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The Land Cover Classification System (LCCS) is a comprehensive, standardized a priori classification system, designed to meet specific user requirements, and created for mapping exercises, independent of the scale or means used to map. Any land cover identified anywhere in the world can be readily accommodated. The classification uses a set of independent diagnostic criteria that allow correlation with existing classifications and legends. Land cover classes are defined by a combination of a set of independent diagnostic criteria--the so-called classifiers--that are hierarchically arranged to assure a high degree of geographical accuracy. Because of the heterogeneity of land cover, the same set of classifiers cannot be used to define all land cover types. The hierarchical structure of the classifiers may differ from one land cover type to another. Therefore, the classification has two main phases: an initial Dichotomous Phase, where eight major land cover types are distinguished; and a subsequent Modular-Hierarchical Phase where the set of classifiers and their hierarchical arrangement are tailored to the major land cover type. This approach allows the use of the most appropriate classifiers and reduces the total number of impractical combinations of classifiers. Because of the complexity of the classification and the need for standardization, a software application has been developed to assist the interpretation process. This will reduce heterogeneity between interpreters and between interpretations over time. Because of the flexible manner in which the classification is set up, with creation of classes at different levels of the system and the optional use of modifiers, environmental attributes and specific technical attributes in combination, coupled with the tremendous number of classes possible, this innovative software application assists the user to select the appropriate class using a step-by-step process, i.e., classifier by classifier. The software application will be available both as a stand-alone product and integrated into a digital image interpretation software suite which will allow interpretation of imagery followed by labelling of the mapping units with the land cover classes, the Africover Interpretation and Mapping System (AIMS)

Cover crops --- Land use --- Vegetation classification. --- Environmental Sciences and Forestry. Remote Sensing and Geographical Information Systems --- Geographical Information Systems. --- Vegetation classification --- Classification --- Land use - Classification --- Cover crops - Classification

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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 publishing 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