Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

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

Editor:: Damen, M. C. J.

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:: 4 Renewable resources in rural areas: Vegetation, forestry, agriculture, soil survey, land and water use. Chairman: J. Besenicar, Liaisons: M. Molenaar, Th. A. de Boer

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Comparison of SPOT-simulated and Landsat 5 TM imagery in vegetation mapping. H. Tommervik

Document type:: Multivolume work

Structure type:: Chapter

SPOT simulation 1982-06-30 Time: 12.32.42PM IR Imagery 1982-06-30 Time: 12.32.42PM BW Imagery 1975 Summer (July). 2.4 Laboratory work Digital and visual classification were carried out at the I2S IMAGE PROCESSING SYSTEM (MODEL 575) at Troms0 Telemetry Station (TTS). 2.5 Classification The procedures followed are illustrated in the flow chart (figure 2). A supervised classification method has been elaborated using MAX-LOG-LIKELIHOOD classi- . fier with treshold of 3.00, 4.00 and 5.00, respective ly. In addition to this, several caanael combinations and ratios were used both for digital and visual classification. 2.6 Accuracy assessment Then®are two major types of accuracy assessment prose- dures: 1.Non-site-specific (total area extent) 2.Site-specific (location). Non-site-specific accuracy is usually expressed as the similarity between the total numbers of hectares in each vegetation-cover type as determined by a Landsat or SPOT classification, compared to the corresponding total area determined from the digital vegetation map. The non-site-specific method compares only the total area without regard to location. Site-specific accu^ racy, however, considers the spatial nature of the da ta when two spatially defined data sets (one ground truth) are registred and compared for the amount of agreement (Reichert & Crown 1984). The chosen method in this study is the no-site-specific method. 3.0 RESULTS AND DISCUSSION The results of the prosject is here in this chapter presented and discussed. The basic tabels and figures from the prosject is not presented here, but I refer to my thesis-report (T0mmervik 1985a). 3.1 Floristic and Phytosociology The area is very varioues and rich what species con cern, and of the the 489 plant taxa at species and subspecies levels encountered in the ground truth pro gram, 371 were vascular plants, 82 mosses and 36 lich ens. The result of the phytosociological study which was carried out during this prosject, was that 39 main vegetation-units on several levels in the phytososio- logical hierarchy were picked out. 3.2 Digital vegetation map A digital vegetation map has been generated on basis of the ground truth program, and the legend of this map consists of 31 vegetation cover types. The work with the digital vegetation map has shown that it is a good tool for resource studies and vegetation map ping purposes, and that it can be updated very easily. 3.3 Digital image processing Digital image processing was done both on SPOT-simu- lated imagery and Landsat 5 TM imageries. Unfortuna tely both the spring-scenes from 1982 and 1984 were taken too early in the spring due to snowcover, and the autumn-scene was taken to late in the autumn to Figure 2. Methodology flow chart for detection of the vegetation cover types. give a sufficient basis for a good digital classifi cation . 3.4 Digital classification Digital classification was based on a supervised met hod using MAX-LOG-LIKELIHOOD classifier. The resulting themes left many pixels around vege tation type boundaries unclassified. Boundary pixels presented a special problem, as they represented port ions of different vegetation cover types. Their values were a function of the amount of the area of each vegetation type within the pixel and the relative ref lectance of each material as a whole. This was also the result within the resulting themes, and this was a result of the variation in phenology, heterogeneous vegetation and distribution of snowcover. The interpretation and classification were checked by comparing the classified imagery with the digital map, and the accuracy of the interpretation was asses sed on a quantitative basis. Two areas within the digital map area were checked, Dividal-Cavarre and Saratr0a-Habafjell (Table 1 and Table 2). 3.4.1 Alpine vegetation Hi Extremely dry shrub were rather good mapped by the two sensorsystems (Landsat 5 TM and SPOT-HRV) with a optimal accuracy of 89 % for the SPOT-simulation (treshold: 3.00) and 93 % for Landsat 5 TM (treshold: 5.00). For H7 Rich shrub was the Landsat 5 TM-sensor the best sensor for mapping of this vegetation cover type with a accuracy of 89 % (treshold: 3.00). H2 Dry shrub was rather bad detected, 36 % for the SPOT-simu lation and 66 % for Landsat 5 TM (treshold: 5.00), respectively. 3.4.2 Mire vegetation Q5 Rich mire and P2' Wet shrub were bad detected and mapped, due to the high amount of watercontent and the distribution of snowcover at these types of vegetation. But Q4 Poor mire (intermediate) showed a very good accuracy of 83 % for the SPOT-simulation (treshold:

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