RESUMO
In addition to the largest existing expanse of tropical forests, the Brazilian Amazon has among the largest area of mangroves in the world. While recognized as important global carbon sinks that, when disturbed, are significant sources of greenhouse gases, no studies have quantified the carbon stocks of these vast mangrove forests. In this paper, we quantified total ecosystem carbon stocks of mangroves and salt marshes east of the mouth of the Amazon River, Brazil. Mean ecosystem carbon stocks of the salt marshes were 257 Mg C ha-1 while those of mangroves ranged from 361 to 746 Mg C ha-1 Although aboveground mass was high relative to many other mangrove forests (145 Mg C ha-1), soil carbon stocks were relatively low (340 Mg C ha-1). Low soil carbon stocks may be related to coarse textured soils coupled with a high tidal range. Nevertheless, the carbon stocks of the Amazon mangroves were over twice those of upland evergreen forests and almost 10-fold those of tropical dry forests.
Assuntos
Carbono/análise , Solo/química , Áreas Alagadas , Brasil , EcossistemaRESUMO
The search for tools to perform soil surveying faster and cheaper has led to the development of technological innovations such as remote sensing (RS) and the so-called spectral libraries in recent years. However, there are no studies which collate all the RS background to demonstrate how to use this technology for soil classification. The present study aims to describe a simple method of how to classify soils by the morphology of spectra associated with a quantitative view (400-2,500 nm). For this, we constructed three spectral libraries: (i) one for quantitative model performance; (ii) a second to function as the spectral patterns; and (iii) a third to serve as a validation stage. All samples had their chemical and granulometric attributes determined by laboratory analysis and prediction models were created based on soil spectra. The system is based on seven steps summarized as follows: i) interpretation of the spectral curve intensity; ii) observation of the general shape of curves; iii) evaluation of absorption features; iv) comparison of spectral curves between the same profile horizons; v) quantification of soil attributes by spectral library models; vi) comparison of a pre-existent spectral library with unknown profile spectra; vii) most probable soil classification. A soil cannot be classified from one spectral curve alone. The behavior between the horizons of a profile, however, was correlated with its classification. In fact, the validation showed 85 % accuracy between the Morphological Interpretation of Reflectance Spectrum (MIRS) method and the traditional classification, showing the importance and potential of a combination of descriptive and quantitative evaluations.
Assuntos
Análise Espectral , Características do Solo/análise , Classificação , Espectroscopia de Luz Próxima ao Infravermelho , Tecnologia de Sensoriamento RemotoRESUMO
The search for tools to perform soil surveying faster and cheaper has led to the development of technological innovations such as remote sensing (RS) and the so-called spectral libraries in recent years. However, there are no studies which collate all the RS background to demonstrate how to use this technology for soil classification. The present study aims to describe a simple method of how to classify soils by the morphology of spectra associated with a quantitative view (400-2,500 nm). For this, we constructed three spectral libraries: (i) one for quantitative model performance; (ii) a second to function as the spectral patterns; and (iii) a third to serve as a validation stage. All samples had their chemical and granulometric attributes determined by laboratory analysis and prediction models were created based on soil spectra. The system is based on seven steps summarized as follows: i) interpretation of the spectral curve intensity; ii) observation of the general shape of curves; iii) evaluation of absorption features; iv) comparison of spectral curves between the same profile horizons; v) quantification of soil attributes by spectral library models; vi) comparison of a pre-existent spectral library with unknown profile spectra; vii) most probable soil classification. A soil cannot be classified from one spectral curve alone. The behavior between the horizons of a profile, however, was correlated with its classification. In fact, the validation showed 85 % accuracy between the Morphological Interpretation of Reflectance Spectrum (MIRS) method and the traditional classification, showing the importance and potential of a combination of descriptive and quantitative evaluations.(AU)