João Vianei Soares

Mapping deforestation and land use in amazon rainforest by using SIR-C imagery☆

Abstract In this paper, the potential use of spaceborne polarimetric synthetic aperture radar (SAR) data in mapping landcover types and monitoring deforestation in tropics is studied. Here, the emphasis is placed on several clearing practices and forest regeneration that can be characterized by using the sensitivity of SAR channels to vegetation biomass and canopy structure. A supervised Bayesian classifier designed for SAR signal statistics is employed to separate five classes: primary forest, secondary forest, pasture-crops, quebradao, and disturbed forest. The L- and C-band polarimetric SAR data acquired during the shuttle imaging radar-C (SIR-C)/X-SAR space-shuttle mission in 1994 are used as input data to the classifier. The results are verified by field observation and comparison with the Landsat data acquired in August of 1994. The SAR data can delineate these five classes with approximately 72% accuracy. The confusion arises when separating old secondary forests from primary forest and the young ones from pasture-crops. It is shown that Landsat and SAR data carry complementary information about the vegetation structure that, when used in synergism, may increase the classification accuracy over secondary forest regrowth. When the number of land-cover types was reduced to three classes including primary forest, pasture-crops, and regrowth-disturbed forest, the accuracy of classification increased to 87%. A dimensionality analysis of the classifier showed that the accuracy can be further improved to 92% by reducing the feature space to L-band HH and HV channels. Comparison of SIR-C data acquired in April (wet period) and October (dry period) indicates that multi-temporal data can be used for monitoring deforestation; however, the data acquired during the wet season are not suitable for accurate land-cover classification.

Characterizing landscape changes in central Rondonia using Landsat TM imagery

An analysis of landscape changes in a region of pioneer settlements in central Rondonia, western Brazilian Amazon, was derived from Landsat TM data. Total deforested area increased from 206 x 103 ha in 1977, to 565 x 103 ha in 1985 and to 1210 x 103 ha, or 35.5% of the region, in 1995. Eighty-one per cent of the total 1995 deforestation had occurred in regions within 12.5km from areas of pioneer colonization deforested by 1977. Deforested area exceeded 79% in regions within 12.5km from the regions first road.

Exploratory study of the relationship between tropical forest regeneration stages and SIR-C L and C data☆

Abstract In this article, the relationship between secondary forest regrowth stage and SIR-C SAR data is assessed, for an area located near to the Tapajos National Forest, south of Para State, in the Amazon region. These regeneration stages are mapped by making use of a consecutive annual sequence of Landsat TM (Thematic Mapper) images. Using this map as a mask over the radar images, the tonal means (expressed in dB) and coefficient of variation (CV) for several second-growth succession stages classes are calculated. It is shown that the discrimination between regeneration stages is difficult when individual ″small areas are used, but this discrimination might be possible when L-band means over a “large area are computed. In particular, the LHV band seems to carry more information. The maximum difference of means among classes occurred in this band and it is of about 5 dB. The CV appeared to be less well suited than the mean value for regeneration stage discrimination, although some discrimination among early stages of regeneration may be possible using this measure at L-band.

An investigation of the selection of texture features for crop discrimination using SAR imagery

This article presents a methodology for selecting texture measures to maximize the discrimination of agricultural land use classes in SAR images. The images were acquired during the first flight of the Shuttle Imaging Radar-C (SIR-C) experiment, in April 1994. L (24 cm)- and C (5 cm)-band SAR data at HH (horizontal transmitting and receiving), HV (horizontal transmitting, vertical receiving), and VV (vertical transmitting and receiving) polarizations both in ground range and slant range and in two different passes were analyzed. The kappa statistic was used to identify meaningful texture measures to discriminate seven classes. The results show that the classifications of land use based only on tonal averages produced a kappa coefficient only slightly higher than 0.50. A kappa threshold of 0.90 was reached with the simultaneous inclusion of 15 texture measures for the six images (two bands, three polarizations). It was also found that the inclusion of texture features when only one band and one polarization was used could produce kappa values higher than 0.85.

Earth Observation Applications in Brazil with Focus on the CBERS Program

Brazil is a large and diverse country and almost all remote sensing application fields can be developed there: oceanographic, agricultural, environmental, urban, etc. Some applications are instrumental for environmental governance, some are very useful for business and others are devoted to research. As part of its remote sensing development, Brazil counts on the CBERS (China-Brazil Earth Resources Satellite) Program. Applications and accomplishments of the CBERS Program are presented in this article.

Regrowth biomass estimation in the amazon using JERS-1/RADARSAT SAR composites

Synthetic Aperture Radar (SAR) is known to have a response that is directly related to the amount of living material that it interacts with. It is this property that our research seeks to exploit in order to better understand carbon dynamics in the Amazon. The vegetation density causes the radar response to saturate such that vegetation that is more dense than some threshold is indistinguishable from each other. However, the areas of regrowth are likely to have a low enough biomass during the first 10 years of regrowth to be accurately assessed using radar. Our efforts involve obtaining appropriate pairs of radar images at L and C bands from different sites and for both seasons. These data are then orthorectified to allow accurate calibration and incidence angle correction. The seasonality of the data is used to deal with the moisture sensitivity of the data, and the different frequency data is used to help classify the data into several classes for use in class-specific biomass estimates. We have chosen 2 sites in Brazil for our study. we use the JERS-1 (L-band) and RADARSAT (C-band) data to create a 2-channel composite. These data are then classified into the following classes: flat area (water, bare soil), short vegetation, regrowth, and trees. We report on the accuracy of both our classification and biomass estimation efforts.

Water budget model of a eucalyptus forest using a canopy characterization by remote sensing techniques and a soil water flux parameterization

This paper deals with the development of a water budget model for Eucalyptus forest, using a conceptually simple onedimensional mass balance approach within the root zone of the forest. The model uses Leaf Area Index to quantify the forest structure important for mass and energy exchange, and this represents a key simplification for regional scale applications. Remote Sensing vegetation indexes and mixture modeling techniques were used to estimate LAI. A five-layered water balance model, with water movement between layers along hydraulic gradients, was developed and parameterized for a eucalypt plantation (Eucalyptus grandis Hill ex.Maiden hybrids) in Brazil. Available soil water controls stomatal conductance and hence transpiration, which is calculated by the Penman-Monteith equation. The remote sensing derived LAI was used to compute the canopy conductance that drives the Penman-Monteith formulation. The model accounts for changes in the depths of the water table. The test period was from October 1995 to September 1996 in a nine-year-old plantation in an experimental catchment in eastern Brazil. Total transpiration for the year was 1116 mm, with 119 mm intercepted and re-evaporated and another 79 mm soil surface evaporation, giving evapotranspiration of 1314 mm compared to rainfall of 1396 mm. The water balance was closed by net flow below the root zone of about 53 mm and an increase in water storage (in the first layer) of 29 mm. The model also estimated a water deficit of 135 mm (difference between the potential and current transpiration) for the period. Upward flux from the water table was around 81 mm and piezometric measurements showed 1.5 m recession for the same period. The upward flux into the root zone was about 1 mm day-1 at the end of a long dry season; that kept the water storage in that zone to about 15% of capacity and helped prevent complete stomatal closure. Comparison between estimated water storage and measurements confirmed that this model is a very promising tool for calculating water use by plantations. It can also provide water balance information and information about stomatal conductance for growth prediction models.

Selection of texture features for crop discrimination using SAR imagery

This paper presents a methodology for selecting texture measures to maximize the discrimination of agricultural land use classes in SAR images. The images were acquired during the first flight of the Shuttle Imaging Radar-C experiment, in April 1994. L and C band SAR data at HH, HV and VV polarizations, both in ground range and slant range and in two different passes were analyzed. The kappa statistic was used to identify meaningful texture measures to discriminate seven classes. The results show that the classifications of land use based only on tonal averages produced a kappa coefficient only slightly higher than 0.50. A kappa threshold of 0.90 was reached with the simultaneous inclusion of 15 texture measures for the six images.