The amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land-cover and land-use change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site-specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no spaceborne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old-growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300Mgha(sup 1) here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300Mgha(sup 1). Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200Mgha(sup 1). The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and below ground biomass, is 86 PgC with +/- 20% uncertainty.

Distribution of Aboveground Live Biomass in the Amazon Basin

Dual-season mapping of wetland inundation and vegetation for the central Amazon basin

http://usstudy.goes.msu.edu/wp-content/uploads/2013/10/A-method-for-integrating-MODIS-and-Landsat-data-for-systematic-monitoring-of-forest-cover-and-change-in-the-Congo-Basin_Hansen-et-al_2008_rs.pdf

A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

Numerous large-area, multiple image-based, multiple sensor land cover mapping programs exist or have been proposed, often within the context of national forest monitoring, mapping and modelling initiatives, worldwide. Common methodological steps have been identified that include data acquisition and preprocessing, map legend development, classification approach, stratification, incorporation of ancillary data and accuracy assessment. In general, procedures used in any large-area land cover classification must be robust and repeatable; because of data acquisition parameters, it is likely that compilation of the maps based on the classification will occur with original image acquisitions of different seasonality and perhaps acquired in different years and by different sensors. This situation poses some new challenges beyond those encountered in large-area single image classifications. The objective of this paper is to review and assess general medium spatial resolution satellite remote sensing land cover cl...

Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas

Summary


1
The floristic variation in Amazonian lowland forests is poorly understood, especially in the large areas of non-inundated (tierra firme) rain forest. Species composition may be either unpredictable as abundances fluctuate in a random walk, more-or-less uniform, or it may correspond to environmental heterogeneity.

2
We tested the three hypotheses by studying the floristic variation of two phylogenetically distant plant groups along a continuous 43-km long line transect that crossed tierra firme rain forest in northern Peru.

3
The observed floristic patterns were compared to patterns in the spectral reflectance characteristics of the forest as recorded in a Landsat TM satellite image. The topography of the transect was measured in the field, and surface soil samples were collected to document edaphic conditions. The two plant groups, pteridophytes and the Melastomataceae, were assessed in 2-m wide and 500-m long sampling units.

4
Floristic similarity (Jaccard index) between sampling units ranged from 0.01 to 0.71 (mean = 0.27), showing that some units were almost completely dissimilar while others were very alike.

5
Spatially constrained clustering produced very similar subdivisions of the transect when based separately on satellite image data, pteriophytes, and Melastomataceae, and the subdivisions were also related to topography and soil characteristics. Mantel tests showed that floristic similarity patterns of the two plant groups were highly correlated with each other and with similarities in reflectance patterns of the satellite image, and somewhat less correlated with geographical distance.

6
Our results lend no support to the uniformity hypothesis, but they partially support the random walk model, and are consistent with the hypothesis that species segregate edaphically at the landscape scale within the uniform-looking forest.

Floristic patterns along a 43-km long transect in an Amazonian rain forest

The 100 meter JERS-1 Amazon mosaic image was used in a new classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first order texture measures derived from the 100 m data by using a 10/spl times/10 independent sampling window. The classification approach included two interdependent stages: 1) a supervised maximum a posteriori Baysian approach to classify the mean backscatter image into 5 general land cover categories of forest, savanna, inundated, white sand, and anthropogenic vegetation classes, and 2) a texture measure decision rule approach to further discriminate subcategory classes based on taxonomic information and biomass levels. Fourteen classes were successfully separated at 1 km scale. The results were verified by examining the accuracy of the approach by comparison with the IBGE and the AVHRR 1 km resolution land cover maps.

/pdf/mapping-land-cover-types-in-amazon-basin-using-1-km-jers-1-1py7t5vb6a.pdf

B. Nelson

Papers

Mapping land cover types in Amazon basin using 1 km JERS-1 mosaic