Combination of artificial neural network with multispectral remote sensing data as applied in site quality evaluation in Inner Mongolia

Croatian Journal of Forest Engineering

Volumn 36, Issue 2, 2015, Pages 307-319

  Yan, Fei ^a   Gong, Yinxi ^b   Feng, Zhongke ^a  

^a BEIJING FORESTRY UNIVERSITY   (China)

^b GIS Research and Development Center The First Institute of photogrammetry and Remote Sensing SBSM   (China)

Author keywords

Multispectral remote sensing; Neural network; Site quality evaluation

Indexed keywords

BACKPROPAGATION; FORESTRY; QUALITY CONTROL; REMOTE SENSING; SURVEYS;

GROUND SURFACES; INNER MONGOLIA; MODEL-BASED OPC; MULTISPECTRAL REMOTE SENSING; NEURAL-NETWORKS; QUALITY EVALUATION; REMOTE SENSING DATA; SITE QUALITY; SITE QUALITY EVALUATION; SURVEY DATA;

NEURAL NETWORK MODELS;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CONIFEROUS TREE; GROUND-BASED MEASUREMENT; OPTIMIZATION; QUALITATIVE ANALYSIS; REMOTE SENSING; SATELLITE DATA; SURVEY METHOD;

CHINA; NEI MONGGOL;

LARIX;

EID: 84943176424     PISSN: 18455719     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (23)

1. Bruce, F., 1981: The use of overlays in site quality mapping. Canadian Journal of Forest Research 11(2): 362-370.
2. Carmean, W. H., 1978: Site index curves for northern hardwood sinnorthern Wisconsin and upper Michigan. New York, Department of Agriculture 3-18.
3. Curt, T., Bouchaud, M., Agrech, G., 2001: Predicting site index of Douglas fir plantations from ecological variables in the Massif Central area of France. Forest ecology and management 149(1): 61-74.
4. Ens, J., Farrell R. E., Bélanger, N., 2013: Effects of edaphic conditions on site quality for Salix purpurea »Hotel« plantations across a large climatic gradient in Canada. New Forests 44(6): 899-918.
5. Farrelly, N., Dhubháin, Á. N., Nieuwenhuisb, M., 2011: Site index of Sitka spruce (Picea sitchensis) in relation to different measures of site quality in Ireland. Canadian Journal of Forest Research 41(2): 265-278.
6. Francl, L. J., Panigrahi, S., 1997: Artificial neural network models of wheat leaf wetness. Original Research Article. Agricultural and Forest Meteorology 88(1-4): 57-65.
7. Guo, Y. R., Wu, B. G., Liu, Y., 2012: Research Progress of Site Quality Evaluation. World Forestry Research 25(5): 47-52.
8. Huang, J. R., Ma, T. X., Wang, Y. M., 2006: Forest site evaluation model studies on the basis of BP Neural Network. Journal of Mountain Agriculture and Biology 25(6): 479-483.
9. He, Z. H., Chen, X. X., Liang, H., 2012: Study of Remote Sensing Monitoring of Karst Basin Water-holding Dynamic Changing Based On Vegetation Indices Taking Guizhou Province as a Case. Territory&Natural Resources Study 4: 48-51.
10. López, G., Rubio, M. A., Martı́nez, M., Batlles, F. J., 2001: Estimation of hourly global photo synthetically active radiation using artificial neural network models Original Research Article. Agricultural and Forest Meteorology 107(4): 279-291.
11. Louwa, J. H., Scholes, M., 2002: Forest site classification and evaluation: a South African perspective. Forest Ecology and Management 171(1-2): 153-168.
12. Luan, Z. P., 2011: Study on growth site conditions of wild Benedict bilberry based on the BP neural network. Shandong Forestry Science and Technology 195(4): 11-16.
13. Ma, M. D., Jiang, H., Liu, S. R., 2006: The preliminary analysis of forest ecosystem site index using remote sensed data. Acta Ecologica Sinica 26(9): 2810-2816.
14. Meng, X. Y., 1996: Forest measurements. The second edition. Beijing: China Forestry Publishing House 99-106.
15. Mutlu, A. C., Boyaci, I. H., Genis, H. E., 2006: Prediction of wheat quality parameters using near infra-red spectroscopy and artificial neural networks. European Food Research and Technology 233(2): 267-274.
16. Niall, F., Áine, N. D., Maarten, N., 2011: Site index of Sitka spruce (Picea sitchensis) in relation to different measures of site quality in Ireland. Canadian Journal of Forest Research 41(2): 265-278.
17. Skovsgaard, J. P., Vanclay, J. K., 2008: Forest site productivity: a review of the evolution of dendrometric concepts for even-aged stands. Forestry 81(1): 13-31.
18. Swenson, J. J., Waring, R. H., Fan, W., 2005: Predicting site index with a physiologically based growth model across Oregon. Canadian Journal of Forest Research 35(7): 1697-1707.
19. Waring, R. H., Milner, K. S., Jolly, W. M., 2006: Assessment of site index and forest growth capacity across the Pacific and Inland Northwest U. S. A. with a MODIS satellite-derived vegetation index. Forest Ecology and Management 228(1-3): 285-291.
20. Wu, J., Peng, D. L., 2011: Advances in researches on hyperspectral remote sensing forestry. Spectroscopy and Spectral Analysis 31(9): 2305-2312.
21. Wu, J., Peng, D. L., 2011: Tree Species information extraction of farmland returned to forests based on improved support vector machine algorithm. Spectroscopy and Spectral Analysis 31(4): 1038-1041.
22. Zhang, W. R., Sheng, W. T., Jiang, Y. X., 1992: Classification of Forest Site System in China. Forest Research 5(3): 251-262.
23. Zhang, X. L., You, X. X., 1998: Application of 3S Technology to Forest Site Type Classification and Site Quality Evaluation in Beijing. Journal of Remote Sensing 2(4): 292-296.