XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: FOREST RESOURCES STUDY IN MONGOLIA USING ADVANCED SPATIAL TECHNOLOGIES D. Amarsaikhan, M. Saandar, V. Battsengel, Sh. Amarjargal

Document type:: Multivolume work

Structure type:: Chapter

2. THE STUDY AREA AND DATA SOURCES Mongolia has a relatively low forest cover, although, the country has a total land area of 156.5 million hectares. Forest reserve land comprises 18.3 million hectares with 12.9 million hectares of forest-covered area. This includes 10.5 million hectares of coniferous and hardwood forests. The forests are mainly located in the northern and central parts of the country, forming a transition zone between the Siberian boreal forest and the Central Asian steppe. Climate change, global warming and negative human activities are directly and negatively influencing the Mongolia’s forest resource and its quality. As the forests in Mongolia are state-owned, the Ministry for Nature, Environment and Tourism takes the overall responsibility for forest resource management. The main objective of the forest management is to protect and develop the existing forests of Mongolia so that they make maximum contributions to soil and watershed protection, and conservation of existing ecosystems. In the meantime, the forests are expected to produce, on a sustainable basis, increased volumes of industrial wood, fuel wood and minor forest products for the needs of people (Ykhanbai 2010). For a test site, a coniferous forest-dominated area around the Lake Khuvsgul located in northern Mongolia has been selected. The lake is considered as the second largest fresh water lake in Asia after the Lake Baikal with 100 km in length, 35 km in width, and over 265 m in depth. The lake lies at 1645m above sea level where mountains on the western shore rise up to 2961m and the mountains on the northern shore even up to 3491m.The area surrounding the Lake Khuvsgul represents a forest ecosystem and is characterized by such main classes as coniferous forest, deciduous forest, grassland, light soil, dark soil and water. The annual precipitation in the region is about 350-400 mm and it makes the area as the most humid region in the country. Figure 1. Landsat ETM+ image of the test area (R=B4, G=B3, B=B2). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia As data sources, multispectral Landsat ETM+ data of 12 August 2007 with a spatial resolution of 28m, a panchromatic Landsat ETM+ image of 28 August September 2007 with a spatial resolution of 15m and ALOS PALSAR L-band HH polarization image of 17 August 2007 with a spatial resolution of 25m, a topographic map of 1986, scale 1:100,000 and a forest taxonomy map of 1984, scale 1: 500,000 have been used. The selected test site in the Landsat ETM+ image frame is shown in figure 1. 3. GEOMETRIC CORRECTION OF THE IMAGES AND SPECKLE SUPPRESSION OF THE SAR IMAGE Initially, the multispectral and panchromatic Landsat ETM+ images were geometrically corrected to a UTM map projection using a topographic map of the study area, scale 1:100,000. In general, the panchromatic image was used to improve textural variety of the forests especially in bordering areas with other classes. The ground control point (GCP)s have been selected on clearly delineated crossings of rivers and other clear sites. In total 15 points were selected. For the transformation, a second order transformation and nearest neighbour resampling approach have been applied and the related root mean square (RMS) errors were 0.96 pixel and 0.83 pixel, respectively. In order to geometrically correct the PALSAR image, 18 more regularly distributed GCPs were selected comparing the locations of the selected points with other information such as Landsat ETM+ image and the topographic map. Then, the image was georeferenced to a UTM map projection using the topographic map of the study area. For the actual transformation, a second order transformation and nearest neighbour resampling approach were applied and the related RMS error was 1.18 pixel. As the microwave images have a granular appearance due to the speckle formed as a result of the coherent radiation used for radar systems; the reduction of the speckle is a very important step in further analysis. The analysis of the radar images must be based on the techniques that remove the speckle effects while considering the intrinsic texture of the image frame (Serkan ef al. 2008, Amarsaikhan ef al. 2012). In this study, four different speckle suppression techniques such as local region, lee-sigma, frost and gammamap filters (ERDAS 1999) of 3x3 and 5x5 sizes were applied to the PALSAR image and compared in terms of delineation of forest and other texture information. After visual inspection of each image, it was found that the 5x5 gammamap filter created the best image in terms of delineation of different features as well as preserving content of texture information. In the output image, speckle noise was reduced with very low degradation of the textural information. After the speckle suppression, the SAR image was added to the optical bands, thus forming multisource images. 4. DERIVATION OF FEATURES AND STANDARD MAXIMUM LIKELIHOOD CLASSIFICATION Generally, it is desirable to add some orthogonal features to any classification process to increase its decision-making. In the present study, for this aim, texture features have been used. To derive the texture features from the combined Landsat and PALSAR images, contrast and dissimilarity measures (using an 11x11 window size) have been applied and the results were compared. The bases for these measures are the co-occurrence measures that use a grey-tone spatial dependence matrix to calculate texture values, and the matrix shows the number of occurrences of the relationship between a pixel and its

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