Identifying areas susceptible to desertification in the Brazilian northeast

Abstract

Approximately 57% of the Brazilian northeast region is recognized as semi-arid land and has been undergoing intense land use processes in the last decades, which have resulted in severe degradation of its natural assets. Therefore, the objective of this study is to identify the areas that are susceptible to desertification in this region based on the 11 influencing factors of desertification (pedology, geology, geomorphology, topography data, land use and land cover change, aridity index, livestock density, rural population density, fire hot spot density, human development index, conservation units) which were simulated for two different periods: 2000 and 2010. Each indicator were assigned weights ranging from 1 to 2 (representing the best and the worst conditions), representing classes indicating low, moderate and high susceptibility to desertification. The results indicate that 94% of the Brazilian northeast region is under moderate to high susceptibility to desertification. The areas that were susceptible to soil desertification increased by approximately 4.6% (83.4 km²) from 2000 to 2010. The implementation of the methodology provides the technical basis for decision-making that involves mitigating actions and the first comprehensive national assessment within the United Nations Convention to Combat Desertification framework.

Solid Earth, 6, 347360, 2015 www.solid-earth.net/6/347/2015/ doi:10.5194/se-6-347-2015 Author(s) 2015. CC Attribution 3.0 License.

R. M. S. P. Vieira1, J. Tomasella1,2, R. C. S. Alval2, M. F. Sestini1, A. G. Affonso1, D. A. Rodriguez1, A. A. Barbosa2,A. P. M. A. Cunha2, G. F. Valles1, E. Crepani1, S. B. P. de Oliveira3, M. S. B. de Souza3, P. M. Calil4,M. A. de Carvalho2, D. M. Valeriano1, F. C. B. Campello5, and M. O. Santana5

1Instituto Nacional de Pesquisas Espaciais, So Jos dos Campos, Brazil

2Centro Nacional de Monitoramento e Alertas de Desastres Naturais, Cachoeira Paulista, Brazil

3Fundao Cearense de Meteorologia e Recursos Hdricos, Fortaleza, Brazil

4Secretaria de Agricultura Agropecuria e Abastecimento de Gois, Goinia, Brazil

5Secretaria de Extrativismo e Desenvolvimento Rural Sustentvel, Braslia, Brazil Correspondence to: R. M. S. P. Vieira ([email protected])

Received: 4 November 2014 Published in Solid Earth Discuss.: 10 December 2014 Revised: 11 February 2015 Accepted: 13 February 2015 Published: 18 March 2015

Abstract. Approximately 57 % of the Brazilian northeast region is recognized as semi-arid land and has been undergoing intense land use processes in the last decades, which have resulted in severe degradation of its natural assets. Therefore, the objective of this study is to identify the areas that are susceptible to desertication in this region based on the 11 inuencing factors of desertication (pedology, geology, geomorphology, topography data, land use and land cover change, aridity index, livestock density, rural population density, re hot spot density, human development index, conservation units) which were simulated for two different periods: 2000 and 2010. Each indicator were assigned weights ranging from 1 to 2 (representing the best and the worst conditions), representing classes indicating low, moderate and high susceptibility to desertication. The results indicate that 94 % of the Brazilian northeast region is under moderate to high susceptibility to desertication. The areas that were susceptible to soil desertication increased by approximately4.6 % (83.4 km2) from 2000 to 2010. The implementation of the methodology provides the technical basis for decision-making that involves mitigating actions and the rst comprehensive national assessment within the United Nations Convention to Combat Desertication framework.

Identifying areas susceptible to desertication in the Brazilian northeast

1 Introduction

Drylands (arid, semi-arid and dry sub-humid areas) cover approximately 41 % of the Earths surface and approximately 10 to 20 % of these regions are experiencing degradation processes (Deichmann and Eklundh, 1991; Reynolds et al., 2007), resulting in a decline in agricultural productivity, loss of biodiversity and the breakdown of ecosystems. According to the United Nations Conference to Combat Desertication (UNCCD), when land degradation happens in the worlds drylands it often creates desert-like conditions. Land degradation occurs everywhere but is dened as desertication when it occurs in the drylands, resulting from various factors, including climatic variations and human activities (UN, 1979; UNCCD, 2012). The vegetation is composed of scrub-lands patches (high plant cover) interspersed with herbaceous patches (low plant cover)(Aguiar and Sala, 1999). This heterogeneity is induced by overgrazing, one of the main causes of the increase of bare soil that facilitates water and wind erosion and accelerates the desertication process (Cerd and Lavee, 1999; Krp et al., 2013; Pulido-Fernndez et al., 2013; Ziadat and Taimeh, 2013).

Forty-four percent of global agricultural areas and almost 2 billion people are located over the drylands, and the majority (90 %) are in developing countries (DOdorico et al., 2013). Overexploitation of natural resources in extremely vulnerable regions can accelerate land degradation and desertication process, affecting ecosystem functions and de-

Published by Copernicus Publications on behalf of the European Geosciences Union.

348 R. M. S. P. Vieira et al.: Identifying areas susceptible to desertication

Figure 1. Study area location and its main biomes.

creasing productivity, biodiversity and landscape heterogeneity, and represents a major threat to the environment and human welfare (Mainguet, 1994; Reynolds and Stafford Smith, 2002; Montanarella, 2007; Salvati and Zitti, 2008; Cerd et al., 2010; Santini et al., 2010; Kashaigili and Majaliwa, 2013; Pulido-Fernndez et al., 2013; Bisaro et al., 2014).

In South America, the United Nations Convention to Combat Desertication report (ONU, 1997) concluded that, until 2025, one-fth of the productive land could be affected by the desertication process. The most susceptible areas are located in Argentina, Bolivia, Chile, Mexico, Peru and Brazil (Arellano-Sota et al., 1996). In Brazil, the most critical desertication hot spots are located in the semi-arid northeast. In this region the climate is one of the factors that control the desertication process. Soil type, geology, landscape, vegetation, socioeconomic factors and land management also are considered important aspects of this process (IBGE, 2004). The main causes of desertication in this region are (i) deforestation to produce fuel wood and explore clay deposits; (ii) intensive land use employing poor agricultural methods, such as slash and burn, harvesting and land clearing; (iii) salinization; and (iv) extensive herding and overgrazing (Nimer, 1988).

Considering that the Brazilian semi-arid region is the worlds most populous dry land region (Marengo, 2008), with more than 53 million inhabitants and a human population density of approximately 34 inhabitants per km2 (IBGE, 2010), and that global climate change scenarios indicate that the region will be affected by increased aridity in the next

century, this area is seen as one of the worlds most vulnerable regions to climatic change (IPCC, 2007).

The UNCCD recognizes desertication as an environmental problem with huge human, social and economic costs (Hulme and Kelly, 1993).

The most accepted denition currently states that desertication is land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities (UN, 1979). Due to the complex social interactions and the biophysical processes, the identication and assessment of the desertication areas have been addressed through a multidisciplinary framework across different spatial and temporal scales (e.g., Prince et al., 1998; Diouf and Lambin, 2001; Thornes, 2004; Santini et al., 2010).

Several methods have been successfully applied for desertication analysis based on indicators and indices (Kepner et al., 2006; Sommer et al., 2011). For instance, the MEDALUS methodology, developed for the European Mediterranean environment, is widely used because of its simplicity and exibility. The MEDALUS methodology is based on the environmentally sensitive area index (ESAI; Parvari et al., 2011;Salvati et al., 2011; Izzo et al., 2013; Jafari and Bakhshandehmeh, 2013). In order to identify areas potentially affected by land degradation, the method analyzes four main variables: climate, soil, vegetation and land management (Kosmas et al., 1999, 2006; Lavado Contador et al., 2009). It has been validated on regional and local scales (Basso et al., 2000; Brandt et al., 2003; Salvati and Bajocco, 2011) and was

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Table 1. Indicators of land degradation/desertication.

Indicators Scale/Spatial resolution Period Source

Geology 1 : 500 000/90 m 2010 INPE/MMA Geomorphology 1 : 500 000/90 m 2010 INPE/MMA Pedology 1 : 500 000/90 m 2010 INPE/MMA Land use and land cover 1 : 500 000/90 m 2000 and 2010 INPE/MMA Aridity index 1 : 500 000/5 km 19702000 INMET/CPTEC Slope angle 1 : 500 000/90 m 2010 INPE Rural population density Per municipality 2000 and 2010 IBGE Livestock density Per municipality 2000 and 2010 IBGE Fire hot spot density 1 : 500 000/1 km 19992003 and 20082012 CPTEC Human development Per municipality 2000 and 2010 FJP Conservation units 1 : 500 000/90 m 2010 MMA

CPTEC Center for Weather Forecasting and Climate Research; INMET National Institute of Meteorology; FJP Joo Pinheiro Foundation, INPE National Institute For Space Research; MMA Ministry of the Environment; IBGE Brazilian Institute of Geography and Statistics.

applied to quantify the impact of mitigation policies against desertication (Basso et al., 2012).

Symeonakis et al. (2014) estimated the environmental sensitivity areas on the island of Lesvos (Greece) through a modied ESAI, which included 10 additional parameters related to soil erosion, groundwater quality, demographic and grazing pressure, for two dates (1990 and 2000). This study identied areas that are critically sensitive on the eastern side of the island mainly due to human-related factors that were not previously identied.

Although several studies have been conducted to detect desertication or to identify the drivers (indicators) of the process in critical hot spots in the Brazilian northeast (Matallo Jnior, 2001; Lemos, 2001; Sampaio et al., 2003; Aquino and Oliveira, 2012), there have been no studies addressing the entire region.

Crepani et al. (1996) developed a methodology based on the concept of the eco-dynamic principles, proposed by Tri-cart (1977), and on the relationship between morphogenesis and pedogenesis to identify areas that are susceptible to soil erosion. The author provided an integrated view of the physical environment and the conceptual basis for developing humannature relationships. However, this study did not include socioeconomic and management indicators as parameters that can inuence soil loss.

Therefore, this paper presents a novel approach which integrates the MEDALUS project and the methodology developed by Crepani et al., 1996 to identify areas that are susceptible to desertication in the northeastern region of Brazil and the northern regions of the states of Minas Gerais and Esprito Santo by combining social, economic and environmental indices. This study was conducted considering two reference periods: early 2000s and 2010. The results will be useful for providing basic information for the diagnosis and prognosis of desertication in the region and providing sub-

sidies for the technical support for mitigation and adaptation actions.

2 Study area

The study area is located in the equatorial zone (121 S, 3249 W), totaling an area of 1 797 123 km2, which corresponds to 20 % of the Brazilian territory (Fig. 1).

The climatology of the northeast of Brazil includes three different rainfall regimes: (i) in the south-southwest area, the rainy season occurs from October through February, which is associated with the displacement of cold fronts coming from the south; (ii) in the north of the region, rainfall occurs from February to May, which is associated with the southward movement of the Intertropical Convergence Zone; and nally, (iii) in a narrow area that is close to the coast at the east, the rainy season occurs from April through August, triggered by temperature differences between the oceans and the sea shore (Kousky, 1979; Marengo, 2008). The evaporation rate in the region is very high and can reach 1000 mm yr1 in the coastal region and up to 2000 mm yr1 in the interior (IICA, 2001), based on 11 stations distributed in the semi-arid region and on historical series (Molle, 1989). Annual evaporation average is 2700 to 3300 mm, with the highest values occurs from October to December and the lowest from April to June.

Because of the high evaporation rates and the short duration of the wet season, most of the rivers are temporary, and ash oods occur only during the rainy season (MMAIBAMA, 2010).

In the northeast region of Brazil, natural vegetation includes rainforests, riparian forests, savannas and montane forests, among others (Foury, 1972). However, the natural vegetation that dominates 62 % of Brazilian semi-arid region is caatinga (MMA, 2007). Caatinga vegetation is composed of shrubs and small trees, usually thorny and decidu-

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350 R. M. S. P. Vieira et al.: Identifying areas susceptible to desertication

Table 2. Land use and land cover classes.

Land use and land cover classes Description

Evergreen forest Evergreen broadleaf closed/openWater body Rivers, streams, canals, lakes, ponds or puddlesBeach Beach areaSeasonal forest Type of forest characterized by trees that seasonally shed their leavesRestinga Herbaceous and arbustive vegetation, distributed along the coastal zoneUrban area Cities and townsSavanna (Cerrado) Grasslands, shrublands and woodlandsFluviomarine MangroveAlluvial Similar characteristics to the evergreen forest but differsbecause of its physiographical position (alluvial plain)

Campo Maior complex Prevailingly herbaceous vegetation; presence of carnaubais (coconut type) in ood plains Steppe Savanna (caatinga) Vegetation typical of the Brazilian semi-arid region characterized byxeric shrubland and thorn forest that primarily consists of small,thorny trees that shed their leaves seasonallyShrimp farming Producing shrimpPasture Pasture area (both natural and planted)

Agriculture Cultivated areas (temporally and permanent crops)

Baixada Maranhense Low plain area that is ooded in the rainy season, creating large lagoonsBare soil Bare soil areas without natural coveringDunes Sand dunes along the coastRock outcrops Exposed rock areasSalt elds Areas where sea salt is produced

ous, that lose their leaves in the early dry season. Caatinga is a highly dynamic ecosystem that responds quickly to climatic conditions. The dominant factor that controls the structure and distribution of vegetation is the precipitation, with an annual mean of 500800 mm and high spatial and temporal variability (Hastenrath and Heller, 1977; Oliveira et al., 2006). Caatinga, in comparison with other xeric areas in South America, presents climatic distinctiveness that resulted in numerous important morphological and physiological adaptations to aridity by many species of plants (Mares et al., 1985). Nowadays, more than 10 % of the semi-arid area has already undergone a very high degree of environmental degradation, being susceptible to desertication (Oyama and Nobre, 2004).

3 Methods

To identify areas susceptible to desertication, we evaluated 11 indicators of susceptibility to desertication (Table 1) based on previous studies of the area (Vasconcelos Sobrinho, 1978; Ferreira et al., 1994; Matallo Jnior, 2001; Lemos, 2001). From Table 1, each indicator was sub-divided into various uniform classes. Each class received a weight factor, related to the potential inuence on desertication process, that ranged between 1 (low susceptibility) and 2 (high susceptibility), producing 11 susceptibility maps (SM). The weight factors were assigned based on previous analyses of the literature (Crepani et al., 1996, Torres et al., 2003; Alves,

2006; Santini et al., 2010; Symeonakis et al., 2013). These indicators were grouped into two groups as described below.

3.1 Physical indicators

3.1.1 Slope data, geology, geomorphology and pedology maps

The basic topographic data set used was a 30 m spatial resolution digital elevation model (DEM), derived from TOPA-DATA, which was developed based on Shuttle Radar Topography Mission data (Farr and Kobrick, 2000; Van Genderen et al., 1987). The DEM was processed to derive elevation and slope angle and used to identify breakline surface discontinuities where changes occurred in the vertical curvature which are linked to lithological, pedological, geomorphological and vegetation characteristics. Therefore, breaklines often indicate the boundary between adjacent units on a map.

Geomorphology and geology maps were extracted from RADAMBRASIL Project (Projeto RADAMBRASIL 1973 1981) and from the Geological Survey of Brazil (CPRM Companhia de Pesquisa de Recursos Minerais), both with a spatial scale of 1 : 1 000 000. These basic maps were digitized and then rescaled to the scale of 1 : 500 000 using the processed DEM, following the procedure suggested by Vale-riano and Rossetti (2012).

Soil maps (EMBRAPA, 1999) were rescaled from 1 : 5 000 000 to 1 : 500 000 based on the topographic map information. The Brazilian System of Soil Classication

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is based on soil pedogenetic characteristics, and also uses morphological, physical, chemical and mineralogical criteria (Camargo et al., 1987). The system is hierarchical and opened which allows the inclusion of new classes and enables the classication of all soil types that occur in Brazil.

3.1.2 Aridity index (AI)

The aridity index is considered to be one of the most important indicators of areas that are susceptible to desertication (UNESCO, 1979; Sampaio et al., 2003). In this study, the AI was obtained by the following formula:

AI = P /PET, (1)

where P is the precipitation and PET is the potential evapotranspiration calculated using the PenmanMonteith equation (Monteith, 1965).

3.2 Socioeconomic indicators

3.2.1 Land use and land cover maps

Between 2000 and 2010, northeast Brazil was the fastest-growing economic (IBGE, 2010) region of the country and has been undergone severe land use and land cover changes.Therefore, it is crucial to asses if the combination of both effects fast growth and severe land use changes have impacted the susceptibility to desertication/degradation of the region. Thus, 90 Landsat-TM images (30 m resolution) of the dry period (July to September) of 2010 and 2011 were selected and geocoded based on the orthorectied Landsat images from the Global Land Cover Facility (NASA). These images were used to update the land use and land cover map derived by the ProVeg Project (Vieira et al., 2013), which was based on Landsat images from 2000. Additionally, land use and land cover maps from the PROBIO (Project for Conservation and Sustainable Use of Biological Diversity) (MMA, 2007) project, with a spatial scale of 1 : 500 000, and high-resolution images from Google Earth were used as auxiliary data. The land use and land cover classes mapped in this study are presented on Table 2.

3.2.2 Rural population density

These data were extracted from IBGE census data (available at http://downloads.ibge.gov.br/downloads_estatisticas.htm

Web End =http://downloads.ibge.gov.br/downloads_estatisticas.htm ).The rural area boundaries and the number of inhabitants were dened considering information for both 2000 and 2010.

3.2.3 Livestock density

Livestock density data, based on the total number of cattle and goat herds per municipality in 2000 and 2010, were extracted from IBGE agricultural census.

Figure 2. Combination of indicators for the determination of the ESAI; adapted from Benabderrahmane and Chenchouni (2010).

3.2.4 Fire hot spot density

Fire hot spot data were obtained from INPEs Fire Monitoring Project (INPE, 2012). Fire hot spot density maps were derived for two periods: (i) the average number of satellite hot spots from 1999 to 2003, which was used to represent the year 2000, and (ii) the average for the period 2008 to 2012, which was used as an indicator for the year 2010. To convert point data to continuous smooth surfaces, Kernel density estimation was applied to re hot spots point using a 50 km radius (Koutsias et al., 2004; de la Riva et al., 2004). This estimator improves visualization and enables comparison with continuous environmental variables (Silverman, 1986).

3.2.5 Conservation units

Conservation unit data were obtained from the Ministry of the Environment. In the present study, the number of conservation units for 2000 and 2010 did not change. There are two basic categories of conservation units: integral protection units and the conservation units for sustainable use (Rocco, 2002). The former forbids the use of natural resources and includes national parks, ecological stations, biological reserves and wildlife sanctuaries. The latter includes national forests, extractive reserves and sustainable development reserves where the sustainable use and the management of natural resources are allowed under certain regulations.

3.2.6 Human development index (HDI)

The HDI indicators for the years 2000 and 2010 were obtained from the Joo Pinheiro Foundation (http://atlasbrasil.org.br/2013/

Web End =http://atlasbrasil. http://atlasbrasil.org.br/2013/

Web End =org.br/2013/ ). Population data, as well as HDI, are essential to understand the territorial dynamics. The calculation

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352 R. M. S. P. Vieira et al.: Identifying areas susceptible to desertication

Table 3. Classes and weights of parameters used for environment quality assessment.

Susceptibility class

Geomorphological types and features Susceptibility weight

Terrace formations structural and at tops landforms; the roughness of the topographic relief is characterized by being very slightly dissected; at relief and planation surface without intense erosive action.

1.00

Low Flat and convex tops landforms; the roughness of the topographic relief is characterized by being lightly to moderately dissected; at relief and planation surface with signicant erosive action; slightly undulating relief with gentle slopes.

1.25

Moderate Convex tops landforms; the roughness of the topographic relief is characterized by being moderately dissected; undulating relief with steep slopes.

1.50

High Convex and sharp tops; the roughness of the topographic relief is characterized by being highly dissected; strong undulating relief with very steep slopes; karstic relief.

1.75

Geology type

Quartzite, metaquartzite, banded iron formation, metagranodiorite, metatonalite

1.00

1.05

Low Granodiorite, quartz-diorite, granulite 1.10

Migmatite, gneiss, orthogneiss 1.15

Nepheline syenite, trachyte, quartz-monzonite, quartz-syenite 1.20

Andesite, basalt 1.25

Gabbro, anorthosite 1.30

Moderate Biotite, quartz-muscovite, itabirite, metabasite, mica schist 1.35

Amphibolite, kimberlite 1.40

Hornblende, tremolite 1.45

Schists 1.50

Phyllite, metasiltite 1.55

Slate rock, metargillite 1.60

Marble 1.65

Quartz arenites (sandstones), ortoquartizites 1.70

High Conglomerates 1.75

Arkoses 1.80

Siltstones, Argillite 1.85

Shale 1.90

Limestone, dolostone 1.95

Unconsolidated sediments (colluvial and alluvial deposits, sandy deposits, etc.)

Rhyolite, granite, dacite, metasyenogranite, monzogranite, syenogranite, magnetite, metadiorite, metagabbro

2.00

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Table 3. Continued.

Susceptibility class

Geomorphological types and features Susceptibility weight

Soil type (EMBRAPA, 1999)

Low Latosols, organic soils, hydromorphic soils, humic soils 1.00

Moderate Podzolic soils, brunizem, planosol, brunizem, structured dusky red earth

1.33

1.66

laterites, rocky outcrop 2.00

Slope (%)

Low 26 1.00 Moderate 618 1.50 High > 18 2.00

High CambisolNon-cohesive soils, immature soils,

Applied Economic Research and the Joo Pinheiros Foundation the Brazil have reduced the inequalities between its sub-indices of education, income and longevity in 2010.

3.3 Environmentally sensitive area index

The methodology used to map susceptible areas to desertication was based on the MEDALUS methodology (Mediterranean Desertication and Land Use, by Kosmas et al., 1999), which uses geometric means of environment-state and response indicators. Each index is estimated from a combination of indicators of desertication, which depends on geology, pedology, land management, human occupation and conservation policies (Fig. 2).

These maps were then grouped according to four quality indexes (Kosmas et al., 1999).

Physical land quality index (PLQI):

PLQI = (Is Ig Igm Id)1/4, (2)

where Is is the soil SM, Ig is the geology SM, Igm is the geomorphology SM and Id is the slope SM.

Management quality index (MQI):

MQI = (Iuc Ip Ifq Iucob)1/4, (3)

where Iuc is conservation units SM, Ip is the livestock density SM, Ifq is the re density SM and Iucob is the land use and land cover SM.

Climate quality index (CQI):

CQI = Ia, (4)

where Ia is the aridity index SM.

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Figure 3. (a) Physical land quality index; (b) management quality index; (c) climate quality index; (d) social quality index.

of the HDI includes three kinds of data: longevity, education and economic income. HDI scale ranges from 0 to 1, where values from 0 to 0.49 represent low HDI, 0.5 to 0.59 medium HDI, 0.60 to 0.79 high HDI, and 0.8 to 1.0 very high HDI. According to the Atlas of Human Development of Brazil 2013, developed by a partnership between United Nations Development Program (UNDP, 2010), the Institute of

354 R. M. S. P. Vieira et al.: Identifying areas susceptible to desertication

Table 4. Classes and weights of parameters used for management quality assessment.

Susceptibility Land use/land cover Susceptibility class change classes weight

Evergreen forest, water body, beach, urban area 1.00 Low Deciduous forest 1.40

Restinga 1.45

Savanna (Cerrado), uviomarine pioneer, alluvial pioneer 1.50 Complex of Campo Maior, Baixada Maranhense 1.55 Moderate Caatinga 1.60

Shrimp farming, pasture 1.80 Agriculture 1.90

High Bare soil, dunes, rocky outcrop 2.00

Livestock density data

Low 0 to 30 1.00 Moderate 30 to 75 1.50 High above 75 2.00

Fire density data

Low 0 to 1000 1.00 Moderate 1000 to 2000 1.50 High above 2000 2.00

UC data

Low Integral protection units 1.00 Moderate Conservation units for sustainable use 1.50 High Without conservation unit 2.00

Social quality index (SQI):

SQI = (IHDI IPop)1/2, (5) where IHDI is the human development index SM and

Ipop is rural population density SM.

The geo-database was developed using SPRING (Cmara, et al., 1996).

Finally, to obtain an ESAI, the geometric mean is calculated among the variables inside each factor through the following equation:

ESAI = (PLQI MQI CQI SQI)1/4. (6)

Based on these calculations, three types of ESAs were assigned: (a) low-susceptibility areas (ESAI 1.00 1.25), (b)

moderate-susceptibility areas (ESAI 1.25 1.50) and (c)

high-susceptibility areas (ESAI > 1.50).

3.4 Validation

In this study, the 2010 susceptibility map was validated using the method proposed by Van Genderen et al. (1978). This method assumes that the probability of making f interpretation errors when taking x samples from a remote-sensing-based classication map follows a binomial probability distribution function. The method allows the determination of

Table 5. Classes and weights of parameters used for climate quality assessment.

Susceptibility Climate types Susceptibility class weight

Low Wet sub-humid 1.00(AI above 0.65)

Moderate Dry sub-humid 1.50(AI between 0.51 to 0.65)

High Semi-arid 2.00(AI between 0.21 to 0.50)

the minimum sample size required for validating the map, avoiding the risk of accepting a map with low accuracy.

Based on this methodology, 110 random samples were selected from the low-, medium- and high-susceptibility classes and compared with high-resolution images from Google Earth (Ginevan, 1979; Congalton and Green, 1999) and in situ images. Thus, the points from high-susceptibility classes were compared to their corresponding images to ob-serve the degraded areas of exposed soil.

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4 Results and discussion

This work presents the rst effort to identify the areas that are most susceptible to desertication in the semi-arid region of Brazil through a system that enables continuous and integrated analysis of the factors that provide the best explanation of the desertication processes.

The weight factors assigned to each indicator are described in Tables 3, 4, 5 and 6.

Analyses from 11 indicators stress that areas with predominantly humid and sub-humid climate are potentially susceptible to desertication due to inadequate soil management, which is a key factor for adaptation and mitigation of climate change (IPCC, 2007).

On the MEDALUS methodology, variables like HDI and conservation units were not included. However, these two indicators were considered important in the semi-arid region Brazil based on the fact that the region has relatively low development indexes and several inadequate land uses practices, and previous studies in other regions of Brazil (Trancoso et al., 2010) have shown that conservation enforcement in protected areas is crucial for avoiding degradation.

4.1 Physical land quality index

In terms of soil types, the northeast and southern portions of the region are largely covered by Podzolic soils (23 %) that are more prone to erosion due to the low permeability of the B clayey horizon. Lithosols (21 % of the area) occur in the semi-arid region, associated with rock outcrops. Lastly, the Latosols (18 %) dominate the northwest region, associated with Savanna vegetation, where the relief is plain and favors the mechanized agriculture increasing soil compaction (Cavaliere et al., 2006; Arajo et al., 2007).

The eastern part of the study area is dominated by crystalline rocks. However, there is a predominance of sedimentary basins located in coastal regions and in the western part of the study area. To the south of the region, extensive karst formations can be found. Most of the study area consists of at and undulating relief, but the occurrence of steep formations and the presence of inselbergs have also been noted.

According to the spatial distribution of the physical land quality index (Fig. 3a), 52 % of the study area has a moderate susceptibility. The areas with high susceptibility are on soil types that are more vulnerable to erosion processes, such as podzols (23 %) and lithosols (21 %).

4.2 Management quality index

The analyses showed an increase of 3 % of the area with high susceptibility for a period of 11 years between 2000 and 2010 (Table 7). Areas with high susceptibility reached 87 % (1 571 033 km2) of the studied area in 2000, while in 2010 the percentage increased to 90 % (1 622 716 km2). Among the factors that might be contributing to the increase in area

Table 6. Classes and weights of the parameters used for social quality assessment.

Human development index Susceptibility Per municipality Susceptibility class weight

Low 0.70 to 1.00 1.00 Moderate 0.60 to 0.70 1.50 High 0 to 0.60 2.00

Rural population density

Low 0 to 25 1.00 Moderate 25 to 50 1.50 High above 50 2.00

are shrimp farming, agriculture, livestock and re hot spots.

Analyzing the results of use land and land cover, it is possible to observe that the natural vegetation is being replaced by pastures and agriculture. According to the land use/cover map developed by Vieira et al. (2013), the typical vegetation of the semi-arid of Brazil, known as caatinga, has been replaced by pasture and agricultural activities. Approximately 40 % of the caatinga has been converted to these uses, and the remaining area is being transformed at a rate of 0.3 % per year (IBAMA/MMA, 2010).

In recent years, agribusiness has become one of the most dynamic segments in the northeastern states with the production of fruits, such as papayas, melons, grapes, watermelons, pineapples and mangos. The activities related to shrimp farming covered an area of 69.7 km2 in 2000, which increased to 136.7 km2 in 2010. Northeastern Brazil is responsible for 94 % of all shrimp production in Brazil (Ferreira, 2008).

Even though areas located in sub-humid and humid areas are less vulnerable from a climatic point of view, they are susceptible to land degradation and desertication due to inadequate land use and management. In the northwestern portion of study area, for example, the deforestation is one of main causes to land degradation. The natural vegetation is being replaced by pasture and agriculture, increasing from 106 568 in 2000 to 143 323 km2 in 2010 and from 10 425 in 2000 to 20 100 km2 in year 2010. In livestock areas of the region, re is routinely used as a method for clearing land from bushes and for the re-establishment of pasture (Miranda, 2010). In the present work, the number of re hot spot increased from 26 181 in 2000 to 73 429 in 2010.

4.3 Climate quality index

According to the climate quality index (Fig. 3c, Table 7), 42 % of the area is a highly susceptible semi-arid climate, while 38 % is classied as moderate susceptible dry sub-humid. Finally, 20 % of the area, where the climate is sub-humid to humid, is considered as having a low susceptibil-

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356 R. M. S. P. Vieira et al.: Identifying areas susceptible to desertication

Figure 4. Environmental susceptibility area for (a) 2000 and (b) 2010. (c) Difference between 2000 and 2010.

Table 7. Percentage of the land area covered by each susceptibility class of the four quality indices in 2000 and 2010.

Index Susceptibility class 2000 (%) 2010 (%)

Physical land quality index Low 24.5 24.5 (PLQI) Moderate 52.7 52.7

High 22.9 22.9

Management quality index Low 1.0 0.8 (MQI) Moderate 11.6 8.9

High 87.4 90.3

Climate quality index Low 19.5 19.5 (CQI) Moderate 38.2 38.2

High 42.3 42.3

Social quality index Low 42.4 48.1 (SQI) Moderate 34.8 32.9

High 22.8 19.0

ity. From a climatic point of view, rainfall exceeds 1250 mm in the coastal region annual. To the west, annual rainfall is around 1500 mm, while in the semi-arid interior annual rainfall is less than 1000 mm, ranging from 350 to 750 mm (IBGE, 1996).

4.4 Social quality index

The social quality index showed that 42 % of the region had low susceptibility in 2000, while the value increased to 48 % in 2010 (Table 7). According to IBGE (2010), the HDI improved in this period in response to the countrys economic growth. The region is marked by socioeconomic inequality;

the highest HDI is in the northern (0.682) and eastern (0.684) regions and the lowest is in the northeast (0.631).

4.5 Susceptibility areas to desertication

The areas susceptible to desertication in the Brazilian semi-arid region for both 2000 and 2010, as well as the changes that occurred between these periods, are presented in Fig. 4. The results showed that 94 % of the semi-arid region is moderately (59.4 %) or highly (35 %) environmentally sensitive for both periods: 2000 (94.4 %) and 2010 (94 %). High-sensitivity areas increased from 35 to 39.6 %, which corresponds to 83 348 km2. Moderate regions decreased almost 5 % (89 856 km2), while low-sensitivity areas increased from

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R. M. S. P. Vieira et al.: Identifying areas susceptible to desertication 357

5.6 % (2000) to 6 % (2010). The most susceptible areas were mapped, both in 2000 and 2010, in the central-eastern regions that include the four desertication hot spots ofcially recognized by the Brazilian Ministry of the Environment: Gilbus (PI), Irauuba (CE), Cabrob (PE) and Serid (RN) (MMA, 2007).

The results also showed several areas with high susceptibility, specically in the south of the study area. According to the eld survey, desertication in this area is increasing due to inadequate soil management and indiscriminate deforestation (MMA, 2005). The human activities are the dominant factor for desertication expansion. However, in the northwest of the study area, several spots showed low susceptibility. Government incentives in the last decades have turned this region into a tropical fruit producer (Araujo and Silva, 2013).

From these results, it is clear that the management quality index is the main driver of desertication in the study region (Fig. 3b). Therefore, mitigation actions for reducing the susceptibility to degradation in the region depend heavily on changes in management practices towards more sustainable land use.

Finally, it is important to note that the validation results indicated that the environment susceptibility map has an accuracy of 85 %, which is considered acceptable due to the extent and complexity of the study area.

5 Final considerations

The environmentally sensitive area index calculated in the present study allowed a better understanding of the degradation/desertication process in the Brazilian semi-arid region.The study showed that desertication susceptibility ranges from moderate to high in the Brazilian semi-arid region.

From a climatic point of view, the humid and sub-humid areas have low vulnerability. However, when management issues associated with land use are taken into consideration, these areas become potentially susceptible to degradation.

The northwestern part of the study area is highly susceptible to land degradation due to inadequate soil management associated with intensive agricultural land expansion. In the last 50 years, the area received millions of migrants looking for better opportunities created by agriculture expansion.

This study is the rst effort to produce a comprehensive diagnosis of the desertication processes for the entire region and combines the existing experience from previous studies in the region with a consolidated methodology. Additionally, new indicators were included in the methodology of this study, such as HDI (social indicator) and conservation units (management indicator), because previous knowledge indicated that they would be relevant in the study area.

In addition, it was possible to obtain a database with biophysical and social information on the same scale and reso-

lution, which allowed the integrated analysis of the desertication indicators.

One of the major issues facing humanity today is the development of knowledge in regards to the occupation of land in regions affected by desertication in a sustainable way. Then it becomes critical to dene adaptation alternatives for living in semi-arid regions. Furthermore, it can be applied in multi-scale studies, showing the magnitude of the risk in different areas and the factors that may contribute to triggering the process. The approach was based on the use of indicators that are routinely surveyed in the area, allowing for continuous monitoring of the desertication processes. The proposed methodology proved to be a useful, timely and cost-effective tool to identify areas that are susceptible to degradation/desertication.

Acknowledgements. The authors are grateful to the Brazilian Ministry of the Environment and Inter-American Institute for Cooperation on Agriculture (IICA) for providing logistical and nancial support, to Soil EMBRAPA, from Recife, for supplying the soil data and to the National Council for Scientic and Technological Development.

Edited by: A. Cerd

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