Taking the interaction between spatial and temporal resolution of remote sensing data into consideration, the authors hold that there is no satellite sensor that can produce images with both high spatial and temporal resolution, and spatiotemporal fusion of remote sensing data is an effective method to solve this problem. This paper introduces main research achievements of spatiotemporal fusion model obtained both in China and abroad. Based on the comparative analysis of the mainstream fusion models, these models can be divided into two categories, i.e., the transformation-based model and the pixel-reconstruction-based model. Furthermore, the authors divide the pixel-reconstruction-based model into mixed linear model and spatial and temporal adaptive reflectance model, and then introduce the basic principles, methods of these models. This paper makes a comparative analysis of the advantages and disadvantages of various aspects of the model. At last, the data, application and scale prospect of spatiotemporal fusion models are put forward.
Synthetic aperture Radar (SAR) is widely used in tropical rainforest and desert areas remote sensing geological survey by virtue of its all-day and all-weather earth observation capability and unique penetrating imaging characteristics. In this paper, the geological bodies microwave scattering mechanism which is the theoretical basis of the SAR geological application is expounded. This paper summarizes the domestic and foreign applications of SAR in geological archeology, mineral exploration, lithology and geological structure identification, reports the research progress of multi - source image fusion technology in Radar geology applications, and explains its important function in combination with practical project examples. Constrained by image characteristics, processing technology and data sources, the domestic SAR geological application remains at a very low level. With the development of hardware and software technology, the Radar remote sensing technology will become more and more important in domestic geological applications.
Influenced by the global warming,coral reef bleaching phenomenon is more and more serious and approximately 1/3 of the world’s coral is facing possible extinction. Sea water temperature anomaly is one of the most important causes of coral reef bleaching and mortality. NOAA has developed thermal stress satellite products for coral reef bleaching monitoring based on sea surface temperature(SST),including 50 km and 5 km spatial resolution. This paper presents the research status of coral reef bleaching,and introduces the methods and algorithms that NOAA has developed for monitoring coral reef bleaching. There is also a case study of coral reef bleaching monitoring in South China Sea based on NOAA’s coral reef bleaching monitoring products. It is shown that it is very probable that coral reef bleaching already occurred in the study area in June 2015. This paper expounds the necessity and urgency of exploring the related research on coral bleaching warning methods in China through research review and case study,and provides relevant research and technical reference.
The method of change vector analysis in posterior probability space(CVAPS) does not take into consideration the correlation between the bands of remote sensing image, which may result in unreliable change detection. In view of such a situation, the authors introduced multivariate change detection(MAD)method and, in combination with CVAPS, proposed an improved method for automatic updating of land use / cover change(LUCC) classification. The method firstly introduces MAD to reduce bands-correlation for improving the reliability of train-samples and accordingly improving LUCC updating maps, and then included an iterative Markov random field(IR-MRF)model to fully employ the contextual information in post-processing to reduce the noise of “salt-and-pepper”. Choosing Changting County of Fujian Province as the study area, the authors used Landsat5 TM and Landsat8 OLI data acquired in 2003 and 2013 respectively, and took OLI as the base image to update the classification map in 2003. The experimental results show that the proposed method significantly outperforms the CVAPS in that its overall accuracy could reach 80% with the improvement rate being about 3%.
In order to enhance the capability of local feature representation of remote sensing images and to make full use of sparse decomposition of over-complete dictionary, this paper proposes a new method of remote sensing image retrieval based on sparse representation feature. In this method first the local invariant features are extracted from the training remote sensing image database and trains over-complete dictionary based on local features, and thus the sparse representation will be obtained under the dictionary update; the authors regard the sparse representation as the image’s final feature description. Secondly, the authors construct a visual dictionary using sparse representation features, and obtain the sparse histograms by spatial pyramid matching algorithm. Finally, the SVM classification model is trained based on the sparse features, by using the classification model, the images classified as one category with the query image to be output. The similarity matching is carried out in the output image set, and an image with the largest similarity is returned to achieve the image retrieval. Experimental result shows that the features extracted by the new method not only possess the robustness of local invariant features but also provide the necessary semantic information, which is of great practicality and applicability in image retrieval research field.
Remote sensing-based soil moisture content inversion is an indispensable procedure in drought monitoring; however, the image acquisition process is often influenced by bad weather such as cloud cover and snowfall, or sensor performance defects, which causes missing data. The existing filtering interpolation methods based on time series images have a high requirement on input data and thus are difficult to be widely applied, while the spatial interpolation methods do not work well for the images with missing blocks. In view of the above problems, this paper proposes a missing data filling method based on optimum interpolation, which predicts and fills missing data with the ground observation data as a reference. The authors selected Ningxia as the study area and obtained the soil moisture content in multiple periods using the VCADI index, and conducted missing pixel interpolation using the proposed method with the ground observation data of 16 national meterological stations. Experimental results show that the proposed method performs well in all regions with different levels of missing data. The authors simulated the images with missing blocks and different levels of missing data, and compared the performances between the inverse distance weighted interpolation method, the Kriging interpolation method and the optimum interpolation method. Experimental results show that the method proposed by the authors can obtain more accurate interpolation results.
Evaluation the application potential of the new generation L-band sensor by ortho-rectifying of the PALSAR-2 image has an important significance. The error of orbital parameter in the rectification process will affect the final ortho-rectification precision. To tackle this problem, this paper proposes an ortho-rectification algorithm for PALSAR-2 image based on orbit parameters modulation and simplified calculation. Orbit parameters are modulated by registration of the simulate image and the real image. By using the modulated orbit parameters and simplified calculation of the range doppler(RD) model, ortho-rectification is performed. The method was applied to PALSAR-2 image and PALSAR image at the same time and it was compared with the PALSAR-2 ortho-rectification image without orbit parameters modulation. The result shows that the algorithm has strong operability and high geometric accuracy and that the new generation L-band sensor image’s rectification accuracy is higher, which further confirms that the new generation L-band sensor has better performance and greater application potential.
The available standard products of the domestic satellite HJ-1A/B CCD image are level II images, which are generated with systematic correction without considering the distortions caused by terrain undulation. Lacking of the geometric imaging model, it is difficult to do a reliable image rectification on the level II image with the existing methods in the range with big terrain undulation. To solve this problem, the authors propose a method to reconstruct the imaging geometric model based on the observation angle information, which is stored in a SatAngle.txt file. With the reconstructed RPC model, the HJ-1A/B CCD image can be orthorectified just like an original image with rigorous imaging geometric model. The experimental results show that the proposed method can effectively improve the rectification precision and the stability of geometric model fitting.
3D modeling of urban buildings is one of the key technologies for smart city construction. The traditional modeling methods have many disadvantages in the process of data collection, such as operating difficulty, high cost and low efficiency in 3D modeling. In this paper, the authors propose a 3D city modeling approach based on oblique photography technology of unmanned aerial vehicle(UAV) for consumption. The cradle is used to control the direction of lens, and multi-angle slanted images are obtained. Then the 3D model is constructed by using aerial triangulation principle, and textures of building walls are extracted from multi-angle images. Finally the texture is mapped to the corresponding models, and the true 3D model is built. Result shows that the approach can not only improve modeling efficiency but also reduce the cost during data producing process.
In this study, the authors examined the estimation of soil moisture with various drought indices in Huihe River Basin of East China. MODIS data were used for the estimation. Such drought indices as apparent thermal inertia (ATI) and vegetation supply water index (VSWI) were used for the estimation. On the basis of these drought indices, the authors integrated the drought indices into a comprehensive drought index (CDI) for the study to estimate soil moisture in East China. As dimensionless data, CDI cannot represent the actual soil moisture. The authors introduced the measured data, and built the correlation model between CDI and measured data. CDI can therefore be converted to soil moisture through the model. Finally, the authors used the measured data to verify the reliability and accuracy of the estimation results. The results show that the correlation between measured data and estimation data is high, and R 2 values are around 0.7. The method in this study has great application value for estimating soil moisture in large area.
The traditional manifold learning algorithms are based on the assumption that categories of data are located in the same manifold structure; nevertheless, due to the different features of different data categories, it is more reasonable that the data are in respective different manifold structures. Hence, the assumption of multi-manifold is more applicable for data classification. This paper adopts the thought of multi-manifold spectral clustering algorithm, mainly focuses on multiple manifolds LE algorithm, and applies this algorithm to the processing of hyperspectral data. Combined with the features of the hyperspectral data, the multiple manifolds LE algorithm is further improved by adding the spatial information and data maker information. The experimental results show that, in many kinds of hyperspectral data, the multi-manifold LE algorithm has higher precision than the LE algorithm. In addition, the improved multi-manifold LE algorithm could classify data with higher precision than the LE algorithm and multi-manifold LE algorithm. The authors have reached the conclusion that the assumption of multi-manifold is in better agreement with the features of hyperspectral data and the improved algorithm is of high performance.
With the development of image dense matching method, point clouds can be obtained from multi-view oblique aerial images, whose accuracy and density can be comparable with LiDAR point clouds. However, the currently derived colored point clouds lack classification information. In view of such a situation, this paper proposes an object-based classification method for oblique photogrammetric point clouds. The first step of this method is to calculate features of each point. Then, SLIC algorithm is used to divide the corresponding image into super-pixels. After that, point clouds are clustered into super-voxels as objects according to the relationship between point clouds and images, and features of each object are calculated afterwards. Random forests algorithm is used to classify these super-voxels. Finally, contextual information is adopted to optimize the initial classification results. Two sets of data were employed for evaluating the proposed method, and the overall accuracy could reach up to 91.2% and 88.1% respectively, which improves the precision by 2.3% and 8.2% compared with the point-based classification.
In view of the fact that the traditional change detection algorithm mainly depends on the spectral information and fails to effectively use image feature detection advantage, the authors put forward a multi-feature fusion of remote sensing image change detection algorithm. First, color histogram and edge histogram of gradient image object with multi-scale segmentation is statistically analyzed based on the calculation of each object. Then, the object color distance and edge linear characteristics distance between different periods are calculated by using the earth mover’s distance method; the adaptive weighted method is used to combine color distance and edge linear characteristics distance so as to construct object heterogeneity. Finally, the images change detection results are analyzed by using histogram curvature. The experimental results show that the method can fully fuse the color and edge line features and improve the accuracy of detection.
The current panchromatic (PAN) / multispectral (MS) fusion methods do not comprehensively consider the characteristics of the remote sensing images from China’s domestic high-resolution satellites. Therefore, this paper proposes a variational fusion method for China’s domestic high-resolution images. On the one hand, the three-dimensional spectral high-fidelity model based on the spectral gradient is proposed by comprehensive consideration of the relations between the spectral bands. On the other hand, according to the existing blurring characteristics of the PAN image acquired by China’s domestic high-resolution satellites, the spatial enhancement model in consideration of the blurring degradation is developed. Finally, the fusion energy function is constructed by combining the prior knowledge of the remote sensing images, and it is solved by the classical gradient decent methods to obtain the fused image. The proposed method was tested and verified by the Gaofen-1 (GF-1) and Gaofen-2 (GF-2) satellite datasets. In addition, the popular GS, PRACS, and ATWT-M3 methods were applied for comparison from both qualitative and quantitative aspects. The experimental results show that the proposed variational high-fidelity PAN/MS fusion method comprehensively considers the characteristics of China’s domestic satellites, and hence it can maximally preserve the spectral information while effectively improve the spatial resolution of the MS images, thus achieving the best fused results.
The hydrocarbon microseepage detection method with remote sensing technology is a direct way for oil and gas investigation. According to several anomalous phenomena above oil and gas reservoirs, such as the enrichment of low-grade iron elements, the abundance of clay minerals and high carbonate content, this paper proposes an oil and gas alteration information extraction theory with hyperspectral method. Based on the theory, the authors analyzed the spectral response characteristics of various hydrocarbon alteration materials with hyperspectral data of the Tiangong-1(TG-1), highlighted targeted mineral feature information and at the same time suppressed the information of other ground objects, selected the high-absorption and high-reflection bands of the different interpretation signs, and then used the band ratio method to highlight and extract feature information. With the TG-1 hyperspectral data of Qingyang City, Gansu Province, the authors conducted oil and gas micro-seepage extraction and the results show that the distribution of the abnormal information of surface alteration is not only in good consistency with the local geological analysis results but also in good agreement with the actual oil and gas area data, thus verifying the feasibility of the method proposed in this paper and demonstrates the detection potential of TG-1 hyperspectral data.
ing at overcoming the shortcomings of existing denoising algorithms, such as the poor denoising capability, the noise error evaluation, and the damaging of the image edge and texture details, this paper proposes an image denoising algorithm of joint bilateral filter and wavelet threshold shrinkage. Firstly, the original noise image is divided into high-contrast and low-contrast layers by bilateral filter. Secondly, different appropriate filters are employed for different hierarchical layers. i.e., the bilateral filter and wavelet threshold shrinkage are adopted for high-contrast and low-contrast layers, respectively. Finally, the final denoising image is obtained by integrating high-contrast with low-contrast layers’ denoising images, which suppresses noises and at the same time enhances the image more efficiently. Experimental results show that peak signal to noise ratio (PSNR) of this method reaches 40.99 dB, which is higher than the ratio of non-local means filter, bilateral filter, wavelet threshold shrinkage and partial differential equation algorithms by 7.79%, 3.56%, 11.22% and 1.91%, respectively. Moreover, the proposed algorithm can not only remove the noises efficiently but also preserve the image edge and texture details very well.
In this study, the authors used TM remote sensing images in 1995, 2000, 2005, 2010 and OLI remote sensing image in 2015 as data sources, classified the image by decision tree method based on CART (classification and regression tree)to obtain the land use information of Lucheng City of Shanxi Province and did accuracy assessment. Then the dynamic change of land use was analyzed by such means as the extent of land use change, the single land use dynamics, and the integrated index of land use change degree. In addition, the GM(1, 1) model was built using first four data and was verified by the actual data in 2015 . At last, the land use of Lucheng City in 2020 was predicted by using the GM (1,1) model. According to the results obtained, the forest area and the residential area increased, the agriculture area and the unused land area decreased, and the water area remained about the same in the 20 years from 1995 to 2015 in Lucheng City; the development degree achieved the medium level and the land use structure remained about the same. In 2020, the predicted value of agriculture area in Lucheng City will be 22 759.32 hm 2 and the predicted value of residential area will be 8 854.76 hm 2.
GWSA (groundwater storage anomaly) data of North China Plain from 2003 to 2015 were estimated from terrestrial water storage change (TWSC) data retrieved by monthly GRACE (gravity recovery and climate experiment). The EOF (empirical orthogonal function) method was applied to analyzing the GWSA, and it is shown that cumulative contribution rate of the first three EOF modes reached up to 96.35%. The explanation rate of the total variance of first mode reached about 80%. It is shown that GWSA in the North China Plain behaved consistently descending in the whole region with obvious seasonal fluctuations, caused by groundwater exploitation and precipitation. The second and third mode, with an explanation rate of about 12% and 5%, showed that spatial pattern in northeast-southwest direction and that in northwest-southeast direction were obviously opposite. However, no significant temporal diversification was found, presumably mainly controlled by water cycle under the coastal-inland, piedmont-plain and hydrogeological conditions. This study helps to further understand the spatiotemporal characteristics and drive mechanism of groundwater change in North China Plain.
The study of the changing pattern of urban thermal environment from different spatial scales can provide a scientific reference for the construction of urban human settlements. On the basis of MODIS and Landsat TM/OLI/TIRS data, land surface temperature is retrieval. Spatial pattern of thermal field in Lanzhou-Xining agglomeration (LXA) was analyzed from macro scale and micro scale. Diurnal variation, seasonal variation, and annual variation of urban heat island effect of LXA were explored. The heat island ratio index was introduced to describe the variation characteristics of thermal field pattern in LXA from 1992 to 2015. The results show that, on the large scale, there is no obvious urban heat island effect in LXA, whereas the spatial pattern of urban heat island effect in internal areas of central urban region of Lanzhou and Xining-Haidong changed greatly from 1992 to 2015. The urban sprawl had a spatial consistency with the urban heat island extension. Specifically, the heat island ratio index first increased and then decreased in central urban area of Lanzhou, whereas the heat island ratio index continuously increased significantly in central urban area of Xining-Haidong. As a typical valley agglomeration, the temperature of central cities was lower than that of surrounding loess hilly regions. The main influence factors were vegetation and the duration and amount of solar radiation. It seems that the land surface temperature is negatively correlated with normalized difference vegetation index(NDVI) and positively correlated with normalized difference building index(NDBI).
The distribution of the mineral abundances on lunar surface has a significant meaning. Hapke model is one of the most usually used methods for studying lunar surface, and particle size is one of the parameters that must be clearly understood in doing model calculation. Nevertheless, the research on grain size remains very insufficient. To study the distribution of the abundances of five main minerals, i.e., clinopyroxene, orthopyroxene, plagioclase, olivine and ilmenite, the authors considered the influence of grain size and built inverse models of these five minerals by using fully constrained linear-unmixing method with Relab data based on Hapke radioative transfer model, with the correlation coefficients of these five minerals being 0.98, 0.98, 0.83, 0.91 and 0.50. Furthermore, the accuracy of this models was verified by using data of Apollo sampling points . At last, the lunar minerals abundance distribution maps of Sinus Iridum were compiled by applying the models to the M 3 hyperspectral data,which shows that the fully constrained linear-unmixing method in consideration of mineral grain sizes can be used to study lunar mineral abundance distribution.
It appears that most lakes are controlled by tension faults, as shown by the study of the shapes,size,numbers and distribution of lakes in the hinterland of the Tibetan Plateau using remote sensing. The shapes and distribution of lakes have visible regularity and are in accordance with measurement data of GPS which reflect the conditions of regional stress field. The territorial characteristics of lakes on patterns and distribution in different stress fields constitute an embodiment of different tectonic backgrounds. The fact that the lithosphere matters in middle Tibetain Plateau escaped southeastward might have been an important factor for the formation of a large number of rift lakes. The distribution regularity of lakes is a window to research on tectonics of the Tibetan Plateau, and can be used to supervise the prospecting for groundwater.
For the purpose of understanding the performance of the high resolution satellite image in the geological prospecting field and finding out the metallogenic geological environment of Hongshan Region in Gansu Province, the authors interpreted the ore-controlling strata, structures and rock masses in Hongshan Region by using the Gaofen-1 satellite(GF-1)remote sensing images. On the basis of reviewing and summarizing the results of previous studies and field verification, the geological characteristics and metallogenic regularity of Hongshan Region were analyzed comprehensively, the geological bodies closely related to the polymetallic deposit were summarized, and the GF-1 image interpretation keys of the important ore-controlling strata, ore controlling structure and ore rock were established. Based on the analysis of metallogenic geological characteristics and metallogenic regularity of the important ore-controlling geological units, the authors analyzed and excavated the characteristics of the ore-controlling geological factors of typical ore deposits, integrated the geological environment of the typical ore deposits and ore-controlling information; the multi-source anomaly characteristics of the typical polymetallic deposit were comprehensively analyzed, and the ore-prospecting model was established. The remote sensing prospecting was carried out, and the favorable areas for prospecting were delineated. Copper, molybdenum, iron, zinc and other metal mineralization clues were newly discovered through the field investigation, which are located in the important ore- controlling geological units according to the GF-1 image interpretation, and the better results of prospecting were achieved. The results show that the domestic satellite data can obtain good application results in the field of geological mineral exploration.
Crop growth condition monitoring is one of the key contents of crop monitoring. The growth condition of different periods is obviously different especially in a large region because of phenology. In order to improve the accuracy of the research on crop monitoring in the large region and long time series, the authors extracted heading dates of winter wheat in Shandong Province from 2001 to 2015 based on MOD09A1 datasets and then analyzed the spatio-temporal changes of the condition during the heading period of winter wheat. The main conclusions are as follows: ① Heading dates from EVI have a better consistency with ground observation data than results of NDVI. ② Heading stage is mainly concentrated in mid-April to late-April and gradually postponed from south to north, and so is the situation from west to east. ③ Compared with other four indexes, PI_NDVI gets a better resultant index for monitoring the actual growth conditions of winter wheat in the study area. ④ Founded on the results of PI_NDVI, irrigation condition of winter wheat during the heading stage was on the rise from 2001 to 2015. However, interannual fluctuation was obvious. Conditions of winter wheat exhibited an obvious difference in different areas of the same year. However, the growing conditions are consistent in most of the study region, close to the average level of 15 years. The results in this paper are concordant with the records of situ measurement and previous researches in the same area, and this indicates that the research thinking in this paper can provide certain references for the study of crop condition using remote sensing.
The expansion of impervious surfaces(IS)exacerbates the pollution of water resources in the island city, which is one of the important human factors affecting the vulnerability of island ecosystem. Landsat images acquired in three days of the same season were applied to monitor the dynamics of IS in Zhoushan Islands during 1990―2011. Firstly, the non-IS region was masked by the land use data set of the study area by supervised classification. Then, the complement of vegetation coverage was used to extract IS in 1990, 2000 and 2011. The results show that the IS expansions have occurred continuously over the past 20 years. The IS area in Zhoushan Islands increased from 47.96 km 2 (accounting for 6.28% of the total study area) in 1990 to 114.40 km 2 in 2011 (16.27%), and the increased IS were mostly located around the old city of Zhoushan Islands and along the periphery of surrounding islands. It is observed that the height of new IS was gradually changing to greater depth with time. The analysis indicates that the topography and the policy as well as the functions and transportation convenience are the dominant factors controlling the spatial patterns of IS and its expansions in Zhoushan Islands.
To study the spatio-temporal pattern of the air temperature in Guangzhou City, the authors used MODIS monthly normalized difference vegetation index (NDVI) acquired in 2015 and extracted the normalized difference built-up index (NDBI) with Landsat8 OLI data. The correlation analysis method was used to explore the relationship between air temperature and NDVI, NDBI. The experimental results show that there is a negative relation between NDVI and air temperature and a positive relation between NDBI and air temperature. On such a basis, the spatial lag model (SLM) and spatial error model (SEM) were established to discuss the spatial relations between air temperature and NDVI, NDBI in different seasons, respectively. The SLM and SEM results were compared with the ordinary least square regression (OLS) model, which shows the best performance of the SLM and SEM models. The SLM model with higher R 2 and lower AIC values performs slightly better than the SEM model. NDVI has more influence on air temperature from spring to autumn than NDBI. In the SLM model, the positive and significant spatial autoregressive coefficients indicate an active influence from neighboring meteorological stations.
Based on dimidiate pixel model and using Landsat TM/OLI remote sensing images of 4 periods in 25 years (1989—2014) and geological hazards investigation data, the authors analyzed the characteristics of vegetation spatial-temporal pattern in Yanchi County by GIS and discussed the relationship between vegetation coverage and geological hazards. The results showed that the vegetation coverage of study area took on the features of relatively high vegetation coverage in the east and relatively low vegetation coverage in the west. The average vegetation coverage was on the low side generally and presented the characteristics of increase-decrease-increase; correspondingly the vegetation appeared the repetitive process of restoration-degeneration-restoration but had a tendency of recovery as a whole, with restoration distributed in the southeast and northwest and degeneration in the mid-west. Geological hazards point density was high in the south and low in the north, which suggests that the hazards points were concentrated in the south and dispersed in the north. A negative correlation between vegetation coverage and density of geological hazards points was discovered, which suggests the regularity of density of geological hazards points descending with the increasing of vegetation coverage: the higher the vegetation, the lower the density of geological hazards points.
Invisible fault identifying in loess area is a difficult problem in active fault study in northern China. Detailed stratigraphic division of loess area by the naked eye is very difficult due to the insignificant difference of the granularities and the colors, which would affect the identification of the obscured fault and paleo-seismic event. Spectral technique has been used for magnetic susceptibility estimation. Magnetic susceptibility (MS) has been considered to be a measure of the degree of pedogenic activity and excellent proxies for terrestrial climatic fluctuations. In this study, multiple linear regression was used to build MS estimation models based on the spectral features. A model was built and was applied to hyperspectral image. Test of datasets indicates that this model is very successful. The applying of this model to hyperspectral image shows that the intensity distribution of MS could be used for stratigraphic division.
In the process of urbanization, a phenomenon called “urban sprawl” usually occurs. In the aspect of measuring and comparing urban sprawls among metropolitan areas, consistent and easy measuring or calculations are lacking although they are urgently required. Therefore, this study aims to introduce a set of metrics of urban sprawl, which include intensity, coefficient of variation, poly-centers and centrality extracted from the night-time light (NTL)data. Moreover, 50 metropolitan areas in China were used to test the feasibility of these metrics in representing urban sprawl and clarify the situation in China. The results show that these metrics are independent of each other, and can represent the urban sprawl accurately from various perspectives. The methods proposed in this paper would provide the useful tools for government and urban planners to understand urban sprawl so as to make appropriate policy and plan to achieve sustainable development of the metropolitan areas.
MODIS snow product data constitute one of the most common data in the real-time monitoring and the research on snow cover; nevertheless, the cloud is the biggest factor affecting the application of MODIS snow cover products (MOD10A1 and MYD10A1) to real-time monitoring and researching daily snow cover in Xinjiang region. By introducing such data as the interactive multi-sensor snow ice mapping system (IMS) data and the meteorological station observation data and combining the existent cloud removal methods based on temporal filter method, spatial filter method and multi-sensor data fusion method, the authors established a new cloud removal method based on multi-source remote sensing data, with which the 15-year-long daily snow cover product in clear air of the study area from 2002 to 2016 was generated. In addition, the accuracy of the cloud removed product was evaluated with field experiment data. The results show that the overall monitoring accuracy of the new product after cloud removal reaches 90.61% in the study area, which is close to the overall monitoring accuracy (93.3%) of the MODIS clear air snow cover product before cloud removal in the study area.
The urban roof greening has the effects such as water interception and ecological environment improvement, and can be an important part of sponge city construction. Taking Gulou, Taijiang, Cangshan Districts of Fuzhou City as the study objects and the remote sensing image of Landsat8 OLI as the main data, the authors extracted the roof greening rate based on sequential maximum angle convex cone(SMACC), constructed the relational models of roof greening rate and global vegetation moisture index(GVMI) humidity indicator, and then simulated and analyzed the roof greening rates. The results show that the roof greening rate in the three districts of Fuzhou is overall low, with an average of only 17.34%; the proportion of greening rate of 10%~20% is 66.55%, and only 5.11% is higher than 50%. The greening rates are different, and there are also changes in humidity, indicating that the roof vegetation has remarkable water interception capacity. The quadratic fumction model of roof humidity h and greening rate r is the optimization model. When the roof greening rate is higher than 16.30%, the intercepting effect begins to be obvious. In the process of greening rate increasing from 30% to 60%, the increasing speed of intercepting capacity becomes the fastest, with an average of up to 57.9%. Two typical blocks were selected and the roof greening rates were simulated and analyzed, which further proves the rationality of the above model. The result confirms the intercepting capacity of roof greening and determines the roof greening threshold under the target of water interception, which provides important reference for sponge city construction.
Lithologic identification and classification can provide important basic information for regional geological survey and mineral resource exploration. Topographic variables constitute the quantitative parameters of digital expression for topography, and are very important in improving the accuracy. Based on the classification validity and correlation of 10 topographic variables such as elevation, slope, profile curvature, surface roughness, and surface cutting depth in the known lithologic area, the authors screened the topographic variables and used the variables under the best scale for the classification of lithology. The result shows that the combination of elevation, profile curvature, surface cutting depth, surface roughness and plane curvature is very useful and, in terms of the capability of identification, each variable has the corresponding lithology. The adding of the best terrain variables combination to fully express terrain characteristics in identifying each type of lithology is helpful to improving the recognition and classification of lithology.
Using the Beidou navigation technology to supervise UAV flying operations is a new mode in the UAV flying supervisory system. This paper comprehensively presents the research idea, development environment and function structure of the latest system, and investigates in detail some key technologies such as registration of UAV resources and tasks, reception of Beidou flight parameters, UAV flying supervisory and alarm, and resources planning and allocation. Many kinds of test application show that the system has a reliable data transmission, stable supervision function and good visualization.
In order to meet the needs of the application of ecological quality meteorological evaluation technology, the authors designed a service system by using open-source GIS(MapWinGIS) technology in C# develop environment. This system has carried out the analysis and processing of climate data according to the needs of service process. By using MapWinGIS interfaces and class libraries, this platform has achieved the design and development of GIS function. And by self-developed writing class, this platform has the function of the processing of meteorological evaluation data including the MODIS remote sensing data and index calculation, finally obtaining the comprehensive evaluation index of ecological quality by using the GIS map. This system provides a standardized work flow for the ecological quality evaluation.