Abstract
ABSTRACT The objective of this study was to apply the Simple Algorithm For Evapotranspiration Retrieving (SAFER) with MODIS images together with meteorological data to analyze evapotranspiration (ET) and biomass production (BIO) according to indicative classes of pasture degradation in Upper Tocantins River Basin. Indicative classes of degraded pastures were obtained from the NDVI time-series (2002-2012). To estimate ET and BIO in each class, MODIS images and data from meteorological stations of the year 2012 were used. The results show that compared to not-degraded pastures, ET and BIO were different in pastures with moderate to strong degradation, mainly during water stress period. Therefore, changes in energy balance partition may occur according to the degradation levels, considering that those indicatives of degradation processes were identified in 24% of the planted pasture areas. In this context, ET and BIO estimates using remote sensing techniques can be a reliable indicator of forage availability, and large-scale aspects related to the degradation of pastures. It is expected that this knowledge may contribute to initiatives of public policies aimed at controlling the loss of production potential of pasture areas in the Upper Tocantins River Basin in the state of Goiás, Brazil.
Highlights
Upper Tocantins River Basin has undergone increasing changes in the use and occupation of land as the agricultural frontier has been expanding
For each degradation indicative class (Figure 2), average values of monthly evapotranspiration (ET) and biomass in the pasture were estimated by means of MODIS images (16-day composition) for the year 2012
Those differences were more marked in the water stress period, in pastures classified as moderate to strong degradation indicative
Summary
Upper Tocantins River Basin has undergone increasing changes in the use and occupation of land as the agricultural frontier has been expanding. It is essential to combine development and economic growth with environmental sustainability, since a significant part of cultivated pastures shows some indications of degradation (Andrade et al, 2013b). The application of remote sensing techniques on a large scale may help in a dynamic manner to diagnose and to obtain indicators relating to economic and environmental sustainability of pasture areas, contributing, for example, with Low-Carbon Agriculture Program (LCAP), which predicts several mitigating actions of greenhouse effect gas emission (GHGs). It should be highlighted that some studies have successfully applied the data of orbital remote sensing to identify and to monitor plant conditions by means of vegetation indices, such as the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). Evapontranspiration (ET) and plant biomass (Bio) can be estimated via remote sensing and, for that, algorithms and models are implemented
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