HAND, a new terrain descriptor using SRTM-DEM - DPI - Inpe

RSE-07152; No of Pages 13 

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

environments in Amazonia 

Camilo Daleles Rennó a, ⁎, Antonio Donato Nobre b , Luz Adriana Cuartas a , João Vianei Soares a , 

Martin G. Hodnett c , Javier Tomasella a,b , Maarten J. Waterloo c 

a Instituto Nacional de Pesquisas Espaciais, Av. Astronautas, 1758, São José dos Campos, SP, 12227-010, Brazil 

b Instituto Nacional de Pesquisas da Amazonia, Escritório Regional do INPA, INPE Sigma, Av. dos Astronautas, 1758, São José dos Campos, SP, 12227-010, Brazil 

c Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands 

ARTICLE INFO 

Article history: 

Received 29 May 2007 

Received in revised form 24 March 2008 

Accepted 29 March 2008 

Available online xxxx 

1. Introduction 

ABSTRACT 

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 km 2 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 

⁎ Corresponding author. Tel.: +55 12 39456521; fax: +55 12 39456468. 

E-mail address: camilo@dpi.inpe.br (C.D. Rennó). 

0034-4257/$ – see front matter © 2008 Elsevier Inc. All rights reserved. 

doi:10.1016/j.rse.2008.03.018 

ARTICLE IN PRESS 

Remote Sensing of Environment xxx (2008) xxx-xxx 

Contents lists available at ScienceDirect 

Remote Sensing of Environment 

journal homepage: www.elsevier.com/locate/rse 

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. 

© 2008 Elsevier Inc. All rights reserved. 

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 

Please cite this article as: Rennó, C. D., et al., HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments 

in Amazonia, Remote Sensing of Environment (2008), doi:10.1016/j.rse.2008.03.018

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. 

2. Algorithm development 

2.1. Conditioning procedures 

The new descriptor algorithm requires a hydrologically coherent 

DEM as input, with resolved depressions (sinks), computed flow 

directions for each grid point and a defined drainage network. The 

procedures to develop these are presented below. 

2.1.1. Fixing DEM topology and computing flow directions 

Topography is a hydrologic driver since it defines the direction and 

speed of flows. Flow directions define hydrological relations between 

different points within a basin. Topological continuity for the flow 

directions is therefore necessary for a functional drainage to exist. 

Hydrological connections by flow direction between two points on a 

surface are not the same as those based on Euclidian distances. As seen 

in Fig. 1, point A is spatially closer to C, but it is hydrologically connected 

to point B because superficial water (runoff) will flow towards the latter. 

The flow directions can be represented using different approaches 

(Zhou and Liu, 2002). For a DEM represented by a grid, the simplest 

and most widely used method for determining flow directions is 

designated D8 (eight flow directions) initially proposed by O'Callaghan 

and Mark (1984). In this method, the flow from each grid point is 

assigned to one of its eight neighbors, towards the steepest downward 

slope. The result is a grid called LDD (Local Drain Directions), whose 

values clearly represent the link to the downhill neighbor. A pit is 

defined as a point none of whose neighbors has a lower elevation. For 

a pit, the flow direction is undefined. 


2 C.D. Rennó et al. / Remote Sensing of Environment xxx (2008) xxx-xxx 

Defining G as a set of pairs of Cartesian coordinates of a grid with c 

columns and r rows, 

G ¼ fhi; jiji ¼ ½1; cŠoN; 

j ¼ ½1; rŠoNg; 

ð1Þ 

we can represent LDD as a function that associates to each grid point 

hi,ji the flow direction LDD(hi,ji) which can assume a value according 

to its orientation: N, NE, E, SE, S, SW, W, NW or null. The null value is 

assigned to all points with undefined flow direction (pits). 

The real hydrological meaning of the LDD depends on the quality of 

the DEM. The C Band interacts strongly with the vegetation with the 

result that the actual topography represented in the SRTM data is 

roughly that of the upper canopy (Valeriano et al., 2006). Therefore, 

for areas where the soil surface is covered by dense or tall vegetation it 

must be expected that a variable degree of relief masking occurs in the 

SRTM data (Kellndorfer et al., 2004), producing pits and extensive 

unresolved flat areas. Some of these features can be real properties of 

the relief, but often they represent artifacts in the data. SRTM data, 

perhaps because of radar speckle (noise) or vegetation effects, have 

more spurious points than other DEMs (Curkendall et al., 2003). 

Besides, forests have characteristic sylvigenetic dynamics with a 

relatively high occurrence of tree gaps (Oldeman, 1990), which will 

appear in the SRTM-DEM as depressions. Such depressions, if they 

occur on a stream, for example, create false interruptions in the 

topological continuity of that drainage (apparent impounding). 

Another particular feature of SRTM data is related to abrupt 

transitions of vegetation types or land uses where vegetation is 

suddenly absent, revealing the soil surface in a patchy manner. 

According to Lindsay and Creed (2005), the depression artifacts 

arising from underestimation of elevation should be filled and the 

features caused by elevation overestimation should be corrected by 

breaching. In general, filling methods involve raising the inner area of 

a depression to the elevation of its outlet point, defined as a point 

through which water could leave the depression. The outlet is usually 

Fig. 1. Hydrological connection between points A and B. Red line indicates the profile 

shown above, blue lines represent the drainage network and the arrows indicate flow 

direction. B and C are points on streams. (For interpretation of the references to colour 

in this figure legend, the reader is referred to the web version of this article.) 



defined as the lowest point along the border of the depression basin. 

On the other hand, breaching methods create an artificial channel 

lowering some points across a divide in order to connect two neighboring 

depressions. 

In this work, both sink and flat areas were eliminated by using a 

process similar to depression breaching suggested by O'Callaghan and 

Mark (1984), Band (1986) and Jenson and Domingue (1988). Although 

depression breaching can create strong anomalies at the edge of the 

artificial drainage channels, it produces the least amount of change on 

most of the remaining points of the DEM. Fig. 2 exemplifies the 

approach used in the DEM fixing process. The first step is to delimit 

the sink area (or closed basin) to be corrected based on the LDD 

obtained from the original DEM. Note that there are two sink areas in 

this example (52 and 50 elevation, the pits in darker hues). Next, the 

outlets of these sinks, generally falling on a saddle in the relief, are 


C.D. Rennó et al. / Remote Sensing of Environment xxx (2008) xxx-xxx 

identified (here, 55 for the first pit). This outlet point will be the 

neighbor of the lowest point along the limits of the adjacent sink area 

(54). A topological trajectory passing through the outlet and 

connecting the two pits is then generated. Taking into account the 

distance between the two extremities, the new elevation of every 

point along this trajectory for cutting the hill or breaching is calculated 

by a linear interpolation. The result is a new functional flow path that 

eliminates one sink. Finally, a coherent new LDD is obtained based on 

this corrected DEM. 

2.1.2. Computing drainage network 

Many methods of automatic extraction of drainage networks from 

DEMs have been developed. Soille et al. (2003) grouped the drainage 

extraction algorithms into two classes, the first based on morphological 

features and the second on hydrological terrain characteristics. 

Fig. 2. DEM fixing process. Red arrows indicate flow directions changed during correction. (For interpretation of the references to colour in this figure legend, the reader is referred to 

the web version of this article.) 


in Amazonia, Remote Sensing of Environment (2008), doi:10.1016/j.rse.2008.03.018 

3

Table 1 

Δ Function definition 

Flow direction Δ (Flow direction) 

N h0,−1i 

NE h1,−1i 

E h1,0i 

SE h1,1i 

S h0,1i 

SW h−1,1i 

W h−1,0i 

NW h−1,−1i 

Null h0,0i 

Montgomery and Dietrich (1988) discussed the criteria to predict the 

point where channels should begin (headwater). One of the first 

procedures for delineating drainage networks (O'Callaghan and Mark, 

1984) used a minimum contributing area threshold to identify 

channel beginning. The drainage network is thus defined by those 

grid points that have a contributing area greater than a given 

threshold. The contributing area is computed by counting the number 

of points whose flow paths converge to the considered point. 

Considering a given point h i,j i, we can determine its neighborhood 

as 

Nðhi; jiÞ 

¼ fhk; liaGjk ¼ fi1; i; i 1g; 

l ¼ fj1; j; j þ 1g; 

hk; liphi; jig 

ð2Þ 

The points in N(hi,ji) are called the neighbors of h i,j i.Ifpointh i,j i is 

not on the border grid (i≠1, i≠c, j≠1 andj≠r), then it has 8 neighbors, 

that is 

Nðhi; jiÞ 

¼ fhi1; j 1i; hi 1; ji; hi 1; j þ 1i; hi; j 1i; hi; j þ 1i; 

hi þ 1; j 1i; hi þ 1; ji; hi þ 1; j þ 1ig 

otherwise, it will have less than 8 neighbors. 

Based on the D8 method, the point hi,ji can be hydrologically 

connected to only one of its neighbors. If F(hi,ji) is the point to which hi,ji 

flows, it can be defined as: 

Fðhi; jiÞ 

¼hi; jiþDðLDDðhi; jiÞÞ 

ð4Þ 

where Δ is a function that represents the relative position hΔi,Δji 

between two hydrologically connected points. Table 1 shows the 

correspondence between the flow directions and the relative positions. 

Note that if point hi, ji is a pit, then F(hi, ji)=hi, ji. 

The contributing area of point hi, ji, defined as A(hi,ji), is computed 

through an iterative process. 

Let A t (hi, ji) be the contributing area of the point hi, ji in the t th 

iteration. In the first iteration (t=1) 

A 1 ðhi; jiÞ 

¼ 

1 ifhi; jigFðGÞ 0 otherwise 

where F(G) represents the set of all upward points connected to any 

given point, that is 

FG ð Þ ¼ fhk; liaGjahi; jiaNðhk; liÞjFðhi; 

jiÞ 

¼ hk; lig: 

ð6Þ 

In this first iteration, the contributing area of all points that initiate 

a flow path is equal to one. 

For the other iterations (tN1) 

A t A 

ðhi; jiÞ 

¼ 

t 1ðhi; jiÞ 

if At 1ðhi; jiÞp0 

1 þ P 

F−1ðhi;jiÞAt 1ðhk; liÞ 

if 0gAt 1 F 1 8 

< 

ðhi; jiÞ 

: 

0 otherwise 



ð3Þ 

ð5Þ 

ð7Þ 

where F − 1 (hi, ji) represents a set of all neighbors of point hi,ji that flow 

to it, that is 

F 1 ðhi; jiÞ 

¼ fhk; liaNðhi; jiÞjFðhk; 

liÞ 

¼ hi; jig 

ð8Þ 

The iteration process stops (t=n) when, for any point hi,ji, A t −1 (hi,ji)≠0, 

that is, A(hi,ji)=A n (hi,ji). 

The method based on the contributing area is very easy to 

implement and therefore widely used. Tarboton (2003) pointed out 

that a significant question with this method is the choice of 

contributing area threshold. The basic hypothesis is that channel 

heads, where there is a transition from convex to concave profiles, is 

also where concentrated fluxes begins to dominate over diffusive 

fluxes (Tarboton et al., 1991, 1992). Some authors have called for 

objective criteria to define channel heads, such as the relationship 

between slope and contributing area (Tarboton et al., 1991). Others use 

local curvatures to account for spatially variable drainage densities 

(Tarboton and Ames, 2001). Montgomery and Foufoula-Georgiou 

(1993) compared the constant contributing area with the slopedependent 

contributing area. Although these approaches represent 

progress towards the automatic determination of channel heads, the 

tests we conducted for Central Amazonia showed that the application 

of these methodologies to SRTM data did not estimate drainage 

density properly. We suspect that vegetation masking ground 

topography may be the explanation. For this reason, we decided to 

use the contributing area threshold as one of the main criteria to 

define the drainage network, validating channel heads with field data. 

To define the channel heads we used an additional criteria based on 

simplifications of horizontal and vertical geomorphic curvatures. 

Firstly, the channel head element should represent a convergent point, 

meaning it should have two or more overland flux paths converging to 

it (horizontal curvature). Secondly, the channel profile should be 

concave, i.e. where the channel head profile point has a smaller 

change of elevation than the mean of the elements located uphill and 

downhill of it (vertical curvature). 

2.2. The height above the nearest drainage terrain descriptor 

To quantify relevant parameters that could uniquely identify generalizable 

spatial properties of hill slopes there was the need to have a local 

frame of variable topographic reference that should be more useful than 

the all encompassing and generic height ASL, or the third dimension in 

the DEM. Horizontal distances of DEM grid points to connected drainage 

channels (slope length) have been computed by a number of approaches 

(e.g. Tucker et al., 2001). As the initial interest in this study was to predict 

potential hydrological properties for each DEM grid point, especially the 

depth to the permanently saturated zone, this approach wasn't useful. 

The gravitational potential energy difference between any given grid 

point and the other extremity of the hill slope flow path, at the functional 

stream outlet (explicit in the LDD), defines a unique and permanent 

property of that grid point that we call draining potential. The vertical 

distance of a given grid point to its drainage outlet (which is 

hydrologically important) can in most cases be expressed as a relative 

height, or the height difference between those points. Thus there will be 

no gravitationally driven water movement between two hydrologically 

connected points that share the same height. Classifying all grid points 

according to their respective draining potentials allows them to be 

grouped into classes of equipotential (equivalent draining gravitational 

potential), defining environments or zones with inferred similar 

hydrological properties. The linking of every grid point to its outlet on 

the drainage system allows for the whole DEM to be normalized for the 

drainage network (adjusting point heights in relation to the drainage), 

which in this way becomes a distributed frame of topographic reference. 

If D is a set of drainage network points, identified by a number 

through a bijective function in order that each point of the drainage 



network be identified by a unique identification number k a N⁎. We 

define that 

Dðhi; jiÞ 

¼ 

k if hi; ji is a drainage network point 

0 otherwise 

and its inverse function as 

D 1 ðÞ¼ k fhi; jiaGjDðhi; jiÞ 

¼ kg: 

ð10Þ 

Following respective flow paths, each and every point in the grid is 

necessarily connected to a drainage point. Let I(hi,ji) be the function 

that identifies the drainage point connected to the point hi,ji. This 

function is computed through an iterative process and the result is a 

grid that associates the identification number of the drainage point 

that each point is connected to. If I t (hi,ji) is the drainage identification 

number of the point hi, ji in the t th iteration. In the first iteration (t=1), 

I 1 ðhi; jiÞ 

¼ Dðhi; jiÞ: 

ð11Þ 

For the other iterations (tN1) 

I t I 

ðhi; jiÞ 

¼ 

t 1ðhi; jiÞ 

if It 1ðhi; jiÞp0 

It 1ðFðhi; jiÞÞ 

if It 1 8 

< 

: 

0 

ðFðhi; jiÞÞp0 

otherwise: 

ð9Þ 

ð12Þ 

The iteration process stops (t=m) when, for any point hi,ji, I t (hi,ji)≠0, 

that is, 

Iðhi; jiÞ 

¼ I m ðhi; jiÞ: 

ð13Þ 

Assuming that all points belong to a flow path and that all flow 

paths are associated to respective drainage points, we define the 

HAND value of any given point hi, ji as 

HANDðhi; jiÞ 

¼ 

H hi; ji ð Þ HDðhi; jiÞ 

if HDðhi; jiÞbHðhi; 

jiÞ 

0 otherwise 



Fig. 3. Procedure to calculate the HAND grid. BBBBlue squares represent grid points belonging to the drainage network. Only black arrows are considered as flow paths. 

ð14Þ 

where H(hi,ji) represents the height of the point hi, ji given by the 

original DEM and H D(hi, ji) is the height of drainage point hydrologically 

connected to point hi,ji following the flow path, that is, 

HDðhi; jiÞ 

¼ HD 1 ðIðhi; jiÞÞ 

: ð15Þ 

It is important to note that if the point i,j is a point belonging to 

the drainage, then 

Iðhi; jiÞ 

¼ Dðhi; jiÞ 

ð16Þ 

D 1 ðIhi; jiÞ 

¼ hi; ji ð17Þ 

HDðhi; jiÞ 

¼ Hðhi; jiÞ 

ð18Þ 

and so 

HANDðhi; jiÞ 

¼ Hðhi; jiÞ 

Hðhi; jiÞ 

¼ 0: ð19Þ 

In other words, all grid points belonging to the drainage network 

are zeroed in height, which implies that the draining potential 

(according with the HAND definition) along the stream channel is 

disregarded. Although the drainage channel is also a flow path, which 

effectively drains, the HAND descriptor uses the whole channel as a 

flat relative topographic reference, the end of all non channel flow 

paths. By definition, the HAND drainage outlet grid point is the nearest 

draining point to itself, therefore it can only be subtracted from itself. 

It is important to note that the breaching process, required to reach a 

topologically sound and accurate drainage network, introduces occasional 

canyon like artifacts into the DEM, as a result of aberrant height 

differences adjacent to the drainage network. These artifacts would be 

transferred to the HAND grid if it were computed from the corrected 

DEM. When using original SRTM data for the HAND grid computation, 

some associations prescribed by the corrected drainage connection grid 

result in small negative differences that could be interpreted as points 



5

elow their associated drainage points (impoundment). Considering the 

1 m elevation accuracy of the SRTM and the disturbances caused by the 

forest canopy, these small negative differences fell within the noise in the 

data, and were zeroed. Therefore, in the HAND algorithm we combined 

the more meaningful need to reach a topologically coherent LDD 

(requiring the topological correction of the DEM, leading to an accurate 

drainage network), with the use of the original non-corrected DEM 

(which has dispersed sinks) for the computation of the final HAND grid, 

thus avoiding the canyon aberrations associated with the corrected DEM. 

Fig. 3 shows an example of the HAND procedure. Based on a LDD 

grid, overlaid with the computed drainage network, all flow paths are 

coded according to the nearest associated drainage point. The marked 

point with height 72 in the DEM is connected to the drainage point with 

height 53 (both coded 2), resulting in a HAND of 19. This means that the 

marked grid point is 19 m above its corresponding drainage point. Fig. 4 

summarizes all operations involved in obtaining the HAND grid. 

3. Application 

3.1. Study area 

Data from a 37 km square study area located in the Cuieiras Biological 

Reservation (central Amazonia NW of Manaus, Fig. 6), was used to test 



Fig. 4. Processing steps for the height above the nearest drainage descriptor. 

the new HAND terrain descriptor. The area, representative of large areas 

in Amazonia and including the K34 LBA fluxtower site (Araujo et al., 

2002) and the Asu hydrological catchment (Waterloo et al., 2006; 

Tomasella et al., 2007; Cuartas et al., 2007), is covered by pristine, terrafirme 

(terrain not subject to flooding by the annual flood cycle of the 

Amazon and its major tributaries) rain forest vegetation, with the 

canopy height varying from 20 m to 35 m. Forest biomass in the 

vicinities is highly heterogeneous and has been reported ranging from 

215 to 492 ton/ha (Laurance et al., 1999; Castilho, 2004). Details about 

the wet equatorial seasonal climate (annual rainfall larger than 

2000 mm) can be found in Araujo et al. (2002) and Cuartas et al. 

(2007). The landscape in and around the Asu catchment (Fig. 5), is 

composed mostly of flat plateaus (90–105 m ASL) incised by a dense 

drainage network within broad swampy valleys (45–55 m ASL). 

Ground truth data were collected in various field campaigns. A total 

of 120 points were visited in the Asu catchment for testing the HAND 

application. These points fell along a hydrological transect (site C1), 

across two 1st order sub catchments, and along two 2.5 km-long N–S 

and E–W orthogonal transects, crossing the catchment edge-to-edge. 

Field positions of streams and stream heads were also logged for 

verification of the calculated drainage network. Points beneath the 

forest canopy were located in the field using accurate geo positioning 

(obtained with a 30 m horizontal accuracy) in conjunction with 



vegetation and soil pattern evaluation, aided by subsurface water table 

depth data (multiyear time series, logged by an irregular sampling 

network of 27 piezometers installed in valleys, major stream heads, and 

along a hillslope hydrological transect). Hydrological details about the 

Asu instrumented catchment can be found in Tomasella et al. (2007). 

Non-floodable local environments were clearly identified in the field 

through topography, vegetation, soils and hydrological cues. 

3.2. Results 

The SRTM data for the study area were corrected in order do 

produce the hydrologically coherent LDD. Using this corrected LDD, 

the contributing area grid was computed and with it the drainage 

network was derived, using a selected contributing area threshold. 

The HAND grid was then obtained using this drainage as reference. 

The results of these steps are presented below. 

3.2.1. Drainage network sensitivity 

Drainage density is an important parameter for the accuracy of the 

HAND descriptor. Fig. 6 shows networks extracted from the SRTM data 

using three different contributing area thresholds. For the HAND 



Fig. 5. Study area NW of Manaus. (a) Landsat TM 5,4,3 (RGB) 2001, (b) SRTM-DEM, showing rich details in the forest topography. Flood lands in the SRTM-DEM, identified using JERS-1 

data (Melack and Hess, 2004), were removed from the analysis of this study (masked in black). 

descriptor, the contributing area is the tunable parameter. Note that 

the lower the threshold, the higher the density of the resulting drainage 

network. Automatic extraction of the drainage network from a DEM still 

lacks competence to represent channel heads realistically and with 

robustness, which requires field verification for an appropriated 

representation of drainage density. The ground truth for channel heads 

carried out in this study indicated that the contributing area threshold of 

50 grid points produces the most accurate drainage network density. 

From an exploratory analysis of the relationship between drainage 

density and contributing area threshold, we found that varying thresholds 

within the range of 47 through 92 did not change channel hierarchy 

at a verification point in a 3rd order stream. Smaller or larger thresholds 

produced higher or lower orders respectively. Fig. 7 shows the impact of 

these three contributing area thresholds (47, 50 and 92), plus two chosen 

extreme points (5 and 500) on the HAND grid distribution of heights. 

The skewness in the HAND distribution of heights is directly proportional 

to the smoothness of the HAND grid. Higher frequencies of the 

small HAND values, for example, result in a smoother topography of the 

HAND grid, which implies a lower ability to distinguish and resolve 

contrasting local environments. If the calculated drainage network 

remains within the range that realistically captures the order hierarchy 

Fig 6. Contributing area (left) and drainage networks plotted on SRTM image, with varying contributing area thresholds (in number of grid points). 



7

of the drainage network, then the effect of slightly varying channel 

heads on the HAND grid (and therefore on other estimations based on 

it), will not be significantly great. In a forthcoming paper we report this 

to be a robust result, verified for a wider area around the Asu catchment. 

But for other areas with distinct geomorphologies this finding still needs 

verification. 



Fig. 7. Sensitivity of the HAND grids based on different drainages with varying contributing areas. 

Fig. 8. HAND grid DEM (using 50 grid points threshold for drainage contributing area). 

3.2.2. The HAND grid DEM 

Fig. 8 shows a HAND grid shaded relief of the test area. Altimetric 

differences along the drainage channels are no longer visible as they 

were in the original DEM (Fig. 5b). The capacity of the HAND 

descriptor to make evident locally significant terrain-controlling 

factors is apparent. 



Fig. 9. Profile comparing original SRTM data and the HAND grid normalized for the 

drainage network. 

The main difference between the original SRTM-DEM and the 

HAND grid DEM (Fig. 9) lies in the effect of the topographic 

normalization for the drainage network. In fact, the HAND grid 

converts into absolute the relative nature of the distributed frame of 

topographic reference in the drainage network. Thus, although the 

HAND grid loses the height reference to sea level, it enhances 

meaningful local relative variations in height. This local relevance is 

especially useful because height differences now found in the HAND 

grid have hydrological significance, and can potentially reveal 

previously hidden local environments. 

3.2.3. Mapping local environments 

The HAND algorithm can be applied to the SRTM-DEM of any terrain, 

producing HAND grid DEMs with implicit geomorphologic and hydrological 

meaning. However, the significance in practical applications is 



Fig. 11. Box-plot of HAND for ground truth points: 36 points from the hydrological 

transect, 84 spread out within the Igarapé Asu catchment, with water table inferred 

from data of 27 piezometers. 

provided by finding HAND classes (ranges of heights) that match with 

relevant soil water and land cover characteristics. For this application, 

the most detailed field data was produced along the hydrological 

transect (Fig. 10), running orthogonally from the top of the K34 

fluxtower plateau to the 2nd order Asu stream (Hodnett et al., in 

preparation). This transect encompassed and represented all topographic 

features for the area, containing measurement and sampling 

points for soil water, vegetation, soil and topographic ground truth 

verification. 

The vertical cross-section of the hydrological transect (Fig. 10a), 

reveals the relation of the HAND grid profile with the surveyed ground 

topography and water table data. Note the convergence of the water 

table with the topography in the lowland, towards the stream. Note also 

that the HAND grid profile hovers above the ground topography by the 

distance of the forest vegetation height (the C-band radar data from the 

SRTM interacted strongly with the forest canopy). Local environments 

and their relative extents were defined by matching ground truth of 

vegetation and topography with groundwater data. These environments 

were easily recognized in the field by pattern observation, and were 

classified as waterlogged (considering only the zone where soil is 

perennially saturated to the surface), ecotone (shallow water table, 

usually covered by Campinarana – sensu, Anderson et al.,1975)orupland 

Fig. 10. a) Cross-section of the Igarapé Asu hydrological hillslope transect (from A to A') with HAND grid profile superimposed on ground topography and water table data. b) Overlay 

of ground truth points onto SRTM-based HAND grid DEM (grayscale, with top contour lines in red) along the hillslope hydrological transect (from A to A'). 



9

(deep water table). The last category was arbitrarily split into slope 

(upland with slope N3°) and plateau (flat upland). 

The overlaying of the ground truth points onto the HAND grid 

(Fig. 10b) suggests a coherent matching between local environments 

identified in the field and corroborated by groundwater data, with 

drainage-normalized canopy-topography represented by the HAND 

grid. This coherence suggests that local environments can be 

associated with height classes in the HAND grid. An exploratory 

quantitative analysis matching the distribution of ground truth points 

in the Asu catchment with respective heights in the HAND grid (Fig.11) 



Fig. 12. Four-class HAND map overlaid with ground truth points to site C1. 

Fig. 13. Four-class HAND map for entire study area. 

suggests a compelling separation of HAND classes, especially between 

waterlogged, ecotone and upland environments. 

Taking these into account, HAND grid values of 5 m and 15 m were 

selected as preliminary best-guess thresholds between the three 

classes. To optimize this separation (lessen errors on class inclusion) 

we applied the simplex algorithm (Cormen et al., 2001), finding 5.3 m 

and 15.0 m as the best thresholds between classes for the set of ground 

truth points available. 

The upland class, in comparison to the other two lowland classes, 

represented well and in a relatively homogeneous way a single soil 



water condition (well drained soil, deep water table). However there are 

many distinct substrate effects in the definition of an environment on a 

slope compared to one on a plateau. The waterlogged and plateau classes 

are quite well defined in that they share low slope angles, and are well 

separated from ecotone and slope. This analysis reveals that the HAND 

height is a good separator between the three soil water relevant classes. 

It also shows that, in the case of the Igarapé Asu catchment — with its 

fairly uniform plateau heights, it can also separate the upland class into 

slope and plateau. However, for other catchments with plateaus at 

variable HAND heights, it might not score so well. Slope angle, on the 

other hand, can be used to identify flat surfaces independently of HAND 

height. However the HAND height can still be useful to set aside plateaus 

from flat alluvial terrain in the valley bottoms. As both slope and aspect 

are terrain descriptors that do not require field verification, slope will be 

a better separator when applied exclusively for the upland class. The 

upland class (HANDN15.0 m) was then split on the basis of slope, with 

the initial threshold value arbitrarily selected at 6.5% slope, and then 

optimized with the simplex algorithm to 7.6% (Fig. 12). 

The ground truth survey was accurate in identifying the local 

environment for each chosen point, and as a result, for most points, the 

matching between field environments with HAND predicted environments 

was exceptionally good. Nevertheless, unavoidable localization 

errors were responsible for a few mismatches, which happened only to 

the extreme values. Area estimation indicates that the distinctive 

waterlogged plus ecotone environments (valley bottoms) occupied 43% 

of the surface, whereas slope and plateau occupied only 26% and 31% 

respectively (Fig. 13). The two valley bottom classes have been widely 

considered as a minor proportion of the landscape and as a result have 

been largely disregarded by most integrative studies of terra-firme. The 

HAND descriptor clearly and quantitatively reveals that the valley 

bottom classes are very important in this landscape. The HAND class test 

carried out in this study might suffer from some site specific bias, but as 

it uses site-independent gravitational potential as the main driver for its 

description of terrain, we expect that it will show robustness when 

applied in the Amazon outside the test area, and even anywhere else 

with a similar terrain covered by thick forests with a relatively uniform 

canopy height across the landscape. 

4. Discussion 

Topography in the SRTM shaded relief image is plainly discernible to 

the trained eye. When the computed drainage network is plotted on the 



SRTM image of a rainforest area, hidden local environments, such as 

riparian zones, appear to pop up out of the continuous canopy carpet. 

However this perception reveals an imaginary presence, which at best 

represents only a qualitative indication. An objective and quantitative 

descriptor of these hidden environments was lacking. Pure hypsometry, 

as that in the shaded relief, does not offer much. A given valley area at 

200 m ASL, for example, might have a similar environment to another 

valley area at 650 m ASL, the latter lying kilometers upstream. 

Conversely, at 200 m ASL one can find a range of different local 

environments. Geographic Information System (GIS) buffer zoning is a 

common tool used for delineating local environments, most often in 

association with streams and other superficial waters or distinctive 

landscape features. Buffer analysis to define areas of riparian influence 

creates a zone of predefined width around the drainage, usually based 

on simple Euclidean distance (Burrough and McDonnell, 1998). 

However, without functional variables driving the buffer zoning process, 

geometric distances present little topographic, pedological and hydrological 

meaning or consistency. The uncertainty in this local environment 

delineation is especially troublesome for tropical rain forests 

(Bren, 2000; McGlynn and Seibert, 2003). The widely variable zone of 

floodable forests in Amazonia (Hess et al., 2003), for example, suggests 

that it would be grossly misrepresented if a simple geometric GIS buffer 

were applied. Therefore, geometric buffering zones also fail to provide a 

local environment descriptor, as the spatial extents of these perceived 

environments are highly irregular, and do not have extractable 

geometries that would correlate with the drainage network or other 

emergent feature in any easy way. Topographic patterns associated with 

contrasting environments, which are very evident from within the rain 

forest, needed to be rendered in quantitative manner. 

A porous medium (soil) presents a fine and interconnected network of 

void spaces and channels through which water can flow, following 

gradients of potential energy, always seeking equilibrium in the condition 

of least energy. This is a fundamental hydraulic principle. Flow routes 

from any given point on the terrain, or hill slope flow paths (overland or 

groundwater in the saturated zone), will seek trajectories of least 

resistance and will be propelled by gradients of gravitational energy 

(neglecting both matric and osmotic potentials for a saturated soil or for 

overland flow). These hill slope flow paths will invariably end somewhere 

on the drainage network, with the principle of least energy requiring a 

discharge into the nearest stream. Therefore, for each grid point in the 

SRTM-DEM, there must be a unique and topologically consistent flow 

path connecting it with its stream outlet. These connecting flow paths 

Fig. 14. Comparison between the HAND (vertical distance do the nearest drainage) and horizontal distance to the nearest drainage (along water flow path) values, according to 

algorithm proposed by Tucker et al. (2001). 



11

ear all topological components, extractable from the SRTM-DEM, which 

allowed for the development of the new HAND terrain descriptor. The 

results of the test of the HAND descriptor reported here demonstrate its 

capacity to resolve meaningfully the delineation of local environments. 

There are a host of generic terrain descriptors, e.g. topographic 

indexes, which could be compared to the HAND, but an exhaustive 

comparison is beyond the aim of the present work. The closest 

descriptor to the vertical distance along a flow path is the non- 

Euclidian horizontal distance along the same flow path (Tucker et al., 

2001). To test the similarity between the two descriptors, 1000 

independent points (pixels) were randomly drawn from the test area 

SRTM-DEM. According to the linear regression, the horizontal distance 

can only explain 55% of the total variance that can be explained by the 

HAND (Fig. 14). 

There is an obvious relation between horizontal and vertical 

distances because it is expected that as one moves away from the 

drainage, the terrain will get higher. However, the other 45% of the 

variance is new information that only the HAND descriptor can give. 

Points with large horizontal distances but low HAND are indicative of 

great flat areas connected to the drainage (swampy areas). The innate 

swampy characteristic of these terrains, for example, would never be 

seen with horizontal distances alone. With a large HAND value 

(vertical flow path distance) and a small horizontal flow path distance, 

the terrain is quickly rising away from the stream, i.e. a well etched 

drainage valley. But, although the HAND has this evident advantage, 

the horizontal flow path distance is a great advance over plain 

Euclidian distances, because the latter may connect and relate points 

on the terrain that do not belong to the same catchment. Furthermore, 

horizontal flow path distances to the nearest drainage might have a 

bearing in terms of span of time spent in draining flows, determined 

by hydraulic conductivity and slope along the flow path. Height above 

the nearest drainage correlates directly with gravitational potential, 

and this fact alone sets this descriptor aside as unique. 

5. Conclusions 

The height above the nearest drainage algorithm was developed on 

top of the local drain directions and drainage networks, two well 

established and basic topographic descriptors. The HAND has added 

the height difference along flow paths, or draining potential, as a 

significant and unique terrain descriptor. The HAND terrain descriptor 

produces a normalized digital elevation model (HAND grid) that can 

be applied to classify terrain in a manner that is related to local soil 

water conditions. The HAND grid, a DEM where all pixels have been 

mapped according to their draining potential, has a wide range of 

potential applications. The application of the HAND descriptor in 

classifying the terrain within a monitored hydrological catchment in 

Amazonia revealed strong correlations 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. To our knowledge no previous study has noticed or reported the 

height above the nearest drainage as likely being a good terrain 

descriptor. It increases usability of the SRTM-DEM data and provides a 

new quantitative view on the steady state landscape, one that was 

missing in the repertoire of terrain descriptors. 

Acknowledgements 

This work was developed within the Environmental Physics Group 

of GEOMA modeling network (Brazil's Ministry of Science and 

Technology funding), with invaluable collaboration and co-funding 

from the LBA project (Igarapé Asu instrumented catchment study). 

This work would not have been possible without full support of INPE 

and INPA. The Igarapé Asu catchment study was also funded by the 



PPG7/FINEP Ecocarbon project and by CTEnerg and CThidro (Energy 

and Water Resources Sectorial Funds). Brazilian Science Funding 

Agency, CNPq and Fapeam, also contributed with grants and scholarships 

of personnel involved in the project. Thanks to Gerard Banon 

(DPI/INPE), two anonymous reviewers and the editor for their valuable 

comments and questions. We thank dearly Antonio Huxley for support 

in the field work. We thank also Evlyn Novo, Adriana Affonso and John 

Melack for the cession of the JERS-1 based flood land numerical mask. 

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