Designing a sustainable rangeland information system for Turkey

Year 2018, , 87 - 97, 01.10.2018

Alper Akar , Ertan Gökalp

https://doi.org/10.26833/ijeg.412222

Cited By: 3

Abstract

The purpose of this study is to identify the deficiencies of the rangeland information system currently used in Turkey and, as an alternative, design a sustainable rangeland information system. In the study, both the extent of changes that occurred over time in the rangelands and the factors that caused such changes were identified, and solutions were suggested to eliminate those factors. The rangelands located in the Akçaabat district of Trabzon province were selected as the study area. Land use maps were produced by using the object-based classification method. According to the results of change analyses made with this information system, it was found out that, from 1973 to 2012, a surface area of 159.8 hectares had been degraded, demonstrating that the current information system had not been successful enough in the management of rangelands. For that reason, a sustainable rangeland information system free from all deficiencies was designed.

Keywords

Rangeland Information System, Worldview-2, Unmanned Aerial Vehicle, Support Vector Machine, Object Based Classification

References

• Altin, M., Gökkuş, A., Koç, A., 2011. Çayırvemerayönetimi [Grassland and rangeland management], 1st ed, OnurGrafik, Ankara, pp 376.
• An, R., Wang, Z., Wang, H. L., Wu, H., Quaye-Ballard, J.A., 2014. Monitoring rangeland degradation on the “Three River Headwaters” region in 1990 and 2004, Qinghai, China, Proceedings of the 35th IEEE International Geoscience and Remote Sensing Symposium, July 13-18 Quebec-Canada, IEEE, 3526 – 3529.
• Avcioğlu, R., 2012. Turkish grasslands and acquisitions by pasture law, Research Journal of Agricultural Sciences, 1, 24-32.
• Baatz, M. and Schäpe, A., 2000. Multiresolution segmentation an optimization approach for high quality multi scale image segmentation, J. Strobl, T. Blaschke, G. Griesebner (Eds.), Angewandte Geographische Informations Verarbeitung XII, Wichmann Verlag, Heidelberg, pp. 12–23.
• Boswell, A., Petersen, S., Roundy, B., Jensen, R., Summers, D., Hulet, A., 2017. Rangeland monitoring using remote sensing: comparison of cover estimates from field measurements and image analysis, AIMS Environmental Science, 4 (1), 1-16.
• Breckenridge, R.P. and Dakins, M.E., 2011. Evaluation of bare ground on rangelands using unmanned aerial vehicles: a case study, GIScience and Remote Sensing, 48 (1), 74-85.
• Brown, J.R. and Thorpe, J., 2008. Climate change and rangelands: responding rationally to uncertainty, Rangelands, 30 (3), 3-6. Burrough, P.A. and Mcdonnell, R.A., 1998. Principles of Geographical Information Systems, Oxford University Press, New York, 34 p.
• Carter, D.B., 1998. Analysis of Multiresolution Data Fusion Techniques, M.Sc Thesis, Blacksburg, Virginia: Virginia Polytechnic Institute and State University, 54 p.
• Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., Mills, J., 2015. Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7- 27.
• Chen, Z.Y. and Gao, B.B., 2014. An object based method for urban land cover classification using airborne lidar data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4243-4254.
• Congalton, R.G. and Green, K., 1999. Assessing the accuracy of remotely sensed data: principles and practices, Lewis Publishers, Boca Raton, 137 p.
• Congalton, R.G., 2001. Accuracy assessment and validation of remotely sensed and other spatial information, International Journal of Wild land Fire, 10(4), 321–328. Cortes, C. And Vapnik, V., 1995. Support vector networks, Machine Learning, 20(3), 273-297.
• eCognition. eCognition Developer 8.7.2 User Guide, Definiens AG, Germany. ERDAS Imagine, 2013.
• ERDAS Imagine 2013 User Guide, 2801 Buford Highway, N.E. Atlanta, Georgia, USA.
• Eswaran, H., Van Den Berg, E., Reich, P., 1993. Organic carbon in soils of the world, Soil Science Society of America Journal, 57, 192–194.
• Farnoosh, R. And Zarpak, B., 2008. Image segmentation using Gaussian Mixture Model, International Journal of Engineering Science (IUST), 19(1-2), 29-32.
• Gonzalez-Audicana, M., Saleta, J.L., Catalan, R.G., Garcia, R., 2004. Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1291–1299.
• Gül, B., Yıldırmış, Ç.,Değer, A., Erdoğan, M., Ulubay, A.2013. Görüntübirleştirmeyöntemlerinin spectral değerlerivegörüntükalitesinikorumaaçısındankarşılaştırıl ması: Worldview-2 uygulaması [A comparison of image fusion methods in terms of spectral values of images and protection of image quality: Worldview-2 application], Harita Dergisi, 150, 8–17.
• Han, S.S., Li, H.T., Gu, H.Y., 2008. The Study on Image Fusion for High Spatial Resolution Remote Sensing Images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7 Beijing, 1159–1164.
• Huang, F., Wang, P., Zhang, J.J. 2012. Grasslands changes in the Northern Songnen Plain China during 1954–2000, Environmental Monitoring and Assessment, 184, 2161– 2175.
• Iscan, F. and Ilgaz, A, 2017. Analysıs Of Geographıc/Urban Informatıon System Web Presentatıons Of Local Goverments In Turkey, International Journal of Engineering and Geosciences (IJEG), Vol; 2; Issue; 03, pp. 75-83.
• Karakus, P., Karabork, H., Kaya, S., 2017. A Comparison Of The Classification Accuracies In Determining The Land Cover Of Kadirli Region Of Turkey By Using The Pixel Based And Object Based Classification Algorithms, International Journal of Engineering and Geosciences (IJEG), Vol; 2; , Issue; 02, pp. 52-60.
• Kavzoğlu, T. and Çölkesen, I., 2010. Destekvektörmakineleriileuydugörüntülerininsınıflandırı lmasında kernel fonksiyonlarınınetkilerininincelenmesi [Investigation of the effects of kernel functions in satellite image classification using support vector machines], HaritaDergisi, 144, 73–82.
• Kavzoglu, T., Çölkesen, I., Yomralioğlu, T., 2015. Object-based classification with rotation forest ensemble learning algorithm using very high resolution WorldView-2 image, Remote Sensing Letters, 6(11), 834-843.
• Keno, B., Suryabhagavan, K.V.2014. Multitemporal remote sensing of landscape dynamics and pattern change in Dire district, southern Ethiopia, Journal of Geomatics, 8 (2).
• Klonus, S. and Ehlers, M., 2009. Performance of evaluation methods in image fusion, 12th International Conference on Information Fusion, International Society of Information fusion, Seattle, 6-9 July, Washington.
• Laliberte, A.S., Winters, C., Rango, A., 2011. UAS remote sensing missions for rangeland applications, Geocarto International, 26, 141–156.
• Lewinski, S., 2006. Applying fused multispectral and panchromatic data of Landsat ETM+ to object oriented classification, 26th EARSeL Symposium, New Developments and Challenges in Remote Sensing, May 29-June 2, Warsaw, Poland.
• Li, S., Xie, Y., Meng, L., 2011. Monitoring land cover changes in HulunBuir by using object oriented method, Proceedings of the Multitemporal Conference, IEEE, pp. 29-32.
• Liu, D., Li, B., Liu, X., Warrington, D.N., 2011. Monitoring land use change at a small watershed scale on the Loess Plateau, China: applications of landscape metrics, remote sensing and GIS, Environmental Earth Sciences, 64, 2229–2239.
• Liu, X.Y., Liang, T.G., Guo, Z.G., Long, R.G., 2014. A rangeland management pattern based on functional classification in the Northern Tibetan Region of China, Land Degradation and Development, 25(2), 193–201.
• Mansour, K., Mutanga, O., Adam, E., Abdel-Rahman, E.M., 2016. Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors, Geocarto International, 31(5), 477–491.
• Mcgwire, K.C., Weltz, M.J., Finzel, A., Morris, C.E., Fenstermaker, L.F., Mcgraw, D.S., 2013. Multiscale assessment of green leaf cover in a semi-arid rangeland with a small unmanned aerial vehicle, International Journal of Remote Sensing, 34(5), 1615– 1632.
• Nikolakopolos, K. And Oikonomidis, D., 2015. Quality assessment of ten fusion techniques applied on Worldview-2, European Journal of Remote Sensing, 48, 141–167.
• Qian, Y., Zhou, W., Yan, J., Li, W., Han, L., 2015. Comparing machine learning classifiers for object based land cover classification using very high resolution imagery, Remote Sensing, 7, 153-168.
• Rango, A., Laliberte, A., Herrick, J.E., Winters, C., Havstad, K., Steele, C., Browning, D., 2009. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management, Journal of Applied Remote Sensing, 3, 1–15.
• Rokni, K., Ahmad, A., Solaimani, K., Hazini, S., 2015. A New approach for surface water change detection: integration of pixel level image fusion and image classification techniques, International Journal of Applied Earth Observation and Geoinformation, 34, 226–234.
• Schowengerdt, R.A., 1980. Reconstruction of multispatial, multispectral image data using spatial frequency content, Photogrammetric Engineering and Remote Sensing, 46(10), 1325–1334.
• Scurlock, J.M.O. and Hall, D.O., 1998. The global carbon sink: a grassland perspective, Global Change Biology, 4(2), 229–233.
• Stehman, S.V., 1997. Selecting and interpreting measures of thematic classification accuracy, Remote Sensing of Environment, 62(1), 77 – 89.
• Song, X.F., Duan, Z., Jiang, X.G., 2012. Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image, International Journal of Remote Sensing, 33, 3301–3320.
• Tso, B. And Mather, P.M., 2009.Classification methods for remotely sensed data, Second Edition, Taylor & Francis Group, United States of America, 376 p.
• Ünal, S., Mutlu, Z., Mermer, A., Urla, Ö.,Ünal, E., Özaydın, K.A., Avağ, A., Yıldız, H., Aydoğmuş, O., Şahin, B., Arslan, S., 2012. A Study on Determination of Condition and Health of Rangelands in Çankırı Province, Research Journal of Agricultural Sciences, 5(2), 131-135.
• Wang, J., Brown, D.G., Bai, Y., 2014. Investigating the spectral and ecological characteristics of grassland communities across an ecological gradient of the Inner Mongolian grasslands with in situ hyperspectral data, International Journal of Remote Sensing, 35(20), 7179– 7198.
• Wang, Z., Ziou, D., Armenakis, C., Li, D., Li, Q., 2005. A comparative analysis of image fusion methods, IEEE Transactions on Geoscience and Remote Sensing, 43(6), 1391–1402.
• Wu, W., Zucca, C., Karam, F., Liu, G., 2016. Enhancing the performance of regional land cover mapping, International Journal of Applied Earth Observation and Geoinformation, 52, 422-432.
• Yilmaz, M., Uysal, M., 2017. Comparing Uniform And Random Data Reduction Methods For DTM Accuracy, International Journal of Engineering and Geosciences (IJEG), Vol;2, Issue;01, pp. 9-16.