C. da Costa Freitas

Identification of the tropical forest in Brazilian Amazon based on the DEM difference from P and X bands interferometric data

In this paper the difference between digital elevation models, derived from P and X bands interferometric data, is used as a main information to identify land cover classes. The radar data used in this work were collected on September of 2000 over Tapajos National Forest, which is a region of Brazilian Amazon, Para State. The SAR images were acquired from an airborne polarimetric system, AeS-1, that could provide P and X bands interferometric data. During the radar mission ground survey was carried out, and the georeferenced information about the forest typology were acquired, and used as a support for the thematic identification and calibration of the remoted sensing data. The X-band DEM was generated using one-pass interferometric data and the P-band DEM was generated using two-pass interferometric data. The grid of the DEMs has a spatial resolution of 2.5 meters. Images from P and X bands and coherence maps were also used in order to improve the classification. Supervised and unsupervised classifications techniques are used and their results are shown.

Evaluating the Potential of L Band PolSAR Data to Discriminate Deforestation Increment Areas in Amazon Rain Forest

The main objective of this work is to evaluate the potential of L band PolSAR data to discriminate deforestation increment. In order to achieve this purpose, it was performed a coherent attributes exploratory analysis and a Maximum Likelihood - ICM (Iterated Conditional Modes) classification of R99B sensor L band PolSAR data. The PolSAR data classification obtained good agreement with PRODES Digital project reference map (k=0,71). This result might indicate that R99B L band PolSAR data have good potential to discriminate deforestation increment areas in Amazon Forest.

SAR interferogram phase filtering based on the Von Mises distribution

We propose the use of the Von Mises distribution for circular data to model interferometric phase images. Based on the kappa parameter of the Von Mises distribution a locally adaptive phase filter is developed. A two-sided confidence interval on the kappa values is constructed, and only phase measurements where kappa values fall inside this interval are input to the filter. We additionally put more importance to spatially closer pixels than to pixels further away from the pixel in question. The latter idea is achieved by narrowing the confidence interval depending on the distance from the central pixel.

Risk Mapping of the Schistosomiasis in the Minas Gerais State, Brazil, Using Modis and Socioeconomic Spatial Data

Schistosomiasis mansoni is a disease with social and behavioral characteristics, and distributed mainly in poor regions of Brazil. From 1995 to 2005 more than a million positive cases of the disease were reported, 27% of them reported in Minas Gerais state. The objective of this work is to estimate the prevalence risk of schistosomiasis in the Minas Gerais state through the characterization of the habitat of the snail. Two approaches were used for modeling the risk, by making use of the following types of variables: remote sensing, climate, socioeconomic, and variables that characterizes the neighborhood. In the first approach a unique regression model was generated and used to estimate the disease risk for the entire state. In the second approach, the state was divided in four regions, and four models were generated and used to estimate the disease risk across state, one for each region. The coefficients of determination for these two approaches were 0.424 and 0.717, respectively.

The use of JERS-1 and RADARSAT images for land use classification in the Amazon region

The objective of this paper is to compare the potential of RADARSAT and JERS-1 images to discriminate primary forest, secondary forest and recent activities areas in Amazon. The surrounds of Tapajos National Forest (Para State, Brazil) was used as a study site. Tonal and textural parameters were used in the analysis. It is shown that the discrimination between primary forest and secondary forest was only possible with RADARSAT image, and recent activities was better classified with the use of the JERS-1 image.

JERS-1 backscatter temporal behavior of land use types in the Tapajos National Forest, Brazilian Amazonia

The JERS-1 backscatter temporal behavior of some land cover types in Tapajos National Forest is analyzed. The following land cover types were investigated: bare soil, agriculture, pasture, secondary forest from one to 23 years old and primary forest. The differences in backscatter of these land cover types are discussed, based on the analysis of previous land use and human impacts such as fires and selective logging.

The discriminatory capability of polarimetric SAR data for land use classification

A SIR-C data is used to assess the discriminatory capability of full polarimetric data for several classes of land-use. It is analysed the contribution of each type of data (phase difference, intensity ratio, intensity pair and intensity-phase difference pair), using the iterated conditional modes (ICM) classifier. It is shown that each class was better classified using a different type of polarimetric data. The result of the classification (measured by the confusion matrix and the Kappa coefficient of agreement) was considered very good, allowing the discrimination of nine land use classes, which includes different cultivation stages of some crops.

Assessment of operational radar satellite for monitoring land cover change in Amazo/spl circ/nia

The objective of this paper is to verify the viability of using existing radar satellite (ERS and RADARSAT), as an operational tool for monitoring land cover in Amazo/spl circ/nia. It is well known that C band radar data are not adequate for land applications, but as cloud cover in Amazo/spl circ/nia is a constant problem, particularly in certain areas, radar data can help, as complimentary information, for change monitoring. In this paper ERS and RADARSAT images are classified using texture measures, in several classes of land use, and then the adequacy of using these classes for change detection is analyzed. Progressive sequential feature selection, using the Kappa coefficient of agreement as a selection criterion, chooses a subset of the texture layers that maximizes that coefficient. It was observed that even for the best feature set, the Kappa coefficient was considered too low and unsuitable to be used for change detection. However, it is shown that this coefficient progressively increases when classes are merged sequentially. When only two classes are considered, identified as forest/non-forest, the overall accuracy is higher than 85%, which was considered adequate for change detection. The classifications of the 1992, 1993 and 1996 ERS1/2 images over the Tapajo/spl acute/s National Forest, Brazil, were performed using the iterative contextual mode (ICM) classifier. Deforestation was detected for those points changing from forest in one year to non-forest in other year, with very good agreement with the results obtained with optical imagery sequence. Similar results were obtained using RADARSAT imagery for the year 1996.