Classification of arable land using multitemporal

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1 Mr. Anser Mehmood Classification of arable land using multitemporal TerraSAR-X data Duration of the Thesis: 6 months Completion: April 2013 Tutor: Dipl.- Geogr. René Pasternak Examiner: Prof. Dr.-Ing. Alfred Kleusberg Abstract Agriculture can be considered as an essential ingredient in modern society. It plays an important role in removing hunger, improving the economy and standard of livelihood. Everyone in the modern world is being beneficial from nutrition, energy and fiber. There is continuous need of agricultural land due to increase in the population. Cultivating activities harm natural resources which cause erosion, pollution and deforestation in the world. Therefore careful management is needed for sufficient production of food and minimizes the impact on natural resources. Remote sensing data is widely used for monitoring and mapping the arable land. The aim of this study was to classify different vital types of crops in Baden Württemberg from back scattered value of TerraSAR-X. Temporal data was used to implement on the defined study of research. Two different test sites (LGTN upper part and LGTN lower part) were considered who s harvesting and sowing seasons were same. The main purpose of our study was to check whether a back scattered values of one crop (e.g. wheat, rapeseed) is same in both of the test sites or not. TerraSAR-X data with VV polarization was used of four months (July, August, September, and October) for rule based classification. Radiometric calibration of radar data was done but the speckle noise was not reduced or eliminated. Optimal result can t be obtained by removing speckle noise because it can smooth the whole image so the texture information is not enough to evolve valuable results. Rule sets which evolved from one test site (LGTN upper part) using backscattered statistical values were also implemented on the other test site (LGTN lower part). Rule sets which were defined for one test side had the accuracy of 90.61% and the rule sets implemented on other test site had the accuracy 78.43%.

2 Problem statement and Justification Classification of the arable land, using high-resolution SAR data in a well-organized way, is the aim of the current study. Several studies about arable land classification have verified the effectiveness of using high spatial resolution SAR data in combination with temporal series figures. Numerous types of methods have been adopted and developed to map different land covers from SAR high-resolution data. These methods consist primarily of visual interpretation, pixel based classification, and object based classification. Visual interpretation permits definition of classes based on interpreter s knowledge. The separation between classes can be incredibly exact. But on the other hand, visual interpretation is very costly, time taking and boring. Therefore, it is not used somewhat commonly. Image classification on pixels has been accomplished from the launch of Landsat satellite and it is still widely into practice. In the pixel-based classification, the pixels are classified on the bases of spectral or backscatter properties. For addition this method has been constrained in the mean of training data which is gathered for assembling specific signature of given study area and the limitation of the signature is transferability to other study area. In high resolution SAR data the single pixel no longer represents the true signature (Yu et al., 2006). Along with backscatter value, there is other useful information such as shape, texture and neighborhood. For significant monitoring of the arable land cover, the neighborhood information has immense meaning. Remote sensing business is now stirring from pixel-based to object-based classification because it has advantages over it. An increase in accuracy of the classification has been observed by incorporating the auxiliary data in classification. In the region Baden-Württemberg, there are different intense situations of climate in terms of annual precipitation and temperature. These situations directly influence farmer s activities in the fields in context of sowing and harvesting time. Even a small change in temperature can make entire crops wilt or be completely destroyed. As global warming impacts the whole world, Baden- Württemberg cannot be considered differently. The overall temperature has been raised by 0.6 C during the last 50 years. Climate change effects all the departments of life. For example, it affects the water balance and agriculture in such a manner that water has lower ground level in summer whereas flooding in winter, and the agricultural production of winter wheat is less but more corn maybe produced. This depicts a glance of the warmer climate in future (LUBW, 2010). In addition, due to unusual altitude and terrain characteristics of the area, the development phases of crops are different in various parts of the region. 1

3 Taking all these aspects into account, it would be awfully convenient if the time sequential backscatter signatures of the crops, which are obtained from one area of the region, can be reassigned to another nearby area of the same region exclusive of performing all classification processes once more and without compromising the achievable classification out comes. Objectives General objective of research was to classify the arable land using TerraSAR-X back scattered values, independent of the traditional methods i.e. nearest neighbor classification. This study specifically focused on the creation of back scattered response signatures of arable land. Furthermore, the Rule-based classification of the images had to be determined on the basis of these back scattered response signatures. Methodology The steps which were involved in this study are explained in figure 1.a below. All the processes were treated in systematic way for getting the highly accurate outcomes. Figure 1: Showing stepwise procedure involved in whole study 2

4 Mean-Standard Deviation (db) Mean-Mean (db) Results and Analysis Overall comparison between crops It has been evident that all the crops except wheat and barley, are easy to classify using statistical back scattered value, i.e. mean back scattered value and standard deviation. But wheat and barley are difficult to classify. Fields of rapeseed and corns can be mixed into another because they have approximately same temporal back scattered response. Potatoes and sugar beet have also almost same back scattered value which is the result of mixing one field to another. Mean of back scattered value is shown in the graph 1.a. Texture response is also useful to classify different type of crops as shown in the graph 1.b Temporal backscatter response of different crop types July August September Barley Wheat Corn Rapeseed Sugar Beet Potato Graph 1.a: Temporal backscatter response of different crop Types 4,6 4,5 4,4 4,3 4,2 4,1 Temporal Textural Responce of Different Crop Types July August September Barley Wheat Corn Rapeseed Sugar Beet Potato Graph 1.a: Temporal mean-std Backscatter Response of Different Crop Types 3

5 Classification Considering the statistical analysis, Object oriented and rule based classification has been done in ecognition software, which has the main process of segmentation. In this study one field has been considered as an object or segment for classification of Terra SAR-X s data. Some rules have been defined through statistical calculations which were applied on each field. As discussed earlier, it has not been easy to classify wheat and barley crops so somehow faced the same problem by classifying using ecognition. That s why wheat and barley crops will be considered as one class and other classes are sugar beet, potato, corn, and rapeseed. Accuracy assessment of Classification LGTN upper part Overall accuracy of upper part was 90.62%. 125 fields were considered in accuracy assessment of sugar beet. 121 have been successfully classified and other 4 fields were misclassified as 2 fields for Potatoes and 2 for Wheat-Barley. Field 8009 was not classified as potato but sugar beet field because it has forest in surroundings and the trees are the main cause of the phenomena named foreshortening. Table 1.1: Error Matrix for upper Part of LGTN 4

6 User accuracy was 96.8% and producer accuracy was 95.2%. 31 fields of potatoes gave the user accuracy of 87.10% and producer accuracy was 84.38%. 27 fields were perfectly classified and 3 unclassified fields were of sugar beet and 1 was corn field. For accuracy assessment, 35 fields of corn were considered. 31 were purely classified and other 3 fields were mixed with rapeseed and 1 field was mixed into potato field. Field was identified as rapeseed rather than corn because it has the same back scattered value in July and August. User accuracy was 88.58% and producer accuracy was 79.49%. 14 fields were considered of rapeseed. 12 fields were exactly classified and produced user accuracy as 85.70% and producer accuracy as 66.66%. Wheat- Barley has 292 fields to be considered for accuracy assessment. All fields have been perfectly classified. That s why 100% user accuracy was obtained and producer accuracy was 92.12%. LGTN Lower part Rules applied on the upper part were applied in the same manner on the lower part to get the user and producer accuracies. For accuracy of lower part of LGTN, sugar beet showed the 100% user accuracy out of 18 fields and producer accuracy was 81.82%. Overall accuracy of lower part was 78.43%. 15 fields of corns were considered in which 12 were exactly classified whereas 2 fields were misclassified as rapeseed and 1 field as Wheat-Barley. Table 1.2: Error Matrix for lower Part of LGTN 5

7 User and producer accuracy was 80% and 75% respectively. Total 5 rapeseed fields were exactly classified with 100% user accuracy and producer accuracy as 55.5%. 45 fields of Wheat-Barley were exactly classified and 1 field was mixed with rapeseed out of 46 total Wheat-Warley fields. User accuracy was 97.82% and producer accuracy was 81.82%. Figure 2: Classification map of LGTN upper part, RGB for JAS DLR 2010 ecognition 6

8 Figure 3: Classification map of LGTN lower part, RGB for JAS DLR 2010 ecognition Conclusion Remotely sensed high-resolution SAR imagery has been an impressive approach in classifying different agriculture crops in the Leingarten area (LGTN). Ground resolution 1.25m of X-band gives an optimum accuracy to classify seasonal crops. It is concluded that after considering four time-sequential SAR images of July, August, September and October that only first three images 7

9 Chapter 7: References resulted in the productive analysis because new crop season starts in October so that image was not much fruitful in classifying the crop types but maybe important for season Segmentation is carried out on one hierarchical level after implementing the rule sets. The hierarchical unit level represents the one field of crops. The defined rule sets which are based on backscattered mean value and standard deviation, has proved that different crop types (sugar beet, potatoes, corn and rapeseed) can be classified separately. But wheat and barley cannot be classified separately for the reason that these two crops have approximately same growth behavior. The rules applied on object based classification in ecognition Software, provides an opportunity to implement different features within one class according to the requirements of user but he should have deep knowledge of classes to identify variation within class. These rules can be executed very fast and easily transferred in other study areas, which is the significant use of radar data. Reference LUBW, 2010, Climate change in Baden-Württemberg Facts Impacts Perspectives. (last accessed ). Yu Q., Gong P., Clinton N., Biging G., Kelly M., and Schirokauer D., 2006, Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery, Photogrammetric Engineering and Remote Sensing, vol. 72, no. 7, pp , jul