TUTORIAL PERCEPCION REMOTA:

1. Definición 
y conceptos
fundamentales


2. Naturaleza de las radiaciones 
electro-
magnéticas.

3. Interacción
de la radiación 
con la materia 
y origen de 
los espectros.
 

4. Interacción 
de las radiaciones 
con los objetos 
de la superficie
terrestre 


5. Interacciones 
atmosféricas.
 

6. La adquisición 
de datos y las 
plataformas
satelitales.
 

7. Sensores 

8. Estructura de
las imágenes 
digitales


9. Procesamiento
de las imágenes
digitales


10. Algunas 
aplicaciones 
de la percepción 
remota


Apendice I: nociones básicas sobre sensores de radar

Apendice II: bandas 
espectrales de algunos satélites actuales. 

Apendice III: 
bibliografia sugerida

 

  

  

  

SATELLITE REMOTE SENSING AND ITS FORESTRY APPLICATIONS

OTROS ITEMS DE INTERES 

Galería de imágenes

Plataformas de observación

Aeropuertos del mundo

Imágenes satelitales y seguros

¿Qué es la resolución?

Petróleo

Forestación

Estudios de viabilidad

Mercados de futuros

Cultivo del arroz

Nuestra misión

Nuestros servicios

¿Qué es la percepción remota?

¿Qué es una imagen satelital?

Uso del GPS

Estación rastreadora

Pasturas

Monitoreo de incendios

Sequías

Recursos naturales

Cultivo del tabaco

 

 

 

 

 

 

forestacion-321-sim-pancror.jpg (13933 bytes)

 

forestacion-rgb-321r.jpg (16028 bytes)

forestacion-rgb-453r.jpg (44689 bytes)

i0.jpg (55521 bytes)

 

d1.jpg (14348 bytes)

g1.gif (3621 bytes)

d2.jpg (13873 bytes)

g2.gif (3365 bytes)

d5.jpg (13231 bytes)

g3.gif (3266 bytes)

d7.jpg (8510 bytes)

g4.gif (3112 bytes)

 

g0.gif (11861 bytes)

Consulte por nuestros servicios:

 

February 1 of 1997 - Landsat-5 Image (30mts of resolution by pixel) Bordering Zone between the Paysandú and Río Negro Uruguayan counties, between the villes of Piedras Coloradas and Algorta.

forestacion-321-sim-pancror.jpg (13933 bytes)

Expand image #1

Image # 1: Panchromatic Simulation generated with the Spectral Bands 3-2-1, corresponding to the visible rank of the electromagnetic spectra ("what our eyes can see").

The obtained product is comparable to the classic aerial photography (black and white) but with a smaller resolution (30mts per pixel). Knowing beforehand that this is an densely forested area, we can only conclude that the dark zones correspond to woods: it's impossible for us to discern between different trees species (eucaliptus and pines, in this case)

forestacion-rgb-321r.jpg (16028 bytes)

Expand image #2

Image # 2. Using the same spectral bands, we generated a colored composition of the same zone. 

In this case, the result would correspond approximately with a photo obtained with film color: Despite aggregate the element color, the difficulty for the species discrimination still persist.

forestacion-rgb-453r.jpg (44689 bytes)

Expand image #3

Image # 3. The same scene but in a RGB composition of bands 4 (near infrared), 5 (middle infrared) and 3 (red of the visible spectra).

Satelite sensors like Landsat TM not only send information of the visible rank of the electromagnetic spectra (examples 1 and 2). Also they are sensible to other wavelengths such as the near and middle infrared. The radiation reflected in these regions of the spectra (that it's outside the reach of our vision), contains a lot of information, very specially about vegetable biomass. It's for that reason, that all that we couldn't see using classic or conventional methods, appears here in a remarkable way when we introduce in a color combination, information obtained from the infrared region of the spectra. In this case we not only clearly differentiated between eucaliptus and pines, but that also we can, having some field reference data, reach conclusions about the age and handling variability inherent to each arboreal species. Returning to the treated image, we identified eucaliptus in those zones that the color vary from orange, an intense, to red almost purple; the population of pines is identified in a rank that goes almost from black, ,through the brown to dark green (this in a first look). This chromatic variability is directly associated not only with the nature of each species in, but with its age and kind of handling. In a deeper analysis it's possible to classify and to detach the different situations that can be present in a certain forest population. The terrestrial objects, illuminated by solar radiation, reflects it after to introduce modifications induced by the its own structure and composition (reflectance). These modifications in the radiation of the radiant body (in this case the sun) generates what we call spectral pattern response. This pattern or spectral signature allow us to interpret different states of an object, in this case, a wood. The trees can have different ages, different species and/or variety, and trated or not by the man handling. Therefore, if we combined all these variables, we were obtain an ample range of forest objects possible spectral signatures. Knowing with certainty the location and the characteristics of the test woods previously selected (groud truth) we can, using of satellite images, to know which is the spectral signature of these trees (satelite truth) and to infer that of all those zones that respond to the same spectral pattern spectral, have similar characteristics. In this way is possible to generalize, using asuitable sampling criteria, characteristics of our test woods in extensive forestry areas. In addition, it's possible to make an estimation of planted surface for each individual object. Although this kind of interpretation and analysis is enough more complex and goes beyond the objective of this page, we will propose some examples about how respond differets layers of a forestry area from the spectral point of view.

Let us select of the image # 3, four woods whose ground truth we know (image #3a)

i0.jpg (55521 bytes)

image #3a

Now, let us compare test ground photos of those mounts with its corresponding spectral patterns (the colors of each graph correspond with those of the regions selected in image #3a):

wavelenght  = wavelenght measured in µm

value          =  reflectance

d1.jpg (14348 bytes)

g1.gif (3621 bytes)

d2.jpg (13873 bytes)

g2.gif (3365 bytes)

d5.jpg (13231 bytes)

g3.gif (3266 bytes)

d7.jpg (8510 bytes)

g4.gif (3112 bytes)

 

Finally, we compare between the different spectral response patterns:

g0.gif (11861 bytes)

As it can be seen in this sequence, the sensor of the Landsat satellite was able to detect different characteristics from these woods. Inclusively, see that those that are more approximate according to its carecterísticas (Pines of 28 and 25 years) show very similar spectral pattern responses, more compatible between them, respect to the yougest layers of pines. 

By above exposed, it's worth to insist in the advantages that the Satellite Remote Sensing technology offers. As we said (and we saw) previously, the infrared region of the sepctra "says us" a lot about the nature and state of the different vegetable species. And this is not only valid in the forestry case: whenever we treat vegetable biomass as object of study, the information originated in the infrared region of the spetra will have to acquire vital character.

    CONTACTO

Imágenes satelitales

Plataformas de observación

TUTORIAL IMÁGENES

Cultivo del arroz

Atractivo turístico

Forestación 

Estudios de viabilidad

Aeropuertos del mundo

Satélites y seguros

Mercados de futuros

Petróleo

¿Qué es la resolución?

Nuestra misión

Nuestros servicios

Sedimentos