Close Window

CD-17 Abstract

Validating, Scaling and Parameterizing a Forest Regrowth Model for the Amazon Region Using Aircraft and Spaceborne Sensors and GIS

Diogenes Salas Alves — DPI (INPE) (SA-PI)
Mark J Ducey — University of New Hampshire (US-PI)

This proposal is in response to the NASA-Carbon Cycle

Science and LBA-Ecology Program request for remote sensing research and

the development of methods and datasets for exploring the response of

ecosystems to disturbance at the regional scale.  We propose a four-step, incremental approach directed toward

the spatially explicit modeling and mapping of forest regrowth potential

for the Amazon region. Each of the four steps will make a significant

contribution to current understanding of the response of ecosystems to

disturbance at the regional scale.  Developing

an ability to predict forest regrowth potential has considerable

implications for our understanding of carbon dynamics in a future

characterized by increased conversion of old-growth Amazonian forests and

the subsequent abandonment of many areas originally cleared for

agricultural activities. A central focus of our approach is the

development of remote sensing approaches for quantifying vegetation

recovery and changes in biomass following disturbance, determination of

the optimal scale for these approaches, and testing of

disturbance-specific parameters that may influence rates of forest

regrowth in Amazonia.

Step 1 is the

production of preliminary forest regrowth potential maps for the region

using an empirical model of biomass accumulation in global secondary

forests (Johnson et al.2000; see Appendix B).  The maps are a spatially explicit application of model algorithms

within a GIS framework constrained by readily available regional datasets

on climate and soil texture.  Step

1 products include maps illustrating potential biomass accumulation in

Amazonian regrowth forests at 5-year intervals (Zarin et al. submitted;

see Appendix C).

Step 2 is the

definition of a set of normalized spectral indices of forest regrowth

optimized for the Amazon region.  These

indices will be derived from MASTER and multi-temporal Landsat TM/

ETM+data and calibrated against field measurements of regrowth structure

and estimates of canopy height and vertical distribution. The ideal

resolution for these spectral indices will be quantified. Expected

products from Step 2 include forest regrowth data bundles that will

contain age and land use history for each stand, temporal sequences of

regrowth indices, derived canopy height, canopy profiles and canopy

structural properties for a subset of regrowth stands.

Step 3 is the

testing of the reliability of the preliminary maps (Step 1 product) and

the remote sensing indices of regrowth structure (Step 2 products). We

will employ traditional validation methods to compare the spatially

explicit model predictions with forest inventory data not included in the

development of the model.  Validation

is used here in a strictly statistical sense.  Using independent data, we will assess the bias and variance

characteristics of the global model when used as a regional predictor,

including partitioning the variance components into local (within-site)

and regional (between-site) components.  And although the model was developed with reference to regrowth

biomass, we will also test its applicability to the prediction of regrowth

structure as expressed by the spectral indices defined in Step 2.  AIRSAR estimates of biomass will be tested and compared to assess

between-site and seasonal differences in SAR based biomass retrievals.

Expected products from Step 3 are spatially explicit accuracy assessment

of regrowth maps for biomass and structure, based on both field and

remotely-sensed data. 

Step 4 is the

refinement of the global model to enhance its regional applicability by

including known disturbance-specific parameters shown to explain a

significant amount of variance between measured and modeled regrowth

biomass and structure.  We

will test parameters that describe disturbance type, size, perimeter:area

ratio and land-use history, including the number of agricultural cycles

and persistence of the various stages of vegetation cover.  Remote sensing based observations are required to adequately test

the importance of these disturbance-specific parameters with a sufficient

number of observations. A subset of the forest inventory data will be used

to calibrate this regional enhancement of the model.  Model selection will employ cross-validation with reserved

independent data, or resampling cross-validation techniques, to avoid

overfitting and to assess expected prediction errors.  The resulting spatial pattern of predictions will also be compared

to the spatial pattern of the spectral indices defined in Step 2. The

principal products of Step 4 will be a regionally refined and

accuracy-assessed forest regrowth potential model and derivative maps that

will provide our best predictions (and corresponding error estimates) for

the regional responses of biomass and forest structure to current and

future land-use patterns in Amazonia.

Close Window