Wetland ecosystem and resources in Beijing have huge service function, and are very important in maintaining balance of ecosystem and urban ecological security. Because of change of globle climate change and population increaseing of population, area of Beijing wetlands is decreasing sharply, and water is heavily polluted heavily. Service function of wetland ecosystem is degrading by a large margin. Based on remote sensing survey and field investigations, this study has established the classification system, the index system framework and methods of evaluating the serving functions and value of wetland ecosystem in Beijing city with an integrated use of comprehensively applying resource economics and ecological economics methods and then evaluated, tThe service value of wetland ecosystem of Beijing in 2009 was evaluated. In the evaluation, the serving functions of wetland ecosystem were divided into economic service function value, social service funcion value and the ecological service function value. The total wetland area of Beijing in 2009 was 49921.48 hm2 and its total ecosystem service value was quantitatively estimated at 213.7.31 billion Yuan （Chinese RMB）, with. The 3.399 billion RMB of economic service function value, 134.094 billion RMB of social service funcion value and 76.238 billion RMB of ecological service function value. They respectively was 33.99 billion Yuan contributed 1.59%, 62.74% and 35.67% toof the total value., among which, fishery value was 10.47 billion Yuan, water supply value was 20.20 billion Yuan and pumped storage power value was 3.32 billion Yuan. The social service funcion value was 1340.94 billion Yuan contributed 62.74% of the total value, among which, culture and travel value was 297.76 billion Yuan, landscape added value was 1042.92 billion Yuan and scientific research and education value was 0.26 billion Yuan. The ecological service function value was 762.38 billion Yuan contributed 35.67%, among which, climate regulation value was 457.73 billion Yuan, water resources conservation value was 91.12 billion Yuan, environmental purification value was 10.06 billion Yuan, biodiversity value was 78.30 billion Yuan, flood control and disaster reduction value was 125.15 billion Yuan, water and soil conservation value was 0.02 billion Yuan. According to the amount of these servicethese services value, landscape added value, climate regulation value and culture and travel value took occupied the main positions in the total value of wetland ecosystem service of Beijing in 2009. These service value results, to some extent, objectively reflected the position and function of wetland ecosystem in Beijing in which served economic development and people＇s living in the aspects of production, life and ecology city constructiony to some extent and. It canould provide scientific basis for the protection and recovery of wetland ecosystem in Beijing. However, some deficiencies and uncertainties still existed in this study, which should be improved in the future studyies.

Crop acreage information is very important for accurate estimating crop production,the policy-making of agriculture and ensuring national food security.This paper summarized the spatial sampling methods on crop area estimation,and reviewed the evolvement of spatial sampling survey on crop area estimation of USA,European and China.Finally,It analyzed the existing problems and discussed the develop tendency,and hoped to provide guidance on the research and application of crop area estimation.

In this paper,a change intensity indicator of land cover based on cross correlogram spectral matching(CCSM) technique was employed to generate the change intensity image of the cropping vegetation cover area in North China between two TM images in different periods.It was first considered that the change intensity of image pixel of the two-order neighbor in the change intensity image obeyed the hidden markov random field model,and then the vegetation cover change area was extracted from the change intensity image using maximum a posteriori estimation of markov random field(MRF-MAP) model.The experiment has proved that the proposed method could precisely extract vegetation cover change and inhibit effectively the same object with different spectra due to exogenous noises in the cropping vegetation cover area.However,this method seems to perform unsatisfactorily over the water area.

Spatial Framework is the important foundation in building digital earth,digital city and digital industry.The Construction of Spatial Framework in Statistics and Remote Sensing(SFSR) provides spatial location benchmark and carrier for the transformation from traditional catalog statistics to late-model spatial statistics.It also provides a multi-scale,multi-resolution,multi-source data spatial framework database and achieves the conformity and communion of statistical information and resource on the basis of spatial location.The aim,task and content of the SFSR Construction were presented in this paper first.Then,the SFSR data system,criterion system,technique system and organization system were introduced in detail.The SFSR application example in crop plant area monitoring and yield estimating system was exhibited finally.

Time-series data of normalized difference vegetation index (NDVI), derived from satellite sensors, can be used to support land-cover change detection and phenological interpretations, but further analysis and applications are hindered by residual noise in the data. As an alternative to a number of existing algorithms developed to compensate for such noise, we develop a simple but computationally efficient method (which we call the changing-weight filter method) to reconstruct a high-quality NDVI time series. The new algorithm consists of two major procedures: (1) detecting the local maximum/minimum points in a growth cycle along an NDVI temporal profile based on a mathematical morphology algorithm and a rule-based decision process and (2) filtering an NDVI time series with a three-point changing-weight filter. This method is tested at 470 test points for 55 vegetation types and a test region in China using a 250-m 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product. Comparing our results to those of three other well-known methods-asymmetric Gaussian function fitting, double logistic function fitting, and Savitzky-Golay filtering-the new method has many of the advantages of existing methods, while in some cases, the changing-weight filter method more effectively preserves the curve shape as well as the timing and the amplitude of the local maxima/minima in the NDVI time series for a broad range of phenologies. Moreover, the response of the filtering algorithm is relatively insensitive to the exact values of its design parameters, making the new method more flexible and effective in adjusting to fit a variety of classes of NDVI time series.

An M 7.3 strong earthquake occurred in Port-au-Prince,Haiti on Jan.12,2010 has destroyed a large number of buildings.From the post-earthquake very high resolution remote sensing image,we can find there are a lot of rubbles around collapsed and partly damaged buildings.Therefore,we take these rubbles as seismic-damage characteristics of the collapsed buildings and use the following three methods—pixel-based method,object-oriented method and object-oriented method combined with geo-statistical texture to extract rubbles automatically.Finally,we assess the accuracy of the three classifications of the remotely-sensed data through the three methods.It turns out that the accuracy of pixel-based method is less than that of object-oriented method.The overall Kappa coefficient and user's precision of rubbles have improved remarkably when adding texture feature.However,there is no obvious improvement of the producer precision of the rubbles.

不同尺度遥感数据源的选取将直接影响到作物种植面积测量的精度,ç ”ç©¶å°ºåº¦å› å­åœ¨å†œä½œç‰©é¢ç§¯é¥æ„Ÿæµ‹é‡ä¸­çš„ä½œç”¨,å°ºåº¦ä¸Žé¢ç§¯æµ‹é‡ç²¾åº¦çš„å®šæ€§å’Œå®šé‡å ³ç³»æ˜¯éžå¸¸å¿ è¦çš„ã€‚ä¸ºæ­¤,本文利用SPOT5卫星数据,以尺度变化对农作物种植面积遥感测量精度影响的分析为主线,è¿ç”¨ç©ºé—´ç»Ÿè®¡åˆ†æžæ–¹æ³•å’Œå¤šç§ç²¾åº¦è¯„ä»·æŒ‡æ ‡,从不同空间分辨率、不同空间范围、不同农作物百分比等角度系统分析了农作物种植面积遥感测量中的尺度效应问题。为基于多尺度遥感数据复合的农作物种植面积测量业务化运行中的数据选择和精度保证问题提供理论与实验基础。

Maximum light use efficiency (εmax) is a key parameter for the estimation of net primary productivity (NPP) derived from remote sensing data. There are still many divergences about its value for each vegetation type. The εmax for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The vegetation classification accuracy is introduced to the process. The sensitivity analysis of εmax to vegetation classification accuracy is also conducted. The results show that the simulated values of εmax are greater than the value used in CASA model, and less than the values simulated with BIOME-BGC model. This is consistent with some other studies. The relative error of εmax resulting from classification accuracy is −5.5%–8.0%. This indicates that the simulated values of εmax are reliable and stable.

植被最大光利用率是净初级生产力(NPP)遥感估算的一个关键参数,对它的大小一直存在分歧.利用NOAA/AVHRR遥感数据、气象数据和中国NPP实测资料,根据NPP遥感估算的建模思路,采用改进的最小二乘法对中国典型植被的最大光利用率进行了系统的模拟,并针对不同植被分类精度可能带来的误差对最大光利用率进行了敏感性分析.结果表明: 文中模拟得到的中国典型植被最大光利用率介于光能利用率模型(CASA模型)和生理生态过程模型(BIOME-BGC)的模拟结果之间,与前人研究结果比较一致; 由植被分类精度所带来的最大相对误差仅为-5.5%~8.0%,说明了本文模拟结果具有一定的可靠性和稳定性.

With the measurement of net primary productivity and vegetation coverage fraction based on remote sensing data,the terrestrial ecosystem service value of China in 2000 was quantitatively estimated as 9.17 x 10(12) yuan (RMB). The spatial distribution of the ecological service value showed a decreasing trend from southeast China to northwest China, which was consistent with the regional distribution of vegetation types. The service value varied with different vegetations, e. g., forests had the highest service value of 18 789 yuan x hm(-2), accounting for 40.80% of the total terrestrial ecosystem service value, and bushes and farmlands had a higher service value of 13 789 yuan x hm(-2) and 13054 yuan x hm(-2), which was 10.79% and 24.23% of total value, respectively. The service value was also varied with different ecosystem functions, i.e., gas regulation contributed the highest value of 45.16% to the total service value, and the contribution of soil conservation and water conservation was 28.83% and 14.44%, respectively. The integrated approach coupling ecology and remote sensing data provided a new method to measure the ecological service value, which could estimate the value objectively and spatial-explicitly. However, some uncertainties still existed in this approach, which should be improved in the future studies.

An estimation model for ecological service value was presented. With the measurement of the net primary productivity and vegetation coverage fraction based on remote sensing data, the terrestrial ecosystem service value of Chinas in 2000 was quantitatively estimated at 9.17 X 10(12) yuan (RMB). The spatial distribution of the ecological service value showed a decreasing trend from south east Chinas to north west China. It was consistent with the regional distribution of vegetation types. The service value varied with the different vegetation. Forests had the highest service value of 18789 yuan center dot hm(-2). It was 40.80% of the total terrestrial ecosystem service value. Bushes and farmlands had a higher service value of 13789 yuan center dot hm(-2) and 13054 yuan center dot hm(-2), which was 10.79% and 24.23% of the total value respectively. The service value was also varied with different ecosystem functions. Gas regulation contributed the highest value of 45.16% to the total service value. The contribution of soil conservation and water conservation was 28.83% and 14.44% respectively. The integrated approach, coupling ecology and remote sensing data, provided a new method to measure ecological service value. It can he estimated objectively and spatial-explicitly. However, some uncertainties were still existed in this approach, and should be improved in the future studies.

Based on the remote sensed and weather data, the paper tried to discuss the temporal and spatial changes of vegetation light utilization efficiency (LUE) in the eastern Asia area (70 degrees E-170 degrees E, 10 degrees N-70 degrees N). The results showed that: 1) NDVI of the vegetations in the study area during January and December in 1999 mainly reached the maximum on. July or August, and the fittest temperature range for vegetation growth was 13 degrees C-29 degrees C (about 69.25% to the whole temperature grades); 2) spatial distribution of interannual average LUE which ranged from 0.166 gC.MJ(-1) to 0.714gC.MJ(-1) had the characters of belt, namely decreasing from the south to the north, and from the east to the west; 3) climate changes had obviously effect on LUE, and LUE in the winter half year (January-April, November and December) was about 79.5% of that in the summer half year (May-October); 4) To most vegetations, water factor affected the LUE more possible than temperature factor in a year.