Hyperspectral Remote Sensing of Agriculture and Vegetation

• Autore: Pascucci Simone
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (266 p.)
• Soggetto topico: Environmental economics; Research & information: general
• Soggetto non controllato: abaxial; adaxial; analytical methods; AOTF; artificial intelligence; biodiversity; BRDF; canopy spectra; chlorophyll content; classification; classification of agricultural features; continuous wavelet transform (CWT); correlation coefficient; crop properties; discrimination; DLARI; Eragrostis tef; Ethiopia; expansive species; feature selection; field spectroscopy; future hyperspectral missions; grapevine; heavy metals; high-resolution spectroscopy for agricultural soils and vegetation; hyperspectral; hyperspectral data as input for modelling soil, crop, and vegetation; hyperspectral databases for agricultural soils and vegetation; hyperspectral imaging; hyperspectral imaging for vegetation; hyperspectral LiDAR; hyperspectral remote sensing; hyperspectral remote sensing for soil and crops in agriculture; invasive species; leaf chlorophyll content; macronutrient; MDATT; micronutrient; MLR; multi-angle observation; Natura 2000; new hyperspectral technologies; object-oriented segmentation; partial least square regression (PLSR); partial least squares; peanut; plant; plant traits; platforms and sensors; PLS; precision agriculture; product validation; proximal sensing data; proximal sensor; random forest; Red Edge; remote sensing; replicability; reproducibility; soil characteristics; spectra; spectral reflectance; spectroscopy; support vector machine; SVM; vegetation; vegetation classification; vegetation parameters; waveband selection
• Persona (resp. second.): PignattiStefano; CasaRaffaele; DarvishzadehRoshanak; HuangWenjiang; PascucciSimone
• Sommario/riassunto: This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.
• Titolo autorizzato: Hyperspectral Remote Sensing of Agriculture and Vegetation
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557691803321
• Lo trovi qui: Univ. Federico II