The Preparation of Updated Vegetation Maps by Processing Satellite Images: A Way in Sustainable Management of Agriculture
الموضوعات :
Ali Mohammadi Torkashvand
1
(Department of Soil Science, Rasht Branch, Islamic Azad University, Rasht, Iran.)
Shahryar Sobhe Zahedi
2
(Research Center of Agriculture and Natural Resources, Guilan, Iran)
الکلمات المفتاحية: Vegetation, Sustainable agriculture, Satellite Images, Agriculture management,
ملخص المقالة :
An important factor in sustainable agriculture and economic management is to calculate areas under different crops that the inputs of agriculture connect to this topic. Planning of agricultural mechanization, fertilizer and pesticide requirements, pests and diseases control, estimates of agricultural production, income and tax and financial planning, all linked to the cultivated areas and estimation of agricultural products. One of the problems in the agricultural section of Iran is the lack of accurate statistics of cultivated crops areas that this is much higher for horticultural products. Over time, it varies the area of land under cultivated crop, and orchards and bare lands; consequently the estimation of yield is not done as well due to these changes caused some problems in planning and management. Land Surveying is time-consuming and expensive, while mapping farms and orchards lands through classified satellite images is a high speed and low cost way. Nowadays, the satellite image processing techniques have developed for the estimation of crops, pest control, agricultural macro planning and preparing updated maps. A principal problem is the interference of plants spectral reflections that different methods have been proposed by researchers to differentiate vegetation on satellite images. At this paper, remote sensing imagery in mapping vegetation or various plants are investigated.
1- Ahmadpour, A., Solaimani, K., Shokri, M., & Ghorbani, J. (2011). Comparison of three common methods in supervised classification of satellite data for vegetation studies. Journal of Applied RS and GIS Techniques in Natural Resource Science, 2(2), 69-81.
2- Alavipanah, S.K. (2004). Application of remote sensing in the earth sciences (soil). University of Tehran Inc., Tehran, Iran.
3- Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI & APAR assesment. Remote Sensing of Environment, 35, 161-173.
4- Berni, J.A.J., Zarco-Tejada, P.J., Sepulcre-Cantó, G. Fereres, E., & Villalobos, F. (2009). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment, 113, 2380-2388.
5- Baugh, W.M. & Groeneveld, D.P (2009). Broadband vegetation index performance evaluated for a low-cover environment. International Journal of Remote sensing, 27(3), 4715-4730.
6- Berberoglu, S., Lloyd, C.D., Atkinson, P.M., & Curran, P.J. (2000). The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Comput. Geoscience, 26, 385–396.
7- Brandt, C.J., & Thornes, J.B. (1996). Mediterranean desertification and land Use. John Wiley Inc., London, UK.
8- Cˇerna´, L., & Chytry´, M. (2005). Supervised classification of plant communities with artificial neural networks. Journal of Vegetation Science, 16, 407-14.
9- Chen, J. M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Remote Sensing of Environment, 22, 229-242.
10- Clevers, J.G.P. (1989). The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting soil moisture. Remote Sensing of Environment, 29, 25–37.
11- Crippen, R.E. (1990). Calculating the vegetation index faster. Remote Sensing of Environment, 34, 71–73.
12- Committee on Global Change Research, National Research Council (1999). Global environmental change: Research pathways for the next decade. National Academy Press, Washington, DC.
13- Darvishsefat, A.A., & Zareh A. (1998). Investigating potential of satellite data in the preparation of vegetation in arid and semiarid regions (case study, Qaen region). Journal of Natural Resources, 51(2), 39-45.
14- Darvishzadeh, R., Matkan, A.A., Hosseiniasl, A., & Ebrahimi Khusefi, M. (2012). Estimation of vegetation fraction in the Central arid region of Iran using satellite images (Case study: Sheitoor basin, Bafgh). Arid Biome Scientific and Research Journal, 2(1), 25-38.
15- Darvishzadeh, R., Skidmore, A., Atzberger, C., & Wieren, S. (2008). Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture. International Journal of Applied Earth Observation and Geoinformation, 10, 358-373.
16- Das, P.T., Tajo, L. & Goswami, J. (2009). Assessment of citrus crop condition in umling block of Ri-bhoi district using RS and GIS technique. Journal of the Indian Society of Remote Sensing. 37(2), 317-324
17- Domacx A., & Su¨zen, M.L. (2006). Integration of environmental variables with satellite images in regional scale vegetation classification. International Journal of Remote Sensing, 27, 1329-50.
18- Duda, T., & Canty, M. (2002). Unsupervised classification of satellite imagery: choosing a good algorithm. International Journal of Remote Sensing, 23, 2193-2212.
19- Ebrahimi, M. (2010). Estimation of vegetation fraction using vegetation indices and artificial neural network in order to improve modified perpendicular index. RS & GIS department, Shahid Beheshti University, Tehran, Iran.
20- Egbert, S.L., Park, S., & Price, K.P. (2002). Using conservation reserve program maps derived from satellite imagery to characterize landscape structure. Comput Electron Agric, 37, 141-56.
21- Filippi, A.M., & Jensen, J.R. (2006). Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sens Environ, 100:512–30.
22- Fletcher, R.S. (2005). Evaluating high spatial resolution imagery for detecting citrus orchards affected by sooty mold. International Journal of Remote Sensing, 26(3), 495-502.
23- Gad, S., & Kusky, T. (2006). Lithological mapping in the Eastern Desert of Egypt, the Barramiya area, using Landsat thematic mapper (TM). Journal of African Earth Science, 44, 196-202.
24- He, C., Zhang, Q., & Li, Y. (2005). Zoning grassland protection area using remote sensing and cellular automata modeling: a case study in Xilingol steppe grassland in northern China. Journal of Arid Environment, 63, 814-26.
25- Hill, R.A., Grancia, K., Smith, G.M., & Schardt, M. (2007). Representation of an alpine treeline ecotone in SPOT 5 HRG data. Remote Sensing of Environment, 110, 458-467.
26- Huete, H. (1988). A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25, 295–309.
27- Huete, H., Liu, Q., Batchily, K., & Van Leeuwen, W. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59, 440-451.
28- Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50, 663-666.
29- Hugget, R.J. (1993). Modelling the human impact on nature. Oxford University Inc. Oxford, UK.
30- Kaufman, Y.J., & Tanre, D. (1992). Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience & Remote Sensing, 30, 261-270.
31- Jung, M., Churkina, G., & Henkel, K. (2006). Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing Environment, 101, 534-53.
32- Lambin, E.F., Turner, B.L., Helmut, J. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global Environment Change, 11, 261-9.
33- Lawrence, R.L., & Ripple, W.J (1998). Comparisons among Vegetation Indices andBandwise Regression in a Highly Disturbed، Heterogeneous Landscape: Mount St. Helens Washington. Remote Sensing of Environment, 64, 91-102
34- Lenka, C., & Milan, C. (2005). Supervised classification of plant communities with artificial neural networks. Journal of Vegetation Science, 16, 407-414.
35- Loveland, T.R. (2000). The patterns and characteristics of global land cover. In: M.J. Hill and R.J. Aspinall, Editors, Spatial Information and Land Use Management, Gordon and Breach, Reading 13–24.
36- Major, D.J., Baret, F., & Guyot, G. (1990). A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing, 11, 727-740.
37- Mohammadi, M.,Ebrahimi, A., & Haqzade, A. (2012). Capability of IRS satellite on vegetation cover estimation (Case study: Chaharmah-va-Bakhtiari). Renewable Natural Resources Research, 3(1), 41-53.
38- Matkan, A.A., Darvishzadeh, R., Hosseini, A.A. & Ebrahimi, M. (2011). Capability using satellite images and neural network in estimating vegetation percentage in arid region. Journal of Environmental Erosion, 1, 7-27.
39- Mohammadi Torkashvand, A. (2011). The Preparation of Paddy Map by Digital Numbers of IRS images and GIS. Journal of American Science, 7(1), 382-385.
40- Mohammadi Torkashvand, A., & Eslami, A. (2012). Proposing a methodology in preparation of olive orchards map as an important medicinal plant in Iran by remote sensing (RS) and geographical information system (GIS). Journal of Medicinal Plants Research, 6(5), 680-688
41- Muschen, B., Flugel, W.A., Hochschild, V., Steinnocher, K. & Quiel, F., (2001). Spectral and spatial classification methods in the ARSGISIP project. Physics and Chemistry of the Earth, 26 (7-8), 613-616.
42- Naghibi, S.J., Habibian,S.H., & Habibian, S.M.R.(2010). Determination of optimum vegetation indices for vegetation cover percentage modelling, using satellite spectral reflectance. Journal of Eco-physiology, 1 (3), 63-74.
43- Pinty, B., &Verstraete, M. (1992). GEMI: A nonlinear index to monitor global vegetation from satellites. Vegetation, 10, 15-20.
44- Qi, J., Chehbouni, A., Huete, A. R., & Kerr, Y. H., Sorooshian, S. (1994). A Modified Soil Adjusted Vegetation Index (MSAVI). Remote Sensing of Environment, 48, 119-126.
45- Ramos, M.I., Gil, A.J., Feito, F.R., & Garcia-Ferrer, A. (2007). Using GPS and GIS tools to monitor olive tree movements. Computer and Electronics in Agriculture, 57: , 135-148.
46- Rembold, F., Carnicelli, S., Nori, M., & Gioranni, A.F. (2000). Use of aerial photographs, Landsat TM imagery and multidisciplinary field survey for land-cover change analysis in the lakes region (Ethiopia). International Journal of Applied Earth Observation and Geoinformation, 2(3-4), 181-189.
47- Richardson, A.J., & Wieg, C.L.(1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering & Remote Sensing, 43, 1541-1552.
48- Rondeaux, G., Steven, M., & Baret, F. (1996). Optimisation of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95−107.
49- Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., & Harlan, J.C. (1974). Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/GSFC, Type III, final report, Greenbelt, MD.
50- Senay, G.B., & Elliott, R.L. (2002). Capability of AVHRR data in discriminating rangeland cover mixtures. International Journal of Applied Earth Observation and Geoinformation, 23(2), 299-312.
51- Shafei, H., & Hosseini, S.M. (2012). A study of vegetation in Sistan region through satellite data. Journal of Plant Ecophysiology, 3, 91-105.
52- Shrestha, D.P., & Zinck, J.A. (2001). Land use classification in mountainous areas: Integration of image processing, digital elevation data and field knowledge (application to Nepal). International Journal of Applied Earth Observation and Geoinformation, 3, 78–85.
53- Sohn, Y., & Rebello, N.S. (2002) Supervised and unsupervised spectral angle classifiers. PhotogrammEng Remote Sens, 68, 1271–80.
54- Teresa Barata, P., & Granado, I. (2000). Classification of forest cover types in ortho and satellite images using a methodology based on mathematical morphology. 11th Portuguese Conference on Pattern Recognition, Lisbon, Portugal..
55- Tso, B., & Olsen, R. (2005). Combining spectral and spatial information into hidden Markov models for unsupervised image classification. International Journal of Remote Sensing, 26, 2113–33.
56- Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.
57- Unal, E., Mermer, A., & Mete Dogan, H. (2004). Determining major orchard (pistachio, olive, and vineyard) areas in Gaziantep province using remote sensing techniques. The International Archives of the Photogrammetry, Remote sensing and Spatial Information Sciences. 34, part XXX.
58- Wu, J., Ransom, M.D., Kluitenberg, G.J., Nellis, M.D., & Seyler, H.L. (2001). Land-use management using a soil survey geographic database for Finney County, Kansas. Soil Science Society American Journal, 65, 169–177.
59- Xu, M., Watanachaturaporn, P., & Varshney, P.K. (2005) Decision treeregression for soft classification of remote sensing data. Remote SensingEnvironment, 97, 322-36.
60- Zhang, J., & Foody, G.M. (1998).A fuzzy classification of sub-urban land cover from remotely sensed imagery. International Journal of Remote Sensing, 19, 2721–38.
61- Zhang, Y.X., Li, X.B., & Chen, Y.H. (2003). Overview of field and multi-scale remote sensing measurement approaches to grassland vegetation coverage. Advanced Earth science, 18, 85-93.
