L.S. Araujo

Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data

The objective of this study is to show the relation among backscatter signals of JERS-1 images and biophysical parameters (biomass values) of forest and savanna formations. Two contact zones involving these vegetation units in Brazilian Amazonia (Roraima and Mato Grosso States) were selected. A regression model was applied during the analysis of these two variables, based on the best fit function and taking into account the data dispersion. Maps were generated showing biomass spatialization of the vegetation typology found in the study areas. The importance of this study is the innovation referring to the joint analysis of JERS-1 data of these two contact zones in Amazonia, representing both an abrupt contact and a smooth contact along a transition zone of savanna/tropical rainforests formations.

Power spectrum analysis of SAR data for spatial forest characterization in Amazonia

Power spectrum analysis was used for the analysis of spatial forest features from airborne X‐band synthetic aperture radar (SAR) data in the Brazilian Amazon. Spectral estimates were arrived at empirically by periodograms and correlograms, and from autoregressive moving‐average (ARMA) models. The spectral estimates derived from SAR data were validated by those derived from ground data with locational match. The results obtained by ARMA modelling revealed particularly good correspondence between remote sensing and reference data: repeating patterns at pixel level could be detected in the images. These patterns were shown to arise from canopy structure and distances between major tree individuals; and thus allowed the extraction of parameters of spatial forest structure, particularly of forest density. The method was applied to an example area of primary tropical forest, and its spatial patterns were modelled.

Tropical forest stand table modelling from SAR data

Diameter distributions of forest stands are modelled by the three-parameter Weibull distribution for a variety of different forest types in the Brazilian Amazon. The parameters of the function are arrived at by estimation: (1) only from tree diameters as measured in the field, and (2) only from backscatter coefficients and interferometric height measurements as obtained from airborne synthetic aperture radar. The models yield good results of fit, the modelling of forest structure, i.e. of the stand tables, is equally precise from ground data as from remote sensing data. The adopted approach provides a convincing basis for the monitoring of forest structure over huge areas at low cost by adding a new feature to the analysis of high precision SAR data.

Inventory of forest biomass in Brazilian Amazon: a local approach using airborne P-band SAR data

The objective of this study is to explore the use of airborne P-band SAR polarimetric data, to stratify biomass by primary and secondary vegetation typology. To ensure that different landscapes of Amazon upland forest are represented, a test-site located in the lower Rio Tapajos region, Pari State, was selected. The backscatter signals derived from the complex image of the P-band SAR were correlated with field data obtained from a forest inventory, for different physiognomic-structural aspects of the tropical rainforest The estimation of above-ground biomass for these forest types was modeled by DBH and total height measurements, including the use of general allometric equations. Statistical regression models were applied to establish the relationship between biomass and radar data at HH, HV and VV polarization. The overall objective of this P-band experiment is to improve the regional monitoring process of biomass dynamics as well as landscape changes, due to human action.

Identificação de áreas prioritárias para recuperação florestal com o uso de rede neural de mapas auto-organizáveis

O objetivo deste trabalho foi identificar areas prioritarias para a recuperacao florestal e analisar variaveis a elas relacionadas atraves da rede neural artificial (RNA) de Mapas Auto-Organizaveis (SOM), em duas escalas. Primeiramente, procurou-se identificar uma sub-bacia hidrografica prioritaria para a recuperacao florestal na Unidade de Gerenciamento de Recursos Hidricos Paulista (UGRHI) do rio Paraiba do Sul por SOM. Para isto, foram utilizadas variaveis de conectividade ambiental e cobertura florestal. Definiu-se uma sub-bacia hidrografica situada na represa do Jaguari, municipio de Igarata, para estudo em uma escala de maior detalhe. Nas Areas de Protecao Permanentes (APPs) englobadas nesta sub-bacia hidrografica, foi realizada uma nova analise por SOM. Neste caso, foram consideradas variaveis de distância a fragmentos florestais, a areas urbanas, a estradas pavimentadas e a construcoes rurais, assim como o Indice de Vegetacao por Diferenca Normalizada e o Potencial Natural de Erodibilidade Laminar. Em ambas as escalas, as areas prioritarias para a recuperacao florestal foram determinadas atraves de histogramas do somatorio dos valores dos Mapas Auto-Organizaveis de cada variavel por agrupamentos delimitados. Por fim, foi gerado um mapa de contribuicao de amostras para neuronios vencedores, o que permitiu uma nova abordagem para a analise dos agrupamentos gerados.

Allometric equations for tropical forest estimation and its relationship with P-band SAR data

The objective of this research is to show the capacity of P-band polarimetric images to define the space of attributes of primary forest and secondary succession, through the analysis of the relation among backscatter and biomass data (estimated by four specific allometric equations). Based on the best fitted regression equation (heat capacity model) a biomass map was elaborated. This was done through the backscatter image, whose each segment were derived from the thematic classification of Bivariate Intensities HH-HV image, with the �1 ° mean values of each resulting segment converted into biomass by the best fit function. As a conclusion, the best allometric equations for primary and secondary forest biomass estimation was defined, considering the different polarizations of P-band SAR data. The advance that was reached with the treatment of P-band data could improve the regional monitoring process of the land cover change, a process whose speed was accelerated as a result of human action in the Amazon during the last two decades. The airborne SAR images were obtained by a system that acquires both P- (polarimetric) and X-band interferometric data. Technical characteristics of the P band imaging system: Wavelength = 72 cm; Middle frequency = 415 MHz; Depression angle = 45°; Range resolution = 1.5 m; Azimuth resolution = 0.7 m, for 1 look slant range image. The radar tracks were radiometrically corrected according to the antenna pattern using a function based on homogeneous extended areas; and afterwards, the polarimetric calibration was done for each polarization (slant range mode), based on the 8 corner reflectors (placed in the field along the flight strips using differential GPS measurement). The �1 ° of P-band were estimate by the procedure adopted by (1). During the field survey, biometric features for the primary forest and secondary sucession were collected in several samples, with measurements of DBH >5 cm and also tree height were made in plots of 2,500 and 1,000 m 2

Estimation of basal area from Amazon tropical rain forest using airborne P-band SAR data

Shows the capacity of P-band polarimetric images to define the space of attributes of primary forest and secondary succession, through the analysis of the relation among backscatter and basal area data. To ensure that different landscapes of Amazon upland forest are represented, a test-site located in the Tapajo/spl acute/s region was selected. Statistical regression model was used to verify the relation between basal area values (37 plots) and backscatter data derived from P/sub HH/, P/sub HV/ and P/sub VV/ images. An analysis of the spatial configuration of backscatter values for primary forest and secondary succession is made, considering the intrinsic variations of these formations in terms of biometric parameters. For this thematic stratification, a contextual Markovian classification technique (Iterated Conditional Modes CM) was used in polarimetric P-band images, to delineate primary forest and regeneration areas, in association with basal area intervals. The advance that was reached with the treatment of P-band data could improve the regional monitoring process of the land cover change, a process whose speed was accelerated as a result of human action in the Amazon during the last two decades.