Abstract
While remote sensing and geographic information systems have been used successfully to classify forest health using recent image, applying this process to older images is problematic because contemporaneous field data are not available to measure the accuracy of the classification of historical images. Data ranges of normalized difference water index(NDWI) were established for each Robinia Pseudoacacia health class using a contemporary image and field data by sequential cluster analysis. These ranges were used to separate Landsat Thematic Mapper (TM) images acquired from 1999 to 2007 into a series of health-class maps, By applying cross-tabulation procedures to pairs of classified images, we can see how the Robinia Pseudoacacia health class of each pixel in the images of the study area had changed over time. The resulting maps provide a look back at forest conditions of the past and can be used to identify areas of special interest. Further analysis carried out between environmental factors(soil salt, soil texture, soil type, DEM, groundwater depth and groundwater salinity)and the Robinia Pseudoacacia health led to identify the likely causes of these negative trends.
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References
Wang, S.M.: Quicken steps in greening in the Yellow River Delta. Land Greening, Beijing 3, 18 (2002)
Schelhaas, M.J., Nabuurs, G.J., Schuck, A.: Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biol. 9, 1620–1633 (2003)
Rull, V.: Sustainability, capitalism and evolution-nature conservation is not a matter of maintaining human development and welfare in a healthy environment. EMBO Rep. 12, 103–106 (2011)
Coppin, P., Lambin, E., Jonckheere, I., Nackaerts, K., Muys, B.: Digital change detection methods in ecosystem monitoring: a review. Int. J. Remote Sens. 25, 1565–1596 (2004)
Kennedy, R.E., Cohen, W.B., et al.: Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens. Environ. 110(3), 370–386 (2007)
Cohen, W.B., Goward, S.N.: Landsat’s role in ecological applications of remote sensing. Bioscience 54, 535–545 (2004)
Kennedy, R.E., Yang, Z., et al.: Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. landtrendr — temporal segmentation algorithms. Remote Sens. Environ. 114(12), 2897–2910 (2010)
Hall, R.J., Fernandes, R.A., Hogg, E.H., Brandt, J.P., Buston, C., Case, B.S., Leblanc, S.G.: Relating aspen defoliation to changes in leaf area derived from field and satellite remote sensing data. Can. J. Remote Sens. 29, 299–313 (2003)
Liu, G.H., Drost, H.J.: Atlas of the Yellow River Delta. The publishing House of Surveying and Mapping, Beijing, pp.23 (1997)
Yao, L., Liu, G.H., et al.: Remote sensing monitoring the health of artificial Robinia Pseudoacacia Forest. Geomatics Inf. Sci. Wuhan Univ. 35(7), 863–866 (2010)
LeMarie, M., van der Zaag, P., et al.: The use of remote sensing for monitoring environmental indicators: the case of the incomati estuary, Mozambique. Physics Chemistry Earth, Parts A/B/C 31(15–16), 857–863 (2006)
Reich, R.M.: Geils, B.W.: Review of spatial analysis techniques. In: Liebhold, A.M.: Barrett, H.R. (eds.) Proceedings: Spatial Analysis and Forest Pest Management, Mountain Lakes, Virginia, USA, 27–30 April 1992, pp. 142–149 USDA Forest Service General Technical Report NE-175 (1992)
Legendre, P.: Spatial autocorrelation: trouble or new paradigm? Ecology 74(6), 1659–1673 (1993)
Cliff, A.D., Ord, J.K.: Spatial autocorrelation. Pion, London Congalton RG a review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35–46 (1991)
Acknowledgment
This research work was jointly supported by a grant from the National Natural Science Foundation of China (Project No. 40771172) and a grant from National Key Technology R&D Program of China (Project No. 2008 BAC34B06) and a grant from Innovative Program of The Chinese Academy of Sciences (Project No. kzcx2-yw-308 and 066U03003SZ) and a grant from Special Project of Water Body Contamination Control and Curement of China (Project No. 2008ZX07526-007).
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Yao, L., Liu, Q., Liu, G. (2016). Monitoring Health of Artificial Robinia Pseudoacacia Changes in the Yellow River Delta by the Analysis of Multiyear NDWI Data. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2015 2015. Communications in Computer and Information Science, vol 569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49155-3_32
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