Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

Bibliographic data

Multivolume work

Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

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

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WP-3 FOREST INVENTORY APPLICATIONS]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: USE OF DIGITAL SATELLITE DATA FOR STAND DELINEATION AND ESTIMATION OF STAND VARIABLES BY REGRESSION ANALYSIS AND FIELD INVENTORY. Olle Hagner

Document type:: Multivolume work

Structure type:: Chapter

Coordinates for the plots were digitized from orthophoto maps at scale 1:10 000. The actual plot locations were marked during field inventory. Only permanent plots were used in this study because only the nominal coordinates were available for the temporary plots. The figures of volume/hectare, diameter, age, basal area etc. were updated to the level of 1989, by applying growth models developed by Soderberg (1986). 2.4 Image and map data Ihe digital satellite data used are summarized in Table 1. Ihe satellite scenes were precision- corrected by the Swedish Space Corporation in Kiruna using ground control points and orbit modeling techniques. Ihe SPOT XS and PAN data used at test site 1 were merged into a multispectral composite, with spatial resolution of 10 m, using the method described by Jaakkola & Hagner (1988). The Landsat quarter-scene acquired 1989, was used to provide multispectral signatures corresponding to NFI-plots. The intensities were extracted without correcting for displacements due to eleva tion. Landuse information and administrative boundaries at the test sites were digitized from public maps at scale of 1:10 000. Ihe digitized vector data were converted into raster overlays for use in the segmentation procedure. Table 1. Digital image data used in the study. Type Resolution Date of acquisition SPOT 1 HKV PAN 10 m 18 June 1986 SPOT 1 HKV XS 20 m 18 June 1986 Landsat 5 TM 30 m 21 June 1989 2.5 t-ratio segmentation In the case of multiple bands, the statistic ncg*j here is calculated as the square—root of summed, squared t-ratios, computed for each feature band (Equation 2), which is equivalent to Hotellings ^ the case of uncorrelated bands (Manly, 1986). Note that this assumes independence among features. b T 2 = Sum t 2 A (2) Where: t = t-ratio, band j. b = Number of feature bands. Ihe t-ratio segmentation procedure is started by entering input parameters and defining the initial set of regions. Ihe input parameters are: minimum and maximum region size allowed, a final t-ratio threshold, and the number of iteration steps. Any digital image data may be used as the source of feature bands. Ihe segmentation is guided by an overlay band, which can be used to define diff erent administrative regions and land use classes. Individual pixels, or results of a lew level segmentation (e.g. directed trees segmentation), may be used as initial regions. In the special case of single pixel "regions", when the t-ratio is not defined, the pixel is merged with the adjacent region (or pixel) closest in feature space. In order to control the order of merging, so that the most similar regions are merged first, the merging is done in several iteration steps. A temporary t-ratio threshold is initialized at a lew starting level and raised for each step, until the final threshold level is reached. Note that a region can be claimed by, and merged with more than one other region during an iteration step. If the number of steps is sufficiently large (10-15), most regions will be merged properly at the end. The t-ratio segmentation method (Algorithm 1) is a type of region growing algorithm. Ihe basic idea behind the method is that spatially adjacent regions should be merged if they can not be separ ated with a given certainty. A criterion for merging of regions should describe the chance for two regions to be of the same type, i.e., test the hypothesis that the spectral inten sities of the two regions are in fact observations on the same population. Hence, the absolute dis tance in feature space between regions must be set in relation to the population variance and number of observations (pixels). A criterion with the properties described above is the well-known t-ratio (Manly, 1986) (Equation 1). For each step, the following operations are per formed in parallel for all regions: A temporary threshold is calculated. Each region is compared to all adjacent regions and the one closest in feature space is selected. If the t-ratio for the crurrent and selected region is less than the temporary threshold, and the total size of the two regions does not exceed the maximum size allowed, the two regions are listed for merging. At the end of each pass, all regions listed are merged and statistics are calculated for the new regions. When no more regions are merged, the next itera tion step is started. Ihe iteration ends when the final t-ratio threshold is reached. Finally, all remaining regions smaller than the minimum size are merged with the adjacent region closest in feature space. An example of t-ratio region seg mentation is shewn in Figure 2. t-ratio x i - x 2 ( s 2 i + s2 x )0-5 n l n 2 (1) where: X^ = Mean of region i s 2 i = Variance region i ni =number of pixels region i 395

Access restriction

Copyright

fullscreen: Proceedings of the Symposium on Global and Environmental Monitoring (Part 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation