Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A semi-automated method for analysis of landscape elements using shuttle radar topography mission and landsat ETM+data
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Environmental and Natural Resources Information System.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Environmental and Natural Resources Information System.
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.

Place, publisher, year, edition, pages
2009. Vol. 35, no 2, 373-389 p.
Keyword [en]
Self-organizing map, SRTM, Landscape, Neural network, ETM
National Category
Other Environmental Engineering
Identifiers
URN: urn:nbn:se:kth:diva-7175DOI: 10.1016/j.cageo.2007.09.019ISI: 000263398200019Scopus ID: 2-s2.0-58349099655OAI: oai:DiVA.org:kth-7175DiVA: diva2:12104
Note
Uppdaterad från submitted till published: 20101104. QC 20101104Available from: 2007-05-23 Created: 2007-05-23 Last updated: 2011-09-01Bibliographically approved
In thesis
1. Artificial neural networks: applications in morphometric and landscape features analysis
Open this publication in new window or tab >>Artificial neural networks: applications in morphometric and landscape features analysis
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:nbn:se:kth:diva-4392 (URN)978-91-7178-669-2 (ISBN)
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
2. Morphometric and Landscape Feature Analysis with Artificial Neural Networks and SRTM data: Applications in Humid and Arid Environments
Open this publication in new window or tab >>Morphometric and Landscape Feature Analysis with Artificial Neural Networks and SRTM data: Applications in Humid and Arid Environments
2008 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

This thesis presents a semi-automatic method to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as an unsupervised Artificial Neural Network algorithm in two completely different environments: 1) the Man and Biosphere Reserve “Eastern Carpathians” (Central Europe) as a complex mountainous humid area and 2) Lut Desert, Iran, a hyper arid region characterized by repetition of wind-eroded features.

In 2003, the National Aeronautics and Space Administration (NASA) released the SRTM/ SIR-C band data with 3 arc seconds (approx. 90 m resolution) grid for approximately 80 % of Earth’s land surface. The X-band SRTM data were processed with a 1 arc second (approx. 30 m resolution) grid by the German space agency, DLR and the Italian space agency ASI, but due to the smaller X-SAR ground swath, large areas are not covered. The latest version 3.0 SRTM/C DEM and SRTM/X band DEM were re-projected to 90 and 30 m UTM grid and used to generate morphometric parameters of first order (slope) and second order (cross-sectional curvature, maximum curvatures and minimum curvature) by using a bivariate quadratic surface. The morphometric parameters are then used in a SOM to identify morphometric features (or landform elements) e.g. planar, channel, ridge in mountainous areas or yardangs (ridge) and corridors (valley) in hyper-arid areas.

Geomorphic phenomena and features are scale-dependent and the characteristics of features vary when measured over different spatial extents or different spatial resolution. Morphometric parameters were derived for nine window sizes of the 90 m DEM ranging from 5 × 5 to 55 ×55. Analysis of the SOM output represents landform entities with ground areas from 450 m to 4950 m that is local to regional scale features. Effect of two SRTM resolutions, C and X bands is studied on morphometric feature identification. The difference change analysis revealed the quantity of resolution dependency of morphometric features. Increasing the DEM spatial resolution from 90 to 30 m (corresponding to X band) by interpolation resulted in a significant improvement of terrain derivatives and morphometric feature identification.

Integration of morphometric parameters with climate data (e.g. Sum of active temperature above 10 ° C) in SOM resulted in delineation of morphologically homogenous discrete geo-ecological units. These units were reclassified to produce a Potential Natural Vegetation map. Finally, we combined morphometric parameters and remotely sensed spectral data from Landsat ETM+ to identify and characterize landscape elements. The single integrated data set of geo-ecosystems shows the spatial distribution of geomorphic, climatic and biotic/cultural properties in the Eastern Carpathians.

The results demonstrate that a SOM is a very efficient tool to analyze geo-morphometric features under diverse environmental conditions and at different scales and resolution. Finer resolution and decreasing window size reveals information that is more detailed while increasing window size and coarser resolution emphasizes more regional patterns. It was also successfully applied to integrate climatic, morphometric parameters and Landsat ETM+ data for landscape analysis. Despite the stochastic nature of SOM, the results are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible with consistent results.

Abstract [sv]

Avhandlingen presenterar en halvautomatisk metod för att analysera morfometriska kännetecken och landskapselement som bygger på Self Organizing Map (SOM), en oövervakad Artificiell Neural Nätverk algoritm, i två helt skilda miljöer: 1) Man and Biosphere Reserve "Eastern Carpathians" (Centraleuropa) som är ett komplext, bergigt och humid område och 2) Lut öken, Iran, en extrem torr region som kännetecknas av återkommande vinderoderade objekt.

Basen för undersökningen är det C-band SRTM digital höjd modell (DEM) med 3 bågsekunder rutnät som National Aeronautics and Space Administration släppte 2003 för ungefär 80 % av jordens yta. Dessutom används i ett mindre område X-band SRTM DEM med 1 bågsekund rutnät av den tyska rymdagenturen DLR. DEM transformerades till 90 och 30 m UTM nätet och därav genererades morfometriska parametrar av första (lutning) och andra ordning (tvärsnittböjning, största och minsta böjning). De morfometriska parametrar används sedan i en SOM för att identifiera morfometriska objekt (eller landform element) t.ex. plan yta, kanal, kam i bergsområden eller yardangs (kam) och korridorer (dalgångar) i extrem torra områden.

Geomorfiska fenomen och objekt är skalberoende och kännetecken varierar med geografiska områden och upplösning. Morfometriska parametrar har härletts från 90 m DEM för nio fönsterstorlekar från 5 × 5 till 55 × 55. Resultaten representerar landform enheter för områden från 450 m till 4950 m på marken dvs. lokal till regional skala. Inflytande av två SRTM upplösningar i C och X-banden har studerats för identifikation av morfometriska objekt. Förändringsanalys visade storleken av upplösningsberoende av morfometriska objekt. Ökning av DEM upplösningen från 90 till 30 m (motsvarande X-bandet) genom interpolation resulterade i en betydande förbättring av terräng parametrar och identifiering av morfometriska objekt.

Integration av morfometriska parametrar med klimatdata (t.ex. summan av aktiv temperatur över 10° C) i SOM resulterade i avgränsningen av homogena geoekologiska enheter. Dessa enheter ha används för att producera en karta av potentiell naturlig vegetation. Slutligen har vi kombinerat morfometriska parametrar och multispektrala fjärranalysdata från Landsat ETM för att identifiera och karaktärisera landskapselement. Dessa integrerade ekosystem data visar den geografiska fördelningen av morfometriska, klimatologiska och biotiska/kulturella egenskaper i östra Karpaterna.

Resultaten visar att SOM är ett mycket effektivt verktyg för att analysera geomorfometriska egenskaper under skilda miljöförhållanden, i olika skalor och upplösningar. Finare upplösning och minskad fönsterstorlek visar information som är mer detaljerad. Ökad fönsterstorlek och grövre upplösning betonar mer regionala mönster. Det var också mycket framgångsrikt att integrera klimatiska och morfometriska parametrar med Landsat ETM data för landskapsanalys. Trots den stokastiska natur av SOM, är resultaten inte känsliga för slumpvisa värden i de ursprungliga viktvektorerna när många iterationer används. Detta förfarande är reproducerbart med bestående resultat.

Place, publisher, year, edition, pages
Stockholm: KTH, 2008. viii, 71 p.
Keyword
Self Organizing Map, Neural Network, Morphometric Feature, Landscape, Yardang, Lut Desert, Potential natural vegetation, geoecosystem, Landform, Landsat ETM+, Morphometric Parameters, SRTM, Resolution, Curvatures, DEM.
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-4789 (URN)978-91-7415-010-0 (ISBN)
Public defence
2008-06-11, F3, Lindstedsvägen 26, 13:15
Opponent
Supervisors
Note
QC 20100924Available from: 2008-06-02 Created: 2008-06-02 Last updated: 2011-09-01Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Ehsani, Amir HoushangQuiel, Friedrich
By organisation
Environmental and Natural Resources Information System
In the same journal
Computers & Geosciences
Other Environmental Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 173 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf