5.3 Argumentos y políticas defendidas que sustentaron el debate
5.3.1 Parte I: Argumentos
5.3.1.2 Argumentos de los críticos al prohibicionismo
Spectral imaging data is the most commonly used data source in tree species clas- sification. Airborne images have been acquired in the context of forest inventories for several years. In the beginning, aerial images were taken by analog cameras and
the images were then digitalized by scanning them as described in [28].
Fig. 2.1 shows examples of six tree species spectra. The visible part of the spec- trum ranges from 0.38 µm to 0.78 µm as defined in [29] and contains the blue band at ∼0.475 µm, the green band at ∼ 0.51 µm and the red band at ∼ 0.65 µm. The near
by the Labrador Current which confers to the Island mild
winters and cool summers. The forest cover is composed
mostly of coniferous species like balsam fir, white and black
spruce and tamarack.
B. Spectroradiometric data
The spectroradiometric measurements were done according
to the method described by Jackson et al. [5] with the ASD
between 350 and 2500 nm. The sampling step of the
instrument was performed at 1.4 nm for the 350-1050 nm
interval and at 2 nm for the 1000 to 2500 nm intervals. The
spectroradiometer executed a resampling at a step of 1 nm for
the visible, the NIR and the SWIR region. For our
measurements, the instrument collected 20 spectra and
calculated the average. The measurements were acquired with
a 25º FOV’s at Anticosti Island between August 5
thand 7
th2001. To benefit from maximum illumination and good
acquisition geometry, the measures were acquired between
11: 20 and 15: 05. When the measurements were performed,
the sky was mainly clear and the wind was light. The
observing angle was kept vertical during all the measurement
acquisitions process to minimise the BRDF effect. In total,
two deciduous species (trembling aspen and white birch), four
coniferous species (white and black spruce, tamarack and
balsam fir) and one sample of herbaceous vegetation were
measured.
C. Data processing
In order to proceed with the spectroradiometric analysis, all
noises caused by the detectors and by the water vapour
absorption around 1950 nm were eliminated from the spectra.
The analysis was performed on the spectral interval of 400 to
1800 nm and also between 1950 to 2400 nm. To quantify the
differences in the reflectance between the forest species, a
reference spectrum (tamarack) was chosen. The value of the
reference spectrum was subtracted from the other studied
spectra.
III. ANALYSIS AND DISCUSSION
Figure 1 presents the spectral signatures of the seven types
of forest cover present in the study. The comparison of these
spectral responses to a reference spectrum was used to
quantify the reflectance variation between spectra (fig. 2). The
reference spectrum is the tamarack, which is the forest species
under study with the lowest reflectance. Although some
studies [2, 6] refer to the visible region as offering a
possibility to distinguish between species, these results were
not observed in our study (fig. 3). In the visible, the vegetation
spectral response is influenced by the high absorption of
chlorophyll a and b, and by carotenoids [7]. The comparison
between the spectral signatures in this region shows a spectral
difference lower than 5% for the blue and red portion.
Meanwhile, the green peak region (between 500 and 600 nm)
allows discrimination between deciduous and coniferous
species. In fact, the white birch has a spectral difference of 8%
with the reference spectrum and a difference of 6% with the
black and white spruce (fig.3). These constitute the coniferous
species with the highest reflectance in the visible region.
Figure 1 : Spectral signatures of studied forest species
Figure 2 : Comparison of spectral signatures to a reference spectrum (Tamarack)
Figure 3 : Comparison of spectral signatures to a reference spectrum (Tamarack) for the visible region (400 to 700 nm)
However, this region is not appropriate for a spectral
discrimination between four coniferous species since a low
spectral variation of 2% is observed. The same observation
occurs for the spectral discrimination between the two
deciduous species where the spectral variation is not higher
than 3%.
The SWIR-1 region is important in forest cover studies
because of its rich information content (700 to 1350 nm). A
distinction between coniferous and deciduous species can be
performed (fig.4). In opposition to the visible region, spectral
differences between species belonging to the same groups can
be observed. In the SWIR-1, three precise regions can be used
to distinguish between the forest species under consideration
that are 770 to 920 nm, 1000 to 1150 nm and 1150 to 1350 nm
(fig.4). In these regions, white birch constitutes the species
with the highest reflectance, where a reflectance difference of
about 5% can be observed. The coniferous species are
spectrally distinct, except for the black and white spruce,
0,00 0,20 0,40 0,60 400 900 1400 1900 2400 Wavelength Ref lectan ce White Spruce Black Spruce Trembling Aspen White Birch Tamarack Herb. veg. Balsam Fir -0,05 0,00 0,05 0,10 0,15 0,20 0,25 0,30 400 900 1400 1900 2400 Wavelength R ef lectan ce White Spruce Black Spruce Trembling Aspen White Birch Tamarack Herb. veg. Balsam Fir 0,00 0,02 0,04 0,06 0,08 0,10 400 450 500 550 600 650 700 Wavelength R ef lectan ce
White Spruce Black Spruce
Trembling Aspen White Birch
Tamarack Herb. veg.
Balsam Fir
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4302
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4302
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Figure 2.1: Hyperspectral responses of six tree species. Wavelengths in [nm]. Reprinted from [30] with permission.
infrared (NIR) area ranges from 0.78 to 1.4 µm and is also called short wavelength range of the near infrared (IR-A) by the International Commission of Illumination and the German Institute for Standardization [29]. The transition between red and NIR is often called red edge (RE). Subsequent to the NIR, the wavelength area between 1.4 to 3 µm is called short wavelength infrared (SWIR) or long wave- length range of the near infrared (IR-B). The longer wavelengths are called mid, long and far infrared (IR-C) and are sometimes subdivided into mid wavelength infrared (MWIR) from 3–8 µm, long wavelength infrared (LWIR) from 8–15 µm and far infrared (FIR) from 15 µm to 1 mm.
For tree species classification, remote sensing data in the range between the vis- ible and the SWIR part of the spectrum can be useful. Therefore, in addition to aerial photographs, color infrared (CIR) images are often acquired. In early appli- cations, special photographic films that are sensitive to infrared wavelengths were used. Later, electronic sensors emerged. CIR images are usually stored as 3-band images with the infrared data in the red band, the data from the red spectral area in the green band of the image and the blue band of the image contains the spectral data of the green range. An example of an airborne digital image and an airborne infrared image are shown in Fig. 2.2.
(a) RGB image (b) CIR image Figure 2.2: Airborne RGB and CIR image
The achievable resolution of the airborne images depends on the used cameras and the altitude of the aircraft. Widely used resolutions are between 1 cm and 1 m per pixel. In addition to airborne images, multispectral satellite data sources are used for tree species classification and for land cover and land use classification. Satellite data offers multispectral bands, which can include bands in the visible areas as violet or yellow, bands in the transition between red band and NIR — also called RE — and in the infrared area. In contrast to airborne images they sometimes offer more than one infrared bands and additional SWIR bands. The resolution is usually lower than with airborne images. New satellites like WorldView-2, which was launched in 2009, offer resolutions down to 0.5 m [31]. Other satellites like OrbView-2, which was launched in 1997, offer multispectral data of less than 1 km resolution [32]. Fig. 2.3 shows two examples of satellite images as false-color composites with the used bands given in brackets.
In addition to multispectral data, hyperspectral sensors have been developed. In [33], Shippert stated that hyperspectral sensors measure reflectance values at a series of contiguous and narrow wavelength bands, whereas multispectral sensors usually measure defined bands, which often are not adjacent. Hyperspectral sensors give more detailed information on the spectrum of 1 pixel, as the results are similar to what would be measured in a spectroscopy laboratory. Hyperspectral data sets can
(a) RapidEye image (NIR-R-RE) (b) SPOT image (NIR-R-G) Figure 2.3: Satellite images. Values were stretched for better representation and often do have very large numbers of bands. However, it is not the number of bands that characterizes a hyperspectral sensor, but the contiguous and narrow na- ture of the measured bands. As an example the Airborne Imaging Spectroradiometer for Applications (AISA) series [34] offers sensors with 84 to 500 bands ranging from the visible spectrum to the long wavelength infrared spectrum. Today’s hyperspec- tral satellite sensors like Hyperion [35] and Proba [36] offer resolutions of 30 m and 20 m respectively.
For large area classifications the price of hyperspectral data, which is usually much more expensive than multispectral data, has to be taken into account and often is a limiting factor for the use of hyperspectral data in operational systems. Apart from the data acquisition, data handling needs to be taken into account. Hyperspectral data sets have much higher memory requirements due to the high number of bands. Because of the high redundancy of hyperspectral data sets, special algorithms have to be developed [37].