Data Analytics and Remote Sensing-Based Analysis for Sustainable Development

Inhaltsverzeichnis

1. Frontmatter

2. Data Analytics and Remote Sensing for Sustainable Development

   Deepak Kumar, Nick P. Bassill, Khushboo Rani

   Abstract

   Data analytics and remote sensing have advanced sustainable development by providing critical insights into environmental, social, and economic systems. The integration of data analytics with remote sensing technologies enables the efficient monitoring and management of natural resources, climate change, and urban development. The stakeholders make informed decisions to promote sustainability and resilience with the use of satellite imagery, sensor networks, and advanced data processing techniques. This work reports the role of data analytics and remote sensing in achieving sustainable development goals and highlights key applications like agricultural optimization, disaster management, and environmental conservation. It emphasizes the importance of interdisciplinary collaboration and innovative approaches in harnessing these technologies to create a more sustainable and equitable future.

3. Mapping and Identification of Coconut Plantations Through a Pixel-Based Classification Algorithm with Sentinel 2 Datasets

   B. Anand, R. Shanmathi Rekha, M. Vijaya Prabakaran, R. Amirtha Varshini, M. Kavya, B. Reshma, K. Ramaswamy

   Abstract

   Agriculture is an important natural resource of our country, whereas coconut is one of the vital horticultural crops suitable for humid regions. Due to the impact of climate change and less awareness of farmers, major crop areas were converted into coconut plantations over the decades in the Western part of Tamil Nadu region. Even though numerous applications utilizing remote sensing tools have been attempted, regional mapping of coconut growing areas has not been studied at regular intervals. In this study, it is necessary to understand the dominance of coconut crop pattern changes in selected parts of the Coimbatore region. Multispectral-based Sentinel 2 data were used for unsupervised classification-based ISODATA algorithm and analysis. Three clusters were isolated as vegetation for NDVI mapping based on their spectral signature. The training data are assigned to import the maximum-likelihood algorithm of supervised classification to classify the heterogeneous coconut palm, and it is identified that nearly 310.99 km² area belongs to coconut plantation and ground truth observation was carried out in assist of field mapping which contributes that overall accuracy of 83%. This finding showcases that the change in crop pattern will create awareness among the farmers about their high-yield income crops which can be extracted with a limited span of maintenance and less yield of water over different climatic conditions. The study comprises the importance and effectiveness of remotely quantifying the coconut plantation that will help stakeholders, agricultural statistics, NGOs, and farmers to detect the crop rate per unit of land that can assist in the implementation of appropriate agricultural practices to increase coconut palm production profits.

4. Remote Sensing-Based Error Assessment of Mathematical Modelling for Predicted Urban Sprawl with Stochastic Approaches

   Gaurav Kumar Mishra, Amit M. Deshmukh

   Abstract

   The population burst has become a very important topic for discussions during recent periods. Migration has also become one of the most problematic issues and has become an environmental issue. Remote sensing-based error assessment is a critical method for enhancing the reliability of mathematical models for the prediction of urban sprawl. The integration of high-resolution satellite imagery with stochastic approaches quantifies the uncertainties to detect discrepancies in spatial simulations. The technique provides planners with robust data for decision-making, enabling the optimization of land use and infrastructure planning. Error quantification expands model calibration for stochastic simulations to capture inherent variability. These factors play a major role in case of expansion of urban sprawls. The same scenario may be applied here to make urban centers sustainable, first we must assess the sustainability. It is well known that economic, social, and environmental aspects play a major role in the assessment of overall sustainability. These urban sprawls are very important in assessing for environmental well-being in urban life. These may affect the social and economic aspects of the centers negatively. Remote sensing offers error assessment for sustainable urban design and management, ensuring resilient planning under environmental and societal influences while driving innovation.

5. Data Analytics and Integration of CCME-Water Quality Index, Water Pollution Index (WPI), and Contamination Index (CI) with GIS Approach for a Better Understanding of Drinking Water Quality

   Abhijeet Das

   Abstract

   Water is a vital yet limited resource, increasingly threatened in both availability and quality as the global population continues to grow. Amid rapid urbanization and industrialization, assessing and predicting drinking water quality presents significant challenges due to the diversity of pollution sources and the complexity of temporal variations. The objective of the current study is to determine the concentration of physiochemical parameters in surface water intended for drinking, together with any potential dangers to human health, and to identify any substantial anthropogenic pressures in the Mahanadi River in Odisha. Monitoring and evaluating often is necessary to maintain the good status of the water quality (WQ). The water quality index (WQI) model is one of the most used methods for evaluating the quality of water. Further, the basis for the water quality evaluation program is in line with Canadian Council of Ministers of the Environment water quality index (CCME-WQI), water pollution index (WPI), and contamination index (CI) analysis, employing computed excursions, during the estimation of WQI values. For the purpose of this research, samples of water were gathered from nineteen locations, during monsoon season (June–September), encompassing both urbanized and non-urbanized parts during 2022–2023, and its acquired specimens were analyzed for twenty usual WQ variables. It is noticed that most of the WQ indicators were behind the guideline value of the World Health Organization (WHO) for surface water except TKN and coliform. It is predominantly found in the overall region that all water samples show mild-alkaline behavior. The TKN content is discovered to be inversely correlated with the presence of nitrogen fertilizer and shell debris, whereas the coliform is produced from inflow from rivers and the coast, that serves as a principal contributor. The CCME-WQI result displayed that the quality of water is found to be “good” to “poor” quality, and rest 10.53% water is only viable under specific circumstances for specific goals. Further, on the basis of WPI and CI, the physicochemical state of the river, i.e., 63.16% of samples, was referred to as “good,” and therefore rest 36.84% of locations requires monitoring for ecosystem sustainability. The findings of this ongoing research highlight that urban pressures, parent rock weathering, and urban-industrial activities significantly influence river water quality. To mitigate effluents from surrounding agricultural and industrial sources, continuous monitoring and strategic guidance are essential. However, by integrating the employed methodologies, their individual strengths are preserved, and valuable insights into water management are uncovered, that shedding light on the underlying factors affecting water quality in different types of surface water bodies. Ultimately, this study contributes to preserving the river’s diverse ecosystem and ensuring the provision of high-quality water to support sustainable urban development.

6. Comprehensive Study of Ground Experiences for Flood Resilience and Sustainable Development

   Puja Verma, Ambrina Sardar Khan

   Abstract

   It has been stated that building flood resilience comes through experience. Although it is questionable whether exposure to flooding always results in increased flood resilience, it is unclear what such a learning process entails. Experiencing floods can significantly enhance an individual's flood-related knowledge through direct exposure to the event and its consequences. This knowledge acquisition process is influenced by several key factors. Primarily, the availability of learning opportunities during and after a flood event plays a crucial role in shaping one's understanding. Furthermore, the individual's motivation to learn and engage with flood-related information is essential for effective knowledge gain. Followed by the pre-existing knowledge about floods and related topics serves as a foundation upon which new information can be built and integrated. The interplay of these factors determines the extent and depth of flood-related knowledge acquired through personal experience, ultimately contributing to an individual's overall preparedness and resilience in the face of future flood events. Flood management and/or other actions may be influenced by flood-related knowledge, but they may also be constrained by constraints such as lack of information and resources, attitudes, social capital, and policy restrictions. Together, flood-related information and the action that followed are regarded as the lesson learnt. This lesson then changes flood ability, recoverability, adaptability, and/or transformability, which in turn influences flood resilience. We analyse the various learning processes and their individual impacts on flood resistance in two environments. It implies that learning about flood mitigation is not possible in a setting that is well-protected by flood control infrastructure. In light of climate change, we then advocate for learning-based flood mitigation to promote flood resilience.

7. Habitat Suitability Modelling for Snow Leopards in Biosphere Reserve with Remote Sensing-Based Approaches

   Vipul Maurya, Parag Madhukar Dhakate, Ashish Mani, Ashish Panda, S. B. Lal

   Abstract

   The Nanda Devi Biosphere Reserve (NDBR) in Uttarakhand represents a critical sanctuary for biodiversity, hosting a plethora of rare and endemic flora and fauna. The extensive field survey was conducted to underscore the reserve’s role as a stronghold for elusive species like the snow leopard. Utilising camera traps over a grid system and trail surveys, we could gather vital data on the population and distribution of key species, adapting their methods seasonally to account for changes in animal behaviour and habitat use. The application of the maximum entropy model to predict habitat suitability is particularly noteworthy, offering a scientific basis for conservation efforts by identifying areas with the highest potential for snow leopard presence. This meticulous approach has revealed that approximately 10% of NDBR’s terrain is favourable for snow leopards, with a notable concentration in the Malari and Niti regions, thus providing a foundation for targeted conservation strategies. The study’s findings are instrumental in informing future preservation initiatives, ensuring that these regions remain a refuge for one of nature’s most magnificent predators.

8. A Comprehensive Review of Brick Kilns Mapping with Artificial Intelligence, Deep Learning and GIS Methods for Sustainable Development

   Yamini Agrawal, Hina Pande, Poonam Seth Tiwari, Shefali Agrawal

   Abstract

   Brick kilns play a significant role in meeting the global demand for construction materials, but their operations have raised environmental concerns, ranging from air pollution to deforestation and soil degradation. A lack of documented inventory of their operation have created a need to map these kilns for policymakers. This review article provides a comprehensive analysis of various brick kiln mapping methods, from traditional approaches to state-of-the-art deep learning algorithms. The paper explores a wide array of mapping technologies, including aerial remote sensing, satellite imagery, machine learning, deep learning, and geographic information systems (GIS), utilized to monitor brick kiln activities and their impact on the environment. The review highlights the strengths and limitations of different techniques and discusses recent advancements in the field. Early mapping methods which relied on manual surveys and basic tools transformed into visual inspection using aerial photography and satellite imagery which introduced more efficient mapping possibilities. Remote sensing data, combined with field observations, aided in assessing the extent of brick kiln impacts. After the boost of artificial intelligence (AI), the use of machine learning and deep learning algorithms has emerged as a promising approach for accurate and automated brick kiln mapping. The review addresses comprehensive assessment of studies which have employed various deep learning architectures to detect brick kilns in different regions, showcasing their potential for scalable and automatic mapping. To enhance mapping accuracy and spatial analysis, the integration of deep learning methods with geographic information systems (GIS) has been explored. The review further addresses the environmental consequences of brick kiln emissions, such as greenhouse gases, black carbon, and particulate matter, impacting air quality and climate change. The paper also highlights the socio-economic aspect, discussing the exploitation of workers and their health risks associated with brick kiln labor. This review article is the first of its kind which aims to aid policymakers and researchers in selecting appropriate mapping techniques and promotes sustainable solutions for accurately mapping brick kilns. By critically analyzing existing methodologies and exploring the potential of emerging technologies such as remote sensing and GIS, this review contributes to the advancement of brick kiln mapping practices, benefiting the environment, labor conditions, and the brick manufacturing industry as a whole.

9. Assessment of Hydro-Meteorological Variables in Gandak River Basin with an Integrated Approach for Climate Resilient Sustainable Water Resource Management

   Rina Kumari, Ujjawal Kumar, Nang Swatie Manpoong, Virendra Padhya

   Abstract

   Encompassing Mid-Gangetic Plain, state Bihar has abundant water resources, both the ground water and surface water resources. About 90% of the area of the state is occupied with rich alluvial deposits with high yielding aquifers sustaining livelihood of millions of populations and river ecosystem. Being an agrarian economy, Bihar is highly dependent on rainfall as well as availability of ground and surface water resources which is linked with ongoing climate change. Moreover, the high dependency by the growing population coupled with agricultural and industrial development, imposed severe stress to the freshwater resource of the state. The present study is using an integrated approach of hydro chemical, isotopic and remote sensing to assess the water resources and other hydrometeorological variables in the area. It was observed that both surface and groundwater are polluted at few locations. Isotopic analysis suggests surface water is admixture of glacier melt and precipitation. Also, it was observed that land surface temperature is increasing in the basin which is showing an impact on vegetation and moisture in the basin.

10. Remote Sensing of Active Tectonics and Geomorphic Signatures for Sustainable Development

    Gurav Chandrakant, Md Babar

    Abstract

    This research aims to explore the tectonic influences shaping the development of streams within the Lendi River sub-basin. In this research employed geomorphic indices of active tectonic (GIAT) parameters, such as the bifurcation ratio (Rb), basin elongation ratio (Re), hypsometric integral analysis (HI), basin asymmetry factor (AF), transverse topographic symmetry (T), stream gradient length ratio (SL), river longitudinal profile (RLP), lineaments, and abnormal stream behavior. Geologically, the Lendi River flows over three types of rock formations, namely Quaternary deposits (Pleistocene to Holocene age), Deccan Volcanic Basalt (DVB) from the upper (late) Cretaceous to lower (early) Eocene, and the Peninsular Gneissic Complex (PGC) from the Precambrian age. This study reveals that Lendi river exhibits a semi-elongated shape, and while basin asymmetry suggests symmetry, transverse topographic symmetry indicates a tilted basin. Hypsometric integral analysis indicates an early mature stage of basin evolution. The river’s longitudinal profile value of 26.3% and stream length gradient ratio index of 6.73 suggest a second-order anomaly. Other parameters, like, abnormal stream behavior concerning the main river, trellis drainage pattern, lineament analysis, waterfall breaks, gorges, and Quaternary sediment analysis, contribute to the GIAT analysis of watersheds. Notably, tectonic evidence including stike-slip faults in granite terrain is observed in Precambrian rock formations as well as Quaternary sediments near the Degloor town in the Lendi river area. This extensive analysis sheds light on the tectonic influences shaping the Lendi river basin evolution and helps comprehend the ongoing geological processes impacting stream behavior and landscape formation.

11. Exploring the Relationship Between Women's Health and WASH Conditions in Slum Areas Towards Sustainable Development

    Renu Dhupper, Suhasini Jindal

    Abstract

    The relationship between women’s health and water, sanitation and hygiene (WASH) conditions in slum areas is a critical yet underexplored facet of sustainable development. This research investigates how inadequate WASH infrastructure disproportionately affects the health and well-being of women in urban slums, exacerbating gender inequities and hindering progress towards global development goals. Slum areas often experience poor access to clean water, sanitation facilities and hygiene resources, leading to heightened health risks such as waterborne diseases, reproductive health complications and chronic illnesses. Women are particularly vulnerable due to biological and sociocultural factors, including their roles in caregiving and water collection. This study combines quantitative data analysis with qualitative insights gathered through fieldwork, focusing on slums. It evaluates the impact of WASH conditions on women’s physical and mental health, as well as their ability to participate in economic and educational activities. The findings reveal significant correlations between inadequate WASH infrastructure and adverse health outcomes, emphasising the need for gender-sensitive interventions in slum development policies. Improved WASH conditions not only enhance health outcomes but also empower women by reducing time burdens, ensuring safety and fostering greater equality.

12. Science of Data Analytics and Role of Microclimate in Crop Disease Prediction for Sustainable Development

    Pritimoy Sanyal

    Abstract

    The intersection of data analytics and microclimate science offers transformative potential in predicting crop diseases, a critical aspect of sustainable agricultural development. This research explores how advanced data analytics methodologies, combined with the understanding of microclimate dynamics, can enhance disease prediction accuracy and inform proactive measures in agriculture. Microclimates (localized weather conditions) are influenced by factors like topography, vegetation, and human activities and play a pivotal role in shaping disease dynamics in crops. Leveraging data analytics allows researchers to process extensive datasets, including microclimatic parameters, soil health indicators, and crop-specific vulnerabilities, to identify patterns that contribute to disease outbreaks. Machine learning algorithms, predictive modelling, and geospatial analysis serve as key tools in extracting actionable insights from complex, multi-dimensional datasets. This research highlights successful applications of data analytics in predicting diseases such as blight and blast across various crops. It emphasizes the need for integrating real-time sensor data, satellite imagery, and historical climatic information to develop robust predictive models. Furthermore, this study underscores the importance of translating predictions into localized, sustainable practices that mitigate disease impacts while promoting ecosystem health. The findings of this research demonstrate that combining data analytics and microclimate science not only empowers farmers and policymakers with precision tools but also advances global efforts towards sustainable agriculture. This approach supports the long-term goals of food security and environmental preservation with reduction in crop loss, improving resource efficiency, and minimizing chemical dependency,

13. Mitigating Climate Change Effects on Food and Nutrition Security for a Sustainable Future

    Rosy Bansal, Gorakh Singh Teja, Monika Hans

    Abstract

    Our health and the environment are both impacted by what we eat and how it is produced. Food must be produced, processed, distributed, prepared, eaten, and occasionally disposed of. Climate change severely impacts food and nutrition security, threatening global sustainability. Effective mitigation strategies include promoting climate-resilient agriculture, reducing food system emissions, and strengthening nutritional interventions. Each of these processes produces greenhouse gases, which trap the heat of the sun and cause climate change. Food is linked to about one-third of all greenhouse gas emissions that are created by humans. People believe that the main effects of climate change are increasing temperatures. The noise does not begin with the temperature increase. Because the ecology is interrelated, changes in one area will therefore have a similar effect on others. The world’s wealthiest nations will experience less change in their local climate and crop yields as a result of well-built information systems in place, while low-income or less developed nations will suffer more in terms of food security and food safety as a result of climate change and less resilient crop infrastructure. On the one hand, rising CO₂ levels are beneficial for crop growth, but on the other hand, CO₂ emissions are causing frequent climatic changes, such as extreme heat, severe weather, and droughts, which pose a serious threat to crops in high demand like wheat and maize. The worst effects of these consequences are likely to fall on nations already struggling with concerns like violence, pollution, deforestation, and other problems. Integrated policies addressing environmental and socioeconomic factors are critical to ensuring equitable food access and sustainable development. Collaborative global efforts can safeguard food security for future generations.

14. Challenges and Future Trends of Data Analytics and Remote Sensing Based Analysis for Sustainable Development

    Deepak Kumar, Nick P. Bassill, Khushboo Rani

    Abstract

    Data analytics and remote sensing are crucial for progressing sustainable development to provide real-time, large-scale data to monitor and manage natural resources, ecosystems, and environmental changes. Issues like data quality and accuracy, high costs, data integration and interoperability, and a shortage of skilled professionals obstruct the effective utilization of remote sensing and data analytics. Besides, challenges related to data access, privacy concerns, and uncertainties in predictive modelling complicate long-term planning and decision-making hindering the utilization despite their enormous potential. Despite these obstacles, the integration of data analytics and remote sensing proposes significant opportunities to optimize resource management, enhance disaster response, and support policy development for achieving global sustainability goals. Future trends point towards advances in satellite technology, increased data availability, and improved machine learning algorithms will enhance the precision and accessibility of these tools. The continued evolution of these technologies, combined with interdisciplinary collaboration and capacity-building, holds the potential to overcome existing challenges and create a more sustainable, data-driven future. The growth of cloud computing and open-source platforms will facilitate broader access to remote sensing data, particularly in developing countries, empowering local communities and organizations to contribute to sustainable development initiatives.