Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing

Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing

Geoderma 302 (2017) 39–51 Contents lists available at ScienceDirect Geoderma journal homepage: www.elsevier.com/locate/geoderma Soil class and attr...

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Geoderma 302 (2017) 39–51

Contents lists available at ScienceDirect

Geoderma journal homepage: www.elsevier.com/locate/geoderma

Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing

MARK

José A.M. Demattêa,⁎, Veridiana Maria Sayãoa, Rodnei Rizzob, Caio T. Fongaroa a Department of Soil Science, College of Agriculture Luiz de Queiroz, University of São Paulo (ESALQ/USP), Av. Pádua Dias, 11, CP 9, Piracicaba, SP Code 13418-900, Brazil b Center of Nuclear Energy in Agriculture, University of São Paulo, Av. Centenário, 303 - São Dimas, Piracicaba, SP Code 13416-000, Brazil

A R T I C L E I N F O

A B S T R A C T

Keywords: Vegetation spectral sensing NDVI Soil attributes Soil–vegetation relationship Detection of soil by vegetation

The development of a strategy to reach soil data through vegetation has the potential to improve soil mapping in large areas covered by forests. This study aimed to develop a strategy to identify the relationship between soil information, such as texture, fertility and pedological classes, through vegetation spectral sensing obtained by a satellite sensor. The study area is located in Angola, Africa, covering 20,000 ha. Landsat images corresponding to dry and wet seasons were obtained, atmospheric corrected and transformed into reflectance. We built a mechanism to extract soil information and keep only vegetated areas in images. Afterward, the Normalized Difference Vegetation Index (NDVI) was calculated and its pixel was linked with soil samples collected in the field (450 points at four depths, A: 0–0.2; B: 0.4–0.6; C: 0.8–1.0; D: 1.0–1.2 m), and the wet analysis was performed. The non-parametric tests were used to verify the NDVI variation according to soil classes and Landsat image acquisition date. Higher correlation values were obtained in the images from 2008, for Al saturation, Al content, and base saturation. Overall, soil attributes best related with vegetation were the ones directly or indirectly involved with Al. The best correlation occurred in the wet season, because vegetation expressed better its relation with soils and water availability. This relation did not vary much at depth. The higher Al content was associated with the development of vegetation adapted to this condition, i.e. a native savanna-type usually occurring in regions of dystrophic soils such as Ferralsols, which presented the highest NDVI mean and were statistically different from the other soil groups. Pedological classes drive water dynamics, which interferes on vegetation. Thus, we found an important relationship between NDVI and soil classes. Depending on the season, soil class varies on the NDVI projection, due to its morphological and attributes configuration. Thus, we conclude that only one soil attribute is not sufficient to drive the NDVI result, but it has to be combined with the soil class. Wet season is indicated as the best period to analyze vegetation in order to reach soil information. The results indicate an important strategy to assist digital soil mapping in vegetated areas.

1. Introduction FAO indicates the need to increase the current world food production by 70% in order to feed populations in 2050 (FAO, 2009). The main countries with agricultural lands in the world are Brazil, China, United States, and Australia. Nevertheless, African countries also play a very important role in this context and most of them have serious issues regarding food security. To meet the demand for food production, soil maps are extremely necessary. The importance of spectroscopy for soil science, thus, for soil mapping, has been discussed throughout the scientific community in more recent years (Viscarra-Rossel et al., 2016; Ge et al., 2011). The continuous need for soil maps for environmental monitoring has



Corresponding author. E-mail address: [email protected] (J.A.M. Demattê).

http://dx.doi.org/10.1016/j.geoderma.2017.04.019 Received 31 March 2016; Received in revised form 5 April 2017; Accepted 18 April 2017 0016-7061/ © 2017 Elsevier B.V. All rights reserved.

prompted the expressive development of new techniques. In fact, the contribution of new technologies for digital soil mapping has been reported in several studies (Minasny and McBratney, 2016). The research community has been discussing the problem regarding soil mapping, since the areas are very large and there are few pedologists to carry out studies. To solve this problem, strategies, such as digital soil mapping (McBratney et al., 2003) have been evaluated, which encompass remote and proximal sensing (Mulder et al., 2011). Therefore, remote sensing (RS) using satellite images is a great strategy to map large areas. Studies have linked the importance of RS to soil evaluation (Nanni et al., 2014; Dewitte et al., 2012). The usual approach reported in many papers focuses on soils. However, soils are usually covered by vegetation and the evaluation of their spectral

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chemical variations from surface to undersurface, driven by water dynamics, and expressed by pedological classes.

information extracted from satellite images is conditioned to the availability of bare soil. Studies, such as Shabou et al. (2015), have used a strategy to achieve bare soil from satellite images, which is quite important mostly in agriculture areas. However, some questions remain for areas with dense forests. The African continent has approximately 674 million km2 (FAO, 2010) covered with forests. How to map this extensive area using traditional mapping? Many multidisciplinary studies show the efficiency of technological tools applied to soils. Rossiter (2005) indicates that some soil properties can be assessed directly by its spectral signature only where the soil surface is visible. Spectroscopy studies show a strong relationship between spectral responses and soil properties, such as the cation exchange capacity, organic carbon, iron oxides and clay (Chang et al., 2001). Reschke and Hüttich (2014) used multitemporal Landsat data to ensure soil information and environmental quality. Thus, this technique needs to be improved in order to access soil information. Studies, such as Fölster et al. (2001), Li et al. (2008) and Solon et al. (2012) investigated the relationship between soil characteristics and vegetation through satellite RS, suggesting that these two elements are closely related and their interactions are worth investigating. Regarding the satellite RS approach, works such as Lozano-García et al. (1991), Farrar et al. (1994), Todd and Hoffer (1998), Sumfleth and Duttmann (2008), Rivero et al. (2009), and Zanzarini et al. (2013) used vegetation spectral indexes to reach soil attributes, showing that this approach may be an efficient way to reach accurate results. Nevertheless, most authors point to the need to develop techniques in different regions, since correlation of vegetation with soils is complex. Soil chemical and physical attributes strongly influence on vegetation development, and the soil as a body drives the dynamics of nutrients, water and roots. Thus, it is also important to study soil classes and their relationship with forests. Africa is a continent with extensive lands and natural forests, and studies on the satellite RS approach are scarce. Regarding land use, Africa has approximately 40% as agriculture and 20% as forestland (FAO, 2016), which justifies the importance to study soils in this region. The development of research in areas with natural forests is to ensure the possibility to reach pedological information using characteristics from the current vegetation, since traditional soil mapping methods have major difficulties. The need for food in the world is leading to agricultural expansion in continents, such as Africa, where deforestation is currently a serious issue (FAO, 2016). How to develop sustainable agriculture without damaging the environment is a question that still needs to be answered. Soil maps are important tools for this purpose and still need to be developed and/or improved. The presence of vegetation is one of the main difficulties in digital soil mapping, which requires the development of a strategy to reach soils through satellite images in these conditions. Thus, this study focuses on the relationships between native forest and soil attributes. The questions raised are: Is it possible to map soils and their attributes through vegetation? Can we reach soil information using a sensor located 800 km from the target and superposed by vegetation? Soil classes are constituted by soil attributes along a profile. Does this dynamics interfere on vegetation development? Natural vegetation adapts to local soil chemical, physical and water dynamics characteristics, possibly due to soil classes and/or their characteristics (considering the same climate region). In fact, relating one soil attribute with vegetation is less complicated than with a soil class, which is the result of several attributes and additional environmental factors. The answers for these questions can improve soil mapping in vegetated areas. In this context, this study aimed to develop a strategy to identify the relationship between soil attributes (such as texture and fertility) at different depths, and pedological classes with vegetation spectral information, obtained from Landsat images. We expect that a determined type of forest is best suited for a pedological class as well as soil fertility is related with spectral vegetation data. The hypothesis guiding this research is that natural vegetation is related with soil physical and

2. Material and methods 2.1. Study site The study site for this research is located in the African continent, in Angola. The region has hot and humid summers with higher precipitation occurring from November until March and decreasing between June and October, which corresponds to the drier period. The maximum and minimum annual average temperatures are 28 °C and 18 °C, respectively. The average annual precipitation is approximately 1200 mm, and the altitude is about 1000 m. The geology of the area is characterized by the predominance of gneiss rocks and sandstones (Diniz, 1998). The relief is gently rolling to flat with long and uniform landforms. The slope is between 0 and 6%. There is predominance of Ferralsols in the region with fine texture, ranging from clay to loam sandy, and occurrence of Cambisols, Lixisols and Alisols (Diniz, 1998). Soils are dominantly kaolinitic with contribution of Fe and Al oxides. Eventually there is occurrence of 2:1 minerals in waterlogged soils such as Gleysols. Regarding the vegetation, the area is covered by Savanna, characterized by sparse vegetation with scattered, twisted trees (European Commission, 2013). The area covers 20,000 ha, where a pedological map was performed following the methodology of Legros (2006). For this, 450 points were located by collecting soil samples with augers at four depths (0–0.2; 0.4–0.6; 0.8–1.0; 1.0–1.2 m) and 33 profiles inside the main soil units were evaluated. It was determined soil particle size, coarse and fine sands, silt and clay by the densimeter method (Bouyoucos, 1927) and chemical attributes according to Raij et al. (2001). Soils were classified according to the World reference base for soil resources (IUSS Working Group WRB, 2015). Fig. 1 summarizes the methodology adopted. Steps of the work are as follows:

2.2. Landsat image acquisition Images of Landsat satellite from the years 2001, 2003, 2006, 2008 and 2014 were obtained. Two images were obtained per year from April to May (end of moist season) and June to August (dry season). The distinction between these seasons is because vegetation expresses its maximum biomass development during the wet season, while radicular development at greater depths may be associated with the dry season, when plants need to reach water stored at deeper soil layers. The images from 2014 are from Landsat 8 OLI/TIRS, and the others are from Landsat 5 TM. Only images from 2001 and 2003 are from Landsat 7 ETM+. Fig. 2 illustrates images from both dry and wet seasons, from Landsat 5 TM.

2.3. Atmospheric correction Atmospheric correction, processing and transformation into reflectance were performed using FLAASH (Line-of-sight Atmospheric Analysis of Spectral Hypercubes) algorithm through ENVI 5.1 software (Exelis Visual Information Solutions, Boulder, Colorado). FLAASH manages data from a variety of sensors, including all the Landsat sensors used in this study, and it incorporates algorithms for water vapor and aerosol retrieval (Cooley et al., 2002). Before using this tool, which transforms gray levels into reflectance, it was necessary to convert data from each image into radiance. First, the images were processed through the Radiometric Calibration tool, which creates the radiance images. The resulted images were processed through FLAASH, where they were actually transformed into reflectance. 40

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Fig. 1. Flowchart with the methodology adopted.

2.4. NDVI determination and classification

2.5. Mask for vegetation

After the atmospheric correction, the NDVI was calculated on ENVI 5.1 (Exelis Visual Information Solutions, Boulder, Colorado), using the SPEAR Vegetation Delineation tool. This tool enables to identify the presence of vegetation, through NDVI, and to visualize its vigor level. The NDVI was calculated and then four display ranges were created, representing three vegetation classes (sparse, moderate, and dense) and one no vegetation class. The default NDVI ranges are generated according to general procedures and may not apply in all cases; therefore, adjustments were made in order to suit to the data from the study site. The numbers displayed for each range indicate the bottom limits of NDVI. The NDVI thresholds used for the listed classes were − 1 for no vegetation; 0.4 for sparse vegetation; 0.5 for moderate vegetation and 0.7 for dense vegetation. The Normalized Difference Vegetation Index (NDVI) is the most used index to differentiate vegetation in satellite images, especially from different dates. The index evaluates the variation of green area in a certain period. The index varies from − 1 to 1, and it is calculated according to Eq. (1) (Rouse et al., 1974).

The classification described in Section 2.4 allowed the distinction of all elements from an image that do not represent vegetation such as water, bare soil and rocks. The creation of a mask enables to clip a classification raster, such as the NDVI, from the total study area. A mask was built to contrast only vegetation elements included in the three vegetation classes obtained before (sparse, moderate and dense). This aimed to keep only the green area of an image, which enables to collect the value of NDVI points regarded only to vegetation. This mask was created as the objective of the research was to relate vegetation with soil attributes, and depending on the period of the year, areas that were covered by vegetation in the moist season changed into lower vegetation and/or bare soil areas in the dry season or, due to land use change, turned into agricultural land. Generally, NDVI values between 0.2 and 0.4 represent a threshold of green vegetation and bare soil with very sparse vegetation. The use of a mask eliminates biased areas in an image, which have a null value (0) after the mask is applied.

NDVI =

NIR − R NIR + R

2.6. Correlation analysis The Spearman's rank correlation coefficient (r) was determined between NDVI values of different Landsat images and each soil attribute in the physical and chemical analyses separately, considering two depths designated as A and C (0–0.2 m, A and 0.8–1.0 m, C). The chemical attributes are: pH in H2O, P (mg kg− 1), K (mmolc/kg), Ca (mmolc/kg), Mg (mmolc/kg), Al (mmolc/kg), H + Al (mmolc/kg), Sum of Cations (SC - mmolc/kg), Cation Exchange Capacity (CEC - mmolc/

(1)

where: NDVI – normalized difference vegetation index value; NIR – reflectance value on near infrared band; R – reflectance value on red band.

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2.8. Soil attributes mapping based on vegetation spectra The algorithm Spectral Angle Mapper (SAM), available at ENVI 5.1 (Exelis Visual Information Solutions, Boulder, Colorado), was used to map soil attributes, such as Al saturation (m) and clay from layers A and C, using vegetation spectra extracted from the Landsat image of May 20, 2008. The SAM algorithm creates classes based on the spectral similarity of pixels that are related with soil information. The similarity of pixels is inferred by the calculated angle between two spectra, considered vectors in a space where the dimensionality is represented by the number of bands used (Kruse et al., 1993). The bands B1, B2, B3, B4, B5 and B7 were used in this section. The clay content was mapped based on the following classes: 0–150 g kg− 1; 151–250 g kg− 1; 251–350 g kg− 1 and 351–600 g kg− 1. For Al saturation, the classes were < 5%; 5–10%; 10.1–20%; 20.1–45% and > 45%. The available database with 450 points, excluding samples that do not represent vegetation pixels identified after the application of the mask in Section 2.5, was randomly divided between 70% of samples for calibration and 30% of samples for validation. For the validation of maps, a confusion matrix was performed from which Global Accuracy (GA) was calculated. The GA represents the sum of all pixels correctly classified by the SAM classifier, divided by the total number of pixels used in the validation.

3. Results and discussion 3.1. Soil characteristics There is a wide diversity of soils in the study site, resulting in a wide range of attributes (Table 1). Most soil texture is sandy to sandy-loam. The sand content decreases slightly at depth, as the clay content increases. The silt content remains constant at depth. In general, soil fertility is very poor with low base saturation, having an average of V% at depths B, C and D < 20%. Only topsoil (layer A) presented higher V%, due to the presence of organic matter (OM). The OM content is higher in the topsoil and it decreased > 50% in the subsequent layers. The presence of essential plant nutrients, such as Ca, Mg, K and P, followed the same trend observed for OM, with higher contents in horizon A (Table 1). Actually, the content of these essential nutrients is very low (with the exception of horizon A), according to Raij et al. (1997). Fig. 3 indicates the variation of some soil properties along depth for the main soil classes in the region. Ferralsol and Arenosol have a very similar clay content along depth, despite one is clayey and the other sandy. On the other hand, Lixisol has a texture gradient, which gives this soil a great characteristic to store water. In all cases, soil aluminum and aluminum saturation increases, as base saturation decreases. Another important aspect regarding soil fertility is the Al content (Al) and Al saturation (m%) (Table 1). With pH values below 5.0, Al is present in the soil solution (Delhaize and Ryan, 1995) and it occupies adsorption sites that, in other pH conditions, would be occupied by essential cations thus decreasing soil fertility. This explains why the Al content, which increases at depth (Fig. 3), is higher than the essential nutrients. In addition, when present at high concentration in the soil solution, Al can be intensely absorbed by plants. In fact, Al can be toxic to certain plant types, restricting their development. Even though Al toxicity is one of the major growth-limiting factors for plants in acid soils, some plants are tolerant to the Al presence in the soil, and the mechanisms for this tolerance are genetically controlled. Al tolerance tends to be associated with NH4 tolerance as the nitrification process for N release is substantially hindered in acid soils (Kabata-Pendias, 2011). All these characteristics indicate soils typical from the tropics, with kaolinite and iron oxides mineralogy.

Fig. 2. (a) RGB (red, green, blue) Landsat 5 TM composite image from the study site from the end of the moist season (May 2008) and (b) from the dry season (August 2008). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

kg), Organic Matter (OM - g kg− 1), V % (base saturation = SC/ CEC ∗ 100), m % (aluminum saturation = Al/(SC + Al) ∗ 100) and for the particle size analysis, sand, silt, and clay were determined. All statistical analyses in this section were performed in R environment (R Core Team, 2015), using the package corrplot (Wei and Simko, 2016).

2.7. NDVI variation according to soil class Using the soil map (scale 1: 20,000) and images from year 2008 representing wet and dry seasons, NDVI variations (mean value) were determined according to each soil class: Ferralsol, Lixisol, Cambissol, Plinthosol, Leptosol and Arenosol. Four Landsat TM 5 images were used, from April, May, June and August, due to the availability of good quality (cloud-free) images only in these months (only for the year 2008). April and May represent the end of the wet season, June represents the beginning of the dry season, and August represents the dry season itself. The Shapiro-Wilk test (Shapiro and Wilk, 1965) showed that NDVI follows a normal distribution allowing to proceed with appropriate statistical analyses in the means. The analysis of NDVI means is required in case of significant differences among NDVI and soil classes, as well as satellite scene acquisition dates (representing wet and dry seasons). All statistical analyses in this section were performed in R environment (R Core Team, 2015). In addition, the variation of the vegetation spectrum related with soil types was determined for both the wet and the dry seasons. The spectral curve from each soil class was made by plotting mean values of reflectance in each Landsat TM band – B1 (0.45–0.52 μm), B2 (0.52–0.60 μm), B3 (0.63–0.69 μm), B4 (0.76–0.90 μm), B5 (1.55–1.75 μm) and B7 (2.08–2.35 μm). The Shapiro-Wilk test (Shapiro and Wilk, 1965) was also performed.

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3.2. Soils versus vegetation (NDVI)

Table 1 Minimum, maximum and mean of soil attributes determined in wet-analyses. Min Max Sand (g kg− 1)

Mean

Min Max Silt (g kg− 1)

Mean

Min Max Clay (g kg− 1)

Mean

Deptha A B C D

427.0 351.0 308.0 316.0

692.4 623.9 596.9 585.6

7.0 22.0 14.0 23.0

126.3 132.7 136.6 137.8

13.0 31.0 26.0 10.0

181.3 243.4 266.5 276.5

867.0 883.0 917.0 895.0

Mg (mmolc/dm3)

pH H2O A B C D

4.0 4.2 4.3 4.4

7.3 7.9 6.8 6.7

5.3 5.1 5.1 5.1

Ca (mmolc/dm3) A B C D

0.0 0.0 0.0 0.0

174.4 254.4 53.2 100.6

0.3 0.2 0.1 0.1

196.7 272.3 75.3 142.1

0.0 0.0 0.0 0.0

10.5 3.8 2.6 2.9

1.0 1.0 1.0 1.0

a b c

9.7 10.5 5.1 5.6

203.8 275.0 82.5 152.0

5.4 2.5 2.2 2.1

17.8 7.5 5.8 5.9

0.0 0.0 0.0 0.0

28.1 27.6 34.3 29.1

0.3 0.2 0.1 0.1

5.2 2.3 2.0 2.0

4.5 2.0 2.0 2.0

0.8 0.4 0.5 0.4

1.8 1.1 1.0 0.9

55.9 15.0 13.6 11.4

16.3 7.2 5.5 4.8

H + Al (mmolc/dm3) 7.9 11.6 12.2 12.4

0.2 0.0 1.3 1.6

V (%)b 49.4 35.6 32.3 31.9

7.4 7.1 10.7 5.1

Organic Matter (g/dm3)

Al (mmolc/dm3)

CEC (mmolc/dm3) A B C D

33.1 20.0 21.7 39.6

55.0 31.0 8.0 17.0

432.0 508.0 544.0 531.0

K (mmolc/dm3)

P (mg/dm3)

SB (mmolc/dm3) A B C D

321.0 297.0 300.0 275.0

3.2.1. Correlation between NDVI and soil attributes Higher r values, relating NDVI with soil attributes, were obtained using Landsat 5 images from 2008 at the end of the wet season (May), indicating that the expression of vegetation occurred in this period. Indeed, in the dry season, the vegetation loses all green parts that affects NDVI data (Fig. 4). Fig. 4 shows the correlation results in moist and dry seasons for year 2008. This figure is divided into numeric correlation values (lower portion) and a visual representation (upper portion), and the variables are indicated in the diagonal. In addition, values marked with an “X” are not statistically significant at the 0.05 level. The first column and row represent NDVI correlation with each soil attribute. The following columns and rows represent correlation between soil attributes: a and c are results from May (for layers A and C, respectively), b and d are results from August (for layers A and C, respectively). At the end of the wet season, highest correlation data were obtained for the following chemical attributes: m% (0.56 for layer A and 0.57 for layer C), Al content (0.57 for layer A and 0.47 for layer C) and V% (− 0.55 for layer A and − 0.56 for layer C). For the dry season (August), there was lower correlation, however, the highest values in this period were obtained for similar soil attributes as previously observed, m% (0.29 for layer A and 0.30 for layer C), Al (0.28 for layer A and 0.31 for layer C), and V% (− 0.27 for layer A and − 0.28 for layer C). All this information indicates that the image spectra are related with these chemical attributes in the soil and the vegetation of this area is adapted to acid soils. Indeed, soils from Angola are mostly leached and acidic (White, 1983). The correlation kept the same trend at all depths and seasons, highlighting the strong evidence of the NDVI correlation.

100.4 64.7 49.4 49.7

30.5 27.0 25.5 24.9

m (%)c 96.5 99.0 91.3 93.5

34.0 19.6 18.3 17.8

0.0 0.0 0.0 0.0

97.4 98.8 99.2 99.2

3.2.2. Correlation between soil attributes The correlation values between soil attributes (Fig. 4) show a strong negative correlation between sand and clay (from − 0.93 to −0.98), which can be associated with the occurrence of Arenosols in the study site (soils with low clay content). The chemical attributes show a positive correlation between pH and V% (from 0.71 to 0.85) and negative correlation between pH and m% (from −0.72 to −0.86). Higher percentages of Al saturation occur with low pH values (< 5), characterizing an acidic environment. In addition, a positive correlation

38.6 65.2 69.5 71.1

A: 0–20; B: 40–60; C: 80–100; D: 100–120 cm. m (%) = Al ∗ 100 / CEC. V (%) = SB ∗ 100 / CEC.

Fig. 3. Variation of soil properties along depth.

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Fig. 4. Spearman's rank correlation coefficient (r) for wet season (a and c) and dry season (b and d); a and b represent layer A (0–20 cm); c and d represent layer C (80–100 cm).

associated with dystrophic soils, such as the Ferralsol class. In Brazil, this condition is common in areas of native savanna-type vegetation known as ‘Cerrado’, which largely occurs in the central portion of the country. The study site covers savanna areas (it also has Ferralsols as one of its main soil classes), with sparse medium-sized scleromorphic trees and shrubs, common characteristics of this vegetation class usually adapted to dystrophic soils and seasonal droughts (Haridasan, 1982). Ruggiero et al. (2002), studying soil-vegetation relationships in Cerrado regions in Brazil, found that native species from this forest physiognomy present high level of exchangeable Al at the surface soil (0.05–0.25 m), varying from 10 to 20 mmolc/kg. Therefore, it is suggested that the high levels of Al found in the surface soil in Cerrado areas are associated with the presence of litter, which contains Al decomposed from plant leaves. According to Diniz (1998), the Malange province, where the study site is situated, fits in the phytogeographic areas of open forest (known as “mata de panda”), which also includes related formations of savanna woodland. These areas are located in the central Angolan plateau and are characterized by an interchange of well-defined dry and rainy seasons and, in general, are correlated to Ferralsols. The open forests are peculiarly dominated by an upper tree stratum of Brachystegiae (mainly B. spiciformis and B. tamarindoides), Isoberlinia angolensis and Julbernardia paniculata as well as a lower shrub stratum of sparse elements, with the soil usually covered by sparse grass. Due to changes

between OM and CEC (with high values only for A layer, such as 0.84 and 0.81) was obtained, as the presence of OM increases CEC, especially in these soils that are naturally poor in fertility. These observations are in agreement with tropical soils from Angola (Hartemink and Huting, 2008). The correlation patterns and the correlation values, in general, remained similar according to the two soil depths analyzed. This fact may be attributed to the presence of homogeneous soil profiles with very low variations of physical and chemical attributes content at depths. Examples of homogeneous soil profiles include Ferralsols and Arenosols, with very similar horizons in terms of colors, minerals and fertility, with the exception of the topsoil, which is influenced by the OM content. 3.2.3. Vegetation and aluminum in the soil Overall, the soil attributes that showed a better correlation to vegetation were involved directly or indirectly with the Al content. The higher Al content in the study site may be associated with the development of vegetation adapted to acidic soils (Makhado et al., 2014), which explains the significant correlation between NDVI and soil attributes in relation to Al. In several parts of Africa, soil acidity and free Al hinder the availability of plant nutrients (Steiner, 1991), which inhibit the vegetation development that is not tolerant to this environmental condition. Some types of vegetation are adapted to Al presence, usually 44

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Fig. 5. Variation of mean NDVI values for six soil classes, in four months analyzed in the year of 2008.

in land use, typical savanna-shrub communities are replacing the initial plant physiognomy of primitive open forest areas, as observed in this work. Soils from the study site have an average pH (measured in H2O) of 5.2, and at pH below 5, Al in its trivalent form (Al3 +) is available in soil solution (Delhaize and Ryan, 1995), which implies an increase of Al cations concentration (Fig. 3), consequently decreasing the availability of exchangeable bases such as Mg, Ca, and K. This natural soil condition does not restrict plant development; however, forest communities in this region have evolved in such a way that they are adapted to this acidic environment, tolerating acidic soils with relatively low fertility.

3.2.4. Vegetation and water dynamics in the soil classes Vegetation behavior changed when different Landsat scenes were analyzed, as observed by a slight difference in the NDVI values according to each soil class (Fig. 5). Ferralsols showed the highest means for the NDVI values in April and May, which correspond to the end of dry season, followed by Lixisols. Ferralsols are deep, have good drainage and are intensely leached. Their mineralogy is essentially composed by Fe and Al oxides. When these soils are clayey, they are usually associated with good water storage capacity and enhanced root development. Cambisols and Plinthosols showed lower means for the NDVI values, as these soils limit root development. Cambisols have a poor developed B-horizon with the presence of rocky material. In Plinthosols, the presence of plinthite and, particularly, petroplinthite represent a serious obstacle to root penetration and water flow thus compromising tree establishment. In the dry season, notably in August, the NDVI values decreased substantially. The spectral pattern, in turn, remained similar for the soil classes. Ferralsols, associated with clay texture, showed the highest NDVI values in the moist season (April and May, Fig. 5), and had low NDVI values in August; still, the values were higher than in Arenosols. Arenosols had the lowest NDVI values in April and in August (Fig. 5), due to their low water availability. Lixisols had almost the same NDVI values as Ferralsols did in June and August (Fig. 5), as they also have good water storage at depth. The significant NDVI difference obtained between June and August (Fig. 5) shows the maximum expression of the dry season, when vegetation loses its green parts thus decreasing the NDVI values. The Shapiro-Wilk test was performed in the residuals generated in the Analysis of Variance (ANOVA). The NDVI data in function of soils

Fig. 6. Results of Dunn's test for (a) soils and (b) scene acquisition dates.

and season did not follow a normal distribution (p-value = 6.1 ∗ 10− 6). The Kruskal-Wallis rank sum test (Kruskal and Wallis, 1952), recommended for non-parametric data, was used to proceed with the statistical analysis. The Kruskal-Wallis one-way analysis of variance by ranks test is applicable in cases when the data do not follow requirements for one-way ANOVA (Pohlert, 2014). The Kruskal-Wallis rank sum test identified significant statistical differences regarding the NDVI values considering the six soil classes and four months analyzed (April, May, June and August), both with a pvalue < 0.01. The Dunn's test (Dunn, 1961) for multiple comparisons of independent samples verified which soil classes presented statistical differences in terms of the NDVI rank sum (Fig. 6a) and if this rank differed considering the months analyzed (Fig. 6b). In the case of soil classes, three groups were formed (Fig. 6a): a (only Arenosols), ab (Cambisols, Leptosols, Plinthosols and Lixisols) and b (only Ferralsols). Only groups a and b were considered statistically different. Arenosols differ from Ferralsols in the study site mainly because of texture, as Arenosols have a sandy to very sandy texture and are not significantly 45

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Fig. 7. Results of Dunn's test for (a) April, (b) May, (c) June and (d) August.

nutrients, presenting high contents of Fe and/or Al oxides. However, for this study site, besides soil fertility, the presence and storage of water in the soil strongly influence on plant development. Ferralsols have the best conditions for plant development, supporting the assumption that savanna vegetation is adapted to soils with low fertility and high Al content, within an acidic environment, which is in agreement with tropical soils from Brazil that occur in the central region of the country (Demattê and Demattê, 1993). In August (Fig. 7d), the maximum expression of the dry season led to a general decrease in the NDVI values, regardless of the soil class, which were statistically classified as equal. Still, Ferralsol and Lixisol store more water and resist further to water shortage. Cambisols are not well drained and thus do not provide good conditions for plant development, which is why in April (Fig. 7a) and May (Fig. 7b), their NDVI mean rank is lower. However, in the dry season, even though there is lack of water associated with this incipient soil class, nutrients are available as they are young soils. Nutrients help the maintenance of plants in this season. The same occurs with Leptosols that are usually affected by water shortages. Ferralsols have good drainage as they present clay texture, related with microaggregation. In the wet season, Ferralsols are associated with high water retention, but they do not saturate, providing good conditions for plant development. In the dry season, Ferralsols still keep water due to their high clay content. Lixisols have a sandy layer in horizon A and a clayey one in horizon B. This leads to low water loss in the moist season and, consequently, good water storage in the dry

associated with clay minerals in their composition. Ferralsols were differentiated because of their clay content from topsoil to undersurface layers. All months were considered statistically different in terms of NDVI (Fig. 6b) and the NDVI mean rank decreases with the occurrence of dry season, from April until August. Considering this significant difference, additional Kruskal-Wallis tests were performed for each month in order to test differences between soil classes, followed by the Dunn's test. The Kruskal-Wallis test detected differences in April, May and June (p-values < 0.01, respectively; for August, p-value = 0.3), and the Dunn's test verified differences in the groups formed (Fig. 7). In April, three groups were formed (Fig. 7a), similar to the ones presented in Fig. 6a. Arenosols were grouped with Lixisols (a), as they present surface horizons with high sand content, and this group differed from Ferralsols (b). Cambisols, Leptosols and Plinthosols were grouped together and, even though they did not statistically differ from the other groups, they are soils that restrict plant development at different levels. Differences in soil classes in May were intensified (Fig. 7b) and five groups were formed. Arenosols (a), Ferralsols (b) and Lixisols (c) were statistically different. In June, Ferralsols (b) and Arenosols (a) were again classified as different (Fig. 7c). Ferralsols were different from at least one group in all cases, except for August (Fig. 7d). Regardless of the month, Ferralsols enhanced the NDVI values and thus provided the best water dynamics conditions in the soil for plant development in the study site. Regarding fertility, Ferralsols are highly leached soils and poor in terms of essential plant 46

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Fig. 8. Vegetation spectral curve in (a) Moist and (b) Dry Seasons (B1: 0.45–0.52 μm; B2: 0.52–0.60 μm; B3: 0.63–0.69 μm; B4: 0.76–0.90 μm; B5: 1.55–1.75 μm; B7: 2.08–2.35 μm).

to infer certain vegetation characteristics as reported by Liu et al. (2012), Labrecque et al. (2006) and Foody et al. (2003). Overall, Ferralsols had higher chlorophyll absorption (in B1, B2 and B3) and higher water absorption (in B5) at the end of the moist season (Fig. 8a). Regarding the Near Infrared Region (NIR, ranging from 0.7 to 1.3 μm and expressed by B4), Ferralsols showed higher reflection, which indicates that vegetation in these soils are more vigorous, because it is assumed there is a larger number of green area in a healthy forest canopy when reflectance on NIR wavelength is higher. According to Ponzoni (2002), in this spectral region, there is usually a slight absorption of the electromagnetic radiation and a considerable spread inside the leaf. Nevertheless, water absorption is low and spectral reflectance is likely to be constant. In fact, spectral reflectance of leaves in the NIR region is attributed to an interaction between the incident energy with the mesophyll structure. Besides this, environmental factors such as water availability may alter the relation between water and air in the mesophyll thus modifying leaf reflectance in this region. Reflectance values from Landsat TM bands do not follow a normal distribution (p-value < 0.01); therefore, the Dunn's test was applied to

period. These two soil classes kept regular NDVI values in the dry season (Fig. 7d). The NDVI, corresponding to different soil classes and thus environmental factors, allowed to evaluate plant development under different soil conditions and, consequently, water dynamics. Moreover, these results suggested that vegetation type, soil texture, and soil waterholding capacity are directly related to NDVI, in agreement with Lozano-García et al. (1991). 3.3. Vegetation spectrum and soil classes Fig. 8a presents the vegetation spectral curve according to the soil class to which the extracted pixels belong, for the moist season (May 2008). Given that within the use of Landsat Thematic Mapper (TM) scenes there is only reflectance information regarding six bands, only the characteristics in these specific wavelengths attributed to each spectral band can be observed. These spectral bands have large ranges and approach discontinued portions of the spectrum, which implies in certain limitations for a more accurate analysis of absorption features from vegetation (Carvalho et al., 2003). On the other hand, it is possible 47

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Table 2 Dunn's test results for the comparison between reflectance values for each TM band (image from the end of moist season); □ represents Band 1; ◊ represents Band 2; ○ represents Band 3; ● represents Band 4; Δ represents Band 5; ⌂ represents Band 5. Symbols in red represent significant values at 0.01 level.

Cambisol

Ferralsol

Plinthosol

Lixisol

Leptosol

Arenosol

Cambisol Ferralsol Plinthosol Lixisol Leptosol Arenosol

vegetation spectral behavior indicate the presence of leaf pigments such as chlorophylls and carotenoids (Carvalho et al., 2003; Jensen, 2007). In the dry season, there is more reflectance in B1 and B3 (rather than the chlorophyll absorption attributed to Fig. 8a), which suggests the presence of non-photosynthetic vegetation in this period, including dry matter. Vegetation is usually in its dormancy period, as there is not enough water available. This fact is also supported by the reflectance pattern of B5 (Fig. 8b), higher than B4. Band 5 is associated with water stress when it presents higher reflectance than band 4 thus showing lower biomass (Carvalho et al., 2003). Fig. 8a also shows high reflectance values in B5, indicating a decrease of water content in plant leaves, given that the period analyzed corresponds to the end of the moist season. In relation to soils, in the dry season, Ferralsols presented lower reflectance values in B5, and it is the soil class best suited for resisting water stress conditions attributed to this period. For the NIR region, Leptosols presented higher reflectance values. Leptosols are not related to the development of vigorous vegetation. Besides, there are only small areas covered by this soil (especially after the application of the mask, when only four Leptsol auger points were kept for the extraction of reflectance values, while there are at least 15 points for soil classes such as Arenosols, Ferralsols, and Lixisols); therefore, the spectral behavior of vegetation in this soil class (Fig. 8b) may not be sufficiently representative of the real conditions of plants. The results of the Dunn's test to compare reflectance values for each TM band in August (Table 3) presented fewer differences between soils than in May. Ferralsols were statistically different considering reflectance values in B1, B2, B3 and B5 from Lixisols, Leptosols and Arenosols, but they were not differentiated from Plinthosols and Cambisols. This may be attributed to the presence of dry matter influencing the vegetation spectra thus masking soil conditions that can be otherwise identified in the moist season.

detect statistical differences between bands in relation to soils. The results for the comparison between reflectance values for each TM band in May (Table 2) show that for B4 Ferralsols were statistically different from all other soils, confirming that vegetation in this soil type is more vigorous comparing to the others. Analyzing the spectral curves in Fig. 8a, B4 is actually more differentiated considering all soil classes as this band represents well the spectral behavior of vegetation. Plinthosols presented low reflectance in May (Fig. 8a), which can be associated to vegetation presence under different conditions, such as diminished water storage due to physical conditions that act as an obstacle to root penetration. Leptosols and Cambisols also presented low reflectance due to similar reasons and the spectral curve of these soils were considered statistically equal (Table 2). Arenosols have a sandy texture with high contents of minerals such as quartz, which implies in a higher reflectance pattern when compared to clayey soils. Arenosols were statistically different in all bands in relation to Ferralsols (Table 2), which have the presence of oxides, are darker and with lower reflectance patterns than Arenosols. The influence of soil background could be causing the high reflection presented by these sandy soils (Todd and Hoffer, 1998; Kancheva and Borisova, 2004). There are clear differences of vegetation between Fig. 8a (moist season) and 8b (dry season). The influence of water on vegetation spectral signature is observed in the decrease of reflectance from band 4 to band 5 (Fig. 8a). In the dry season, the opposite occurs (Fig. 8b). According to Jensen (2007), when the water content of leaves decrease, reflectance in the region of Short Wave Infrared (SWIR) is likely to increase, and the wavelength ranges between 1.5 and 1.8 μm and 2.1–2.3 μm are very sensitive to changes in the water content of plants. A sensor such as Thematic Mapper (Landsat 5) has band 5 (1.55–1.75 μm) and band 7 (2.08–2.35 μm) in order to capture this sensibility in the water content of plants. Moving from wet to dry season, the reflection increases in band 5, in agreement with the spectral signature (Fig. 8b). This also agrees with Fig. 5, where the NDVI decreases from wet (around 0.80) to dry season (around 0.40) thus indicating water stress to plants. Differences in the visible (B1 to B3) and NIR (B4) spectra for

3.4. Soil attributes thematic maps The maps of Al saturation (m) in layers A (Fig. 9a) and C (Fig. 9b)

Table 3 Dunn's test results for the comparison between reflectance values for each TM band (image from the dry season); □ represents Band 1; ◊ represents Band 2; ○ represents Band 3; ● represents Band 4; Δ represents Band 5; ⌂ represents Band 5. Symbols in red represent significant values at 0.01 level.

Cambisol

Ferralsol

Plinthosol

Cambisol Ferralsol Plinthosol Lixisol Leptosol Arenosol

48

Lixisol

Leptosol

Arenosol

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Better fertility situation in surface

Lower fertility in depth

Fig. 9. Maps of Aluminum Saturation (%) in layers A (a) and C (b) and Clay (g kg− 1) in layers A (c) and C (d) obtained from the algorithm Spectral Angle Mapper.

The maps of clay content in layer A (Fig. 9c) and layer C (Fig. 9d) presented GA values of 0.45 and 0.41, respectivelly. Following the same trend of m, the clay content was usually higher in undersurface than in topsoil, particularly in areas with clay content from 251 to 350 g kg− 1. This relation is common for the Lixisols present in the study site, soils with clay accumulation in undersurface layers due to pedogenetic processes. The soil map of the study site (Fig. 10) shows this increase in

presented Global Accuracy (GA) of 0.43 and 0.57, respectively. In layer C, the values of m are higher than in layer A, with Al accumulation with increasing soil depths. In fact, the accuracy of the map in layer C was higher than that from layer A. This occurs because vegetation spectra was better related with the undersurface soil than with the surface layer for Al. In the topsoil, the presence of OM decreases Al availability in the soil, because Al may be trapped in organic colloids.

Fig. 10. NDVI calculated from image of May 20, 2008 with soil map lines, representative soil profiles of main classes and the vegetation in the dry season. Where AR = Arenosol, FL = Ferralsol, LX = Lixisol, PT = Plinthosol. Cambisols (CM) and Leptsols (LP) occur in a very small extent and it was not possible to represent them in this map. In the upper right corner, the spectral signature of FL, LX and AR and the vegetation spectra from the same pixels in an image of May 20, 2008.

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vegetation in the wet season. NDVI indicated correlation with soil attributes at depth. 3. The soil attributes best related with vegetation were directly or indirectly involved with Al. The higher Al content in the study site is associated with the development of vegetation adapted to this condition, a native savanna-type, particularly in acidic and leached Ferralsols. 4. Soil classes indicated variation in vegetation spectral signatures and NDVI. This occurred due to chemical and particle size of soil attributes and the water dynamics inherent to each soil class architecture. Thus, vegetation information can be used to assist the mapping of soils.

clay content from layer A to C occurs mainly in the areas covered by Lixisols. The increase in clay content in undersurface layers may contribute to the increase of Al saturation, considering that clay mineralogy is composed by Fe and Al oxides. Areas with higher NDVI in May (Fig. 10) presented higher clay and Al saturation, which remains similar in soil layers A and C. Higher NDVI values are associated with an acidic and poor environment in terms of fertility, as with clayey soils. This strongly suggests that the development of plants in this region is more conditioned by the presence of certain environmental factors that can be considered harmful to some plant types than by the absence of others, such as good soil fertility. Analyzing the presence of water in soils only by NDVI, plants are more vigorous when there is water available, as in the case of the end of the moist season. Therefore, the wet season is indicated as the best period to analyze vegetation in order to reach soil information. This affirmation is also supported by the association between high NDVI values (Fig. 10) and high clay content (Fig. 9a and b), in regions with occurrence of Ferralsols (mainly) and Lixisols (Fig. 10). Clayey soils have higher water storage capacity due to a higher colloidal surface area and the presence of both macro and micro pores in soil structure. These factors enhance the water storage capacity when compared to much lower surface areas attributed to the soil sand fraction also associated with a more significant presence of macro pores that enhance water drainage – characteristics of Arenosols. Spectra of main soils and their respective vegetation cover are presented in Fig. 10. As observed, spectra of bare soil indicated a flat curve for Ferralsol and low intensity in band 5, and the inverse for Arenosol, due to mineralogy and clay texture. Looking at the vegetation over these soils in the end of the moist season, there is not much alteration considering what was discussed in Section 3.3.

Acknowledgments We wish to thank the Department of Soil Science from Luiz de Queiroz College of Agriculture (University of São Paulo) and the São Paulo Research Foundation (FAPESP) for the second author scholarship (FAPESP Process 2014/04005-0) and Biocom for sampling and soil analysis, which enabled to carry out this research, as well as the Geotechnologies in Soil Science group (http://esalqgeocis.wix.com/ english) for all the support. References Bouyoucos, G.J., 1927. The hydrometer as a new method for mechanical analysis of soils. Soil Sci. 23 (5), 343–352. Carvalho, A.P.F., Bustamante, M.M.C., Guimarães, R.F., Carvalho Júnior, O.A., 2003. Classificação de Padrões de Vegetação na Região de Transição entre o Cerrado e a Floresta Amazônica. In: Anais XI Simpósio Brasileiro de Sensoriamento Remoto, Belo Horizonte, Brasil, 05–10 abril 2003. INPE, pp. 2679–2687. Chang, C., et al., 2001. Near infrared reflectance spectroscopy – principal components regression analysis of soil properties. Soil Sci. Soc. Am. J. 65, 480–490. Cooley, T., et al., 2002. FLAASH, a Modtran4-based atmospheric correction algorithm: its application and validation. IEEE Trans. Geosci. Remote Sens. 3, 1414–1418. Delhaize, E., Ryan, P.R., 1995. Aluminum toxicity and tolerance in plants. Plant Physiol. 107, 315–321. Demattê, J.L.I., Demattê, J.A.M., 1993. Comparações entre as Propriedades Químicas de Solos das regiões da Floresta Amazônica e do Cerrado do Brasil Central. Sci. Agric. 50 (2), 272–286. Demattê, J.A.M., Morgan, C.L.S., Chabrillat, S., Rizzo, R., Franceschini, M.H.D., Terra, F.S., Vasques, G.M., Wetterlind, J., 2016. Spectral sensing from ground to space in soil science: state of the art, applications, potential, and perspectives. In: Thenkabail, P.S. (Ed.), Land Resources Monitoring, Modeling, and Mapping with Remote Sensing. Remote Sensing Handbook Series, vol. 2. CRC Press, Boca Raton, pp. 661–732. Dewitte, O., Jones, A., Elbelrhiti, H., Horion, S., Montanarella, L., 2012. Satellite remote sensing for soil mapping in Africa: an overview. Prog. Phys. Geogr. 36 (4), 514–538. Diniz, A.C., 1998. Angola: o meio físico e potencialidades agrárias. Cooperação Portuguesa, Lisboa (176 p). Dunn, O.J., 1961. Multiple comparisons among means. J. Am. Stat. Assoc. 56, 52–64. European Commission, 2013. Soil Atlas of Africa. http://eusoils.jrc.ec.europa.eu/library/ maps/africa_atlas/index.html (accessed 17.02.14). FAO, 2016. State of the World's Forests 2016. Forests and Agriculture: Land-Use Challenges and Opportunities (Rome). FAO, 2010. Global Forest Resources. Assessment 2010. Main Report (Rome). FAO, 2009. How to feed the world in 2050. http://www.fao.org/fileadmin/templates/ wsfs/docs/expert_paper/How_to_Feed_the_World_in_2050.pdf (accessed 13.02.14). Farrar, T.J., Nicholson, S.E., Lare, A.R., 1994. The influence of soil type on the relationship between NDVI, rainfall and soil moisture in semi-arid Botswana. Remote Sens. Environ. 50, 121–131. Fölster, H., Dezzeo, N., Priess, J.A., 2001. Soil-vegetation relationship in base-deficient premontane moist forest-savanna mosaics of the Venezuelan Guayana. Geoderma 104, 95–113. Foody, G.M., Boyd, D.S., Cutler, M.E.J., 2003. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sens. Environ. 85, 463–474. Ge, Y., Thomasson, J.A., Sui, R., 2011. Remote sensing of soil properties in precision agriculture: a review. Front. Earth Sci. 5 (3), 229–238. Haridasan, M., 1982. Aluminium accumulation by some cerrado native species of central Brazil. Plant Soil 65, 265–273. Hartemink, A.E., Huting, J., 2008. Land cover, extent, and properties of Arenosols in southern Africa. Arid Land Res. Manag. 22, 134–147. IUSS Working Group WRB, 2015. World Reference Base for Soil Resources 2014, Update 2015: International soil Classification System for Naming Soils and Creating Legends for Soil Maps. World Soil Resources Reports n. 106 FAO, Rome (203 p). Jensen, J.R., 2007. Remote Sensing of the Environment: An Earth Resource Perspective, second ed. Prentice Hall, Upper Saddle River, NJ (592p). Kabata-Pendias, A., 2011. Trace Elements in Soils and Plants, fourth ed. CRC Press, Boca

4. Final remarks The importance of new strategies to characterize soils is a real necessity (Demattê et al., 2016). Vegetation is usually an expression of several factors such as climate and soils. Regarding soils, two points must be considered: (a) soil attributes – the unique inherent characteristic, a soil property, such as physical (clay, sand and silt) or chemical (such as Ca, Mg, K, Al); and (b) soil class – related to the pedon, which is a body composed by several attributes, disposed in different ways, contents and in a singular morphology architecture. Thus, soil attributes indicate a single data that can influence vegetation. On the other hand, soil classes indicate the water dynamics along the profile. The results of this study show the importance of evaluating both situations, since an Arenosol can have good drainage but no water storage. A Lixisol indicates variation of texture between horizons A and B, which creates a situation where soil stores more water. In both cases, chemistry and texture (soil attributes) in horizon A can be the same, but their intrinsic architecture is different and this affects water dynamics and thus vegetation. Indeed, obtaining information from vegetation-covered soil is a different method. On the other hand, vegetation expression showed important inferences and correlations with soil attributes and classes. This methodology can assist soil mapping in natural forested areas with difficult access. It is indicated to have the soil map before altering or managing natural vegetation domains; otherwise, the soil cannot be assessed through this approach after vegetation removal. 5. Conclusions 1. It was possible to obtain significant correlation between NDVI from natural vegetation and soil attributes such as Al content, Al saturation and base saturation. The NDVI indicated strong correlation between vegetation and soil attributes from images obtained at the end of moist season when compared to the dry season. 2. Top and undersurface soil analyses showed great relationship with 50

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