Evaluation of Land Cover Changes Ysing Remote Sensing Technique (Case study: Hableh Rood Subwatershed of Shahrabad Basin)
محورهای موضوعی : Environmental policy and managementKhadijeh Abolfathi 1 , Marzieh Alikhah-Asl 2 , Mohammad Rezvani 3 , Mohammad Namdar 4
1 - M.Sc. Student in Assessment and Land Use Planning, Natural Resources and Environmental Engineering Department,
Faculty of Agricultural Science, Payame Noor University (PNU), Tehran, Iran
2 - Assistant Professor, Natural Resources and Environmental Engineering, Faculty of Agricultural Science, Payame Noor
University (PNU), Tehran, Iran
3 - Assistant Professor, Natural Resources and Environmental Engineering, Faculty of Agricultural Science, Payame Noor
University (PNU), Tehran, Iran
4 - Forest, Rangeland and Watershed Management Organization, Tehran, Iran
کلید واژه: Remote Sensing, Supervised classification, Land Use Change, Land cover,
چکیده مقاله :
The growing population and increasing socio-economic necessities creates a pressure on land use/land cover. Nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. This study investigates the land use changes in part of Hableh Rood Watershed of Iran using Landsat 7 and 8 (Sensor ETM+ and OLI) images between 2001 and 2013. Supervised classification was used for classification of Landsat images. Four land use classes were delineated including rangeland, irrigated farming and plantations land, and dry farming lands,urban. Visual interpretation, expert knowledge of the study area and ground truth information accumulated with field works to assess the accuracy of the classification results. Overall accuracy of 2001 and 2013 image classification was 81.48 (Kappa coefficient: 0.7340) and 87.04 (Kappa coefficient: 0.7841), respectively. The results showed considerable land cover changes for the given study area. Land cover change detection showed that in a period of 12 years, 277.57 hectares of dry farming lands and 340 hectares of dense range have been lost. But, 341 hectares for low dense range, 280 hectares for semi dense range and 1.4 hectares for urban areas, have been added in area.
The growing population and increasing socio-economic necessitiescreates a pressure on land use/land cover. Nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. This study investigates the land use changes in part of Hableh Rood Watershed of Iran using Landsat 7 and 8 (Sensor ETM+ and OLI) images between 2001 and 2013. Supervised classification was used for classification of Landsat images. Four land use classes were delineated including rangeland, irrigated farming and plantations land, and dry farming lands,urban. Visual interpretation, expert knowledge of the study area and ground truth information accumulated with field works to assess the accuracy of the classification results. Overall accuracy of 2001 and 2013 image classification was 81.48 (Kappa coefficient: 0.7340) and 87.04 (Kappa coefficient: 0.7841), respectively. The results showed considerable land cover changes for the given study area. Land cover change detection showed that in a period of 12 years, 277.57 hectaresof dry farming lands and 340 hectares of dense range have been lost. But, 341 hectares for low dense range, 280 hectares for semi dense range and 1.4 hectares for urban areas, have been added in area.
1- Abd El-Kawy, O.R, Rod, J.K, Ismail, H.A, Suliman, A.S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2), 483-494.
2- Ahmadizadeh, S., Yosefi, M., & Saghafi, M. (2014). Land use change detection using remote sensing and artificial neural network-application to birjand, Iran. Computational Ecology and Software, 4(4), 276-288.
3- Alqurashi, A.F., & Kumar, L. (2013). Investigating the use of remote sensing and gis techniques to detect land use and land cover change: A review. Advances in Remote Sensing, 2 (2), 193-204. DOI:10.4236/ars.2013.22022
4- Aplin, P., & Atkinson, P.M. (2004). Predicting missing field boundaries to increase per-field classification accuracy. Photogrammetric Engineering and Remote Sensing, 70(1), 141-149.
5- Butenuth, M., Gosseln, G.V., Tiedge, M., Heipke, C., Lipeck, U., & Sester, M. (2007). Integration of heterogeneous geospatial data in a federated database. ISPRS Journal of Photogrammetry and Remote Sensing, 62(5), 328-346.
6- Carrao, H., Gonçalves, P., & Caetano, M. (2008). Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sensing of Environment, 112(3),986-997.
7- Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Review article digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing,25(9), 1565-1596.
8- Foody, G. (2002). Status of land covers classification accuracy assessment. Remote Sensing of Environment, 80(1), 185-201.
9- Friedl, M..A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168-182.
10- Guerschman, J.P., Paruelo, J. M., Bella, C.D., Giallorenzi, M.C., & Pacin, F. (2003). Land covers classification in the Argentine Pampas using multitemporal Landsat TM data. International Journal of Remote Sensing, 24(17), 3381-3402.
11- Imagine, E. (2002). ERDAS Field Guide Sixth Edition. Leica Geosystems. Atlanta, Georgia, USA.
12- Imam, E. (2011). Use of geospatial technology in evaluating landscape covers type changes in Chandoli National Park, India. Computational Ecology and Software, 1(2), 95-111.
13- Ioannis, M., & Meliadis, M. (2011). Multi-temporal Landsat image classification and change analysis of land cover/use in the Prefecture of Thessaloniki, Greece. The International Academy of Ecology and Environmental Sciences, 1(1), 15-25.
14- Jung, M., Henkel, K., Herold, M., & Churkina, G. (2006). Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101(4), 534-553.
15- Kassa, A. (1990). Drought risk monitoring for Sudan using NDVI, 1982-1993. Unpublished thesis, University College, London.
16- Knorr, W., Pytharoulis, I., Petropoulos, G.P., & Gobron, N. (2011). Combined use of weather forecasting and satellite remote sensing information for fire risk, fire and fire impact monitoring. Computational Ecology and Software, 1(2), 112-120.
17- Kogan, F.N. (1993). United States droughts of late 1980's as seen by NOAA polar orbiting satellites In Geoscience and Remote Sensing Symposium, 1993. IGARSS'93. Better Understanding of Earth Environment. International (pp. 197-199). IEEE.
18- Lark, R. M., & Stafford, J.V. (1997). Classification as a first step in the interpretation of temporal and spatial variation of crop yield. Annals of Applied Biology, 130(1), 111-121.
19- Lillesand, T.M., & Kiefer, R.W. (1994). Remote sensing and photo interpretation. John Wiley and Sons: New York, 750.
20- Lillesand, T.M., Kiefer, R.W., & Chipman, J.W. (2004). Remote Sensing and Image Interpretation. New York: John Wiley and Sons.
21- Lu, D., & Weng, Q. (2004). Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering & Remote Sensing, 70(9), 1053-1062.
22- Naiman, R.J., Elliott, S.R., Helfield, J.M., & OKeefe, T.C. (1999). Biophysical interactions and the structure and dynamics of riverine ecosystems: the importance of biotic feedbacks. Hydrobiologia, 410, 79-86.
23- Podobnikar, T., Schoner, M., Jansa, J., & Pfeifer, N. (2009). Spatial analysis of anthropogenic impact on karst geomorphology (Slovenia). Environmental Geology, 58(2), 257-268.
24- Ramadan, E., Feng, X.Z., & Cheng, Z. (2004).Satellite remote sensing for urban growth assessment in Shaoxing City, Zhejiang Province. Journal of Zhejiang University SCIENCE, 5(9), 1095-1101.
25- Rana, S.V.S. (2005). Essentials of ecology and environmental sciences. Chaudhary Charan Singh University. PHI Learning Pvt. Ltd.
26- Reddy, T.B., & Gebreselassie, M. A. (2011). Analyses of land cover changes and major driving forces assessment in middle highland Tigray, Ethiopia: the case of areas around Laelay-Koraro. Journal of Biodiversity and Environmental Sciences, 1(6), 22-29.
27- Rogan J., Bumbarger, N., Kulakowski, D., Christman, Z.J., Runfola, D.M., & Blanchard, S. D. (2010). Improving forest type discrimination with mixed lifeform classes using fuzzy classification thresholds informed by field observations. Canadian Journal of Remote Sensing, 36(6), 699-708.
28- Rouse, Jr., Haas, RH., SchellJ, A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. published by NASA, Washington, D.C., p.309.
29- Roy, P.S., Sharma, K.P., & Jain, A. (1996). Stratification of density in dry deciduous forest using satellite remote sensing digital data an approach based on spectral indices. Journal of Biosciences, 21(5), 723-734.
30- Shalaby, A., & Tateishi, R. (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1), 28-41.
31- Singh, P., & Khanduri, K. (2011). Land use and land cover change detection through remote sensing & GIS technology: Case study of Pathankot and Dhar Kalan Tehsils, Punjab. International Journal Geomatics Geosci, 1(4), 839-846.
32- Srivastava, P.K ., Han, D., Rico-Ramirez, M.A. , Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land covers change investigation. Advances in Space Research, 50(9), 1250-1265.
33- Tahir, M., & Hussain, T. (2008). Environmental, Jawahar Publishers and Distributors; New Delhi, India.
34- Tillmann, C., Holzel, N., & Volker, A. (2012). Supervised Classification and Change Detection of Agricultural Land Use in the Forest Steppe Zone of West Siberia Using Multitemporal Satellite Imagery. Unpublished thesis in the subject Landscape Ecology. P:44.
35- Torahi, A. A., ChandRai, S.C. (2011). Land cover classification and forest change analysis, using satellite imagery-a case study in Dehdez area of Zagros Mountain in Iran. Journal of Geographic Information System, 3(-1), 1.
36- Townshend, J. R., & Justice, C.O. (1986). Analysis of the dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing, 7(11), 1435-1445.
37- Tucker, C. J., Townshend, J.R., & Goff, T.E. (1985). African land-cover classification using satellite data. Science, 227(4685), 369-375.
38- Verhoef, W., Menenti, M., & Azzali, S. (1996). Cover a colour composite of NOAA-AVHRR-NDVI based on time series analysis (1981-1992). International Journal of Remote Sensing, 17(2), 231-235.
39- White, J., Shao, Y., Lisa, M.K., & Campbell, J.B. (2013). Campbell landscape dynamics on the island of La Gonave, Haiti, 1990–2010. Land, 2(3), 493-507
40- Wolter, P.T., Mladenoff, D.J., Host, G.E., & Crow, T.R. (1995). Improved forest classification in the Northern Lake States using multi-temporal Landsat imagery. Photogrammetric Engineering and Remote Sensing, 61(9), 1129-1144.
41- Yang, X., Lo, C.P. (2002). Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23(9), 1775-1798.
42- Yuan, F., Sawaya, K.E., Loeffelholz, B.C., & Bauer, M .E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote
sensing. Remote sensing of Environment, 98(2), 317-328.
43-Zhang, W.W., Yao, L., Li, H., Sun, D. F., & Zhou, L. D. (2011). Research on land use change in Beijing Hanshiqiao Wetland Nature Reserve using remote sensing and GIS. Procedia Environmental Sciences, 10, 583-588.
44- Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83-94.
