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This book provides a first synthetic view of an emerging area of ecology and biogeography, linking individual- and population-level processes to geographic distributions and biodiversity patterns. Problems in evolutionary ecology, macroecology, and biogeography are illuminated by this integrative view. The book focuses on correlative approaches known as ecological niche modeling, species distribution modeling, or habitat suitability modeling, which use associations between known occurrences of species and environmental variables to identify environmental conditions under which populations can be maintained. The spatial distribution of environments suitable for the species can then be estimated: a potential distribution for the species. This approach has broad applicability to ecology, evolution, biogeography, and conservation biology, as well as to understanding the geographic potential of invasive species and infectious diseases, and the biological implications of climate change. The authors lay out conceptual foundations and general principles for understanding and interpreting species distributions with respect to geography and environment. Focus is on development of niche models. While serving as a guide for students and researchers, the book also provides a theoretical framework to support future progress in the field.

Niche (Ecology). --- Niche (Ecology) --- Biogeography. --- Biogeography --- Mathematical models. --- Niche écologique --- Biogéographie --- Modèles mathématiques --- Microhabitat --- Biotic communities --- Competition (Biology) --- Ecology --- Habitat (Ecology) --- Areography (Biology) --- Geographical distribution of animals and plants --- Species --- Species distribution --- Biology --- Geography --- Geographical distribution --- Mathematical models --- BAM diagram. --- Eltonian Noise Hypothesis. --- Eltonian niche. --- Grinnellian niche. --- absence data. --- algorithm. --- ancillary data. --- biodiversity. --- biogeography. --- biotic interactions. --- biotically reduced niche. --- climate change. --- commission error. --- conservation biology. --- conservation planning. --- conservation reserve network planning. --- data preparation. --- data quality. --- disease distribution. --- disease ecology. --- disease transmission risk. --- disease transmission. --- distributional area. --- distributional ecology. --- diversification. --- ecological niche evolution. --- ecological niche modeling. --- ecological niche. --- ecology. --- environmental data. --- environmental space. --- epidemiology. --- evolutionary ecology. --- extinction risk. --- extrapolation. --- feedback loop. --- geographic distribution. --- geographic space. --- geography. --- habitat suitability modeling. --- hypervolume. --- infection. --- invasive species. --- macroecology. --- model calibration. --- model evaluation. --- model performance. --- model prediction. --- model projection. --- model significance. --- model. --- niche concept. --- niche conservatism. --- niche evolution. --- niche models. --- niche. --- nonequilibrium distribution. --- nonnative species. --- occupied distributional area. --- overfitting. --- pathogens. --- population biology. --- populations. --- potential distributional area. --- predictability. --- primary occurrence data. --- resolution. --- risk mapping. --- sampling bias. --- scenopoetic variables. --- sensitivity. --- sisterгpecies pairs. --- spatial extent. --- species distribution modeling. --- species distribution. --- species invasions. --- species limit. --- species occurrence. --- species reintroduction. --- statistical theory. --- transferability. --- unknown species.

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Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others.

Research & information: general --- Geography --- geographic information system (GIS) --- pocket beaches --- coastal management --- Interreg --- climate change --- remote sensing --- drone --- Sicily --- Malta --- Gozo --- Comino --- systematic literature review --- anomaly intrusion detection --- deep learning --- IoT --- resource constraint --- IDS --- evapotranspiration --- penman-monteith equation --- artificial neural network --- canopy conductance --- Ziz basin --- water quality --- satellite image analysis --- modeling approach --- nitrate --- dissolved oxygen --- chlorophyll a --- time series analysis --- environmental monitoring --- water extraction --- modified normalized difference water index (MNDWI) --- machine learning algorithm --- hyperspectral --- proximal sensing --- panicle initiation --- normalized difference vegetation index (NDVI) --- green ring --- internode-elongation --- Sentinel 1 and 2 --- Copernicus Sentinels --- crop classification --- food security --- agricultural monitoring --- data analysis --- SAR --- random forest --- 3D bale wrapping method --- equal bale dimensions --- mathematical model --- minimal film consumption --- optimal bale dimensions --- round bales --- Sentinel-2 --- SVM --- RF --- Boufakrane River watershed --- irrigation requirements --- water resources --- sustainable land use --- agriculture --- invasive plants --- precision agriculture --- rice farming --- site-specific weed management --- nitrogen prediction --- 1D convolution neural networks --- cucumber --- crop yield improvement --- mango leaf --- CCA --- vein pattern --- leaf disease --- cubic SVM --- chlorophyll-a concentration --- transfer learning --- overfitting --- data augmentation --- guava disease --- plant disease detection --- crops diseases --- entropy --- features fusion --- machine learning --- object-based classification --- density estimation --- histogram --- land use --- crop fields --- soil tillage --- data fusion --- multispectral --- sensor --- probe --- temperature profile --- forest roads --- simulation --- autonomous robots --- smart agriculture --- environmental protection --- photogrammetry --- path planning --- internet of things --- modeling --- convolutional neural networks --- machine vision --- computer vision --- modular robot --- selective spraying --- vision-based crop and weed detection --- Faster R-CNN --- YOLOv5 --- band selection --- CNN --- NDVI --- hyperspectral imaging --- crops --- urban flood --- Sentinel-1a --- Synthetic Aperture Radar (SAR) --- 3D Convolutional Neural Network --- multi-temporal data --- land use classification --- GIS --- Coatzacoalcos --- algorithms --- clustering --- pest control --- site-specific --- virtual pests --- rice plant --- weed --- hyperspectral imagery --- sustainable agriculture --- green technologies --- Internet of Things --- natural resources --- sustainable environment --- IoT ecosystem --- hyperspectral remoting sensing --- crop mapping --- image classification --- deep transfer learning --- hyperparameter optimization --- metaheuristic --- soil attribute --- ordinary Kriging --- rational sampling numbers --- spatial heterogeneity --- sampling --- soil pH --- spatial variation --- ordinary kriging --- Land Use/Land Cover --- LISS-III --- Landsat --- Vision Transformer --- Bidirectional long-short term memory --- Google Earth Engine --- Explainable Artificial Intelligence