Above-ground biomass of forest has great research and application value in the forest ecological system. There are mainly three types of models for estimating above-ground biomass of forest, i.e., forest measuring method, remote sensing method and integrated method. Remote sensing technique has become an important means for obtaining above-ground biomass of forest at the regional scale. There are mainly four types of remote sensing models, namely empirical, ANN, physical and NPP based models. This paper has analyzed and discussed the present methods for estimating above-ground biomass of forest based on remote sensing as well as their advantages and disadvantages. Finally, this paper points out that the integrated method combining remote sensing technique and forest succession model can be generally used to estimate above-ground biomass of forest at the regional scale in future.

Near surface air temperature is an important environment variable in many earth system models，because it is a key factor in the energy and water exchanges between land surface and atmosphere. Detailed measurements of spatial and temporal variations of near surface air temperature are critical for the effective understanding of climate, hydrology, ecology, agriculture and terrestrial life processes. Traditionally meteorological observation could provide accurate air temperature data at the point scale, but most earth system models need gridded input variables. Satellite remote sensing provides a straightforward and consistent way to observe air temperature at regional and global scales with more spatially detailed information than meteorological data. This paper systematically reviews the air temperature retrieving algorithms for thermal remote sensing data, which include TVX approaches, statistical approaches, neural network approaches and energy balance approaches. The main advantages and limitations of these four methods are also discussed. Finally, the development tendencies of estimating air temperature by remote sensing are pointed out, such as intensive research on thermal radiant transfer model, spatial-temporal scaling of air temperature and improvement of cloud detection.

This paper has dealt with some key problems existent in land cover mapping and analyzed objective adaptability of the classification system，which include the impacts of land cover definition uncertainty on classification effects, the features and adaptability of the classification algorithms, the gap and effects of fine and coarse scale monitoring techniques， the capability of land cover scaling for application, the scaling effects on classification, the procedures，problems and solutions of the classification system of land cover，the classification algorithm and its accuracy assessment, and the factors and solutions of accuracy and errors of the current monitoring systems.

High altitude photographic work such as aerial photogrammetry and UAV photography has very high demands on geometric optical parameters of the CCD camera. As one millimeter or one pixel error of geometric optical parameter tends to cause the error of a few meters or even dozens of meters on the ground, the camera’s high-precision geometric optical parameter calibration is a crucial step for data geometric process after aerial photogrammetry. In this paper, the initial value of inner orientation elements (the main distance and principal point offset) and the exterior orientation elements (three elements in a straight-line and three angles elements) were acquired by DLT method，and then the single-image space resection and multi-image space resection methods were used to get the geometric optical parameters of the camera. It is concluded that the multi-image space resection is superior to the single-image space resection.

 DS (Dempster-Shafer) evidence theory has the capability of combining multisource data，and has been used more and more widely in the remote sensing classification field. However，it is not true that all the data sources can improve target category’s Basic Probability Assignment (BPA) so as to improve the remote sensing classification accuracy. The evaluation of the effect of combined evidence is therefore a key point in the application of DS evidence theory. This paper proposed the evidence combine index (eci) for evaluating the combined evidence. The authors chose band 5 and band 7 of TM image as verification data，applied the eci index to evaluate the combining effect，and used the variation of kappa coefficient before and after evidence combination classification to validate the eci. The results show that the eci index can reflect the effect of evidence combination and thus lay the foundation for evaluating supervised evidential classification quantitatively.

Although composite data present lower atmospheric contamination than raw time series, MODIS NDVI products are still contaminated by clouds, especially when cloud cover lasts longer than the composite period. e.g., in the rainy season. The long-time cloud cover will weaken the application of MODIS NDVI time series data. To remove the effect of these clouds from NDVI data and reconstruct high-quality NDVI data, the authors propose three algorithms for cloud removal, namely SPLINE function, HANTS and Savizky-Golay. The capabilities of the three algorithms in cloud removal was compared with each other in this study, with the MODIS NDVI time series data in Shandong province serving as the test data. The results show that the three algorithms can remove the effect of cloud from NDVI time series data effectively, with each algorithm having its own advantages and disadvantages. For the algorithm of SPLINE function, the result of cloud removal mainly depends on the quality of cloud data and sometimes extreme values will occur;  this algorithm fails to change the values of pixels which have not been contaminated atmospherically. When HANTS and Savizky-Golay algorithms are used, most of the pixels will lose their original values, and the parameters have to be determined after conducting many experiments because there is no objective rule to set them.

 This paper proposes an algorithm based on the variable step estimation of fractal dimension for spectral response curve of hyperspectral image. The algorithm carries out sampling on the spectral response curve at different sampling rates, computes the differential value between two consecutive points, and then counts the total sum of differential values about these sampling points. Finally, the fractal dimension is calculated by using the least squares method. To improve computation efficiency, the algorithm divides the hyperspectral image into several parts by using the multi-thread technology and then estimates the fractal dimension by the parallel computation of the polynuclear computer. Experimental results indicate that the algorithm is effective in that it solves the problem of computational inefficiency, low-fidelity, and weak separability in the algorithm of grid and step.

 In order to provide more suitable surface retrieval methods, the authors reconducted forward modeling and made algorithm improvement for multiple parameters retrieval of AMSRE-E data simulated by Njoku-developed Levernberg-Marquardt iteration algorithm, and realized project code in MATLAB. The result shows that the improved retrieval method not only has somewhat higher precision than the original method but also can make retrieval of measurable roughness parameters of the exposed surface. In addition, it can attain relatively high precision in a wider water content range of vegetation.

In this paper，a new filtering method has been proposed on the basis of the WLS error criterion，and the fractional order calculus has been used in the method. The coefficient of the filter can be altered with the order. Validated by the Riemann-Liouville rule，the method can be applied to remote sensing image processing. Studies show that the method can attain the balance between anti-interference and detail extraction. Thus the technique displays fairly good filtering effect.

 A progressive linear prediction filtering algorithm is proposed for extracting DEM from LiDAR data. Some processes are inserted in the ordinary linear prediction algorithm, such as gross error detection, initial value selection, landform analysis, and smoothness detection. The authors used this algorithm to process 6 datasets published by ISPRS as standard filtering test data. The results show that the improvement in the traditional methods can increase the precision of DEM.

Based on the experimental data collected in the Taihu Lake on October 27 and 28, 2003,  the authors utilized the numerical simulation method to study the relationship between the errors of experimental data and the empirical constant of chlorophyll-a concentration retrieval model. The results indicate that, when the errors of experimental data obey normal distribution and uniform distribution respectively, the empirical constant of the retrieval model becomes divergent with the increasing error of the experimental data. In addition, according to the relationship between errors of experimental data and retrieval accuracy, 26.58% experimental errors are corresponding to 30% retrieval accuracy. This means that under the prerequisite that the errors produced during the processing of remote sensing images are not taken into account, the strict control of the errors of experimental data within 26.56% constitutes one of the key elements that break the bottleneck of the retrieval accuracy at ±30%.

A model of DVI is built in this paper. The computation was conducted by near-infrared and red bands of SPOT 5 image and observed concentration of PM10 (inhalable particulate matter) to retrieve concentration of PM10 in SPOT 5 image of Beijing urban area acquired in 2007. It is shown that the spatial distribution trends of PM10 are basically identical between the retrieved result and the observed data. In 2007，PM10 pollution in northern part of Beijing urban area was more serious than that in southern part. The concentration of PM10 was relatively low near the greenbelt and water body，and was comparatively high around roads.

Linear spectral unmixing（LSU）is the most common method for solving mixed pixel problem; nevertheless, if the number of the pixels’ endmember is regarded as unchangeable, the traditional pixel unmixing algorithm cannot attain a good result. In the light of the characteristic that pixels usually have the same composition as their neighboring pixels, the authors proposed a grid-based dynamic endmember linear spectral unmixing（DELSU） model.  The land cover classification experiment was conducted with the adoption of the Landsat TM image as the experimental data. The abundance map of winter wheat derived from the visual interpretation of the QuickBird image was used for accuracy evaluation. The experimental results show that the use of the DELSU model could extract the area of winter wheat at a precision higher than that of the traditional maximum likelihood method and the LSU model. This model absorbs the traditional classification advantages and improves the measurement accuracy of the target features. As an improved method of LSU，DELSU is also applicable to the measurement of other land use/cover types.

 The DVB-S is a new digital satellite broadcasting technology which meets the industrial standard. Based on this technology，China Meteorological Administration built up a low-cost，wildly-applied satellite remote sensing data sharing platform. The users can acquire the near-real-time satellite remote sensing data from US EOS，NOAA and Chinese FY satellite series. Due to the signal disturbance and other reasons，some received data have lots of abnormal data，which influence their utilization. Considering the multiple receiving stations in the DVB-S system，there would be lots of overlapped areas in their receiving range. A method for modifying the abnormal data in the MODIS 1B swath image by using the correspondent pixels in the overlapped area is introduced in this paper. Its key steps include image selection，overlapped area matching，abnormal data replacement etc.， and the automatic data processing code is realized by using Visual C++.

To deal with the Level 2 HSI data from the newly-launched HJ-1A satellite, this paper introduced in details the entire flow and relevant algorithms for data preprocessing. The introduction includes calibration, vertical stripes elimination, and atmospheric correction geometric correction. Standard spectral reflectance products with precise geo-locations were produced. Spectral reflectance data from EO-1 Hyperion of close dates was used to simulate the band reflectance of HJ-1A HSI. Comparisons of spectral reflectance data between simulated and actual HJ-1A HSI were made to validate the effect of the data preprocessing. The average correlation coefficient of spectral reflectance between actual and simulated HJ-1A HSI is 0.947 with its standard deviation being 0.017, suggesting a high consistency. The mean and standard deviation of differential bands between real and simulated HJ-1A HSI are close to 0. The result shows that the reflectance from HJ-1A HSI is consistent with that of simulated data from Hyperion, and hence the data processing flow could provide necessary support for quantitative use of HJ-1A HSI data.

Registration between remote sensing images and DEM data is difficult because it is hard to find the Ground Control Points (GCPs) between the two images. In this paper, an illumination model was introduced and served as a proxy in the image registration to increase the registration accuracy. The aspect and slope were computed for each pixel of the image based on DEM data. The solar elevation angle and azimuth were obtained from the header file of the remote sensing image. Based on these parameters, the authors constructed the terrain illumination model based on the Lambert reflectance model, which displays very similar texture to the remote sensing image. Thus, the GCPs can be easily identified in the two images. This method was tested by using sub ETM+ image and DEM in Guangdong province. The result shows that the total RMSE of this method is 13.373 m, which is less than one pixel. This method is effective in the registration of remote sensing image and DEM, especially in mountain areas.

 This paper takes the Zhongteng Cu-Mo deposit in Pinghe County of Fujian Province as an example and makes a brief introduction to Hyperion hyper-spectral data processing technology. The author adopted the differential spectrum technology based on feature bands and the spectrum matching techniques based on full wave features in remote sensing study to make mineral identification. Phyllic rock in the ore district was recognized preliminarily and confirmed by geological data，field reconnaissance as well as rock and mineral identification. The purpose of this paper is to exchange the techniques of rock and mineral information extraction based on high-spectral remote sensing data and to explore the application of the remote sensing technology to the high vegetation coverage area.

Although the East Kunlun-Tuotuohe section of the Qinghai-Tibet railway is one of the regions where the geological study is in the lowest degree in western China，the metallogenic conditions there are still hopeful．With the remote sensing technology as the main method，the authors adopted the comprehensive investigation and extraction of the mineralization alternation information for the mineral resources in this area．Through a comprehensive analysis，seven prospect areas were delineated，and the evaluation of the ore prospecting potential based on the remote sensing anomaly and integration of the known data of geological，ore resources，geochemical and geophysical information was carried out．

The main purpose of the mineral resources potential assessment research based on the remote sensing method in Tibetan region is to extract abnormal remotely sensed information， make several kinds of geological maps，and provide basic maps and data for other cooperating research groups. Four typical problems have appeared in the project because of vast area， multi-temporal remote sensing images，too much software，complex processes and technical differences in technicians’ professional background and experience. The first problem is the incorrect understanding of the datum and map projection and the ignoring of the remote sensing image map projection distortion； the second problem is deleting too much or too little interference information and incomplete spatial topological relations during the anomaly information extraction； the third problem is the inappropriate strategy of remote sensing image mosaicking and color balancing； the fourth problem is low accuracy in the interpretation of geological features of mineral resources. Focused on these four problems，this paper proposed a practical solution which ensures the successful completion of the remote sensing project.

 As the surface of the manganese ore deposit always contains a large amount of hydroxyl and ferric ions，manganese mineralization anomaly can be characterized to some extent by the hydroxyl and iron anomaly. The authors therefore proposed the adoption of such methods as band ratio，threshold segmentation and principal component analysis to extract the hydroxyl and iron abnormal alteration information from ETM+ remote sensing images，and analyzed the extracted abnormal alteration information in combination with data from known ore spots by using GIS. With manganese deposits in western Guangxi and southeastern Yunnan as examples and through the manganese mineralization alteration information extraction and comprehensive analysis，the authors verified the relationship between manganese mineralization and remote sensing alteration information as well as the effectiveness of the methods used in this paper and, on such a basis, put forward new ideas and methods for the prediction and evaluation of manganese ore deposits. Using extracted alteration information，the authors divided the study area into three kinds of favorable ore-forming zones on the basis of ore-forming favorable degree and delineated six potential districts for ore-prospecting in this area.

In this paper, two HJ-1 images before and after typhoon Morakot were used. Decorrelation stretch (DS) and maximum likelihood classification (MLC) were adopted to extract the regions of landslide and debris flow. Furthermore, TRMM precipitation data and digital elevation model (DEM) were combined to analyze landslide and debris flow. The results show that landslide and debris flow can be monitored effectively by HJ-1. Good classification results are obtained: the overall accuracies of two phases are above 90%, and Kappa coefficients are higher than 0.9. The rainstorm brought by typhoon Morakot led to landslide and debris flow, so vegetation and settlements were changed into bare land, which is reflected clearly on the classification map and 3D map.

Using GIS technology as well as theory and methods of landscape ecology，the authors studied landscape pattern of land consolidation. Exemplified by the land consolidation key project for Huainan city，the authors selected three targets and probed into the land use situation and landscape pattern before and after land consolidation. The changes of land use structure and landscape patterns based on land consolidation were analyzed using Geographical Information System combined with the method of landscape ecology, and some results were obtained. First，paddy fields are predominant in the landscape pattern, and the patch fractal dimension and the shape parameters tend to decrease gradually, indicating that the pattern of patches has become regular and simple. Second，the number of landscape patches and patch densities are relatively low，the average patch area and the maximum patch area increase，and the maximum patch index rises steadily. All this shows that landscape diversity has been reduced simultaneously with the increase of the landscape distribution uniformity.  

 NASA MODIS sea ice products provide global information of sea ice extent and ice surface temperature (IST). However, when it is used for local and regional sea ice mapping, its accuracy needs further validation. Taking sea ice in Liaodong Bay as an example, the author analyzed MODIS sea ice products, and the result shows that almost all the sea ices in this area are identified as clouds. Based on NASA sea ice algorithm, the author extracted sea ice extent by using sea ice reflectance and ice surface temperature respectively. The results show that ice surface temperature can extract the distribution of sea ice effectively.

 Land use in urban areas is crucial for urban land management decision-making, environment monitoring and urban planning. According to the landscape ecology concept that the landscape patterns within the same land use type are similar, this paper presents a new land use classification approach which integrates landscape characteristics and high-spatial resolution remote sensing data. Some key landscape metrics were selected to quantify the landscape patterns of different land uses. Then, the integration of SPOT image and landscape characteristics was applied to land use classification within the 5th Ring Road of Beijing. The overall land use classification accuracy was 85.9% with Kappa parameter being 71.1%. The results show that the specific landscape patterns of different land use types would significantly contribute to improving land use classification, and could potentially be applied to other urban areas.

 On the basis of the techniques of remote sensing and geographical information system (GIS)，it is possible to extract water information from the remotely sensed images for mapping and pattern study by using the method of spectrum relations. In this paper, water information was derived from the remote sensing images of Shanghai section obtained on the northern bank of Hangzhou Bay in 1992，2001 and 2008, respectively. The landscape information including a number of patches，areas，densities，shape indices and fractals were calculated for further analysis. The results show that the landscape pattern of water was changed remarkably with the reduction of total area and patches as well as the complication of the water shape from 1992 to 2008.

 With Gurbantunggut desert as the study area and by using moderate resolution imaging spectrometer (MODIS) data, which is a kind of optical remote sensing data，in combination with snow reflectance spectra simultaneously measured by ASD Field Spec, the authors evaluated the correction capability of FLAASH model. Some results have been attained: ① Snow reflectance from 1st to 7th band of MODIS is similar to the simultaneously measured snow reflectivity，and the correlation coefficient of all bands is 0.82 on the whole， indicating that FLAASH can greatly enhance the capability for identifing surface features of M0DIS . ② On the basis of linear relationship between snow reflectance after correction at MODIS channel 6 and NDSI and measured snow density, a snow density remote computation model can be built by way of regression and fitting.

Based on Remote Sensing and GIS, this paper quantified the landscape fragmentation of Changsha city by using landscape pattern metrics such as patch density, edge density, mean patch fractal dimension, landscape contagion index and Shannon diversity index. To reflect the spatial change in landscape fragmentation from the extent of human influence on the environment, the authors selected two transects in the orientation from north to south (N-S,40 km×8 km) and from west to east (W-E, 40 km×8 km) through Changsha’s spatial gravity center. Eight concentric areas with different radii from Changsha’s spatial gravity center were also selected to analyze the temporal change in landscape fragmentation. The results show that the spatial gravity center has moved 2.45 km in east-southeast direction for about 75°. The outward expansion of the construction land in the form of concentric zone model has a great impact on the landscape pattern of the suburbs. The study of urban-to-rural transects has also showed that the agglomeration effects in urban areas are obvious. In contrast to the city center, fragmentation of landscape has been increasing and spatial heterogeneity has been changing dramatically in suburban areas.

 In this paper, we propose a framework of GIS service chain for geodata and geoprocessing web services to handle complex geospatial tasks in web environment using XML web service, OGC standard and workflow techniques. An online image fusion web service is developed as an example based on the framework. This web service allows different images can be fused on-line. The prototype system integrated industry standards, including Web services, OGC WCS, OGC WPS, WSDL and BPEL4WS, allows invoke geoprocessing functionality in a platform and language independent manner on the Internet.