Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM's shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

Four distinctly different classifiers were used to analyze multispectral data. Which of these classifiers is most suitable for a specific study area is not always clear. This paper provides a comparison of minimum-distance classifier (MDC), maximumlikelihood classifier (MLC), extraction and classification of homogeneous objects (ECHO), and decision-tree classifier based on linear spectral mixture analysis (DTC-LSMA). Each of the classifiers used both Landsat Thematic Mapper data and identical field-based training sample datasets in a western Brazilian Amazon study area. Seven land-cover classesmature forest, advanced secondary succession, initial secondary succession, pasture lands, agricultural lands, bare lands, and water-were classified. Classification results indicate that the DTC-LSMA and ECHO classifiers were more accurate than were the MDC and MLC. The overall accuracy of the DTC-LSMA approach was 86 percent with a 0.82 kappa coefficient and ECHO had an accuracy of 83 percent with a 0.79 kappa coefficient. The accuracy of the other classifiers ranged from 77 to 80 percent with kappa coefficients from 0.72 to 0.75.

Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation. KEYWORDStexture, aboveground biomass, TM image, correlation, Amazon Explorando texturas de imagens TM e suas relações com estimativas de Biomassa em Rondônia. (i.e., média, variância, homogeneidade, contraste, dissimilaridade, entropia, segundo momento e correlação), associadas com sete diferentes tamanhos de janela (5x5, 7x7, 9x9, 11x11, 15x15, 19x19 e 25x25) e cinco bandas TM (TM 2, 3, 4, 5 RESUMO Muitas medidas de textura têm sido desenvolvidas e utilizadas para melhorar a acurácia de classificações de cobertura das terras, mas raramente têm-se avaliado a importância dessas medidas em estimativas de biomassa. Este trabalho utilizou dados Landsat TM para explorar as relações entre texturas de imagens TM e biomassa em Rondônia, Amazônia. Foram analisadas oito medidas de textura baseadas em matrizes de co-ocorrência de tons de cinza

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Heterogeneous Amazonian landscapes and complex forest stand structure often make aboveground biomass (AGB) estimation difficult. In this study, spectral mixture analysis was used to convert a Landsat Thematic Mapper (TM) image into green vegetation, shade, and soil fraction images. Entropy was used to analyze the complexity of forest stand structure and to examine impacts of different stand structures on TM reflectance data. The relationships between AGB and fraction images or TM spectral signatures were investigated based on successional and primary forests, respectively, and AGB estimation models were developed for both types of forests. Our findings indicate that the AGB estimation models using fraction images perform better for successional forest biomass estimation than using TM spectral signatures. However, both models based on TM spectral signatures and fractions provided poor performance for primary forest biomass estimation. The complex stand structure and associated canopy shadow greatly reduced relationships between AGB and TM reflectance or fraction images. Description of the Study Area Rondônia has experienced high deforestation rates during the past two decades (INPE, 2002). Following the national

Deforestation and colonization in Amazônia have attracted substantial attention. This article focuses on an area of 3,000 km 2 within the Brazilian State of Rondônia. Two adjacent settlements were compared to assess the role of their different designs in landscape change. Anari was planned following an orthogonal road network. Machadinho was designed with attention to topography in laying out roads and farm properties, while including communal reserves. Field research was undertaken in conjunction with multi-temporal classifications of remotely sensed data (1988, 1994, and 1998) and landscape ecology methods.The results indicate that large patches of communal reserves play an important role in maintaining lower levels of fragmentation. Analyses of landscape structure confirmed that forest patches in Machadinho are less fragmented, more complex, and preserve more interior habitat. By comparing the effects of different settlement designs on landscape change and forest fragmentation, this article contributes to the debate about colonization strategies in Amazônia.

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