Annual cropland mapping using data mining and OLI Landsat-8

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%.

Evaluating the Impact of Crop Layout Changes on N and P Nutrient Balance: A Case Study in the West Liaohe River Basin, China

“…Figure 2 illustrates the framework of mapping cropland and crop types and the N and P nutrient balance model. In the first step, the cropland boundary of the study area was extracted based on Landsat 8 OLI [38]. In the second step, crop types were extracted and a crop layout database was constructed based on MOD09Q1 data within the cropland boundary.…”

A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

“…To evaluate the use of digital image analysis for forest plot composition and tree identification only the NAIP and UAS imagery were used. Our analysis began with evaluating visual interpretation because numerous research projects opt for visual interpretation of remotely sensed imagery as their source for reference data (e.g., Google Earth or airborne imagery) [14,16,69,70]. These data yield a synoptic view, can be cost effective, in modern times are high resolution, and in some cases provide multi-date or multispectral inferences.…”