Proc. Indian Acad. Sci. (Engg. Sci.), Vol. 6, Pt. 3, September 1983, pp. 197-208. �9 Printed in India.

Soil mapping using remote sensing techniques

R L K A R A L E , Y P BALI and K V S E S H A G I R I R A O All India Soil and Land Use Survey, Hyderabad 500017, India

Abstract. Advancements in space technology opened application possibilities of remote sensing in soil mapping. Reviewing the achievements in this field, various techniques of remote sensing applications are outlined and their merits and limitations discussed in the context of soil mapping of Mahaboobnagar area. The products of different techniques including visual interpretation of imageries, 12S and computer analysis of LANDSAT digital data are compared with aerial photo interpretation and soil map drawn by conventional method.

The study reveals that both airborne and spaceborne data afford greater accuracy, economy and efficiency than the conventional method at reconnaissance level of mapping. The efficiency in terms of time spent on soil mapping by computer techniques, aerial photo- interpretation and conventional method was in the ratio of 1 : 5 : 10. A case study of erosion mapping by remote sensing techniques is also presented.

Further scope for expanding application possibilities through future generation satellites and microwave sensing is highlighted.

Keywords. Remote sensing; aerial photo-interpretation; digital data; enhancement tech- niques; M-DAS system; clustering; soil maps; erosion mapping; stereosat; microwave sensing

1. Introduction

The beginning o f soil surveys in India dates back to early twenties when sporadic at tempts were made to characterise and classify soils mostly on edaphic considerations. Acceptance o f the Steward report and act ion on the specific recommendat ions o f Dr. Rickens, the soil specialist o f USA paved the way for systematic soil surveys in the country with the establishment o f the All India Soil and Land Use Survey Organisation. A decade or so thereafter, soil survey techniques were standardised and fairly perfected; several guidelines were brought out; a soil survey manual was generated and aerial pho to base was introduced in mapping operations. The Indian Photo-In terpre ta t ion Institute (now Indian Institute o f Remote Sensing, IIRS) established at Dehra Dun by about the same time greatly accelerated the progress in developing systematic aerial photo- interpretat ion techniques for soils mapping.

2. Remote sensing

Broadly, remote sensing is acquisition o f data about specific objects or phenomena by an information gathering device not in intimate contact with the subject under investigation. Remote sensing, in a stricter sense connotes a technology o f acquisition and interpretation o f data about the terrestrial and atmospheric objects and processes in the form of photographs , imageries, video-tapes or other forms o f recordings in and beyond the range o f human vision and photographic sensitivity.

197

198 R L Karale, Y P Bali and K V Seshagiri Rao

3. Aerial photo-interpretation

Panchromatic vertical aerial photographs are being used in soil mapping since a couple of decades. The major approaches followed in photo-interpretation are (i) pattern analysis (ii) physiognomic (iii) element analysis and (iv) physiographic. The pro- cedures commonly followed are (i) Sample area technique and (ii) adjusted photo- interpretation technique. A detailed discussion on these aspects is provided in several standard text books on the subject. A combination of physiographic and photo- element approach with sample area technique supported by limited field check has been widely used because of its adaptability to different terrain conditions and varying scales of mapping.

Work carried out by several individuals and organisations helped to make aerial photo-interpretation techniques operational in soils mapping at semi-detailed and reconnaissance levels (Karale et al 1970b; Hilwig & Karale 1970; Murthy & Hirekert.+r 1972; Srinivasan 1972; Mathur et al 1980; Kolarkar et al 1980; Joshi & Dhir 1980 and Ahuja & Manchanda 1980).

Soil map generated by this technique is illustrated in figure 1, The special advantages of this technique are efficiency, accuracy and low costs. A mapping efficiency of about 150 % was achieved in semi-detailed soil surveys of Ganges plain by using 1 : 25,000 scale aerial photos as compared to conventional surveys (Karale et a11970). In the same investigation cost reduction to an extent of 54 % was reported.

Soil degradation mapping by photo-interpretation was first reported by Govindarajan & Mouttapa (1967). Kamphrost & Iyer (1972) could categorise the ravine areas of Northern India into four major classes by aerial photo studies based on depth and width of ravines. In either studies they have used systematic photo- interpretation combined with morphometric parameters obtained by parallax bar measurements.

In the saline soil studies conducted in Haryana and Punjab three levels of soil salinity could be identified and mapped by photo interpretation techniques (Shanwal et a11980, Bhargava & Sharma 1980).

Results were not so spectacular in the case of detailed soil mapping through photo- interpretation. They were favourable only in specific terrains where minor differences in texture and landscape are reflected in landuse (Jawade 1972; Biswas 1977). The scope of wide utility of this technique in all landscapes is yet to be established. Field adjusted interpretation facilitated accuracy in soil boundaries in detailed soil mapping but failed to show appreciable saving in cost and time (Karale et al 1978a).

Colour aerial photographs have a definite additional advantage over the pan- chromatic products since the soils are depicted in near natural colours. But, their usage has been very limited in India, in view of the prohibitive costs although encouraging results have been reported from USA, tJSSR, Australia and parts of Europe.

4. Spaceborne studies

A new era dawned in the techniques of resources surveys since 1972 with the advent of launching of LANDSAT series. Widespread use of data from the satellite products for soil resources surveys was specially initiated since the last 6-7 years. The LANVSAT imageries provide a unique set of characteristics such as: (i) Synoptic view of 3.3 million ha. of

Remote sensing in soil mapping

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Figure 1. Comparison of soil maps, Mahboobnagar area (AP) produced by different techniques

land; (ii) Availability of data on the condition of soils and stage of vegetation recorded at a time; (iii) Temporal feature permitting the studyofsoils, and vegetation with time; (iv) Acquisition and processing of information in several bands of one spectral region (multi-band) and various regions of spectrum (multispectral); (v) Near orthogonal projection permitting registration with topographic maps of comparable scale.

Several extraneous factors considerably influence the tonal response so that the characteristic pattern of soils hold good for a specific area in a specific season thus facilitating identification of specific spectral signatures. Venkataratnam & Rao (1977) provided a good account on these aspects.

Visual interpretation and the computer techniques are both deployed in soil studies with LANDSAT data.

5. Visual interpretation of LANDSAT products

Small scale soil mapping carried out by Krishnamurthy & Srinivasan (1973) in parts of up and Bihar with Apollo and Gemini photographs offered great possibilities of

200 R L Karale, Y P Bali and K V Seshaoiri Rao

application of LANDSAT data in soil studies. Their results proved to be more superior than the existing soil information of the area.

5.1. Soil map of Mahboobnagar area

Visual interpretation technique was successfully employed in abstracting soil map of a sample area of 12000 ha in Mahboobnagar district (Andhra Pradesh). LANDSAT imagery of bands 5 and 7 in the scales of 1 : 1000,000 and 1 : 250,000, and 70 mm additive colour viewer compatible chips were used for this purpose.

Black and white imagery of bands 5 and 7 in both the scales were systematically analysed with the help of 10X hand lens using such image attributes as tone, texture and pattern. Geologic boundaries were affirmed by references to relevant geologic map. Additional information on topographic features was used from relevant Survey of India toposheets. By systematic analysis of imagery with adjunctive data, physio- graphic-cum-image analysis maps were prepared for different bands in different scales. A similar map was produced from additive colour viewer (I2S). Soil observation points were carefully located on the image-analytical maps of the different products and by correlative approach image-analytical maps were translated in terms of soil classes at Great group levels. Final products, brought to a uniform scale of 1 : 250,000 (approx.), were compared with the reconnaissance soil map of the area drawn by conventional field traversing method. The significant results are illustrated in figure 1.

Comparison between maps obtained by interpretation of imageries in bands 5 and 7 with that of I2S revealed that band 5 yielded more delineations possibly due to better differentiation of landscape elements based on tone. Visual interpretation of contact paper prints in the scale of 1 : 1000,000 yielded similar results compared to enlarged prints in the scale of 1 : 250,000. This may be owing to lack of any change in spectral resolution. Spatial resolution was, however, better afforded by the enlarged prints and the delineation of boundaries could be carried out with great ease. However, in both the cases it was possible to map associations of 2-4 Great-groups only. I2S has provided somewhat better differentiation of the image but the results are not very striking. The output in 1 : 500,000 did not offer any special advantage.

The final visual interpretation soil map drawn by combination of the different approaches (bands 5, 7 and 70 mm chips on I2S) shows better segregation of soil classes and reliably precise boundary delineations as compared to conventional soil map (figure 1).

A fair degree of accuracy and saving in time of mapping have also been reported by various workers. Particularly, results reported by Hilwig et al (1974); Mirajkar & Srinivasan (1975); Hilwig (1976) proved that the visual interpretation of LANDSAT products is very promising in small scale soil mapping upto 1:50,000 scale.

Visual LANDSAT interpretations were also successfully used in soil degradation mapping. Singh (1977) delineated ravine watersheds and reclamation units in a more efficient manner and at lower costs by visual interpretation. In a study carried out by NRSA (1981a,b), ravines were categorised into five depth classes and mapped with appreciable precision using 1:250,000 scale FCC products.

Remote sensing in soil mapping 201

6. Computerised techniques

In view of the complexity of digital data made available by LANDSAT series, the scope of utilising the entire data by visual studies is essentially very limited. Automative techniques using computers widened the scope and the results obtained through various studies are very encouraging.

Various techniques and methodologies have been developed in recent years in using computer techniques for soil studies. A majority of soil maps prepared by computerised techniques are produced either by NRSA or by using the M-DAS, Bendix system installed at NRSA.

6.1 M-DAS system--soil map

A soil map for a part of Mahboobnagar district of Andhra Pradesh using computer techniques with limited field check was prepared by the All India Soil & Land Use Survey in collaboration with NRSA. The data available in the form ofccxs were analysed at N•SA, Hyderabad using BENDIX (M-DAS) system. The area was projected on the video screen and the geographical location as well as soilscapes were identified with the help of ground reference data available in the form of aerial photographs and conventional soil map. Training sets of small area for each class were entered using a cursor. The spectral signatures of the specified classes were extracted by the computer from the training sites and the entire scene was classified. The codified colour composite was obtained by the optronics system. A line map was subsequently prepared and the various units were translated to soil association units by supervised analysis. The computer-generated soil map was compared with the products of aerial photo interpretation and conventional field traversing (figure 2).

In all cases, the level of abstraction attained was Great-groups and their association. Mapping of Chromusterts, Ustifluvents and Haplaquents was found to be more efficient in all the three systems. The computer-aided map was more superior than the conventional RR survey map and well comparable with the API map (Karale et al 1978b).

The computer based RS map showed more details compared to RR map but over 70 ~o of the area represented associations of Subgroups as against 10-15 ~ in case of API map at 1:50,000 scale. Soil boundaries are better expressed on RS map compared to conventional soil map. Correlation of LANDSAT analysis with that of API presented in

Table 1, Comparative study of mapping efficiency and levels of characterisation

RR API LANDSAT

Details map map computer aided

No. of units in legend 9 8 Total No. of boundaries 58 186 Total length of boundaries (km) 376 550 Level of soil taxonomic Subgroups and association

classification Time involved including field data

collection (hr) 90 45

7 171 425

Great groups and association

Sample area: 180 km 2

202 R L Karale, Y P Bali and K V Seshaoiri Rao

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Remote sensin 0 in soil mappino 203

table 1 highlights the various factors involved in soil mapping by these techniques. The efficiency in terms of time to prepare soil maps by the three techniques of computer analysis, ApI and conventional method was in the ratio of 1 : 5: 10. Similar techniques were employed by Singh et al (1977) for delineation of salt affected lands in a part of Ganges plain. However, in these initial studies, further categorisation of the salinity was not comparable with the results obtained through aerial photo-interpretation.

Subsequently, using image emhancement techniques for colour separation, Venkataratnam (1981) segregated highly saline areas from moderately saline and non- saline areas in Karnal, Kurukshetra and Jind areas of Haryana.

6.2. Soil erosion map

In respect of soil degradation studies, successful mapping was carried out by the All India Soil & Land Use Survey, and three levels of erosion in granite landscape were differentiated using computer techniques.

Three sample areas covering 480kin 2 in Peddavagu subcatchment of Nagatjunasagar catchment, Andhra Pradesh were studied to assess the feasibility of utilising LANDSAT imagery in erosion assessment surveys. CCTS were analysed at NRS^, Hyderabad using M-DAS (Bendix) system. The area was displayed on the video screen and training sets of small areas, for which ground data were available, have been entered using a cursor. The colour coding was carried out with operator interaction panel and the classified output obtained. The classified tape was fed to the printer plotter and the digital soil and erosion map was obtained.

The same area was studied systematically using panchromatic black and white aerial photographs. Physiographic approach in conjunction with photo-element was fol- lowed in the analysis. The derived map was converted into a physiographic-soil map by field studies.

The maps thus obtained by LANDSAT analysis and ^pI were compared with the RR soil map prepared by conventional field survey. The erosional sequence prevailing in the terrain and the results obtained are presented in figures 3a and b.

Broad agreement in soils and erosional levels is evident in all the three nmlm. Apparently, identification of clayey soils and severely eroded lands is quite remarkable in LANDSAT imagery. As compared to other techniques, the areas classified as 'severely eroded' are larger in extent in the LANOSAa" map. Obviously the peripheral slight to moderately eroded areas of gravelly coarse loamy soils having very light surface colours recorded very high reflectance thus leading to their grouping along with the severely eroded areas in the computer analysis. It may be possible to separate these two classes of lands by density slicing techniques.

7. Recent advances

Special techniques in decoding digital data such as band stretching, enhancement, ratioing and utilising the computer aided statistical functions and clustering techniques proved to be more helpful in soil mapping. Image enhancement technique was successfully used by Venkataratnam (1980) to differentiate shallow red soils from deep ones that registered same spectral response.

204 R L Karate, Y P Bali and K V Seshaoiri Rao

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Figure 3. a. Erosion model showing sequence of erosion in granite terrain (Peddavagu Subcatchment), A.P.b. Soil and erosion maps of sample area obtained by different techniques

Remote sensing in soil mapping 205

7.1 Clustering technique

Computer-derived data on magnitude and Vis/IR ratios were used for segregation of vegetation and soilscapes (table 2). The work was carried out at LARS, USA by one of the authors (KVSR) using IBM system. Clustering was carried out initially and the means data obtained. A study of the data reveals distinct behaviour of each of the field classes in the four dimensional space (MSS "bands).

For the purpose of these studies 14 field classes covering sand bar, saline area, three vegetation classes, six soil classes, two soil + crop classes and one water class were considered. The calibrated curves of each of the classes are presented in figure 4a. The water bodies, vegetation classes, saline and sandy soils stand out conspicuously. Other soil groups are all projected together and narrowly defined in the two-dimensional space. For further segregation the magnitude and ratio (Vis/IR) are plotted against each (figure 4b). It may be perceived that the classes which are not so clearly separable by calibrating the mean values of each cluster, could be differentiated from other classes as also individually among themselves.

A bi-spectral plot was obtained and with the help of information abstracted by the above technique, pooling and deleting of the classes were carried out with a suitable threshold value. The statistical data thus generated were used in the maximum likelihood classifier and the computer classification of the study was carried out. The results obtained were quite satisfactory.

The combined and reciprocal information on varied statistical data obtained through computer programming can thus be efficiently used in soil mapping. Application of various other classifiers like Bayesian maximum likelihood, Euclidian Minimum distance, aggregate Gaussian standard, parallelopiped, ECHO, etc have shown encouraging results and need further studies in different terrain conditions.

Table 2. Statistical data (Uncalibrated)

Means of bands Code Class 4 5 6 7 Magnitude Ratio Vis/IR

1 Sand bar 57-77 56-86 48-69 t9-77 183-10 1-6746 2 Saline 49.94 48.87 43-31 17-79 159-90 1-6174

14 Dense forest 33.52 22-94 63-92 37-54 157-92 0-5563 13 Bushy veg 40.00 35.67 50-21 25.22 151-10 1.0038 3 Light soil 1 41.75 42.75 42-04 18.38 144.92 1.3984 4 Light soil 2 39.62 35.97 34-47 15.24 125.30 1.5206 5 Dark clay 38.90 33.86 28-36 11-04 112.15 1-8467 6 Emerging crop 31.76 28-44 34-51 16-07 110.78 1.1903 7 Loamy soil 1 33.00 28'58 28-16 12-53 102.28 1.5132 8 Soil + crop 28.56 23'50 30-67 14.94 97.67 1.1414 9 Dark clay 32.88 27.11 22.34 8-67 91.00 1.9348

10 Soil+crop 26.15 20-52 29-15 14-61 90.44 1.0665 11 Loamy soil 2 25.42 19.73 21-30 9.86 76.30 1.4488 12 Water 29.91 21.34 12.65 3.03 66"93 3.2685

(Representative classes cited)

(EnO0. Sci.)-- 4

206 R L Karale, Y P Bali and K V Seshagiri Rao

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Figure 4. a. Calibrated curves for the candidate classes, b. Complimentary effect of magnitude vs ratio of candidate classes

8. Future scope

A review of the achievements so far indicates great scope for remote sensing techniques in soil mapping. More investigations have to be carried out deploying the automative techniques in differentiating soil and landscapes as also various agro-climatic zones. On experience it is found that no specific spectral signature can be assigned to soil in view of

Remote sensing in soil mapping 207

its changing spectral response with changes in crop growth, stage of vegetation, moisture content and surface conditions of soils, etc. Specific situations with specific condition, have to be investigated to arrive at a range of signatures for each of the candidate classes encountered in a particular terrain.

8.1 Future generation satellite data

The major limitations inherent in current generation satellite data in respect of spatial resolution (presently 0-5 ha) and lack of stereoscopic coverage inhibit efficient soil mapping at medium and large scales. The expected higher resolution and stereo- coverage in SPOT may widen the scope of soil studies in the near future. Stereosat and Mapsat will also generate stereo-pairs facilitating more reliable image interpretation for physiographic features and processes comparable to aerial photographs. The Indian satellite, IRS-1 offers a further promise as it is designed to provide pictures with better spatial resolution than LANDSAT 1, 2 and 3.

8.2 Microwave sensing

Compared to the regions of spectrum available for visible and infrared sensing, the microwave region, which is free from major atmospheric absorption offers a vast zone for remote sensing. Despite its immense potential, no significant work has so far been carried out on radar sensing in India. Plan position indicator radar (PP0, side looking airborne radar (SEAR) and synthetic aperture radar (SAR) are reported to have been used for thematic mapping with varying results in other countries.

Stereoscopic radar imageries are possible with SLAR system by either flying parallel flight lines which cover the same target area or by flying the same flight line at two different altitudes. Studies are also in progress in USA to provide design parameters for the development of a special satellite-borne radar unit.

Some potential applications of radar sensing as related to soil studies are indicated below:

--Standard pulse radar sensor can provide information that permits an estimate of moisture content of deep homogeneous soils.

--Large wavelength radar (P band) reflects from subsurface soil-water interface or layered materials thereby providing information on the subsurface features.

- -The longer wavelength radar (e.g. P band) is not influenced by various heights of vegetation unlike those of the shorter wavelength. This suggests that the shorter wavelength radar system could be used to measure vegetation parameters whereas the longer wavelength radar system could be used to analyse the soil condition below.

In view of the exceedingly fast developments in space technology, it is to be hoped that soil studies will result in efficient and reliable systems.

References

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208 R L Karale , Y P Bali and K V Seshagiri R a g

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Biswas R R 1977 J. Indian Soc. Soil. Sci. 25 347 Govindarajan S V & Mouttapa F 1967 J. Post-graduate School 5 226 Hilwig F W 1976 J. Int. Training Centre 1 127 Hilwig F W & Karale R L 1970 Aerial photo-interpretation for detailed soil survey of salt-affected soils near

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photographs as base maps; Presented at the Seminar on Application of Photo-interpretation and RS techniques for Natural Resources Survey and Environmental Analysis, Dehradun, October

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Interactive Symposium and Workshop on applications of remote sensing for rice production, Hyderabad, September