LANDSCAPE PLANNING

Remotely sensed data sources

Data characteristics are the most important issue to select appropriate available one for a LUC mapping. Both airborne and spaceborne data have various spatial, radiometric, spectral and temporal resolutions. Large numbers of studies have focused on characteristics of remotely sensed data (Barnsley 1999, Lefsky and Cohen 2003). Additionally, scan width (cover size in one scene), data availability (accessibility) and lunch date (data archive potential) are the other important factors (Table 3).

Sensor

Organization

Spatial

resolution

Spectral

resolution

Radiometric

resolution

Temporal

resolution

Imaging

Swath

Lunch

date

Geoeye

Geoeye ABD

Pan: 0.41m MS: 0.61m

4 VNIR bands (450­920nm)

11 bit

~3 days

15X15km

2008

Quickbird-2

Digital globe ABD

0.61m

4 VNIR bands (450­890)

11 bit

3.5 days

16.5X16.5km

2002

Ikonos

Geoeye ABD

Pan: 1m MS: 4m

4 VNIR bands (450­880nm)

11 bit

3.5 days

11X11km

1999

Rapideye

Rapideye AG Germany

5m

4 VNIR bands and red edge (440-850nm)

12 bit

5.5 days at nadir

77X77km

2008

World view (2) and (3)*

Digital globe ABD

Pan: 0.5m MS: 2m at nadir

4 VNIR standart bands & 4 VNIR unique bands (400­1040nm)

11 bit

1-3days

16.4X16.4km

2009 (2) 2014 (3)

Spot 5

SpotIMAGE

France

Pan:2.5m

VNIR:10m

SWIR:20m

3 VNIR & 1 SWIR bands (490-1750)

8 bit

26 days

60X60km

2002

AVIRIS

airborne

hyperspectral

NASA ABD

17m

224 VNIR & SWIR bands (400-2500nm)

16 bit

airborne

11X11km

-

Alos (AVNIR2)

JAXA Japan

10m

4 VNIR bands

8 bit

46 days

70X70km

2006

ASTER

Japan&ABD

VNIR: 15m SWIR: 30m TIR: 90m

4 VNIR bands 6 SWIR bands 5 TIR bands (520­11650nm)

VNIR:8 bit SWIR: 8 bit TIR: 12 bit

16 days

60X60km

1999

Landsat 8*

NASA ABD

Pan: 15m MS: 30m

5 VNIR bands 2 SWIR bands

1 cirrus band (433-2300nm)

8 bit

16 days

185X185km

Dec.

2012

CHRIS proba hyperspectral

ESA EU

17-34m

18-62 VNIR bands (400­1100nm)

16 bit

16 days

15X15km

2002

Sensor

Organization

Spatial

resolution

Spectral

resolution

Radiometric

resolution

Temporal

resolution

Imaging

Swath

Lunch

date

Hyperion

hyperspectral

NASA ABD

30m

220 VNIR & SWIR bands (400-2500nm)

16 bit

16 days

7.7X185km

2000

244 VNIR &

2014­

2015

EnMAP*

DLR Germany

30m

SWIR bands

14 bit

4 days

30X30km

(420-2450)

36 VNIR &

MODIS

NASA ABD

250-1000m

SWIR & TIR

12 bit

1 day

2330km

2000

bands

MERIS

ESA EU

300-1200m

15 VNIR bands

16 bit

1 day

1150km

2002

AVHRR NOAA 15

NASA ABD

1090m

6 VNIR & TIR bands

10 bit

1 day

1446km

1978

1998

Table 3. The most used optical sensor specifications in LUC mapping. (*) planned missions.

LANDSCAPE PLANNING

Data dependent (machine learning classifiers)

Data dependent classifiers are based on non-parametric rules. Particularly, the machine learning classifiers use different approaches according to classifier type. In this chapter, largely used non-parametric classifiers were assessed such …

Model based classifiers (traditional)

Model based classifiers are run using basic statistical theories like mean, variance and standard deviation of the dataset. The most used ones at the literatures are supervised MLC, MD, LDA …

Classification techniques

There are two basic approaches to the classification process: supervised and unsupervised classification. With supervised classification, one provides a statistical description of the manner in which expected land cover classes …

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