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:: 3 Spectral signatures of objects. Chairman: G. Guyot, Liaison: N. J. J. Bunnik

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Multitemporal analysis of LANDSAT Multispectral Scanner (MSS) and Thematic Mapper (TM) data to map crops in the Po valley (Italy) and in Mendoza (Argentina). M. Menenti & S. Azzali, D. A. Collado & S. Leguizamon

Document type:: Multivolume work

Structure type:: Chapter

295 August Italy, dur- 1 1 , ¿2 ] such is accepting Je can final- ,ng for each ] and [1 1 , re not the f the :round cover', which a Figure 2. Effect of intercropping on the Transformed Vegetation Index (TVI) as obtained with LANDSAT Multi- spectral Scanner (MSS) measurements in band 7 (MSS 7: 0.8 to 1.1 pm) and band 5 (MSS 5: 0.6 to 0.7 pm) for irrigation districts in the region of Mendoza in Argentina given fraction, 80% say, of the observed area reach this stage. 2.3 Intercropping Intercropping is a characteristing feature of small holders agricultural land. Sub-pixel combinations of different crops will give pixel reflectances which cannot be predicted from the reflectances of the sub pixel components. One feasible approach is by apply ing directly the LANDSAT measurements to establish typical temporal profiles for each main intercropping pattern. Such results are given in Figure 2. It can be seen that the TVI-values for the combination 'vineyard with olive trees' lie between the TVI-values for 'vineyard' and 'olive trees'. Our opinion is that the practical aspects of agriculture can be efficiently translated in terns of LANDSAT measurements by com bining directly field inquiries on a number of plots with the LANDSAT measurements applying to these plots. In the following sections we will illustrate how this can be done and how to assess the significance of the observed differences between agricultural units, as defined in terms of crops and of agricul tural practices. 3 APPROACH Differences between crops in growth cycle are better defined than differences in spectral reflectances. So multitemporal crop discrimination on the basis of growth cycle is in principle easier than monotemporal crop discrimination on the basis of spectral reflec tances. After having established characteristic val ues of some vegetation index for each crop and growth stage, crop mapping is basically done by applying simple and fast density-slicing procedures. The required characteristic values can be estab lished by combining published measurements of spec tral-temporal crop properties with crop phenology in a particular area (Azzali 1985a, 1985b, 1986). To es tablish identification labels for the crops being cultivated in the Po valley different vegetation in dices have been combined to give a multi-index multi temporal crop identification method. The identifica tion scheme is presented in Figure 3. This scheme has been established by comparing different selections of overpass-dates and of index-values. The availability of a large number of options makes crop discrimina tion easier. Moreover, as shown in Figure 3, many crops can be discriminated in the early growing sea son. This is essential if one intends to apply LAND SAT TM measurements during the second part of the growing season to detect crop stress, e.g. due to water shortage. The irrigation districts of Mendoza, Argentina are characterized by a mediterranean - small holders - type of agriculture. Intercropping is a widespread occurrence, thus making it impossible to establish crop identification labels on the basis of literature, the more so since spectral measurements on vineyards and olive trees are quite rare. So, the characteristic vegetation index values have been established by lo cating a number of ground-control plots in three LANDSAT-images during the growing season 1984-1985 and then extracting the required reflectance measure ments from each image. The result is presented in Ta ble 2, which shows that there is at least one combina tion of index values and satellite overpass dates Table 2. Characteristic values of the ratio 1^ = (MSS 7/MSS 5). 60 and of Io - TVI.100 applying to the main crops cultivated in Mendoza, Argentina, as ob tained with LANDSAT MSS measurements Crop Dates 28-8 -1984 27-2' -1985 15-3 -1985 T 1 I 2 X 1 T 2 T 1 h Onion 69 83 89 87 82 86 Vineyard 73 82 140 98 125 96 Alfalfa 121 96 143 98 158 100 Olive trees 105 93 122 96 96 90 Vineyard with grass 155 100 150 100 107 93 Olive trees with vineyard 79 85 128 97 114 94 Fruit trees 67 80 113 88 113 88 Table 3. Cultivated area (1980 farming year) as percentage of the total agricultural area for the 5 main crops cultivated in Crande Bonifica Ferrarese: A, as estimated by means of the multi -temporal multi- index method; B, according to ISTAT agricultural statistical data Crop A B Winter wheat 20. 5 23 Alfalfa 21. 5 14.5 Corn 9 8 Sugar beet 16 20 Trees 16 12 Rice 3 3

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