identification of hydrologically active areas in a watershed using satellite data

Available online at www.sciencedirect.com ScienceDirect Aquatic Procedia (2015) 1339 – 1344 INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015) Identification of Hydrologically Active Areas in a Watershed using Satellite Data Kumar Raju B Ca *, Lakshman Nandagiria a Department of Applied Mechanics & Hydraulics, National Institute of Technology Karnataka, Surathkal -575025, India Abstract Information on the spatial distribution of Hydrologically Active Areas (HAAs) in a watershed is an important input for many applications, such as hydrological modeling, water resource planning and flood estimation HAAs can be delineated using a wetness index derived from either a Digital Elevation Model (DEM) or from satellite data The purpose of this study was to develop and apply a methodology to delineate the HAAs in the Harangi (535 km2) and Hemavathy (2974 km2) watersheds located in Karnataka, India Spatial distributions of HAAs derived from the DEM and from satellite data (Landsat ETM+ sensor) were compared It was found that wetness index obtained from satellite data was better able to capture the HAAs in comparison to the use of DEM The delineated HAAs will be useful in identifying runoff generation areas and improve process representation in distributed hydrological modeling of the watershed © 2015 2015The TheAuthors Authors Published by Elsevier © Published by Elsevier B.V.B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of organizing committee of ICWRCOE 2015 Peer-review under responsibility of organizing committee of ICWRCOE 2015 Keywords: Hydrologically Active Areas; Wetness Index; Satellite Data; DEM Introduction Protection and conservation of Hydrologically Active Areas (HAAs) in watersheds help to achieve long-term water quality and also contributes to the sustainable water resources management The HAAs is auxiliary to identify the runoff contributing areas and also analyzing movement of pollutants in the watersheds Although Land Use/Land Cover (LU/LC) changes exhibit significant impacts on HAAs, not every part of watersheds contributes equally to these impacts Therefore, the mapping of HAAs is an important challenging task in heterogeneous watersheds * Corresponding author Tel.:+91 9742501982 E-mail address: kumarrajubc@gmail.com 2214-241X © 2015 The Authors Published 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/) Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi:10.1016/j.aqpro.2015.02.174 1340 B.C Kumar Raju and Lakshman Nandagiri / Aquatic Procedia (2015) 1339 – 1344 located in the humid tropics There is an urgent need to develop appropriate techniques for identifying and mapping spatial patterns of HAAs with the highest accuracy and also to quantify the uncertainties associated with such techniques In the Western Ghats there is importance in delineating HAAs that contribute storm runoff and non point source pollutant loads to rivers Studies on vegetated hilly humid areas shows that the saturation does not occur over the whole valley floor even during peak rainfall and also found that infiltration rates in these areas were higher than the rainfall intensities (Hewlett and Hibbert, 1967, Hewlett and Nutter, 1970) The direct rainfall on the saturated area explains only a very small part of the total volume of storm runoff (Ando et al., 1983) The remaining amount of volume of runoff from saturated areas were observed from a return flow occurring from soil pipe outlets (Jones, 1971, Bryan and Jones, 1997, Putty and Prasad, 2000b, Putty and Prasad, 2000a) There is a need for identifying runoff generating area, which contribute more flow to rivers and it also important for analyzing movement of pollutants These areas expand and contract during the course of the year (Dunne and Black, 1970, Dunne, 1978, Beven and Freer, 2001, Needelman et al., 2004), making delineation difficult Active and passive remote sensing data can be used to delineate the HAAs over large areas, with economical and less time consuming In active remote sensing technique, microwave radiation emits low radiation from saturated area whereas dry soils emit much higher levels of radiation (Guha and Lakshmi, 2002, Wang and Schmugge, 1980) However it is difficult to separate the microwave radiations emitted from saturated and dry soils due to competing effects of moisture content, surface roughness, vegetation, precipitation, and complex topography (Schmugge, 1985, Bindlish et al., 2003) One of the earliest distributed model called TOPMODEL developed by Beven and Kirkby (1979), based on wetness index derived from the topographic data This wetness index is static and will never capture the temporal variability of the soil water content Based on passive remote sensing data De Alwis et al (2007) delineate the HAAs using wetness index derived from Normalized Difference Water Index (NDWI) Hence, in this study passive remote sensing data from Landsat satellite and topographic data from SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) used to delineate the HAAs over a watershed The objective of this work is to delineate HAAs using remote sensing data and topographic data to map the spatial patterns of the wetness over the basin This aim will be achieved through the use of a Modified Normalized Differential Water Index (MNDWI) derived from remote sensing data and a Soil Topographic Index (STI) derived from topographic data Methods 2.1 Study area and Data The proposed approach for indentifying HAAs was applied to two sub watershed of the Upper Cauvery Basin in Karnataka, India The Harangi watershed is tributary of the Cauvery basin and it originates in the Pushpagiri Hills of Western Ghats The watershed area of Harangi River is about 535 km2 (Fig 1a) The Harangi joins the Cauvery near Kudige in Madkeri and its length from the origin to the joining with the Cauvery River is 50 km Topography of Harangi watershed ranges from 1635 to 818 m above mean sea level The Hemavathy watershed is also a tributary to Cauvery basin and it originates in the mountainous Western Ghats region The Hemavathy watershed has an area of 2974 km2 (Fig 1b) and elevation ranges from 1795 to 843 m above mean sea level Data required for the delineating HAAs includes topography and remote sensing data Topographic data was obtained in the form of DEM at 90 m resolution from the SRTM Landsat imagery (Enhanced Thematic Mapper Plus (ETM+) sensor) was chosen as it provides the necessary information with high quality and moderate resolution ETM+ sensor provides eight channels (3 visible, near infrared, mid infrared, thermal infrared and panchromatic) at 28.5 resolutions (60 m resolution for the thermal infrared and 15 m resolution for panchromatic band) B.C Kumar Raju and Lakshman Nandagiri / Aquatic Procedia (2015) 1339 – 1344 1341 Fig Location and topography of the Harangi (a) and Hemavathy (b) watersheds 2.2 Calculation of STI and MNDWI A STI map of a watershed is generated by dividing the watershed into a grid of cells and calculating the index for each cell by: STI § · a áá ln ăă â T tan E (1) where ‘a’ is the upslope contributing area for the cell per unit of contour line (m), ‘tanE’is the topographic slope of the cell and ‘T’ is the transmissivity of the uppermost soil layer(m2/d) STI classifies each unit of a watershed into a relative tendency to become saturation In Landsat image, pixels are scaled as Digital Numbers (DN) from to 255 and not represent real spectral reflectance as measured by the sensor These values however need to be obtained first in order to calculate spectral indices First, radiance was calculated from the DNs From radiance, reflectance was calculated There are many different pairs of band combinations offered to calculate the wetness index In general Green and Near Infrared (NIR) (McFeeters, 1996), NIR and Middle Infrared (MIR) (Gao, 1996; Wilson and Sader, 2002; Xiao et al., 2002), Green and MIR (Xu, 2006) and Tasseled Cap transformation (Crist, 1985; Kauth and Thomas, 1976) were used calculate wetness index In the present study the green and MIR band is used for calculating MNDWI, which will help to delineate HAAs The MNDWI is expressed as follows: MNDWI Green  MIR Green  MIR (2) 1342 B.C Kumar Raju and Lakshman Nandagiri / Aquatic Procedia (2015) 1339 – 1344 2.3 Unsupervised classification of STI and MNDWI In this study, an unsupervised ISODATA (Self-Organizing Data Analysis Technique) (ERDAS IMAGINE 9.2) iterative clustering algorithm was used to analyze the digital wetness map to delineate the HAAs The ISODATA clustering algorithm makes a large number of passes through an image using a minimum spectral distance formula to form clusters It begins with arbitrary cluster means and each time the clustering repeats the means of these clusters are shifted The new cluster means are used for the next iteration This iterative process continues until statistically distinct features emerge The ISODATA technique divided the STI and MNDWI values of the image based on saturation index of the regions Table shows the parameter used for ISODATA clustering algorithm Table Parameter used in the ISODATA clustering algorithm Number of classes Maximum iterations 1000 Convergence threshold 0.99% Initializing Options Initialize mean along Diagonal axis Scaling Range Automatic Fig MNDWI (a) and STI (b) derived HAAs for Harangi and Hemavathy watersheds 1343 B.C Kumar Raju and Lakshman Nandagiri / Aquatic Procedia (2015) 1339 – 1344 Results and Discussion The purpose of this study was to delineate HAAs of Harangi and Hemavathy watersheds based on two different approaches and to compare the results The HAAs were delineated using wetness index derived from topographic and remote sensing images An average value of STI and MNDWI for DEM and Landsat image were identified by the ISODATA clustering method Table shows the percentage share of contributing areas of various wetness classes in Harangi and Hemavathy watersheds Index distribution dependence on vegetation cover was investigated Fig compares the spatial distributions of HAAs obtained from STI and MNDWI techniques and it is observed that there is a region among all land cover types that is saturated and even unsaturated There is no connection between STI distribution and land cover However, MNDWI values change with land cover distribution The spatial distribution of the relative saturation tendency is comparable The DEM agree on the spatial distribution of flow pattern whereas in the Landsat image the spatial distribution of flow pattern seems significantly distorted Table Percentage share of contributing areas of various wetness classes in Harangi and Hemavathy watersheds Harangi Wetness class Hemavathy STI MNDWI STI MNDWI % % % % 242.38 0.45 9401.31 17.57 1147.61 0.39 48536.73 16.75 Very dry (Class 2) 15397.63 28.73 15749.45 29.44 92632.59 31.16 48967.06 16.90 Dry (Class 3) 16196.86 30.22 15281.92 28.56 97964.59 32.95 51198.72 17.67 Wet (Class 4) 11786.45 21.99 9941.13 18.58 68739.10 23.12 70600.85 24.36 Very Wet (Class 5) 7625.55 14.23 2386.72 4.46 25990.19 8.74 62151.26 21.45 Extremely wet (Class 6) 2345.41 4.38 744.91 1.39 10840.50 3.65 8357.57 2.88 Driest (Class 1) Conclusions This study examined two completely different methods of wetness index approach for delineating HAAs The HAAs delineating from STI and MNDWI techniques were applied on two humid watersheds of Upper Cauvery Basin The use of remotely sensed satellite imagery derived wetness index was able to identify the HAAs spatial patterns using an unsupervised classification technique The technique used in this study is advantageous because it allows identification of HAAs independent of field measurements at a high spatial resolution Unsupervised classified wetness index derived from the DEM and from Landsat image were compared The results have shown that Landsat image to represent the spatial distribution of saturated areas was better compared to spatial distribution of saturated areas delineated from DEM The technique of delineating HAAs will be useful in land use planning, management practices to reduce pollution sources, identifying runoff generation areas and improve process representation in distributed hydrological modeling of the watershed For instance, identifying runoff generation areas provides a valuable tool for water resource managers in regions with runoff from VSAs with which to assess and ultimately improve water quality practices References Ando, Y., Musiake, K.,Takahasi, Y., 1983 Modelling of hydrologic processes in a small natural hillslope basin, based on the synthesis of partial hydrological relationships Journal of Hydrology, 64, 311-337 Beven, K.,Freer, J., 2001 Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology Journal of Hydrology, 249, 11-29 Beven, K J.,Kirkby, M J., 1979 A physically based, variable contributing area model of basin hydrology Hydrological Sciences Bulletin, 24, 43-69 1344 B.C Kumar Raju and Lakshman Nandagiri / Aquatic Procedia (2015) 1339 – 1344 Bindlish, R., Jackson, T J., Wood, E., Gao, H., Starks, P., Bosch, D.,Lakshmi, V., 2003 Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States Remote Sensing of Environment, 85, 507-515 Bryan, R B.,Jones, J A A., 1997 The significance of soil piping processes: inventory and prospect Geomorphology, 20, 209-218 Crist, E P., 1985 A TM Tasseled Cap equivalent transformation for reflectance factor data Remote Sensing of Environment, 17, 301-306 De Alwis, D A., Easton, Z M., Dahlke, H E., Philpot, W D.,Steenhuis, T S., 2007 Unsupervised classification of saturated areas using a time series of remotely sensed images Hydrol Earth Syst Sci., 11, 1609-1620 Dunne, T., 1978 Field studies of hillslope flow processes Hillslope hydrology, 227, 293 Dunne, T.,Black, R D., 1970 Partial area contributions to storm runoff in a small New England watershed Water Resources Research, 6, 1296-1311 Gao, B C., 1996 NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space Remote Sensing of Environment, 58, 257-266 Guha, A.,Lakshmi, V., 2002 Sensitivity, spatial heterogeneity, and scaling of C-band microwave brightness temperatures for land hydrology studies Geoscience and Remote Sensing, IEEE Transactions on, 40, 2626-2635 Hewlett, J D.,Hibbert, A R., 1967 Factors affecting the response of small watersheds to precipitation in humid areas Forest hydrology, 275290 Hewlett, J D.,Nutter, W L.,1970 The varying source area of streamflow from upland basins Interdisciplinary Aspects of Watershed Management, ASCE, 65-83 Jones, A., 1971 Soil Piping and Stream Channel Initiation Water Resources Research, 7, 602-610 Kauth, R J.,Thomas, G S.,1976 The tasselled cap - a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat LARS Symposia, 159 McFeeters, S K., 1996 The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features International Journal of Remote Sensing, 17, 1425-1432 Needelman, B A., Gburek, W J., Petersen, G W., Sharpley, A N.,Kleinman, P J A., 2004 Surface Runoff along Two Agricultural Hillslopes with Contrasting Soils Soil Sci Soc Am J., 68, 914-923 Putty, M R Y.,Prasad, R., 2000a Runoff processes in headwater catchments—an experimental study in Western Ghats, South India Journal of Hydrology, 235, 63-71 Putty, M R Y.,Prasad, R., 2000b Understanding runoff processes using a watershed model - a case study in the Western Ghats in South India Journal of Hydrology, 228, 215-227 Schmugge, T 1985 Remote sensing of soil moisture Hydrological forecasting MG Anderson, TP Burt, John Wiley, New York Wang, J R.,Schmugge, T J 1980 An Empirical Model for the Complex Dielectric Permittivity of Soils as a Function of Water Content Geoscience and Remote Sensing, IEEE Transactions on, GE-18, 288-295 Wilson, E H.,Sader, S A., 2002 Detection of forest harvest type using multiple dates of Landsat TM imagery Remote Sensing of Environment, 80, 385-396 Xiao, X., He, L., Salas, W., Li, C., Moore, B., Zhao, R., Frolking, S.,Boles, S., 2002 Quantitative relationships between field-measured leaf area index and vegetation index derived from vegetation images for paddy rice fields International Journal of Remote Sensing, 23, 3595-3604 Xu, H., 2006 Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery International Journal of Remote Sensing, 27, 3025-3033  ... data Methods 2.1 Study area and Data The proposed approach for indentifying HAAs was applied to two sub watershed of the Upper Cauvery Basin in Karnataka, India The Harangi watershed is tributary... distribution of saturated areas was better compared to spatial distribution of saturated areas delineated from DEM The technique of delineating HAAs will be useful in land use planning, management practices... to delineate the HAAs over a watershed The objective of this work is to delineate HAAs using remote sensing data and topographic data to map the spatial patterns of the wetness over the basin This