International Water Management Institute (IWMI). CGIAR Initiative on Low-Emission Food Systems (Mitigate+). Colombo, Sri Lanka

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technologies as related to the impacts of climate change and its potential to prevent/remediate the effects. As such, different types of algorithms, mathematical relations and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact, chances of improving and saving lives and creating a healthier world. This book covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e., climate action. - Includes case studies on the application of AI and machine learning for monitoring climate change effects and management - Features applications of software and algorithms for modeling and forecasting climate change - Shows how real-time monitoring of specific factors (temperature, level of greenhouse gases, rain fall patterns, etc.) are responsible for climate change and possible mitigation efforts to achieve environmental sustainability

Recognized and advocated as a powerful tool, the role of remote sensing in identifying, mapping, and monitoring soil salinity and salinization will continue to expand. Remote Sensing of Soil Salinization: Impact on Land Management delineates how to combine science and geospatial technologies for smart environmental management. Choose the Right Tech

The water resources of the Mekong river catchment area, from China, through Thailand, Cambodia and Laos to Vietnam, are increasingly contested. Governments, companies and banks are driving new investment in roads, dams, diversions, irrigation schemes, navigation facilities, power plants and other emblems of conventional "development." Their plans and interventions pose multiple burdens and risks to the livelihoods of millions of people dependent on wetlands, floodplains, fisheries and aquatic resources.

This book about the Mekong Delta presents a unique collection of state-of-the-art contributions by international experts from different scientific disciplines about the characteristics and pressing water-related challenges of the Mekong Delta in Vietnam. The Mekong Delta belongs to one of the areas, which are to expect the largest challenges concerning environmental change and climate change induced sea level rise . The Delta acts as the “rice bowl” of Southeast Asia and is home to over 17 Million people, who need to cope with ecologic as well as socio-economic changes linked to the rapid economic development of the country. Annual floods, severe droughts, salt water intrusion, degrading water quality, tropical cyclones, hydrologic changes due to hydropower projects in the upstream of the Mekong, coastal erosion, and the loss of biodiversity are some of the problems in the region. Heterogeneous resource management responsibilities, and the fact that the Mekong – and thus also the Delta – is influenced by six countries aggravate the situation. Integrated water resources management and fostered cooperation and information exchange are pressing needs for the sustainable development of the Delta.

On top of a decade of exacerbated disaster loss, exceptional global heat, retreating ice and rising sea levels, humanity and our food security face a range of new and unprecedented hazards, such as megafires, extreme weather events, desert locust swarms of magnitudes previously unseen, and the COVID-19 pandemic. Agriculture underpins the livelihoods of over 2.5 billion people – most of them in low-income developing countries – and remains a key driver of development. At no other point in history has agriculture been faced with such an array of familiar and unfamiliar risks, interacting in a hyperconnected world and a precipitously changing landscape. And agriculture continues to absorb a disproportionate share of the damage and loss wrought by disasters. Their growing frequency and intensity, along with the systemic nature of risk, are upending people’s lives, devastating livelihoods, and jeopardizing our entire food system. This report makes a powerful case for investing in resilience and disaster risk reduction – especially data gathering and analysis for evidence informed action – to ensure agriculture’s crucial role in achieving the future we want.

Ending poverty and stabilizing climate change will be two unprecedented global achievements and two major steps toward sustainable development. But the two objectives cannot be considered in isolation: they need to be jointly tackled through an integrated strategy. This report brings together those two objectives and explores how they can more easily be achieved if considered together. It examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win†? situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.

A flagship publication of the Ministry of Agriculture, this is an authentic source of data on various facets of the agriculture and allied sector. It gives a comprehensive picture of Indian agriculture by presenting statistical data across states and time periods, covering diverse aspects, such as crop production and productivity, land use, agricultural inputs, terms of trade, price support and procurement, domestic and international trade, rainfall, credit and insurance. Though primarily based on data being continuously generated by the Ministry of Agriculture through a nation-wide agricultural data collection system and from various other central and state government Departments, the 2014 edition includes key indicators of Situation Assessment Survey of Agricultural Households 2013, made public in December 2014.

Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.