AVHRR Continuous Fields Tree Cover Product

Description

treecover

Characterization of terrestrial vegetation from the Advanced Very High Resolution Radiometer (AVHRR) on the global to regional scale has traditionally been accomplished using classification schemes with discrete numbers of vegetation classes. Representation of vegetation into a limited number of homogeneous classes does not account for the variability within land cover, nor does the portrayal recognize transition zones between adjacent cover types. An alternative paradigm to describing land cover as discrete classes is to represent land cover as continuous fields of vegetation characteristics using a linear mixture model approach. This prototype data set contains 1km cells estimating:

  1. Percent tree cover
  2. Percentage cover for two layers representing leaf longevity (evergreen and deciduous)
  3. Percentage cover for two layers estimating leaf type (broadleaf and needleleaf)

Each pixel in the layers has a value between 10 and 80 percent. These layers can be directly used as parameters in models or aggregated into more conventional land cover maps. For the latter, the product offers the flexibility to derive land cover maps based on user's requirements for a particular application. The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial data sets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks.

The aim of this research is to 1) develop methodologies for global representation of vegetation characteristics and 2) produce continuous fields of vegetation characteristics at 1km which are accessible to the global change research community.

Legend and Values

For: treecover

  • 10 - 80 percent tree cover
  • 254 non-vegetated
  • 255 tree cover less than 10%

For: evergreen; deciduous; broadleaf; and needleleaf

  • 10 - 80 percent cover for indicated leaf longevity and type (% evergreen + % deciduous = % tree cover; and % broadleaf + % needleleaf = % tree cover)
  • Band interleaving: Band sequential
  • Mask: Sea mask applied=255

Quantization:

  • 8-bit unsigned integer

Downloadable file formats:

  • UNIX compressed (".gz" for UNIX) also works with winzip for Windows.

Origin

An important requirement for global models of the earth system is reliable, geographically-referenced, consistent data on global vegetative cover. The only truly synoptic view of the earth is provided by satellites and may improve the quality, consistency and reproducibility of global land cover information. To this end this project aims at providing an alternative to traditional land cover classification by representing vegetative cover as a gradient over the landscape using satellite data. Data acquired in 1992-93 from NOAA's AVHRR at a 1km spatial resolution and processed under the guidance of the International Geosphere Biosphere Program (IGBP) (Eidenshink and Faudeen 1994) were used to derive the tree cover, leaf type and leaf longevity maps.

Procedure

Combining a land cover map based on IGBP land cover definitions (Hansen et al. in press) with percentage cover estimations of trees, leaf longevity and leaf type (DeFries et al. in press) produced the layers provided here. The land cover map was joined with the mixture model results to bind the minimum and maximum value defined for each cover type to the IGBP definitions of that cover type. For example, if all pixels classified as woodland according to Hansen et al. (in press) in a particular continent ranged in percent woody cover from 30 to 70 percent according to DeFries et al. (in press), the values would be scaled to within the 40 to 60 percent range as defined as woodland. Values obtained for leaf type and leaf longevity in DeFries et al. (in press) were adjusted so that the total percentage summed to the adjusted percent woody value. Furthermore pixels classified as agriculture in the tropics were adjusted to range from 10 to 25 percent tree cover because of the known! ! presence of mixed farming with an overstory. Both approaches use the same IGBP processed data set from the AVHRR in 1992-1993. The merging of the data sets was performed to:

  1. Overcome the difficulty in distinguishing percentage tree cover, leaf longevity and leaf type at extreme high and low percent cover. The difficulty in determining high and low values is likely due to cloud contamination in humid forests and saturation of the spectral signature at high percent cover and the overwhelming influence of soil and understory background on the spectral signature at low percentage cover. Therefore the layers of tree cover, leaf type and leaf longevity have values ranging from 10 to 80 percent, with a value of 80 percent cover being equal or greater than 80 percent and a value of 10 being equal or less than 10 percent cover.
  2. Constrain the observed overestimation of tree cover in locations known to have intermediate values of percent canopy coverage characteristics of woodland (defined by the IGBP as 40 to 60 percent canopy cover). Proportions of trees, leaf type and leaf longevity were (Defries et al. in press) adjusted so that the percent woody coverage in each pixel is within the range of percent canopy coverage defined by the classification result (Hansen et al. in press).
  3. Adjust the range of agricultural pixels form 10 to 25 percent cover because of known mixed farming with an overstory.

The two data sets merged in this study were developed using different methods to describe vegetation. A brief explanation of each of the methods used to derive the data sets is given below:

  1. Global maps of proportional cover for three vegetation characteristics, leaf form (percent woody vegetation, percent herbaceous vegetation and percent bare ground cover), leaf type (percent needleleaf and percent broadleaf) and leaf longevity (percent deciduous and percent evergreen). The procedure for deriving the continuous fields of vegetation characteristics is fully explained in DeFries et al. (in press) and utilizes a linear mixture model approach applied to 1km AVHRR data. A set of 156 Landsat Multispectral Scanner data were used to train the linear models for vegetation characteristics permitting estimation of endmember values (DeFries et al. 1998). The spectral response of the AVHRR data is then unmixed using the endmembers and estimates of leaf longevity (percent evergreen and percent deciduous), leaf type (percent broadleaf and percent needleleaf) and percent tree cover are identified. A separate model was developed for each continent to determine the mixture! ! es of broadleaf evergreen, broadleaf deciduous, needleleaf evergreen, and needleleaf deciduous woody vegetation depending on which forest types are present in each continent. The approach is based on the annual phenological cycle of vegetation derived from 30 metrics acquired from the AVHRR. These metrics are the annual maximum, minimum, mean and amplitude for the annual time series of the Normalized Difference Vegetation Index (NDVI), and channels 1 through 5 of the AVHRR. The 24 metrics were calculated from April to April (1992-93) to account for the full growing season in both hemispheres, but only the eight months with the highest NDVI are used to describe green vegetation. Six metrics based on surface temperature were also derived from channel 4 of the AVHRR to account for snow cover at higher latitudes. Linear discriminates or linear combinations of the weighted metrics were then made to reduce the statistical complexity and error associated with using 30 metrics in the linear model. The resulting data set thus represents a percentage map where each cell is composed of between 10% and 80% of the respective vegetation characteristic.
  2. A global land cover classification (Hansen et al. in press) containing 12 cover types based on requirements identified by the IGBP. The classification methodology used to make this map is described in Hansen et al. (in press) and DeFries et al. (1998). Briefly approximately 150 Landsat scenes were interpreted with ancillary data and consultation with experts on land cover to obtain training data for the classification method. A decision tree algorithm was then employed using 41 metrics derived from the annual temporal profile of the NDVI and the five individual bands of the AVHRR to obtain a classification of cover types.
AVHRR Tree Cover
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