Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems
- 2023
- Book
- Editors
- Chaitanya B. Pande
- Kanak N. Moharir
- Sudhir Kumar Singh
- Quoc Bao Pham
- Ahmed Elbeltagi
- Book Series
- Springer Climate
- Publisher
- Springer International Publishing
About this book
This book on the climate change, natural resources, landscape and agricultural ecosystems describes the contributing challenges related to natural resources, soil erosion, irrigation planning, water, landscape, sustainable crop yield agriculture and biomass estimation. Natural resources and agricultural ecosystems include factors from nearby regions where landscape and agriculture practices (direct or indirect) interface with the water, vegetation, irrigation planning and ecology. Changes in climatic situations impact all the natural resources, ecology, and landscape of agricultural systems, which affects productivity.
This book summarizes the various aspects of soil erosion, soil compaction, soil nutrients, aquifer and water with respect to vegetation, crops, pest and sustainable yields and management for the future. It also focuses on the use of precision techniques, remote sensing, GIS technologies, IOT and climate related technology for the sustainability of ecology, natural resources and agricultural areas, along with the capacity and flexibility of natural resources and agricultural societies under climate change. This book presents both theoretical and applied aspects and will help as a guide for future research. The contents will appeal to researchers, scientists, and NGOs working in climate change, environmental sciences, agriculture engineering, remote sensing, natural resources management, remote sensing, GIS, hydrologist, soil sciences, agricultural microbiology, plant pathology and agronomy.
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Table of Contents
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Frontmatter
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Chapter 1. Impact of Climate Change on Livelihood Security and Biodiversity – Issues and Mitigation Strategies
Gyanaranjan Sahoo, Prasannajit Mishra, Afaq Majid Wani, Amita Sharma, Debasis Mishra, Dharitri Patra, Ipsita Mishra, Monalisa BeheraThe chapter 'Impact of Climate Change on Livelihood Security and Biodiversity – Issues and Mitigation Strategies' delves into the multifaceted effects of climate change on agricultural production, rural livelihoods, and biodiversity. It discusses the projected temperature increases and their impact on food security, particularly in tropical countries. The text highlights the key drivers of climate change, such as rapid land use changes and fossil fuel burning, and underscores the need for effective adaptation and mitigation strategies. It also explores the role of biodiversity in ecosystem services and the potential feedback loops between climate change and natural systems. The chapter emphasizes the importance of sustainable agriculture practices and the need for collaborative efforts between governments, scientists, and local communities to address the challenges posed by climate change. By providing a detailed analysis of the issues and potential solutions, the chapter offers valuable insights for professionals working in environmental conservation, agriculture, and public policy.AI Generated
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AbstractClimate change is one of the most pressing issues of our day, posing a threat to the lives and livelihoods of billions of people worldwide. Natural disasters, biodiversity loss, and rising temperatures destroy crops, diminish ecosystems, put livelihoods in jeopardy, and accelerate the spread of fatal diseases. Climate change mixes population trends, migration, and greater urbanisation, putting the most vulnerable people at risk. Climate change is the most important impediment to achieving sustainable development through biodiversity conservation, and it threatens to impoverish millions of people. Species distributions have changed to higher altitudes at a median pace of 11.0 m and 16.9 km per decade to higher latitudes as a result of climate change. As a result, under migration scenarios, extinction rates for 1103 species range from 21–23% with unrestricted migration to 38–52% with no migration. When an environmental change happens on a period shorter than the plant’s life, a plastic phenotypic may emerge as a reaction. Phenotypic flexibility, on the other hand, might protect species against the enduring impacts of climate change. Climate change also has an impact on food security, especially in people and areas that rely on rainfed agriculture. Crops and plants have growth and yield limits that must be respected. As a result, agricultural productivity in Africa alone might plummet by more than 30% by 2050. Climate change is already wreaking havoc on people’s lives, especially the impoverished. Because rural people rely on natural resources, their livelihoods are jeopardised by frequent climate change. The impact of climate change on natural resource-based rural livelihoods is anticipated to be uneven and ecosystem resilience will be strengthened as a result of biodiversity conservation, and ecosystems will be better able to deliver critical functions in the face of increasing climate stresses. Moreover, as a consequence of global influence, the warming trend has changed significantly over the years. In addition to ensuring the livelihood security of rural people, a number of adaptation approaches species and ecosystems in a changing climate may be recommended. -
Chapter 2. Desertification Intensity Assessment Within the Ukraine Ecosystems Under the Conditions of Climate Change on the Basis of Remote Sensing Data
Vadym І. Lyalko, Alexandr А. Apostolov, Lesya A. Elistratova, Inna F. Romanciuc, Iuliia V. ZakharchukThe chapter delves into the assessment of desertification intensity within Ukraine's ecosystems under the influence of climate change. Utilizing remote sensing data, the study develops a drought index to monitor vulnerable ecosystems and identify areas susceptible to desertification. The research highlights the importance of continuous satellite monitoring to combat land degradation and desertification, focusing on the unique challenges faced by Ukraine. By analyzing meteorological data and remote sensing indices, the study offers valuable insights into the spatial and temporal dynamics of drought and its impact on ecosystems. The proposed drought index serves as a crucial tool for early warning systems and sustainable management strategies, making the chapter a significant contribution to the field of environmental monitoring and climate change research.AI Generated
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AbstractThe study of the climate conditions modern transformations in the different countries acquires theoretical and practical significance that is determined by the high activity of climate changes in natural and social processes of the region. Many climate scenarios and forecasts make accent on increasing the frequency of adverse events, including the drought processes under the conditions of climate change. One of the most pronounced manifestations of modern climate change in Ukraine is the growing aridity. It is manifested in the increasing duration and intensity of droughts. This process is associated with the significant reduction of moisture content, leading to consequences that affect functioning of both natural environment and society. Among the landscape components, biota, water (surface and groundwater), and soil cover undergo to the greatest changes due to intensification of arid phenomena. Steady tendencies to gradual changes within the boundaries of natural zones of the studied area are already visible. Ukraine’s economy faces serious challenges due to this fact. The illustrative impact of climate changes is demonstrated by the negative consequences for agriculture. An effective tool for monitoring of the natural environment processes under the influence of climate change, in particular the arid phenomena, is the applying the remote sensing data. This study uses the data from the TERRA/MODIS satellites along with the drought indices. The detailed maps of arid-zonation were developed. Such studies represent the basis of justifying measures for adapting of society to the existing climate change. -
Chapter 3. Climate Change Effect on the Urbanization: Intensified Rainfall and Flood Susceptibility in Sri Lanka
M. D. K. Lakmali Gunathilaka, W. T. S. HarshanaThe chapter examines the profound effects of climate change on urbanization, with a focus on Sri Lanka. It discusses how rising temperatures and erratic rainfall patterns exacerbate flood risks and other meteorological challenges. The text explores the interplay between urban growth, deforestation, and greenhouse gas emissions, highlighting the need for sustainable urban planning to mitigate these effects. It also provides case studies and data-driven insights into the vulnerabilities and resilience strategies of urban areas in the face of climate change.AI Generated
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AbstractClimate change is inevitable with the interference made by various anthropogenic activities. Urbanization and development are two main processes that affect climate change. Thus, urbanization and climate change are like two sides of the same coin. The conjunction between climate change and urbanization already has created several issues in urban areas. Such issues are more common in developing countries due to unsustainable development, rapid urbanization, and population growth. The Sri Lankan scenario is also the same. The increased intensity and frequency of rainfall and increased land surface temperature are observable in urban areas in Sri Lanka. Colombo is the main administrative city and the largest urban area lies within the lower Kelani River basin. Due to the frequent flooding, the low lands usually inundate for days. Along with the wetland shrinkage and unsustainable development, the inundation period has increased creating a higher magnitude of flooding disaster. The damage due to frequent flooding usually creates an economic burden which in turn affects the development of the country. Sustainable development and greening cities are highly recommended to reduce the impacts of climate change in urban areas. -
Chapter 4. Climate Change, a Strong Threat to Food Security in India: With Special Reference to Gujarat
Diwakar KumarThe chapter delves into the significant threat climate change poses to food security in India, with a particular focus on Gujarat. It begins by highlighting Gujarat's economic achievements and its proactive approach to climate change through the establishment of the Department of Climate Change. The text explores the various policies and initiatives undertaken by the state to mitigate climate impacts and promote sustainable development. It also discusses the specific challenges faced by the agricultural sector in Gujarat, including temperature and drought effects on crop productivity. The chapter further examines the impact of climate change on cropping patterns and irrigation systems, emphasizing the need for adaptive strategies to ensure food security. Throughout, the text provides a detailed analysis of the state's response to climate change and its implications for the future of food security in the region.AI Generated
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AbstractGujarat contributes to around 16% of industrial and 12% of agricultural production in India (GoI, India: greenhouse gas emissions 2007. Technical report. Ministry of Environment and Forests, Government of India, New Delhi, 2010b). The Government of Gujarat acknowledges that Climate Change is not just a threat to the environment; it has profound implications for economic expansion, social progress, and nearly all other aspects of human wellbeing (Grafton et al., Nat Clim Chang 3:315–321, 2013). A Department of Climate Change has been established by the government of Gujarat to deal with climate change (GoI, Twelfth five year plan (2012–2017). Economic sectors. Government of India, New Delhi, 2013). It includes Missions on Solar Energy, Augmented Power Efficiency, Resilient Ecosystems, Water, Green procurement India, Climate resilient Agriculture, and Collaborative Knowledge for Climate Change (Bring et al., Earth’s Future 3:206–217, 2015). The state of Gujarat has put in place a variety of policies and programs to address some of the issues associated with Climate Change while also assuring the attainment of sustainable development goals (Doll and Bunn, The impact of climate change on freshwater ecosystems due to altered river flow regimes. In: Climate change 2014. Assessment report of the Intergovernmental Panel on Climate Change, pp 143–146, 2014). Efforts are being taken to make farming more environmentally friendly, like setting up agro-meteorological field stations, setting up automatic weather stations, and studying Climate Change in State agriculture universities (Douglas et al., Glob Planet Chang 67:117–128, 2009). According to the Department of Agriculture, Gujarat, about 51% of the state’s land is used for farming. Agriculture makes up about 18.3% of India’s most populous state’s GDP (GoI, Climate change and India: a 44 assessment a sectoral and regional analysis for 2030s. Technical report. Ministry of Environment and Forests, Government of India, New Delhi, 2010a). Despite the Government’s efforts to address climate change, challenges persist. Agriculture in India faces numerous difficulties, one of which includes environmental unpredictability (Gosling et al., Hydrol Earth Syst Sci 7:279–294, 2011). A report from the IPCC says that by 2080–2100, India could lose 10%–40% of its crop production due to climate change. The cumulative result is expected to be a reduction in the viability of terrain for agriculture in arid and semi-arid regions. Salt concentrations’ infiltration is an issue in Gujarat due to its lengthy shoreline (Garduno et al., India groundwater governance case study. Technical report. World Bank, Washington, DC, 2011). An increase in CO2 will increase the output of rice, wheat, legumes, and oilseeds by 10–20%. With each degree Celsius increase in temperature, yields of grains such as wheat, soybeans, mustard, peanuts, and potato are expected to fall by 3%–7%. There is a probability that yields of chickpeas, rabi, maize, millets, and coconuts will increase on the west coast of India (Hsu et al., J Geophys Res Atmos 118:1247–1260, 2013). In particular, to the state of Gujarat, there are not nearly enough data on the impacts of climate change on agriculture. It is anticipated that irrigated rice production in some parts of Gujarat will go down by 2030 (Gordon et al., Natl Acad Sci 102:7612–7617, 2005). According to the most recent available information, climate change will almost certainly result in more people at threat of going hungry. In 2080, the number of people who are not well-fed could rise by 5%–26% because of climate change. Agriculture, according to some assessments, is likely to be impacted in coastal regions since agriculturally productive areas are subject to flooding and soil salinity (Ghose, J Sci Ind Res 60:40–47, 2001). Climate change will have different impacts on food security in different regions of the state of Gujarat. Climate change will make it more difficult for people living in poor socio-economic regions to get their food and make food insecurity even more important (Hoff, Understanding the nexus. Background paper for the Bonn 2011. Stockholm Environment Institute, Stockholm, 2011). The future policy environment will have a significant impact on the long-term effects of climate change (GRDC, Long-term mean monthly discharges and annual characteristics of GRDC stations/online provided by the Global Runoff Data Centre of WMO 3 19. http://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html, 2020). -
Chapter 5. Livelihood Vulnerability Assessment and Drought Events in South Africa
Israel R. OrimoloyeThis chapter delves into the complex interplay between drought events and livelihood vulnerability in South Africa. It begins by highlighting the increasing population growth and climate-related risks, such as drought disasters, that affect urban livelihoods. The study examines South Africa’s varied climate, which is influenced by its unique geographical position, and the recurring droughts that have significant socio-economic impacts. The authors discuss the concept of vulnerability assessment, focusing on the Livelihood Vulnerability Index (LVI) as a tool for evaluating farmers’ susceptibility to climate change and disasters. The chapter also provides a detailed analysis of the impact of droughts on various sectors, including water resources, agriculture, and employment. It concludes by emphasizing the need for proactive strategies and institutional cooperation to manage drought risks effectively, ensuring the resilience of vulnerable communities.AI Generated
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AbstractLivelihood and the economies of South Africa are highly vulnerable to climatic fluctuations. Drought, in particular, is one of the most significant natural factors contributing in many parts of South Africa to agricultural losses, poverty, famine, and environmental degradation. Several factors rely on the cumulative effect of drought on a given area and its ability to recover from the resulting social, economic, and environmental impacts. South Africa’s vulnerability to climate variability and the risks posed by climate change and other natural disasters needs to be mitigated urgently. “This paper seeks to highlight the challenges of drought in South Africa and examines the existing livelihood vulnerability of drought, particular assets, and well-being vulnerability. This study indicates that a pragmatic strategy that incorporates innovative technology, institutional, and policy solutions to manage risks within vulnerable communities implemented by institutions operating at different levels (community, regional, and national) is considered to be the way forward for the management of drought and climate variability. This study recommends that a pragmatic strategy that incorporates innovative technology, institutional, and policy solutions to manage risks posed by recurring droughts on vulnerable communities must be continually explored. This calls for firm partnership cooperation by implementing institutions operating at different levels (community, regional, and national) as the way forward for managing drought and climate variability. -
Chapter 6. Possible Influence of Urbanisation on Rainfall in Recent Past
Prabhat Kumar, Archisman Barat, P. Parth Sarthi, Devendra Kumar TiwariThe chapter delves into the complex relationship between urbanisation and rainfall patterns, highlighting how the rapid growth of urban areas alters microclimates and influences convective available potential energy. It discusses the formation of urban heat islands and their role in enhancing precipitation downwind of cities. Additionally, the chapter explores the impact of anthropogenic aerosols on cloud microphysics and rainfall patterns, showing how aerosol concentration can both suppress and enhance precipitation depending on local conditions. The chapter also reviews key studies and modelling efforts that have sought to understand these complex interactions. By providing a detailed analysis of the mechanisms at play, the chapter offers valuable insights into the potential impacts of urbanisation on regional weather patterns and climate.AI Generated
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AbstractThe detection of weather and climate change caused by urbanisation is an important issue to understand and future projection of local weather change due to anthropogenic activities. Observational and climatological studies of alteration in convective phenomena over and around the urban area are reviewed with a focus on urban-modified downwind enhancement of rainfall. Causative factors for the alteration of urban precipitation can be urban heat island, surface roughness and anthropogenic aerosol. Monitoring of urban climate through high-resolution datasets was found to be quite important in today’s era of climate change. A detailed study through high-resolution CHIRPS-gridded data has been done for the cities of Patna and Gaya. The Mann–Kendall test and Pettitt’s test also indicated a changing trend in the rainfall intensity regime in the recent past for Patna, while a decreasing rainfall over Gaya has been envisaged by time-series analysis. -
Chapter 7. Influence of Climate Change on Crop Yield and Sustainable Agriculture
M. Aali Misaal, Syeda Mishal Zahra, Fahd Rasul, M. Imran, Rabeea Noor, M. FahadThe chapter delves into the profound effects of climate change on crop yield and sustainable agriculture, emphasizing the urgent need for mitigation and adaptation strategies. It discusses how climate change alters weather patterns, leading to increased temperatures, erratic rainfall, and extreme events like floods and droughts. These changes significantly impact agricultural production, food security, and livelihoods, particularly in vulnerable regions like Pakistan. The study highlights the importance of understanding the complex interactions between climate change and agriculture to develop effective policies and practices. It also explores the potential of improved seeds, agronomic management, and diversified crop genetics to enhance resilience against climate-related stresses. The chapter offers valuable insights into the challenges and opportunities in achieving sustainable agriculture in the face of climate change, making it a must-read for professionals and researchers in the field.AI Generated
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AbstractClimate change (CC) is one of the serious matters regarding the global food supply, safety, and sustainability of agriculture. The population of the world is increasing at a rapid pace, especially in South Asia. To fulfill the food consumption of the growing population and encountering CC at the same time in agriculture is the toughest challenge of today’s world. The events like severe hot summer and extremely cold winters, uncertain prolonged seasons, and randomness of precipitation are the biggest evidence of CC. Global warming and increased anthropogenic human activities are meant to be the cause of CC. Crops at early and even advanced stages are significantly affected by CC, the processes like photosynthesis, evapotranspiration, plant growth, metabolic activities, and other chemical reactions. The variability of climate at the specific crop period slows down reactions within the plant which results in decreased crop production. There is a wide range of farmers with different socioeconomic conditions and life experiences. The progressive farmers have conceptualized the climate in recent years and updated their farm practices, while the others are still trying to cope up with CC. Thus, this is the need of time that farmers must be educated with the ongoing environmental conditions and best cropping pattern, crop rotation, and practices are informed so that the emerging issue of food security and safety of the developing world can be overcome. The review is carried out to understand the effect of CC on crop production and sustainable agriculture. -
Chapter 8. Hybrid Daily Streamflow Forecasting Based on Variational Mode Decomposition Random Vector Functional Link Network-Based Ensemble Forecasting
Salim HeddamThe chapter presents a detailed study on hybrid daily streamflow forecasting using Variational Mode Decomposition (VMD) and Random Vector Functional Link Network-Based Ensemble Forecasting. It discusses the importance of accurate streamflow prediction for water resource management and flood control. The authors compare the performances of Extreme Learning Machine (ELM) and Random Vector Functional Link (RVFL) models, both with and without VMD signal decomposition. The results show that the hybrid models using VMD significantly improve forecasting accuracy, with RVFL models demonstrating higher stability and better performance. The chapter also highlights the challenges posed by standalone models and the advantages of using hybrid models for streamflow forecasting. The study concludes that robust, stable, and well-validated models are crucial for effective streamflow prediction and water resource management.AI Generated
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AbstractStreamflow forecasting using advance machine learning models have received great importance during the last few years regarding its importance for water resources management, especially for facing climate change. Several approaches based on the exploitation of a wide variety of models have been proposed and successfully applied for accurately daily and monthly streamflow forecasting. However, since streamflow and rainfall are closely interconnected, they were always combined for building more robust forecasting models. While, other climatic variables, i.e., temperature and evapotranspiration, were rarely, if ever, combined for streamflow forecasting, an important part of the developed models used only the value of streamflow measured at previous time lag as input variables. Recently, the use of signal processing decomposition algorithms, i.e., wavelet decomposition (WD) and more recently the variational mode decomposition (VMD), has attracted considerable attention and its success was highlighted up to this date without serious criticism. In the present chapter, we introduce a new scheme for daily streamflow forecasting using the random vector functional link network (RVFL) combined with the VMD. The VMD was used for decomposing the streamflow signal, and then the different intrinsic mode functions (IMF) were used as input variables. For more in depth conclusions, obtained results using the RVFL were compared to those using the extreme learning machine (ELM). Models accuracies were evaluated using several performances metrics and, overall, our best estimation resulted in an overall low RMSE and MAE, and high correlation between measured and predicted streamflow. Furthermore, the best forecasting accuracies were obtained using the RVFL combined with the VMD, for which the R and NSE values were ranged from 0.922 to 0.995 and from 0.850 to 0.991 using the RVFL_VMD compared to the values of 0.836–0.947 and 0.691–0.898 obtained using the ELM_VMD. It was found that the gained improvement in terms of model performances was more significant using the RVFL models compared to the ELM models. -
Chapter 9. Climate Change and Natural Hazards in the Senegal River Basin: Dynamics of Hydrological Extremes in the Faleme River Basin
Cheikh FayeThe chapter delves into the hydroclimatic risks of the Senegal River Basin, particularly focusing on the Faleme River Basin. It examines the dynamics of extreme discharge events, such as floods and droughts, and their impact on the region's water resources and socio-economic conditions. The study highlights the significant variability in discharge patterns, reflecting the impact of climate change and the need for effective adaptation measures. The analysis of hydrological indices and historical data provides a detailed understanding of the region's water management challenges and the potential for future hydrological extremes.AI Generated
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AbstractExtreme events punctuate the climate variability that directly affects the national economies of West African countries, those of the Sahel in particular, due to the low level of water control and poor reservoir filling conditions. This paper examines the dynamics of hydrological extremes and thus droughts and floods from the Faleme basin to the Gourbassi and Kidira stations, taking into account the context of climate change. The analyses are based on daily maximum (Dmax) and minimum (Dmin) discharge data for the period 1954–2019. The XLStat and KhronoStat software made it possible to calculate a set of indices (irregularity index, drying coefficient and Myer coefficient A). These software were also used to determine trends in the temporal evolution of the data. The discharges of the Faleme River make it possible to distinguish the hydroclimatic conditions in the basin over the study period. At station level, the Dmin underwent three successive breaks, in 1966–67 and 2007–08, with a decrease in values of more than 50%. These ruptures reflect the transition from wet conditions to a marked drought. From 2007 onwards, conditions became particularly contrasted, with the return of years with excess discharges. On average, Myer’s coefficient A is 7.2. This reflects a low flood strength. The annual value of the Dmax/Dmin ratio is very high, reflecting the variability of the discharge. The drying coefficient is generally low (0.06/day on average). Not all of the catastrophic floods in the study area occurred in wet years. This reflects the important role played by other factors, such as the spatial distribution of rainfall. -
Chapter 10. Review of Various Impacts of Climate Change in South Asia Region, Specifically Pakistan
Rabeea Noor, Chaitanya B. Pande, Syeda Mishal Zahra, Aarish Maqsood, Azhar Baig, M. Aali Misaal, Rana Shehzad Noor, Qaiser Abbas, Mariyam AnwarThis chapter delves into the multifaceted impacts of climate change in South Asia, with a particular focus on Pakistan. It explores how rising temperatures, extreme weather events, and melting glaciers are affecting various sectors such as agriculture, water resources, and energy. The review highlights the vulnerabilities and challenges faced by Pakistan, including increased flooding, droughts, and heatwaves, and discusses the critical need for adaptation and mitigation strategies. The chapter also examines the socioeconomic and environmental impacts of climate change, emphasizing the urgent need for policy interventions and public awareness. By providing a detailed analysis of the current state and future projections, the chapter offers valuable insights into the complex dynamics of climate change in the region.AI Generated
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AbstractClimate change is a dire and increasing crisis worldwide in South Asian region, especially Pakistan. This region is highly vulnerable to climate change, while awareness of climate change issues and adaptation strategies is very low. Pakistan faces a perpetual threat in its ecosystem, biodiversity, and oceans. Much of the country’s threats stem from poverty, a lack of financial resources, and natural disasters. Pakistan endures ongoing seasonal changes, including extreme climate events, an ongoing shortage of water, pest diseases, human health issues, and more. The country has a low adaptive capacity to these threats, given its ongoing economic struggles and varying living conditions from season to season. The likely effects of climate change on the common Pakistani citizens are devastating, especially for local animals like lions, tortoises, dolphins, and vultures. These animals, regardless of their small global impact, will face extinction. In fact, the effects on local people, such as the per capita impact on global greenhouse gas emissions, are severe. The findings of this review support the theory that GHG emissions cause climate change. The effects of this global phenomenon have been seen in Pakistan in agriculture, livestock, precipitation and temperature trends and patterns, food and energy reliability, water resources, and community. According to a recent sectorial assessment, this review evaluates climate change alleviation and adaptation techniques in the sectors mentioned above, which caused a huge economic loss in Pakistan every year. A new study finds that governmental intervention is necessary for recent climate policy development. The research suggested that strict accountability of resources and regulatory actions are vital to creating climate policy. -
Chapter 11. Future Hydroclimatic Variability Projections Using Combined Statistical Downscaling Approach and Rainfall-Runoff Model: Case of Sebaou River Basin (Northern Algeria)
Bilel Zerouali, Mohamed Chettih, Zak Abda, Mohamed MesbahThe chapter delves into the significant impacts of climate change on water resources and hydrological systems, using the Sebaou River Basin in Northern Algeria as a case study. It combines statistical downscaling approaches with the GR2M rainfall-runoff model to project future hydroclimatic variability. The study reveals expected decreases in rainfall and increases in temperatures, which will significantly affect water availability and basin flows. The GR2M model demonstrates effectiveness in simulating hydrological behavior, providing crucial data for water resource planning and management under future climate scenarios. The chapter also discusses the challenges and uncertainties associated with climate modeling and the need for advanced techniques to reduce these uncertainties.AI Generated
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AbstractDue to its location in the Mediterranean basin, Algeria is one of the most countries vulnerable to the effects of climate change. The aim of this study is to assess future flow rate projections of Sebaou basin (Northern Algeria), using the coupling of statistical downscaling approach (SDSM) based on the general circulation model Hadley Centre Coupled Model version 3 (GCM-HadCM3) of the Royaume-Uni with an anthropogenic forcing SRES A2a (pessimist) and SRES B2a (optimistic) and GR2M model for rainfall-runoff transformation. The use of GR2M rainfall-runoff model has been able to control the hydrological functioning of the basin with very satisfactory performance values expressed by the Nash values over 80% for most subbasins, except for the degradation the Nash coefficient after the commissioning of the Taksebt dam in the Oued Aissi subbasin after 2001. The combining approach showed, on one hand, a decrease in rainfall ranging from 18% to 14% and that the maximum, average, and minimum temperatures could continue to increase with a maximum of 1.1–0.65 °C, 1.1–1.25 °C, and 2.7–3.4 °C, respectively, for the H3A2 and H3B2 emission scenarios until the long-term horizon 2080. On the other hand, the model indicated that these climatic changes have an effect on decreases in the basin’s water resources and that the 2050 and 2080 horizons are the most deficient with a decrease in flows estimated from −35% to −49% for A2 and from −45 to −57% for B2 scenarios, respectively, which represents approximately 500–300 Hm3 by the end of the twenty-first century. -
Chapter 12. Predication of Sugarcane Yield in the Semi-Arid Region Based on the Sentinel-2 Data Using Vegetation’s Indices and Mathematical Modeling
Chaitanya B. Pande, Sunil A. Kadam, J. Rajesh, S. D. Gorantiwar, Mukund G. ShindeThe chapter discusses the significance of monitoring agronomy crop situations using remote sensing and mathematical modeling. It focuses on the prediction of sugarcane yield in semi-arid regions based on Sentinel-2 data and vegetation indices such as NDVI, EVI, and GCVI. The study area in Sadegaon village, Maharashtra, India, is used to develop linear regression models for yield forecasting. The chapter explores the correlation between vegetation indices and observed yield, highlighting the potential of these models in enhancing agricultural practices and supporting farmers. It also discusses the challenges of conventional yield assessment methods and the benefits of advanced remote sensing and GIS technologies in improving crop yield estimation.AI Generated
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AbstractThis paper is aimed at developing the model of prediction of the sugarcane yield based on the satellite data and mathematical modeling. Remote sensing satellites have been monitoring agricultural crops with regard to growing, harvesting, and other periods. Satellite data have provided accurate information on the earth surface and then easily interpreted which crop is in good health and which is unhealthy, and vegetation indices can give more valuable information for the prediction of crop yield. In this regard, if farmers can estimate the yield before harvesting this is very helpful to the farmers and countries. In this study, ground truth data were collected by farmer’s fields and validated with satellite indices and predicted yield. In this model sugarcane crop is first selected for the prediction of yield because 72% of crops consist of sugarcane and the duration of this crop is 8–12 months. During the survey, information was collected on crop yield, the demonstration plots were verified and the observed yield computed. Sentinel-2 data were selected for crop yield forecasting. This crop model used three vegetation indices (Normalized Difference Vegetation Index [NDVI], Enhanced Vegetation Index [EVI], and Green Chlorophyll Vegetation Index [GCVI]), which have been computed from sentinel-2 data using Raster Calculator. To correlate the sugarcane observed crop yield, NDVI, EVI, and GCVI values were computed by linear model. Sugarcane crop yield correlated strongly with the NDVI, EVI, and GSVI (NDVI: R2 = 0.65, EVI: R2 = 0.598 and GCVI: R2 = 0.746). The GCVI index has a high correlation with observed yield using a linear model. Therefore, three linear correlation models have been developed by vegetation indices to determine which indices correlated best with the prediction yield. The observed yield data were compared with normalized vegetation index and other indices. The sugarcane yield is high compared with the observed yield. -
Chapter 13. Effect of Urbanism on Land Surface Temperature (LST) in a River Basin and an Urban Agglomeration
J. Brema, Ahmed Khalid Alsalmi, C. Mayilswami, Jenita ThinakaranThe study investigates the effect of urbanization on land surface temperature (LST) in a river basin and an urban agglomeration. It delves into the phenomenon of urban heat islands (UHIs), which are caused by increased absorption of solar radiation and reduced evapotranspiration in urban environments. The research utilizes remote sensing techniques to monitor LST and UHI, demonstrating how urban vegetation and land cover changes influence temperature dynamics. By comparing LST and normalized difference vegetation index (NDVI) values over time, the study highlights the importance of green spaces in mitigating urban heat. The analysis covers two distinct regions—a semiarid river basin and a coastal humid area—providing a detailed examination of temperature variations and their implications for sustainable urban planning.AI Generated
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AbstractThe environmental and social consequences of predicted climate change are expected to be magnified in urban regions due to the elevated temperatures, which are due to the continuous manmade processes. A research work was carried out by utilizing remote sensing and geographic information systems to explore the interactions between land surface temperatures (LST) and normalized difference vegetation index (NDVI) in an urban area and in a basin area. Increase in vegetative cover can provide microclimate formation through the process of evapotranspiration by increasing the amount of urban vegetation. This might prove to be a highly effective solution in reducing the effect of temperature toward urbanization. The results were presented based on the observations carried out using Landsat 8/Sentinel 2 satellite imageries and from the field by deriving land-use data as input and by comparing the temperature variations, normalized difference vegetation index (NDVI), normalized differential build-up index (NDBI), and normalized differential water index (NDWI) derived from satellite imageries (2004–18) in an urban limit (Chennai City, India) and a subbasin (Noyyal River, India). The correlation revealed that both the spatial and temporal variation in vegetation sprawl and surface temperature affect the local climatic temperature. It has been suggested that future modeling studies should account for anthropogenic heating in the case of urban planning for better climatic control characteristics. -
Chapter 14. Estimation of Land Surface Temperature and Urban Heat Island by Using Google Earth Engine and Remote Sensing Data
Komal Gadekar, Chaitanya B. Pande, J. Rajesh, S. D. Gorantiwar, A. A. AtreThe chapter delves into the estimation of land surface temperature (LST) and urban heat island (UHI) effects in Nashik, India, using Google Earth Engine and remote sensing data. It discusses the significance of LST in climate, atmospheric, and urban planning studies, highlighting the impact of urbanization on air and surface temperatures, soil water, and air pollution. The methodology involves analyzing satellite data using Google Earth Engine to develop thematic LST maps, study UHI and non-UHI areas, and measure indices like NDVI and NDBI. The study reveals the relationship between LST, NDVI, and NDBI, showing strong negative correlations in semiarid areas. The research provides valuable insights into urban thermal dynamics, which can inform urban planning and environmental management strategies.AI Generated
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AbstractThis chapter aims at the surface temperature of urbanization, nonurban heat island (NUHI), and urban heat island, an important factor for heat changes that affect the surface of the earth. Therefore, due to local and global environmental changes and man-made operations, block land surface temperatures in so many areas of Nashik town are rising. The estimation of urban heat island, NDVI, and NDBI indices was calculated using GEE, machine learning algorithm, and also remote sensing data. In this chapter, the relationship and correlation between the LST, NDVI, and NDBI indies were established for the estimation of the surface temperature of the Nashik urban area and other areas of the Maharashtra block. The NDVI and NDBI indices were estimated using the machine learning algorithm and satellite data. The GEE platform has provided easy access to all satellite data with a java script algorithm for analysis and LST relationship between the built-up area and the vegetative land. Various urban thermal islands (UHIs) have demarcated as higher temperatures in urban areas within city borders due to more man-made activities and climate change factors. The UHI value threshold for 2015 was measured at 41.03 °C and in 2019 at 43.28 °C. The relationship between LST–NDVI and LST–NDBI was identified quantitatively by a correlation analysis based on the algorithm and the GEE platform. LST shows a strong negative correlation (−0.41 for 2015 and −0.57 for 2019) with NDVI and a strong positive correlation (0.31 for 2015 and 0.71 for 2019) with NDBI throughout the Nashik region. The non-UHI zones (green areas and water bodies) remain almost unchanged if any change is assumed to be very little altered, but only the UHI zones are in severe heat stress due to urban air pollution. The study field results can help the urban, agricultural, and ecological planners decide on the sustainable practices of ecological and climate change. -
Chapter 15. Study on Irrigated and Nonirrigated Lands in Ukraine Under Climate Change Based on Remote Sensing Data
Artur Ya. Khodorovskyi, Alexander A. Apostolov, Lesya A. Yelistratova, Tetiana A. OrlenkoThe chapter delves into the critical issue of climate change and its impact on agricultural lands in Ukraine. It discusses the decline in water resources and the increasing demand for irrigation due to global warming. The study focuses on the use of remote sensing data to monitor soil moisture and vegetation conditions, highlighting the importance of irrigation and drainage systems. The authors also analyze soil erosion processes and their impact on agricultural productivity. The chapter emphasizes the need for sustainable water management and the integration of advanced technologies in agricultural practices to adapt to climate change.AI Generated
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AbstractAccording to the UN, potential of Ukraine’s agricultural sector allows feeding of 450–500 million people. However, nowadays, its capabilities are exploited only by a third. The key factors, which characterize increasing average annual temperature, are primarily related to climate change. Increasing drought and desertification caused by global warming occur against the background of almost unaltered precipitation in the steppe zone of Ukraine. Climate change will derate the condition of the humidity. Therefore, the role of irrigation and drainage in agricultural production increases. However, at present, melioration agriculture is in crisis concerning the level of use of the facilities engineering and infrastructure of irrigation and drainage in Ukraine. Irrigation system recovery is a vital tool in the present conditions. Firstly, the development of the agricultural economy sector and increasing the export potential of Ukraine. Secondary, climate impact minimizing on the processes of socioeconomic nature of the regions. Climate change monitoring is a crucial problem, in specific, due to the aridity of the climate, especially regarding regional changes on agroecosystems determining its impact on long-term development and food security. Information on moisture conditions and the occurrence of the degradation process is continuously up to date with recent developments. Obtaining of the monitoring data would necessitate integrated remote sensing at the global, regional, and local scale. In addition, high spatial resolution and low satellite revisit period will supplement the use of optical and radar data from satellite images to overcome the problem of cloud cover. Remote sensing data of the Earth allows identifying and establishing regular variation in agroecosystems structure and determining their productivity. This research is based on remote sensing of the Earth, monitoring and underpinned by international experience and internal capacity, including integrated approaches and methods for reclaimed lands. The results of this study will enable the effective use of reclaimed land. Furthermore, proposals based on the monitoring outcome allow improved governance in the agriculture of Ukraine. -
Chapter 16. Hybrid Kernel Extreme Learning Machine-Based Empirical Wavelet Transform for Water Quality Prediction Using Only River Flow as Predictor
Salim HeddamThe chapter introduces a hybrid kernel extreme learning machine-based empirical wavelet transform for predicting river water pH and specific conductance using river discharge as the sole predictor. The empirical wavelet transform decomposes river discharge into multiresolution analysis components, which are then used as input variables for the kernel extreme learning machine models. The study compares three kernel functions—radial basis, polynomial, and wavelet—and demonstrates that the empirical wavelet transform significantly enhances the models' predictive accuracy. The chapter presents results from two USGS stations, highlighting the improvements in prediction metrics such as correlation coefficient, Nash-Sutcliffe efficiency, RMSE, and MAE. This innovative approach offers a promising tool for water quality monitoring and prediction, with potential applications in other water quality variables.AI Generated
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AbstractDuring the last few years, monitoring and controlling water quality in freshwater ecosystems was strongly facilitated by the increasing number of in situ stations, certainly in combination with the high number of developed models. Several water quality variables have received a great deal of attention regarding their environmental importance, while other variables have rarely been studied in detail using modeling strategies. Generally speaking, water variables were linked to building robust models and rarely are the models using fewer variables. Machine learning algorithm aiming to accurately build relationships between water quality variables are widely used and acknowledged. In the present investigation, we tried to introduce a new modeling strategy for predicting two water quality variables: water pH and specific conductance (SC) using kernel extreme learning machine models (KELM). The major contribution of our study is that we used only the river flow as relevant predictor and a single-input and single-output (SISO) model was proposed for predicting water pH and SC. Two scenarios were analyzed and compared. First, SISO models were developed and compared. Second, to greatly increase the performances of the KELM models, we have used the empirical wavelet transform (EWT) algorithm for decomposing the river flow time series into several multiresolution analysis components (MRA), which were used as new input variables. Data collected at the USG websites were used to test the proposed algorithms, and we find that the EWT clearly exhibited high accuracies compared with the SISO models, and it provides a very robust estimate of the water pH and SC. For water pH, it was found that the KELM models based on EWT were more accurate compared with the models without EWT, exhibiting R, NSE, RMSE, and MAE values ranging from 0.888 to 0.981, from 0.767 to 0.961, from 0.038 to 0.074, and from 0.027 to 0.058, respectively. In addition, for the SC, it was found that KELM models based on EWT were more accurate exhibiting R, NSE, RMSE, and MAE values ranging from 0.897 to 0.974, from 0.804 to 0.947, from 2.352 to 5.374, and from 1.528 to 4.152, respectively. -
Chapter 17. Assessment of Climate Change Impact on Land Use-Land Cover Using Geospatial Technology
Syeda Mishal Zahra, Muhammad Adnan Shahid, Rabeea Noor, M. Aali Misaal, Fahd Rasul, Sikandar Ali, M. Imran, M. Tasawar, Sidra AzamThe chapter delves into the assessment of climate change impact on land use and land cover (LULC) in Sindh, Pakistan, using geospatial technology. It introduces the significant influence of climate change on agriculture and environmental shifts, highlighting the role of human activities in exacerbating these changes. The study employs remote sensing and GIS techniques to map and analyze LULC variations, with a particular focus on the Normalized Difference Vegetation Index (NDVI) to track vegetation changes. The research covers the period from 2000 to 2021, revealing substantial changes in agricultural practices, urbanization, and vegetation patterns. The findings underscore the need for effective land use planning and government intervention to mitigate the adverse effects of climate change on the region's agricultural sector and overall environment.AI Generated
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AbstractMonitoring land cover variations is imperative for global resource management. Climatic variation influences land use–land cover (LULC) distribution steadily. Geospatial techniques are among the most comprehensive and efficient approaches for developing LULC categorization maps, which greatly enhance the overall utilization of agricultural, industrial, and urban areas of any region. The primary causes of LU change are urbanization and variation in temperature and precipitation leading to climate change. People have the innate desire to be close to nature, which drives them to relocate from densely populated places toward less heavily populated regions. Therefore, as a result, agricultural lands are being replaced by new communities created by deforestation and disrupting overall ecology. In the perspective of urbanization and variation in climatic parameters, i.e., precipitation and temperature, the present and previous LULC have been examined in this study by using geospatial techniques. Geographic information systems enable the examination of the changing patterns of LULC through monitoring by satellites. The key categorization indices in this study are the normalized difference vegetation index (NDVI) ranging between −0.28 and 0.74. As per analysis, the forthcoming trend of climate change and its impact on LULC has been detected for the better management of land resources. The outcomes of this research will aid in the formulation of mitigation as a result of climate change. -
Chapter 18. Impacts of Climate-Induced Events on the Season-Based Agricultural Cropping Pattern and Crop Production in the Southwestern Coastal Region of Bangladesh
Shimul Roy, Rezuana Afrin, Md. Younus Mia, Sanjoy Kumar MondolThe chapter delves into the severe impacts of climate change on agricultural crop production in Bangladesh, particularly in the Southwestern coastal region. It discusses how climate-induced events such as cyclones, floods, and sea-level rise significantly affect crop yields and cropping patterns. The study focuses on the districts of Khulna and Satkhira, where detailed data analysis reveals the correlation between climatic variability and crop production. The chapter also highlights the need for effective adaptation strategies to mitigate the impacts of climate change on agriculture in this vulnerable region.AI Generated
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AbstractAgricultural cropping pattern and crop production in the Southwestern coastal region of Bangladesh is affected severely by climate-induced events and climatic variability. This study shows the impacts of climatic-induced events (e.g., cyclone Sidr and Aila) on agricultural cropping patterns and crop production in two disaster-prone Southwestern coastal districts (i.e., Khulna and Satkhira) in Bangladesh. For analyzing the trend of climatic variability (e.g., temperature, rainfall, and relative humidity), 35 years (1980–2014) of climatic data were used. This study shows that the agricultural crop production in the selected Southern coastal region of the country had declined significantly when the two major cyclones (i.e., Sidr and Aila) approached in 2007 and 2009, respectively. A correlation analysis has been performed between annual average crop production and annual average climatic data to identify the influence of climatic variability on crop production. -
Chapter 19. Toward Smart Agriculture for Climate Change Adaptation
Rinku Moni DeviThe chapter delves into the pressing issue of climate change and its impact on agriculture, particularly in India. It highlights the potential of smart agriculture using IoT-based tools to enhance resource management, improve crop yield, and mitigate climate change effects. The text discusses the advantages of integrating IoT with good agricultural practices, including better water use efficiency, soil health, and reduced environmental footprint. Additionally, it explores the challenges faced by the Indian agriculture system and recommends the adoption of smart farming technologies to overcome these obstacles. The chapter concludes with a call for policy support and institutional innovations to promote sustainable and climate-resilient agricultural practices.AI Generated
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AbstractAgriculture plays a significant role in food security and forms the backbone of the economic system of a country. The increase in population has led to an urgent need to balance demand and supply, threatening sustainability and putting pressure on agricultural systems. Furthermore, climate change challenges like extreme weather conditions, climatic changes, and environmental impact have adversely impacted agriculture and linked resources. Besides this, about 85% of Indian farmers are marginal and small landholders. About 60% of the net sown area is under rainfed agriculture, and this makes India vulnerable to climate change considerably affecting the cropping system, livestock, and soil and increasing pests and diseases. Climate change would have a serious impact on Indian agriculture in the coming years which would negatively impact some important crops leading to food insecurity. The present trend and scenario are evident that without an efficient measure, it would be very difficult to meet agro-demand of the country. Therefore, there is an urgent need of efficient measures of adaption and mitigation. Therefore, smart agriculture using IoT (Internet of Things) technology has opened up extremely productive ways for farmers, helps in managing agricultural systems, and deals with weather uncertainties and challenges improving resource management. It enables farmers to collect real-time data related to weather updates, irrigation, production, yield quality, and soil moisture and predict pest, diseases, and market information and strengthen good agricultural practices in farms. Additionally, IoT solutions along with smart practices in agriculture offer opportunities for innovation in climate adaptation reducing the ecological footprints and enhancing the livelihoods of farmers. Thus, the present paper aims to review the current and future trends of IoT in the Indian agriculture system, highlighting potential challenges and also its role in combating climate change. Additionally, the study recommends adoption of good agricultural practices, capacity building, and switching from traditional to precise farming with IoT-based technology. For future scope, institutional innovations, networking of farmers, regulatory authorities, clear policies supporting the necessary legal and market architecture for smart farming, and transparent data management system will be required. -
Chapter 20. Flood Impact and Damage Assessment Based on the Sentitnel-1 SAR Data Using Google Earth Engine
Sachin Shinde, Chaitanya B. Pande, V. N. Barai, S. D. Gorantiwar, A. A. AtreThe chapter delves into the critical issue of flood impact and damage assessment using Sentinel-1 SAR data and Google Earth Engine. It introduces the study area in Kolhapur, India, and discusses the methodology for flood mapping, including data preparation, pre-processing, change detection, and refining the flood extent layer. The results are analyzed to calculate the flood extent, exposed population density, and affected cropland and urban areas. The chapter highlights the importance of cloud-based platforms like Google Earth Engine for efficient flood management and disaster response, providing valuable insights for planners and managers in mitigating flood risks.AI Generated
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AbstractFloods, as cataclysmic events, are often commonly brought on by floods and heavy downpours or by overflowing streams, rivers or seas; this sort of destructive occurrence is one of the most frequently recognised and affects nearly every sector and Earth area. This recommended practice involves supplying vital disaster data for both short- and long-haul flood worries. The tool offers a flood scale chart using Sentinel-1 SAR (synthetic aperture radar) images, just as cropland and community focus data presentations have been affected to address the entirety of essential issues caused by floods. Remote sensing data is a valuable asset for outlining areas. As of late, the availability of free satellite data has dramatically increased in terms of form and recurrence, making it easier to build flood maps across the globe. Propose a semiautomatic flood mapping system right now with free satellite imagery and open-source tools in mind. Google Earth Engine (GEE) is given an essential platform for impact analysis and damage assessment based on the SAR data. Rapid analysis of SAR data also identified how much area was affected due to flood. The possibility of flooding causes a significant loss of life and property, leading to the instability of human civilisation. Flood risk analysis is also needed to understand flood singularities, particularly for development and mitigation determinations. The central portion of the Panchganga River was chosen for current research. The essential purpose of the present thesis was to use Google Earth Engine mapping to determine the possible flood risk regions of the Panchganga River. A flood setup was done based on the SAR data of 5 August 2005 around the river area. The holding method was used to SAR data applied on separate flooded and non-flooded areas. Area outcomes can be significant for flood planning and damage. -
Chapter 21. Application of Hyperspectral Remote Sensing Role in Precision Farming and Sustainable Agriculture Under Climate Change: A Review
Chaitanya B. Pande, Kanak N. MoharirThe chapter delves into the pivotal role of hyperspectral remote sensing in precision farming and sustainable agriculture, particularly under the challenges posed by climate change. It begins by explaining the technology behind hyperspectral imaging, which captures detailed spectral information across hundreds of bands. This data is crucial for identifying and classifying crops, detecting diseases, and estimating crop yields. The chapter also covers the processing of hyperspectral data and the development of vegetation indices specific to hyperspectral imagery. It highlights the importance of these technologies in improving crop management, water stress detection, and soil analysis. Additionally, the chapter discusses the current state of hyperspectral sensors and data providers, showcasing their increasing application in agricultural decision-making. The text concludes by emphasizing the potential of hyperspectral remote sensing in transforming agricultural practices, despite the challenges in implementing these technologies at a grassroots level.AI Generated
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AbstractEach year, scholars, agronomists, scientists, and engineers have implemented several technologies to improve low-cost agricultural production, but this has detrimental environmental impacts. Precision agriculture deals with the study of the use of hyperspectral remote sensing (RS) and other technologies to boost cultivation as opposed to traditional farming methods which reduce harmful environmental consequences. Hyperspectral remote sensing technology plays a significant role in agricultural precision and agricultural growth, with its use in precision agriculture providing different ways to enhance agricultural practices. The idea of precision farming has attracted significant interest from farmers and scholars in the entire globe. The decision-making method includes making the best management choices based on the knowledge on uncertainty obtained from evidence gathered in the sector. The hyperspectral remote sensing and the various field data such as slope, dimension of plant indices, soil nutrients, crop quality, and yield can be calculated. This analysis illustrates hyperspectral remote sensing technologies, GIS, RGB, and multispectral, thermal imagery and gives you an understanding of how accurate farming and agricultural development can be useful. -
Chapter 22. Tools and Solutions for Watershed Management and Planning Under Climate Change
Abbas Mirzaei, Nasser Valizadeh, Hassan AzarmThe chapter delves into the multifaceted challenges of watershed management under climate change, highlighting the importance of integrated approaches. It discusses various tools and solutions, including the WEAP model for hydrological simulation, economic-hydrological modeling for optimal water allocation, and agent-based models for behavioral simulation. The chapter also emphasizes the need to consider environmental factors and stakeholder participation in sustainable watershed management. By offering a holistic view, the chapter provides valuable insights into effective strategies for managing water resources in a changing climate.AI Generated
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AbstractWater resources in watersheds are severely affected by climate change, water scarcity, and frequent droughts. This leads to a conflict between different users of water, especially between agriculture and the environment sectors. Due to the multidimensional and multiscale nature of watershed management and climate change, it is necessary to provide some tools and solutions for sustainable management and planning of watersheds under climate change. In this regard, the purpose of this study was to develop a conceptual framework in the field of tools and solutions for integrated watershed management and planning to optimally allocate water resources between different sections in the watersheds. In order to achieve this goal, the literature on the watershed management was reviewed, and it was found that there are three categories of studies in this field. The first category includes studies that have managed the watershed using hydrological and economic simulation tools. The second category is studies that consider environmental aspects as well and combine hydrological, economic, and environmental simulation tools. The third category is studies that consider users’ behavior very important in watershed management and employ some behavior-specific tools to simulate the behavioral complexities of users. The results showed that the combination of all hydrological, socioeconomic, environmental, and behavioral components and the use of component-specific tools to simulate these components can achieve sustainable watershed management. Furthermore, the present integrated framework can be used in various case studies around the world due to its comprehensiveness and operationality for integrated watershed management. -
Chapter 23. Isotopic Proxy to Identify Climate Change During the Anthropocene
Manpreet Singh, Prosenjit GhoshThe chapter delves into the role of human activities in altering atmospheric CO2 levels during the Anthropocene, focusing on the industrial revolution period. It discusses the use of stable isotopes in tree rings to understand climatic fluctuations and introduces paper samples as a new proxy for climate reconstruction. The study compares δ13C values from paper samples with atmospheric CO2 trends, highlighting the potential of this method despite data variability. The findings challenge previous studies, suggesting an upward trend in δ13C values over time, and call for further research to validate this novel approach.AI Generated
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AbstractStable isotopes are widely used in past climate reconstruction studies. They find a wide range of applications in climatology, and isotopic values in tree rings, ice cores, and marine sediments enable us to decipher past climatic conditions at the global scale. Since the onset of the industrial revolution in the eighteenth century, the burning of fossil fuels had accentuated the rate of CO2 rise and exacerbated global warming. The CO2 uptake by plants is reflected in 13C variations in the atmosphere and helps us in understanding how plants responded to past climatic conditions. However, climatic reconstruction using tree rings is an invasive sampling technique, and hence this chapter attempts to check whether paper samples obtained from trees may also preserve the climate record or not. Therefore, using paper samples from 1832 to 1880, an attempt has been made to reconstruct the climate record using 13C variations in that period. Our results show that paper samples may act as a significant archive for climatic reconstruction especially in Anthropocene due to the prolific growth of the printing industry in that period. Our results further show that there is a net positive trend in 13C values from 1832 to 1880. The paper sample is a more cost-effective method and does not require field-intensive sampling for taking samples of tree rings. Therefore, it may act as an important substitute for tree rings for climatic reconstruction in Anthropocene. -
Chapter 24. Estimation of Land Surface Temperature for Rahuri Taluka, Ahmednagar District (MS, India), Using Remote Sensing Data and Algorithm
J. Rajesh, Chaitanya B. PandeThe chapter explores the estimation of land surface temperature (LST) in Rahuri Taluka, Ahmednagar District, using remote sensing data from LANDSAT-8. It delves into the significance of LST in various fields such as hydrology, meteorology, and surface energy balance. The methodology involves the use of thermal infrared bands to calculate LST, with a focus on the algorithm's accuracy and validation against near-surface air temperatures. The study area's unique climate and urbanization patterns are highlighted, making the chapter valuable for understanding the impacts of climate change and urban heat islands. The chapter concludes with the potential applications of LST measurements in water management and climate change planning.AI Generated
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AbstractAs an outcome of the global warming influence on the atmosphere, India faces grave problem like the rest of the globe. Land surface temperature (LST) is more significant for urban LULC (land use/land cover), climate change, crop water requirement, temperature measurement studies and other essential input materials that contribute to atmosphere models. LANDSAT satellite data have provided many opportunities to use remote sensing and GIS (geographic information system) methods to study the Earth’s surface analysis. This study showed that topography, in an extra to human activity, also significantly impacts on the land surface temperature. Such type of research has provided the automated LST developed by LANDSAT-8 images based on algorithms. The majority of climate modelling and analytic applications require this. Remote sensing and geographic information systems suggest many possible uses in climate change assessments when they have been used to calculate LST. The results are presented in that area, where the standard deviation calculated was 4.83 °C LST, as the NDVI (normalized difference vegetation index) values have attained by red and near-infrared bands. Thermal infrared bands were utilized to determine land surface emissivity (LSE). The NDVI, LSE and LST studies have given sufficient accuracy for understanding the temperature variability. Results of study area show the lowest temperature in between 26.65 and 32.31 °C (1.85%), tolerance of 37.73–40.64 °C (24.05%) and deeply below at 43.47–47.89 °C (32.05%) during April 2019. -
Chapter 25. Analytical Hierarchy Process (AHP) Based on the Spatial Assessment of an Endangered Alpine Medicinal Herb Aconitum heterophyllum in the Western Himalayan Environment
Arun Pratap Mishra, Naveen Chandra, Juan James Mandy, S. K. Dwivedi, Ali Alruzuq, Chaitanya B. PandeThe chapter explores the rich plant biodiversity of the Himalayan region, focusing on the endangered medicinal herb Aconitum heterophyllum. It introduces the Analytical Hierarchy Process (AHP) as a decision-making tool to identify suitable sites for the conservation of A. heterophyllum in the Western Himalayas. The study area, comprising alpine meadows in Uttarakhand, is analyzed using various criteria such as temperature, rainfall, topographic wetness index, soil texture, forest type, aspect, elevation, and slope. The AHP method is employed to determine the weight of each criterion, and a weighted overlay analysis is conducted to generate a suitability map. The findings reveal specific areas with high, moderate, and low suitability for the species, providing valuable insights for conservation strategies. The chapter also discusses the impact of climate change and human activities on the species' distribution and suggests in situ and ex situ conservation measures. The integration of GIS and AHP in this study offers a comprehensive approach to habitat suitability analysis, making it a significant contribution to the conservation of endangered species.AI Generated
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AbstractRare and endemic species comprise globally a priority conservation concern in view of being at a higher risk of extinction. Recording the occurrence data for such species, especially in hardly accessible alpine habitats, is a rather challenging task. Modeling serves as an effective tool for predicting habitat suitability as well as in practicing artificial introductions for such species with encouraging conservation implications. A. heterophyllum is a critically endangered and endemic medicinal herb that is distributed along 2400–4500 m in Western Himalayas. The excessive demand for Aconitum heterophyllum in the herbal and pharmaceutical industry due to the biologically active compounds (aconitine) has led to extensive exploitation of the species from the wild, which has made its survival in its natural habitat miserable. In the present communication, effective criteria were identified in determining suitable areas for the Aconitum heterophyllum, which include the eight criteria of precipitation, temperature, slope, aspect, elevation, topographic wetness index (TWI), vegetation type, and soil texture based on the purpose of the study, regional characteristics, field surveys, local information, and expert opinions. The analytical hierarchy process (AHP), a pair comparison method, was used to determine the incompatibility rate of the criteria based on the questionnaire. -
Chapter 26. Land Use and Cover Variations and Problems Associated with Coastal Climate in a Part of Southern Tamil Nadu, India, Using Remote Sensing and GIS Approach
B. Santhosh Kumar, J. Rajesh, Chaitanya B. Pande, Abhay VaradeThe chapter investigates the transformations in land use and cover in a coastal region of southern Tamil Nadu, India, between 2001 and 2017. Using remote sensing and GIS, the study reveals significant changes driven by population growth, industrial activities, and climate change. Key findings include an increase in built-up land and industrial areas, and a decrease in cropland and waterlogged areas. The analysis underscores the importance of monitoring these changes for sustainable development and environmental conservation. The study also highlights the impact of coastal industries on the environment, emphasizing the need for effective land management policies to mitigate these issues.AI Generated
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AbstractLand use is an important factor in planning and managing land resources. Increasing pressure due to population and human resources in the world’s resources to meet growing needs contributes to significant land reform in various land uses. Remote sensing and GIS (geographic information system) techniques have been used to study land use change and land cover on the Tuticorin coast in Tamil Nadu. This study examines land use and land cover (LULC) changes from 2001 to 2017 for the coast of Tuticorin. The main objective of this study was to assess changes under the NRSC (National Remote Sensing Center) classification using Landsat ETM + and OLI images using visual interpretation with the help of image interpretation keys. The digitized land use and land cover features are categorized as aquaculture, built-up land, water bodies, cropland, fallow land, forest, a forest plantation, industrial area/mining, mangrove/swamp area, plantation, salt-affected land, saltpan, sandy areas, land with scrub, land without scrub, and waterlogged area. Apparently the whole study from 2001 to 2017 found that built-up land (+20.44 sq. km) and industrial/mining activities (+5.78 sq. km) were increased and cropland (−8.04 sq. km) and plantation (−7.62 sq. km) were decreased. The ground truth verification of the LULC features performed is made with an effective assessment of the changes. This study shows a significant environmental impact in the study area. In addition, it is crucial to strongly monitor the land use/land cover changes to maintain sustainable growth and in-depth coastal management requirements that can be taken to protect human health and property. -
Chapter 27. Classification of Vegetation Types in the Mountainous Terrain Using Random Forest Machine Learning Technique
Raj Singh, Arun Pratap Mishra, Manoj Kumar, Chaitanya B. PandeThe chapter delves into the classification of vegetation types in mountainous regions using advanced machine learning techniques, particularly the Random Forest algorithm. It underscores the significance of remote sensing data, such as NDVI time-series, and topographic and climatic variables, in achieving high classification accuracy. The study emphasizes the utility of Google Earth Engine as a powerful tool for processing large datasets, showcasing its potential to revolutionize the field of vegetation mapping. The research compares the classified maps with existing vegetation maps, highlighting the effectiveness and efficiency of the proposed methodology. The chapter also discusses the challenges and future directions in vegetation classification, encouraging further exploration of machine learning algorithms and remote sensing technologies.AI Generated
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AbstractClassification of vegetation into appropriate classes is important for management and conservation planning. Field-based observations are now extensively supported with remote sensing-based observations for such classifications. We demonstrate here the application of a machine learning technique using the random forest (RF) to classify Landsat imageries in the mountainous terrain of the Indian Western Himalayas. The region represents a mega-diverse area having a wide variation in climate and vegetation types with a varied topography. In mountainous regions, vegetation classification is crucial to identify the natural resources for its conservation and management planning. Normalized difference vegetation index (NDVI) using near infra-red and red bands was created for the period 2013–2019. As the imageries are available at a temporal resolution of 16 days, a Fourier transformation was done to compress a large amount of data. To achieve a better accuracy of classification, topographic variables of elevation and slope together with climate variables of temperature and precipitation were considered while implementing the classification algorithm. We successfully characterized the mountainous terrain into the classes of non-forest, evergreen needle leaf trees, evergreen broadleaf trees, moist deciduous trees, dry deciduous trees, shrub, and agriculture with an overall accuracy of 80%. We compared the classified maps with existing vegetation type maps to see inconsistency in mapping with the demonstrated approach. The methodology demonstrated in this study can be used for classifying the landscape into distinct classes with improved accuracy for various purposes. -
Chapter 28. Water Conservation Structure as an Unconventional Method for Improving Sustainable Use of Irrigation Water for Soybean Crop Under Rainfed Climate Condition
Chaitanya B. Pande, Kanak N. Moharir, Abhay VaradeThe chapter discusses the challenges of rainfed agriculture, particularly in India, where the majority of agricultural land relies solely on rainfall. It highlights the irregular climatic conditions and the need for water conservation structures to mitigate these challenges. The study focuses on the Kajaleshwar watershed area in Maharashtra, where rainwater harvesting structures were implemented to improve soybean crop yields. The chapter presents data on rainfall, groundwater levels, and crop yields before and after the implementation of these structures. It also discusses the impact of these structures on groundwater regime development and the potential for replicating this approach in other rainfed areas. The detailed analysis and practical implications of the study make it a valuable resource for professionals in the field.AI Generated
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AbstractRainwater harvesting through water conservation structures and techniques is playing a vital role under the rainfed agriculture conditions of the Vidarbha region in Maharashtra. Soil and water conservation activity is one of the most important components for agronomy practices in rainfed conditions. In view of that, the Department of Agriculture (M.S.) is undertaking various projects with the objective of developing rainwater harvesting structures for sustainable use of harvested rainwater for mitigating the need for protective irrigation to the rainfed crops grown at the Akola District. That is utilized for protective irrigation during prolonged dry spells to the kharif crops grown in the vicinity of existing drainage line developed under high surface runoff area. During the kharif seasons 2016 and 2017, ten demonstrations in watershed were conducted under rainfed condition. Protective irrigation has resulted in significant increase in yield as compared to rainfed condition/without irrigation at the watershed area. There was a 19.14–33% increase in yield during the kharif season of 2017–2018 as compared to 2016–2017. It was observed due to protective irrigation provided during the critical growth stage and dry spells. The results of study area should be more helpful for agriculture crops and what impacts of groundwater level and that is directly an impact on farmer’s income and production yields under different climatologically factors. All of these factors have been considered for dryland conditions based on rainwater conserved in the rainwater harvesting structures for protective irrigation for agronomy crops under water stress situations. -
Chapter 29. Study of Image Segmentation and Classification Methods for Climate Data Analysis
Ahmed Elbeltagi, Kouadri Saber, Djamal Bengusmia, Behnam Mirgol, Chaitanya B. PandeThis chapter delves into the historical context of image processing, from ancient drawings to modern digital photography and artificial neural networks. It focuses on advanced techniques such as deep learning and convolutional neural networks for image segmentation and classification. Key methods like fully convolutional networks, dilated convolutions, and conditional random fields are discussed in detail. The chapter also explores practical applications in various fields, including medical diagnostics, water science, agriculture, and disaster management. The integration of these techniques in everyday life is emphasized, showcasing how they can revolutionize data analysis and decision-making processes.AI Generated
This summary of the content was generated with the help of AI.
AbstractArtificial intelligence (AI) has revolutionized information technology and has shaped the way we live. AI is a computational model that allows computer to learn out from data and approximate solutions for nonlinear, multi-input functions and doesn’t depend upon physical models. Due to their flexibility and robustness, AI has been widely applied in large-scale fields ranging from robotics to airplane flight control. This section of book aims to discuss the advances in all aspect of AI, including machine/deep learning (ML-DL), data mining (DM), computer vision (CV), multi-agent systems (MS), evolutionary computation (EC), and fuzzy logic (FL) methods in image segmentation and classification. This chapter focuses specifically on various applications of AI related to mapping, classification, and segmentation of aerial images, including non-classification-/classification-based methods. Applications of AI are also discussed and showed the importance of AI in performing segmentation and outline extraction on aerial imagery. AI is performing very well on the understanding of climate data analysis. Brief introductions of AI with their adaptability for accurate segmentation, classification methods, and outline extraction are also interpreted. Furthermore, we illustrated how the AI tool will help the decision-makers, and developers, in achieving better performance with less computational cost. -
30. Correction to: Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems
Chaitanya B. Pande, Kanak N. Moharir, Sudhir Kumar Singh, Quoc Bao Pham, Ahmed Elbeltagi -
Backmatter
- Title
- Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems
- Editors
-
Chaitanya B. Pande
Kanak N. Moharir
Sudhir Kumar Singh
Quoc Bao Pham
Ahmed Elbeltagi
- Copyright Year
- 2023
- Publisher
- Springer International Publishing
- Electronic ISBN
- 978-3-031-19059-9
- Print ISBN
- 978-3-031-19058-2
- DOI
- https://doi.org/10.1007/978-3-031-19059-9
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