Landcover Mapping Using Lidar Data and Aerial Image and Soil Fertility Degradation Assessment for Rice Production Area in Quezon, Nueva Ecija, Philippines
Land-cover maps were important for many scientific, ecological and land management purposes and during the last decades, rapid decrease of soil fertility was observed to be due to land use practices such as rice cultivation. High-precision land-cover maps are not yet available in the area which is important in an economy management. To assure accurate mapping of land cover to provide information, remote sensing is a very suitable tool to carry out this task and automatic land use and cover detection. The study did not only provide high precision land cover maps but it also provides estimates of rice production area that had undergone chemical degradation due to fertility decline. Land-cover were delineated and classified into pre-defined classes to achieve proper detection features. After generation of Land-cover map, of high intensity of rice cultivation, soil fertility degradation assessment in rice production area due to fertility decline was created to assess the impact of soils used in agricultural production. Using Simple spatial analysis functions and ArcGIS, the Land-cover map of Municipality of Quezon in Nueva Ecija, Philippines was overlaid to the fertility decline maps from Land Degradation Assessment Philippines- Bureau of Soils and Water Management (LADA-Philippines-BSWM) to determine the area of rice crops that were most likely where nitrogen, phosphorus, zinc and sulfur deficiencies were induced by high dosage of urea and imbalance N:P fertilization. The result found out that 80.00 % of fallow and 99.81% of rice production area has high soil fertility decline.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131886Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 Selcuk, R., Nisanci, R., Uzun, B., Yalcin, A., Inan, H., Yomralioglu, T., “Monitoring land-use changes by GIS and remote sensing techniques: case of Trabzon” (2003).
 Choi J, and Lo, C.P., “A hybrid approach to urban land use/cover mapping using Landsat 7 enhanced thematic mapper plus (ETM+) images, Inter. J. Rem. Sen., 25 (14), 2687–2700 (2004).
 Phil-LiDAR 2.,“Object-Based Image Classification using KNN and SVM, Nationwide Detailed Resources Assessment using LiDAR, University of the Philippines, Quezon City (2014).
 Eswaran H, Lal R, Reich PF., “Land degradation: An overview”. In: Bridges, EM, Hannam ID, Oldeman LR, Pening de Vries FWT, Scherr SJ, Sompatpanit S, eds. Responses to Land Degradation. Proc. 2nd. International Conference on Land Degradation and Desertification, Khon Kaen, Thailand. Oxford Press, New Delhi, India (2001).
 Snel M, Bot A., “Draft Paper: Suggested indicators for land degradation assessment of drylands”. FAO, Rome (2003).
 Gobabeb, T. S. K. A, “Review of Land Degradation Assessment Methods” Land Restoration Training Programme Keldnaholt, 112 Reykjavík, Iceland (2008) http://www.unulrt.is/static/fellows/document/taimi-1-.pdf (November 28. 2016)
 Kapalanga T. S.,” A Review of Land Degradation Assessment Methods” Land Restoration Training Programme Keldnaholt, 112 Reykjavík, Iceland, Final project (2008) http://www.unulrt.is/static/fellows/document/taimi-1-.pdf (December 11, 2016).
 FAO., “Agro-Ecological Zoning and GIS application in Asia with special emphasis on land degradation assessment in drylands (LADA)”. Proceedings of a Regional Workshop, Bangkok, Thailand10–14 November (2003). ftp://ftp.fao.org/agl/agll/docs/misc38e.pdf, (December 5, 2016).
 Hontoria C., Rodríguez-Murillo J. C., and Saa A., “Relationships between soil organic carbon and site characteristics in peninsular Spain,” Soil Science Society of America Journal, 63(3), 614–621(1999).
 Rong L., Li S. J., and Li X. W., “Carbon dynamics of fine root (grass root) decomposition and active soil organic carbon in various models of land use conversion from agricultural lands into forest lands,” Acta Ecologica Sinica, 31(1), 137–144 (2011).
 Alberto R. T., S. C. Serrano, G. B. Damian, E. E. Camaso, A. B. Celestino, P.J. C. Hernando, M. F. Isip, K. M. Orge, M.J. C. Quinto, and R. C. Tagaca., “Object based agricultural land cover classification map of shadowed areas from aerial image and lidar data using support vector machine”. XXIII International Society for Photogrammetry and Remote Sensing, Prague, Czech Republic (2016). http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/45/2016/doi:10.5194/isprs-annals-III-7-45-2016 (November 25, 2016).
 Dragut, L., Csillik, O., and Tiede, D., “Automated parameterization for multi-image segmentation on multiple layers.” ISPRS Journal of photogrammetry and Remote Sensing, 88, 119-127 (2014).
 Definiens, “Definiens Developer Reference Guide”, (2010).
 Lu D., Q Weng., “A survey of image classification methods and techniques for improving classification performance”, International journal of remote sensing, 28(5), 823-873 (2007).
 Huang, C., DAVIS, L., and Townshend, R., “An assessment of support vector machines for land cover classification.” Int. J. Remote Sensing, 23(4), 725–749 (2002).
 Shaban M A and O Dikshit, “Improvement of classification in urban areas by the use of textural features: the case study of Lucknow city, Uttar Pradesh”. International Journal of remote sensing, 22(4), 565-593 (2001).
 Paola, J. D., and Schowengerdt, R. A.,“A review and analysis of back propagation neural networks for classification of remotely sensed multi-spectral imagery”. International Journal of Remote Sensing, 16, 3033–3058 (1995).
 Hixson, M., Scholz, D., Fuhs, N., and Akiyama, T., “Evaluation of several schemes for classification of remotely sensed data”. Photogrammetric Engineering and Remote Sensing, 46, 1547–1553 (1980).
 Lu, D., Hetrick, S., and Moran, E., “Land Cover Classification in a Complex Urban-Rural Landscape with QuickBird Imagery.” Photogrammetric Engineering & Remote Sensing, 76(10), 1159-1168 (2010).
 Concepcion, R., “Precision soil and plant nutrition management. Issues in the Management of Agricultural Resources”. FFTC Book (2001.http://www.fao.org/docrep/010/ag120e/AG120E16.htm (December 5, 2016).