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Relationships among soil fertility dynamics and remotely sensed measures across pasture chronosequences in Rondônia, Brazil
Remote Sensing of Environment 87 (2003) 446 – 455 www.elsevier.com/locate/rse
Relationships among soil fertility dynamics and remotely sensed measures across pasture chronosequences in Rondoˆnia, Brazil I. Numata a,*, J.V. Soares b, D.A. Roberts a, F.C. Leonidas c, O.A. Chadwick a, G.T. Batista d a
Department of Geography, EH 3611, University of California at Santa Barbara, Santa Barbara, CA 93106, USA b Instituto Nacional de Pesquisas Espaciais, Sa˜o Jose´ dos Campos, Sa˜o Paulo, Brazil c Centro de Pesquisa de sistema Agro-florestal, Empresa Brasileira de Pesquisa Agropecua´ria, Rondoˆnia, Brazil d Universidade de Taubate´, Taubate´, Sa˜o Paulo, Brazil Received 15 June 2002; received in revised form 14 July 2002; accepted 18 July 2002
Abstract This study analyzed the relationships between soil fertility and remotely sensed measures over three pasture chronosequence sites in the state of Rondoˆnia, in the western Brazilian Amazon region. Remotely sensed measures included shade, nonphotosynthetic vegetation (NPV), green vegetation (GV) and soil (derived from spectral mixture analysis), and the normalized difference vegetation index (NDVI). These were correlated against soil fertility parameters such as phosphorus, potassium, calcium, and base saturation. In temporal analysis, it was observed that NPV dominated the spectral responses of pasture canopies and tended to increase with pasture age as well. The increase of NPV appeared to be related to the decline of soil fertility, but soil texture variation also played a role. In the correlation analysis, soil P, known as the most limiting nutrient for pasture productivity, showed the highest correlation with remotely sensed measures, followed by soil K and base saturation. However, this result was not observed at the sites where nutrient availability was very low. D 2003 Elsevier Inc. All rights reserved. Keywords: Pasture chronosequence; Soil fertility dynamics; Remotely sensed measures
1. Introduction The conversion of tropical rain forest to cattle pasture is one of the most common patterns of land-use change in the Amazon Basin. Rapid pasture degradation and abandonment due to low soil fertility and inappropriate pasture management is a critical factor sustaining high deforestation rates in this region (Buschbacher, 1986; Falesi & Veiga, 1987; Fearnside, 1980; Nepstad, Uhl, & Serra˜o, 1991). In order to understand the ecosystem-scale impacts of land conversion, it is necessary to study how biophysical and biogeochemical processes operate in pastures as they age (Kauffman, Boone, Cumminga, & Ward, 1998; Moran, Brondizio, Mausel, & Wu, 2000; Serra˜o & Toledo, 1991). Remotely sensed data are critical for quantitative estimates of the different land-cover types and land-cover change detection over time (Alves, Pereira, De Souza, Soares, & Yamaguchi, 1999; Roberts, Batista, Pereira, Wal-
* Corresponding author. E-mail address: [email protected] (I. Numata). 0034-4257/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2002.07.001
ler, & Nelson, 1998; Skole & Tucker, 1993). The combination of field data with temporal analysis of remotely sensed data has provided useful information for the analysis of ecological processes such as CO2 flux and sequestration associated with forest regeneration (Foody, Pulubinskas, Lucas, Curran, & Honzak, 1996; Lucas et al., 1996). Focusing on grassland, the normalized difference vegetation index (NDVI) has been widely used for evaluating the spatial pattern of vegetation type and its productivity (Gamon, Field, Roberts, Ustin, & Valentini, 1993). In addition to photosynthetic materials, largely related to plant leaves, nonphotosynthetic materials (NPV), such as dry leaf and litter, are very important features for characterization of grassland, as well as background soil types (Asner, 1998; Roberts, Smith, & Adams, 1993; van Leeuwen & Huete, 1996). Although many studies have evaluated the ability of remote sensing to quantify biophysical measures such as leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fAPAR), and biomass (Asrer, Myneni, & Choudhury, 1992; Gao, Huete, Ni, & Miura, 2000; van Leeuwen et al., 1997), the relationships between
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biogeochemistry and remote sensing have not been studied in depth. Since pasture productivity in the Amazon region is highly dependent on soil fertility, the analysis of the relationship between remotely sensed measures focused on pasture biological features and soil physical and chemical characteristics could provide useful information for monitoring ecological change in this important land-cover class. Asner, Townsend, and Bustamante (1999) observed that hyperspectral estimates of LAI and nonphotosynthetic vegetation area index (NPVAI) of pasture were correlated with soil P and Ca concentrations across a pasture chronosequence in the central Amazon. The primary objective of this study was to analyze the relationships between remotely sensed biophysical measures such as spectral fractions [shade, NPV, green vegetation (GV), and soil] and NDVI, and soil physical and chemical properties over pasture chronosequences (pastures with different ages of installation). In this study, we addressed the following questions: (1) Which ecosystem nutrients are the most correlated to remotely sensed measures? (2) Which remotely sensed measures are appropriate for expressing pasture condition related to soil fertility dynamics?
2. Methods 2.1. Study site The study was conducted in the state of Rondoˆnia, in the western Brazilian Amazon, in the municipalities of Theobroma, Jaru, and Ariquemes (Fig. 1). The climate is humid tropical, Awi, according to the Ko¨ppen classification, with a well-defined dry season during July and August. Annual precipitation is around 2250 mm (RADAMBRASIL, 1978). The average temperature is 24 jC, while relative humidity
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varies between 80% and 85%. The natural vegetation consists of dense tropical forests and open tropical forests (INPE, 2000). This site is flat to gently rolling. The dominant soil types in this region are ‘‘Podzo´lico Vermelho Amarelo’’ and ‘‘Podzo´lico Vermelho Escuro’’ according to the Brazilian soil classification system—classes that fall into the oxisol and ultisol soil orders in the US soil classification system. Local variation of soil classes occurs and we do not classify the soils that were sampled for this study, preferring instead to present the relevant soil nutrient properties. The conversion of primary forest to pasture is the dominant form of land-use change in Rondoˆnia. Pasture size can vary from 100 – 200 ha to more than 2000 ha. Other land-use types in this region include perennial crops such as coffee, and annual crops such as corn, rice, and soybean (Pedlowski, Dale, Matricardi, & Filho, 1997). 2.2. Field work and soil sampling Fieldwork was conducted between July and August 1998, during the dry season, at three study sites (A, B, and C) (Fig. 1). Each site had a pasture chronosequence consisting of 1– 2-year-old, 3 –5-year-old, 6 –10-year-old, and >10-year-old age classes. To identify pasture age classes, a land-use age map was developed using multitemporal shade fraction images (Numata, Soares, Batista, Roberts, & Chadwick, 2000). Pasture management history for the study sites was obtained through interviews with landowners (Table 1), and consistency of information was checked against the multitemporal Landsat TM data. The three study sites have been subjected to similar management: similar pasture grasses, animal density, fallow period, etc. None of the sites was fertilized. Although pioneer species were observed in the youngest pastures, they were not subsequently invaded by second growth shrubs and forests.
Fig. 1. Map showing the study region and locations of the study sites (A, B, and C).
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Table 1 Pasture management for the study sites Study sites
A
B
C
Species
Brachiaria brizantha cv. Marandu Dry season: 1.6; rainy season: 2.4 40 – 50 days
B. brizantha
B. brizantha
Dry season: 1.4; rainy season: 2 40 – 50 days
Dry season: 1.6; rainy season: 2.4 40 days
Cattle/ha
1
Fallow period Pasture fire Two times at the beginning of pasture formation Clearing Not mechanized method Fertilization None
Two times in the Two times at the beginning of beginning, and pasture formation then in each 4 – 5 years, if necessary Not mechanized Not mechanized None
None
Soil samples were collected from a 0- to 20-cm depth in each age class at each site. Each sample was aggregated from three to four subsamples, taken at a distance of 150 –200 m apart, in topographically homogeneous locations. In total, 44 aggregated samples were analyzed. Soil samples were dried at 110 jC for 24 h, and sieved at 2 mm. Soil chemical analyses included available P, and exchangeable K, Ca, Mg, Al, pH, effective cation exchangeable capacity (ECEC), and base saturation, according to the techniques described by EMBRAPA (1997). The Melich extractor was used to extract available P and exchangeable K. This extraction procedure utilizes 0.025 N H2SO4 and 0.05 N HCl to remove labile forms of these nutrients. Phosphorus was determined using spectrophotometry, while exchangeable K was determined by flame photometry. Exchangeable Ca and Mg were extracted with 1 N KCl, and quantified by titration with EDTA. Exchangeable Al was extracted with 1 N KCl and quantified by titration with 0.025 N NaOH. Soil reaction (pH) was measured using a standard calomel electrolyte pH meter. Base saturation was calculated as: V = 100S/CEC, where S = Ca + Mg + K and CEC = S + H + Al. Detailed information on soil chemical analysis is given in Numata (1999). For comparative analysis to the remotely sensed data in this study, available P, exchangeable K and Ca, ECEC, and base saturation were considered.
the modified empirical approach (Roberts et al., 1998; Smith, Susan, Adams, & Gillespie, 1990). Using this approach, encoded radiance from Landsat TM data was regressed against laboratory and field-measured reflectance from soils, water, and NPV collected over two Amazon sites, Rondoˆnia and Manaus (see Roberts et al., in press). Because no plant spectra were available for tall trees at an appropriate spatial scale, spectra of broadleaf deciduous and evergreen forests in North America measured by the Airborne Visible Infrared Imaging Spectrometer were used. A more detailed description of reflectance retrieval is provided by Roberts et al. (in press), in which retrieved reflectance from a 1992 reference image was ported to a time series of TM images using relative radiometric calibration techniques (Furby & Campbell, 2001) using 25 invariant targets. Reference endmembers, spectra of known materials, were selected from the same spectral library used to retrieve surface reflectance. The methods for endmember selection are described in Roberts et al. (1998). The spectral mixture model was calculated over the image and generated four fraction images including shade, green vegetation, nonphotosynthetic vegetation (NPV) and soil, and root mean square (RMS) error image. The RMS error was not used in our analysis. In addition, the normalized difference vegetation index image was calculated using Band 3 (Visible R) and Band 4 (NIR) from the Landsat ETM calculated from sensor radiance. Spectral fractions and NDVI data were extracted from corresponding soil sample plots for each study site using GPS coordinates obtained in the field. Mean values and corresponding standard deviations were calculated for each age class. Based on the fraction data, two additional vegetation indices were included for this study: NPV + Soil and NPV/(GV + NPV). Since it is difficult to distinguish NPV from soil spectrally (Roberts et al., 1993), the sum of both fractions would represent the nonvegetation part. On the other hand, NPV/(GV + NPV) indicates the proportion of the dead or senesced vegetation relative to total vegetation amount. In order to evaluate the relationships between soil fertility data and remotely sensed data, we carried out linear regression analysis between these two datasets.
2.3. Remotely sensed data 3. Results A Landsat Enhanced Thematic Mapper (ETM) scene from path 232, row 67 was used for this study, in order to derive the remote sensing attributes of the pasture-covered land. Because no cloud-free image was available for the time frame in which the fieldwork was conducted, we employed an image acquired on August 4, 1999, during the dry season. These data were coregistered using 26 tie points to a georectified PRODES image from 1999, provided by the ‘‘Brazilian Institute for Space Research’’ (INPE), and then resampled using the nearest neighbor resampling. To compare satellite image data to laboratory or field measured spectra, reflectance retrieval was performed using
3.1. Biogeochemical dynamics in pasture chronosequence The soil biogeochemical data for the study sites included P, K, Ca, ECEC, base saturation, and clay content (Table 2). Site A had the highest value of both P and base saturation. Soil P at site A varied from 4 to 7.5 times higher than sites B and C. Other elements such as K, Ca, and ECEC were similar among the sites. Site B appeared to be the least fertile with the highest variability in soil fertility, as suggested by the standard deviation of its chemical properties. Particle size separation demonstrated that site A had the lowest clay
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Table 2 Soil physical and chemical data in pasture chronosequence in the study sites (standard deviation in parentheses) Age (years)
N
Clay content (g/kg)
P (mg/dm3)
K (cmol/dm3)
Ca (cmol/dm3)
ECEC (cmol/dm3)
Base saturation (%)
Site A 1–2 3–5 6 – 10 >10
3 4 3 4
150 156 177 271
(75.0) (104) (85.0) (49.0)
15.00 10.80 6.00 4.80
(0.00) (1.71) (0.00) (0.50)
0.43 0.34 0.40 0.23
(0.06) (0.08) (0.26) (0.06)
1.55 1.85 0.97 0.93
(0.21) (0.81) (0.29) (0.21)
2.83 3.22 2.10 1.95
(0.22) (1.01) (0.46) (0.34)
54.5 46.5 40.7 30.8
(16.3) (7.0) (7.2) (6.0)
Site B 1–2 3–5 6 – 10 >10
4 5 6 3
284 280 258 223
(73.0) (94.0) (49.0) (71.0)
3.50 3.80 1.00 1.00
(1.91) (2.17) (0.00) (0.00)
0.54 0.42 0.18 0.42
(0.14) (0.12) (0.05) (0.31)
1.15 1.24 0.65 0.53
(0.99) (0.32) (0.29) (0.21)
2.42 2.68 1.78 1.79
(0.91) (0.54) (0.39) (0.10)
39.0 37.0 22.5 25.3
(17.6) (8.2) (6.4) (2.5)
Site C 1–2 3–5 6 – 10 >10
4 3 4
226 (74.0) 255 (45.0) 284 (36.0)
2.00 (0.82) 1.00 (0.00) 1.25 (0.50)
0.35 (0.17) 0.44 (0.09) 0.14 (0.09)
1.68 (1.01) 1.35 (0.21) 1.15 (0.68)
2.98 (1.23) 2.54 (0.23) 2.32 (0.80)
41.5 (21.1) 43.5 (2.1) 27.8 (13.4)
N = number of aggregated samples. One sample is composed of three to four subsamples.
content, except for >10-year-old age class. While the >10year age class had similar clay content to sites B and C, the younger classes of site A had 30% less clay, indicating that these pastures are on different soil. The impact of differences of clay content among sampling sites is discussed below. Temporal analysis indicated a trend of decreasing P, Ca, ECEC, and base saturation over time (Table 2). Phosphorus at site A decreased from 1 to 2 years old, while site B showed decay in P from the 3 –5-year-old age class. Calcium and ECEC in all study sites increased up to 3– 5 years old, and then started decreasing. Potassium, unlike the other soil parameters, did not linearly decrease over time. This fact may be attributed to the recycling efficiency of this element in the soil – plant –animal system (Serra˜o, Falesi, Veiga, & Teixeira, 1982). Overall, the results appear very similar to those described by Correa and Reichardt (1995), Teixeira and Bastos (1989). There are two major stages controlling the dynamics of these soil parameters in pasture over time. The first is the period of early pasture installation (about the first 5 years), where the dynamics depends largely on ash deposition derived from burning following deforestation and subsequent burning of other vegetations such as regrowing forest species, weeds, and woody debris. After this period, when no ash addition occurs in pastures, the soil fertility dynamics is controlled mostly by environmental factors such as soil type (physical and chemical properties) and erosion and leaching, in addition to pasture management practices (types of grass and cattle density) according to Falesi and Veiga (1987) and Serra˜o and Toledo (1991). 3.2. Changes of remotely sensed measures in pasture chronosequence Fig. 2 and Table 3 show changes in remotely sensed measures over time. NPV increased with pasture age, while
other fractions such as GV, shade, soil, and NDVI showed the opposite pattern across all study sites. The dynamics of these fractions with pasture age should be largely related to changes of vegetation composition and structure in pasture canopies over time. In general, young pastures consisted of high amounts of residual woody debris from deforestation, shrubs, and very low grass cover, while older pastures show a homogeneous land cover with higher amounts of biomass in herbaceous materials, including litter/dead grass components and low amounts of woody debris (Kauffman et al., 1998; Numata, 1999). Senesced plant materials tend to increase with pasture age because old leaves of forage become photosynthetically less efficient and tend to be dead or senesced (Corsi & Junior, 1994). Older pastures consisted almost entirely of NPV. In the >10-year-old class, NPV reached around 85% or more for all study sites (85%, 93%, and 90% for A, B, and C, respectively). The ratio of NPV to total vegetation (NPV/ (NPV + GV)) also indicated that senesced material was the dominant form of vegetation for the >10-year-old pastures, showing values of 1.01, 1.01, and 0.99 for A, B, and C, respectively. The prevalence of NPV is partly due to the timing of image acquisition. The image was acquired during the dry season, when most pastures are partly or wholly senesced due to water deficit (some pastures in Rondoˆnia remain green throughout the year due to the quality of management; Paul Steudler, personal communication). Low GV fractions may also be attributed to poor sensitivity of Landsat TM to sparse amounts of live vegetation ( < 10%; Elmore, Mustard, Manning, & Lobell, 2000). For example, Asner (1998), using a radiative transfer model to test the sensitivity of canopy reflectance to biophysical and structural attributes, noted that changes in litter biomass had a far greater impact on canopy reflectance variability than a comparable change in LAI in grassland biomes. Okin,
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Fig. 2. Changes in remotely sensed measures over pasture chronosequence at the study sites: (a) shade, (b) GV, (c) NPV, (d) soil, (e) NPV+Soil, (f) NDVI, and (g) NPVI/(GV+NPV).
Roberts, Murray, and Okin (2001), using simulated mixtures of soils, NPV, and vegetation, noted that hyperspectral data could not confidently retrieve vegetation cover at cover fractions below 10% in the case of a high spectral contrast between vegetation and soils. As canopy spectra became less distinct from a soil/litter background, the minimum threshold for mapping vegetation cover accurately increased to as much as 30%. In essence, given Landsat TM data, sparse amounts of green vegetation are not likely to be mapped accurately within a matrix dominated by NPV, although changes in GV from one date to the next are
likely to be mapped with greater accuracy (Elmore et al., 2000). At site B, high NPV and low GV were observed in the 1 – 2-year-old class—a result opposite to site A and attributable to burning just prior to the acquisition of the satellite imagery. Consequently, the corresponding remotely sensed measures at the same age class were low in GV and very high in NPV, with similar values of older pasture classes. The biophysical interpretation related to pasture structural changes over time, as discussed above, follows the shade fraction behavior. The changes in vegetation structure, from
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Table 3 The means and standard deviations of remotely sensed measures for the study sites Age (years)
Site
N
1–2
A B A B C A B C A B C
72 108 126 135 108 81 171 72 135 81 108
3–5
6 – 10
>10
Shade 14.2 (5.7)a 7.1 (9.7)b 10.8 (6.2)a 15.5 (4.4)b 13.2 (5.4)c 7.3 (5.7)a 8.0 (6.5)b 11.3 (6.0)c 6.9 (7.7)a 1.0 (16.4)b 1.4 (8.1)b
NPV 52.1 82.7 68.1 63.7 67.1 87.5 77.5 73.3 85.1 93.2 90.2
GV (8.9)a (21.0)b (9.9)a (10.9)b (12.6)a,b (10.4)a (12.0)b (8.7)c (13.9)a (30.5)b (12.7)a,b
21.9 (4.1)a 0.9 (8.6)b 5.3 (3.7)a 8.6 (6.3)b 8.7 (6.7)b 2.4 (5.0)a 2.4 (5.8)b 6.2 (3.5)c 1.3 (5.6)a 2.8 (9.3)a 1.1 (3.6)b
Soil
NDVI
9.8 (2.2)a 7.3 (4.0)b 13.9 (3.6)a 10.9 (2.3)b 9.1 (4.1)c 5.7 (2.9)a 10.2 (3.4)b 7.1 (3.9)a 7.3 (2.7)a 8.5 (6.1)a 5.3 (3.8)b
0.30 0.18 0.18 0.21 0.21 0.16 0.18 0.20 0.15 0.15 0.19
(0.03)a (0.04)b (0.02)a (0.04)b (0.04)b (0.02)a (0.04)b (0.03)c (0.03)a (0.03)a (0.02)b
NPV + Soil
NPV/ (GV + NPV)
61.9 (8.6)a 90.0 (17.7)b 82.1 (8.7)a 73.9 (9.9)b 76.1 (11.0)b 93.2 (8.4)a 87.7 (11.0)b 80.4 (9.2)c 92.5 (12.2)a 101.7 (25.3)b 95.4 (10.8)b
0.70 0.98 0.92 0.88 0.88 1.03 0.97 0.92 1.01 1.01 0.99
(0.08)a (0.10)b (0.05)a (0.10)b (0.10)b (0.06)a (0.07)b (0.05)c (0.07)a (0.10)a (0.04)a
a, b, and c mean statistical difference (Tukey, 95%). N = number of sampled pixels.
heterogeneous and tall vegetation structures in young pasture to smooth and low structures in older pasture, may account for the decreased shade fraction over time as observed in the study sites. On the other hand, this inference linking canopy structure to pasture age may not be valid in some cases. For example, abandoned pastures could have very similar structures to young pastures, due to the presence of regenerating species and weed invasion (Uhl, Buschbacher, & Serra˜o, 1988), which may lead to confusion with other land-cover classes such as young pasture and second growth forest. In our study region, where the rates of pasture abandonment are low, this kind of problem should not be of a major concern. The soil fraction showed similar behavior to the shade and GV fractions, but with a much smaller overall magnitude. Low values of the modeled soil fractions occur largely because the endmembers selected for the model were calibrated to provide fractions comparable to field estimates and NPV was observed to be the dominant component within pastures. However, considerable caution must also be exercised when interpreting the soil fraction due to potential spectral similarity between NPV and soils. Although many soils are spectrally distinct from NPV using TM wavelengths, the only definitive separation between these two materials is the presence of lignocellulose bands in senesced materials (Nagler, Daughtry, & Goward, 2000; Roberts et al., 1993). For this reason, an alternate approach towards reporting separate fractions for NPV and soil is to report the sum of NPV and soil, in effect creating the equivalent of a threeendmember model (Roberts et al., 1998). Because NPV is the dominant material within most pastures, NPV + Soil resembles NPV dynamics. Soil clay content may impact the behavior of the measured vegetation fractions such as NPV and GV. For example, higher NPV and NPV/(GV + NPV) and lower GV are observed in the 1– 2-, 3– 5-, and 6– 10-year age classes in site A, where the clay content is lower than other sites. This pattern is probably due to low soil water holding
capacity related to coarser soil texture and it is more likely that water deficit (Table 2) can lead to early senescence. Indeed, the >10-year-old class at the same site has higher clay content and the NPV and NPV/(GV + NPV) fractions are lower than the 6– 10-year-old class, which suggests less senesced pasture in the finer-textured soil. 3.3. Analysis of the relationships between biogeochemical and remotely sensed measures for pasture Possible relationships between soil fertility and remotely sensed data were investigated using linear regression (Table 4). The highest correlation occurred for P at site A, with high r2 values for GV and NPV. NPV had the highest correlation, with an r2 = 0.86, followed by NPV + Soil, NPV/(GV + NPV), and GV. Soil showed the lowest r2 for this element. High correlation for P was observed at site A, but did not occur at the other sites (Fig. 3). The r2 for P at sites B and C was around 0.21, except for NDVI at site C. For site B, low correlation may be related to burning practices in the 1– 2year-old age class, which increased NPV and decreased GV in this age class as already mentioned Section 3.2. Another factor that may account for this observation would be an artifact of the low resolution of the soil P measurements. Many sampling plots showed 1 mg/dm P, the minimum value measured in this analysis, but the same sampling plots showed considerable variation in remotely sensed measures (Fig. 3). Soil K at sites C and A correlated well with remotely sensed measures for pasture when we neglected one outlier. There is one sample that showed very high K at site A, probably related to high ash concentration caused by periodic burning. Removing the outlier from the analysis, the correlation between K and remotely sensed measures at site A showed similar patterns as P, such as high correlation with the vegetation measures of NPV and GV, and low correlation with soil. For site C, the highest correlation was observed between the shade fraction and soil K with an r2 of 0.68,
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Table 4 Linear regression between soil chemistry and remote sensed data (r2 values) Soil P
Soil K
Soil Ca
ECEC
Base saturation
0.45* 0.33* 0.13 0.42* 0.07 0.26 0.17
0.50** 0.39* 0.17 0.45* 0.10 0.30* 0.18
0.49* 0.49* 0.39* 0.19 0.36* 0.41* 0.33*
a
b
0.64** 0.86** 0.77** 0.35* 0.72** 0.85** 0.80**
0.06 0.20 0.25 0.08 0.23 0.10 0.06
0.41* 0.64** 0.62** 0.26 0.58** 0.55** 0.58**
Site B Shade NPV GV Soil NDVI NPV + Soil NPV/GV + NPV
0.15 0.13 0.15 0.00 0.19 0.16 0.21
0.00 0.01 0.01 0.04 0.00 0.00 0.00
0.20 0.23 0.26* 0.07 0.26* 0.24* 0.31*
0.23* 0.24* 0.28* 0.04 0.30* 0.27* 0.35*
0.14 0.14 0.18 0.01 0.19 0.16 0.21
Site C Shade NPV GV Soil NDVI NPV + Soil NPV/GV + NPV
0.21 0.19 0.13 0.08 0.51* 0.20 0.12
0.68** 0.54* 0.41* 0.09 0.33 0.62** 0.38*
0.3 0.34 0.27 0.12 0.40 0.36 0.26
0.40* 0.44* 0.44* 0.13 0.28 0.48* 0.43*
0.54* 0.57** 0.49* 0.22 0.25 0.60** 0.48*
Site A Shade NPV GV Soil NDVI NPV + Soil NPV/GV + NPV
Site B showed the lowest correlations in all analyses. One reason for this result was the periodic burning in the 1– 2year-old age class that led to low GV and very high NPV. While this age class showed high soil fertility as a function of new ash addition, the corresponding remotely sensed data resemble observations for older pasture such as high NPV and very low GV. Overall, based on the analysis of correlation for those elements that were statistically significant, NPV correlated better with soil parameters than with GV in pastures. Furthermore, other NPV measures such as NPV + Soil and NPV/(GV + NPV) in some cases showed better correlation than GV. Shade also deserves attention; high correlations for this fraction indicate that vegetation structural change, primarily modifying canopy shadowing, can be related to soil fertility decline over time with fertile sites showing higher shade content. In terms of soil parameters, higher correlations were observed for the most limiting elements, such as P at site A and K at sites A and C, and also by base
a = r2 between K and remotely sensed data with all samples at site A; b = r2 between K and remotely sensed data without one outlier. * p < 0.05. ** p < 0.01.
followed by NPV + Soil and NPV, with 0.63 and 0.57, respectively. In general, soil K correlated more closely with nonphotosynthetic parameters than with green vegetation such as GV and NDVI. Soil Ca and ECEC showed weak relationships with remotely sensed data in comparison to P and K observed at sites A and C. Low correlation between soil parameters and remotely sensed data found at site A may be related to inconsistency between the dynamics of Ca and ECEC and the vegetation structural changes over time. While the increase of NPV and the decrease of GV and NDVI occurred linearly from the 1– 2-year-old class due to burns, the rates of Ca and ECEC kept increasing up to the 3– 5-year-old class, before starting to decrease. For base saturation, which indicates potassium and alkaline earth nutrient availability, the highest correlations were observed with the NPV fractions at site C such as NPV + Soil and NPV, with r2 = 0.60 and 0.53, respectively. Shade also showed a high correlation coefficient, with an r2 of 0.54. A similar pattern was observed at site A, although the r2 was smaller than at site C.
Fig. 3. Correlation between soil P and NPV at the study sites: (a) site A, (b) site B, and (c) site C.
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saturation at sites A and C. Also, total soil element availability, indicated by base saturation, might reflect pasture canopy dynamics.
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Landsat would be a new approach that allows us more accurate and robust analysis of the relationships between soil biogeochemistry and remotely sensed data.
3.4. Discussion: Landsat ETM and hyperspectral data 4. Conclusion Our results show that Landsat ETM data can be used to identify the dynamics of biophysical properties across the pasture chronosequences. In addition, we were able to identify relationships between biogeochemical data and remotely sensed measures derived from this sensor. The capability of Landsat data for multitemporal analysis, especially land-use age detection, is very important for identifying the implications of pasture degradation. On the other hand, the 30 30-m scale resolution and low spectral sensitivity of Landsat ETM limit our ability to capture complex environmental variations at the individual pasture scale. For example, the overall observed correlations are not very strong and even though some reasonable correlations were observed, these results are not robust and could not be replicated across all study sites. The use of a finer spectral and spatial resolution sensor would be expected to minimize these problems due its capability to better characterize biophysical properties. In Asner et al. (1999), ground-based hyperspectral sensor data were used to generate the total vegetation index, leaf area index, and nonphotosynthetic vegetation index (NPVI), using inverse modeling. This index was found to positively correlate with measured pasture biomass and with P and Ca measured across pasture chronosequence as well. Although the dynamics of estimated biophysical properties is similar to our results, such as dominance of senesced vegetation and a decrease of live materials as a function of pasture age, total vegetation index derived from hyperspectral data also showed the decrease of pasture biomass over time. For a more effective analysis of the impact of biogeochemical variation on pasture productivity using remotely sensed data, the total vegetation may represent better pasture biomass in the dynamics of the soil properties, rather than the surface cover estimates of pasture provided by fraction images such as NPV and GV as studied in this paper. The same authors also could distinguish NPV better from bare soil based on the SWIR reflectance shapes at the range between 2000 and 2200 nm, a spectral region that is not included in detail in the Landsat ETM sensor. This feature also can improve the accuracy of the biophysical measurements for pasture productivity. In addition to the approaches mentioned above, hyperspectral sensors can provide other alternatives that are potentially useful for analyzing vegetation productivity such as the use of liquid water derived from AVIRIS data (Roberts, Green, & Adams, 1997a, 1997b), and also vegetation stress as a function of soil properties such as red edge shape and position (Zarco-Tejada & Miller, 1999). Scaling up well-characterized biophysical parameters derived from ground hyperspectral data to coarser resolutions such as
We studied changes in biogeochemical and biophysical properties of pasture classes over time interpreted from the remotely sensed measures using multitemporal analysis. Reasonable correlations exist between soil physical and chemical properties and remotely sensed measures, but at the 30 30-m pixel scale of Landsat ETM, remotely sensed measures over pastures were most strongly influenced by the combined effects of soil nutrient change and pasture management practices. Some high correlations among remotely sensed measures and soil parameters were observed for the most limiting parameters of pasture productivity, such as P, K, and base saturation in some study sites, but this result was not robust across all study sites. Spectral mixture analysis facilitated the biophysical interpretation of the changes in pasture status over time. For pasture age structure in the dry season, vegetation fractions, specifically the NPV fraction and related measures such NPV + Soil and NPV/(GV + NPV), are more likely to respond to the biophysical properties of pasture. It is important to note that this study only focused on three pastures in Rondoˆnia and thus is not representative of all cases of management or soil properties in the region. Neither highly degraded pastures nor well-managed pastures such as Fazenda Nova Vida (Neill, Piccolo, Steudler, Melillo, & Feigle, 1995) were sampled (e.g., well-managed pastures maximize the grazing potential, which tends to lower the amount of senesced vegetation and thus the role of NPV). An important future direction for research is to extend this type of analysis over a larger area and greater diversity of pastures. Additional biophysical measures, such as LAI and NPVAI, are needed to link the soil parameters, vegetation, and remotely sensed data. The use of higher spectral and spatial resolution remotely sensed data such as field spectrometer or hyperspectral data would be encouraged to improve the analysis of biophysical properties related to biogeochemistry over different land-cover classes in the Amazon Region.
Acknowledgements This study was funded by PPG7, project 816/95, ‘‘Carbon sink and emission as a function of land-cover and land-use changes in the Amazon area,’’ and also by NASA grant NCC-282 as part of LBA Ecology. We had technical and logistic support from EMBRAPA-CPAFRO for fieldwork. Eraldo Matricardi (SEPLAN/PNUD, Rondoˆnia) arranged the transport and other facilities for fieldwork. Digital PRODES used as a georeferenced base map was
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supplied by INPE. Finally, we wish to thank Karen Holmes and Trent Biggs for invaluable help and suggestions for this study.
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fertility and land-use on forest succession in Amazonia. Forest Ecology and Management, 139, 93 – 108. Nagler, P. L., Daughtry, C. S. T., & Goward, S. N. (2000). Plant litter and soil reflectance. Remote Sensing of Environment, 71, 207 – 215. Neill, C., Piccolo, M. C., Steudler, P. A., Melillo, J. M., & Feigle, B. L. (1995). Nitrogen dynamics in soils of forest and active pastures in the western Brazilian Amazon Basin. Soil Biology and Biochemistry, 27, 1167 – 1175. Nepstad, D. C., Uhl, C., & Serra˜o, E. A. S. (1991). Recuperation of a degraded Amazonian landscape: Forest recovery and agriculture restoration. Ambio, 20, 248 – 255. Numata, I. (1999). Avaliacßa˜o da conversa˜o de floresta para pastagem na Amazoˆnia usando sensoriamento remoto e fertilidade do solo (p. 104). Master’s Thesis. Instituto Nacional de Pesquisas Espaciais Sa˜o Jose´ dos Campos. Numata, I., Soares, J. V., Batista, G. T., Roberts, D. A., & Chadwick, O. A. (2000). Evaluation of the conversion from forest to pasture using remote sensing for soil fertility analysis. Proceedings of the ISPRS. International Archives of Photogrammetry and Remote Sensing, vol. XXXIII (pp. 1033 – 1038), Amsterdam, Part B7. Okin, G. S., Roberts, D. A., Murray, B., & Okin, W. J. (2001). Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment, 77, 212 – 225. Pedlowski, M. A., Dale, V. H., Matricardi, E. A. T., & Filho, E. P. S. (1997). Patterns and impacts of deforestation in Rondoˆnia, Brazil. Landscape Urban Plan, 38, 149 – 157. RADAMBRASIL (1978). Folha SC. 20 Porto Velho: Geologia, geomorfologia, pedologia, vegetacß a˜o e uso potencial da terra, Rio de Janeiro: Ministerio das Minas e Energia, Departmento Nacional de Producao, 666 pp. Roberts, D. A., Batista, G., Pereira, J., Waller, E., & Nelson, B. (1998). Change identification using multitemporal spectral mixture analysis: Applications in eastern Amazoˆnia. In C. Elvidge, & R. Lunetta (Eds.), Remote sensing change detection: Environmental monitoring applications and methods ( pp. 137 – 161). Ann Arbor: Ann Arbor Press. Roberts, D. A., Green, R. O., & Adams, J. B. (1997a). Mapping deforestation and secondary growth in Rondoˆnia, Brazil, using imaging radar and Thematic Mapper data. Remote Sensing of Environment, 59, 167 – 179. Roberts, D. A., Green, R. O., & Adams, J. B. (1997b). Temporal and spatial patterns in vegetation and atmospheric properties determined using AVIRIS. Remote Sensing of Environment, 62, 223 – 240. Roberts, D. A., Numata, I., Holmes, K., Krug, T., Batista, G. T., Powell, B. R., & Monteiro, A. (2002). Large area mapping of land-cover change in Rondoˆnia using multitemporal spectral mixture analysis and decision tree classifiers. Journal of Geophysical Research, 107(D20), 8073. Roberts, D. A., Smith, M. O., & Adams, J. B. (1993). Green vegetation, non-photosynthetic vegetation, and soils in AVIRIS data. Remote Sensing of Environment, 44, 255 – 269. Serra˜o, E. A. S., Falesi, I. C., Veiga, J. B., & Teixeira, J. F. (1982). Produtividade de pastagens cultivadas em solos de baixa fertilidade das a´reas de floresta da Amazoˆnia Brasileira. In P. A. Sanchez (Ed.), Producßa˜o de pastagens em solos adicodos dos tropicos ( pp. 219 – 251). Brasilia: Editerra Editorial. Serra˜o, E. A., & Toledo, J. M. (1991). The search for sustainability in Amazonian pastures. In A. B. Anderson (Ed.), Alternatives to deforestation. Steps toward sustainable use of the Amazon rain forest ( pp. 195 – 214). New York: Columbia University Press. Skole, D. L., & Tucker, C. (1993). Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988. Science, 260, 1910 – 1950. Smith, M. O., Susan, L. U., Adams, J. B., & Gillespie, A. R. (1990). Vegetation in deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment, 31, 1 – 26. Teixeira, L. B., & Bastos, J. B. (1989). Nutrients nos solos de floresta prima´ria e pastagem de Brachiaria humidicola na Amazoˆnia Central. Boletim de Pesquisa, 98, 8 – 33. Uhl, C., Buschbacher, R., & Serra˜o, E. A. S. (1988). Abandoned pastures in
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Relationships among soil fertility dynamics and remotely sensed measures across pasture chronosequences in Rondoˆnia, Brazil I. Numata a,*, J.V. Soares b, D.A. Roberts a, F.C. Leonidas c, O.A. Chadwick a, G.T. Batista d a
Department of Geography, EH 3611, University of California at Santa Barbara, Santa Barbara, CA 93106, USA b Instituto Nacional de Pesquisas Espaciais, Sa˜o Jose´ dos Campos, Sa˜o Paulo, Brazil c Centro de Pesquisa de sistema Agro-florestal, Empresa Brasileira de Pesquisa Agropecua´ria, Rondoˆnia, Brazil d Universidade de Taubate´, Taubate´, Sa˜o Paulo, Brazil Received 15 June 2002; received in revised form 14 July 2002; accepted 18 July 2002
Abstract This study analyzed the relationships between soil fertility and remotely sensed measures over three pasture chronosequence sites in the state of Rondoˆnia, in the western Brazilian Amazon region. Remotely sensed measures included shade, nonphotosynthetic vegetation (NPV), green vegetation (GV) and soil (derived from spectral mixture analysis), and the normalized difference vegetation index (NDVI). These were correlated against soil fertility parameters such as phosphorus, potassium, calcium, and base saturation. In temporal analysis, it was observed that NPV dominated the spectral responses of pasture canopies and tended to increase with pasture age as well. The increase of NPV appeared to be related to the decline of soil fertility, but soil texture variation also played a role. In the correlation analysis, soil P, known as the most limiting nutrient for pasture productivity, showed the highest correlation with remotely sensed measures, followed by soil K and base saturation. However, this result was not observed at the sites where nutrient availability was very low. D 2003 Elsevier Inc. All rights reserved. Keywords: Pasture chronosequence; Soil fertility dynamics; Remotely sensed measures
1. Introduction The conversion of tropical rain forest to cattle pasture is one of the most common patterns of land-use change in the Amazon Basin. Rapid pasture degradation and abandonment due to low soil fertility and inappropriate pasture management is a critical factor sustaining high deforestation rates in this region (Buschbacher, 1986; Falesi & Veiga, 1987; Fearnside, 1980; Nepstad, Uhl, & Serra˜o, 1991). In order to understand the ecosystem-scale impacts of land conversion, it is necessary to study how biophysical and biogeochemical processes operate in pastures as they age (Kauffman, Boone, Cumminga, & Ward, 1998; Moran, Brondizio, Mausel, & Wu, 2000; Serra˜o & Toledo, 1991). Remotely sensed data are critical for quantitative estimates of the different land-cover types and land-cover change detection over time (Alves, Pereira, De Souza, Soares, & Yamaguchi, 1999; Roberts, Batista, Pereira, Wal-
* Corresponding author. E-mail address: [email protected] (I. Numata). 0034-4257/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2002.07.001
ler, & Nelson, 1998; Skole & Tucker, 1993). The combination of field data with temporal analysis of remotely sensed data has provided useful information for the analysis of ecological processes such as CO2 flux and sequestration associated with forest regeneration (Foody, Pulubinskas, Lucas, Curran, & Honzak, 1996; Lucas et al., 1996). Focusing on grassland, the normalized difference vegetation index (NDVI) has been widely used for evaluating the spatial pattern of vegetation type and its productivity (Gamon, Field, Roberts, Ustin, & Valentini, 1993). In addition to photosynthetic materials, largely related to plant leaves, nonphotosynthetic materials (NPV), such as dry leaf and litter, are very important features for characterization of grassland, as well as background soil types (Asner, 1998; Roberts, Smith, & Adams, 1993; van Leeuwen & Huete, 1996). Although many studies have evaluated the ability of remote sensing to quantify biophysical measures such as leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fAPAR), and biomass (Asrer, Myneni, & Choudhury, 1992; Gao, Huete, Ni, & Miura, 2000; van Leeuwen et al., 1997), the relationships between
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biogeochemistry and remote sensing have not been studied in depth. Since pasture productivity in the Amazon region is highly dependent on soil fertility, the analysis of the relationship between remotely sensed measures focused on pasture biological features and soil physical and chemical characteristics could provide useful information for monitoring ecological change in this important land-cover class. Asner, Townsend, and Bustamante (1999) observed that hyperspectral estimates of LAI and nonphotosynthetic vegetation area index (NPVAI) of pasture were correlated with soil P and Ca concentrations across a pasture chronosequence in the central Amazon. The primary objective of this study was to analyze the relationships between remotely sensed biophysical measures such as spectral fractions [shade, NPV, green vegetation (GV), and soil] and NDVI, and soil physical and chemical properties over pasture chronosequences (pastures with different ages of installation). In this study, we addressed the following questions: (1) Which ecosystem nutrients are the most correlated to remotely sensed measures? (2) Which remotely sensed measures are appropriate for expressing pasture condition related to soil fertility dynamics?
2. Methods 2.1. Study site The study was conducted in the state of Rondoˆnia, in the western Brazilian Amazon, in the municipalities of Theobroma, Jaru, and Ariquemes (Fig. 1). The climate is humid tropical, Awi, according to the Ko¨ppen classification, with a well-defined dry season during July and August. Annual precipitation is around 2250 mm (RADAMBRASIL, 1978). The average temperature is 24 jC, while relative humidity
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varies between 80% and 85%. The natural vegetation consists of dense tropical forests and open tropical forests (INPE, 2000). This site is flat to gently rolling. The dominant soil types in this region are ‘‘Podzo´lico Vermelho Amarelo’’ and ‘‘Podzo´lico Vermelho Escuro’’ according to the Brazilian soil classification system—classes that fall into the oxisol and ultisol soil orders in the US soil classification system. Local variation of soil classes occurs and we do not classify the soils that were sampled for this study, preferring instead to present the relevant soil nutrient properties. The conversion of primary forest to pasture is the dominant form of land-use change in Rondoˆnia. Pasture size can vary from 100 – 200 ha to more than 2000 ha. Other land-use types in this region include perennial crops such as coffee, and annual crops such as corn, rice, and soybean (Pedlowski, Dale, Matricardi, & Filho, 1997). 2.2. Field work and soil sampling Fieldwork was conducted between July and August 1998, during the dry season, at three study sites (A, B, and C) (Fig. 1). Each site had a pasture chronosequence consisting of 1– 2-year-old, 3 –5-year-old, 6 –10-year-old, and >10-year-old age classes. To identify pasture age classes, a land-use age map was developed using multitemporal shade fraction images (Numata, Soares, Batista, Roberts, & Chadwick, 2000). Pasture management history for the study sites was obtained through interviews with landowners (Table 1), and consistency of information was checked against the multitemporal Landsat TM data. The three study sites have been subjected to similar management: similar pasture grasses, animal density, fallow period, etc. None of the sites was fertilized. Although pioneer species were observed in the youngest pastures, they were not subsequently invaded by second growth shrubs and forests.
Fig. 1. Map showing the study region and locations of the study sites (A, B, and C).
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Table 1 Pasture management for the study sites Study sites
A
B
C
Species
Brachiaria brizantha cv. Marandu Dry season: 1.6; rainy season: 2.4 40 – 50 days
B. brizantha
B. brizantha
Dry season: 1.4; rainy season: 2 40 – 50 days
Dry season: 1.6; rainy season: 2.4 40 days
Cattle/ha
1
Fallow period Pasture fire Two times at the beginning of pasture formation Clearing Not mechanized method Fertilization None
Two times in the Two times at the beginning of beginning, and pasture formation then in each 4 – 5 years, if necessary Not mechanized Not mechanized None
None
Soil samples were collected from a 0- to 20-cm depth in each age class at each site. Each sample was aggregated from three to four subsamples, taken at a distance of 150 –200 m apart, in topographically homogeneous locations. In total, 44 aggregated samples were analyzed. Soil samples were dried at 110 jC for 24 h, and sieved at 2 mm. Soil chemical analyses included available P, and exchangeable K, Ca, Mg, Al, pH, effective cation exchangeable capacity (ECEC), and base saturation, according to the techniques described by EMBRAPA (1997). The Melich extractor was used to extract available P and exchangeable K. This extraction procedure utilizes 0.025 N H2SO4 and 0.05 N HCl to remove labile forms of these nutrients. Phosphorus was determined using spectrophotometry, while exchangeable K was determined by flame photometry. Exchangeable Ca and Mg were extracted with 1 N KCl, and quantified by titration with EDTA. Exchangeable Al was extracted with 1 N KCl and quantified by titration with 0.025 N NaOH. Soil reaction (pH) was measured using a standard calomel electrolyte pH meter. Base saturation was calculated as: V = 100S/CEC, where S = Ca + Mg + K and CEC = S + H + Al. Detailed information on soil chemical analysis is given in Numata (1999). For comparative analysis to the remotely sensed data in this study, available P, exchangeable K and Ca, ECEC, and base saturation were considered.
the modified empirical approach (Roberts et al., 1998; Smith, Susan, Adams, & Gillespie, 1990). Using this approach, encoded radiance from Landsat TM data was regressed against laboratory and field-measured reflectance from soils, water, and NPV collected over two Amazon sites, Rondoˆnia and Manaus (see Roberts et al., in press). Because no plant spectra were available for tall trees at an appropriate spatial scale, spectra of broadleaf deciduous and evergreen forests in North America measured by the Airborne Visible Infrared Imaging Spectrometer were used. A more detailed description of reflectance retrieval is provided by Roberts et al. (in press), in which retrieved reflectance from a 1992 reference image was ported to a time series of TM images using relative radiometric calibration techniques (Furby & Campbell, 2001) using 25 invariant targets. Reference endmembers, spectra of known materials, were selected from the same spectral library used to retrieve surface reflectance. The methods for endmember selection are described in Roberts et al. (1998). The spectral mixture model was calculated over the image and generated four fraction images including shade, green vegetation, nonphotosynthetic vegetation (NPV) and soil, and root mean square (RMS) error image. The RMS error was not used in our analysis. In addition, the normalized difference vegetation index image was calculated using Band 3 (Visible R) and Band 4 (NIR) from the Landsat ETM calculated from sensor radiance. Spectral fractions and NDVI data were extracted from corresponding soil sample plots for each study site using GPS coordinates obtained in the field. Mean values and corresponding standard deviations were calculated for each age class. Based on the fraction data, two additional vegetation indices were included for this study: NPV + Soil and NPV/(GV + NPV). Since it is difficult to distinguish NPV from soil spectrally (Roberts et al., 1993), the sum of both fractions would represent the nonvegetation part. On the other hand, NPV/(GV + NPV) indicates the proportion of the dead or senesced vegetation relative to total vegetation amount. In order to evaluate the relationships between soil fertility data and remotely sensed data, we carried out linear regression analysis between these two datasets.
2.3. Remotely sensed data 3. Results A Landsat Enhanced Thematic Mapper (ETM) scene from path 232, row 67 was used for this study, in order to derive the remote sensing attributes of the pasture-covered land. Because no cloud-free image was available for the time frame in which the fieldwork was conducted, we employed an image acquired on August 4, 1999, during the dry season. These data were coregistered using 26 tie points to a georectified PRODES image from 1999, provided by the ‘‘Brazilian Institute for Space Research’’ (INPE), and then resampled using the nearest neighbor resampling. To compare satellite image data to laboratory or field measured spectra, reflectance retrieval was performed using
3.1. Biogeochemical dynamics in pasture chronosequence The soil biogeochemical data for the study sites included P, K, Ca, ECEC, base saturation, and clay content (Table 2). Site A had the highest value of both P and base saturation. Soil P at site A varied from 4 to 7.5 times higher than sites B and C. Other elements such as K, Ca, and ECEC were similar among the sites. Site B appeared to be the least fertile with the highest variability in soil fertility, as suggested by the standard deviation of its chemical properties. Particle size separation demonstrated that site A had the lowest clay
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Table 2 Soil physical and chemical data in pasture chronosequence in the study sites (standard deviation in parentheses) Age (years)
N
Clay content (g/kg)
P (mg/dm3)
K (cmol/dm3)
Ca (cmol/dm3)
ECEC (cmol/dm3)
Base saturation (%)
Site A 1–2 3–5 6 – 10 >10
3 4 3 4
150 156 177 271
(75.0) (104) (85.0) (49.0)
15.00 10.80 6.00 4.80
(0.00) (1.71) (0.00) (0.50)
0.43 0.34 0.40 0.23
(0.06) (0.08) (0.26) (0.06)
1.55 1.85 0.97 0.93
(0.21) (0.81) (0.29) (0.21)
2.83 3.22 2.10 1.95
(0.22) (1.01) (0.46) (0.34)
54.5 46.5 40.7 30.8
(16.3) (7.0) (7.2) (6.0)
Site B 1–2 3–5 6 – 10 >10
4 5 6 3
284 280 258 223
(73.0) (94.0) (49.0) (71.0)
3.50 3.80 1.00 1.00
(1.91) (2.17) (0.00) (0.00)
0.54 0.42 0.18 0.42
(0.14) (0.12) (0.05) (0.31)
1.15 1.24 0.65 0.53
(0.99) (0.32) (0.29) (0.21)
2.42 2.68 1.78 1.79
(0.91) (0.54) (0.39) (0.10)
39.0 37.0 22.5 25.3
(17.6) (8.2) (6.4) (2.5)
Site C 1–2 3–5 6 – 10 >10
4 3 4
226 (74.0) 255 (45.0) 284 (36.0)
2.00 (0.82) 1.00 (0.00) 1.25 (0.50)
0.35 (0.17) 0.44 (0.09) 0.14 (0.09)
1.68 (1.01) 1.35 (0.21) 1.15 (0.68)
2.98 (1.23) 2.54 (0.23) 2.32 (0.80)
41.5 (21.1) 43.5 (2.1) 27.8 (13.4)
N = number of aggregated samples. One sample is composed of three to four subsamples.
content, except for >10-year-old age class. While the >10year age class had similar clay content to sites B and C, the younger classes of site A had 30% less clay, indicating that these pastures are on different soil. The impact of differences of clay content among sampling sites is discussed below. Temporal analysis indicated a trend of decreasing P, Ca, ECEC, and base saturation over time (Table 2). Phosphorus at site A decreased from 1 to 2 years old, while site B showed decay in P from the 3 –5-year-old age class. Calcium and ECEC in all study sites increased up to 3– 5 years old, and then started decreasing. Potassium, unlike the other soil parameters, did not linearly decrease over time. This fact may be attributed to the recycling efficiency of this element in the soil – plant –animal system (Serra˜o, Falesi, Veiga, & Teixeira, 1982). Overall, the results appear very similar to those described by Correa and Reichardt (1995), Teixeira and Bastos (1989). There are two major stages controlling the dynamics of these soil parameters in pasture over time. The first is the period of early pasture installation (about the first 5 years), where the dynamics depends largely on ash deposition derived from burning following deforestation and subsequent burning of other vegetations such as regrowing forest species, weeds, and woody debris. After this period, when no ash addition occurs in pastures, the soil fertility dynamics is controlled mostly by environmental factors such as soil type (physical and chemical properties) and erosion and leaching, in addition to pasture management practices (types of grass and cattle density) according to Falesi and Veiga (1987) and Serra˜o and Toledo (1991). 3.2. Changes of remotely sensed measures in pasture chronosequence Fig. 2 and Table 3 show changes in remotely sensed measures over time. NPV increased with pasture age, while
other fractions such as GV, shade, soil, and NDVI showed the opposite pattern across all study sites. The dynamics of these fractions with pasture age should be largely related to changes of vegetation composition and structure in pasture canopies over time. In general, young pastures consisted of high amounts of residual woody debris from deforestation, shrubs, and very low grass cover, while older pastures show a homogeneous land cover with higher amounts of biomass in herbaceous materials, including litter/dead grass components and low amounts of woody debris (Kauffman et al., 1998; Numata, 1999). Senesced plant materials tend to increase with pasture age because old leaves of forage become photosynthetically less efficient and tend to be dead or senesced (Corsi & Junior, 1994). Older pastures consisted almost entirely of NPV. In the >10-year-old class, NPV reached around 85% or more for all study sites (85%, 93%, and 90% for A, B, and C, respectively). The ratio of NPV to total vegetation (NPV/ (NPV + GV)) also indicated that senesced material was the dominant form of vegetation for the >10-year-old pastures, showing values of 1.01, 1.01, and 0.99 for A, B, and C, respectively. The prevalence of NPV is partly due to the timing of image acquisition. The image was acquired during the dry season, when most pastures are partly or wholly senesced due to water deficit (some pastures in Rondoˆnia remain green throughout the year due to the quality of management; Paul Steudler, personal communication). Low GV fractions may also be attributed to poor sensitivity of Landsat TM to sparse amounts of live vegetation ( < 10%; Elmore, Mustard, Manning, & Lobell, 2000). For example, Asner (1998), using a radiative transfer model to test the sensitivity of canopy reflectance to biophysical and structural attributes, noted that changes in litter biomass had a far greater impact on canopy reflectance variability than a comparable change in LAI in grassland biomes. Okin,
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Fig. 2. Changes in remotely sensed measures over pasture chronosequence at the study sites: (a) shade, (b) GV, (c) NPV, (d) soil, (e) NPV+Soil, (f) NDVI, and (g) NPVI/(GV+NPV).
Roberts, Murray, and Okin (2001), using simulated mixtures of soils, NPV, and vegetation, noted that hyperspectral data could not confidently retrieve vegetation cover at cover fractions below 10% in the case of a high spectral contrast between vegetation and soils. As canopy spectra became less distinct from a soil/litter background, the minimum threshold for mapping vegetation cover accurately increased to as much as 30%. In essence, given Landsat TM data, sparse amounts of green vegetation are not likely to be mapped accurately within a matrix dominated by NPV, although changes in GV from one date to the next are
likely to be mapped with greater accuracy (Elmore et al., 2000). At site B, high NPV and low GV were observed in the 1 – 2-year-old class—a result opposite to site A and attributable to burning just prior to the acquisition of the satellite imagery. Consequently, the corresponding remotely sensed measures at the same age class were low in GV and very high in NPV, with similar values of older pasture classes. The biophysical interpretation related to pasture structural changes over time, as discussed above, follows the shade fraction behavior. The changes in vegetation structure, from
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Table 3 The means and standard deviations of remotely sensed measures for the study sites Age (years)
Site
N
1–2
A B A B C A B C A B C
72 108 126 135 108 81 171 72 135 81 108
3–5
6 – 10
>10
Shade 14.2 (5.7)a 7.1 (9.7)b 10.8 (6.2)a 15.5 (4.4)b 13.2 (5.4)c 7.3 (5.7)a 8.0 (6.5)b 11.3 (6.0)c 6.9 (7.7)a 1.0 (16.4)b 1.4 (8.1)b
NPV 52.1 82.7 68.1 63.7 67.1 87.5 77.5 73.3 85.1 93.2 90.2
GV (8.9)a (21.0)b (9.9)a (10.9)b (12.6)a,b (10.4)a (12.0)b (8.7)c (13.9)a (30.5)b (12.7)a,b
21.9 (4.1)a 0.9 (8.6)b 5.3 (3.7)a 8.6 (6.3)b 8.7 (6.7)b 2.4 (5.0)a 2.4 (5.8)b 6.2 (3.5)c 1.3 (5.6)a 2.8 (9.3)a 1.1 (3.6)b
Soil
NDVI
9.8 (2.2)a 7.3 (4.0)b 13.9 (3.6)a 10.9 (2.3)b 9.1 (4.1)c 5.7 (2.9)a 10.2 (3.4)b 7.1 (3.9)a 7.3 (2.7)a 8.5 (6.1)a 5.3 (3.8)b
0.30 0.18 0.18 0.21 0.21 0.16 0.18 0.20 0.15 0.15 0.19
(0.03)a (0.04)b (0.02)a (0.04)b (0.04)b (0.02)a (0.04)b (0.03)c (0.03)a (0.03)a (0.02)b
NPV + Soil
NPV/ (GV + NPV)
61.9 (8.6)a 90.0 (17.7)b 82.1 (8.7)a 73.9 (9.9)b 76.1 (11.0)b 93.2 (8.4)a 87.7 (11.0)b 80.4 (9.2)c 92.5 (12.2)a 101.7 (25.3)b 95.4 (10.8)b
0.70 0.98 0.92 0.88 0.88 1.03 0.97 0.92 1.01 1.01 0.99
(0.08)a (0.10)b (0.05)a (0.10)b (0.10)b (0.06)a (0.07)b (0.05)c (0.07)a (0.10)a (0.04)a
a, b, and c mean statistical difference (Tukey, 95%). N = number of sampled pixels.
heterogeneous and tall vegetation structures in young pasture to smooth and low structures in older pasture, may account for the decreased shade fraction over time as observed in the study sites. On the other hand, this inference linking canopy structure to pasture age may not be valid in some cases. For example, abandoned pastures could have very similar structures to young pastures, due to the presence of regenerating species and weed invasion (Uhl, Buschbacher, & Serra˜o, 1988), which may lead to confusion with other land-cover classes such as young pasture and second growth forest. In our study region, where the rates of pasture abandonment are low, this kind of problem should not be of a major concern. The soil fraction showed similar behavior to the shade and GV fractions, but with a much smaller overall magnitude. Low values of the modeled soil fractions occur largely because the endmembers selected for the model were calibrated to provide fractions comparable to field estimates and NPV was observed to be the dominant component within pastures. However, considerable caution must also be exercised when interpreting the soil fraction due to potential spectral similarity between NPV and soils. Although many soils are spectrally distinct from NPV using TM wavelengths, the only definitive separation between these two materials is the presence of lignocellulose bands in senesced materials (Nagler, Daughtry, & Goward, 2000; Roberts et al., 1993). For this reason, an alternate approach towards reporting separate fractions for NPV and soil is to report the sum of NPV and soil, in effect creating the equivalent of a threeendmember model (Roberts et al., 1998). Because NPV is the dominant material within most pastures, NPV + Soil resembles NPV dynamics. Soil clay content may impact the behavior of the measured vegetation fractions such as NPV and GV. For example, higher NPV and NPV/(GV + NPV) and lower GV are observed in the 1– 2-, 3– 5-, and 6– 10-year age classes in site A, where the clay content is lower than other sites. This pattern is probably due to low soil water holding
capacity related to coarser soil texture and it is more likely that water deficit (Table 2) can lead to early senescence. Indeed, the >10-year-old class at the same site has higher clay content and the NPV and NPV/(GV + NPV) fractions are lower than the 6– 10-year-old class, which suggests less senesced pasture in the finer-textured soil. 3.3. Analysis of the relationships between biogeochemical and remotely sensed measures for pasture Possible relationships between soil fertility and remotely sensed data were investigated using linear regression (Table 4). The highest correlation occurred for P at site A, with high r2 values for GV and NPV. NPV had the highest correlation, with an r2 = 0.86, followed by NPV + Soil, NPV/(GV + NPV), and GV. Soil showed the lowest r2 for this element. High correlation for P was observed at site A, but did not occur at the other sites (Fig. 3). The r2 for P at sites B and C was around 0.21, except for NDVI at site C. For site B, low correlation may be related to burning practices in the 1– 2year-old age class, which increased NPV and decreased GV in this age class as already mentioned Section 3.2. Another factor that may account for this observation would be an artifact of the low resolution of the soil P measurements. Many sampling plots showed 1 mg/dm P, the minimum value measured in this analysis, but the same sampling plots showed considerable variation in remotely sensed measures (Fig. 3). Soil K at sites C and A correlated well with remotely sensed measures for pasture when we neglected one outlier. There is one sample that showed very high K at site A, probably related to high ash concentration caused by periodic burning. Removing the outlier from the analysis, the correlation between K and remotely sensed measures at site A showed similar patterns as P, such as high correlation with the vegetation measures of NPV and GV, and low correlation with soil. For site C, the highest correlation was observed between the shade fraction and soil K with an r2 of 0.68,
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Table 4 Linear regression between soil chemistry and remote sensed data (r2 values) Soil P
Soil K
Soil Ca
ECEC
Base saturation
0.45* 0.33* 0.13 0.42* 0.07 0.26 0.17
0.50** 0.39* 0.17 0.45* 0.10 0.30* 0.18
0.49* 0.49* 0.39* 0.19 0.36* 0.41* 0.33*
a
b
0.64** 0.86** 0.77** 0.35* 0.72** 0.85** 0.80**
0.06 0.20 0.25 0.08 0.23 0.10 0.06
0.41* 0.64** 0.62** 0.26 0.58** 0.55** 0.58**
Site B Shade NPV GV Soil NDVI NPV + Soil NPV/GV + NPV
0.15 0.13 0.15 0.00 0.19 0.16 0.21
0.00 0.01 0.01 0.04 0.00 0.00 0.00
0.20 0.23 0.26* 0.07 0.26* 0.24* 0.31*
0.23* 0.24* 0.28* 0.04 0.30* 0.27* 0.35*
0.14 0.14 0.18 0.01 0.19 0.16 0.21
Site C Shade NPV GV Soil NDVI NPV + Soil NPV/GV + NPV
0.21 0.19 0.13 0.08 0.51* 0.20 0.12
0.68** 0.54* 0.41* 0.09 0.33 0.62** 0.38*
0.3 0.34 0.27 0.12 0.40 0.36 0.26
0.40* 0.44* 0.44* 0.13 0.28 0.48* 0.43*
0.54* 0.57** 0.49* 0.22 0.25 0.60** 0.48*
Site A Shade NPV GV Soil NDVI NPV + Soil NPV/GV + NPV
Site B showed the lowest correlations in all analyses. One reason for this result was the periodic burning in the 1– 2year-old age class that led to low GV and very high NPV. While this age class showed high soil fertility as a function of new ash addition, the corresponding remotely sensed data resemble observations for older pasture such as high NPV and very low GV. Overall, based on the analysis of correlation for those elements that were statistically significant, NPV correlated better with soil parameters than with GV in pastures. Furthermore, other NPV measures such as NPV + Soil and NPV/(GV + NPV) in some cases showed better correlation than GV. Shade also deserves attention; high correlations for this fraction indicate that vegetation structural change, primarily modifying canopy shadowing, can be related to soil fertility decline over time with fertile sites showing higher shade content. In terms of soil parameters, higher correlations were observed for the most limiting elements, such as P at site A and K at sites A and C, and also by base
a = r2 between K and remotely sensed data with all samples at site A; b = r2 between K and remotely sensed data without one outlier. * p < 0.05. ** p < 0.01.
followed by NPV + Soil and NPV, with 0.63 and 0.57, respectively. In general, soil K correlated more closely with nonphotosynthetic parameters than with green vegetation such as GV and NDVI. Soil Ca and ECEC showed weak relationships with remotely sensed data in comparison to P and K observed at sites A and C. Low correlation between soil parameters and remotely sensed data found at site A may be related to inconsistency between the dynamics of Ca and ECEC and the vegetation structural changes over time. While the increase of NPV and the decrease of GV and NDVI occurred linearly from the 1– 2-year-old class due to burns, the rates of Ca and ECEC kept increasing up to the 3– 5-year-old class, before starting to decrease. For base saturation, which indicates potassium and alkaline earth nutrient availability, the highest correlations were observed with the NPV fractions at site C such as NPV + Soil and NPV, with r2 = 0.60 and 0.53, respectively. Shade also showed a high correlation coefficient, with an r2 of 0.54. A similar pattern was observed at site A, although the r2 was smaller than at site C.
Fig. 3. Correlation between soil P and NPV at the study sites: (a) site A, (b) site B, and (c) site C.
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saturation at sites A and C. Also, total soil element availability, indicated by base saturation, might reflect pasture canopy dynamics.
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Landsat would be a new approach that allows us more accurate and robust analysis of the relationships between soil biogeochemistry and remotely sensed data.
3.4. Discussion: Landsat ETM and hyperspectral data 4. Conclusion Our results show that Landsat ETM data can be used to identify the dynamics of biophysical properties across the pasture chronosequences. In addition, we were able to identify relationships between biogeochemical data and remotely sensed measures derived from this sensor. The capability of Landsat data for multitemporal analysis, especially land-use age detection, is very important for identifying the implications of pasture degradation. On the other hand, the 30 30-m scale resolution and low spectral sensitivity of Landsat ETM limit our ability to capture complex environmental variations at the individual pasture scale. For example, the overall observed correlations are not very strong and even though some reasonable correlations were observed, these results are not robust and could not be replicated across all study sites. The use of a finer spectral and spatial resolution sensor would be expected to minimize these problems due its capability to better characterize biophysical properties. In Asner et al. (1999), ground-based hyperspectral sensor data were used to generate the total vegetation index, leaf area index, and nonphotosynthetic vegetation index (NPVI), using inverse modeling. This index was found to positively correlate with measured pasture biomass and with P and Ca measured across pasture chronosequence as well. Although the dynamics of estimated biophysical properties is similar to our results, such as dominance of senesced vegetation and a decrease of live materials as a function of pasture age, total vegetation index derived from hyperspectral data also showed the decrease of pasture biomass over time. For a more effective analysis of the impact of biogeochemical variation on pasture productivity using remotely sensed data, the total vegetation may represent better pasture biomass in the dynamics of the soil properties, rather than the surface cover estimates of pasture provided by fraction images such as NPV and GV as studied in this paper. The same authors also could distinguish NPV better from bare soil based on the SWIR reflectance shapes at the range between 2000 and 2200 nm, a spectral region that is not included in detail in the Landsat ETM sensor. This feature also can improve the accuracy of the biophysical measurements for pasture productivity. In addition to the approaches mentioned above, hyperspectral sensors can provide other alternatives that are potentially useful for analyzing vegetation productivity such as the use of liquid water derived from AVIRIS data (Roberts, Green, & Adams, 1997a, 1997b), and also vegetation stress as a function of soil properties such as red edge shape and position (Zarco-Tejada & Miller, 1999). Scaling up well-characterized biophysical parameters derived from ground hyperspectral data to coarser resolutions such as
We studied changes in biogeochemical and biophysical properties of pasture classes over time interpreted from the remotely sensed measures using multitemporal analysis. Reasonable correlations exist between soil physical and chemical properties and remotely sensed measures, but at the 30 30-m pixel scale of Landsat ETM, remotely sensed measures over pastures were most strongly influenced by the combined effects of soil nutrient change and pasture management practices. Some high correlations among remotely sensed measures and soil parameters were observed for the most limiting parameters of pasture productivity, such as P, K, and base saturation in some study sites, but this result was not robust across all study sites. Spectral mixture analysis facilitated the biophysical interpretation of the changes in pasture status over time. For pasture age structure in the dry season, vegetation fractions, specifically the NPV fraction and related measures such NPV + Soil and NPV/(GV + NPV), are more likely to respond to the biophysical properties of pasture. It is important to note that this study only focused on three pastures in Rondoˆnia and thus is not representative of all cases of management or soil properties in the region. Neither highly degraded pastures nor well-managed pastures such as Fazenda Nova Vida (Neill, Piccolo, Steudler, Melillo, & Feigle, 1995) were sampled (e.g., well-managed pastures maximize the grazing potential, which tends to lower the amount of senesced vegetation and thus the role of NPV). An important future direction for research is to extend this type of analysis over a larger area and greater diversity of pastures. Additional biophysical measures, such as LAI and NPVAI, are needed to link the soil parameters, vegetation, and remotely sensed data. The use of higher spectral and spatial resolution remotely sensed data such as field spectrometer or hyperspectral data would be encouraged to improve the analysis of biophysical properties related to biogeochemistry over different land-cover classes in the Amazon Region.
Acknowledgements This study was funded by PPG7, project 816/95, ‘‘Carbon sink and emission as a function of land-cover and land-use changes in the Amazon area,’’ and also by NASA grant NCC-282 as part of LBA Ecology. We had technical and logistic support from EMBRAPA-CPAFRO for fieldwork. Eraldo Matricardi (SEPLAN/PNUD, Rondoˆnia) arranged the transport and other facilities for fieldwork. Digital PRODES used as a georeferenced base map was
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supplied by INPE. Finally, we wish to thank Karen Holmes and Trent Biggs for invaluable help and suggestions for this study.
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