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Tardin, A. T.

Publications and source records attributed to Tardin, A. T..

Development and testing of a rural credit supervision system at the level of counties and rural properties utilizing remote sensing techniqes

Remote sensing techniques for supporting the rural credit supervision system were developed and tested. The test area comprised the counties of Aracatuba and Guararapes, located in the State of Sao Paulo. Aerial photography, LANDSAT images and topographic charts were used. Aerial photographs were extremely useful for the out lining of properties boundaries with financing of sugarcane plantations by the Banco do Brasil S.A.. The percentage of correctly interpreted sugarcane on LANDSAT images, considering the 85 analyzed properties, was of 63.12%. The occurrence of atypical conditions such as excessive raining, sugarcane in bloom, and wind damaged sugarcane and sugarcane not harvested due to planning failures verified during the period the images were obtained, were some of the contributing factors associated with a low interpretation performance. An alternative approach was developed using several LANDSAT overpasses and auxiliary field data, which resulted in 91.77 percent correct.

Batista, G. T.↗

Elevation of a cane-growing area of the state of Sao Paulo using LANDSAT data

Images at a scale of 1:250.000 were visually interpreted for identification and area estimates of sugar cane plantations in Sao Paulo. The basic criteria for crop identification were the spectral characteristics of channels 5 and 7 and their temporal variations observed from different LANDSAT passes. Using this technique, it was possible to map the sugar cane areas as well as the sugar cane already harvested. An area of 801,950 hectares was estimated within the study area. The confidence interval of correct classification ranged from 87.11% to 94.71%.

Dejesusparada, N.↗

Wheat cultivation: Identifying and estimating area by means of LANDSAT data

Automatic classification of LANDSAT data supported by aerial photography for identification and estimation of wheat growing areas was evaluated. Data covering three regions in the State of Rio Grande do Sul, Brazil were analyzed. The average correct classification of IMAGE-100 data was 51.02% and 63.30%, respectively, for the periods of July and of September/October, 1979.

Dejesusparada, N.↗

Wheat cultivation: Identification and estimation of areas using LANDSAT data

The feasibility of using automatically processed multispectral data obtained from LANDSAT to identify wheat and estimate the areas planted with this grain was investigated. Three 20 km by 40 km segments in a wheat growing region of Rio Grande do Sul were aerially photographed using type 2443 Aerochrome film. Three maps corresponding to each segment were obtained from the analysis of the photographs which identified wheat, barley, fallow land, prepared soil, forests, and reforested land. Using basic information about the fields and maps made from the photographed areas, an automatic classification of wheat was made using MSS data from two different periods: July to September and July to October 1979. Results show that orbital data is not only useful in characterizing the growth of wheat, but also provides information of the intensity and extent of adverse climate which affects cultivation. The temporal and spatial characteristics of LANDSAR data are also demonstrated.

Dejesusparada, N.↗

Remote sensing in forestry: Application to the Amazon region

The utilization of satellite remote sensing in forestry is reviewed with emphasis on studies performed for the Brazilian Amazon Region. Timber identification, deforestation, and pasture degradation after deforestation are discussed.

Dejesusparada, N.↗

Surveying the area of deforestation of the Amazon by LANDSAT satellite imagery

LANDSAT imagery was used to determine the amount of deforestation in a study area comprising 55 million hectares of the Amazon region. Results show that more than 4 million hectares were deforested. Maps and pictures of the deforested area in relation to the total area of the Amazon are included.

Dejesusparada, N.↗