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Assessment of land degradation using comprehensive geostatistical approach and remote sensing data in GIS-model builder
The Egyptian Journal of Remote Sensing and Space Sciences xxx (xxxx) xxx
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Research Paper
Assessment of land degradation using comprehensive geostatistical approach and remote sensing data in GIS-model builder Mohamed A.E. AbdelRahman a,⇑, A. Natarajan b, Rajendra Hegde b, S.S. Prakash c a
Division of Environmental eStudies and Land Use, National Authority for Remote Sensing and Space Sciences (NARSS), Egypt National Bureau for Soil Survey and Land Use Planning, Indian Council of Agriculture Research, India c Soil Science and Agricultural Chemistry Department, V.C, Farm Mandya (UAS Bangalore), G.K.V.K., Bangalore, Karnataka, India b
a r t i c l e
i n f o
Article history: Received 19 September 2017 Revised 16 January 2018 Accepted 6 March 2018 Available online xxxx Keywords: Chamrajanagar district Hyperspectral Multispectral Geostatistical GIS spatial model Land degradation
a b s t r a c t This study was conducted to assess Land Degradation (LD) status under different land use. Geostatistical technique used to interpolate spatially distribution of soil physical, chemical and biological properties. Salinity indices were applied on Hyperspectral and Multispectral Data to predict the salt affected areas. Arc GIS model-builder implemented to integrate the available LD methodologies and produce the overall degradation map of the study area in Chamrajanagar district (CDK), Karnataka, India. Remote sensing data was found to be useful tools to map land resources, especially in the areas where accessibility is limited like mountains. This study determined spatial distribution by calculating different soil properties for soils profiles. For LD calculations, eighteen soil profiles were dug and 79 samples were analyzed. This along with the parameters taken into consideration i.e., soil, slope, rainfall, DEM, land use, and land characteristics maps. It was found by adopting the logical criteria that LD of CDK categorized as very high, high, moderate, low and very low. The result of this research work could be potentially used as a useful tool to guide policy decision makers for sustainable land resource management in CKD. Based on the imagery interpretation and soil map unit description, hotspots were identified for representing different types of degraded soils. From the physical and chemical characteristics of pedons studied, it has been found that soils of CDK are exposed to degradation in the surface and sub surface horizons. Ó 2018 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
1. Introduction Land Degradation is a serious problem facing the agricultural production. Various methodologies versions are available to estimate LD extensions. Quantification assessment of LD is depending on variety of land characteristics. LD assessment requires use of different form of data and information (soil properties, climate, land use, topography, etc.) and its weights and rates could be constructed in the ArcGIS model builder to perform the LD assessment methodologies in spatially distributed ways using the geostatistical approach (AbdelRahman 2009, AbdelRahman 2014, AbdelRahman et al., 2016b). Utilizing of remote sensing (RS) techniques is important to describe and understand dynamic changes in the landscape (Kouchoukos et al., 1997). RS is a good tool which
recommended for its potential to detect, map and monitor different degradation types and its problems (Hellden and Stern, 1980; Sabins, 1987; Frederiksen, 1993; Mohammed, 1993; Raina et al., 1993; Tripathy et al., 1996; Sujatha et al., 2000; AbdelRahman et al., 2008) including its degree of spread and range effects with time (Sommer et al., 1998; AbdelRahman, 2009). Survey using satellite images overcomes that traditional survey which is expensive and time-consuming, especially in areas difficult to access (AbdelRahman et al., 2016a). Geographic Information System (GIS) is a flexible and powerful tool that provides storing large volumes of different kinds of data sets, manipulating and combining different data sets into other sets which can be presented in thematic maps (Marble et al., 1984; Foote and Lynch, 1996; Al-Mashreki et al., 2010). GIS allows construction models able to produce thematic map from a set of thematic maps (Harasheh,
Peer review under responsibility of National Authority for Remote Sensing and Space Sciences. ⇑ Corresponding author. E-mail addresses: [email protected], [email protected], maekaoud@ narss.sci.eg (M.A.E. AbdelRahman).
1994). GIS support LD by providing a good platform for storage of base data, simple modelling and presentation of results and development of a user interface in combination with a GPS, controlling the navigation of degradation data (AbdelRahman 2014).
https://doi.org/10.1016/j.ejrs.2018.03.002 1110-9823/Ó 2018 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Geostatistical techniques are being used rapidly in the field of soil science to study spatial variation of different soil properties on scales ranged from centimeters up to kilometers (White et al., 1997; Goovaerts, 1998; Castrignano et al., 2000; Yang et al., 2001). These techniques were used to process the information of means to characterize and quantify spatial variation for rational interpolation, and were applied to estimate the variance of
interpolated values (Isaaks and Srivastava, 1989; McBratney and Pringle, 1999; Webster, 2001; Gaston et al., 2001; Stenger et al., 2002). This study was undertaken to develop a new qualitative and quantitative LD method using GIS and RS technologies in CDK. In addition, the study identified which kinds of data input into a GIS and how this data was used to create useful interpretive maps.
Fig. 1. Location map of study area and 3D Surface View.
Fig. 2. Flowchart for generating Land degradation maps.
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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M.A.E. AbdelRahman et al. / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx Table 1 Remote sensing indices and equations. Indices
Equation
Normalized difference salinity index using Hyperion Data Salinity index using Hyperion Data Normalized difference salinity index using IRS Salinity index using IRS
References
ð10:40 12:5lmÞ ð0:75 0:90lmÞ ð10:40 12:5lmÞ þ ð0:75 0:90lmÞ SI ¼ fð0:43 0:515lmÞ ð0:63 0:690lmg band3 band4 NDSIð1Þ ¼ band3 þ band4 SIð1Þ ¼ ðband1 band3Þ 12
Iqbal, 2008; Iqbal, 2010
NDSI ¼
1 2
Khan et al., 2005 Tripathi et al.,1997 Tripathi et al.,1997
Fig. 3. SI using Hyperion.
Fig. 4. NDSI using Hyperion.
LD maps were generated by integration of information gathered from RS data and from both universal soil loss equation (USLE) and revised universal soil loss equation (RUSLE). Soil conservations and practices recommendations were generated in a thematic map to CDK local stakeholder Based on soil’s physical and chemical properties.
from Indian Remote Sensing satellite (IRS P-6) LISS III and LISS IV sensors and Hyperion. Also slope’s length and degree were derived from SRTM and Topographic maps. Ancillary data like Topo sheets used (1: 50,000) and (1: 2, 50,000) were used. (i.e., Topo sheets 1: 50,000 were 57H/7, 57H/4, 57H/8, 57H/12, 57H/16, 58A/9, 58A/13, 58E/1, 58E/5, 58E/9, 58A/6, 58A/10,and 58A/14 while Topo sheets 1: 250,000 were; 57 A, 57 E, 57 D, and 57H). The soil site characteristics at each location were studied and recorded in a standard Performa (Natarajan and Sarkar, 2009). The site characteristics include parent material, geology, climate, rainfall, topography, elevation, gradient and length of slope, erosion, runoff, drainage, pH, EC, stoniness, rock outcrops, natural vegetation, crop yield and present land use. At the selected sites soil profiles were exposed by making pits (1 1 m), down to the parent material and the morphological characteristics were studied horizon wise for each pedon. The characteristics studied and recorded were thickness of the horizon, boundary characteristics, colour, texture, coarse fragments, structure, dry, moist and wet consistency, size and type of pores and roots, type, thickness and quantity of cutans, size and quantity of nodules. Samples were collected horizon-wise for laboratory analysis from each pedon. Total of 79 soil samples and 18 pedons were collected for laboratory analysis. Satellite imagery of Hyperion and IRS were used to detect salt affected soils. The equations used to extract the salt affected soil are available in Table 1.
2. Materials and methods The geographical area of CDK is about 5101 Km2, located in the southern tip of Karnataka state and lies between the North latitude 11⁰400 5800 and 12⁰060 3200 and East longitude 76⁰240 140 0 and 77⁰460 550 0 . It falls in the southern dry zone of the state. Topography is undulating and mountainous with north south trending hill ranges of eastern ghats. The administrative division of the district is given in Fig. 1. The study area is a combing of arid zone, semi-arid zone and rain zoon (CGWB, 2008). Therefore USDA (1997) used in forest area, FAO (1979) in arid zone and FAO/ISRIC (2004) for chemical degradation, FAO/UNEP (1978 and 1984) and FAO/UNEP and UNESCO (1979) in rain fed land. LD maps generated according to GLASOD 1991. The procedures followed starting from imagery interpretation, field traversing, transect selection, profile study, sample collection, sample analysis and identify LD indicators by land survey, and producing LD types and hazard showing in (Fig. 2). Used RS data are
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Fig. 5. SI using IRS.
Fig. 6. NDSI using IRS.
3. Results and discussion 3.1. Soils Soil orders which found in CDK are Alfisols, Inceptisols, Entisols, Vertisols, Ultisols. The soil map has 24 mapping unit consisting of soil family associations with dominant phases based on land form analysis, field survey, laboratory investigation, field reviews and after (Prasad et al., 1998; AbdelRahman 2014). 3.2. Detection of salt affected soil using Hyperspectral and Indian Remote sensing (IRS) satellite imageries Detection of soil salinity is usually done by laborious soil sampling. To delineate surface soil salinity in selected area of
CDK, the study employed an index-based approach of using optical RS data in combination with GIS. The effectiveness of two satellite imagery indicators was examined. This index has been development using several combinations of the ratio of signals received in different spectral bands. Near infrared and thermal IR spectral bands proved to be most effective as this combination helped easy detection of salt affected area from the non-saline area. Results showed that 8.9 per cent of Hyperion image area (32610 ha) in the selected area of CDK is salt affected using SI and 1.8 per cent using NDSI as shown in Figs. 3 and 4 respectively. These results are in agreement with the field survey data. Using IRS, results showed that 0.11 per cent of CDK is salt affected applying SI while 11.4 per cent applying NDSI as shown in Figs. 5 and 6 respectively.
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Fig. 7. Assessment of the risk of Water Erosion.
Fig. 8. Assessment of present water erosion.
3.3. Land degradation indicators, types, and hazard and status maps LD indicators were collected from the direct observations from field and served as ground truth to verify results obtained from RS image interpretation. Survey was based on direct field observations, using diagnostic criteria and simple and visual indices given in (FAO/UNEP and UNESCO, 1979). 3.3.1. Water erosion The parameters (variables that take into account the soil erosion) have been generated from fieldwork data and have been classified in integer values to obtain the ranges for the assessment of
the risk of Water Erosion (Fig. 7) and assessment of present Water Erosion (Fig. 8). Integrating different types of the factors affecting land erosion, the results show that the majority of CDK fall under the moderate land erosion classes occupying 72.2 per cent of the total area. High LD class occupying 27.8 per cent has been found also in areas affected by high soil loss. FCCs obtained from LISS III and IV sensors (with 24 m and 5.8 m spatial resolution) were evaluated for delineation of eroded areas. Based on soil, slope, and land use/land cover, current soil erosion status was mapped as shown in Fig. 9; none or slight occupied 26.24 per cent of the total area, Moderate occupied 415.54 per cent, Severe occupied 12.99 per cent, and Very severe occupied
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Fig. 9. Erosion based on satellite images.
Fig. 10. Assessment of salinity risk.
8.15 per cent of the total area and the remain areas are for Rock land 11.06 per cent and Water bodies 0.02 per cent of the total area. Visual interpretation of IRS data helped in delineation distribution of various LD categories as shown in the Fig. 9. FCC used to identify distinctly eroded areas as a result of erosion of soil by running water which are more common on sloping surface. Fig. 9 shows the soil erosion map of the site on 1:50,000 scale.
3.3.2. Wind erosion According to both USLE (FAO, 1979 and FAO/ISRIC 2004) and RUSLE (USDA, 1997) calculations, the whole CDK has been subjected to non to slight wind erosion hence it has wind speed ranging from 8.4 to 14.1 kmph.
3.3.3. Salinization and sodification (Alkalinization) This estimation is based upon the calculated climatic index without considering the salinity of the ground water. The soil, topography and human activity cause higher values for the present state and risk of degradation by salinization and risk of degradation by sodification. The geostatistical approaches of GIS were used to produce the spatial variability for salinization and sodification maps of the studied area. Assessment of salinization risk was classified into four classes according to the USLE as shown in Fig. 10; none to slight occupying 41.41 per cent, moderate 42.92 per cent, high 7.40 per cent and very high 8.27 per cent of the total area. while assessment of sodification risk was classified into four classes according to the USLE as shown in Fig. 11; none to slight occupying 15.85 per cent, moderate 25.56 per cent, high 42.92 per cent and very high 15.67 per cent of the total area.
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Fig. 11. Assessment of sodification (Alkalinization) risk.
Fig. 12. Assessment of Chemical Degradation (Leaching) Risk.
3.3.4. Chemical degradation (Leaching) Leaching of bases; Soils with free drainage and low cation exchange capacity lose its bases through leaching. This loss of bases leads to the soil becoming more acidic in reaction, the main cause of this is the rainfall upon the upper portions of highland of landform which coarse in texture as a result of erosion, may also be lost each year by leaching down to lower horizons beyond the reach of crop roots. The geostatistical approaches of GIS were used to produce the spatial variability for sodification map. Assessment of chemical degradation (Leaching) risk was classified into four classes according to the USLE as shown in Fig. 12; none to slight occupying 7.71 per cent, moderate 46.25 per cent, high 31.79 per cent and very high 14.25 per cent of the total area. 3.3.5. Physical degradation Tillage and natural processes can re-loosen the topsoil Nevertheless topsoil compaction is almost impossible to be avoided
while the much more persistent and difficult to remove is subsoil compaction. The subsoil loosening artificially has been proven to be disappointing because the loosened subsoil is recompacted easily and therefore many physical properties are strongly reduced. Based on soil properties (bulk density), climate and land use, Provisional map of physical degradation risk initial levels was generated and can be inferred from the maps shown in Figs. 13 and 14. Assessment of Physical degradation risk initial levels was classified into four classes according to the USLE; none to slight occupying 23.30 per cent, moderate 60.73 per cent, high 8.68 per cent and very high 7.29 per cent of the total area. 3.3.6. Biological degradation (BD) Content of organic matter (OM) is directly and positively related to length of cultivation of crops. This is because additional amounts of OM by physical application as organic manures and through the addition of high amount of crop residues in such fields. Irrespective
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Fig. 13. Assessment of bulk density.
Fig. 14. Assessment of physical degradation risk initial levels.
of its location in the landscape, wherever farmers are cultivating with the help of irrigation wells, the OM content is high. The fields without water supply are cultivated with one crop under rainfed conditions. Here the OM content is low. Low OM content combined with lighter soils is prone for more soil erosion and loss of soil nutrients. The upland soils cultivated with only rainfed crops are more prone to such problems in the region. As it is obvious from Fig. 15 risk on BD and its assessment of present degradation should be low under natural vegetation; for the cultivated areas, it is dependent upon the length of the cropping period, on the amount of crop residue left after harvest and on management techniques such as recycling of organic material. The C/N ratio of the raw organic material added to the soil is also very important. At present, BD of the cultivated areas is likely to be at least moderate. BD was classified according to the USLE into four classes as shown
in Fig. 15; none to slight occupying 25.49 per cent, moderate 35.96 per cent, high 28.05 per cent and very high 10.51 per cent of the total area. Some of the observations which are considered as simple criteria of BD are increased sealing, crusting, and runoff; decreased aggregation of soil particles in surface, and decrease of earthworm, ants and rodents, and most of the criteria are used for identification of physical degradation. 3.3.6.1. Overall land degradation. Arc GIS Model Builder Fig. 16 was used to develop a final overlay map Fig. 17 for LD types in the studied area based on integration of USDA (1997), FAO (1979) and FAO/ ISRIC (2004), FAO/UNEP (1978 and 1984) and FAO/UNEP and UNESCO (1979). The overall LD types are shown in Fig. 17. The values resulted from interpolation techniques in GIS were reclassified to produce maps. Assessment of overall LD types was classified
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Fig. 15. Risk on biological degradation.
Fig. 16. Overlay Model of land degradation types.
into four classes according to the USLE; none to slight occupying 6.14 per cent, moderate 12.80 per cent, high 53.32 per cent and very high 27.74 per cent of the total area. Spatial analysis processes were used to precede the overlay operation based on the value of weightage of each subclass within each thematic map. All factor layers maps were combined using weighted overlay into new information to produce individual value for new map. The maps resulted from weightage values through the overlay operations are only performed on raster. The weigh-
tages were given based on the influence of every subclass. It should be noted that the heuristic value for factor and classification is given from 1 to 4 in this study, where value of 1 show the influence or result towards very low while value 4 is very high. The concept of overlay model, which contains major steps of the weightage are schematically shown in 17 and 18. 3.3.6.2. Producing land degradation status maps according to GLASOD 1991. There is a great need for common classifications and
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Fig. 17. Overlay map of land degradation types according to USDA (1997), FAO (1979) and FAO/ISRIC (2004), FAO/UNEP (1978 and 1984) and FAO/UNEP and UNESCO (1979).
Fig. 18. Land degradation types based on deferent methodologies with integration of GLASOD classification.
methodology to organize existing data and to direct future efforts. A general classification was developed, referred to as the GLASOD (Global Assessment of Soil Deterioration) classification. The classification of type, severity and cause has been reproduced. Storing and processing data methodology into maps is provided by SOTER (Global soils and terrain digital database) (Van Engelen and Wen Tin-Tiang, 1993). The different degradation type’s assessment has been carried out through integrating RS, GIS and GLASOD approaches. Seven land degradation types according to GLASOD Classification of Soil Degradation (Oldeman et al., 1991) as shown in Fig. 18 based on the integration of the RUSLE according to USDA (1997) and USLE according to FAO (1979) and FAO/ISRIC (2004) have been obtained:
B; Biological degradation occupying 32.56 per cent, Cs; salinisation/(Alkalinization) occupying 5.19 per cent, Cs; (Salinisation)/ alkalinization occupying 7.86 per cent, Ct; Dystrification/(chemical degradation, leaching) occupying 12.46 per cent, Pc; Compaction occupying 14.79 per cent, W; Wasteland occupying 4.58 per cent and Wt, Water erosion, Loss of topsoil occupying 22.57 per cent, of the total area.
3.3.6.3. Forest degradation. Forest degradation in this study considered as removal of forest cover to an extent that allows for alternative land use. By detecting the changes between Land SAT satellite image 2000 and IRS satellite images 2006 and 2012 found that the
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Fig. 19. Degraded forest in Chamarajanagar district.
forest degraded by reducing in area by 1.73 per cent of total area of district, and 4.16 per cent of forest area as it is shown in Fig. 19. 4. Conclusions Remotely sensed data provide timely, accurate and reliable information on degraded lands. The results showed active degradation in CDK like salinzation, alkalinzation and physical degradation. The geostatistical analysis was performed for all degradation types (water erosion, wind erosion, salinization, sodification (alkalinization) and chemical degradation (leaching), physical degradation and biological degradation) and was performed for fertility and land evaluation maps in the district. Overlay map of land degradation types was produced using spatial model tools. Land degradation status map was produced according to GLASOD (1991) and based on that land improvement and recommendations were suggested to the district. From the physical and chemical characteristics of the soils studied, it has been found that the soils of the area are exposed to degradation in the surface and sub surface horizons in all the lowland areas and non-saline and nonsodic in both upland and midland areas. Maximum low land area had higher sub surface sodicity than surface horizon. Maximum upland areas are exposed to water erosion and Maximum low land area are exposed to salinization, alkalinization, and physical degradation Acknowledgments The authors wish to express their sincere thanks to the department of Soil Science & Agricultural Chemistry, University (UAS Bangalore), G.K.V.K., Bangalore, Karnataka, India for facilitating this study. The authors are also grateful to the National Bureau of Soil Survey and Land Use Planning [NBSSLUP], Regional Center, Hebbal, Bangalore, Karnataka, India for facilitating the soil survey and laboratory analysis.
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Sujatha, G., Dwivedi, R.S., Sreenivas, K., Venkataratnam, L., 2000. Mapping and monitoring of degradaded lands in part of Jaunpur district in Uttar Pradesh using temporal spaceborne multispectral data. Int. J. Remort Sens. 21, 519–531. Tripathi, N.K., Rai, B.K., Dwivedi, P., 1997. Spatial modelling of soil alkalinity in GIS environment using IRS data. J. ACRS. Retrieved from http://www. geospatialworld.net/paper/application/ArticleView.aspx?aid=285#sthash. C7dAYmrY.dpuf. Tripathy, G.K., Gohosh, T.K., Shah, S.D., 1996. Monitoring of desertification process in Karnataka state of India using multi-temporal remote sensing and ancillary information using GIS. Int. J. Remote Sensing 17, 2243–2257. Van Engelen, V.W.P., Wen Tin-Tiang (Eds.), 1993. The SOTER manual (Procedures for small scale digital map and database compilation of soil and terrain conditions, 5th edition). FAO/ISRIC/ISSS/UNEP. Webster, R., 2001. Statistics to support soil research and their presentation. Eur. J. Soil Sci. 52, 331–340. White, J.G., Welch, R.M., Norvell, W.A., 1997. Soil zinc map of the USA using geostatistics and geographic information systems. Soil Sci. Soc. Am. J. 61, 185–194. Yang, J.I., Mosby, D.E., Casteel, S.W., Blanchar, R.W., 2001. Microscale pH variability for assessing of phosphoric acid treatment in lead-contaminated soil. Soil Sci. 66 (6), 374–381.
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Research Paper
Assessment of land degradation using comprehensive geostatistical approach and remote sensing data in GIS-model builder Mohamed A.E. AbdelRahman a,⇑, A. Natarajan b, Rajendra Hegde b, S.S. Prakash c a
Division of Environmental eStudies and Land Use, National Authority for Remote Sensing and Space Sciences (NARSS), Egypt National Bureau for Soil Survey and Land Use Planning, Indian Council of Agriculture Research, India c Soil Science and Agricultural Chemistry Department, V.C, Farm Mandya (UAS Bangalore), G.K.V.K., Bangalore, Karnataka, India b
a r t i c l e
i n f o
Article history: Received 19 September 2017 Revised 16 January 2018 Accepted 6 March 2018 Available online xxxx Keywords: Chamrajanagar district Hyperspectral Multispectral Geostatistical GIS spatial model Land degradation
a b s t r a c t This study was conducted to assess Land Degradation (LD) status under different land use. Geostatistical technique used to interpolate spatially distribution of soil physical, chemical and biological properties. Salinity indices were applied on Hyperspectral and Multispectral Data to predict the salt affected areas. Arc GIS model-builder implemented to integrate the available LD methodologies and produce the overall degradation map of the study area in Chamrajanagar district (CDK), Karnataka, India. Remote sensing data was found to be useful tools to map land resources, especially in the areas where accessibility is limited like mountains. This study determined spatial distribution by calculating different soil properties for soils profiles. For LD calculations, eighteen soil profiles were dug and 79 samples were analyzed. This along with the parameters taken into consideration i.e., soil, slope, rainfall, DEM, land use, and land characteristics maps. It was found by adopting the logical criteria that LD of CDK categorized as very high, high, moderate, low and very low. The result of this research work could be potentially used as a useful tool to guide policy decision makers for sustainable land resource management in CKD. Based on the imagery interpretation and soil map unit description, hotspots were identified for representing different types of degraded soils. From the physical and chemical characteristics of pedons studied, it has been found that soils of CDK are exposed to degradation in the surface and sub surface horizons. Ó 2018 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
1. Introduction Land Degradation is a serious problem facing the agricultural production. Various methodologies versions are available to estimate LD extensions. Quantification assessment of LD is depending on variety of land characteristics. LD assessment requires use of different form of data and information (soil properties, climate, land use, topography, etc.) and its weights and rates could be constructed in the ArcGIS model builder to perform the LD assessment methodologies in spatially distributed ways using the geostatistical approach (AbdelRahman 2009, AbdelRahman 2014, AbdelRahman et al., 2016b). Utilizing of remote sensing (RS) techniques is important to describe and understand dynamic changes in the landscape (Kouchoukos et al., 1997). RS is a good tool which
recommended for its potential to detect, map and monitor different degradation types and its problems (Hellden and Stern, 1980; Sabins, 1987; Frederiksen, 1993; Mohammed, 1993; Raina et al., 1993; Tripathy et al., 1996; Sujatha et al., 2000; AbdelRahman et al., 2008) including its degree of spread and range effects with time (Sommer et al., 1998; AbdelRahman, 2009). Survey using satellite images overcomes that traditional survey which is expensive and time-consuming, especially in areas difficult to access (AbdelRahman et al., 2016a). Geographic Information System (GIS) is a flexible and powerful tool that provides storing large volumes of different kinds of data sets, manipulating and combining different data sets into other sets which can be presented in thematic maps (Marble et al., 1984; Foote and Lynch, 1996; Al-Mashreki et al., 2010). GIS allows construction models able to produce thematic map from a set of thematic maps (Harasheh,
Peer review under responsibility of National Authority for Remote Sensing and Space Sciences. ⇑ Corresponding author. E-mail addresses: [email protected], [email protected], maekaoud@ narss.sci.eg (M.A.E. AbdelRahman).
1994). GIS support LD by providing a good platform for storage of base data, simple modelling and presentation of results and development of a user interface in combination with a GPS, controlling the navigation of degradation data (AbdelRahman 2014).
https://doi.org/10.1016/j.ejrs.2018.03.002 1110-9823/Ó 2018 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Geostatistical techniques are being used rapidly in the field of soil science to study spatial variation of different soil properties on scales ranged from centimeters up to kilometers (White et al., 1997; Goovaerts, 1998; Castrignano et al., 2000; Yang et al., 2001). These techniques were used to process the information of means to characterize and quantify spatial variation for rational interpolation, and were applied to estimate the variance of
interpolated values (Isaaks and Srivastava, 1989; McBratney and Pringle, 1999; Webster, 2001; Gaston et al., 2001; Stenger et al., 2002). This study was undertaken to develop a new qualitative and quantitative LD method using GIS and RS technologies in CDK. In addition, the study identified which kinds of data input into a GIS and how this data was used to create useful interpretive maps.
Fig. 1. Location map of study area and 3D Surface View.
Fig. 2. Flowchart for generating Land degradation maps.
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M.A.E. AbdelRahman et al. / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx Table 1 Remote sensing indices and equations. Indices
Equation
Normalized difference salinity index using Hyperion Data Salinity index using Hyperion Data Normalized difference salinity index using IRS Salinity index using IRS
References
ð10:40 12:5lmÞ ð0:75 0:90lmÞ ð10:40 12:5lmÞ þ ð0:75 0:90lmÞ SI ¼ fð0:43 0:515lmÞ ð0:63 0:690lmg band3 band4 NDSIð1Þ ¼ band3 þ band4 SIð1Þ ¼ ðband1 band3Þ 12
Iqbal, 2008; Iqbal, 2010
NDSI ¼
1 2
Khan et al., 2005 Tripathi et al.,1997 Tripathi et al.,1997
Fig. 3. SI using Hyperion.
Fig. 4. NDSI using Hyperion.
LD maps were generated by integration of information gathered from RS data and from both universal soil loss equation (USLE) and revised universal soil loss equation (RUSLE). Soil conservations and practices recommendations were generated in a thematic map to CDK local stakeholder Based on soil’s physical and chemical properties.
from Indian Remote Sensing satellite (IRS P-6) LISS III and LISS IV sensors and Hyperion. Also slope’s length and degree were derived from SRTM and Topographic maps. Ancillary data like Topo sheets used (1: 50,000) and (1: 2, 50,000) were used. (i.e., Topo sheets 1: 50,000 were 57H/7, 57H/4, 57H/8, 57H/12, 57H/16, 58A/9, 58A/13, 58E/1, 58E/5, 58E/9, 58A/6, 58A/10,and 58A/14 while Topo sheets 1: 250,000 were; 57 A, 57 E, 57 D, and 57H). The soil site characteristics at each location were studied and recorded in a standard Performa (Natarajan and Sarkar, 2009). The site characteristics include parent material, geology, climate, rainfall, topography, elevation, gradient and length of slope, erosion, runoff, drainage, pH, EC, stoniness, rock outcrops, natural vegetation, crop yield and present land use. At the selected sites soil profiles were exposed by making pits (1 1 m), down to the parent material and the morphological characteristics were studied horizon wise for each pedon. The characteristics studied and recorded were thickness of the horizon, boundary characteristics, colour, texture, coarse fragments, structure, dry, moist and wet consistency, size and type of pores and roots, type, thickness and quantity of cutans, size and quantity of nodules. Samples were collected horizon-wise for laboratory analysis from each pedon. Total of 79 soil samples and 18 pedons were collected for laboratory analysis. Satellite imagery of Hyperion and IRS were used to detect salt affected soils. The equations used to extract the salt affected soil are available in Table 1.
2. Materials and methods The geographical area of CDK is about 5101 Km2, located in the southern tip of Karnataka state and lies between the North latitude 11⁰400 5800 and 12⁰060 3200 and East longitude 76⁰240 140 0 and 77⁰460 550 0 . It falls in the southern dry zone of the state. Topography is undulating and mountainous with north south trending hill ranges of eastern ghats. The administrative division of the district is given in Fig. 1. The study area is a combing of arid zone, semi-arid zone and rain zoon (CGWB, 2008). Therefore USDA (1997) used in forest area, FAO (1979) in arid zone and FAO/ISRIC (2004) for chemical degradation, FAO/UNEP (1978 and 1984) and FAO/UNEP and UNESCO (1979) in rain fed land. LD maps generated according to GLASOD 1991. The procedures followed starting from imagery interpretation, field traversing, transect selection, profile study, sample collection, sample analysis and identify LD indicators by land survey, and producing LD types and hazard showing in (Fig. 2). Used RS data are
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Fig. 5. SI using IRS.
Fig. 6. NDSI using IRS.
3. Results and discussion 3.1. Soils Soil orders which found in CDK are Alfisols, Inceptisols, Entisols, Vertisols, Ultisols. The soil map has 24 mapping unit consisting of soil family associations with dominant phases based on land form analysis, field survey, laboratory investigation, field reviews and after (Prasad et al., 1998; AbdelRahman 2014). 3.2. Detection of salt affected soil using Hyperspectral and Indian Remote sensing (IRS) satellite imageries Detection of soil salinity is usually done by laborious soil sampling. To delineate surface soil salinity in selected area of
CDK, the study employed an index-based approach of using optical RS data in combination with GIS. The effectiveness of two satellite imagery indicators was examined. This index has been development using several combinations of the ratio of signals received in different spectral bands. Near infrared and thermal IR spectral bands proved to be most effective as this combination helped easy detection of salt affected area from the non-saline area. Results showed that 8.9 per cent of Hyperion image area (32610 ha) in the selected area of CDK is salt affected using SI and 1.8 per cent using NDSI as shown in Figs. 3 and 4 respectively. These results are in agreement with the field survey data. Using IRS, results showed that 0.11 per cent of CDK is salt affected applying SI while 11.4 per cent applying NDSI as shown in Figs. 5 and 6 respectively.
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Fig. 7. Assessment of the risk of Water Erosion.
Fig. 8. Assessment of present water erosion.
3.3. Land degradation indicators, types, and hazard and status maps LD indicators were collected from the direct observations from field and served as ground truth to verify results obtained from RS image interpretation. Survey was based on direct field observations, using diagnostic criteria and simple and visual indices given in (FAO/UNEP and UNESCO, 1979). 3.3.1. Water erosion The parameters (variables that take into account the soil erosion) have been generated from fieldwork data and have been classified in integer values to obtain the ranges for the assessment of
the risk of Water Erosion (Fig. 7) and assessment of present Water Erosion (Fig. 8). Integrating different types of the factors affecting land erosion, the results show that the majority of CDK fall under the moderate land erosion classes occupying 72.2 per cent of the total area. High LD class occupying 27.8 per cent has been found also in areas affected by high soil loss. FCCs obtained from LISS III and IV sensors (with 24 m and 5.8 m spatial resolution) were evaluated for delineation of eroded areas. Based on soil, slope, and land use/land cover, current soil erosion status was mapped as shown in Fig. 9; none or slight occupied 26.24 per cent of the total area, Moderate occupied 415.54 per cent, Severe occupied 12.99 per cent, and Very severe occupied
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Fig. 9. Erosion based on satellite images.
Fig. 10. Assessment of salinity risk.
8.15 per cent of the total area and the remain areas are for Rock land 11.06 per cent and Water bodies 0.02 per cent of the total area. Visual interpretation of IRS data helped in delineation distribution of various LD categories as shown in the Fig. 9. FCC used to identify distinctly eroded areas as a result of erosion of soil by running water which are more common on sloping surface. Fig. 9 shows the soil erosion map of the site on 1:50,000 scale.
3.3.2. Wind erosion According to both USLE (FAO, 1979 and FAO/ISRIC 2004) and RUSLE (USDA, 1997) calculations, the whole CDK has been subjected to non to slight wind erosion hence it has wind speed ranging from 8.4 to 14.1 kmph.
3.3.3. Salinization and sodification (Alkalinization) This estimation is based upon the calculated climatic index without considering the salinity of the ground water. The soil, topography and human activity cause higher values for the present state and risk of degradation by salinization and risk of degradation by sodification. The geostatistical approaches of GIS were used to produce the spatial variability for salinization and sodification maps of the studied area. Assessment of salinization risk was classified into four classes according to the USLE as shown in Fig. 10; none to slight occupying 41.41 per cent, moderate 42.92 per cent, high 7.40 per cent and very high 8.27 per cent of the total area. while assessment of sodification risk was classified into four classes according to the USLE as shown in Fig. 11; none to slight occupying 15.85 per cent, moderate 25.56 per cent, high 42.92 per cent and very high 15.67 per cent of the total area.
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Fig. 11. Assessment of sodification (Alkalinization) risk.
Fig. 12. Assessment of Chemical Degradation (Leaching) Risk.
3.3.4. Chemical degradation (Leaching) Leaching of bases; Soils with free drainage and low cation exchange capacity lose its bases through leaching. This loss of bases leads to the soil becoming more acidic in reaction, the main cause of this is the rainfall upon the upper portions of highland of landform which coarse in texture as a result of erosion, may also be lost each year by leaching down to lower horizons beyond the reach of crop roots. The geostatistical approaches of GIS were used to produce the spatial variability for sodification map. Assessment of chemical degradation (Leaching) risk was classified into four classes according to the USLE as shown in Fig. 12; none to slight occupying 7.71 per cent, moderate 46.25 per cent, high 31.79 per cent and very high 14.25 per cent of the total area. 3.3.5. Physical degradation Tillage and natural processes can re-loosen the topsoil Nevertheless topsoil compaction is almost impossible to be avoided
while the much more persistent and difficult to remove is subsoil compaction. The subsoil loosening artificially has been proven to be disappointing because the loosened subsoil is recompacted easily and therefore many physical properties are strongly reduced. Based on soil properties (bulk density), climate and land use, Provisional map of physical degradation risk initial levels was generated and can be inferred from the maps shown in Figs. 13 and 14. Assessment of Physical degradation risk initial levels was classified into four classes according to the USLE; none to slight occupying 23.30 per cent, moderate 60.73 per cent, high 8.68 per cent and very high 7.29 per cent of the total area. 3.3.6. Biological degradation (BD) Content of organic matter (OM) is directly and positively related to length of cultivation of crops. This is because additional amounts of OM by physical application as organic manures and through the addition of high amount of crop residues in such fields. Irrespective
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Fig. 13. Assessment of bulk density.
Fig. 14. Assessment of physical degradation risk initial levels.
of its location in the landscape, wherever farmers are cultivating with the help of irrigation wells, the OM content is high. The fields without water supply are cultivated with one crop under rainfed conditions. Here the OM content is low. Low OM content combined with lighter soils is prone for more soil erosion and loss of soil nutrients. The upland soils cultivated with only rainfed crops are more prone to such problems in the region. As it is obvious from Fig. 15 risk on BD and its assessment of present degradation should be low under natural vegetation; for the cultivated areas, it is dependent upon the length of the cropping period, on the amount of crop residue left after harvest and on management techniques such as recycling of organic material. The C/N ratio of the raw organic material added to the soil is also very important. At present, BD of the cultivated areas is likely to be at least moderate. BD was classified according to the USLE into four classes as shown
in Fig. 15; none to slight occupying 25.49 per cent, moderate 35.96 per cent, high 28.05 per cent and very high 10.51 per cent of the total area. Some of the observations which are considered as simple criteria of BD are increased sealing, crusting, and runoff; decreased aggregation of soil particles in surface, and decrease of earthworm, ants and rodents, and most of the criteria are used for identification of physical degradation. 3.3.6.1. Overall land degradation. Arc GIS Model Builder Fig. 16 was used to develop a final overlay map Fig. 17 for LD types in the studied area based on integration of USDA (1997), FAO (1979) and FAO/ ISRIC (2004), FAO/UNEP (1978 and 1984) and FAO/UNEP and UNESCO (1979). The overall LD types are shown in Fig. 17. The values resulted from interpolation techniques in GIS were reclassified to produce maps. Assessment of overall LD types was classified
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Fig. 15. Risk on biological degradation.
Fig. 16. Overlay Model of land degradation types.
into four classes according to the USLE; none to slight occupying 6.14 per cent, moderate 12.80 per cent, high 53.32 per cent and very high 27.74 per cent of the total area. Spatial analysis processes were used to precede the overlay operation based on the value of weightage of each subclass within each thematic map. All factor layers maps were combined using weighted overlay into new information to produce individual value for new map. The maps resulted from weightage values through the overlay operations are only performed on raster. The weigh-
tages were given based on the influence of every subclass. It should be noted that the heuristic value for factor and classification is given from 1 to 4 in this study, where value of 1 show the influence or result towards very low while value 4 is very high. The concept of overlay model, which contains major steps of the weightage are schematically shown in 17 and 18. 3.3.6.2. Producing land degradation status maps according to GLASOD 1991. There is a great need for common classifications and
Please cite this article as: M. A. E. AbdelRahman, A. Natarajan, R. Hegde et al., , The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/ 10.1016/j.ejrs.2018.03.002
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Fig. 17. Overlay map of land degradation types according to USDA (1997), FAO (1979) and FAO/ISRIC (2004), FAO/UNEP (1978 and 1984) and FAO/UNEP and UNESCO (1979).
Fig. 18. Land degradation types based on deferent methodologies with integration of GLASOD classification.
methodology to organize existing data and to direct future efforts. A general classification was developed, referred to as the GLASOD (Global Assessment of Soil Deterioration) classification. The classification of type, severity and cause has been reproduced. Storing and processing data methodology into maps is provided by SOTER (Global soils and terrain digital database) (Van Engelen and Wen Tin-Tiang, 1993). The different degradation type’s assessment has been carried out through integrating RS, GIS and GLASOD approaches. Seven land degradation types according to GLASOD Classification of Soil Degradation (Oldeman et al., 1991) as shown in Fig. 18 based on the integration of the RUSLE according to USDA (1997) and USLE according to FAO (1979) and FAO/ISRIC (2004) have been obtained:
B; Biological degradation occupying 32.56 per cent, Cs; salinisation/(Alkalinization) occupying 5.19 per cent, Cs; (Salinisation)/ alkalinization occupying 7.86 per cent, Ct; Dystrification/(chemical degradation, leaching) occupying 12.46 per cent, Pc; Compaction occupying 14.79 per cent, W; Wasteland occupying 4.58 per cent and Wt, Water erosion, Loss of topsoil occupying 22.57 per cent, of the total area.
3.3.6.3. Forest degradation. Forest degradation in this study considered as removal of forest cover to an extent that allows for alternative land use. By detecting the changes between Land SAT satellite image 2000 and IRS satellite images 2006 and 2012 found that the
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Fig. 19. Degraded forest in Chamarajanagar district.
forest degraded by reducing in area by 1.73 per cent of total area of district, and 4.16 per cent of forest area as it is shown in Fig. 19. 4. Conclusions Remotely sensed data provide timely, accurate and reliable information on degraded lands. The results showed active degradation in CDK like salinzation, alkalinzation and physical degradation. The geostatistical analysis was performed for all degradation types (water erosion, wind erosion, salinization, sodification (alkalinization) and chemical degradation (leaching), physical degradation and biological degradation) and was performed for fertility and land evaluation maps in the district. Overlay map of land degradation types was produced using spatial model tools. Land degradation status map was produced according to GLASOD (1991) and based on that land improvement and recommendations were suggested to the district. From the physical and chemical characteristics of the soils studied, it has been found that the soils of the area are exposed to degradation in the surface and sub surface horizons in all the lowland areas and non-saline and nonsodic in both upland and midland areas. Maximum low land area had higher sub surface sodicity than surface horizon. Maximum upland areas are exposed to water erosion and Maximum low land area are exposed to salinization, alkalinization, and physical degradation Acknowledgments The authors wish to express their sincere thanks to the department of Soil Science & Agricultural Chemistry, University (UAS Bangalore), G.K.V.K., Bangalore, Karnataka, India for facilitating this study. The authors are also grateful to the National Bureau of Soil Survey and Land Use Planning [NBSSLUP], Regional Center, Hebbal, Bangalore, Karnataka, India for facilitating the soil survey and laboratory analysis.
References AbdelRahman, M.A.E., 2009. Quantification of land degradation indicators using remote sensing and GIS techniques. M.Sc. Thesis submitted to Zagazig University, Egypt.
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