HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia

Abstract

Optical imagery can reveal spectral properties of forest canopy, which rarely allows for finding accurate correspondence of canopy features with soils and hydrology. In Amazonia non-floodable swampy forests can not be easily distinguished from non-floodable terra-firme forests using just bidimensional spectral data. Accurate topographic data are required for the understanding of land surface processes at finer scales. Topographic detail has now become available with the Shuttle Radar Topographic Mission (SRTM) data. This new digital elevation model (DEM) shows the feature-rich relief of lowland rain forests, adding to the ability to map rain forest environments through many quantitative terrain descriptors. In this paper we report on the development of a new quantitative topographic algorithm, called HAND (Height Above the Nearest Drainage), based on SRTM-DEM data. We tested the HAND descriptor for a groundwater, topographic and vegetation dataset from central Amazonia. The application of the HAND descriptor in terrain classification revealed strong correlation between soil water conditions, like classes of water table depth, and topography. This correlation obeys the physical principle of soil draining potential, or relative vertical distance to drainage, which can be detected remotely through the topography of the vegetation canopy found in the SRTM-DEM data.

Introduction

Tropical terrain covered by rainforest presents rich mosaics of very distinctive environments, often hidden from remote view. The overwhelming challenge of describing 5.5 million km² of such environments and associated dense, tall and closed-canopy vegetation in Amazonia has made its complete inventory a seemingly impracticable task. Passive optical remote sensing imagery (such as Landsat and CBERS) can reveal spectral properties of forest canopy with detail (e.g. Wulder, 1998), but rarely allows for finding accurate correspondence of canopy features with soils and local hydrology. In Amazonia even seasonally flooded tropical forests could not be easily spotted and distinguished from non-floodable terra-firme forests using this type of data (Novo et al., 1997). The usually flat optical imagery can hint at relief through either bright or shadow reflection artifacts on the slope pixels of steeper areas, or where spectral signatures of the vegetation are distinctive because of local environmental effects (Guyot et al., 1989, Novo et al., 1997, Nobre et al., 1998), but without stereo images it lacks the ability to describe relief quantitatively. Optical stereo images (e.g. obtained from ASTER or SPOT) can be used to produce digital elevation models (DEM). However, the cost and difficulty of obtaining cloud-free coverage for many areas of the world, compounded with the requirement of sun angles below 25° (from Nadir) to avoid long shadows (Jacobsen, 2003), has limited the possibilities for producing DEMs of large, continuous areas (Hirano et al., 2003). In addition, because ground truth accessibility to large areas is limited, many aspects of landscape complexity in those vast tropical surfaces remain shrouded in mystery.

However, some of these drawbacks are quickly being overcome with imagery from active space borne sensors, such as synthetic aperture radars (SAR). Canopy penetrating L-Band SAR imagery from the Japanese Earth Resources Satellite (Siqueira et al., 2000) revealed with unprecedented detail patches of flooded vegetation (Barbosa et al., 2000, Melack and Hess, 2004). Careful mapping of such previously hidden seasonally flooded biomes has suggested their occurrence over a far wider area than formerly believed (Siqueira et al., 2003, Hess et al., 2003). Accurate topographic data are required for the understanding of land surface processes at finer scales. Topographic detail has now become available on a global scale through the C-Band SAR imagery (van Zyl, 2001) of the Shuttle Radar Topographic Mission (SRTM). The spaceborne SRTM circled the globe over a wide swath covering all the tropics and more, generating radar data that allowed for the digital reconstruction of the surface relief, producing the DEM. The SRTM-DEM data, with a horizontal resolution of 3” (~ 90 m near the equator) and a vertical resolution of 1 m, constitutes the finest resolution and most accurate topographic data available for most of the globe. Detailed information on the accuracy and performance of SRTM can be found in Rodriguez et al. (2006). In contrast to the passive optical imagery, this new DEM shows the feature-rich relief of lowland rain forests, adding to the ability to identify and map rain forest environments through many quantitative terrain descriptors.

A range of topographic algorithms are available, which allow various quantitative relief features to be obtained from the DEM. Slope and aspect (e.g. Jenson and Domingue, 1988), and drainage network and catchment area (e.g. Curkendall et al., 2003) are a few classical descriptors. A range of hydrological parameters such as superficial runoff trajectories, accumulated contributing area and groundwater related variables (e.g. Tarboton, 2003) add to the suite of relief descriptors. Relief shape parameters such as curvatures and form factors can also be calculated (Valeriano et al., 2006). The third dimension in a DEM, height, is obviously the key parameter, used to some degree in the derivation of all of the previously mentioned descriptors. Absolute height (above sea level — ASL) can be used on its own as a relief descriptor, as large scale geomorphologic features tend to be associated with altitude relevant geological control (Goudie, 2004). Upon flooding a given catchment for hydro dam development, for example, the height ASL is the descriptor that will predict the reach of the impoundment. However, when local environments in the fine scale relief are considered, height ASL has little, if any, descriptive power. As a result, local scale environments, although of conspicuous importance and clearly defined by characteristic terrain topography that is clearly visible on the SRTM-DEM, have not so far had a good descriptor.

In this paper we present the development of a new quantitative topographic algorithm based on SRTM-DEM data. We crafted and tested the terrain descriptor, applying it for a groundwater, topographic and vegetation dataset from central Amazonia, using ground calibrated terrain classes for mapping the study area.