within five kilometres of the high water mark, - Macedon and McHarg Ranges; - the land within five kilometres of the high water mark of the Bass Coast, - west of Wilsons Promontory; - all land west of the Hume Freeway and the Goulburn Valley Highway; - all land within five kilometres of the high water mark of the coast east of the urban area of Warrnambool; - Urban Growth Zone - Land within five kilometres of major regional cities and centres - within one kilometre of an existing dwelling Above areas have been mapped out and the areas sits in Shire of Moyne have been mapped as the white hatched area on the left.

Conclusion:

-The suitability map are the conclusion of above MCDM. The most suitable development area (top 20% of the most suitable area have been marked out ) could be the guild of phrase 1 development until 2030. -Cross referencing: All existing wind farm and approved future wind farm are within our top 20% area. (Dundonnell Wind Farm AGL Macarthur Wind Farm, Yambuk Wind Farm)

-Landscape Buffering: typologies such as reserves, woodlands can be introduced near the dwelling area to buffer the sight of wind mills in distance.

- Landscape Augmentation: the design of series of parks or highway landscapes to choreograph humans perceptions of travelling through the windfarm in different speed. - Landscape resilience: To enhance the NDVI loss areas by creating a more biodiversity and more suitable environment for local flora and fauna. It is also the opportunity to reverse the damage by urbanisation especially the land use change from farming to hardwood plantation area. -Local Amenities: the developer of windfarm are expected to feedback some community public open space project to benefit the community, such as open park, children playground, education museum, gallery ect. -Tourism opportunities: Where wind farm areas are clustered, themed tourism could be developed, short term stay such as caravel and camping ground, glamping site as well as the holiday park. The landscape projects could enhance the place of identity.