Margareth Simoes

Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI

This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002-2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R 2 = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R 2 = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t· ha −1 . Despite progress in the methodology through the weighted

A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series.

In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013–2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis.

Remote sensing and cropping practices: a review.

For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

Assessing Ecosystem Integrity in the Brazilian Amazon Rainforest to Indicate Biodiversity Loss and Highlight Areas for Adaptation Policies

Ecosystem integrity (EI) may be defined as an equilibrium state of a given natural system able to self-regulate throughout many functional processes. The concept of biodiversity is quite diverse, and it is related to different levels of biological systems ranging from the level of genes, species taxonomic richness to functional groups. In this way, depending on the approach, several indicators, conceptually unrelated, can be used to characterize and quantify the biodiversity of a given natural system. However, in practical terms, the biodiversity, as a characteristic of ecosystems, is an indicator of the ecosystem´s stage regarding its pristine conditions. Then, Ecosystem Integrity has emerged as an important indicator to assess the relationship between biodiversity loss and the impacts on ecosystem services in tropical forests, once EI represents a connection between biodiversity and the ability of ecosystems to maintain the self-organization process. The objective of this chapter is to present a methodological approach developed for generating an Ecosystem Integrity index at regional scale, for different phyto-physiognomies patterns of landscapes, using a probabilistic model based on Bayesian Belief Networks (BBN), and totally free web-available satellite products. The methodology was applied to Brazil’s Legal Amazon region. The results show that it is possible to quantify areas of the Amazon rainforest with high or low Ecosystem Integrity. Using the same Bayesian network, with updated satellite data, it becomes possible to monitor the EI over time, and may even serve to establish a monitoring protocol and planning of mitigation/adaptation procedures.