Interskakeling van LANDSAT-syferdata en landboustatistiek vir die Vermaasontwikkelingsgebied.

• Main Author: Wolfaardt, Petrus Jacobus
• Published: 2014
• Subjects: Landsat satellites.; Crop yields - Forecasts; Crop yields - Data processing; Crop yields - Statistics; Agriculture - Forecasting; Agriculture - Data processing; Agriculture - Statistics
• Online Access: http://hdl.handle.net/10210/10614

D.Litt. et Phil. (Geography) === The aim of this study is to integrate LANDSAT multispectral digital data with agricultural statistics, to analyse, explain and forecast the spatial variation of crop production in the Vermaas development area (south of Lichtenburg, Western Transvaal). This aim answers the urgent need for a reliable agricultural data base that can be quickly and cheaply obtained and used for the timely planning of an environment's limited agricultural resources. With such a data base available, early decisions about imports and exports can be taken in connection with the expected agricultural commodities of an area: the year-to-year fluctuation in crop yields is still the main problem in relation to the overall planning of agricultural food production. The study has been conducted according to two main analytical phases, i.e. (i) the interpretation of the data, which in turn was subdivided into: - the cartographic-analytical evaluation of the agricultural information, and - the recognition of rural land-use patterns from LANDSAT digital data. (i i) the integration process. The LANDSAT land-use information was integrated with the observed agricultural statistics with the aid of two integration models: an empirical and an operational model. The data for the research consisted of the multispectral digital data of LANDSAT-l and available agricultural statistics. The LANDSAT data was acquired from the Satellite Remote Sensing Centre at Hartbeeshoek, while the agricultural data was obtained from the Department of Agriculture (Highveld Region) and other official soures. These analytical phases were conducted at the computer centres of the CSIR and RAU. Existing computer programme packages were used - the VICAR system for pattern recognition, and the BMD and SYMAP systems for the analytical evaluation of the agricultural information and for the implementation of the integration models. The following results were obtained: 3.1 The integration of the LANDSAT information with the agricultural statistics was reasonably successful. The success of any study of this nature can be ascertained from the accuracy with which the necessary information is derived from the LANDSAT multispectral digital data. 3.2 This analysis highl ighted the cultivated area as a major factor for consideration. The type of crop and the area covered by it are the two most important sets of information that can be obtained from the LANDSAT data and used in an integration model. 3.3 The results (predicted crop yields) that were obtained from the integration process could probably be improved, if the detrimental influence of collinearity, which existed between some of the agricultural variables, was el iminated. 3.4 The identification of different crops from the LANDSAT digital data was not possible - a fact which can be attributed to the lack of a crop calendar for this farming area. Besides the above-mentioned results, the following can also be listed: 4.1 The spatial variation In maize production was well analysed in terms of the integration results, In spite of the fact that the accuracy of the agricultural statistics was, in certain cases, questionable. 4.2 The important influence of time upon the spatial variation in crop production could not be implicated, because of the one point in time consideration of this study. 4.3 Only the agricultural variables that were directly related to farm area could be used as input data for this study. 4.4 The potential usefulness of the LANDSAT digital data as geographical information is mainly determined by its quality (cloudcover, resolution, etc.). 4.5 The application of multispectral digital data depends on certain specific techniques, with which the researcher must acquaint himself for a successful and useful interpretation of the digital data.