Soil Mapping and Process Modeling for Sustainable Land Use Management is the first reference to address the use of soil mapping and modeling for sustainability from both a theoretical and practical perspective. The use of more powerful statistical techniques are increasing the accuracy of maps and reducing error estimation, and this text provides the information necessary to utilize the latest techniques, as well as their importance for land use planning. Providing practical examples to help illustrate the application of soil process modeling and maps, this reference is an essential tool for professionals and students in soil science and land management who want to bridge the gap between soil modeling and sustainable land use planning. - Offers both a theoretical and practical approach to soil mapping and its uses in land use management for sustainability - Synthesizes the most up-to-date research on soil mapping techniques and applications - Provides an interdisciplinary approach from experts worldwide working in soil mapping and land management

This textbook provides students and academics with a conceptual understanding of fire behavior and fire effects on people and ecosystems to support effective integrated fire management. Through case studies, interactive spreadsheets programmed with equations and graphics, and clear explanations, the book provides undergraduate, graduate, and professional readers with a straightforward learning path. The authors draw from years of experience in successfully teaching fundamental concepts and applications, synthesizing cutting-edge science, and applying lessons learned from fire practitioners. We discuss fire as part of environmental and human health. Our process-based, comprehensive, and quantitative approach encompasses combustion and heat transfer, and fire effects on people, plants, soils, and animals in forest, grassland, and woodland ecosystems from around the Earth. Case studies and examples link fundamental concepts to local, landscape, and global fire implications, including social-ecological systems. Globally, fire science and integrated fire management have made major strides in the last few decades. Society faces numerous fire-related challenges, including the increasing occurrence of large fires that threaten people and property, smoke that poses a health hazard, and lengthening fire seasons worldwide. Fires are useful to suppress fires, conserve wildlife and habitat, enhance livestock grazing, manage fuels, and in ecological restoration. Understanding fire science is critical to forecasting the implication of global change for fires and their effects. Increasing the positive effects of fire (fuels reduction, enhanced habitat for many plants and animals, ecosystem services increased) while reducing the negative impacts of fires (loss of human lives, smoke and carbon emissions that threaten health, etc.) is part of making fires good servants rather than bad masters.

Reliable predictions of how changing climate and disturbance regimes will affect forest ecosystems are crucial for effective forest management. Current fire and climate research in forest ecosystem and community ecology offers data and methods that can inform such predictions. However, research in these fields occurs at different scales, with disparate goals, methods, and context. Often results are not readily comparable among studies and defy integration. We discuss the strengths and weaknesses of three modeling paradigms: empirical gradient models, mechanistic ecosystem models, and stochastic landscape disturbance models. We then propose a synthetic approach to multi-scale analysis of the effects of climatic change and disturbance on forest ecosystems. Empirical gradient models provide an anchor and spatial template for stand-level forest ecosystem models by quantifying key parameters for individual species and accounting for broad-scale geographic variation among them. Gradient imputation transfers predictions of fine-scale forest composition and structure across geographic space. Mechanistic ecosystem dynamic models predict the responses of biological variables to specific environmental drivers and facilitate understanding of temporal dynamics and disequilibrium. Stochastic landscape dynamics models predict frequency, extent, and severity of broad-scale disturbance. A robust linkage of these three modeling paradigms will facilitate prediction of the effects of altered fire and other disturbance regimes on forest ecosystems at multiple scales and in the context of climatic variability and change.