Integrating landsat TM imagery and See5 decision-tree software for identifying croplands: A case study in Shunyi District, Beijing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6987 LNCS, Issue PART 1, 2011, Pages 251-258

  Zhao, Jinling ^a,b   Zhang, Dongyan ^a   Wang, Dacheng ^a   Huang, Wenjiang ^a  

^a INSTITUTE OF PLANT AND ENVIRONMENT PROTECTION   (China)

^b INSTITUTE OF REMOTE SENSING APPLICATIONS   (China)

Author keywords

Band ratios; cropland identification; Landsat TM; See5 decision tree classification; texture analysis

Indexed keywords

ACCURACY EVALUATION; BAND RATIOS; CLASSIFICATION TOOL; DECISION TREE CLASSIFICATION; DIGITAL ELEVATION MODEL; GROWTH PERIOD; INTEGRAL METHOD; LANDSAT TM; MAPPING TOOLS; SAMPLE POINT; SHUNYI DISTRICT; STATISTICAL DATAS; STUDY AREAS; TEXTURE ANALYSIS; TOPOGRAPHIC CONDITIONS; VEGETATION TYPE;

CLIMATE MODELS; DECISION TREES; INFORMATION SYSTEMS; SURVEYING; TEXTURES; USER INTERFACES; WORLD WIDE WEB;

PLANT EXTRACTS;

EID: 80053405249     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23971-7_33     Document Type: Conference Paper

References (17)

1. Hao, Y., Lal, R., Owens, L.B., Izaurralde, R.C., Post, W.M., Hothem, D.L.: Effect of Cropland Management and Slope Position on Soil Organic Carbon Pool at the North Appalachian Experimental Watersheds. Soil Till. Res. 68, 133-142 (2002)
2. Abdel-Rahman, E.M., Ahmed, F.B.: The Application of Remote Sensing Techniques to Sugarcane (Saccharum spp. hybrid) Production: A Review of the Literature. Int. J. Remote Sens. 23, 3753-3767 (2008)
3. Rao, P.V.K., Rao, V.V., Venkataratnam, L.: Remote Sensing: A Technology for Assessment of Sugarcane Crop Acreage and Yield. Sugar Tech. 4, 97-101 (2002)
4. Rudorff, B.F.T., Batista, G.T.: Yield Estimation of Sugarcane based on Agrometeorological Spectral Models. Remote Sens. Environ. 33, 183-192 (1990)
5. Carlson, R.E., Aspiazu, C.: Cropland Acreage Estimates from Temporal, Multispectral ERTS-1 Data. Remote Sens. Environ. 4, 237-243 (1975-1976)
6. Xiao, X.M., Liu, J.Y., Zhuang, D.F., Frolking, S., Boles, S., Xu, B., Liu, M.L., Salas, W., Moore III, B., Li, C.S.: Uncertainties in Estimates of Cropland Area in China: a Comparison between an AVHRR-derived Dataset and a Landsat TM-derived Dataset. Global Planet. Change 37, 297-306 (2003)
7. Lobell, D.B., Asner, G.P.: Cropland Distributions from Temporal Unmixing of MODIS Data. Remote Sens. Environ. 93, 412-422 (2004)
8. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
9. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W.: Monitoring Vegetation Systems in the Great Plains with ERTS. In: Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, pp. 301-317. NASA, Greenbelt (1974)
10. Gong, P., Pu, R., Biging, G.S., Larrieu, M.R.: Estimation of Forest Leaf Area Index using Vegetation Indices Derived from Hyperion Hyperspectral Data. IEEE Trans. Geosci. and Remote Sens. 41, 1355-1362 (2003)
11. Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N.: Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS. Remote Sens. Environ. 58, 289-298 (1996)
12. Tucker, C.J.: Red and Photographic Infrared linear Combinations for Monitoring Vegetation. Remote Sens. Environ. 8, 127-150 (1979)
13. Huete, A.R.: A Soil Adjusted Vegetation Index (SAVI). Remote Sens. Environ. 25, 295-309 (1988)
14. Roujean, J.L., Breon, F.M.: Estimating PAR Absorbed by Vegetation from Bidirectional Reflectance Measurements. Remote Sens. Environ. 51, 375-384 (1995)
15. Jordan, C.F.: Derivation of Leaf Area Index from Quality Measurements of Light on the Forest Floor. Ecology 50, 663-666 (1969)
16. Crippen, R.E.: Calculating the Vegetation Index Faster. Remote Sens. Environ. 34, 71-73 (1990)
17. Lucas, I.F.J., Frans, J.M., Wel, V.D.: Accuracy Assessment of Satellite Derived Landcover Data: A review. Photogramm. Eng. Rem. Sens. 60, 410-432 (1994)