Introduction to Environmental Data Science

<p>Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research with sections on exploratory data analysis in R including data abstraction transformation and visualization; spatial data analysis in vector and raster models; statistics and modelling ranging from exploratory to modelling considering confirmatory statistics and extending to machine learning models; time series analysis focusing especially on carbon and micrometeorological flux; and communication. <i>Introduction to Environmental Data Science </i>is an ideal textbook to teach undergraduate to graduate level students in environmental science environmental studies geography earth science and biology but can also serve as a reference for environmental professionals working in consulting NGOs and government agencies at the local state federal and international levels.</p><p>Features</p><p>• Gives thorough consideration of the needs for environmental research in both spatial and temporal domains. </p><p>• Features examples of applications involving field-collected data ranging from individual observations to data logging. </p><p>• Includes examples also of applications involving government and NGO sources ranging from satellite imagery to environmental data collected by regulators such as EPA. </p><p>• Contains class-tested exercises in all chapters other than case studies. Solutions manual available for instructors.</p><p>• All examples and exercises make use of a GitHub package for functions and especially data.</p>