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CART and IDC – Based Classification of Irrigated Agricultural Fields Using Multi-Source Satellite Data

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CART and IDC – Based Classification of Irrigated Agricultural Fields Using Multi-Source Satellite Data GEOCARTO INTERNATIONAL, 2016 http://dx.doi.org/10.1080/10106049.2016.1232312 CART and IDC – based classification of irrigated agricultural fields using multi-source satellite data Virupakshagouda C. Patila,b, Khalid A. Al-Gaadia,c, Rangaswamy Madugundua , ElKamil Tolaa, Ahmed M. Zeyadaa, Samy Mareya and Chandrashekhar M. Biradard aPrecision Agriculture Research Chair, King Saud University, Riyadh, Saudi Arabia; bElectron Science Research Institute, Edith Cowan University, Joondalup, Australia; cDepartment of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia; dGeoinformatics Unit, International Center for Agricultural Research in the Dry Areas, Amman, Jordan ABSTRACT ARTICLE HISTORY To understand water productivity of crops cultivated in the Eastern Province Received 30 August 2015 of Saudi Arabia, this study was conducted to generate a reliable crop type Accepted 7 August 2016 map using a multi-temporal satellite data (ASTER, Landsat-8 and MODIS) and KEYWORDS crop phenology. Classification And Regression Tree (CART) and ISO-DATA Crop phenology; decision Cluster (IDC) classification techniques were utilized for the identification tree; spectral separability; of crops. The Ideal Crop Spectral Curves were generated and utilized for vegetation indices the formulation of CART decision rules. For IDC, the stacked images of the phenology-integrated Normalized Difference Vegetation Index were utilized for the classification. The overall accuracy of the classified maps of CART was 76, 77 and 81% for ASTER, MODIS and Landsat-8, respectively. For IDC, the accuracy was determined at 67, 63 and 60% for ASTER, MODIS and Landsat-8, respectively. The developed decision rules can be efficiently used for mapping of crop types for the same agro-climatic region of the study area. 1. Introduction Crop type mapping is a key factor for the efficient management of land and water resources (Biradar et al. 2009; Heller et al. 2012). Several researchers used crop type maps in various agricultural studies, such as cropping patterns based on crop water needs (Alzahrani et al. 2012), quantification of water use efficiency (Patil et al. 2015), irrigation management (Uddin et al. 2004), decisions on crop rotation (Biradar et al. 2008), nutrient management (Patil et al. 2014), yield forecasting (Ferencz et al. 2004) and economic policies and price optimization (Thornton et al. 1997; Wang et al. 2010). The use of satellite-based data-sets for studying agricultural fields and addressing resource man- agement strategies started in the 1990s (de Leeuw et al. 2010; Liaghat & Balasundram 2010). The remote sensing methods used to identify crop types mainly rely on the spectral signatures of crops (Sakamoto et al. 2005; Wardlow et al. 2007; Vincikova et al. 2010) and their temporal profiles of veg- etation indices (Xiao et al. 2005; Biradar & Xiao 2011). Due to the dynamic nature of the agricultural crops, the spectral reflectance of a crop may vary with respect to its phenology. On the other hand, the use of crop phenology-integrated spectral profiles improved the classification accuracy (Blaes et al. CONTACT Rangaswamy Madugundu [email protected] © 2016 Informa UK Limited, trading as Taylor & Francis Group 2 V. C. Patil et al. 2005; Zafar & Waqar 2014). For example, Pena-Barragan et al. (2011) achieved an overall accuracy of 79% in the classification of crops by incorporating phenology. Hence, the Ideal Crop Spectral Curves (ICSCs), which represent the phenology-integrated multi-spectral and multi-temporal profiles of a specific crop, are essential for the generation of an accurate crop type map. For the incorporation of the phenological changes in crop type mapping, the use of multi-temporal image analysis was found to be superior over single image analysis (Wardlow et al. 2007; Ozdogan 2010; Ozdogan et al. 2010; Foerster et al. 2012). However, during multi-temporal image analysis, the spectral reflectance of forage crops, such as alfalfa and Rhodes grass, can be influenced by the cutting schedule, which needs to be considered in discriminating agricultural crops (Yang et al. 2013). Classification of irrigated crops requires not only the detection of significant spectral differences, but also an algorithm that can successfully identify crops. In general, supervised (for example, the Classification And Regression Tree (CART)) and unsupervised (for example, ISO-DATA Cluster (IDC)) classification methods have been widely used to classify agricultural crops (Ahmad & Sufahani 2012). The CART method works on a sequence of binary decisions formulated in the classification strategy (Safavian & Landgrebe 1991). Depending on the decision rule, the first conditional statement leads to the second, the second to the third and so on (Friedl & Brodley 1997). However, a CART decision tree constructed to classify one data-set (e.g. Landsat-8) may not be able to classify another data-set (e.g. ASTER) due to the variation in the spectral profile or band width, where both data- sets do not cover exactly the same regions of the electromagnetic spectrum. On the other hand, the unsupervised IDC algorithm works via an iterative process through which it re-clusters the pixels to achieve relatively homogeneous groups separable in the spectral space (Ball & Hall 1965; Tou & Gonzalez 1977; Shen et al. 2009). The IDC requires a number of clusters and a number of additional user-supplied parameters as inputs to control the clustering process. Numerous studies used CART and IDC classification methods for the discrimination of land use and land cover classes (Hansen et al. 2000; Sesnie et al. 2008; Xie et al. 2008; Tooke et al. 2009; Punia et al. 2011). Most of the researchers used vegetation indices (Normalized Difference Vegetation Index (NDVI), EVI and SAVI) and crop phenology as a base for formulating the decision rules for crop type mapping (Sakamoto et al. 2005; Wardlow et al. 2007; Liu et al. 2014). In addition to vegetation indices, individual bands such as Red, NIR and SWIR were also utilized for crop separability and crop classification studies (Sharma et al. 1995; Dadhwal et al. 1996; Manjunath et al. 1998; Panigrahy et al. 2009; Mondal et al. 2014). A reliable crop type map provides vital information on cropping patterns for the efficient man- agement of agricultural inputs and available water resources. In view of the determination of crop water requirements, this study was carried out to generate a reliable crop type map by employing the CART and IDC classification techniques. The specific key objectives of the study were (i) to generate crop-specific ICSCs using a multi-temporal satellite data (ASTER, Landsat-8 and MODIS) and crop phenology, (ii) to classify and generate crop type maps utilizing the obtained ICSCs and (iii) to com- pare the accuracy of the CART and IDC classified maps. 2. Study area The study was carried out in Todhia Arable Farm (TAF), which spread across an area of 6967 ha with 47 agricultural fields (2400 ha) under centre pivot irrigation systems. Each field was about 50 ha. The TAF was located between Al-Kharj and Haradh cities in the Eastern Province of Saudi Arabia, within the latitudes of 24°10′22.77″ and 24°12′37.25″ N and longitudes of 47°56′14.60″ and 48°05′08.56″ E (Figure 1). The study area was under a dry continental climate with hot summers (40 ± 1.7 °C) and cold to moderate winters (15 ± 1.3 °C) with an average annual temperature of 35 °C. Tube wells located in the TAF were used to supply irrigation water to the cultivated fields. The major crops cultivated in the TAF were wheat, alfalfa, Rhodes grass, corn and barley. Geocarto International 3 Figure 1. Location map of Todhia Arable Farm in the eastern region of Saudi Arabia. 3. Data collection 3.1. Field data A reconnaissance survey was conducted to understand the cropping pattern of the TAF and to deter- mine the sampling approach for the development of classification strategies. Wheat and barley were cultivated during the winter season (November–April), while corn was grown twice a year (March–June and July–November). Rhodes grass and alfalfa were cultivated as biennial multi cut crops. In some instances, Rhodes grass was cultivated as a catch crop after the harvest of wheat or barley. Out of the 47 fields of the TAF, 11 fields (23%) were randomly selected and considered as sample plots. For the convenience of the study, the selected 11 fields were earmarked for ground truth data collection based on the area coverage of each crop (one field for wheat, one field for barley, three fields for corn, three fields for alfalfa and three fields for Rhodes grass). The sample plots were visited at frequent intervals (once in 16 ± 2 days) corresponding to the date of satellite over-pass (ASTER/ MODIS or Landsat-8) during the study period (February 2012–May 2014). From each sample plot, four to five homogeneous patches (>3 × 3 pixels) were identified and used to monitor the changes in the spectral reflectance with respect to crop phenology. The geo-location of each homogeneous patch was recorded, along with the field data, which included crop type, phenology and growth stage (Table 1). 3.2. Satellite imagery A total of 43 cloud-free images were acquired for the study, 15 (ASTER), 15 (MODIS) and 13 (Landsat-8). ASTER data were procured from the Japanese Space Centre (http://ims.aster.ersdac. jspacesystems.or.jp), while MODIS (MOD09A1) and Landsat-8 data were downloaded from the por- tal of the USGS Earth Explorer (http://earthexplorer.usgs.gov). The details of satellite data used in this study are provided in Table 2. The acquired images covered the entire growth period of wheat, barley and corn. However, for alfalfa and Rhodes grass, at least a complete growth cycle between two harvests was covered. 4. Methods In order to generate a reliable crop type map, agricultural crops were classified based on the response of phenology-integrated multi-spectral and multi-temporal profiles (i.e.
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