Policy applications

In this week’s learning we focused on how can remote sensing help in bridging the gap between a policy and execution of the policy.

So for this segment the inspiration of choosing Urban Heat Island was based on MacLachlan et al. (2021), nrdc.org and the heat wave impact in India, 2015 theguardian.

To Observe: Based on nrdc example of Ahmadabad (Western State), India are other parts in India approaching the policy in similar manner?

⭐ Search for one metropolitan policy challenge (any city in the World) that could be solved by incorporating remotely sensed data.

Overview

Geographical Location	Country: India; State: Telangana; Capital: Hyderabad
	SOURCE: britannica
Issue	Reduce the urban heat island effect
Policy	Telangana Cool Roof Policy(Draft) part of Telangana State Heatwave Action Plan – 2021
Implementation period	10 years
Target year	2031
RS data	Impact evaluation of heatwave response activities

Before we begin…..

In this segment, we shall understand a few basic concepts to have an understanding of the issue addressed by the policy.

Roof Cooling Material proposed
SOURCE: Telangana Cool Roof Policy
SOURCE: lowcarbonlivingcrc

SOURCE: Telangana Cool Roof Policy

SOURCE: lowcarbonlivingcrc

Why implement it??

A low cost solution in low rise low income houses Kolokotroni et al. (2018)
Disadvantages & Advantages: Ashtari et al. (2021), Macintyre and Heaviside (2019), unequalscenes, preventionweb

*Heat Wave*
Heat wave is considered if maximum temperature of a station reaches at least 40 degree celsius or more for Plains and at least 30 degree celsius or more for Hilly regions. IMD, NDMA Heat wave guidance: IMD,2022 Heat island India: climate.nasa

Heat wave is considered if maximum temperature of a station reaches at least 40 degree celsius or more for Plains and at least 30 degree celsius or more for Hilly regions. IMD, NDMA

Heat wave guidance: IMD,2022

Heat island India: climate.nasa

*Heat Island*
Heat island: SOURCE: publichealthnotes

Heat island: SOURCE: publichealthnotes

Observation

Policy emphasizes on execution of the methods in a fixed timeline
Lacks clarity on how it would be delivered
Lacks clarity on step by step process of monitoring and recording the observed progress
Emphasizes on role and responsibility of various stakeholders (org)
Based on professional experience I assume it would be tendered as a PPP project or can also be approached by a Suo-Motu proposal.

Question: How do we structure the monitoring aspect for this policy??? - Remote Sensing!

⭐ Identify and evaluate a remotely sensed data set that could be used to assist with contributing to the policy goal

To satisfy the above criteria, we look into the impact, the envisaged milestone, data requirement and the process (under steps, with and without social aspect).

Impact: Health, Nature, economy, Infra, service provision Relation between Land Surface Temperature Data and Land Use Data.

Process

Maps:
- Hyderabad City Area: ward map (Spatial Boundaries)
- Building
Temperature data: Meteorological stations
Population: Census Data
Time period: align it with project mile-stones above
Remote sensing data:
- USGS earth explorer website
- Land use and land cover: Landsat+TIRS (Thermal Infrared Sensor)/OLI – not much clarity on TIRS
Measure: Land surface temperature (LST): infer
- Physiological Equivalent Temperature (PET)
- Limitations: does not fully capture the set of micrometeorological conditions that factor into human thermal comfort or heat stress.
- Used: LST presents data at higher spatial resolutions, thereby enabling comparisons among different neighbourhoods
- Day time and night time LST

Steps:

Pre-process (geometric, atmospheric and topographic corrections)
Masking and sub- setting
Classification: BuA (Emphasis on it because we need to access impact of cool roofing- but BuA has different types of Physical Infra- roads, buildings, rail etc), Vegetation, Openspace, Waterbodies, Agri)—Classification Accuracy??
NDVI (Normalized Difference Vegetation Index)
- (-1 to +1) 0<=barren land/ BuA, +1<=vegetation/forest cover
NDBI (Normalized Difference Built-up Index)
- Landsat SWIR (Short wave infrared) characteristically higher reflectance compared to the near-infrared region
- (-1 to 1)- built up area detection range
LST calculation- (refer this paper:(Halder, Bandyopadhyay and Banik, 2021)
The Urban Thermal Field Variance Index (UTFVI)
- Urban heat island (UHI) along with Urban Thermal Field Variance Index (UTFVI) phenomena)
Land use and Land cover
- Urbanization effects and vegetation (may be a decade? To build existing scenario/ base line, how build-up area increased etc, to understand the cause better)
Correlation analysis with
- LST & NDVI
- LST & NDBI

May be Inclusion of Social Aspect?

Emergency department visit: patient, inpatient/ outpatient in hospitals
Diagnoses -heat related
LST and heat-related ED visits?
- Geographically: Ward,
- Tests: t-tests and boxplot
To determine whether these relationships hold after adjusting for social vulnerability controls and spatial dependency?
- Ordinary least squares (OLS) models
- spatial models on LST
- heat-related ED visits
- outcome variable: Distribution
- outcome variable: transformation (Sq root transformation)
- examined spatial autocorrelation in the residuals.
Result:
- Moran’s I was non-significant> outcome spatial autocorrelation was absent> OLS was done
- Moran’s I was significant> spatial autoregressive model (SAR) as OLS would yield biased
  - SAR, spatial dependence is addressed either as a spatially lagged dependent variable (spatial lag model) or in the error structure (spatial error model)
to determine the appropriate model.
- Lagrange Multiplier (LM) test
Checking for heteroscedasticity
Breusch-Pagan tests> upon revealing significant heteroscedasticity, corrections were applied Refer: Litardo et al. (2020a)

⭐ Demonstrate how this links to global agendas / goals

To satisfy the above requirement, indicated are the links (Provincial, Federal and Global Levels)

State Level/ Provincial Level	Telangana State Heatwave Action Plan – 2021	Issues Identified: Severe heat wave affected the State of Telangana in May 2015	Telangana State Development Planning Society (TSDPS) Revenue Disaster Management Department UNICEF (working together since 2017)
	District Disaster Management Plans (DDMP)	Issues Identified: Heatwaves, various vulnerabilities (sector wise in each district)	Revenue Disaster Management Department UNICEF
	UNICEF Guidance for Risk Informed Programming	Prepared Child Risk and Impact Analysis (CRIA) Identify various risks Impact of natural hazards Impact group: children and women, various social sectors Output: to provide critical services	UNICEF
National Level/ Federal Level	National Guidelines for Preparation of action Plan- Prevention and management of heat wave- 2019	Issue: Heat wave Early Warning and Communication Dealing with heat related illness, mitigation and preparedness Roles and responsibilities and implementation plan. Emphasis on evidence based policy	Central Government (National Disaster Management Authority Ministry of Home Affairs Government of India)
Global Level	Beating the Heat: A Sustainable Cooling Handbook for Cities- 2021	UN Environment programme (UNEP)	UNEP
	SDG 11 & 13 (out of 17 SDGS) SDG 11: Make Cities And Human Settlements Inclusive, Safe, Resilient And Sustainable SDG 13: Take urgent action to combat climate change and its impacts		UN

⭐ Explain how it advances current local, national or global approaches

Emphasis on monitoring and impact evaluation- it would be evidence based, hence required measure can be taken.
- RS would help facilitate it
- Challenges:
  - The roof top would be a very small scale to execute or monitor
  - Though this approach is cost effective, its not a sustainable long term solution. (may be try searching for alternative solutions)
- Solution:
  - Read: umep plug it in as an alternate solution
  - Compare before and after
  - Choose a longer timeframe so that the impact is visible, then relate it to spatial aspects and impact – (refer prat 3, to understand the execution better)
    - Demography and Economy
    - Vegetation and Economy
    - Health and vegetation

Literature review
Wellmann et al. (2020)	Litardo et al. (2020b)	Casali, Aydin, and Comes (2022)	Martinez and Labib (2023)

Wellmann et al. (2020)

Litardo et al. (2020b)

Casali, Aydin, and Comes (2022)

Martinez and Labib (2023)

Other:

Ravanelli et al. (2018)
Litardo et al. (2020b)
“Achieving Sustainable Development Goals Through the Study of Urban Heat Island Changes and Its Effective Factors Using Spatio-Temporal Techniques: The Case Study (Tehran City)” (2022)

References

“Achieving Sustainable Development Goals Through the Study of Urban Heat Island Changes and Its Effective Factors Using Spatio-Temporal Techniques: The Case Study (Tehran City).” 2022. Natural Resources Forum. 46 (1).

Ashtari, Babak, Mansour Yeganeh, Mohammadreza Bemanian, and Bahareh Fakhr. 2021. “A Conceptual Review of the Potential of Cool Roofs as an Effective Passive Solar Technique: Elaboration of Benefits and Drawbacks.” Frontiers in Energy Research 9 (October). https://doi.org/10.3389/fenrg.2021.738182.

Casali, Ylenia, Nazli Yonca Aydin, and Tina Comes. 2022. “Machine Learning for Spatial Analyses in Urban Areas: A Scoping Review.” Sustainable Cities and Society 85 (October): 104050. https://doi.org/10.1016/j.scs.2022.104050.

Kolokotroni, Maria, Emmanuel Shittu, Thiago Santos, Lukasz Ramowski, Adeline Mollard, Kirkland Rowe, Earle Wilson, João Filho, and Divine Novieto. 2018. “Cool Roofs: High Tech Low Cost Solution for Energy Efficiency and Thermal Comfort in Low Rise Low Income Houses in High Solar Radiation Countries.” Energy and Buildings 176 (July). https://doi.org/10.1016/j.enbuild.2018.07.005.

Litardo, J., M. Palme, M. Borbor-Cordova, R. Caiza, J. Macias, R. Hidalgo-Leon, and G. Soriano. 2020b. “Urban Heat Island Intensity and Buildings’ Energy Needs in Duran, Ecuador: Simulation Studies and Proposal of Mitigation Strategies.” Sustainable Cities and Society 62: 102387. https://doi.org/https://doi.org/10.1016/j.scs.2020.102387.

———. 2020a. “Urban Heat Island Intensity and Buildings’ Energy Needs in Duran, Ecuador: Simulation Studies and Proposal of Mitigation Strategies.” Sustainable Cities and Society 62: 102387. https://doi.org/https://doi.org/10.1016/j.scs.2020.102387.

Macintyre, H. L., and C. Heaviside. 2019. “Potential Benefits of Cool Roofs in Reducing Heat-Related Mortality During Heatwaves in a European City.” Environment International 127: 430–41. https://doi.org/https://doi.org/10.1016/j.envint.2019.02.065.

MacLachlan, Andrew, Eloise Biggs, Gareth Roberts, and Bryan Boruff. 2021. “Sustainable City Planning: A Data-Driven Approach for Mitigating Urban Heat.” Frontiers in Built Environment 6. https://doi.org/10.3389/fbuil.2020.519599.

Martinez, Alex de la Iglesia, and S. M. Labib. 2023. “Demystifying Normalized Difference Vegetation Index (NDVI) for Greenness Exposure Assessments and Policy Interventions in Urban Greening.” Environmental Research 220: 115155. https://doi.org/https://doi.org/10.1016/j.envres.2022.115155.

Ravanelli, Roberta, Andrea Nascetti, Raffaella Valeria Cirigliano, Clarissa Di Rico, Giovanni Leuzzi, Paolo Monti, and Mattia Crespi. 2018. “Monitoring the Impact of Land Cover Change on Surface Urban Heat Island Through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems.” Remote Sensing 10 (9). https://doi.org/10.3390/rs10091488.

Wellmann, Thilo, Angela Lausch, Erik Andersson, Sonja Knapp, Chiara Cortinovis, Jessica Jache, Sebastian Scheuer, et al. 2020. “Remote Sensing in Urban Planning: Contributions Towards Ecologically Sound Policies?” Landscape and Urban Planning 204: 103921. https://doi.org/https://doi.org/10.1016/j.landurbplan.2020.103921.