Name: High-Resolution Remote Sensing for Biodiversity Studies (en Inglés)
SKU: 65785802
Price: 901.69 MXN
Availability: InStock
Author: Vanessa Priscila Camphora
ISBN: 9786209441080

portada High-Resolution Remote Sensing for Biodiversity Studies (en Inglés)

High-Resolution Remote Sensing for Biodiversity Studies (en Inglés)

Formato

Libro Físico

Editorial

Ks Omniscriptum Publishing

Autor

Vanessa Priscila Camphora

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9786209441080

High-Resolution Remote Sensing for Biodiversity Studies (en Inglés)

Vanessa Priscila Camphora (Autor) · Ks Omniscriptum Publishing · Tapa Blanda

Libro Nuevo Importado

Envío: 22 a 27 días háb.

$ 1,502.82$ 901.69

-40%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 10 unidades

$ 901.69

¡Envío Gratis! Llega entre el 09 Oct y el 19 Oct a CUAUHTÉMOC, Ciudad de México. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "High-Resolution Remote Sensing for Biodiversity Studies (en Inglés)"

The Atlantic Forest, now only 7.5% of its original size, has the largest continuous areas of remaining forest in the state of São Paulo, where palm trees (Arecaceae) represent an important component of its biodiversity. The typical architecture of palm tree canopies makes them more easily identifiable by remote sensing than other trees in the forest canopy. In this context, the purpose of this research was to evaluate the potential of using high spatial resolution remote sensing images to identify palm trees in the region of Ubatuba, on the northern coast of São Paulo. A QuickBird image (0.6 m spatial resolution) from 2007 was used for the preliminary identification of species by visual interpretation, serving as a basis for both field planning and analysis for digital processing. This approach made it possible to define which palm tree species are identifiable in the high-resolution satellite image, defining the minimum mappable size of canopy palm tree crowns and which morphological attributes best describe each species and aid in identification by remote sensing.