利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像，对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异，以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的，但类型的边界、每一类型斑块的形状和数量均产生较大的差异；经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化，影像所反映的景观结构发生了较大的变化。其中，各覆盖类型的分维数表现出最大差异，表征着空间分辨率的变化对斑块复杂程度的影响最大。

在遥感（RS）、地理信息系统（GIS）、全球定位系统（GPS）-3S支持下，利用连续时间序列的1km×1km分瓣率的NOAA-AVHRR的归一化植被指数（NDVI）数字影像，通过主分理分析和非监督分类，对中国植被进行宏观分类。在多种基础图件的支持下，结合NDVI年内季节变化规律，分出35个植被类型，9个植被型组。通过重建不同植被类型NDVI特征值10年的时间变化序列，划分出4个内部各类型NDVI变化过程具有相对一致性的区域（二级区域），明显的呈现从东南到西北的带状分布。再逐级归并，产生两个变化的一级区域。区域的界线基本与我国三大自然区域的东部季风区西北部的界线相吻合。进而从NDVI动态变化的角度进行了区划，包括2个一级区域，9个二级区域，进一步阐明了中国植被NDVI动态变化的区域差异。

Land use change process is crucial to region ecological security [1], and it is the main reason that produces ecological disaster. It becomes important factor of maintaining and altering watershed ecological security. In this paper, we simulated soil erosion under the land use pattern in 2000, meanwhile, considering proper vegetation coverage of different vegetation types which is based on remotely sensed monitoring in Huangfuchuan watershed of the Yellow River, we presented optimized land use pattern aimed at soil erosion reduction and vegetation cover enhancement ultimately.

气候变化对陆地生态系统的影响及其反馈是全球变化研究的焦点之一。利用气候变量实现对遥感植被指数所表示的植被绿度信息的模拟，可以尝试作为表达生物圈过去和未来状态的一种途径。利用1961-2000年的气温、降和1983-1999年的NOAA/AVHRR资料，分析了中国北方地带性植被类型荒漠草原植被分布区的短尺度气候的年际和季节变化，及其对植被的影响。结果表明，过去40年中该区域年际气候变化表现为增温和降水波动。年NDVI的最大值（NDVImax）可以出现的时间略有提前。综合分析NDVI、植被盖度、NPP、区域蒸散量、土壤含水量及其气候的年际变化，表明增温加剧了土壤干旱化，降水和土壤含水量仍是制约本区植被生长的根本原因。

着重探讨了建立中国东北样带（Northeast China transect，NECT）土地覆盖特征数据集的原理、方法及其在全 球变化研究方面的重要应用。NECT土地覆盖特征数据集是以多时相的1km分辨率的NOAA/AVHRR归一化植被指数NDVI （Normalized difference vegetation index）数字影像为基础，同时采用高程、气候、土壤、植被、土地利用、土地资源、生态区域、行政边界、经济、社会等多源数据作为数据源，并经过标准化处理（如数字化、空间插值、几何配准、投影转换）集成而成。在土地覆盖特征数据集的主要应用方面，如：1）利用多时相、1km分辨率的NOAA/ AVHRR影像完成了中国东北样带土地覆盖分类图。一级分类系统包括森林、草原、荒漠和沙地、灌丛、农田、混合覆盖类型、城镇和水体等8类，二级分类体系包括12类。经过地面采样进行精度检验，分类精度达到81.61%。2）对主要植被类型的植物生长季变化进行的研究。利用多时相的遥感影像构造了能够反映植被年际、季节生长变化的遥感植被指数ND-VImax、NDVI变幅xam以及NDVI的标准偏差x’s 等，分析这3个参数1983～1999年的17年中的变化情况。该数据集的建立是研究该样带土地覆盖特征及其变化规律的基础，对基于样带的全球变化研究有重要的意义。

Asone of the important vegetation parameters, vegetation fractional coverage (VFC) is more difficult to measure accurately among a good many parameters of plant communities. The temperate typical steppe in the north of China was chosen for investigation in the present study and a digital camera was used to measure herb community coverage in the field, adopting methods of ocular estimation, gridding measurement, visual interpretation, supervised classification, and information extraction of colorspatial transformation to calculate the VFC of images captured by the digital camera. In addition VFC calculated by various methods was analyzed and compared VFC, enabling us to propose an effective method for measuring VFC using a digital camera. The results of the present study indicate that: (i) as two common useful and effective methods of measuring VFC with a digital camera, not only does the error of estimated values of visual estimation and supervised classification vary considerably, but the degree of automatization is very low and depends, to a great extent, on the manipulator; (ii) although the method of visual interpretation may assure the precision of the calculated VFC and enable the precision of results obtained using other methods to be determined, as far as large quantities of data are concerned, this method has the disadvantages of wasting time and energy, and the applications of this method are limited; (iii) the precision and stability of VFC calculated using the grid and node method are superior to those of visual estimation and supervised classification and inferior to those of visual interpretation, but, as for visual interpretation and supervised classification, gridding measurements are difficult to apply in practice because they are not time efficient; and (iv) in terms of the precision of calculation of the VFC, an information-extracting model based on an intensity, hue, saturation (IHS) color space-multi-component series segmentation strategy is superior to methods of ocular estimation, gridding measurement, and supervised classification. In terms of practical efficiency, the information-extracting model is superior to visual interpretation, supervised classification, and gridding measurement. It has been proven that estimating the VFC of the north temperate typical steppe using this model is feasible. This is very fundamental research work in grassland ecology.

One of the study objectives of global change is land use/cover change (LUCC) by using multiscale remotely sensed data on global and regional scale. In this paper, field sample, digital camera, Landsat-ETM+ (ETM+, Enhanced Thematic Mapper) image and the National Oceanic and Atmospheric Administration/the advanced very high resolution radiometer (NOAA/AVHRR) image were integrated to detect, simulate and analyze the vegetation fractional coverage of typical steppe in northern China. The results show: (1) Vegetation fractional coverage measured by digital camera is more precise than results measured by other methods. It can be used to validate other measuring results. (2) Vegetation fractional coverage measured by 1 m2 field sample change fluctuantly for different observers and for different sample areas. In this experiment, the coverage is generally high compared with the result measured by digital camera, and the average absolute error is 9.92%, but two groups measure results, correlation coefficient r2=0.89. (3) Three kinds of methods using remotely sensed data were adopted to simulate the vegetation fractional coverage. Average absolute errors of the vegetation fractional coverage, measured by ETM + and NOAA, are respectively 7.03% and 7.83% compared with the result measured by digital camera. When NOAA pixel was decomposed by ETM+ pixels after geometrical registry, the average absolute errors measured by this method is 5.68% compared with the digital camera result. Correction coefficients of three results with digital camera result r2 are respectively 0.78, 0.61and 0.76. (4) The result of statistic model established by NOAA-NDVI (NDVI, Normalized Difference Vegetation Index) and the vegetation fractional coverage measured by digital camera show lower precision (r2=0.65) than the result of statistic model established by ETM+-NDVI and digital camera coverage then converted to NOAA image (r2=0.80). Pixel decomposability method improves the precision of measuring the vegetation fractional coverage on a large scale. This is a significant practice on scaling by using remotely sensed data. Integrated application of multi-scale remotely sensed data in earth observation will be an important approach to promoting measuring precision of ecological parameters.