| Urban green land has notable effect on urban ecological environment, improvement of urban climate, as well as other aspects. With the development of urbanization, urban green land area is consistently increasing. The green land coverage ratio in the built up area has got to39.5%, in8provinces or cities, in2012, throughout the county. A research shows that more than80%of the CO2were from the urban, and the density of CO2in built up areas is times larger than the areas outside the urban, which is called proximity effect. This kind of effect make the urban green land has a greater efficiency to catch and fix the carbon. As a result, urban green land becomes an important carbon sink of the urban ecosystem, playing an important role in the global carbon ecosystem. Nowadays, there are lots of achievements about extracting information and the change of the green land, while there isn’t enough study about green land biomass in large scale and according to chronological order, hardly show the biomass variation course and character in chronological order. Thus, this kind of research is significantly meaningful.The study makes the mode estimating the urban green land biomass in Hang Zhou using the independent variable factors, and’analysis the biomass change in time and space.The conclusions can be listed as follows:1. This paper firstly masks water, and use PPI to select pure pixel, then select three kinds of end members as green land, high reflection land features, and low reflection land features, finally extract green land ratio using liner mode to decompose the pixel, in full constraint.2. The change of urban green coverage rate. The area of urban green coverage decreased from1351.77km2to1193.12km2, as the average annual loss was1.430%, according to the rapidly expansion of urban and lots of no-building area transformed to building area, between the year1996and2000, while the green covered area decreased by79.13km2, from year2000to2005, as the average annual loss was0.516%. From2005to2010, with the establishment of numerous gardens as well as the green buffer and affiliated green space, the area of green coverage increased by85.6km2, and the average annual growth rate was0.558%. 3. Forest biomass estimates by remote sensing model. Through study variety models base on stepwise regression, forward regression, backward regression, partial-least-square regression, it can be concluded that partial-least-square regression method model is the best to match the predictive result, as the fitting precision is81.51%, and the accuracy of prediction is80.27%. The second best method is backward regression, the fitting precision of the model based on it is72.55%, and the accuracy of prediction is71.34%. The following method is stepwise regression, the model’s fitting precision is70.46%, and the accuracy of prediction is69.25%. The worst method is forward regression model whose fitting accuracy is70.31%, and the accuracy of prediction is69.07%. This study uses partial-least-square regression model as the quantitative remote sensing retrieval for the urban green land biomass in Hang Zhou.4. Spatial and temporal variations of green land biomass were analyzed. Comparing the green land biomass in different periods, it showed the highest average green land biomass was48.38t/hm2in1996, and the total biomass was6539863t, followed by the year2000, which has the second large average green land biomass,42.04t/hm2, and a total of5015876t. Both the year2005and2010have the similar average green land biomass and total green land biomass, as33.72t/hm2and33.21t/hm2respectively in average,3756374.28t and3983838.39t respectively in total. On the space station, West Lake District has the largest green biomass. Though the analysis of green biomass in different profiles of different directions, it showed the size of green biomass was increasing in remaining area, while in the regions like highway, the green biomass was reducing remarkably. |
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