1

Remote sensing based characterisation of land cover and terrain properties in the semi-arid Northeast of Brazil Project by: B. Creutzfeldt Complete study: B. Creutzfeldt (2006) Remote sensing based characterisation of land cover and terrain properties for hydrological modelling in the semi-arid Northeast of Brazil. Unpublished MSc thesis, University of Potsdam, Germany 1 Introduction In semi-arid regions, water availability and erosion are major problems. The analysis and prediction of water resources and erosion patterns by modelling using the WASA-SED model require both the determination of land cover patterns and knowledge about the terrain. The objective of this study is to provide data of land cover and terrain characteristics of the Bengué catchment for the hydrological modelling with WASA-SED. Optical remote sensing data (ASTER and CBERS-2 images) are used in order to provide these hydrological model input data. A land cover map is generated by a supervised classification scheme, structuring the Bengué catchment land cover into twelve different classes. In addition, Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) are calculated for each vegetation class based on the transformation of NDVI to LAI and FVC utilizing an empirical equation. Their temporal dynamics is modelled by an empirical regression model relating LAI and FVC to monthly rainfall. A DEM is generated by automated stereocorrelation using ASTER scenes. Through postprocessing, a hydrological sound DEM is generated. The quality assessment shows an improvement of the data availability in comparison to GCPs and existing DEMs (SRTM DEM and DEM from a topographic map). 2 Study area The Bengué catchment is located in the semi-arid Northeast of Brazil, in the Southwest of the Federal State Ceará and in the East of the Federal State Piáui. The Bengué catchment covers most parts of the municipality Aiuaba (Ceará) and a small part of the catchment is located in Pio (Piaui) (see Figure 1). Both states belong to the poorest states of Brazil and both municipalities are located in the rural areas of these states. The economy is mainly based on agriculture and livestock farming. For example, in the municipality of Aiuaba, land use is proportioned in 7 % agriculture, 23 % pasture, 35 % fallow land and 35 % natural vegetation. With regard to husbandry, the number of cattle amounts to 14 433, of pigs 10 063 and of poultry 42 000 (IBGE, 2000; IBGE, 2005). These figures show that the Bengué catchment is located in a rural and developing area, with a low population density and extensive agricultural activity, whereby most people are dependent on a subsistence economy.

2

Figure 1: The Bengué catchment and its location in the Federal State Ceará and Brazil.

The Bengué catchment has a size of ca. 933 km² and the elevation reaches from 400 to 800 m above sea level. The Bengué catchment is a sub-catchment of the Orós catchment, which is a sub-catchment of the Jaguaribe watershed. The main river of the watershed is the Umbuzeiro River, with its main tributaries Juazeiro, Graganta, Boa Vista, Gameleira and Mulungu. The Umbuzeiro River disembogues into the Bengué reservoir. The Bengué reservoir has a storage capacity of 19.6 million m³, an area of ca. 350 ha. and an average inflow of 30 million m³ water per year (Müller et al., 2005). Along with the Bengué reservoir, many small reservoirs and ponds are spread over the catchment (see Figure 1). The Bengué catchment is located in one of the hottest and driest regions of Ceará. Most parts of the Federal State of Ceará are part of the “drought polygon” which is characterised by a semi-arid climate of high variability and a reoccurrence of droughts (Ponce, 1995). The climate is controlled by the position and shift of the Innertropical Convergence Zone (ITC), which specifies the occurrence of the dry and rainy season (see, e.g. Werner and Gerstengarbe (2003)). The climate for the catchment is defined after the Köppen Climate Classification System as a “Bs”-climate (Frischkorn et al., 2003). It is a semi-arid climate with an annual rainfall of 565 mm (1979-2006) and potential evapotranspiration of 2,200 mm (Frischkorn et al., 2003). It is characterised by high hydrological water deficiency and high spatial, seasonal and interannual variability of rainfall. For the period of 1979-2006, the maximum annual precipitation was 1265 mm, the minimum value amounted to 212 mm. The interannual coefficient of variation (Cv) is 0.40. The seasonal distribution of the rainfall is divided into rainy and dry seasons. The rainy season is between December/January and May. The main proportion of the annual rainfall is concentrated on single rainstorm events with maximum values of over 160 mm. During the dry season, the precipitation is negligible. The monthly coefficient of variation is 1.45 (see Figure 2). Real evapotranspiration is 78 % of the precipitation; the rest splits up in 13 % runoff and 9 % percolation (SUDENE, 1980). The mean annual temperature is around 25° C, with low daily and seasonal variability. The hydrological characteristic of the catchment is characterised by extremely variable and highly local rainfall.

3

The dominant runoff is the Horton overland flow. Only little subsurface flow or groundwater recharge occurs in the catchment. Due to the lack of base flow and important groundwater reservoirs the rivers are ephemeral rivers, with water flow only during and shortly after rainstorm events. The effective runoff generation is mainly concentrated on single, intense rainstorms events causing in combination with high rates and a rapid initiation of overland flow runoff flash floods with sharply peak-shaped hydrographs. In general, small or local rainfall events occur frequently, but do not contribute to the effective runoff due to re-infiltration, transmission loss and the storage of water in small reservoirs and ponds (Bull and Kirkby, 2002; Nanson et al., 2002).

1 2 3 4 5 6 7 8 9 10 11 12

0

100

200

300

400

500

Mea

n m

onth

ly p

rcip

itatio

n [m

m]

Month Figure 2: Monthly distribution of rainfall at the Aiuaba climate station (1979-2005). Boxes are limited by the

lower and upper percentile, red line = mean, whiskers marks 10th and 90th percentile and crosses are outliers.

The natural vegetation of the Bengué catchment is Caatinga. “Caatinga” is the Topi-Indian word for “open forest” or “white forest”. The translation “open forest” refers to the physiognomy of Caatinga, with its characteristically patchy coverage, whereby “white forest” is probably derived from the white barks of many trees. Caatinga describes a vegetation complex typical for the semi-arid Northeast of Brazil. Caatinga is a deciduous dry forest and scrub vegetation, with small medium-sized trees, thorny shrubs and extensive patches of bare soil or rock. No final conclusion about the genesis can be drawn (Daly and Mitchell, 2000). It is not known, whether the Caatinga is either the natural vegetation of the Northeast of Brazil or the vegetation that developed under human interference and became an open forest only through population pressure (Andrade-Lima, 1981). Nevertheless, the area has been populated for more than 10,000 years and the population pressure, with subsistence agriculture and extensive husbandry, has had a considerable influence on the present appearance of the Caatinga (Sampaio, 1995). The physical environment of the Caatinga is conditioned by prevailing semi-arid climate. Caatinga species developed different adaptation mechanisms for these conditions. Most species of the Caatinga are deciduous to reduce the water consumption during the dry season. They developed water storage organs, like swollen trunks, underground storage tubers or can store water in their leaves. Other species are succulent Cactaceae. Only a few species keep their leaves during the dry season. Evergreen vegetation can be found predominantly along the riparian zone, with deep sediments and the ability of water storage. In general, the Caatinga vegetation has sclerophyllous and mesophytic leaves. Only limited information is available about the root architecture, but generally, it can be

4

stated that the percentage of root biomass to total plant biomass is higher compared to wet forests (8-50%) (Holbrock et al., 1995). The occurrence of the Caatinga is unconditional to soil and geological properties, i.e. Caatinga appears on deep sandy soils as well as on flat clayey soils, on sedimentary bedrock as well as on crystalline basement. However, different environmental conditions have an influence on the physiognomy and composition of species, so that the Caatinga can be classified in different Caatinga types (Eiten, 1982; Rizzini, 1997; Rodal and Sampaio, 2002; Salgado et al., 1980). During the last century, different classification systems for the Caatinga were developed. These classification systems are mainly based on the physiognomy and/or flora. Lützelburg (1922) was one of the first who introduced a classification system and divided the Caatinga in arboreal and shrubby Caatinga with several subclasses. Several other classification systems followed. The RADAMBRASIL (1983) classification system is more sophisticated and divided the Caatinga into dense Caatinga, open Caatinga and park steppe, whereby the occurrence of the different classes is correlated to the degree of aridity. Andrade-Lima (1981) divided the Caatinga biome into the following six different classes with twelve subunits: tall Caatinga forest, median and low Caatinga and open arboreal Caatinga, low Caatinga, tall to low shrubby Caatinga, low shrubby Caatinga, valley Caatinga. He stated that the basic factor for the occurrence of the different classes is rainfall, followed by geo-pedological factors, the xerothermic index and topography. Then Eiten (1983) introduced eight different Caatinga classes: woody Caatinga, copse Caatinga, closed bushy-arboreal Caatinga, open bushy-arboreal Caatinga, closed bushy Caatinga, open bushy Caatinga, savannah and open savannah. Other classification systems are presented by Salgado (1980), Rodal and Sampaio (2002), Bezerra et al. (2003) and Rizzini (1997). In general, the delimitation of one class to another is difficult, because different vegetation classes gradually pass from one to another. They are not discrete units and only a few of the classes can be spatially allocated. It can be summarised that “arboreal-bushy Caatinga” or “Caatinga proper” is the most common form of Caatinga, with thorny shrubs from 3-5 m and occasional trees 6-8 m tall, “arboreal Caatinga” is used for arboreal to shrubby Caatinga, with tree cover ranging from 10-60 % and “seridó” is a short grass savannah with deciduous shrub (Eiten, 1983). These different classification systems indicate that until now no common classification system for the Caatinga biome is available. In general, the classification systems are based on the proportion of trees, bush and herbaceous vegetation and the fractional vegetation cover of each vegetation type. It is assumed that composition and occurrence of the different vegetation classes is generally correlated to rainfall rate. Concerning remote sensing-based land cover classification, several studies have developed different classification systems for the Caatinga biome (see e.g. (Sousa, 2003; Teotia et al., 2003)). Some studies classify the Caatinga on the basis of the fractional vegetation cover, like dense, semi-dense and open Caatinga (Almeida et al., 2001; Petta et al., 2005). Other studies use physiognomic criteria in their system to classify the Caatinga. A new and comprehensive classification approach of Carvalho and Freitas (2005) and Carvahlo and Júnior (2004) will be presented below. Carvahlo and Júnior (2004) developed a method for the semi-detailed mapping of the Caatinga biome on the basis of satellite images and applied it to three different study areas located in Ceará/Piauí, Paraíba and Pernambuco. They introduced six main thematic classes, which will be presented in the following paragraphs. Dry forest (mata seca) occurs in areas with rainfall over 800 mm which are generally areas with altitudes between 600 and 2,000 m. More than 50% are evergreen species. It is characterised by a

5

nearly closed high tree layer with trees over 8 m, a low tree layer (between 4 and 8m), a shrub and an herbaceous layer. Carrasco is deciduous, dense, not thorn-bearing, bushy vegetation. This vegetation forms a transition from the Caatinga to the cerrado and is characterised by high proportion of dense woody trees, with fine trunks and almost an absence of Cactaceae or Bromeliaceae. Its occurrence is bound on mountainous ranges, with altitudes varying between 400 and 650 m and softly undulated relief, on a climatic transition zone, with summer-autumn rain (January-April), a dry period of 7 to 8 months and an annual average precipitation of 850 mm and on deep sandy soils, with low fertility. The species are not specific and are found in dry forest or arboreal-bushy Caatinga as well as in the Cerrado. Carrasco has an herbaceous, a closed bushy and closed tree layer, whereas the tree layer can be subdivided in three substrata, the emergent one with trees more than 8 m, an intermediate stratum (4-8 m) and lower one consisting of individual trees with less than 4 m. Wood Caatinga (Caatinga florestada or Caatinga hipoxerófila de transição) is a vegetation of transition, located between two or more ecological regions or vegetation types, e.g. where Caatinga disappears and the tropical forest starts. It occurs in areas with heights between 400 and 800 m, a mountainous, strong undulated relief and a mean annual precipitation ranging from 800 to 1,000 mm. Tree Caatinga (Caatinga arbórea) is a deciduous vegetation type characterised by a more or less dense vegetation, with an average height of 5 m, ramified, thorn-bearing branches and three different strata (arboreal (3-8 m), bushy (0.5-4.0 m) and herbaceous (<0.5 m)). This type of vegetation has a great adaptability to different climatic and geo-physical conditions. The climate varies from humid to semi-arid, with rainfall rates of 500 to 1,000 mm and a summer-autumn rain-season. It can be found in landscapes with altitudes varying from 300 to 600 m, with plain to strong undulated relief and on different soil types (Podzol, sandy sediments, Latosols or Solonetz). Only a few specific species appear in this class and therefore, the class (like the woody Caatinga) shows a great affinity to the other types of Caatinga. Tree-shrub Caatinga (Caatinga arbórea-arbustiva) represents the same floristic characteristics of the previous vegetation type, whereby the individual classes that compose it are smaller. The climatic and geological characteristics are similar. Herbaceous Caatinga (Caatinga herbáceo-lenhosa) differs from the other subsections, because its typical physiognomy consists of a dense herbaceous layer with grassy-woody hemicryptophytes and only a few bushes. It can also be considered a degraded Caatinga. For the previously described vegetation classes, Carvahlo (2005) introduces a sub-classification system, dividing each class according to its degree of conservation. Except for these classes he defined three other classes (arboreal complex, arboreal-bushy complex and complex gallery) to catch the transition of one class to another and to take into account problems due to remote sensing technique. He then defines classes for the land affected by human beings: agricultural and husbandry land, urban areas, exposed soils and water bodies (Carvalho and Júnior, 2004). In the context of northeast Brazil, the Bengué catchment is located in a transition zone of geological and geomorphological conditions. The Bengué catchment covers two different geo-environmental units: the Sertões (Hinterland) in the eastern part and Altos Planaltos Sedimentares (high sedimentary plateaus) in the western and southern part (Nogueira and Oliveira, 2003). Both units are separated by an escarpment, a transition from Precambrian crystalline basement to Phanerozoic sedimentary series striking from East to West (Cavalcante and Ferreira, 1983). In each geo-environmental unit, the landscape elements differ significantly and will both described

6

briefly. A strict border definition is difficult, due to the overlay of different fragments, e.g. the occurrence of Inselbergs in the Hinterland.

Figure 3: Geological units and soil types in the Bengué catchment

The geology of the Hinterland Unit is characterised by the crystalline complex, consisting of Granite, migmatitic and banded Gneiss with fascis of Mica Schist. The crystalline fascis are impermeable, while Mica Schist is a groundwater conducting layer (Cavalcante and Ferreira, 1983; Pereira, 1982). In this unit, the dominating soil types (after the Brazilian soil classification system) are Solos Litólicos (Neossolos), Bruno nao Cálcico (Luvissolos) and Planossolo solódico (Planossolos) (Pereira, 1982). In general, these soil types tend to be shallow and have a relatively high clayey and coarse soil material content and average soil fertility (FUNCEME, 2001). A dentritic river system structures the area in a hill country with predominant erosion forms, truncated rocks, low relief energy and long smooth hill ridges, while perpendicular to these ridges, the hillslopes are steeper (Souza, 1982). The Altos Planaltos Sedimentares Unit is characterised by plateaus, mesas and distinctive valley systems. Mesas are elevated areas with flat tops, steep hillslopes, horizontal strata and a protective cap of erosive resistant rocks (Leopold, 1995). Distinctive valley systems structure the landscape into vast flat plateaus, steep slopes and fluvial plains. The geological units at the plateaus and the top of the mesas, consist out of alluvial sedimentary rocks, coarse grained sandstone and conglomerate and, at the bottom of the valleys, migmatitic Gneiss and banded Gneiss are dominant (Cavalcante and Ferreira, 1983). The dominant soil types in this unit are Vermelho-Amarelos Distrófico (Latossolos) and Planossolo solódico (Planossolos). In general, the soils at the plateaus are highly permeable, resulting in a scarcity of surface water, and low fertility (FUNCEME, 2001). At steep hillslopes, they consist mainly of regolith and, at the valley bottom, the crystalline basement is covered by deep grounded sediments. 3 Land cover classification of the Bengue Catchment Land cover classification through remote sensing (multispectral classification) is “the process of sorting pixels into a finite number of individual classes, or categories of data, based on their data file values” (ERDAS, 2003). The land cover classification process is divided into the training and classification process. Training is the process of finding and defining the mathematical criteria by

7

which spectral patterns are recognised. The result of the training process is a set of signatures, whereby each signature corresponds to a class. These signatures are used to sort each pixel of the image into a class after a defined classification decision rule. A decision rule is a mathematical algorithm, which assigns each pixel to a class (ERDAS, 2003). Methods: For the Bengué catchment, no pre-existing information about land-use and vegetation was available. An unsupervised classification (ISODATA algorithm) was carried for ASTER imageries for an adequate preparation of the field survey with the aim to pre-define land cover classes and to check the separability of the different classes. Along with the pre-definition of classes, the ASTER mosaics from the 21.07.03 and the 16.09.03 was segmented into objects using the eCognition software (Baatz et al., 2001). This software allows the segmentation of multi-spectral image into separated regions (objects). The segmented images were used as a mapping basis of the Ground Truth Data (GTD) collection. The advantage of using already segmented images is that the mapping is faster and more accurate, since during the segmentation process already similar spectral pixels are grouped and it is only necessary to determine a group of pixel. During the field campaign from April to May 2005, the pre-definition of land cover classes were compared to the reality. Around 15 land cover classes were considered as separable. GTD was collected based on the pre-definition of the classes and the segmented satellite image and using a mobile GIS consisting of a Trimble GeoXM GPS receiver and a field computer combined with the software ESRI ArcPadTM 6.0. Spectral signatures were developed from GTD and the three images were classified using a maximum likelihood classifier. Results Finally 12 different land cover classes were distinguished as shown in the land cover map in Figure 4 and the succeeding photographic material: 1) permanent water bodies, 2) seasonal waterbodies, 3) roads and exposed soil, 4) urban area, 5) agriculture, 6) pasture and derelict land, 7) degraded tree-shrub Caatinga, 8) preserved tree-shrub Caatinga, 9) dry deciduous forest, 10) preserved Carrasco, 11) degraded Carrasco, 12) regenerated Carrasco. Agriculture areas and exposed soils were treated as the same class, because they do not differ spectrally from another.

Figure 4: Land cover map of the Bengue catchment

Permanent waterbodies: This class summarises all areas which were identified in all satellite images as water (all images of the multi-temporal image had to be classified as water). Around

8

160 reservoirs were identified in the Bengué catchment. The biggest reservoir is the Bengué reservoir and the smallest identified reservoir has a surface area only of 212 m².

Figure 5: Permanent waterbodies (Bengué reservoir)

Seasonal waterbodies: This class contains all seasonal waterbodies. Seasonal waterbodies are waterbodies which run dry during the dry season. In this study all waterbodies were classified as seasonal waterbodies, when at least one image of the multi-temporal image was classified as water. In general, permanent waterbodies are surrounded by seasonal waterbodies, whereby small ponds and reservoirs are seasonal waterbodies.

Figure 6: Seasonal waterbodies

Urban area: Only two bigger villages, Bom Nome and Barra, are located in the Bengué catchment. All other urban areas are small villages consisting of a couple of houses, with no infrastructure, like water pipes or fortified roads. Many homesteads and single houses in the area are not considered in the land cover map.

Figure 7: Urban Area (left: Bom Nome)

9

Agriculture: This class summarises the areas of agricultural use. Small agricultural fields are not considered in this class. The dominant crops are: cotton, maize and beans. Apart from these main agricultural crops many other plants are cultivated, like Guava, Banana, Manioc and Lanting de Momona (castor bean plant), a raw material for the production of bio-diesel, which is promoted by the government. No distinction between the different crops are made, because the agricultural crops consist of a mixture of cotton and maize, beans and maize, maize or beans and cashew and maize. The sowing time of maize and beans is January and the harvest time is April to May. Cotton has the same sowing time but is harvested in May to June. Cashew is harvested in August to September. In general, urban areas and homesteads are surrounded by agricultural land. The southern plateaus of the Bengué are extensivly used by agriculture. In this area the main crops area maize and cashew.

Figure 8: Agriculture (left: maize and cashew; right: beans)

Pasture and derelict land: This class corresponds to what some authors called class Agropecuária (Carvalho and Júnior, 2004). The class summarises a complex gallery of different land-uses. In general, the transition from intensive used land to natural vegetation is gradual. This class covers the transition zone from intensively agricultural use to natural vegetation and includes land-cover, such as pasture, small agricultural plots, derelict land, and grassland and dry season riparian meadows. It is a heterogeneous class, where the mean vegetation height is estimated at 1 m.

Figure 9: Pasture and derelict land

Preserved Tree-Shrub Caatinga: Some authors call this class Caatinga arboreá-arbustiva class (Carvalho and Júnior, 2004), Caatinga arboreá (Figueiredo et al., 1999) or Savana-Estépica Arborizada (IBGE, 1992) class. It is a deciduous vegetation type, consisting of a more or less dense mixture of trees, bushes and cacti, with ramified and thorn-bearing branches. Generally, the vegetation can be divided into three different strata: a open tree stratum, with a mean height of around 4-5 m and single trees reaching more than 7 m, a bush stratum ranging from 0.5 to 3.0 m

10

and a mean height of 1.5 2.0 and a herbaceous stratum. The mean height of the vegetation is assumed to be 3.8 m. It occurs mainly in the eastern part of the Bengué catchment between 330 m and 650 m. The relief is plain to strong undulate. Some typical species (popular name) are jurema-preto, marmeleiro-preto, canafistula, juazeiro, mameleiro-branco and cansanção.

Figure 10: Preserved Tree-Shrub Caatinga

Degraded Tree-Shrub Caatinga: The physiognomy of this class corresponds to the previously described class. Generally, more open patches can be found and the proportion between bushes and trees is higher. The tree stratum is not so distinctive and in some areas missing. The mean vegetation height is assumed to be 2.6 m. This vegetation type represents the transition class between conserved tree-bush Caatinga and the pasture and derelict land class. It is mainly found in the eastern area of the catchment on undulated relief.

Figure 11: Degraded Tree-Shrub Caatinga

Dry deciduous forest: This vegetation class corresponds to the vegetation class Floresta Estacional Caducifólia (Andrade-Lima, 1981) or Floresta seca (Figueiredo et al., 1999). It is a dense vegetation type, with a relative mean height of 4-5 m. It consists of a more or less dense tree layer, a dense, partly impenetrable bush layer and at diaphanous spots an herbaceous layer can be found. This vegetation type occurs in the south-eastern part of the Bengué catchment mainly in the area with the dissected plateaus. The relief is strongly undulated with small valleys and steep hillslopes. The height of the landscape varies between 450 and 670 m. Some typical species (popular name) are Angico-Preto, Amarelo-Verdadeiro, Mameleiro-Branco, Jurema-Branca and Jurema-Preto.

11

Figure 12: Dry deciduous forest

Preserved Carrasco: The name of this vegetation class was taken from Figueiredo et al. (1999) and Carvalho and Júnior (2004). This class correspond to Vegetação arbustiva densa caducifólia não espinhosa (IBGE, 1992) or Vegetação de tabuleiro (FUNCEME, 2005). This vegetation class is constituted of deciduous and evergreen species. It represents transition vegetation from the Caatinga biome to the cerrado biome. The environmental conditions are generally defined through a higher rainfall. It is a dense vegetation type and structured into the following layers: a tree layer (5-6 m), with nearly 100 % coverage and several trees reaching more than 10 m, a dense bush layer and an herbaceous layer is almost absent. The estimated mean vegetation height is 5 m. This vegetation type can be found in the West and Southwest of the Bengué catchment. The occurrence of the vegetation class is strongly correlated to the mesa. This vegetation type appears especially on steep slopes, but also covers the plateau area. In the catchment it covers a height range from 500 to 700 m. Few species are thorn-bearing. Some examples are (popular name): Amorosa, Marmeleiro-Branco, Jurema-Branca, Jurema-Preto, Jurubeba-Roxa, Alho-Bravo.

Figure 13: Preserved Carrasco

Degraded Carrasco: This vegetation class has the same characteristics of the previous class, but is disturbed by human impact. The tree layer is not as distinctive as in the previous class and some open patches can be found. The estimated mean vegetation height is 3.5 to 4.0 m. This class occurs mainly at the plateau regions with a relatively flat relief and forms the transition between intensively used land and natural vegetation. It can be found at altitudes ranging from 520 and 730 m.

12

Figure 14: Degraded Carrasco

Regenerated Carrasco: The characteristics of this class are the same as from the preserved Carrasco class. 15 to 20 years ago the vegetation of this class was clear-cut. The vegetation is regenerating from the clear-cut and in different areas different succession levels can be found, depending on the clear-cutting time. In general, the tall tree layer is missing or is just growing, but the bush and small tree layer is very dense, depending on the succession level. The mean vegetation height is estimated at 3. The occurrence of the vegetation class is restricted to the west of the catchment and to the plateaus of the mesas. The altitude varies between 630 and 780 m.

Figure 15: Regenerated Carrasco

4 Derivation of the Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) In the field of remote sensing application, vegetation indices (VI) have been developed for the qualitative and quantitative description of the vegetation cover. VI are based on the principle that vegetation has a characteristic spectral response; green leaves absorb most of the red spectrum, but reflect most of the green spectrum. The spectral response of vegetation contains information about water content, age, mineral deficiencies parasitic attack, cover geometry, orientation of leafs and leaf distribution. The spectral response of vegetated areas is also affected by the spatial-temporal variation of the atmosphere, the underlying ground (soil moisture, soil brightness, soil colour) and of shadows (Bannari et al., 1995). Different vegetation indices are developed to obtain the relevant information from the spectral responds. Vegetation indices are widely used for different applications. Vegetation indices are used to detect land use change, for crop discrimination or for estimation of model parameters in environmental studies (Bronge, 2004). Fractional Vegetation Cover (FVC) is defined as “the vertical projection of the crown or shoot area of vegetation to the ground surface expressed as a fraction or percent of the reference area”

13

(Purevdorj et al., 1998). Regarding remote sensing techniques, different methods, like NDVI-regression method or endmember models, were developed (for details see, e.g. Xiao (2005)). Leaf Area Index (LAI) is defined as “half the total green leaf area (one-sided area for broad leaves) per unit ground surface” (Bronge, 2004). Stem Area Index (SAI) “is the one-sided stem area per ground area, where “stem” includes dead leaves, branches, and stems” (White et al., 2000). Plant Area Index (PAI) is the sum of LAI and SAI, which is “the plant area per ground” (White et al., 2000). The strict differentiation of LAI, SAI and PAI is not always used in the literature. In general, the aim is the estimation of LAI, because green leaves are the most important parameter concerning plant canopy interaction with the atmosphere, but frequently measurements (optical methods) are distorted by stems and dead leaves and a strict differentiation between LAI, SAI and PAI is not always possible. Methods: Different techniques were developed for the estimation of LAI. In general, these techniques are divided into direct and indirect measurements. Direct methods, like harvesting, tree model or litter trap methods, are the most accurate ones, but extremely time-consuming and they are mostly used for calibration or validation of other methods. The indirect methods are divided into contact (inclined point quadrate or allometric techniques) and non-contact (optical) measurements (Jonckheere et al., 2004). The optical method can either be done by ground (DEMON, Ceptometer, LiCor, LAI-2000, TRAC) (Gower et al., 1999; Welles and Cohen, 1996) or by remote sensing-based measurements. The latter is based on the principle that the red and near-infrared transmittance ratio is correlated with the LAI (Jordan, 1969). These methods can be divided into estimation of LAI by physical or by empirical modelling. The physical model is based on radiative transfer equation (Qi et al., 2000). Empirical models are usually regression models, derived by estimating the LAI during ground measurements and relating them to VI. A commonly used VI is the Normalized Difference Vegetation Index (NDVI), whereby different VI-LAI relationships have been found (Cohen et al., 2003; Fensholt et al., 2004; Kalacska et al., 2004; Nagler et al., 2004; Qi et al., 2000; Turner et al., 1999; Xavier and Vettorazzi, 2004). Generally, for each vegetation type, a VI-LAI relation had to be established. A major limitation of this approach is the sensitivity of VI to vegetation and non-vegetation factors, like canopy geometry (leaf angle distribution function, row orientation, and spacing), shading or leaf and soil optical properties (Qi et al., 2000). Another limitation is that indices generally reach a saturation level asymptotically for LAI ranging from 2 to 6 (Bronge, 2004). For the Bengue Catchment, the normalized difference vegetation index (NDVI) was calculated for all georeferenced and calibrated scenes using ASTER and CBERS imagery. The mean FVC of each land cover class was estimated on the basis of the approach of Carlson and Ripley (1997). They use an approach based on the correlation of the scaled NDVI and the fractional vegetation cover. For the estimation of the PAI the regression model developed by Corrêa et al. (2002) was used. Corrêa et al. (2002) developed this approach for the Caatinga biome in Rio Grande do Norte/Paraíba, Brazil. They estimate the PAI with a LAI-2000, correlate the measurements with the NDVI calculated from a Landsat scene from a rain season. Under the assumption that the FVC and PAI is correlated with rainfall, a simple linear relation between FVC and rainfall is obtained by correlating the FVC and PAI with the mean annual precipitation of four different rainfall stations (Aiuaba, Fazenda Nova, Bom Nome and Barra) taking into account the rainfall of the previous month.

14

Results: Table 1 shows the FVC and PAI for different vegetation covers and dates derived from the correlation of the scaled NDVI and the fractional vegetation cover. The satellite scene from 16.09.03 covers only the eastern part of the catchment and no values for the Caatinga and the dry deciduous forest are available. These data formed the basis for the development of empirical models which are representing the temporal distribution of NDVI, PAI and FVC as displayed in Figure 16. All available data were used for the development of the model and therefore, a validation of the derived models was not possible. The estimated FVC and PAI values for different vegetation classes and dates are relatively low, but considering that they are representing mean plot-level values (bare soil and vegetation), they sound reasonable and comparable to the measured values of Corrêa (2002). The derived temporal models of NDVI, PAI and FVC represent the general temporal development of the vegetation characteristics. Considering that the model input data contain insecurities and that the models were only developed on the basis of seven measurements, the results are reasonable. They are considered to improve the available data availibility for the Bengué catchment and therefore, they are justified, but should, however, be used cautiously.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 310

200

400

600

Mon

thly

P

rec.

[mm

]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31-0.2

0

0.2

0.4

ND

VI

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 310

1

2

PA

I [m

²/m²]

Caatinga arbustiva-arbórea conservadaCaatinga arbustiva-arbórea pertubadaFloresta secaVegetacao de tabuleiro conservadaVegetacao de tabuleiro dissecadaVegetacao de tabuleiro regeneracao

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 310

50

100

Month (Feb. 2003 - Aug. 2005

Frac

. Veg

etat

ion

Cov

er [%

]

Figure 16: Modelled temporal distribution of Normalized Difference Vegetation Index (NDVI), Plant Area Index (PAI) and Fractional Vegetation Cover (FVC)

15

Table 1: Fractional Vegetation Cover (FVC) and Plant Area Index (PAI) for different vegetation classes and dates

Vegetation class AST

ER

21

.07.

03

AST

ER

16

.09.

03

CB

ER

S 07

.07.

04

CB

ER

S 28

.08.

04

CB

ER

S 14

.11.

04

CB

ER

S 10

.06.

05

CB

ER

S 01

.08.

05

preserved tree-shrub Caatinga 4.17 15.48 0.74 0.81 2.95 0.14degraded tree-shrub Caatinga 3.76 13.39 1.41 1.35 3.06 0.50dry deciduous forest 17.94 11.94 70.89 17.46 3.31 41.25 3.75preserved Carrasco 16.32 11.46 56.51 23.84 11.90 38.79 8.66degraded Carrasco 16.74 18.08 72.64 52.73 29.44 71.99 33.77

FVC [%]

regen. Carrasco 2.22 15.15 65.51 63.15 41.88 85.24 37.26preserved tree-shrub Caatinga 0.75 1.07 0.60 0.61 0.84 0.59degraded tree-shrub Caatinga 0.74 1.04 0.62 0.62 0.84 0.61dry deciduous forest 0.91 0.74 1.77 0.75 0.65 1.18 0.65preserved Carrasco 0.89 0.74 1.60 0.79 0.69 1.17 0.70degraded Carrasco 0.90 0.78 1.79 0.91 0.75 1.35 0.83

PAI [m²/m²]

regen. Carrasco 0.69 0.76 1.73 0.97 0.78 1.47 0.84 5 Derivation of a hydrological-sound digital elevation model (DEM) According to Burrough (1986), a Digital Elevation Model (DEM) is a representation of relief over space in a digital form. DEMs are abstractions of 3-dimensional surfaces by storing the relief information in single z-values for each x- and y-coordinate pair. A DEM represents the relief of the bare soil earth surface, whereby a digital terrain model (DTM) represents the earth surface, with all objects, such as buildings or vegetation (Jacobsen, 2003). The DEM data structure can either be an elevation matrix like a raster DEM or the data can be stored in a vectorised form, such as Triangular Irregular Networks (TINs) or points. DEMs play a fundamental role in all disciplines of scientific research involving studies of earth’s land surface. Especially since the increased use of GIS in geo-scientific researches, the use of DEMs has become more and more important. In hydrology and erosion studies, the terrain is one main parameter controlling many different processes. For example, DEMs are used for calculation of the slope which controls the portioning of the different kind of runoffs, or for catchment and stream network delineation. In remote sensing, DEMs are used for example for ortho-rectification of aerial or satellite images or for the support of the land cover classification process. Different applications of DEMs have different requirements on DEMs. Since DEMs are always an approximation of the earth surface and with associated errors, the DEM generation process should meet these requests. Generally, five different methods are used to gather input data for the generation of DEMs: point measurements during a field survey, digitizing of topographic maps, photogrammetric stereo mapping, side-aperture radar mapping or altimetric laser scanning. For DEM generation from these input data different methods are used, whereby the complexity of the processing method increases within the listed order (Jacobsen, 2003; Oksanen and Jaakkola, 2000). The last three methods have in common that they generate DTMs and only through post-processing and filtering it is possible to derive DEMs. Point height information can be measured through traditional survey techniques or through the up-to-date GPS (Global Positioning System) or DGPS (Differential GPS) technique. The problem

16

here is the very high expenditure of time to gather an appropriate amount of point measurements for the interpolation process. Interpolation methods depend on the input data and the desired output DEM. Interpolation techniques range from simple methods like building TINs, a linear interpolation technique and Inverse Distance Weighting or Spline to complex interpolation techniques like Kriging or Co-Kriging. Nonetheless, the method is still necessary for accurate DEMs which are required by flood inundation modelling or volume change calculation from DEMs. Point measurements can also supply data for the accuracy assessment of other DEM generation techniques. Contour data are a common source for DEM generation, thanks to the good availability of topographical maps for most areas of the world. The problem is that contours contain only explicit height information along the contours and implicit information between them. Therefore, the information of the contours has to be interpolated to generate a DEM from topographic maps. Different interpolation techniques are available. The Delaunay triangulation technique is a linear interpolation technique, whereby the contours are converted into mass points. Another interpolation technique is the method developed by Hutchinson (1989), called TOPOGRID which is based on the inexact spline interpolation and is especially developed for the generation of hydrologically sound DEMs. The advantage of the method is that other data such as river networks can be considered in the interpolation process. The photogrammetric stereo mapping procedure is based on the simple principles of geometry described by Pythagoras. For this procedure, at least two optical images showing the same area from different directions are required. The derivation of height information from these stereo images is either done manually which is very time consuming or by automated stereocorrelation. The principle of automated stereocorrelation is to calculate the parallax differences and to derive the height from stereo images (Schnirch, 2002). The implementation of this principle depends on the software, but generally the procedure can be divided into three steps: the collection of GCPs and the registration of the images, the determination of parallax differences using the automatic image matching technique and the post-processing to remove artefacts (Eckert et al., 2005). Automated stereocorrelation is either applied to aerial or satellite stereo images, depending on the desired scale and resolution of the DEM, whereas aerial photos are used for small scale DEMs and satellite images for medium to large scale DEMs. Therefore, the focus will be on DEM generation from satellite images. Different satellite systems have different techniques to acquire stereo images. Generally, they can be divided into systems, which provide across-track images, and systems which record stereo images in an along-track stereo mode. A common across-track system for DEM generation is the SPOT satellite system, and the along-track system is the ASTER satellite sensor. Along-track systems have usually the advantage that the time gap between the acquisition dates of the stereo images is negligible and therefore, both images are recorded under the same physical (same azimuth, same atmosphere,…) and environmental (no change in land cover) conditions (Jacobsen, 2003; Wang and Trinder, 1999). The last two methods for DEM generation, radar mapping and laser scanning, are based on the phase shift of the reflected signal, which depends on the distance between surface and sensor. A frequently used DEM derived from these methods is the DEM from the Shuttle Radar Topographic Mission (SRTM), owing to the fact that the mission provided DEMs for the world from 58° south to 60° north. The principle of the SRTM is based on the method of Interferometric Synthetic Aperture RADAR (InSAR), that is, separating the mixed signal by means of the Doppler method (Büyüksalih et al., 2005; USGS, 2006a). This section focuses on the generation of DEMs from ASTER images for the entire Bengue Catchment and discusses the post-processing method to generate an appropriate DEM for further modelling. It is assessed, whether an improvement of the data availability for further modelling

17

can be achieved, by comparing the derived ASTER DTM with other DEMs. Further on, the quality of the ASTER DTM is assessed comparing the DTM with mapped hill slope profiles and GPS measurements. Method: The procedure of DTM generation was divided in different phases: the acquisition of satellite scenes, GCP collection and image rectification, DTM generation using automated stereocorrelation and post-processing. ASTER images are suitable and have several advantages for the DTM generation. The ASTER sensor records stereo images in the along-track mode, using the nadir (3N) and the back-looking (3B) channel. Both channels record in the same near-infrared wavelength form 0.78 to 0.86 µm, covering a 60x60 km² area at 15 m spatial resolution. The height of Terra orbit is 705 km and the tilt angle of the back-looking channel 27.6°. The time interval between the records of both channel of the same area is approximately one minute and the height base ratio is 0.6. A literature review showed that the accuracy of DEMs of ASTER images is adequate for further modelling. A residual mean square error of ±7 m to ±15 m can be expected, depending on the modelled terrain, the algorithm used and the input data (Eckert et al., 2005; Hirano et al., 2003; Jacobsen, 2002; Kocak et al., 2004; Krupnik, 2000). Three different ASTER scenes were acquired for the DEM generation procedure, since no ASTER image covered the total Bengué catchment. The ASTER scenes from 21.07.2003, covering the eastern part of the catchment and two from 16.09.2003 covering the western part of the catchment were used for the DEM generation. The ASTER scene from 25.10.2000 were processed to a DEM. Due to cloud coverage and the time gap to the other satellite images it was not any further used for the final DEM processing, but is used for the quality assessment of the DEM. The collection of the GCPs was done during the field survey from April to May 2005, whereby changes during the record time of the satellite images and the collection of the GCPs were assumed to be negligible. Around 70 GCPs were collected using a Trimble GeoExplorer. No DGPS receiver was available. To reduce the insecurities of the height measurements of GPS receiver for each GCP, around 10-20 height measurements were made and the arithmetic mean from height measurements were used for the image rectification. The DTM was extracted from ASTER images with the ENVI software (RSI, 2004) using the ASTERDEM extension (CREASO, 2004). The DTM was processed scene by scene. The images were georeferenced using the GCPs and height values were assigned to each GCP. Then the DTM were processed through determination of the parallax differences using automatic image matching technique. The product was an absolute integer DTM. The post-processing was done with ArcGIS and ENVI. During the post-processing DTMs of both images were masked, excluding the areas not belonging to the Bengué catchment. For overlapping areas of both images the mean difference of the DTMs was calculated and added to the ASTER from 16.09.2003, as more GCPs were used to georeference the image from 21.07.2003. The mean difference of DTMs amounted to 1.63 m. A DTM was created covering the total Bengué area by building a mosaic from DEMs with the mosaic blend method implemented in ArcGIS. The DTM was post-processed in two different ways to remove artefacts and anomalies. The first post-processing method is a simple filtering and smoothing approach. This generated DEM will be referred to as “ASTER DEM filtered”. The second method was introduced because the erosion modelling process requires float DTMs and therefore, it was necessary to convert integer DTM to float DTM. A simple conversion from integer to float is not justified, because the result is not a continuous surface but rather a surface with a step character. The other aim of the second post-processing method was to create a hydrologically sound DEM.

18

This can be achieved by removing artificial sinks while still in the process and by considering reservoirs and streams. Generally, for an integer DTM to float DTM conversion, a re-interpolation of the DTM is necessary to create a continuous surface from integer DTM. The general procedure is to convert a raster DTM to a point DTM and then this dataset is subdivided by random selection which is then used for the re-interpolation to a float DTM. A different procedure is introduced here to reduce the information loss and to remove artefacts and artificial sinks. The raster DTM was, on the one hand, converted to a point DTM with an x-, y-, and z coordinate. On the other hand, 1 m spacing contours were derived from the DTM. These contours were processed manually by comparing the derived contours visually with the stream-network and the satellite images. All recognisable artefacts, anomalies and artificial sinks were removed from the contours. Then, the contours were buffered by 2.5 m. The dataset of the point DTM was divided depending on whether a point is located within or outside the buffered contours. Both datasets were then subdivided as usual by a random selection, but with the difference that 60% of the first dataset which were very close to the contours, remained in the dataset, but only 20% of the second dataset, which were not located in the buffer remained in the dataset. Both datasets were merged. The land cover classification process provided information about location and size of reservoirs, whereby waterbodies were divided into permanent and seasonal waterbodies. All points within the distance of 30 m of the waterbodies were extracted from the dataset and all points within a distance of 2.5 m to shorelines of seasonal and permanent waterbodies are used for extraction of height information for the shoreline. The arithmetic mean for each shoreline was calculated and assigned to each shoreline, taking into account the consistency of the dataset. The shorelines were then converted to points and merged with the existing DTM point dataset. This dataset was re-interpolated to a float DEM using the interpolation technique developed by Hutchinson (1989). What follows, this will be referred to as “ASTER DEM interpolated”. Both post-processed DTMs are considered to be DEMs. Results and Quality Assessment Figure 17 shows the ASTER DEM interpolated.

Figure 17: ASTER DEM interpolated and calculated slope of the Bengué Catchment

19

The validation and quality assessment of the DEM was done by different approaches: the comparison of the ASTER DEMs and other DEMs to Tracklog data, the comparison of the DEM among themselves, the visual comparison of DEM shapes and finally, the comparison of DEMs to mapped hillslope profiles. Tracklog data were acquired during the measurement campaign. These are automated gathered GPS measurements, whereby a higher insecurity is associated with the data compared to the GCPs and the data set contains outliers, because they were taken in a dynamic mode. Different DEMs are used for the comparison. The first DEM was created from a digitised topographic map (map scale of 1:50 000, contour spacing of 50 m) using the TOPOGRID function implemented in ArcGIS by interpolating contours and height points into a raster DEM. The second one is a SRTM DEM (USGS, 2006a), with 90 m spatial resolution. The SRTM DEM was masked, rectified to the ASTER DEM and resampled to 15 m spatial resolution. The third one is the DEM from the ASTER image from 25.10.2000. Table 2: Descriptive statistic of differences between Tracklog data and ASTER DEM raw, ASTER DEM filtered, ASTER DEM interpolated, SRTM DEM and the Topo DEM and correlation of heights between Tracklog and the DEMs.

Minimum [m]

Maximum [m]

Mean [m] Std. Deviation Pearson Cor. Coefficient

ASTER DTM raw -29.98 39.63 1.997 9.332 0.988 ASTER DEM filtered -29.27 40.26 2.032 9.043 0.989 ASTER DEM interpolated

-30.28 40.79 2.020 9.114 0.989

SRTM DEM -30.65 34.35 -11.492 7.455 0.993 Topo DEM -87.9 42.13 -23.544 18.304 0.964

Differences of Tracklog data and different DEMs were calculated. The descriptive statistic is summarised in Table 2. The ASTER DEM raw has the smallest mean differences with 2 m. The mean difference increases slightly with the other ASTER DEM, but the standard deviation decreases. The SRTM DEM has a relatively high mean difference, but the lowest standard deviation of all DEMs. Table 2 also includes the calculated correlation between the height of the Tracklog data and the DEMs. The SRTM has the highest Pearson Correlation Coefficient, followed by the two processed ASTER DEM and the ASTER DEM raw. The DEM from the topographic map shows the lowest agreement to the Tracklog data in all values. The comparison of the different DEMs among themselves was done by subtracting the different DEMs from the ASTER DEM raw. The ASTER DEMs are relatively similar. The differences in the mean differences are negligible and the standard deviation is less than 3 m. The mean difference between ASTER DEM raw and ASTER DEM from 25.10.2000 is -4 m and the standard deviation is around 10 m. The mean difference between ASTER DEM raw and SRTM DEM is -14 m and the standard deviation amounts to 7 m. The comparison of contours shows the morphologic quality of the different DEMs very well. An example of the visual comparison can be seen in Figure 18. This figure shows that the surface of the ASTER DEM raw is relatively rough (hackly contours) and has many artefacts (small island contours). In steep areas the slopes are a partly step-shaped, with very close contours. The amount of ASTER DEM filtered artefacts is reduced and the zigzag course of the contours is smoothed out, but this DEM contains large sinks, e.g. see middle of the image at the main valley discharging to the east. For most parts of ASTER DEM, interpolated sinks are removed and the DEM contains only a few island contours. This DEM appears to be smoother than the raw DEM. ASTER DEM interpolated and SRTM DEM are in the visual comparison relatively similar. Here,

20

however, it must be taken into account that the SRTM DEM is resampled to 15 m and appears smoother.

0 500 1000 1500 2000520

540

560

580

600

620

640

Distance [m]H

eigh

t [m

]

Quality Analysis of Profile 10

0 200 400 600 800 1000440

450

460

470

480

490

500

Hei

ght [

m]

Distance [m]

Quality Analysis of Profile 13

0 1000 2000 3000 4000440

460

480

500

520

540

Distance [m]

Hei

ght [

m]

Quality Analysis of Profile 15

0 500 1000 1500450

460

470

480

490

500

510Quality Analysis of Profile 16

Distance [m]

Hei

ght [

m]

ASTER DEM rowASTER DEM filtered

ASTER DEM interpolated

SRTM DEMmapped hillslope profiles

0 500 1000 1500 2000560

580

600

620

640

660

Distance [m]

Hei

ght [

m]

Quality Analysis of Profile 2

Figure 18: Comparison of mapped profiles to profiles of different DEMs

21

Figure 19: Contours of the different DEMs

Figure 19 shows the comparison between mapped profiles and profiles from the different DEM sources. The profiles were mapped by using a barometer for relative height measurements and a Trimble GeoXM mobile GIS for location estimation. The absolute height estimation is based on the GPS measurements, whereby for each profile point, the barometer measurements were subtracted from the GPS height measurement and for each starting point of a profile, the absolute height was estimated by averaging the differences. The profiles show similar characteristics, already represented in the descriptive statistics of the DEMs. The SRTM DEM generally overestimates the earth surface, whereby the ASTER DEMs generally underestimate it. The overall accuracy of the generated DEMs is reasonable considering the input data, the quality of GCPs and difficulties of ASTER images. There are factors that can be optimized: 1) the use of only one satellite scene for the Bengué catchment, 2) the improvement of GCP quality through either post-processing the GCP data by using of GPScorrect software which is especially designed for combination with ESRI® ArcPad™ software to improve the measurements or the use of a real DGPS system, 3) the modification of post-processing, by using e.g. a two dimensional Kalman filter (Wang, 1998) and the height estimation algorithm itself. Generally, nature and technique of photogrammetric DEMs are themselves limiting factors to accuracy. Conversion of DTMs to DEMs through post-processing is extremely difficult and to remove all objects from the earth surface is nearly impossible. The use of two images from two different perspectives results in the fact that no data are available for “backside” slopes of ridges. Regarding hydrology, the surfaces of photogrammetric DEMs are relatively rough compared to interpolated DEMs and are associated with a significant amount of high frequency noise. Especially for hydrological applications it is necessary to post-process DEMs. Through post-

22

processing, it is possible to remove artefacts and smooth the surface, but at the same time the surface is also generalised. Therefore, the post-processing has to find a balance between information loss and artefacts elimination to generate a hydrological sound DEM. In this view, taking into account the results of the quality assessment, the ASTER DEM interpolated represents the terrain of the Bengué catchment best. 6 Conclusion The study showed successfully how remotely sensed data can be used to derive spatially-distributed data on land-use and cover, leaf area index, fractional vegetation cover and terrain properties. It thus provides a valuable source for the parameterisation of hydrological and sediment-transport models for dryland settings such as the Bengue Basin in the drought-triangle of Ceara, Brazil. The full thesis by Creutzfeldt (2006) provides a more comprehensive account on how the here presented information from remotely sensed data were then employed to derive hydrological landscape units and erosion risk map for the Bengue Catchment. Complete study: B. Creutzfeldt (2006) Remote sensing based characterisation of land cover and terrain properties for hydrological modelling in the semi-arid Northeast of Brazil. Unpublished MSc thesis, University of Potsdam, Germany. References Abrams, M., 2000. The Advanced Spaceborne Thermal Emission and Reflection Radiometer

(ASTER): Data products for the high spatial resolution imager on NASA's Terra platform. International Journal of Remote Sensing, 21(5): 847-859.

Abrams, M., Hook, S. and Ramachandran, B., 2002. ASTER User Handbook, Version 2. Jet Propulsion Laboratory/California Institute of Technology., California.

Albertz, J., 2001. Fernerkundung - Grundlagen der Interpretation von Luft- und Satellitenbilder. Wissenschaftliche Buchgesellschaft, Darmstadt.

Albuquerque, A.W.d., Filho, G.M., Santos, J.R., Costa, J.P.V. and Souza, J.L., 2005. Determinação de fatores da equação universal de perda de solo em Sumé, PB. Revista Brasileira de Engenharia Agrícola e Ambiental, 9(2): 153-160.

Almeida, M.G., Rudorff, B.F.T. and Shimabukuro, Y.E., 2001. Mapeamento da cobertura vegetal de áreas de grande extensão através de mosaicos de imagens do NOAA-AVHRR, Anais X SBSR. INPE, Foz de Iguaçu, pp. 377-381.

Alvarenga, R.d.S. et al., 2005. Aplicação comparativa dos produtos dos sensores TM – LANDSAT 5, CCD – CBERS2 e ASTER - TERRA no mapeamento geológico na região do Platô da Ramada, RS., Anais XII Simpósio Brasileiro de Sensoriamento Remoto. INPE, Goiânia, pp. 4537-4544.

Andrade-Lima, D.d., 1981. The caatingas dominium. Revista Brasileira de Botânica(4): 149-153. Antoine, L. et al., 2005. ERDAS IMAGINE 8.7. Leica Geosystems GIS & Mapping. Baatz, M. et al., 2001. eCognition Object Oriented Image Analysis. User Guide. Definiens

Imaging, München, Germany. Bannari, A., Morin, D. and F, B., 1995. A Review of Vegetation Indices. Remote Sensing

Review, 13: 95-120. Baptista, G.M.d.M. et al., 2005. Comportamento sazonal da vegetação da caatinga na região de

Lucrécia, RN, em 2003 e 2004, por meio dos dados do sensor CCD do CBERS-2, Anais XII Simpósio Brasileiro de Sensoriamento Remoto. INPE, Goiânia, pp. 797-804.

23

Becker, A. and Pfützner, B., 1987. EGMO-system approach and subroutines for river basin modelling. Acta hydrophysica 31, 125-141.

Bezerra, C.L.F., Trigueiro, E.R.d.C., Alberto, A., Castro, J.F. and Castro, A.S.F., 2003. Survey of the Vegetation in the State of Ceará. In: T. Gaiser, M. Krol, H. Frischkorn and J.C.d. Aráujo (Editors), Global Change and Regional Impacts: Water Availibility and Vulnerability of Ecosystems and Society in the Semiarid Northeast of Brazil. Springer, Berlin.

Bronge, L.B., 2004. Satellite romote sensing fro estimating leaf area index FPAR and primary production. A literature review, SKB Rapport R-04-24, Stockholm.

Buheaosier, Tsuchiya, K., Kaneko, M. and Sung, S.J., 2003. Comparison of image data acquired with AVHRR, MODIS, ETM + and ASTER over Hokkaido, Japan. Advances in Space Research, 32(11): 2211-2216.

Bull, L.J. and Kirkby, M.J., 2002. Dryland River Charateristics and Concepts. In: L.J. Bull and M.J. Kirkby (Editors), Dryland Rivers

Hydrology and Geomoprhology of semi-arid Channels. Wiley, Chischester. Burrough, P.A., 1986. Principles of Geographical Information Systems for Land Resources

Assessment. Clarendon, Oxford. Büyüksalih, G., Marangoz, A. and Jacobsen, K., 2005. Generation and Analysis of Height

Models based on Satellite Information. Zonguldak Karaelmas University/University of Hannover.

Carlson, T.N. and Ripley, D.A., 1997. On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sensing of Environment, 62: 241-252.

Carvalho, V.C.d. and Freitas, M.W.D.d., 2005. Abordagem integrada para mapeamento da dinâmica da cobertura da terra em três áreaspiloto do bioma Caatinga, Anais XII Simpósio Brasileiro de Sensoriamento Remoto. INPE, Goiânia, pp. 1459-1468.

Carvalho, V.C.d. and Júnior, O.J.P., 2004. Mapeamento semidelhado da cobertura vegetal das áreas de estudo, com uso de sensoriamento remoto e sisteme geográfico de informações, INPE, São José dos Campos.

Cavalcante, J.C. and Ferreira, C.A., 1983. Mapa Geológico do Estado do Ceará (Escala 1:500 000). Ministério das Minas e Energia, Fortaleza.

CBERS, S.S.-B.d.R.T., National Institute for Space Research (INPE). http://www.cbers.inpe.br/en/index_en.htm [Accessed 03/08/05].

Chrysoulakis, N., Keramitsoglou, I. and Cartalis, C., 2003. Hydrologic land cover classification mapping at the local level with the combined use of ASTER multispectral imagery and GPS measurements. In: M. Ehlers (Editor), Proceedings of 10th SPIE Internationl Symposium on Remote Sensing: Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III.

Cohen, W.B., Maiersperger, T.K., Gower, S.T. and Turner, D.P., 2003. An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sensing of Environment, 84(4): 561-571.

Corrêa, T. et al., 2002. Phytomass mapping of the "Seridó Caatinga" vegetation by the plant area and the normalized difference vegetation indeces. Scientia Agricola, 59(4): 707-715.

CREASO, 2004. AsterDTM: Installation and User 's Guide. SulSoft Ltda. Daly, D.C. and Mitchell, J.D., 2000. Lowland Vegetation of Tropical South America -- An

overview. In: D. Lentz (Editor), Imperfect Balance: Landscape Transformations in pre-Columbian Americas. Columbia University Press, New York, pp. 391-454.

DGI, 2005. Divisão de Geração de Imagens: INPE Image Catalog. Instituto Nacional de Pesquisas Espaciais INPE.

24

Eckert, S., Kellenberger, T. and Itten, K., 2005. Accuracy assessment of automatically derived digital elevation models from ASTER data in mountainous terrain. International Journal of Remote Sensing, 26(9): 1943–1957.

Eiten, G., 1982. Brazilian "Savannas". In: H. Walker (Editor), Ecology of Treopical Savvanas. Eiten, G., 1983. Classificacção da Vegetação do Brasil. CNPq, Brasilia. Epiphanio, J.C.N., 2005. Joint China-Brazil remote sensing satellites: CBERS-2. GIM

International, 19(2): 68-71. ERDAS, 2003. ERDAS Field Guide. Leica Geosystems GIS & Mapping, Atlanta. ESRI, E.S.R.I., 2004. ArcGIS, New York. Eweg, H.P.A., Lammeren, R.V., Deurloo, H. and Woldu, Z., 1998. Analysing degradation and

rehabilitation for sustainable land managemenr in the highlands of Ethiopia. Land Degradation and Development, 9: 529-542.

FAO, 1995. Global and National Soils and Terrain Digital Databases (SOTER), FAO. Fensholt, R., Sandholt, I. and Rasmussen, M.S., 2004. Evaluation of MODIS LAI, fAPAR and

the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91(3-4): 490-507.

Figueiredo, M.A., Lima-Verde, L.W., Barros, E.d.O. and Pinto, F.C.B., 1999. Município de Aiuaba - Cobertura Vegetal. PNE, Fortaleza - Brasil.

Foster, G.R. and Wischmeier, W.H., 1974. Evaluating irregular slopes for soil loss prediction, Transactions of the American Society of Agricultural Engineers 17-2.

Francke, T., 2005. Spatial discretisation in semi-distributed hydrological model-ling with WASA using the Landscape Unit Mapping Program (LUMP), Universität Potsdam, Potsdam.

FUNCEME, F.C.d.M.e.R.H., 2001. Solos do Estado do Ceará Classificações Técnicas. Fundação Cearense de Meteorologia e Recursos Hídricos-FUNCEME. FUNCEME, F.C.d.M.e.R.H., 2005. Land cover classification (South of Aiuaba). FUNCEME,

Fortaleza. Gaiser, T., Ferreira, L.G.R. and Stahr, K., 2002. Potential applications of the soil and terrain

information system SPICE in soil degradation risk assessment., Proceedings of the Third International Conference on Land Degradation. Embrapa Solos, Sociedade Brasileira de Ciência do Solo, Rio de Janeiro.

Gaiser, T., Graef, F., Hilger, T.H., Ferreira, L.G.R. and Stahr, K., 2004. An Information System for Land Resources in Piauí and Ceará. In: T. Gaiser, J.C. Araújo, H. Frischkorn and M. Krol (Editors), Global Change and Regional Impacts: Water Availability and Vulnerability of Ecosystems and Society in Semi-Arid Northeast of Brazil. Springer Verlag, Heidelberg, pp. 267-278.

Gower, S.T., Kucharik, C.J. and Norman, J.M., 1999. Direct and indirect estimation of leaf area index, f(APAR), and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70(1): 29-51.

GRASS, D.T., 2005. GRASS. Gregorio, A.D. and Jansen, L.J.M., 2000. Land Cover Classification System (LCCS):

Classification Concepts and User Manual, FAO, Rom. Güntner, A., 2002. Large-scale hydrological modelling in the semi-arid North-East of Brazil,

Universität Potsdam, Potsdam. Hirano, A., Welch, R. and Lang, H., 2003. Mapping from ASTER stereo image data; DEM

validation and accuracy assessment, Challenges in geospatial analysis and visualization. Elsevier, Amsterdam, Netherlands, pp. 356-370.

25

Holbrock, N.M., Whitbeck, J.L. and Mooney, H.A., 1995. Drought responses of neotropical dry forest trees. In: S.H.-. Bullock, H.A. Mooney and E. Medina (Editors), Seasonally Dry Tropical Forests. Cambridge University Press, Cambridge, pp. 243-276.

Hu, J. et al., 2004. Technique of the second satellite of CBERS-1 image multi-stage information extraction on land use and land cover, International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4484.

Hueck, K., 1966. Die Wälder Südamerikas. In: H. Walter (Editor), Vegetationsmonographien der einzelnen Großräume. Gustav Fischer Verlag, Stuttgart, pp. 275-295.

Hutchinson, M.F., 1989. A new procedure for gridding elevaton and streamline data with automatic removal of spurious pits. Journal of Hydrologiy.

IBGE, 1992. Manual Técnico da vegetação brasileira, IBGE - Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro.

IBGE, 2000. Atlas do Censo Demográfico 2000. IBGE - Instituto Brasileiro de Geografia e Estatística.

IBGE, 2005. Produto Interno Bruto dos Municípios, Instituto Brasileiro de Geografia e Estatística - IBGE, Rio de Janeiro.

Jacobsen, K., 2002. Comparison of high resolution mapping from space, INCA, Ahmedabad, India.

Jacobsen, K., 2003. DEM Generation from Satellite Data. Remote Sensing in Transition. Jonckheere, I. et al., 2004. Review of methods for in situ leaf area index determination Part I.

Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1-2): 19-35.

Jordan, C.F., 1969. Deviation of leaf-area index from quality of light on the florest floor. Ecology: 663-666.

Kalacska, M. et al., 2004. Leaf area index measurements in a tropical moist forest: A case study from Costa Rica. Remote Sensing of Environment, 91(2): 134.

Kampel, M., Amaral, S. and Soares, M.L.G., 2005. Imagens CCD/CBERS e TM/Landsat para análise multi-temporal de manguezais no Nordeste brasileiro. Um estudo no litoral do Estado do Ceará., Anais XII Simpósio Brasileiro de Sensoriamento Remoto. INPE, Goiânia, pp. 979-986.

Kinnell, P.I.A. and Risse, L.M., 1998. USLE-M: empirical modeling rainfall erosion through runoff and sediment concentration. Soil Science Society of America Journal, 62(6): 1667-1672.

Kocak, G., Büyüksalih, G. and Jacobsen, K., 2004. Analysis of digital elevation models determined by high resolution space images, XXth ISPRS Congress, Istanbul.

Kooiman, A., 1987. Relation between land cover and land use, and aspects of soil erosion, specifically for the Upper Komering catchment, South Sumatra, Indonesia, Holland.

Krupnik, A., 2000. Accuracy assessment of automatically derived digital elevation models from SPOT images. Photogrammetric Engineering and Remote Sensing, 66(8): 1017-1023.

Leavesley, G.H., Lichty, R.W., Troutman, B.M. and Saindon, L.G., 1983. Precipitation-runoff modelling system - user's manual, US Geological Survey Water Resources Investigation.

Leopold, L.B., 1995. Fluvial Processes in Geomorphology. Courier Dover Publications. Lützelburg, P., 1922. Estudos botânicos do Nordeste. Inspectorias de Obras contra secas, 1-A. Malla, K., 2002. Bio-physical condition of Andhi Khola watershed to estimate the life span of

proposed dam, 23rd Asian Conference on Remote Sensing, Kathmandu. MathWorks, I., 2005. MATLAB. Millward, A.A. and Mersey, J.M., 1999. Adapting the RUSLE to model soil erosion potential in a

mountainous tropical watershed. Catena, 38: 109–129.

26

Mitasova, H., Hofierka, J., Zlocha, M. and Iverson, R.L., 1996. Modeling topographic potential for erosion and deposition using GIS. Int. Journal of Geographical Information Science, 10(5): 629-941.

Moore, I.D. and G. J. Burch, 1986. Physical basis of the length-slope factor in the Universal Soil Loss Equation. Soil Sciences Society America Journal, 50: 1294-1298.

Morgan, R.P.C., 1995. Soil Erosion and Conservation. Second Edition. Longman Group. Müller, E.N. et al., 2005. SESAM – Sediment Export from large Semi-Arid Catchments:

Measurement and Modelling. Annual Report, University of Potsdam, Potsdam. Nagler, P.L., Glenn, E.P., Lewis Thompson, T. and Huete, A., 2004. Leaf area index and

normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River. Agricultural and Forest Meteorology, 125(1-2): 1.

Nanson, G.C., Tooth, S. and Knighton, A.D., 2002. A Global Perspective on Dryland Rivers: Perceptions, Misconceptions and Distinctions. In: L.J. Bull and M.J. Kirkby (Editors), Dryland Rivers - Hydrology and Geomoprhology of semi-arid Channels. Wiley, Chischester.

NASA, 2005a. EOS Data Gateway version 3.6.5. Earth Observing System EOS. NASA, 2005b. TERRA (EOS AM-1). National Aeronautics and Space Adminstration, pp.

http://terra.nasa.gov/. NASA, 2006. Landsat 7. Science data user handbook. http://landsat.gsfc.nasa.gov/ [13.02.2006]. Nogueira, M.J. and Oliveira, V.P.V.d., 2003. Physical and Environmental Context in the State of

Ceará. In: T. Gaiser, M. Krol, H. Frischkorn and J.C.d. Aráujo (Editors), Global Change and Regional Impacts: Water Availibility and Vulnerability of Ecosystems and Society in the Semiarid Northeast of Brazil. Springer, Berlin.

Oksanen, J. and Jaakkola, O., 2000. Interpolation and accuracy of contour-based raster DEMs, Reports of the Finnish Geodetic Institute, Kirkkonummi.

Pereira, R.C.d.M., 1982. Solos. In: J.G.B.d. Oliveira (Editor), Projeto Aiuaba, Fortaleza. Petta, R.A., Ohara, T. and Medeiros, C.N., 2005. Desertification studies in the Brazilian

northeastern areas with GIS database, Anais XII Simpósio Brasileiro de Sensoriamento Remoto. INPE, Goiânia.

Pietroniro, A. and Prowse, T.D., 2002. Applications of remote sensing in hydrology. Hydrological Processes(16): 1537–1541.

Ponce, V.M., 1995. Management of drought and floods in the semiarid Brazilian Northeast - The case for conservation. Journal of soil and water conservation.

Qi, J. et al., 2000. Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region. Remote Sensing of Environment, 73(1): 18.

RADAMBRASIL, 1983. Vegetação. Folhas SC, 24/25: 537-634. Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K. and Yoder, D.C., 1997. Predicting Soil

Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equaton (RUSLE). Agriculture Handbook 703. United States Department of Agriculture, Washington.

Rizzini, C.T., 1997. Tratado de Fitogeografia do Brasil - Aspectos ecológicos, sociológicos e florístícos. Ambito Cultural Edicoes Ltda., Rio de Janeiro.

Rodal, M.J.N. and Sampaio, E.V.S.B., 2002. A Vegetação do Bioma Caatinga. In: E.V.S.B. Sampaio, A.M. Guilietti, J. Virgínio and C.F.L.G. Rojas (Editors), Vegetação e Flora da Caatinga. APNE, Recife.

27

Rosemback, R., França, A.M.S. and Florenzano, T.G., 2005. Análise comparativa dos dados NDVI obtidos de imagens CCD/CBERS-2 e TM/LANDSAT-5 em área urbana, Anais XII Simpósio Brasileiro de Sensoriamento Remoto. INPE, Goiânia.

Rousseva, S., 2003. Ideas for Physical Interpretation of the USLE, Lecture given at the College on Soil Physics, Trieste.

RSI, R.S.I., 2004. ENVI, Boulder. Saito, H., Bellan, M.F., Al Habshi, A., Aizpuru, M. and Blasco, F., 2003. Mangrove research and

coastal ecosystem studies with SPOT-4 HRVIR and TERRA ASTER in the Arabian Gulf. International Journal of Remote Sensing, 24(21): 4073-4092.

Salgado, O.A., Filho, S.J. and Goncalves, L.M.C., 1980. Vegetação - as regioes fitoecológicas, sua natureza e seus recursis economicos.

Sampaio, E.V.S.B., 1995. Overview of the Brazilian caatinga. In: S.H. Bullock, H.A. Mooney and E. Medina (Editors), Seasonally Dry Tropical Forests. Cambridge University Press, Cambrige, pp. 35-63.

Schnirch, M., 2002. Einführung in die Digitale Photogrammetrie. Silva, A.M.d., 2004. Rainfall erosivity map for Brazil. Catena, 57: 251-259. Sousa, C.L.d., 2003. Análise multitemporal da cobertura vegetal para detecçã de mudanças no

município de Cedro-PE, Anais XI SBSR. INPE, Belo Horizonte, pp. 2907-2912. Souza, M.J.N.d., 1982. Geomorfologia. In: J.G.B.d. Oliveira (Editor), Projeto Aiuaba, Fortaleza. Su, Z., 2000. Remote sensing of land use and vegetation for mesoscale hydrological studies.

International Journal of Remote Sensing, 21(2): 213-233. Teotia, H.S., Silva, I.d.F.d., Santos, J.R.d., Junior, J.F.V. and Gonçalves, J.L.d.G., 2003.

Classificacão da cobertura vegetal e capacidade de uso da terra na região do cariri velho (Paraíba), através de sensoriamento remoto e geoprocessamento, Anais XI SBSR. INPE, Belo Horizonte.

Turner, D.P., Cohen, W.B., Kennedy, R.E., Fassnacht, K.S. and Briggs, J.M., 1999. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70(1): 52-68.

USGS, 2006a. Shuttle Radar Topography Mission Home page. http://srtm.usgs.gov. USGS, 2006b. Thematic Mapper (TM). http://edc.usgs.gov/products/satellite/tm.html

[13.02.3006]. Wang, L.W. and Trinder, J.C., 1999. Analysis and evaluation of the DEM derived from TOPSAR

Airborne interferometric SAR system. Australian Surveyor, 44(2): 174-182. Wang, P., 1998. Applying two dimensional Kalman Filtering for Digital Terrain Modelling. In:

D. Fritsch, M. Englich and M. Sester (Editors), ISPRS Commission IV – GIS Between Visions and Applications. The International Archives of Photogrammetry and Remote Sensing, Stuttgart.

Welles, J.M. and Cohen, S., 1996. Canopy structure measurement by gap fraction analysis using commercial instrumentation. J. Exp. Bot., 302: 1335–1342.

Werner, P.C. and Gerstengarbe, F.-W., 2003. The Climate of Piauí and Ceará. In: T. Gaiser, M. Krol, H. Frischkorn and J.C.d. Aráujo (Editors), Global Change and Regional Impacts: Water Availibility and Vulnerability of Ecosystems and Society in the Semiarid Northeast of Brazil. Spriner, Berlin.

White, M.A., Asner, G.P., Nemani, R.R., Privette, J.L. and Running, S.W., 2000. Measuring Fractional Cover and Leaf Area Index in Arid Ecosystems: Digital Camera, Radiation Transmittance, and Laser Altimetry Methods. Remote Sensing of Environment, 74(1): 45.

28

Wischmeier, W.H. and Smith, D.D., 1978. Predicting rainfall erosion losses - A guide to conservation planing. Agriculture Handbook Number 537. United States Department of Agriculture, Washington.

Xavier, A.C. and Vettorazzi, C.A., 2004. Mapping leaf area index through spectral vegetation indices in a subtropical watershed. International Journal of Remote Sensing, 25(9): 1661-1672.

Xiao, J. and Moody, A., 2005. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98(2-3): 237-250.

Yamaguchi, Y. and Naito, C., 2003. Spectrail indices for lithologic discrimination and mapping by using the ASTER SWIR bands. International Journal of Remote Sensing, 24(22): 4311-4323.

Zaluski, M.h., Consort, J.J. and Antonioli, S.B., 2004. Soil Erosion and Deposition Modeling Using ArcGIS. Business and Industry Symposium.