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Artificial neural networks: applications in morphometric and landscape features analysis
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
2007 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [en]

In this thesis a semi-automatic method is developed to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as a unsupervised Artificial Neural Network algorithm. Analysis and parameterization of topography into simple and homogenous land elements (landform) can play an important role as basic information in planning processes and environmental modeling. Landforms and land cover are the main components of landscapes. Landscapes are dynamic systems that involve interrelation between physical characteristics (such as landform, soil) and anthropogenic processes (such as land use).

In morphometry (as general term of geomorphometry) - the qualitative and quantitative measurement of topography - morphometric parameters are calculated such as profile curvature and longitudinal curvature. They are then used in morphometric analysis to identify morphometric features like plane, channel, ridge, peak or pit.

In February 2000 the Shuttle Radar Topography Mission (SRTM), collected data over 80% of the Earth's land surface, to derive a consistent digital elevation model (DEM) for allland areas between 60 degrees N and 56 degrees S latitude. This DEM with about 90 m grid spacing was used to generate morphometric parameters of first order (slope) and second order (minimum curvature, maximum curvatures and cross-sectional curvature) by fitting a bivariate quadratic surface. These surface curvatures are strongly related to landform features and geomorphological processes.

The thesis starts with an overall introduction and literature review. Then two methods for morphometric analysis are compared: morphometric parameterization and feature extraction proposed by Wood (1996a), calculated with Geographic Information Systems (GIS) software and our method implemented with Self Organizing Map (SOM) as an nsupervised artificial neural networks paradigm.

Finally in our method for landscape element analysis morphometric parameters and remotely sensed spectral data are combined. The emphasis is on morphologically homogeneous landscape elements characterized by similar slope and curvature conditions. SOM is used to reduce large multidimensional data sets to one output layer consisting of 20 map units. These map units are interpreted in terms of morphometric features, slope and land cover to identify and characterize landscape elements or geoecological units

Both studies have demonstrated valuable methods for extraction of land information that can be used in geomorphologic applications and geoecosystem modeling. These methods allow important savings in field work and can be used as alternative to labor intensive manual methods. But results may depend on scale and quality of the DEM and the topographic situation; caution should be used in interpretation. Evaluation of these methods in other areas with different morphometric conditions and with multi-scale DEM remains to be done.

Place, publisher, year, edition, pages
Stockholm: KTH , 2007. , viii, 41 p.
National Category
Other Environmental Engineering
Identifiers
URN: urn:nbn:se:kth:diva-4392ISBN: 978-91-7178-669-2 (print)OAI: oai:DiVA.org:kth-4392DiVA: diva2:12106
Presentation
2007-05-25, Seminarierummet, KTH, Brinellvägen 34, Stockholm, 10:15
Opponent
Supervisors
Note
QC 20101104Available from: 2007-05-23 Created: 2007-05-23 Last updated: 2012-03-20Bibliographically approved
List of papers
1. Terrain Features Analysis using Morphometric Parameterization and Neural Networks
Open this publication in new window or tab >>Terrain Features Analysis using Morphometric Parameterization and Neural Networks
2007 (English)In: Geomorphology, ISSN 0169-555X, E-ISSN 1872-695XArticle in journal (Other academic) Submitted
National Category
Other Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-7174 (URN)
Note
QS 20120316Available from: 2007-05-23 Created: 2007-05-23 Last updated: 2017-12-14Bibliographically approved
2. A semi-automated method for analysis of landscape elements using shuttle radar topography mission and landsat ETM+data
Open this publication in new window or tab >>A semi-automated method for analysis of landscape elements using shuttle radar topography mission and landsat ETM+data
2009 (English)In: Computers & Geosciences, ISSN 0098-3004, E-ISSN 1873-7803, Vol. 35, no 2, 373-389 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we demonstrate artificial neural networks-self-organizing map (SOM)-as a semi-automatic method for extraction and analysis of landscape elements in the man and biosphere reserve "Eastern Carpathians". The Shuttle Radar Topography Mission (SRTM) collected data to produce generally available digital elevation models (DEM). Together with Landsat Thematic Mapper data, this provides a unique, consistent and nearly worldwide data set.

To integrate the DEM with Landsat data, it was re-projected from geographic coordinates to UTM with 28.5 m spatial resolution using cubic convolution interpolation. To provide quantitative morphometric parameters, first-order (slope) and second-order derivatives of the DEM-minimum curvature, maximum curvature and cross-sectional curvature-were calculated by fitting a bivariate quadratic surface with a window size of 9 x 9 pixels. These surface curvatures are strongly related to landform features and geomorphological processes.

Four morphometric parameters and seven Landsat-enhanced thematic mapper (ETM +) bands were used as input for the SOM algorithm. Once the network weights have been randomly initialized, different learning parameter sets, e.g. initial radius, final radius and number of iterations, were investigated. An optimal SOM with 20 classes using 1000 iterations and a final neighborhood radius of 0.05 provided a low average quantization error of 0.3394 and was used for further analysis. The effect of randomization of initial weights for optimal SOM was also studied. Feature space analysis, three-dimensional inspection and auxiliary data facilitated the assignment of semantic meaning to the output classes in terms of landfonn, based on morphometric analysis, and land use, based on spectral properties.

Results were displayed as thematic map of landscape elements according to form, cover and slope. Spectral and morphometric signature analysis with corresponding zoom samples superimposed by contour lines were compared in detail to clarify the role of morphometric parameters to separate landscape elements. The results revealed the efficiency of SOM to integrate SRTM and Landsat data in landscape analysis. Despite the stochastic nature of SOM, the results in this particular study are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible for the same application with consistent results.

Keyword
Self-organizing map, SRTM, Landscape, Neural network, ETM
National Category
Other Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-7175 (URN)10.1016/j.cageo.2007.09.019 (DOI)000263398200019 ()2-s2.0-58349099655 (Scopus ID)
Note
Uppdaterad från submitted till published: 20101104. QC 20101104Available from: 2007-05-23 Created: 2007-05-23 Last updated: 2017-12-14Bibliographically approved
3. Artificial neural networks for landscape analysis of the biohere reserve "Eastern Carpathians" with landsat ETM+and SRTM data
Open this publication in new window or tab >>Artificial neural networks for landscape analysis of the biohere reserve "Eastern Carpathians" with landsat ETM+and SRTM data
2007 (English)Conference paper, Published paper (Refereed)
National Category
Other Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-7176 (URN)
Conference
'Landscape classification-theory and practice' in Warsaw, Poland
Note

QC 20101104

Available from: 2007-05-23 Created: 2007-05-23 Last updated: 2012-10-23Bibliographically approved

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