Land Use/Land Cover Classification of Remote Sensing Data and Their Derived Products in a Heterogeneous Landscape of a Khan-Kali Watershed, Gujarat Amee Thakkar1*, Venkappayya Desai1, Ajay Patel2 and Madhukar Potdar2 Department of Civil Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India
1
Bhaskaracharya Institute for Space Applications and Geo-informatics (BISAG), Gandhinagar, Gujarat, India
2
Abstract Remote sensing (RS) plays a vital role in mapping of land use/land cover (LU/LC) and in quantitatively assessing the effect of human intervention on natural resources. The accurate/updated maps are essential in effective management of natural resources, especially in semi-arid regions, where LU/LC passed through rapid change due to applied number of watershed management programs. The different landforms of such regions generally show homogenous spectral characteristics among different LU/ LC classes. The use of only RS data may not be adequate for thematic classification in such region. Ongoing portion of present study focuses on improving the accuracy of the usually adopted supervised classification through multi-source classification using ancillary and multi-spectral data applied to the semi-arid Khan-Kali watershed in Dahod District of Gujarat. The IRSResourcesat2 (R2) multi-spectral LISS-III data and derived indices viz., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are used for maximum likelihood supervised classification on different five, six and original four band combinations. The findings of this study indicate that the data combination of Green, Red, NIR, NDWI and NDVI is improving the classification accuracy by 7.95% in absolute terms as compared to only use of original band combination of Green, Red, NIR and SWIR. The NDVI and NDWI are found to be successfully distinguishing spectrally similar land use/ land cover classes in heterogeneous and semi-arid landscape of Khan-Kali watershed. Key words: Land use/land cover mapping, MLC classification, Remote Sensing (RS), Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Kappa Statistics, Khan-Kali Watershed.
1. Introduction Land use/land cover (LU/LC) change is a highly dynamic and a very complex process. Land cover refers to natural and physical cover of the earth, viz., forest, savannah, desert, river, roads, runways etc. On the other hand, the land use refers to various human uses of land viz., human settlements, agricultural land, transportation networks, reservoirs, etc. In light of natural resource management agenda, the accurate land use/land cover maps are extremely important to represents current and historical change in any kind of watershed management program. Therefore, accurate classification of land use/land cover tops natural resource management priorities worldwide especially in complex and heterogeneous landscapes (Alrababah and Alhamd, 2006).
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Remote sensing is the recent technology to monitor land resources from space and evaluate the land cover changes which typically result in variations in land use/land cover from global to local scale (Shanwad et al. 2008; Dutta et al. 2003; Foody 2002; Chowdary et al. 2001; Csaplovics, 1998). The high spatial, spectral and temporal resolution of remotely sensed data provide synoptic view of large areas of the earth repeatedly, which is effectively used in land use/land cover mapping and change detection purpose. Moreover, remote sensing data have limitation for land use/ land cover mapping of heterogeneous landscape of semi-arid regions. Spectral similarity between different land use/ land cover classes (i.e., settlement and water body, waste land and fallow land, river sand and settlement etc.) are common in
Land Use/Land Cover Classification of Remote Sensing Data and Their Derived Products in a Heterogeneous Landscape of a Khan-Kali Watershed, Gujarat
such regions. Therefore, automatic classification of heterogeneous landscape is challenging to get accurate good classification accuracy (Manandhar, et al. 2009). Several researchers have tested application of ancillary data to increase the classification accuracy. Rozenstein and Karnieli (2011) tested the applicability of hybrid classification using Decision Support System (DSS) based on expert knowledge and Geographical Information System (GIS), and compared with the conventional supervised and unsupervised classification. The result showed that the Decision DSS based on expert knowledge and GIS increases the classification accuracy significantly. Shalaby and Tateishi (2007) reported that the supervised classification integrated with visual image interpretation, where on screen digitization was carried out to in delineate land cover classes that are easily interpretable (i.e., urban land, sabkha) as an ancillary data. This integration in GIS domain improved the classification accuracy by about 10%. The classification accuracy of mountainous region is also improved using NDVI and Digital Elevation Model (DEM) (Saha et al. 2005; Yacouba et al. 2009; Eiumnoh and Shrestha, 2000) layers as proxy data. Arora and Mathur (2001) reported that the classification on the basis of only spectral data from a remote sensing sensor alone may not be sufficient to gather effective land cover information. Hence, it was inferred that a classification approach that incorporates data from other sources was found to be more effective than the one based solely upon the data from a single remote sensing sensor.
Integrated Watershed Management Programme (IWMP) is being practiced in this watershed effectively. An accurate land use/land cover mapping is the key source to quantify the impact of such a natural resource management programmes. The landscape of Khan-Kali watershed is highly heterogeneous. Forest land/dense vegetation, scrub forest/ sparse vegetation, agricultural land are the major land use/ land cover classes. The river sand, other floating vegetation and water body are the minor classes. Spectral similarity among these classes is the major cause of confusion in order to extract land use/land cover information accurately from remote sensing data. Therefore, the objective of the present study is to present the case of Khan-Kali watershed, to accurately derive the land use/land cover information using appropriate technique like multi-source classification etc., where in spectrally similar land use/land cover classes are the major problem.
2. Study Area The physical extent of the study area (shown in Figure 1) of the Khan-Kali watershed is within latitudes 22o 34’ 36.96” N-23o 01’ 23.83”N and longitudes 74o 08’ 22.71” E-74o 28’ 33.77” E. The estimated geographical area of Khan-Kali watershed is about 860.60 km2. The study area is characterized by semi-arid climate as per prevailing classification and it comes under Agro-climatic Zone Number-13. It is administratively located in the Dahod District in the eastcentral part of the western Indian state of Gujarat, and it is bounded by Panchmahal Disttrict of Gujarat to its west, Banswara District of Rajasthan to its north, Jhabua District of Madhya Pradesh to its east and Vadodara District, Gujarat to its south. It is having both undulating as well as plain lands.
In a heterogeneous landscape, the spectral reflectance of vegetation plays an important role in discriminating the vegetation from the surrounding land use/land cover classes. This spectral reflectance depends on theree factors viz., (1) chlorophyll absorption and (2) Leaf mesophyl structure (3) water absorption. NDVI measures the information contained in vegetation based on chlorophyll absorption in Red spectrum (0.62-0.68 µm) relative to the reflectance in the Near Infra Red spectrum (0.77-0.86 µm). Thus, the NDVI represents the response to the greenness of vegetation. Gao (1996) proposed water absorption based index viz., NDWI, which represents the response to vegetation water content. NDWI quantifies the information contained in the vegetation based on its water absorption in Short Wave Infra Red (SWIR) (1.55-1.77 µm) band. Hence, the information contained in SWIR band is different from the Red band. It improves the distinction of vegetation and other land cover classes (Boles et al. 2004). Thus, the consideration of NDWI as another independent index in addition to NDVI is justified (Gao, 1996). Also, it was found useful to integrate the NDVI and NDWI to represent the state of vegetation (Chen et al. 2006; Xiao et al. 2002; Gao, 1996). The research in each of these three studies indicated that the NDWI and NDVI are complementary derived products to perform multi-source classification.
Khan and Kali-II are the main streams, which are ephemeral in nature in this study area, are contributing to the Anas River [a tributary of Mahi River] sub-catchment during rainy season. The effect of this flow contribution is visible especially in the Anas/Mahi river reaches starting from the Jakham confluence point to the Kadana Dam. The mean monthly temperature is 39.4oC in the month of May and 12.1oC in the month of January. Average annual rainfall is about 900 mm, about 96% of annual rainfall is received during four months of June, July, August and September. In May and October, there could be occasional light to moderate showers. Here the monsoon season extends from July to September and winter season extends generally from October to February. The main forest type is of mixed dry deciduous type, consisting of teak trees. In spite of agriculture being the main economic activity, irrigation facilities in this underdeveloped area are less and/or unreliable. Therefore, the rain-fed farming is very common. However, due to watershed development programme over last 15 to 20 years, increase in agriculture is expected. The soil here is sandy loam and/or loam and clay loam. Wheat, corn and paddy are the major crops. Rocks with crystalline minerals like Basalt, quartzite, phyllite, slate and schist are the main geological
The Khan-Kali watershed located in the Dahod District of eastern Gujarat state is selected for the present study. The
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Asian Journal of Geoinformatics, Vol.14,No.4 (2014)
3.1 Data Collection
strata present in the study area. This area with its fragile environment has shown frequent land use/land cover changes especially in last decade due to human impact resulting from implementing many governmental and non-governmental policies as well as large scale natural resource conservation programme. About 49% of the villages of this watershed are being treated under such watershed management programs since 1995. Series of water harvesting structures have been constructed on Khan and Kali-II rivers as part of such programs. The agricultural area constitutes about 70% of the total watershed area and the remaining 30% is having either forest, scrub forest, waterbody, river sand or other floating vegetation.
3. Methodology
Three sets of data were used here. First, Indian Remote Sensing (IRS)-R2 satellite LISS-III image, acquired on 8th November, 2011 is collected. These cloud free satellite data acquired from ISRO by Bhaskaracharya Institute for Space Applications and Geo-informatics (BISAG), Gandhinagar, Gujarat. The map projection of the satellite images is Universal Transverse Mercator (UTM) within zone of 43 NDatum and co-ordinate system of World Geodetic System (WGS) 84. The details of this data are shown in Table 1. Data of all the spectral bands are utilized in the present study. The same data are also used for the generation of NDVI and NDWI layers. Figure 3 shows the False Colour Composite (FCC) of study area.
The methodology adopted for the present work is given as below (Figure 2):
For extracting the Khan-Kali watershed boundary, the GIS format (i.e., shape file) cauterized as per NBSS&LUP was
Figure 1. Location Map of Khan-Kali Watershed.
Tables 1: Details of Satellite Data Used Table Table 1. Details of Satellite Data Used. Satellite Name IRS-R2
Sensor Path LISSIII
95
Row 56
Acquisition Spatial Date Resolution (m) 8 Nov. 2011
23.5 23.5 23.5 70.0
Spectral Resolution (µm) Band 1:Green: 0.52-0.59 Band 2:Red: 0.62-0.68 Band 3:NIR: 0.77-0.86 Band4:SWIR: 1.55-1.70
3 Table 2: Characteristics of IRS-R2 Satellite Data
Repeating cycle (days) 24
Land Use/Land Cover Classification of Remote Sensing Data and Their Derived Products in a Heterogeneous Landscape of a Khan-Kali Watershed, Gujarat
Digital Procedure
IRS-R2 LISS-III 8th Nov.2011
Image pre-processing Geometric corrections Sensor calibration Sun angle corrections
Original LISS-III Bands
Generation of ancillary data [e.g., NDVI & NDWI]
Band Combination Image Processing
1. The ground truth points
2. Agricultural land use class .shp layer of year 2005 [BISAG; 2005] 3. Open Series SoI Toposheets [1:50, 000 scale; 2011] 4. Natural Colour Composite [2012]
Training Site Selection Separability Analysis Image classification-Supervised Classification Accuracy Assessment
Comparison of Different Band Combinations with Original LISS-III Bands Based on Overall Classification Accuracy
Land Use/ Land Cover Map
Figure 2. Methodology Flow Chart.
collected from BISAG. The agricultural land layer, which was developed and updated for the year 2004-05 was also collected from BISAG. This layer is available in the GIS format (i.e., shape file) and prepared from visual interpretation of the corresponding Cartosat data.
between agricultural land and scrub forest land), we also used a Natural Colour Composite, fused product of cartosat and LISS-IV data of year 2012.
3.2 Data Processing A geometric rectification process was applied to each of the four scanned copies of toposheets. To achieve this, a polynomial model having a Geographic (lat-long) type of projection with a spheroid name of ‘Everest 1956’ and an Indian datum (India/Nepal) was used. About 49 GCPs were used for registering of toposheets with rectification errors less than 0.01 pixel using lat-long marked over the toposheets. Care was taken to ensure a uniform distribution all the GCPs over the entire area covered by the toposheets. For this purpose, the GCPs were selected at each of the intersection points of lat-long marked over the toposheets. A total of 36 intersection points were used for each of the toposheets. The mosaic process was run to join all the selected toposheets of
Second, the latest toposheets categorized as “Open Series Toposheets” are acquired from Survey of India (SoI). These toposheets are the first version of year 2011 based on surveyed in 2005. The study area covered four toposheets (F43/6, F43/5, F43/1, and F43/2), of 1:50,000 scale. All the toposheets are scanned using WID Eimage Crystal X L 42 scanner of DPI of 300 and converted to .tiff format subjected to geometric corrections. The field visit is conducted in the month of May, 2014 to collect the ground truth locations. Total 137 ground truth points in study area are visited and verified for accuracy assessment. Finally, for some of the confusion classes (i.e., confusion 4
In view In of view useofofuse multi-sensor of multi-sensor and multi-date and multi-date data indata future, in future, basic calibration basic calibration correction correction {conversion {conversion of dig
numbers numbers (DNs) (DNs) to radiance to radiance at sensor} at sensor} was (2014) carried was carried out. Theoretically out. Theoretically this radiometric this radiometric calibration calibration standardizes standa Asian Journal of Geoinformatics, Vol.14,No.4 the images the images and does andnot does produce not produce any anomalous any anomalous resultsresults in classification. in classification.
The radiance (Lλ) at sensor corresponding to DN of pixel
sensor corresponding corresponding tousing DNtooffollowing DN pixel of pixel value value is estimated is (1) estimated using using following following Equation Equ The radiance The radiance (Lλ) at(Lsensor λ) at value is estimated Equation (Robinove,
1982) applicable for LISS-III data. It is also a standard formula for calibration specified by digital data product format released by ISRO, Ahmedabad, Gujarat data product data product formatformat released released by ISRO, by ISRO, Ahmedabad, Ahmedabad, Gujarat Gujarat state, India. state, India. state, India.
(Robinove, (Robinove, 1982) 1982) applicable applicable for LISS-III for LISS-III data. Itdata. is also It isa also standard a standard formula formula for calibration for calibration specified specified by digb
L L L L max , maxmin , , min , L L ( DN DN ( DN )DN L )L min min min, min, DN DN DN DN max max min min
(1)
(1)
Where,
Where,Where,
2 2 =Spectral radiance at athe sensor for a single Lλat radiance radiance at the sensor the sensor aperture aperture for a single for single band (W/m bandaperture (W/m . Steradian.µm) . Steradian.µm) Lλ=Spectral Lλ=Spectral
band (W/m2. Steradian.µm)
Lmax, λLmax, and λ Land = Scaled spectral spectral radiance radiance (provided (provided in header in header file offile image of image information) information (W/ min, λLmin, λ = Scaled Steradian.µm) Steradian.µm)
Lmax, λ and Lmin, λ = Scaled spectral radiance (provided in header file of image information) (W/m2. Steradian.µm)
DNmaxDN = Maximum possible possible DigitalDigital Number Number (255 for (255 allfor LISS-III) all LISS-III) max= Maximum
DNmax= Maximum possible Digital Number (255 for all LISS-III) DNminDN =Minimum possible possible DigitalDigital Number Number (0 for (0 allfor LISS-III) all LISS-III) min=Minimum
DNmin=Minimum possible Digital Number (0 for all maximum The maximum and minimum and minimum radiance radiance are measured are measured at detector at detector situated situated in IRS-R2 in IRS-R2 satellite. satellite. These These radia Figure 3. False Colour Composite (FCC) ofThe Khan-Kali LISS-III) Watershed Acquired on 8th Nov. 2011(IRS R-2 LISS III allfor theallbands the b valuesvalues are directly are directly provided provided in .txtinfile .txtoffile satellite of satellite imageimage dataset. dataset. The values The values of Lminoffor Lmin data). The maximum and minimum radiance are measured at detector satellite. radiance zero. Table zero. Table 2 represents 2 represents the values the situated values of Lmax of in for LmaxIRS-R2 allfor bands. all bands. The ENVI TheThese ENVI band math band math function function was used was values are directly provided in .txt file of satellite image computation. dataset. The values of Lmin for all the bands are zero. Table the watersheds. This process largely removescomputation. the mismatch 2 represents the values of Lmax for all bands. The ENVI in linear features between one toposheet and an adjacent one. band math function was used for computation. The watershed map extracted from the mosaic toposheet was 8 8 converted into UTM (Zone 43N) WGS 1984 projection Insert Table 2 Here 3.2.2 Sun-Angle Correction system using the first order polynomial models.
3.2.2 Sun-Angle Correction
The position of sun relative to the Earth depending on time The mosaic toposheet map was used for image to map geoof the dayEarth and time of theonyear It required theyear is differen Theyear position sun relative to the depending timeisofdifferent. the day and time of the referencing process. The IRS-R2 image of 2011ofwas sun-angle correction for each of the satellite on its respective already geometrically corrected. It was used for image to required sun-angle correction for each of the satellite on its respective bands. bands. This absolute correction involves dividing the This each absolute correc map geo-referencing using 119 GCPs at the RMSEthe value of pixel DN values in the image data by sine of the solar 0.22 pixel. All the steps were performed using the ERDAS involves dividing the eachelevation pixel DNangle valuesfor in the image data sinelocation of the solar particular timebyand perelevation spectral angle for partic 9.2 and ENVI 4.7 image processing software. band. The spectral radiance corresponding to sun angle time and location per spectral band. The spectral radiance corresponding to sun angle correction is estima 3.2.1 Conversion of DN to Radiance/CalibrationTablescorrection is estimated using Equation (2). In the present study sunData elevation equal toequal 47.780 is considered. It is Italso Correction using Equation (2). In the present study sun elevation to 47.780 is considered. is also provided in Table 1: Details of Satellite Used provided in the header file of image data. header offuture, image data. Spatial In view of use of multi-sensor multi-date datafileinAcquisition Satellite Name and Sensor Path Row Spectral Resolution Repeating basic calibration correction {conversion of digital numbers Date Resolution (µm) cycle L (m) (days) (DNs) to radiance at sensor} was carried out. Theoretically (2) Lcorr, IRS-R2 LISS95 8 Nov. 2011 23.5 Band 1:Green: 0.52-0.59 24 sin ( ) this radiometric calibration standardizes all the56images and III 23.5 Band 2:Red: 0.62-0.68 does not produce any anomalous results in classification. 23.5 Band 3:NIR: 0.77-0.86 α = Sun elevation angle 70.0 Band4:SWIR: 1.55-1.70 α = Sun elevation angle
Lλ= Spectral radiance at the sensor aperture for a single band (W/m2. Steradian.µm) Lcorr, = Sun angle corrected radianceSatellite of each Data of Data. the bands of satellite imageries. Table 2. λCharacteristics of Satellite Table 2: Characteristics of IRS-R2 IRS-R2
Band No 1 2 3 4
Band
IRS-R2
IRS-R2
3.3 Generation(LofmaxDerived ProductsLmax/DNmax )
(W/m2. Steradian.µm) (W/m2. Steradian.µm/unit DN) Normalized Difference Green: 0.52-0.59 52.00 Vegetation Index (NDVI) 0.2039 and Normalized Difference Water Index (NDWI) Red: 0.62-0.68 47.00 0.1843 generated using corrected NIR: 0.77-0.86 31.50 bands of IRS R2 LISS-III 0.1235 (Figure 4). SWIR: 1.55-1.70 7.50 0.0294
3.3.1 Normalized Difference Vegetation Index (NDVI) layer
Table 3: Characteristics of Land Use/Land Cover Classes in Khan-Kali Watershed on Six Layer Stacked FCC 5 The Normalized Difference Vegetation Index (NDVI) is most widely used in remote sensing of vegetation si Land cover class Land cover class Characteristics on six layer long time. The NDVI layer is prepared to make separability among various classes. The NDVI layers Level I characteristics stacked FCC
= RadianceItinisNear-IR channel in the Equation (2). In the present study sun elevation equal to 47.780 isLNIR considered. also provided Land Use/Land Cover Classification of Remote Sensing Data and Their Derived Products in a Heterogeneous Landscape of a Khan-Kali Watershed, Gujarat The value of NDVI in principle ranges from -1 to1. In the present study, it ranges from -0.29 to 0.45. In p file of image data.
n elevation angle
extreme represents water. Low NDVI value around zero represents bare soil, barren land, L Lλ= Spectral radiance for anegative single valesrepresent sparse vegetation i.e., Scrub forest/sparse the sensor aperture (2) Lcorr, at sin ( ) band (W/m2. Steradian.µm) vegetation. High NDVI to 0.5)i.e., Scrub forest snow. Moderate NDVI values (approximately 0.02values to 0.08)(approximately represent sparse 0.2 vegetation represent dense vegetation i.e., forest. of the bands Lcorr, λ = Sun angle corrected radiance of each vegetation. High NDVI values (approximately 0.2 to 0.5) represent dense vegetation i.e., forest. of satellite imageries. 3.3.2 Normalized Difference Water Index (NDWI) Layer
ectral radiance at the sensor aperture for a single band (W/m2. Steradian.µm)
WaterDifference Index (NDWI) 3.3 Generation of Derived Products 3.3.2 Normalized Difference The Normalized Water layer Index
(NDWI) derived from SWIR and NIR bands is used for land use/land cover Normalized Difference Vegetation Index (NDVI) and classification as an additional layer. Its value increases as The Normalized Difference Water Index (NDWI) derived from SWIR and NIR bands is used for land u Normalized Difference Water Index (NDWI) are generated eneration of Derived Products leaf layers increases, because it is sensitive to the total using corrected bands of IRS R2 LISS-III (Figure 4). of liquid water into the stacked cover classification as amount an additional layer. Its valuecontent increasespresent as leaf layers increases, because it is sensi lized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) leaves. The are value of NDWI in principle ranges from -1 to1. 3.3.1 Normalized Difference Vegetation Index the total(NDVI) amount of liquid water content the stacked TheItvalue of NDWI in principle In present study,present NDWIinto ranges from 0leaves. to 0.81. is defined ted using corrected bands of IRS R2 LISS-III (Figure 4). Layer as,
= Sun angle corrected radiance of each of the bands of satellite imageries.
from -1 to1. In present study, NDWI ranges from 0 to 0.81. It is defined as,
Normalized Difference Index (NDVI) is Normalized The Difference Vegetation IndexVegetation (NDVI) layer NDWI ( LNIR - LSWIR ) / ( LNIR + LSWIR ) most widely used in remote sensing of vegetation since long time. The NDVI layer is prepared to make separability ormalized Difference Vegetation Index (NDVI) is most widely used in remote sensing of vegetation since among various classes. The NDVI layers are prepared from Where, Where, me. The NDVIcalibrated layer is prepared make separability among various classes. The NDVI layers are and suntocorrected red and NIR bands of LISS-III LNIR = Radiance in NIR channel sensor, 2011. It is defined as,
(4)
channel NIR = Radiance ed from calibrated and sun corrected red and NIR bands of LISS-IIILsensor, 2011. Itinis NIR defined as,
NDVI ( LNIR LRed ) / ( LNIR + LRed )
LSWIR = Radiance in SWIR channel
LSWIR(3) = Radiance in SWIR channel
3.4 Band Combination
Where,
Insert Figure 4 Here
The selected satellite data bands and two derived (NDVI & NWDI) layers combination consists of six stacked layers. It Radiance in Red channel 3.4 Band Combination included four original bands and two derived data layers. For LNIR = Radiance in Near-IR9 channel better understanding, the layers are numbered as 1, 2, 3, 4, 5 and bands 6 forand Green, Red, (NDVI NIR, SWIR, NDWI NDVI, consists of six s selected satellite data two derived & NWDI) layers and combination The value of NDVI in principle ranges fromThe -1 to1. In the respectively. present study, it ranges from -0.29 to 0.45. In practice layers. It included extreme negative vales represents water. Low NDVI value four original bands and two derived data layers. For better understanding, the lay 3.5 Classification around zero represents bare soil, barren land, rock or snow. numbered as 1, 2, 3, 4, 5 and 6 for Green, Red, NIR, SWIR, NDWI and NDVI, respectively. Moderate NDVI values (approximately 0.02 to 0.08) LRed = Radiance in Red channel
3.5 Classification
10
Figure 4. NDVI and NDWI Derived Layers of Khan-Kali Watershed. 6
2 3 4
Red: 0.62-0.68 47.00 0.1843 NIR: 0.77-0.86 31.50 0.1235 SWIR:Asian 1.55-1.70 7.50 0.0294 Journal of Geoinformatics, Vol.14,No.4 (2014)
Table 3: Characteristics of Land Use/Land CoverClasses Classes in on Six Layer Stacked FCC FCC. Table 3. Characteristics of Land Use/Land Cover in Khan-Kali Khan-KaliWatershed Watershed on Six Layer Stacked Land cover class Level I
Water body
Forest/dense vegetation
Agricultural land Barren land Scrub forest/sparse vegetation Settlement/built-up land River sand Other vegetation
Land cover class characteristics
River, lakes, check dams, permanent open water, reservoirs Dense vegetation areas with dense shrubs, inner recreational areas, river line plantation Fairly dense teak jungles, tall dense trees, river-line plantation Agricultural area or cultivated area planted or irrigated area, fallow land, and paddy crops. Stony waste, bare land exposed soil, open space. Land with sparse or open vegetation/ trees, scrub, bushes based on percentage of vegetation Area like town, villages populated with residential, commercial, industrial and transportation facilities. River sediments on banks, sediments on water harvesting/watershed structures Aquatic plants
Characteristics on six layer stacked FCC
Light to dark blue
Dull red to pinkish smooth surface Dark red with rough texture Dark Greenish colour to light greenish colour with rough surface, dark red patches of paddy crops. Yellowish to light greenish/pinkish smooth texture Light Yellow/pink/green/dull white smooth surface Light cyan having rough texture with white blocks Very light blue to white colour Bright red colour
combination of bands based on signature separability. If the Supervised maximum likelihood (MLC) classification was applied on each of the stacked six layered images using 21 distance between two classes is not significant, the signature may not be distinct enough to produce the successful image processing software ERDAS 9.2. The different phases classification (Gambarova et al. 2010). ERDAS 9.2 computes of this technique are: (1) Training site selection, (2) Training this distance using four methods viz., (1) Euclidean Distance, signature generation and evaluation, (3) Classification and (2) Divergence, (3) Transformed Divergence and (4) Jefferies (4) Classification accuracy assessment. Matusita Distance, and recommends the Transformed 3.5.1 Training Site Selection Divergence (TD) as good indication of class separability. In image classification, TD is needed to assess the quality of Training site selection is most important aspect of the separability of classes prior to image classification (Bhattarai supervised classification based on maximum likelihood et al. 2009). The scale of TD ranges from 0 for completely classifier specifically for heterogeneous landscape region. It overlapping classes to 2000 for completely separated classes is the process of selecting various homogenous land cover (Jensen, 1996). The value close to 2000 or between 1900 to types as training sites. The selection of tanning site were 2000 shows best separability and between 1700 and 1900 made based on Ground truth locations, open series toposheets indicates fairly good separability. The TD value less than (year 2011), Natural Colour Composite Image (fused product 1700 shows poor class separability. of cartoset and LISS-IV), 2012. The detailed description of each of the classes based on FCC on six layer stacked images Generally, five band combinations are considered suitable is given in Table 3. Agricultural land is the dominant land use for classification (Saha et al. 2005). Table 5 represents the class in the study area. 3.5.2 Training Signature Generation and Evaluation/ Table 4.ofNumber of Pixels forUse/Land Each Land Use/Classes Table 4: Number Pixels Selected forSelected Each Land Cover Class Separability Analysis Land Cover Classes. Total 86 training sites selected are merged into eight land use/land cover classes. The total numbers of pixels selected for training site are presented in Table 4. Out of 86 signatures, 22 to forest/dense vegetation/plantation, 6 to settlement/ built-up land, 15 to other vegetation, 15 to scrub forest, 3 to river sand, 10 to water body and 15 to agricultural land are initially selected. These signatures are tested for their separability. Signature separability is the statistical measure representing the distance between two classes. All combination of bands can be tested for their separability. This gives the useful
Land Use/Land Cover Classes
Number of Training Pixels
Water body Forest/Dense Vegetation Agricultural land Barren land Scrub forest Settlement Other Vegetation River Sand Total
313 1139 8651 190 1568 322 177 144 12504
7 Table 5: Layer Combinations and their Respective Average Transformed Divergence (TD) Value
Land Use/Land Cover Classification of Remote Sensing Data and Their Derived Products in a Heterogeneous Landscape of a Khan-Kali Watershed, Gujarat
accuracy assessment is performed on it (Lillesand et al. 2004). For this, the random samples of testing pixels are selected from classified image and their classes compared with the land use/land cover reference map. Selection of sampling scheme is important part of accuracy assessment. Number of sampling pixels is another important aspect of accuracy assessment. ERDAS 2007 recommended 250 or more pixels for accuracy assessment. The reference data are selected based on: (1) The ground truth points, which were collected during the field visit, (2) SOI toposheets of year 2011, (3) The agricultural land use class shape layer of year 3.5.3 Image Classification Using Maximum Likelihood and of (4)Pixels Natural Colourfor Composite, is spectrally Table 4:2005 Number Selected Each Landwhich Use/Land Cover Classes Classification (MLC) Algorithm merged product of Cartosat and LISS-IV images having 2.5m of spatial for year 2012. In the Landresolution Use/Land Number of present study, After selection and evaluation of all the training signatures, Coversamples Classes of 377Training Pixelsare selected stratified random testing pixels the maximum likelihood algorithm based classification was and compared with above reference data. Out of which, 137 applied to perform the supervised classification. It is one of points are of the ground truth locations and 240 are from the the widely used supervised classification algorithm (Yacouba Water bodysources. 313 above other mentioned et al. 2009). It uses Probability Distribution Function (PDF), Forest/Dense 1139 Vegetation which is based on Mahalanobis distance between each pixel The sample layer comprises of at least 50 pixels of each Agricultural land 8651 and centroid of belonging class (Bakr et al. 2010). MLC was major classes (i.e., forest/dense vegetation, agricultural, Barren land 190 applied on six, five layer combinations and original four scrub forest) and 30 pixels of each minor classes (river sand, Scrub forest band combinations (viz., 1, 2, 3, 4). The major problem faced other vegetation, and scrub forest). The1568 accuracy assessment Settlement during the classification was spectral similarity among some measures generated from confusion 322 matrix are: Overall Other Vegetation 177 land use/land cover classes. For instance, the settlement class accuracy, Producer’s accuracy, User’s accuracy and Kappa ∧ River Sand 144 showed confusion with water body (i.e., river line area). This statistic ( K ). The overall accuracy of classification is Total 12504 may be due to rapid irrigation development along the rivercomputed by taking the ratio of sum of major diagonal line. Even on banks of Kali-II River, total 18 community lift numbers (i.e., the correctly classified pixels) and the total irrigation systems have been installed (Jagawat, 2007). The number of pixels in error matrix. Producer’s accuracy is made for up of concrete material,Cover whose Tableirrigation 4: Numbersystems of Pixelsare Selected Each Land Use/Land Classescomputed by taking the ratio of number of correctly classified signatures are confused with building construction. Another pixels of a class to total number of pixels in reference data. It Table 5: Layer Combinations and their Respective Average Transformed Divergence (TD) Va case of confusion between water body and agricultural area Land Use/Land Number of is also a measure of omission error. User’s accuracy is of shallow medium black soil with water held Cover Classes Training Pixelsat 40 to 50% defined as Combination the ratio of correctly classified of a class to Averagepixels Transformed Band of its maximum soil holding capacity. The third case is total number of pixels in that class. Divergence It is also a (TD) measure of between rocky barren land and agricultural land, as commission error. Kappa statistic is considered as standard 1, 2, 3, 4, 5, 6 1995 Water body crystalline rocks have similar spectral 313 signature as of poor measure of agreement by various researchers (Smith, 1999; 1, 2, 3, 4, 5 1983 Forest/Dense 1139 agricultural fallow land. Open shrub land is also misclassified Rosenfield and Fitzpatirck, 1986). It is mainly based on the Vegetation 1, 2, 3, 4, 6 1985 with a poor agricultural fallow land. Overall, difference1,between how much agreement is actually present Agricultural land 8651 the agricultural 2, 3, 5, 6 2000 land use class Barren is often misclassified to190 other land use/land compared1,to2, how expected to be land 4, 5, 6much agreement would be 1997 cover classes. Scrub forest present by1, chance statistic ranges 1568 3, 4, 5, 6alone. The scale of Kappa 1950 4, case 5, 6 of perfect agreement, the 1995 from 1 to2,-1.3,In Kappa statistic Settlement 322 3.5.4 Accuracy Assessment Other Vegetation 177 average TD value for all five and six layer combinations. The layer combination 12356 resulted in highest TD value, though all the TD values are equally good. It shows the land use/land cover classes are separated well. They also shows that, the NDWI and NDVI have added advantage in improving the separability of signatures and minimize the misclassification. As, the range in average TD values is short, all the five band combinations, six layer combination (1, 2, 3, 4, 5, 6) and original four band combination (1, 2, 3, 4) are used for classification and accuracy assessment.
River Sand
144
There is a standard Totalprocedure to assess 12504the classification accuracy. The classification process is incomplete unless
Table 6. The Overall Accuracies for Various Layer Combinations. Table 6: The Overall Accuracies for Various Layer Combinations Band Combination
Table 5. Layer Combinations and their Respective Average
Overall accuracy of classification (%) 2011
Table 5: Layer CombinationsTransformed and their Respective Average Divergence (TD) Value Divergence (TD)Transformed Value. Band Combination
Average Transformed Divergence (TD)
1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5 1, 2, 3, 4, 6 1, 2, 3, 5, 6 1, 2, 4, 5, 6 1, 3, 4, 5, 6 2, 3, 4, 5, 6
1995 1983 1985 2000 1997 1950 1995
1, 2, 3, 4, 5, 6 1, 2, 3, 4, 5 1, 2, 3, 4, 6 1, 2, 3, 5, 6 1, 2, 4, 5, 6 1, 3, 4, 5, 6 2, 3, 4, 5, 6 1, 2, 3, 4
69.50 70.29 69.50 73.47 68.17 71.62 67.37 65.52
22
8 Table 6: The Overall Accuracies for Various Layer Combinations
Asian Journal of Geoinformatics, Vol.14,No.4 (2014)
Figure 5. (A) Land Use/Land Cover Classification with Highest Accuracy Produced from the Layer Combination 1, 2, 3, 5, 6 (i. e., Green, Red, NIR, NDWI and NDVI) (B) Land Use/Land Cover Classification of Original Band Combination 1, 2, 3, 4 (i.e., Green, Red, NIR and SWIR)
Table 7. Error Matrix for 1234 Band Combination. Table 7: Error Matrix for 1234 Band Combination
Classified Data
Waterbody
Forest
Waterbody 27 0 Forest 0 29 Classified Data Waterbody Forest Agricultural land 2 10 Waterbody 270 00 Barren land Forest 00 292 Scrub forest Agricultural 22 100 settlement land Barren 00 00 Other land vegetation Scrub forest 01 20 Rive sand settlement 232 041 Reference Total Other vegetation 0 accuracy 0 Overall classification Rive sand 1 Overall Kappa Statistics0 Reference Total 32 41 Overall classification accuracy
Waterbody
Agricultural land 18 4 Agricultural 90 land 181 43
900 10
Barren land
024 00
03
014 450 30
50
31
240 00
1
0
117
29
0117 0
Shrub forest
0 0 Reference Data 0 0 Barren5land Shrub 45 forest
140 062
029 0
Settlement
Other vegetion
0
3
River sand 0
0 Settlement 9
0 Other vegetion 3
01
00
00
00
025 00
00
00
98
30
0 River sand 0 00
30
02
0
115 833 0
031 25
030 232
0
15
0
30
62
33
31
32
0
Forest
Waterbody 26 0 Forest 0 33 Classified Data Waterbody Forest Agricultural land 4 7 Waterbody 260 00 Barren land Forest 00 331 Scrub forest Agricultural 41 70 settlement land Barren 00 00 Other land vegetation Scrub forest 01 10 Rive sand settlement 132 041 Reference Total Other vegetation 0 accuracy 0 Overall classification Rive sand 1 Overall Kappa Statistics0 Reference Total 32 41
0 3 Agricultural 107 land 04
32 1070 40
Barren land
01
27 00
011 500 10
20
21
270 00
1 117
0117 0
Shrub forest
0 Reference Data 0 0 Barren2land Shrub 50 forest
Settlement
Other vegetion
0
0
River sand 0
0 Settlement 8
0 Other vegetion 3
00
00
00
028 00
00
00
815 00
00
30
0 River sand 1 00
11
110 062 0
010 1533 0
031 28
0
0
10
0
30
29
62
33
31
33
029 0
Overall classification accuracy
Classified Producer's 48 84.38% User's 56.25% Total Accuracy Accuracy 33 70.73% 87.88% 164 4828 3320
76.92% 84.38% 82.76% 70.73% 22.58%
54.88% 56.25% 85.71% 87.88% 70.00%
2047 12 377
22.58% 93.75% 24.24%
70.00% 63.83% 66.67%
164 12 2825
25 65.52 47 0.58 377
9
23 23
54.88% 66.67% 85.71% 100.00%
80.65%
100.00%
93.75%
63.83%
030 133 0
User's Accuracy
Classified Producer's 26 81.25% User's 100.00% Total Accuracy Accuracy 36 80.49% 91.67% 182 2632 3614
91.45% 81.25% 93.10% 80.49% 17.74%
58.79% 100.00% 84.38% 91.67% 78.57%
1442 17 377
17.74% 90.91% 45.45%
78.57% 71.43% 88.24%
182 17 3228
28 73.47 42 0.67 377 73.47 0.67
Overall Kappa Statistics
76.92% 24.24% 82.76% 80.65%
Classified Producer's Total Accuracy
Table 8.8:Error for 12356 layer Combination. Reference Data Table Error Matrix Matrix for 12356 layer Combination Agricultural land
User's Accuracy
65.52 0.58
Table 8: Error Matrix for 12356 layer Combination
Overall Kappa Statistics
Classified Data
Classified Producer's Total Accuracy
Reference Data Table 7: Error Matrix for 1234 Band Combination
91.45% 45.45% 93.10% 90.32%
58.79% 88.24% 84.38% 100.00%
90.32%
100.00%
90.91%
71.43%
Land Use/Land Cover Classification of Remote Sensing Data and Their Derived Products in a Heterogeneous Landscape of a Khan-Kali Watershed, Gujarat
is equal to 1. Chance agreement is represented by the Kappa statistic equal to 0. Negative values indicate agreement is less than the chance. In order to make effective comparison of classification accuracy between different layer combinations, same data set of sampling pixels is used for accuracy assessment.
land use/land cover mapping in the present study. Figure 5 reflects the same results. However, there is marginal decrease in the value of producer’s accuracy for waterbody, scrub forest and river sand classes in 12356 layer combination. This is due to misclassification persistence between settlement and water body land classes as well as between agriculture land and scrub land. The overall Kappa statistic values for 1, 2, 3, 5, 6 layers and 1,2,3,4 bands combinations are 0.67 and 0.58 respectively. It shows the agreement between classification map and reference data is higher for 1, 2, 3, 5, 6 layer combination as compared to 1, 2, 3, 4 band combination.
4. Results and Discussion In the present study, multi-source classification approach is applied to increase the accuracy of supervised classification of Khan-Kali semi-arid watershed area. Accurate information of land use/land cover map is essential to detect long term trend and impact of watershed management measures taken through various programs in such region. The RS data of this watershed is suffers from the spectral overlap among different land use/land cover classes. The aim of the present study is to increase the accuracy of land use/land cover classification using multi-source data of derived NDVI and NDWI indices in addition to original RS data.
It is observed that, NDVI and NDWI layers help to improve the misclassification accuracy in heterogeneous landscape and hilly terrain, and comparatively gives good classification results for all five band combinations as compared to classification based on only original band combination. During classification of all the band combinations, we also observed significant confusion between scrub forest and poor agricultural land classes (fallow land) due to overlapping of spectral signatures. This is due to the crystalline stone patches present in the study area, which show same spectral signature on that of agricultural land class.
The overall accuracy of classification achieved are shown in Table 6. Along with the five band combination, it also shows the overall accuracy of LISS-III original band combination (i.e., 1, 2, 3, 4) and stacked six layer combination (i.e., 1, 2, 3, 4, 5, 6). The overall accuracy of original LISS-III band combination is 65.52%. With the addition of NDWI as a fifth layer, the overall accuracy rises up to 70.29%. With addition of NDVI as sixth layer, the overall accuracy rises up to 69.50%. On removal of band 4 from six layer combination and replacing it with band 3 (i.e., 1, 2, 3, 5, 6), the accuracy is the highest at 73.47%. This combined use of NDWI and NDVI with original bands results in considerable increase of 7.95% in overall accuracy of classification, as compared to only use of original four band combination. The accuracy drops to71.62% on removal of Red band from six layer combination (i.e., 1, 3, 4, 5, 6). The 2, 3, 4, 5, 6 layer combination gives the lowest value of overall accuracy of 67.37% in the category of five layer combination.
5. Conclusions Accurate LU/LC is essential to detect temporal changes and especially watershed areas due to the watershed development programme. Remote sensing imagery are potential source of data for LU/LC mapping. But, they have limitations in terms of finite spatial and spectral resolutions and temporal coverages. RS sensors measure the spectral radiation intensity, which vary not only due to the temporal changes occurring on the ground, but also due to the variations in the solar illumination and reflectance angle, mainly due to topography over the region. In classification of such data, the spectral signatures separability plays vary important role. The supervised maximum likelihood classification only on original bands of IRS LISS-III data is not sufficient to achieve desirable accuracy of land use/land cover map for such region due to overlap of spectral signatures and effect of topography. The topographic effect can be minimized by taking normalized difference transformation of spectral data, such as NDVI and NDWI. The derived products such as NDVI and NDWI along with the original bands of IRS LISSIII data, when used in combination, shows comparatively good results for five land use/land cover classes out of eight. Especially, the misclassification in agricultural land (specifically black agriculture land) due to waterbody is improved from 76.92% to 91.45% in terms of producer’s accuracy. Thus, the added use of derived data products NDVI and NWDI help to reduce the confusion between spectrally similar classes and improve the classification accuracy.
In brief, we can say that NDWI and NDVI individually added to original bands or together without NIR or RED bands do not show much variation in overall accuracy of classification. All layer combination provides good overall accuracy as compared to the classification based on only four original band combination (i.e., 1, 2, 3, 4). The error matrices for 1234 and 12356 layer combination are represented in Table 7 and 8 respectively. The results of producer’s accuracy showed much variation between different land use/land cover classes. In case of forest, settlement, barren land and other vegetation classes value of Producer’s accuracy moderately increases for 12356 layer combination as compared to 1234 band combination. Agricultural land class showed considerable rise from 76.92 to 91.45% in 12356 layer combination. This clearly indicates that the misclassification of water body class in agricultural land has been improved. It is the dominant factor to accurate 10
Asian Journal of Geoinformatics, Vol.14,No.4 (2014)
Acknowledgements
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We are grateful to Shri T.P. Singh, Director, BISAG, Gandhinagar, Gujarat, India for his keen interest in this project for providing satellite data and institutional facilities to carry out this work. We are also thankful to Shri M. H. Kalubarme, Project Director, BISAG, Gandhinagar, Gujarat, for their valuable time and very useful and practical suggestions.
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