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:: Remote sensing in the evaluation of natural resources: Forestry in Italy. Eraldo Amadesi & Rodolfo Zecchi, Stefano Bizzi & Roberto Medri, Gilmo Vianello

Document type:: Multivolume work

Structure type:: Chapter

Phase II: Satellite data analysis and process The adopted scheme for the process of satellite data follows classic criteria: the supervised classification and a maximum-likelihood classifier have been selected because of the possibility of a strict control on the activities in progress. The spectral and spatial resolution of the Thematic Mapper data at European latitudes, the particular accuracy in the training stage and In the preparation of the 'ground truth " and the availability of a sophisticated image analysis system (hardware plus software), lead to very good results. The processing scheme consists of six main steps: a) - Image selection and preliminary process. The selection of the satellite scenes on which to operate heavily depends on the availability of cloud-free images In the requested season. Previous studies show that, for forest mapping from Thematic Mapper data, the period from mid-June to mid-August offers a good compromise between appearance of the vegetation (phenology) and scene illuminance (due to sun azimuth and elevation). For classifying coniferous forest cover, winter data are much better than any other season, but the possibility of a spectral discrimination Is also good enough In summer, as shown in the accuracy evaluation section. Satellite scenes are acquired in the raw format stored on CCT. Different radiometric and geometric corrections are applied in the phases of the process. For classification purposes, the geometric correction doesn't include resampling with cubic convolution methods, which may affect the radiometric response. When necessary, athmosferic refraction and scattering effect (haze) can be locally removed with statistical algorithms. For the preliminary photographic output, oriented to the photointerpretation, more complex algorithms are used. b) - False colour image production False-colour images of the Landsat Thematic Mapper data for the entire area of interest are produced and output on photographic support for preliminary photointerpretation and area stratification. Among the possible Thematic Mappei band combinations the following have been selected (respectively in red, green, blue shadows): - 5-3-1 to enhance terrain morphology - 4-3-2 for vegetation monitoring - 7-4-3 for land-use. Geometric correction, with cubic convolution resampling and North-South image rotation, and edge enhancement is applied to these images to improve the readability and the allocation of the test areas. c) - Scene stratification In order to increase the accuracy of the classification, the image is divider in small zones of defined characteristics. This procedure is called "stratification" and the single zones "strata ". The aim is to divide the area intc subzones relatively homogeneous with respect to the spectral response; this should grant the extension of spectral signatures within the smaller area. The validity of a spectral signature is reduced to few kilometers when processing Thematic Mapper data over a montainous area. For each stratum a separate classification Is executed, using different signatures and different test areas. A post-classification analysis aggregates spectral classes that are stratum-dependent. Aerial photos, topographic maps and Landsat false colour Images (low and medium scale) are used as control data for breaking out these strata. Percentage of vegetation cover, percentage of bare soils, land features arc also used as stratification factors. The stratification is applied to the digital data stored on disk using a bit-map description language which masks the portions of image to be excluded from the process. This procedure increases the cost of processing phase because of the number of separate classifications and o' the final aggregation, but the stratification, when working over large areas, minimizes the "variance", or error due to sampling, in the final estimates. d) - Test area registration The portions of area for which "ground truth ' is available ( aerial photos ot tophographic maps interpretation, direct survays Information) are marked as "test areas". The allocation of such areas on the image is done manually on the available false colour prints, and interactively on the video display, where the image can be presented at the right scale and projection. e) - Stratum classification The classification stratum by stratum follows a classic supervised scheme, and consists of three passes: - selection of training sets over a test area, local evaluation and refinement; - test of the training set over other test areas of the stratum, evaluation and eventual iteration of first pass; - classification of the entire stratum and accuracy assessment. The procedure requires several iterations of the passes; a special emphasis has been therefore put, when designing and developing the Image analysis software package, in the contort of the man-machine interaction and ir the power of training set handling facilities. A maximum-likelihood classifier has been selected and used for the present application. The training areas were delineated on the display in a interactive manner. Decisions to merge or delete training sets were based upon the analysis of statistical parameters, of two-dimensional histograms and confusion matrices (see Tab. 3). A powerful software tools helped in individuating pixels In the training sets causing misclassifications and in their exclusion; this leads to spectral signatures thal ’■locally" are as "pure" as possible. Several training sets for each class are extracted, trying to reproduce the various spectral aspects of a given category within the stratum. Density of the vegetation, sun exposition, terrain slope, haze presence are taken into account; they heavily affect the response of the vegetation. The result is a large number of spectral classes used for the classification and grouped later. The use of a powerful computer reduces the impact of such approach on process time. All the reflective Thematic Mapper bands (1 through 5 and 7) are used for the spectral analysis; the thermal channe (band 6) has been excluded because of the difficulty in the extraction of usefu information. f) - Class aggregation and final output The final aggregation of the classes across the strata, i.e. over the entire area will be performed using a computer look-up table procedure. The final image pixels, when classified, are assigne to one of the possible expected category. The classification tecnique is 'point-by-point", in which each pixel is treated individually. This approach produces maps which may contain even more detail than is actually needed. To avoid a "salt and pepper " effect in the present application isolate dots have been assigned to closer classes, according to a prevalence algorithm. The final classification is output on a film recorder, and printed at the scale of 1:100,000. Pixels assigned to the same class have the colour assigned tc the category bye the final legenda. Non-classified pixels are presented in Known category Number of pixels Percent correct Number of pixels assigned to category 1 2 3 4 5 6 7 8 1 12 91.6 11 0 0 1 0 0 0 0 2 22 90.9 1 20 0 0 0 1 0 0 3 28 96.4 0 0 27 1 0 0 0 0 4 11 100 0 0 0 11 0 0 0 0 5 16 87.5 0 0 0 0 14 0 0 2 6 20 100 0 0 0 0 0 20 0 0 7 16 100 0 0 0 0 0 0 16 0 8 22 81.8 1 0 0 3 0 0 0 18 TAB. 3 - Training sets confusion matrix over a test area 1 conifers ,2-3 mixed, 4-7 conifers , 8 mixed

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