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

527 I— * I CONTROL -I NOMENCLATURE TEST CLASSIFIEO IMAGE ATISFAÇTORV NOT.SATIS detection of the igital classifi- a supervised met ier . els around vege- Boundary pixels represented port- ypes. Their values area of each the relative ref- . This was also es, and this was gy, heterogeneous over. ion were checked with the digital etation was asses- as within the al-Cavarre and e 2) . [ood mapped by the SPOT-HRV) with a •T-simulation it 5 TM (treshold: idsat 5 TM-sensor vegetation cover ild: 3.001. H2 Dry for the SPOT-simu- reshold: 5.00), bad detected and itercontent and the :ypes of vegetation. ?ed a very good ition (treshold: 3.00) and 97 % for the Landsat 5 TM (treshold: 5.00) , respectively. 3.4.3 Forests G7' Rich meadow with willow and birch showed a very good accuracy for the SPOT-simulation (accuracy of 91.3 %). What G7 Birch forests (meadow type) concern, the accuracy was rather low. The same low percent of accuracy were shown by E5b Grey alder forests (poorer type), Bl Birch forests (richer heath type) and A4b Birch forests (shrub type) were rather bad mapped in the area of Dividal-Cavarre by the both systems (ac curacy up to 24 %). In the area of Saratr0a-Habafjell was this vegetation cover type mapped by a accuracy for SPOT-simulation of 87 % (treshold: 3.00) and 93 % for Landsat 5 TM (treshold: 3.00), respectively. E5a and E5c, Grey alder forests, were very good mapped by the both systems, with a accuracy of 94 % for SPOT (treshold: 3.00) and 88 % for Landsat 5 TM (treshold: 3.00), respectively. Table 1. ACCURACY OF THE DIGITAL CLASSIFICATION Dividal - Cavarre Accuracy in percent Covertype Treshold SPOT-sim 3.0 4.0 5.0 Landsat 5 TM 3.0 4.0 5.0 Vegetation- map (ha) A4b Birch forests 7.6 7.8 7.8 7.0 7.6 7.8 31.9 (heath) 23% 24% 24% 21% 23% 23% Bl Birch forests 2.1 1.8 1.6 1.6 1.5 1.5 6.4 (richer heath) 32% 28% 26% 25% 23% 23% E5a,c Grey Alder forests' (very rich 4.8 5.5 5.6 5.2 5.9 5.9 4.6 type) 94% 84% 81 % 88% 78% 78% E5b Grey Alder forests (poorer 5.5 5.8 5.6 8.0 8.1 8.1 1 .6 type) 29% 27% 26% 20% 19% 19% G7 Birch forests (meadow 1.2 1.2 1 .2 11.2 11.6 11.9 0.2 type) 16% 16% 16% 1% 0.7% 0.7% H1 Extremely 24.0 28. 0 29.8 9.2 10.1 10.4 15.8 dry shrub 66% 56% 53% 58% 63% 66% H2 Dry 14.4 16. 2 17.6 47.9 51.3 51.9 27.6 Shrub 52% 59% 64% 57% 53% 53% H7 Rich 6.2 7.2 7.2 1.6 1.4 1.4 1.8 shrub 29% 25% 25% 89% 78% 78% A1/A2 Farmland 0.6 1.0 32% 50% 1 .7 55% 0.4 0.3 0.3 20% 15% 15% 2.0 Unclassified pixels Snowcover 23.6 13. 9.6 11. 6 10.6 5 11.0 7.4 1.7 0.3 3.4.4 Farmland AA1/AA2 Farmland was very bad detected and mapped by both of the sensorsystems of SPOT-HRV and Landsat 5 TM, due to the early phenological stage and the high water- content in the soil. 3.4.5 General comments and discussion The number of unclassified pixels were rather great, due to the early phenological stage with wide varia tion within the vegetation cover types and the distri bution of snowcover. The investigation has shown that the Landsat 5 TM- scene from the springtime almost had the same accuracy by supervised classification as the SPOT-simulated imagery, due to the better radiometric resolution for the TM-sensor compared to the simulated HRV-sensor on the SPOT-satellite, for mapping purposes. By comparing the classifcation results with the "ground truth" - digital map, the main trend was that Table 2. ACCURACY OF THE DIGITAL CLASSIFICATION Saratr0a - Habafjell Accuracy in percent Covertype Treshold SPOT 3.0 -sim. 4.0 5. .0 Landsat 5 TM 3.0 4.0 5.0 Vegetation- map (ha) Va Water 2.4 2.5 2. .6 2.5 2.6 2.7 2.7 (lakes and 89% 92% 96% 92% 96% 100% rivers) A4b Birch forests 27.1 35.0 37.0 25.6 33.0 34.1 23.8 (heath) 87% 68% 64% 93% 72% 69% G7 1 Rich meadow with willow and 2.1 2.1 2.1 2.3 birch 91 % 91 % 91 % H1 Extremely dry 3.5 3.3 3.4 3.0 3.8 4.1 4.4 shrub 89% 76% 78% 68% 86% 93% H2 Dry 8.7 10.0 11.1 11.1 15.3 20.3 30.7 shrub 28% 34% 36% 36% 49% 66% P2 Wet 5.5 6.8 7.5 0.4 0.1 0.1 3.3 shrub 62% 48% 44% 12% 3% 3% Q4 Poor mire 15.2 16.1 17.3 12.0 11.6 12.4 12.7 (intermediate type) 83% 77% 73% 94% 91% 97% Q5 Rich 3.2 3.7 4.3 0.7 1.3 1.4 2.0 mire 62% 54% 46% 35% 65% 70% Unclassified pixels 33.4 16.0 10.8 42.8 30.6 22.5 the vegetation cover types which were most phenologic al develloped, showed the best accuracy in the super vised classification. This is due to the very early phenological stage and the distribution of snowcover in the mountain areas. But even the classification of the vegetation cover types HI Extremely dry shrub and Q4 Poor mire (intermediate type) in the mountain area was successful, with an overall classification accura cy of 90 % or more. Classification of the SPOT-simulated imagery showed that the vegetation cover types within smallareas, were better detected and mapped due to the better spatial resolution compared to the TM-sensor on the Landsat 5 TM satellite. The autumn-scene taken by the Landsat 5 TM-sensor could not be used as a basis for supervised digital classification, due to the the very low sunelevation and to the very late phenological stage. Several authors have discussed the possibilities of using SPOT- and Landsat-imagery as a basis for mapping of vegetation. Sadowski & Sarno (1976) stated the con ventional ML-classifier was too bad for mapping pur poses, and they used a contextual ML-classifier ins tead. The result of this was an improvement of the ac curacy for the classification, and this was also the trend for channels with a spatial resolution > 32 m. Teillfet et al. (1981) compared MSS-data with simula ted TM-data, and stated that the accuracy of classi fication was improved by using TM-data instead of MSS- data (MSS: 67 % of accuracy and TM: 83 % of accuracy) in classification of forest cover types. This improve ment was a result of the better radiometric and spatial resolution for the TM-sensor. In addition they found none significant improvement as a result of improve ment in spatial resolution alone. This is also my ex perience . Jaakkola (1985) stated that it is obvious that SPOT- data will benefit from the use of multi-point (contex tual methods) instead of single-point classifiers. He also stated that texture should be used in the classi fication. I will agree in this statement what SPOT-data concern, but for TM-data with the improved radiometric resolution, it is my meaning that multi-point classi fiers only can give marginal improvemant. Jaakkola (19 85) also stated that forest cover type-classification into six classes was successful with an overall classi fication accuracy of 90 percent or more. For some of the forest cover types and even other vegetation cover types, I got a successful result with an overall clas sification accuracy of 90 percent or more. Especially

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