Renata Ribeiro do Valle Gonçalves

Risk analysis of climate change on coffee nematodes and leaf miner in Brazil

The objective of this work was to assess the potential impact of climate change on the spatial distribution of coffee nematodes (races of Meloidogyne incognita) and leaf miner (Leucoptera coffeella), using a Geographic Information System. Assessment of the impacts of climate change on pest infestations and disease epidemics in crops is needed as a basis for revising management practices to minimize crop losses as climatic conditions shift. Future scenarios focused on the decades of the 2020s, 2050s, and 2080s (scenarios A2 and B2) were obtained from five General Circulation Models available on Data Distribution Centre from Intergovernmental Panel on Climate Change. Geographic distribution maps were prepared using models to predict the number of generations of the nematodes and leaf miner. Maps obtained in scenario A2 allowed prediction of an increased infestation of the nematode and of the pest, due to greater number of generations per month, than occurred under the climatological normal from 1961-1990. The number of generations also increased in the B2 scenario, but was lower than in the A2 scenario for both organisms.

Incubation period of Hemileia vastatrix in coffee plants in Brazil simulated under climate change

Risk analysis of climate change on plant diseases has great importance for agriculture since it allows the evaluation of management strategies to minimize future damages. This work aimed to simulate future scenarios of coffee rust (Hemileia vastatrix) epidemics by elaborating geographic distribution maps using a model that estimates the pathogen incubation period and the output from three General Circulation Models (CSIRO-Mk3.0, INM-CM3.0, and MIROC3.2.medres). The climatological normal from 1961-1990 was compared with that of the decades 2020s, 2050s and 2080s using scenarios A2 and B1 from the IPCC. Maps were prepared with a spatial resolution of 0.5 × 0.5 degrees of latitude and longitude for ten producing states in Brazil. The climate variables used were maximum and minimum monthly temperatures. The maps obtained in scenario A2 showed a tendency towards a reduction in the incubation period when future scenarios are compared with the climatological normal from 1961-1990. A reduction in the period was also observed in scenario B1, although smaller than that in scenario A2.

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 marketplaces. 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 DTW-based method to similarity search in sugar cane regions represented by climate and remote sensing time series

Brazil is an important sugar cane producer, which is the main resource for ethanol production, a renewable source of energy. This agricultural commodity is important to the country economy, becoming fundamental to improve models that assist the crops monitoring process. Vegetation indexes originated from remote sensing images and agrometeorological indexes can be combined to represent sugar cane fields in a regional scale. However, finding different regions with similar patterns to classify or analyze their characteristics is a non-trivial task. Accordingly, this paper presents a method to find similar sugar cane fields represented by series of vegetation and agrometeorological indexes. The proposed method combines a weighted distance function with an algorithm to find similar objects. Results were coincident in the most cases with the classification done by experts, finding regions with similar characteristics of climate and productivity. Consequently, this approach can help in decision making processes by agricultural entrepreneurs.

Land use temporal analysis through clustering techniques on satellite image time series

Satellite images time series have been used to study land surface, such as identification of forest, water, urban areas, as well as for meteorological applications. However, for knowledge discovery in large remote sensing databases can be use clustering techniques in multivariate time series. The clustering technique on three-dimensional time series of NDVI, albedo and surface temperature from AVHRR/NOAA satellite images was used, in this study, to map the variability of land use. This approach was suitable to accomplish the temporal analysis of land use. Additionally, this technique can be used to identify and analyze dynamics of land use and cover being useful to support researches in agriculture, even considering low spatial resolution satellite images. The possibility of extracting time series from satellite images, analyzing them through data mining techniques, such as clustering, and visualizing results in geospatial way is an important advance and support to agricultural monitoring tasks.

Agricultural monitoring using clustering techniques on satellite image time series of low spatial resolution

This paper discuss how to use the clustering analysis to discover and extract useful information from multi-temporal satellite images with low spatial resolution to improve the agricultural monitoring of sugarcane fields. A large database of satellite images and specific software were used to perform the images pre-processing, time series extraction, clustering method applying and data visualization on several steps throughout the analysis process. The pre-processing phase corresponded to an accurate geometric correction, which is a requirement for applications of time series of satellite images such as the agricultural monitoring. Other steps in the analysis process were accomplished by a graphical interface to improve the interaction with the users. Approach validation was done using NDVI images of sugarcane fields because of their economic importance as source of ethanol and as effective alternative to replace fossil fuels and mitigate greenhouse gases emissions. According to the analysis done, the methodology allowed to identify areas with similar agricultural development patterns, also considering different growing seasons for the crops, covering monthly and annual periods. Results confirm that satellite images of low spatial resolution, such as that from the AVHRR/NOAA sensors, can indeed be satisfactorily used to monitor agricultural crops in regional scale.

Agricultural monitoring assessment based on low spatial and high temporal resolution satellite: A comparison of AVHRR and MODIS

In this context, we have assessed the potential of using low spatial resolution satellites in agricultural monitoring comparing long-term NDVI time series from AVHRR and MODIS (1 km and 250 m), analyzing more specifically areas of sugarcane production in the southeastern region of Brazil, the southern hemisphere. The methodology employed in this work is based on time series mining and statistical methods, such as Pearsons correlation and linear regression. Results showed that there is a strong correlation among AVHRR with MODIS 1km or MODIS 250m. Experiments were applied in a region with large areas of sugarcane fields.

Numerical models to forecast the sugarcane production in regional scale based on time series of NDVI/AVHRR images

The use of time series of meteorological satellite images, such as the AVHRR/NOAA, and agrometeorological data can be very useful in developing monitoring and forecasting methods for sugarcane crops because they are based on detection changes of space-time behavior. The knowledge about different sugarcane producing areas and climate in a given region is information required to develop models that can be applied simultaneously to several producing municipalities of sugarcane in order to assess the relation between NDVI and WRSI, the estimated productivity and the detection of similarity between the municipalities through distance functions. Thus, the main goal of this paper is to propose numerical models applied to monitor the sugarcane production based on time series of NDVI/AVHRR images and agrometeorological data. The regression method analyzes the relation between a single dependent variable (sugarcane production) and several independent variables (planted area, NDVI, WRSI), that is, use the independent variables whose values are known to predict the values of the selected dependent variable. The models proposed to estimate the sugarcane production using the variables planted area, NDVI and WRSI presented correlation coefficients (R2) around 0.9 and are able to estimate the sugarcane production for the state of São Paulo in Brazil.

Carbon stock estimation in coffee crops using high resolution satellites

According to IPCC, the increase of greenhouse gases emissions (GHG) in atmosphere is causing global warming, and this phenomenon could increase global temperature. In tropical areas of Brazil, the air temperature is supposed to increase from 1.1°C to 6.4°C causing large impacts in agricultures areas, including coffee production regions. The main objective of this paper was quantify the biomass of Arabica coffee trees above-ground (and carbon stock) using the vegetation index NDVI based on a high resolution image (Geoeye-1) and biophysical measures of coffee trees. In addition, the study aimed to establish an empirical relationship between biophysical measures of Arabica coffee trees, remote sensing data and dry biomass. The study was conducted in the south of Minas Gerais, which is the main producing region of Arabica coffee in Brazil. It was conclude that NDVI based on images of high spatial resolution, such as from Geoeye-1 satellite, has a strong correlation with dry biomass and carbon sink, showing that it is possible to estimate the carbon stock of coffee crops using remote sensing data without destructive methods.

An approach based on satellite image time series mining to identify region susceptible to desertification

After extensive and devastating drought in the Sahel, Africa, in the late 60s and early 70s (1968-1973) which resulted in the deaths of thousands of people and millions of animals, the desertification issue has been considered in the international agenda being ultimately relevant for the scientific community and governments in the worldwide. In 1992, the United Nations (UN), through Agenda 21 Chapter 12, defined desertification as ”land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities”. Desertification exacerbates socio-economic problems such as poverty and migration, which mainly affects the most vulnerable people and communities, bringing risk to global food security [1]. According to the UN, the annual economic losses are close to 4 billion dollars, with a cost for recovery of 10 billion dollars per year on a global scale.