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Land cover classification using Landsat Enhanced Thematic Mapper (ETM) data - year 2000
Identifier:372_1
Publication date:2000
Author(s):
Matthias Moeller
Abstract:
Landsat ETM+ 2000 CAP LTER subset
Keywords:
Phoenix, Arizona, Sonoran Desert, caplter, Central Arizona Phoenix Longterm Ecological Research, urban, metropolitan area, Landsat, land use, land cover, change detection, National Land Cover Dataset (NLCD), Land-Use and Land-Cover Change, Land Use Changes, Project id 20, caplter created, gis, remote sensing
Temporal Coverage:
2000-04-19
Geographic Coverage:
Geographic Description:Central Arizona Phoenix
Bounding Coordinates:
Longitude:-112.786370 to -111.571205
Latitude:33.845147 to 33.192751
Contact:
Data Manager, Arizona State University,
Global Institute of Sustainability,POB 875401,TEMPE
caplter.data@asu.edu
Methods used in producing this dataset:
The land-use/land-cover classification was produced using the object-oriented approach implemented in the Ecognition software. This approach consists of three steps. In the first step, the image is divided into segments basing on gray values of the image bands, chosen for the segmentation. Segments are clusters with a maximum spectral homogeneity (M. Baatz, and A. Schaepe, “Multiresolution Segmentation: an optimization approach for high quality multi-scale image segmentation,� in: Angewandte Geographische Informationsverarbeitung, Vol. XII, J. Strobl, T. Blaschke and G. Griesebnaer Eds., Karlsruhe, Wichmann, pp. 12–23, 2000). The user is able to define parameters such as shape size, shape form and spectral values. Based on these parameters the segments are calculated. The
initial starting points are set randomly by the software and the segments grow until they reach their bordering neighbors. An image segmentation can be performed on several levels. It ranges from a large number of small segments on a low level to a small number of segments with a large size on an upper level. All levels are linked to each other in a parent - child relationship and the segments on each level are also connected to their specific neighbors. In the second step the classes are named and at least these classes have to be defined and outlined using fuzzy membership functions, which is the most important and complex step during the classification [2]. The classes can be described using a large number of class different parameters. Starting with the definition of simple spectral properties (e.g.
gray values or brightness values of the bands against the mean of all bands) the classes can be separated against each other. Also neighborhood relations can be defined (e.g. relative border of class x is xx% to class y). Inherited relations from upper to lower segmentation levels can be used for the description of classes. That enables the performance of a rough classification on an upper level with large image segments. With finer segmentation this upper class can be reclassified into a number of more detailed classes. For
example: the class ‘urban’ was classified manually on a higher level using the click and classify algorithm. Click and classify is an easy to use method that enables to achieve rough classification results in a relatively short time. But these segments did not correctly match the exact border of the urban area to another LULC class. These overlapping regions may belong either to areas of farmland or to undeveloped desert. On a lower level with smaller segments the classification could be refined. For example, the ‘urban’ upper class was split into three more detailed classes: urban developed sparse, urban developed dense and commercial/industrial/transportation. However, there are overlap areas remaining on the finer level which have been classified as ‘urban’ (n the higher level) but belong definitely to one category ‘farmland’, either ‘vegetated farmland’ or ‘fallow farmland’. The spectral properties of these regions were very similar or identical to the
class ‘urban vegetation’. These classes could be differentiated using the neighborhood relations. A segment classified as some kind of vegetation must have a relative border of at least 50% to other urban features, leading to classification as ‘urban vegetation’, otherwise it was assigned to ‘vegetated farmland’. The neighborhood relations were applied to all classes on the finer level. Checking these neighborhood relations on the same segmentation level is an essential tool and it increased the classification results compared to the statistical approach. The accuracy of classification was checked with randomly distributed control points. It resulted in an overall accuracy of 83%.
Entities:
Raster: cap_lter_etm_2000_class
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Attributes:
Attribute:ObjectID
Description:Internal feature number.
Attribute:Cell value
Description:Thematic value of the cell
Domain:
Enumeration:
0: Unclassified
1: 11 open water
2: 21 low intensity residential
3: 22 low intensity residential
4: 23 commerical/industrial/transportation
24: 84 fallow farmland
29: water
30: 81- 83 cultivated farmland vegetation
34: natural open land
35: natural land vegetation
36: urban vegetation
Attribute:Class_names
Description:Class_names
Domain:
Enumeration:
0: Unclassified
1: 11 open water
2: 21 low intensity residential
3: 22 low intensity residential
4: 23 commerical/industrial/transportation
24: 84 fallow farmland
29: water
30: 81- 83 cultivated farmland vegetation
34: natural open land
35: natural land vegetation
36: urban vegetation
Attribute:Number of pixels
Description:Number of pixels of each land use - land cover type used to build a histogram
Measurement Unit:number
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