Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

New genera and species of Neotropical Rhinotragini (Coleoptera, Cerambycidae, Cerambycinae)

Giesberteclipta and Thomasella, two new genera of Rhinotragini Thomson, 1861 (Coleoptera: Cerambycidae: Cerambycinae), are described and illustrated. Six new species are also described and illustrated: Acyphoderes violaceus from Costa Rica; Ischasioides giesberti from Ecuador, and Oxylymma pallida, Pseudagaone williamsi, Stultutragus tippmanni and S. ventriguttatus from Brazil. Keys are provided for the known species of Pseudagaone Tippmann, 1960, Giesberteclipta, and Oxylymma Pascoe, 1859 and for parts of Ischasioides Tavakilian & Penaherrera-Leiva, 2003 and Stultutragus Clarke, 2010. The following new combinations are proposed: Giesberteclipta costipennis (Giesbert, 1991); G. monteverdensis (Giesbert, 1991); Thomasella igniventris (Giesbert, 1991), and Stultutragus romani (Aurivillius, 1919). The following three new country records are reported: Oxylymma durantoni Penaherrera-Leiva & Tavakilian, 2003 (Brazil), Oxylymma sudrei Penaherrera-Leiva & Tavakilian, 2003 (Brazil), and Ommata (Eclipta) faurei Penaherrera-Leiva & Tavakilian, 2003, all from Brazil.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.