Modis Time Series for Land Use Change Detection in Fields of the Amazon Soy Moratorium

Abstract

ABSTRACT:A virtual globe to visualize time series of pixels from the MODIS sensor over the South American continent is available in the Internet and was developed at the Brazilian Institute for Space Research. The MODIS images acquired since the year 2000 were transformed to a vegetation index (EVI2, two-band Enhanced Vegetation Index) with pixel size of 250 m. This study aims to use these time series to identify land use changes (LUC) based on the temporal profile of EVI2 values of deforested polygons between 2007 and 2011 within the context of the Soy Moratorium. Deforested polygons were divided in two strata: with and without soy in crop year 2010/11. From the MODIS/EVI2 time series the following classes were identified: forest, degraded forest, total clearing of the area, regrowth of forest, regrowth with pasture, pasture, agriculture, and soy. For stratum 1, the dominant LUC trajectory was: forest -degradation -regrowth / regrowth with pasture. In the second stratum it was observed two main LUC trajectories: 1) forestdegraded forest -total clearing of the area -annual crop (rice) -soy; and 2) forest -total clearing of the area -annual crop (rice) -soy. For most samples of stratum 2 the LUC trajectory was agriculture (e.g., rice) between total clearing and soy cultivation. These patterns occurred on average over two harvests, which may be considered the necessary time for soil correction and total removal of above ground stumps and roots to enable mechanized soy harvesting. The fast evaluation of one hundred polygons during 11 years was only possible due to the virtual globe to visualize the MODIS time series that proved to be an important tool to improve the understanding of LUC dynamics in the Amazon region.

Using Landsat 8 Image Time Series for Crop Mapping in a Region of Cerrado, Brazil

“…This approach is useful for vegetation studies, especially in agricultural areas, since vegetation cover is quite dynamic in time, and the ability to capture these variations is essential to discriminate different types of crops, through its phenological characteristics. For this purpose, most studies have explored time series of vegetation indices like NDVI or EVI from MODIS data (Jakubauskas et al, 2002;Sakamoto et al, 2005;Wardlow et al, 2007;Esquerdo et al, 2011;Arvor et al, 2011;Körting, 2012;Risso et al, 2012;Coutinho et al, 2013;Borges & Sano, 2014;Tomás et al, 2015;Neves et al, 2016). However, there is a demand to produce more detailed maps, which can be obtained from higher spatial resolution satellites such as Landsat-like (Zheng et al, 2015;Peña et al, 2015;Pan et al, 2015).…”