Laurent Durieux

The impact of deforestation on cloud cover over the Amazon arc of deforestation

Abstract Atmospheric general circulation model (AGCM) simulations predict that a complete deforestation of the Amazon basin would lead to a significant climate change; however, it is more difficult to determine the amount of deforestation that would lead to a detectable climate change. This paper examines whether cloudiness has already changed locally in the Brazilian arc of deforestation, one of the most deforested regions of the Amazon basin, where over 15% of the primary forest has been converted to pasture and agriculture. Three pairs of deforested/forested areas have been selected at a scale compatible with that of climate model grids to compare changes in land cover with changes in cloudiness observed in satellite data over a 10-year period from 1984 to 1993. Analysis of cloud cover trends suggests that a regional climate change may already be underway in the most deforested part of the arc of deforestation. Although changes in cloud cover over deforested areas are not significant for interannual variations, they are for the seasonal and diurnal distributions. During the dry season, observations show more low-level clouds in early afternoon and less convection at night and in early morning over deforested areas. During the wet season, convective cloudiness is enhanced in the early night over deforested areas. Generally speaking, the results suggest that deforestation may lead to increased seasonality; however, some of the differences observed between deforested and forested areas may be related to their different geographical locations.

Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil

Agriculture in Brazilian Amazonia is going through a period of intensification. Crop mapping is important in understanding the way this intensification is occurring and the impact it is having. Two successive classifications based on MODIS (MODerate Resolution Imaging Spectroradiometer)-TERRA/EVI (Enhanced Vegetation Index) time series are applied (1) to map agricultural areas and (2) to identify five crop classes. These classes represent agricultural practices involving three commercial crops (soybean, maize and cotton) planted in single or double cropping systems. Both classifications are based on five steps: (1) analysis of the MODIS/EVI time series, (2) application of a smoothing algorithm, (3) application of a feature selection/extraction process to reduce the data set dimensionality, (4) application of a classifier and (5) application of a post-classification treatment. The first classification detected 95% of the agricultural areas (5 617 250 ha during the 2006–2007 harvest) and correlation coefficients with agricultural statistics exceeded 0.98 for the three crop classes at municipality level. The second classification (overall accuracy = 74% and kappa index = 0.675) allowed us to obtain the spatial variability mapping of agricultural practices in the state of Mato Grosso. A total of 30% of the total planted area was cultivated through double cropping systems, especially along the BR163 highway and in the Parecis plateau region.

High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data

Understanding spatial patterns of land use and land cover is essential for studies addressing biodiversity, climate change and environmental modeling as well as for the design and monitoring of land use policies. The aim of this study was to create a detailed map of land use land cover of the deforested areas of the Brazilian Legal Amazon up to 2008. Deforestation data from and uses were mapped with Landsat-5/TM images analysed with techniques, such as linear spectral mixture model, threshold slicing and visual interpretation, aided by temporal information extracted from NDVI MODIS time series. The result is a high spatial resolution of land use and land cover map of the entire Brazilian Legal Amazon for the year 2008 and corresponding calculation of area occupied by different land use classes. The results showed that the four classes of Pasture covered 62% of the deforested areas of the Brazilian Legal Amazon, followed by Secondary Vegetation with 21%. The area occupied by Annual Agriculture covered less than 5% of deforested areas; the remaining areas were distributed among six other land use classes. The maps generated from this project - called TerraClass - are available at INPEs web site (http://www.inpe.br/cra/projetos_pesquisas/terraclass2008.php).

Impact of land-cover change in the Southern Amazonia climate: a case study for the region of Alta Floresta, Mato Grosso, Brazil

The transformation of forest into pastures in the Brazilian Amazon leads to significant consequences to climate at local scale. In the region of Alta Floresta (Mato Grosso, Brazil), deforestation has been intense with over half the forests being cut since 1970. This article first examines the evolution of precipitation observed in this region and shows a significant trend in the decrease in total precipitation especially at the end of the dry season and at the beginning of the rainy season. The study then compares the temperatures measured in cleared and forested sectors within a reserve in the area of Alta Floresta (Mato Grosso, Brazil) between 2006 and 2007. The cleared sector was always hotter and drier (from 5% to 10%) than the forested area. This difference was not only especially marked during the day when it reached on average 2°C but also seemed to increase during the night with the onset of the dry season (+0.5°C). The Urban Heat Island effect is also evident especially during the night and in the dry season.

Land use monitoring by remote sensing in tropical forest areas in support of the Kyoto Protocol: the case of French Guiana

The new SPOT/Envisat direct receiving station (DRS) operating in Cayenne in the framework of the SEAS-Guyane project was used to produce a global cloudless SPOT mosaic over French Guiana for the year 2006. This mosaic was used to perform a land-use, land-use change and forestry (LULUCF) inventory in the framework of the Kyoto Protocol. Nearly 17 000 sample points were laid down on the SPOT mosaic with a stratified sampling design. The land use at each sample point was determined by visual interpretation of the corresponding SPOT image in 2006 and the Landsat Thematic Mapper (TM) image from the Global Land Cover Facility (GLCF) in 1990. Statistics for the period 1990–2006 were computed and integrated in the first voluntary Kyoto inventory.

Epidemiological analysis of malaria and its relationships with hydrological variables in four municipalities of the State of Amazonas, Brazil

Abstract This work comprises a spatial, temporal and statistical analysis of the epidemiology of malaria occurrence in four municipalities of the State of Amazonas, Brazil: Coari, Codajás, Manacapuru and Manaus, for the period 2003–2009. The number of malaria cases, precipitation, water level and temperature data were analysed in this study. The strength of the relationship between these hydrological/meteorological variables and the occurrence of malaria was determined by employing the Spearman rank correlation coefficient. Seasonal peaks of malaria were registered, on average, about 1–2 months before the annual maximum temperature and after the river’s seasonal high-water level. The phenomenon called repiquete (notable variations in the water level) was observed during periods of between 9 and 56 days. The results showed a statistically significant correlation between malaria, temperature, precipitation and water level. Temperature influenced malaria occurrence the least, while rainfall was the most important factor, especially in the municipality of Coari. Water level had an important influence on the records of malarial occurrence in the municipality of Manacapuru. Editor Z.W. Kundzewicz Citation Wolfarth, B.R., Filizola, N., Tadei, W.P., and Durieux, L., 2013. Epidemiological analysis of malaria and its relationships with hydrological variables in four municipalities of the State of Amazonas, Brazil. Hydrological Sciences Journal, 58 (7), 1495–1504.

Corrections to “Automatic Spectral Rule-Based Preliminary Classification of Radiometrically Calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 Imagery” [Mar 10 1299-1325 and 1326-1354]

In our two-part paper (this issue, pp. 1299-1354), there are two errors which we correct here. The corrected text is presented here.

Object-based Monitoring of Land Cover Changes in Mangrove Ecosystems of Senegal

This paper presents the results of object-based techniques applied to mapping and hi-temporal change detection of mangrove ecosystem in Low Casamance, Senegal. The methodology was applied on SPOT images for 1986 and 2006. Reference data was based on ground truth and visual verification of very high resolution images. High accuracy was obtained in mapping of mangrove cover. Other land cover classes showed however lower accuracy. The segmentation process for the change detection analysis was executed on a multi-date image and classification was then completed with a standard nearest neighbour classifier. Change detection analysis in mangrove ecosystems proved to be particularly difficult due to underlying presence of water and tide influences. Unlikely most mangrove ecosystems worldwide, important negative trends were not identified and low dynamicity was recorded during the two dates. This information questions the generalized preconception over mangrove retreat. The methodology will be further extended to Sine-Saloum the other main mangrove ecosystem of Senegal and should provide updated mangrove inventory for the country.

Ontology-based classification of remote sensing images using spectral rules

Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for domain experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images. HighlightsAn ontology to classify Landsat images based on spectral rules is proposed.The ontology is composed of three knowledge representation types.Image pixels are assigned to semantic classes by a description logic reasoner.Ontologies are useful to formalize remote sensing expert-knowledge.

Accuracy of the malaria epidemiological surveillance system data in the state of Amazonas

The Epidemiological Surveillance System for Malaria (SIVEP-Malaria) is the Brazilian governmental program that registers all information about compulsory reporting of detected cases of malaria by all medical units and medical practitioners. The objective of this study is to point out the main sources of errors in the SIVEP-Malaria database by applying a data cleaning method to assist researchers about the best way to use it and to report the problems to authorities. The aim of this study was to assess the quality of the data collected by the surveillance system and its accuracy. The SIVEP-Malaria data base used was for the state of Amazonas, Brazil, with data collected from 2003 to 2014. A data cleaning method was applied to the database to detect and remove erroneous records. It was observed that the collecting procedure of the database is not homogeneous among the municipalities and over the years. Some of the variables had different data collection periods, missing data, outliers and inconsistencies. Variables depending on the health agents showed a good quality but those that rely on patients were often inaccurate. We showed that a punctilious preprocessing is needed to produce statistically correct data from the SIVEP-Malaria data base. Fine spatial scale and multi-temporal analysis are of particular concern due to the local concentration of uncertainties and the data collecting seasonality observed. This assessment should help to enhance the quality of studies and the monitoring of the use of the SIVEP database.