In this comprehensive analysis encompassing all districts of India, the analysis focuses on the study of the Environment and Socio- Economic dimensions. The study examines Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) under the domain of LAND, focusing on vegetation health, productivity, and societal patterns. The analysis extends to the study of AIR quality, by assessing concentrations of Carbon Monoxide (CO), Ozone (O3), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Methane (CH4) and Formaldehyde (HCHO). In addition to this, the Normalized Difference Water Index has also been taken into consideration under the domain of WATER. Night Time Light Analysis is done for socio- economic study of the districts of India. Anchoring insights in annual district mean values, the study navigates through the nuances of regional variability, temporal dynamics, and the socio-economic fabric. Augmenting satellite-derived data with literature, public reports, and census data, this analysis aspires to contribute a holistic understanding of India's diverse districts, encapsulating the vital interplay between nature and society.
NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), GPP (Gross Primary Productivity), and NPP (Net Primary Productivity) are all important remote sensing indices and metrics used to assess vegetation health, productivity, and overall ecosystem dynamics and are considered under the parameter, Land. Each of them serves a specific purpose and provides unique insights into different aspects of vegetation.
NDVI is a commonly used vegetation index calculated from satellite imagery. It quantifies vegetation health and density based on the differential reflectance of near-infrared (NIR) and red wavelengths. NDVI is a widely used vegetation index that quantifies the health and density of vegetation based on satellite or aerial imagery. The formula for NDVI is calculated using the near-infrared (NIR) and red bands of the electromagnetic spectrum.
The Enhanced Vegetation Index (EVI) is an improved vegetation index designed to minimize atmospheric influences and enhance sensitivity in high biomass regions. It is derived from satellite imagery and provides information about vegetation health and density.
EVI utilizes the near-infrared (NIR), red, and blue bands in the electromagnetic spectrum.
Gross Primary Productivity (GPP) is a key ecological metric that represents the total amount of carbon fixed by plants through photosynthesis within an ecosystem.
GPP provides insights into the total amount of carbon assimilated by vegetation, reflecting the ecosystem's capacity for photosynthesis. It is a crucial parameter for understanding carbon cycling and ecosystem functioning. High GPP values indicate vigorous plant growth and productivity, while low values may suggest limitations such as water stress or nutrient deficiency.
Net Primary Productivity (NPP) is a fundamental ecological metric representing the net amount of organic matter (biomass) synthesized by plants during photosynthesis, minus the energy expended in cellular respiration. NPP is calculated by subtracting the amount of carbon respired by plants during cellular respiration from the GPP.
NPP reflects the actual amount of energy available to support herbivores and higher trophic levels in an ecosystem. It provides insights into the net carbon accumulation in plants and the potential for carbon sequestration. NPP is a critical parameter for assessing the overall productivity and health of ecosystems.
Nighttime Light Intensity refers to the brightness of artificial lights and the radiance emitted during the night. It is often used as a proxy for human activity, urbanization, and economic development. The analysis of nighttime lights can provide valuable insights into changes in land use, population density, and economic growth.
For nighttime light intensity, satellite imagery such as VIIRS (Visible Infrared Imaging Radiometer Suite) is commonly used. VIIRS captures nighttime lights data with high sensitivity, making it suitable for detecting variations in human activity.
The Normalized Difference Water Index (NDWI) is a remote sensing index used to highlight the presence of water in various landscapes. It is particularly effective in identifying open water bodies and monitoring changes in water content.
NDWI typically uses the near-infrared (NIR) and shortwave infrared (SWIR) bands. In satellite imagery, these bands are sensitive to the reflectance properties of water.
The air quality analysis encompasses crucial parameters, addressing both immediate public health concerns and long-term environmental impacts. Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Ozone (O3) are examined following CPCB guidelines to ensure their concentrations align with permissible limits, prioritizing public health and welfare. Additionally, Methane (CH4) and Formaldehyde (HCHO) are scrutinized for their roles in climate change and greenhouse gas emissions, adhering to international standards. This comprehensive approach allows for an understanding of air quality, integrating considerations for immediate health safety and broader environmental implications tied to climate change and greenhouse gas dynamics. The key air quality parameters considered are:
- Carbon Monoxide
- Methane
- Formaldehyde
- Nitrogen Dioxide
- Sulphur Dioxide
- Ozone
- Targeted Implementation Strategies: By identifying critical areas and aspects through this analysis, decision-makers can tailor implementation strategies specific to the unique needs of each region. This targeted approach ensures that resources are efficiently allocated where they are most needed.
- Phased Development Planning: The study aids decision-makers in adopting a phased development approach. It allows for the identification of key areas requiring immediate attention and intervention, facilitating a step-by-step implementation of developmental measures.
- Resource Optimization: With insights from this analysis, decision-makers can optimize resource allocation. By focusing on areas with the greatest environmental or socio-economic challenges, they can ensure that resources, whether financial, human, or infrastructural, are directed toward initiatives with the highest impact.
- Informed Policy Formulation: The study provides a foundation for evidence-based policy formulation. Decision-makers can use the findings to create policies that address specific challenges revealed in the analysis, fostering more effective and targeted governance.
- Prioritization of Funding: Understanding the critical areas and aspects allows decision-makers to prioritize funding allocation. It assists in directing funds toward initiatives that align with overarching developmental goals and have a substantial positive impact on the environment and society.
- Sustainable Development Planning: By integrating environmental and socio-economic factors, this analysis supports decision-makers in crafting sustainable development plans. The holistic understanding gained from the study aids in creating long-term, resilient policies that consider both ecological and societal well-being.
- Community Engagement: The analysis can be used to foster community engagement. Decision-makers can involve local communities in the planning and implementation processes, ensuring that initiatives resonate with the needs and aspirations of the people.
- Interdisciplinary Collaboration: The comprehensive nature of the study encourages interdisciplinary collaboration among various government departments. Decision-makers can work collaboratively across sectors to address complex challenges at the intersection of the environment and socio-economic factors.
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1.
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LAND
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1.1
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NDVI
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- The NDVI map distinctly highlights regions with lush and robust vegetation, such as districts in Kerala and Karnataka within the Western Ghat region, as well as Darjeeling and Siliguri in the Northern districts of West Bengal.
- Conversely, the NDVI reveals areas with sparse vegetation, including the northern districts of Himachal Pradesh characterized by mountainous terrain, and select districts in Gujarat and Rajasthan, which exhibit arid conditions with minimal vegetation.
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1.2
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EVI
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- In case of the EVI analysis done, the negative values indicate the presence of dense and healthier vegetation and the positive values indicate the presence of minimal and unhealthier vegetation.
- It is designed to be less sensitive to atmospheric conditions and background noise, making it a valuable tool for monitoring vegetation dynamics.
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1.3
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GPP
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- GPP is the total energy captured by plants through photosynthesis.
- Like NDVI and EVI, the areas having denser and heathier vegetation are having higher GPP potential and higher strength to capture carbon.
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1.4
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NPP
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- NPP is the energy available for plant growth and consumption by other organisms after deducting what the plant used for its own metabolism.
- Thus, the Western Ghat areas as well as the North Eastern areas where the intensity of the vegetation is higher are having more NPP Potential.
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2
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NIGHT TIME LIGHT
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2.1
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Night Time Light Intensity
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- Urban hubs such as Delhi, Chandigarh, Mumbai, Chennai, and Hyderabad exhibited the highest NTL intensity, indicative of intense economic activities, infrastructure development, and urbanization.
- The proportion of areas with lower NTL intensity was found to be more widespread. This observation could be attributed to various factors, including rural or less densely populated regions as well as because of the varied lifestyle with the change in region as well.
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3
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WATER
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3.1
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NDWI
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- To create the NDWI map, emphasis is placed on surface water bodies, river basins, and watershed areas, which are prominently delineated and observed through satellite imagery. This process facilitates the identification of key regions with significant water content, such as Alappuzha in Kerala, districts in Assam and Bihar along the Brahmaputra River, and areas surrounding Chilika Lake in Odisha as well as in Gujarat.
- The analysis of water content also identifies the areas covered with snow resulting in the highlighting of the districts of Himachal Pradesh as well.
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4
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AIR
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4.1
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Methane (CH4)
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- Agricultural practices, urbanization and industrialization are some of the major producers of Methane gas.
- Both Andhra Pradesh and Telangana have significant agricultural activities, including rice cultivation, which is a known source of methane emissions. Livestock farming is also prevalent. Additionally, rapid urbanization and industrialization in certain areas may contribute to elevated methane levels.
- West Bengal has a diverse landscape, including agricultural regions, urban areas, and wetlands like the Sundarbans. Agricultural practices and industrial activities in urban centers could contribute to methane emissions. Additionally, the presence of wetlands might influence background levels.
- Bihar, being an agrarian state with extensive rice paddies and livestock farming, is likely to experience elevated methane emissions from these sources. Urban areas with industrial activities may also contribute to higher concentrations.
- Himachal Pradesh, with its predominantly mountainous terrain, may have lower methane concentrations due to fewer intensive agricultural and industrial activities. The state's cooler climate might also influence microbial activity and emissions.
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4.2
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Carbon Monoxide (CO)
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- High population density and urbanization in certain areas of Bihar and West Bengal contribute to increased vehicular traffic and industrial activities, both of which are significant sources of CO emissions. Additionally, the prevalence of biomass burning for cooking and heating in rural areas may contribute to elevated CO levels.
- In case of Odisha and Maharastra the proximity to the coast can influence wind patterns, dispersion of pollutants, and the impact of maritime activities. Urban centers and industrial zones along the coast may contribute to higher CO concentrations.
- Himachal Pradesh, with its mountainous terrain and less industrialized profile, leads to lower CO concentrations.
- A mix of urbanization, industrial activities, and traffic emissions can influence South India’s CO levels. However, the presence of relatively higher forest cover contributes to the air quality.
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4.3
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Formaldehyde (HCHO)
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- In Andhra Pradesh, industrial activities, including chemical manufacturing and other processes, may contribute to elevated levels of HCHO. Urban centers with high vehicular traffic and industrial emissions can be significant sources.
- Assam's air quality is affected by a combination of industrial emissions, biomass burning, and agricultural activities. The presence of oil and natural gas industries in certain regions contributes to higher concentrations of pollutants.
- Urbanization and industrialization in parts of Bihar can lead to increased emissions of HCHO. The use of biomass for cooking and heating in rural areas may also contribute, especially during specific seasons when biomass burning is prevalent.
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4.4
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Nitrogen Dioxide (NO2)
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- In case of Odisha the presence of industrial zones, mining activities, and thermal power plants in Odisha can contribute to higher concentrations of NO2. Coastal areas with industrial complexes may experience increased emissions.
- Kolkata, a major urban center in West Bengal, experiences high vehicular traffic, industrial activities, and emissions from thermal power plants. These factors can contribute to elevated NO2 levels.
- Urban areas in northern Uttar Pradesh, including cities like Noida, Meerut, Muzaffarnagar, cause high traffic density and industrial activities. Additionally, contributions from nearby agricultural burning and biomass combustion can impact NO2 levels.
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4.5
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Ozone (O3)
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- In case of Himachal Pradesh, the ozone levels can be influenced by the presence of precursor pollutants, such as nitrogen oxides (NOx) and volatile organic compounds (VOCs). While urban areas may contribute to these precursors, the unique topography and meteorological conditions in Himachal Pradesh can lead to the accumulation of pollutants, contributing to higher O3 levels.
- Urban and industrial activities in the northern parts of Rajasthan, especially in cities like Jaipur and other urban centers, can contribute to the release of ozone precursors. Local emissions and atmospheric conditions may result in elevated O3 levels.
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4.6
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Sulphur Dioxide (SO2)
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- Industrial activities, particularly those related to the combustion of fossil fuels like coal, can release sulfur dioxide. If there are significant industrial sources in Himachal Pradesh or nearby regions, it could contribute to elevated SO2 levels. Additionally, certain geological formations may release natural sulfur compounds.
- In Uttar Pradesh and Odisha, due to the presence of the industrial activities and mining activities respectively, the elevated concentration of Sulphur Dioxide can be overserved in particular districts.
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The recorded values for each parameter are acquired from Google Earth Engine and have undergone thorough verification processes, including rerunning the codes in GEE, employing analytical tools in ArcGIS, ground truth verification via Google Earth Pro, and referencing relevant literature and publicly available reports for the respective districts. It is essential to acknowledge potential variations in the recorded values due to the following reasons:
Regional Variability:
- Different regions exhibit distinct ecological characteristics, climate conditions, and vegetation types.
- Factors such as temperature, precipitation, and land use contribute to variations in GPP and NPP.
- The geographical characteristics, atmospheric conditions, wind patterns etc. can highly affect the concentration of air pollutants.
Temporal Dynamics:
- The timing of satellite observations and the temporal dynamics of vegetation growth cycles influence index values.
- Seasonal variations and vegetation phenology are key factors affecting the observed values.
Data Quality and Processing:
- Ensuring consistency in satellite data used for EVI, GPP, and NPP calculations is crucial.
Topographical Features:
- Example: In states like Himachal Pradesh, where the terrain is mountainous, the satellite data might capture variations in elevation, shadows, and slope, affecting the accuracy of vegetation indices and air quality parameters.
Local Meteorological Conditions:
- Localized meteorological conditions, such as microclimates, can introduce variability. For example, variations in temperature and humidity may impact the concentration of air pollutants
These considerations are imperative for a comprehensive understanding of the data, addressing potential sources of variability, and ensuring the reliability and accuracy of the reported values.
- 1. Landsat Normalized Difference Vegetation Index: Landsat Missions, US Geological Surveys
https://www.usgs.gov/landsat-missions/landsat-normalized-difference-vegetation-index#:~:text=NDVI%20is%20used%20to%20quantify,)%20%2F%20(NIR%20%2B%20R)
- 2. Landsat Enhanced Vegetation Index: Landsat Missions, US Geological Surveys
https://www.usgs.gov/landsat-missions/landsat-enhanced-vegetation-index
- 3. MYD17A2H Version 6 Gross Primary Productivity (GPP) and Net Primary Productivity (NPP): US Geological Surveys
https://lpdaac.usgs.gov/products/myd17a2hv006/#:~:text=The%20MYD17A2H%20Version%206%20Gross,water%20cycle%20processes%2C%20and%20biogeochemistry
https://cpcbenvis.nic.in/envis_newsletter/Air%20pollution%20in%20Delhi.pdf
