Abstract
ABSTRACT In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were compared with traditional methods spectral images and Maximum Likelihood Classifier (MLC). At first, seven classes were mapped (water bodies, sugarcane, urban area, annual crops, forest, pasture and reforestation areas); then, only two classes were considered (annual crops and other targets). When classifying the seven targets, both methods had corresponding results, showing global accuracy near 84%. NDVI temporal metrics showed producer’s and user’s accuracy for the annual crop class of 86 and 100%, respectively. However, if considering only two classes, the NDVI temporal metrics reached global accuracy of near 98% and producer’s and user’s accuracy above 94%.
Highlights
Remote sensing, given its synoptic character and data acquisition promptness, stands out as a technique able to monitor the crops throughout their lifecycle
In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas
The results were compared with traditional methods spectral images and Maximum Likelihood Classifier (MLC)
Summary
Given its synoptic character and data acquisition promptness, stands out as a technique able to monitor the crops throughout their lifecycle. Even though there are several orbital remote sensors with different configurations and resolutions (Toth & Jóźków, 2016), most of the current ones are unable to distinguish different agricultural crops in terms of spectral characteristics (Yao et al, 2015) To overcome this issue, new approaches such as Data Mining (DM) have been tested to assess and improve spectral differentiation (Grande et al, 2016). DM approach has tools to analyze large amounts of data, allowing the development of a learning mechanism (Vintrou et al, 2013) Another procedure to assist in the multispectral classification of images is the multi-temporal analysis of Normalized Difference Vegetation Index (NDVI) (Rouse et al, 1974) since spectral-temporal profiles are strongly tied to agriculture dynamics (Cattani et al, 2017). This type of approach has been used to classify crop types (Chen et al, 2018) and land cover (Jia et al, 2014)
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