INTEGRATING SPECTRAL INDICES AND GEOSTATISTICS BASED ON LANDSAT-8 IMAGERY FOR SURFACE CLAY CONTENT MAPPING IN GUNUNG KIDUL AREA, YOGYAKARTA, INDONESIA Projo Danoedoro1 and Afida Zukhrufiyati1,2 Center for Remote Sensing (PUSPICS), Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 5281, Indonesia Email:
[email protected] 2 Cartography and Remote Sensing Study Program, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia Email:
[email protected] 1
KEY WORDS: spectral index, geostatistics, surface clay content, Landsat8, Indonesia ABSTRACT: This study tried to integrate spectral and geostatistical approaches for mapping soil surface clay content in Gunung Kidul area, Yogyakarta, Indonesia. As an initial stage, a Landsat-8 image was corrected radiometrically and geometrically, so that its pixel values were transformed to at-surface reflectance, while the geometric position of each pixel refers to Indonesian topographic (RBI) map. After that, two spectral indices for accentuating surface clay content, i.e. SRCI=Band7/Band6 and NDCI=(Band7 – Band6)/(Band7 + Band6) were applied for generating two tentative clay content-related images. Since those images also represent areas other than open soils, two other processes were undertaken in order to isolate the open soils. The first process was applying normalised difference vegetation index (NDVI), by which the areas representing vegetation cover and water were masked out. The second one was multispectral classification for generating built up features such as asphalt, concrete and housing rooftops, since those objects could not be separated from open soils using previously mentioned indices. The clay index images were then correlated with field samples containing information on laboratory-analysed clay contents, resulting nearly the same correlation coefficients for Band7/Band6 image (r=0.65) and (Band7– Band6)/(Band7 + Band6) (r=0.63). Regression equations were used to transform the images to surface clay content maps of the barren land. Since these maps only represent clay content in the open soil area, a geostatistical approach in terms of kriging method was utilised to interpolate the pixel values. By this method, every pixel value of surface clay content is considered as point, and thus the semivariogram analysis could be run. As a result, this method could predict surface clay content in the vegetation-covered areas. Accuracy assessment using independent clay content samples showed that SRCI gave a slightly better results than NDCI. Moreover, when the accuracy tester samples were also taken from vegetated areas, the SRCI- and NDCI-based spatial interpolation results gave lower accuracy than those of spectral index-based model. The resultant models were also impeded by the presence of settlements with clay rooftops, which were hard to be automatically separated from the scene. This study also showed that the integration of spectral and geostatistical approach could improve soil characteristics mapping accuracy in the area with scattered, small open soils. 1. INTRODUCTION 1.1 Background and Problem Formulation Soils are one of the most important land resources, which have studied for a long time. Soil mapping methods have been established. The use of remotely sensed data for soil mapping have been carried out since more than 50 years a go. Christian and Stewart (1953) applied a physiographic approach for developing land system maps, which contain information on geology, landform, soil and land-cover/land-use. All approaches mentioned previously were underlain by an assumption that similar soil characteristics develop from the same geological characteristics, especially lithology or parent materials, so that under the same climatic control they develop into similar morphological expression. With the advent of digital remote sensing, spectral approaches have been producing techniques for mapping soil characteristics, e.g. multispectral classification (Gao, 2010; Mather and Koch, 2011), spectral transformation such as soil brightness index (Caloz et al., 1988), as well as hyperspectral analyses making use of a large number of spectral bands (e.g. Clark et al, 2007; Hively et al., 2011). All spectral approaches started with an assumption that soil characteristics can be differentiated based on their spectral response. This assumption works well in areas where the vegetation and cloud covers are minimal, so that arid lands enjoyed the advantages of these approaches.
One of important characteristics of soil is texture, by which proportion of clay, sand and silt fractions are represented. Soil texture is closely related to topographic position, parent materials, soil development intensity (including time and rainfall). Among the three fractions, soil clay content is frequently analyzed using remote sensing (Curran, 1985; Khasanah and Danoedoro, 2013). Images of Landsat series were frequently used for extracting information related to soil clay content, i.e. by combining spectral bands of middle infrared and far infrared regions. This combination is closely related to the spectral characteristics of soil texture, where clay (finer grain size) tends to have higher spectral reflectance than silt and sand (coarser grain size) in all spectral regions. The longer the wavelength, the bigger the spectral reflectance difference between fine and coarse grains of soils (Jensen, 2007), although this condition is also controlled by other factors such as moisture and organic contents. Extraction of soil characteristics information, including clay content, from remotely sensed data is normally hampered by the presence of vegetation cover. Areas that are largely covered with vegetation would not be possible to map with respect to its soil characteristics, if the soil reflectance is used as the only parameter. Traditional methods using landscape or landform approaches worked well if qualitative indicators are in used. However, quantitative measure such as percentage of clay requires more sophisticated methods. One of them is spatial interpolation. On the other hand, Geographical information systems (GIS) offer spatial analytical tools for soil mapping. Burrough and McDonnell (2000) showed how a geostatictical tool could be used for predicting soil characterstics in areas that were not observed, based on the spatially distributed samples. The works of Grunwald et al. (2004), Hengl et al. (2004) Grunwald (2006), showed that remote sensing could be integrated with geostatistical tool. Indonesia is a wet tropical country with a high frequency of cloud coverage. With an approximately five-six months of wet season in many regions, particularly in the western and middle part of the country, the dry season still supports perennial vegetation covers in term of forest, woodland, mixed garden, and shrub in nearly all terrain characteristics and even annual crops in well irrigated lands. The presence of abundant vegetation cover in many areas causes difficulty in using spectral approaches for soil mapping, even when the images were recorded during dry season. The use of spectral indices and hyperspectral analyses were limited by the presence of of vegetation cover, so that mapping of soil characteristics using spectral approach would only possible in the open soil areas. 1.2 Research Objectives This study tried to evaluate the potentials of Landsat 8 OLI imagery for surface clay content mapping, by integrating the spectral approach in terms of clay content index and the spatial interpolation techniques in wet tropical area of Gunung Kidul Area, Yogyakarta Special Region, Indonesia. 1.3 Study Area As shown in Figure 1, the study area is a rectangle consisting of 895 pixels (east-west direction) by 1025 pixels (north-south direction), or approximately 825.64 km2. It is situated in Gunung Kidul Regency, Yogyakarta Special Region. It covers rugged terrain in the northern part of the region, formed by structural landforms, flat terrain in the middle part, and karst ladscape in the south. Old volcanic materials is predominant in the upper part of this morphology, while sedimentary rocks are found interleaved with the volcanic rocks. However, the northwestern corner of the study area consists of fluvio-volcanic plain occupied for ricefields and settlement. In the southern and eastern parts, karst landscape with calcite and dolomite rocks are predominant. Just in the central part of the region, the Wonosari depression was found. It is a nearly flat terrain with relatively thick soils in comparison with the hilly terrains in the surrounding areas. Soils here mostly developed from the carbonate sedimentary rocks. In the southern part, karst topography develops, showing series of domes and valleys, where the domes have very thin soils and even carbonate rock outcrop, but the valleys have relatively more developed, clay soils. The lower left corner of the study area is water body, i.e. Indian Ocean. In terms of land-cover/land-use, the study area is mostly utilized for settlements, dryland cultivation with annual crops, mixed garden, forest, and only small areas are occupied for ricefields. Because of its relatively humid character, during the dry season vegetation covers in terms of grass, annual crops, trees, and shrubs are still found in most areas, except in the karst topographic surface, which has no or very thin soil layer.
Figure 1. Study area as shown in administrative unit and in Landsat 8 OLI multispectral image.
2. MATERIALS AND METHODS 2.1 Materials This study made use of Landsat-8 multispectral dataset containing coastal, blue, green, red, near infrared, shortwave, and thermal infrareds. The image was recorded on 24 June 2013, path/row 120/065. In addition to the image dataset, a set of topographic maps at scale of 1:25,000 covering the same area was also used. Fieldwork tools including soil test kit and GPS receiver were utilized to collect soil samples at the planned locations. Soil laboratory tools supported the samples analyses, particularly for the surface clay content (in percent). ENVI 5.0 image processing software and ILWIS 3.4 Open software were used for processing the data. ENVI was mainly used for radiometric correction, while ILWIS supported the multispectral classification and geostatistical analysis. GPS receiver was utilized for positioning sample locations in the field. Soil laboratory tools supported the field sample analyses. 2.2 Methods This study integrated spectral approach in terms of clay indices and the geostatistical approach with spatial interpolation. The clay indices were based on spectral values of Landsat 8 bands, to be combined with soil field data for generating spectral-based clay content prediction. The spatial interpolation used Kriging methods for interpolating spectral-based clay content prediction, which were only available in the barren lands, to generate surface clay content map in the entire study area including in the vegetation and building covered parts of the image. As an initial stage, a Landsat-8 image was corrected radiometrically and geometrically, so that its pixel values were transformed to at-surface reflectance, while the geometric position of each pixel refers to Indonesian topographic (RBI) map. The Landsat OLI image radiometric correction followed these formulas (LDCM Algorithm Description Document, 2013): Conversion from digital number (DN) to spectral radiance: Lλ = MLQcal + AL
(1)
where Lλ is spectral radiance (Watt / (m2 *srad*µm), ML is radiance multiplicative, which can be seen in image metadata, and AL is radiance additive value, which can be seen in the image metadata, and Qcal is pixel value or DN.
The document also stated that the LI data can be converted to top of atmosphere (TOA) reflectance by rescaling reflectance coefficient available at the product file’s metadata: ρλ' = MρQcal + Aρ
(2)
where ρλ' is TOA reflectance, without sun angle correction, Mρ is reflectance multiplicative value , which can be seen in the metadata, Aρ is reflectance additive value, which can be seen in the image metadata, and Qcal is DN. Since the TOA reflectance has not taken sun angle correction value yet, therefore the following formula is used for correcting that factor:
𝜌𝜌𝜆𝜆 =
𝜌𝜌 𝜆𝜆 ′
cos (𝜃𝜃𝜃𝜃𝜃𝜃 )
=
𝜌𝜌 𝜆𝜆 ′
(3)
sin (𝜃𝜃𝜃𝜃𝜃𝜃 )
where ρλ is TOA reflectance value, ρλ' : TOA reflectance without sun angle correction value, θSZ is local solar zenith angle; θSZ = 90° - θSE, and θSE is sun elevation angle. Atmospheric correction could then be done using dark pixel subtract: ρs = ρi – ρsi
(4)
where ρs is surface reflectance, ρi is TOA reflectance at band I, ρsi is path radiance value at band i. Based on the radiometrically corrected image, two spectral indices for accentuating surface clay content were used, namely Simple Ratio Clay Index (SRCI) and Normalised Difference Clay Index (NDCI), where 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 =
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 =
𝐹𝐹𝐹𝐹𝐹𝐹 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
or 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 =
𝐹𝐹𝐹𝐹𝐹𝐹 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 −𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑙𝑙𝑙𝑙 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
𝐹𝐹𝐹𝐹𝐹𝐹 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 +𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
or
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 7
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 6
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 7−𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 6 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 7+𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 6
(5) (6)
These indices were applied for generating two tentative clay content-related images. These indices generated clay content images by correlating the pixel values with the samples’ clay content collected in the field. Regression equations between SRCI and surface clay content, as well as between NDCI and surface clay content were then be used to transform both images into surface clay content maps. Since those images also represent areas other than open soils, two other processes were undertaken in order to isolate the open soils. The first process was applying normalized difference vegetation index (NDVI), by which the areas representing vegetation cover and water were masked out. The second one was multispectral classification for generating built up features such as asphalt, concrete and housing rooftops, since those objects could not be separated from open soils using previously mentioned indices. The clay index images were then correlated with field samples containing information on laboratory-analysed surface clay contents. Regression equations were used to transform the images to surface clay content maps of the barren land. Non –open soil or non-barren land areas were removed from the image scene by applying NDVI formula: 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 =
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 −𝑅𝑅𝑅𝑅𝑅𝑅
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 +𝑅𝑅𝑅𝑅𝑅𝑅
or
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 =
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 5−𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 4
𝐵𝐵𝑎𝑎𝑎𝑎𝑎𝑎 5+𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 4
(7)
The exact threshold values of water bodies (NDVI 0) would be specified in the field, since there was no pure pixel containing no vegetation in the study area. Following the spectral transformation, a multispectral classification was required to isolate and to remove building rooftops, wich were not possible to mask out using NDVI formula. In order to map the surface clay content of the vegetation and building-covered soils, a geostatistical approach in terms of kriging method was utilised to interpolate the pixel values. By this method, every single pixel value of surface clay content is considered as point, and thus the semivariogram analysis could be run. Spatial distribution of pixels representing the surface clay content were then computed in order to obtain the graph showing spatial
correlation, which could be transformed into semivariogram surface. Analysis of nugget, sill and range was then used as input to the interpolation result. This process was carried out by inputting different values of nugget, sill and range, as well as interpolation methods in order to obtain the best map. Based on the obtained results, several resultant maps were then assessed with respect to their accuracy, by using independent soil samples. Chang (2014) described the use of semivariogram for calculating values of spatial autocorrelation using the following formula: 1
𝛾𝛾(ℎ) = [𝑧𝑧(𝑋𝑋𝑋𝑋) − 𝑧𝑧(𝑋𝑋𝑋𝑋)]2 2
(8)
𝛾𝛾(ℎ) in the equation (8) is the semivariance between sample points Xi and Xj, which are separated by a distance z. The values that input to the semivariogram are the distance between all pairs of point and the semivariance. Results obtained form the plooted semivariance were then processed using binning, uin order to obtain average semivariance with respect to the distance and direction. Binning process generated less number of point sample pairs, using the following formula: 𝛾𝛾(ℎ) =
1
2𝑛𝑛
∑𝑛𝑛1 [𝑧𝑧(𝑋𝑋𝑋𝑋) − 𝑧𝑧(𝑋𝑋𝑋𝑋 + ℎ)]2
(9)
For the ordinary kriging used in this study, Chang (2014) suggested a formula for estimating interpolated value z using the following formula: 𝑍𝑍0 = ∑𝑠𝑠𝑖𝑖=1 𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍
(10)
The interpolation method delivered a raster map with new values filling up blank pixels, so that a quasi-continue map of surface clay content could be presented. 3. RESULTS AND DISCUSSION 3.1 Spectral Processing Radiometric correction and calibration of the image was done up to at-surface reflectance image. At this stage, the multispectral image already have pixel values representing percentage of reflectance, as shown in Figure 3. The image dataset was then processed for generating spectral indices related to surface clay content. It is shown that both images have different ranges of pixel values (expressed by the brightness), due to the different formulation.
Figure 3. Radiometrically corrected band 6 and band 7 (at surface reflectance) were processed to generate NDVI, SRCI and NDCI. The NDVI was used to remove non-barren land from the spectral index results, e.g. SRCI image.
Prior to the application of clay indices, the dataset was processed for separating barren land from the other land-cover types. To do this, a normalized difference vegetation index (formula 7) applied. By using this index, theoretically barren land has value of 0, water is 0. However, field observation found that the NDVI values of barren land range from 0.0059 up to 0.1027, due to the fact that there was no ideal barren land with zero presence of vegetation at the given spatial resolution (30 m). It is normal, since the study area is categorized as wet tropical land, although this region is still relatively dry in terms of annual rainfall. It should be noted that field work was undertaken in the same season, i.e. June 2014, and the time lag between image recording and the fieldwork did not gave a significant effect, since the collected information is relatively stable during the specified period, while changes in vegetation cover types could also be neglected. By correlating the indices with the soil samples, we found that both transformation showed similar strength, The Simple Ratio (SR) gave r2 =0.4255, while the NDCI gave r = 0.4073. Figure 4 (right hand side) depicts this result. By applying the regression equations between the indices and the surface clay content, tentative maps were delivered as shown in Figure 4 (left hand side). However, it was found that the surface clay content images did not only show the barren land or open soil. Application of NDVI to remove non-barren land only successful for vegetated areas and water bodies. Buildings with concrete, metal and clay rooftops remained. In order to remove these cover types, a multispectral classification was applied. Samples focused on areas covered by buildings only, but unfortunately this process did not fully successful since the clay rooftop has spectral similarity with the open soil. According to the Figure 4, areas with clay rooftops have relatively high surface clay content, similar to barren land with clayey soils.
Wonosari
Clay content (%):
0
N
9 km
Figure 4. SRCI-Surface clay content and NDCI-Surface clay content correlations, accompanied by an example of the image-based regression model showing aspatial distribution of surface clay content in the study area, which only covers barren land 3.2 Spatial Interpolation The spectral-based processing of the image dataset delivered tentative surface clay content map in raster data model. It only covers a small portion of the total study area (9.26 %), while the remaining pixels are blank, which contain no information. In order to fill up the estimated values of those blank pixels, a spatial interpolation was run.
In this method, the pixels representing surface clay content values were transformed into point dataset. A point map is presented in Figure 5a. Based on this point map, a statistical analysis of spatial correlation between lag distance and semivariance were computed. Regarding the spatial correlation table, a graph showing scattergram of lag distance (x) and semivariance (y) is presented in Figure 5b. Following this process, different spatial interpolation methods using selected nugget, sill and range values were run, i.e. simple kriging and ordinary kriging . In these methods, spherical semivariogram fitting model was chosen, based on the assumption that there is a progressive decrease of spatial dependence until some distance, beyond which spatial dependence levels off. This assumption works for clay content in the study area.
Figure 5.a
Wonosari
Figure 5.b
0
N
9 km
Figure 5a. Example of point map converted from the SRCI-based surface clay content estimate. Figure 5.b. The graph showing relationship between the lag distance and the semivariance.
The use of different settings of nugget, sill and range values showed that the interpolation results did not exhibit significant differences. Figure 6a shows the interpolation result, which is presented as a map. This map is compared to spectral-index based clay content estimate, which did not exclude the vegetated and building areas. According to the interpolation results, the spatial distribution of the surface clay content of the study area is quite logical. Areas with more intensive deposition process, and thus developed soil more intensively, looked have higher surface clay content. Example of this area was found in the Wonsosari depression, as well as in the northwestern part with fluvio-volcanic plain, which have thicker soils. High surface clay contents were also found in the hilly terrain of the northern part, since the old volcanic materials have been developed to soils. Areas with intensive denudation, and thus have thinner soil, were found in steeper lands, and more specially in the karst domes. However, several pixels of houses with clay rooftops still remain. So does turbid water of Oya River, which is wide enough to be represented as single pixels. The river’s turbid water was considered as a surface containing high percentage of clay. This study showed that spectral approach in terms of SRCI and NDCI could estimate the surface clay content, with the support of field samples. However, it is recommended to mask the mapped area using NDVI for removing vegetation cover land and simple multispectral classification to isolate he buildings. The multispectral classification effort was not very successful since many buildings in the study area use clay rooftops, The study also showed that spatial interpolation method could estimate surface clay content in the large vegetated and building covered areas.
SPATIAL DISTRIBUTION OF SURFACE CLAY CONTENT BASED ON SPECTRAL INDEX (SRCI) WITHOUT REMOVING VEGETATED AND BUILDING AREAS
6a
6b
Figure 6 a: Distribution of surface clay content (%) based on the integration of Simple Ratio Clay Index (SRCI) and Spatial Interpolation. Figur 6b: Distribution of clay content based on SRCI only.
3.2 Accuracy assessment Accuracy assessment of the results was carried out using independent dataset. This dataset contains samples that were not used to bulid up the model, as analyzed separately. The accuracy tester samples were grouped into (a) samples in the barren land, and (b) samples in vegetated land. Standard error of estimates of the dataset were presented in Table 1. The table shows that, when the accuracy tester samples were taken from barren land only, the SRCI performed consistently better (with SE=11.69%) than the NDCI (SE=12,02%), although the difference was not too high. It means that the accuracies highly correlated to the correlation coefficient r. When all accuracy tester samples involving barren land and areas covered with vegetation and buildings were invlved, the SE dropped into 15.26% and 15.68% for SRCI and NDCI respectively. Moreover, the maximum accuracy of the interpolation result of both SRCI- and NDCI-based models were found less as compared to those of barren land-based spectral index model. Anyhow, the spatial interpolation using geostatistics could provide information on surface clay content in vegetation and building-covered areas, in which the spectral indices failed.
R sd SE Min % Error Max % Error Min Accuracy Max Accuracy
Table 1. Accuracy assessment results SRCI NDCI Spatially (B7/B6) (B7-B6/B7+B6) Interpolated SRCI 0,65 0,64 3,23 3,23 4.11 11,69% 12,02% 15.26% 17,58 18,08 17.58 18,71 19,24 25.24 81,29% 80,76% 73.29% 82,42 81,82 74.21%
Spatially Interpolated NDCI 4.17 15.68% 18.08 27.85 71.86% 72.73%
4. CONCLUSIONS Based on the results discussed previously, it can be concluded that Landsat 8 OLI multispectral images could be used as abasis for generating information relevant to surface clay content in the sudy area. The combination between clay-sensitive spectral indices (SRCI and NDCI) and the NDVI for isolating open soil or barren land was effective, even though it did not fully successful to separate buildings with clay rooftops from the scene. However, the spectral index-approach could only model the surface clay content in the barren land, and it did not work for making estimating in the vegetated areas. The use of geostatistics in terms of ordinary kriging interpolation could enhance the results obtained from the spectral indices, so that surface clay content of soils in the vegetated areas could also be estimated. It should be noted, however, that the accuracies of the spatial interpolation model for the vegetated and non-vegetated areas were found lower as compared to those of spectral index model. 5. REFERENCES Burrough and McDonnell (2000), Principles of Geographical Informaion Systems for Land Resources Assessment. Oxford University Press. pp 304-311 Christian, C.S. and Stewart, G.A. 1953. General Report on Survey of Katherine-Darwin region, 1946. CSIRO land Research Series No. 1 Caloz, R. , Abednego, B., and Collet, C. 1988. The Normalization of a soil Brightness Index for the Study of Changes in Soil Conditions. Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing held in Aussois, France, 18-22 January 1988. European Space Agency Chang, K. 2014. Introduction to Geographic Information Systems. New York: McGraw Hill. Pp. 314-3.24 Clark, R. M., Abuzar, and Nathan. 2007. Mapping soil salinity using hyperspectral imagery. Department of Primary Industries, 2007, Victoria, pp 24-37 Curran, P.J. 1985. Principles of Remote Sensing. London: Longman.pp21-23 Gao, J. 2010. Digital Analysis of Remotely Sensed Imagery. New York: McGrawHill. pp 213-224 Grunwald, S. (Ed.), 2006. Environmental Soil-Landscape Modeling–Geographic Information Technologies and Pedometrics. CRC Press, New York. pp 8-12 Grunwald, S., Reddy, K.R., Newman, S., DeBusk, W.F., 2004. Spatial variability, distribution and uncertainty assessment of soil phosphorus in a south Florida wetland. Environmetrics 15, pp 811–825. Hengl, T., Heuvelin, G.B.M., Stein, A., 2004. A generic framework for spatial prediction of soil variables based on regression-kriging.Geoderma 120, 75–93. Hively, W.D. McCarty, G.M, Reeves, J.B., Lang, M.W., Oosterling, R.A., and Delwiche, S.R. 2011. Use of Airborne Hyperspectral Imagery to Map Soil Properties in Tilled agricultural Fields. Applied and Environmental Soil Sciences. Vol 2011, Artcile ID 358193, http://dx.doi.org/10.1155/2011/358191. Jensen, J.R., 2007. Remote sensing of the environment: An Earth resource perspective. Prentice Hall, Upper Saddle River, N.J. Khasanah, A.N. and Danoedoro, P. 2013. Mapping Clay Fraction Using Hyperion Imagery in Relation to Different Kind of Parents Material in D.I.Yogyakarta. Asian Conference on Remote Sensing 2013. LDCM Algorithm Description Document, 2013 Mather, P.M. and Koch, M. 2011. Computer Processing of Remotely Sensed Image. London: John wiley and Sons. pp. 244-251
