Aerial remote sensing technology is an important means in geological survey. In this paper, the concept of aerial remote sensing was discussed, the history of aerial remote sensing technology was briefly reviewed, the development status of aerial remote sensing platforms, sensors and data processing technology were summarized, and the typical applications of aerial remote sensing technology in geological survey were described by taking geological disasters investigation, mineral resources exploration, coastal geological survey, mine monitoring, aero geophysical and remote sensing integrated exploration as examples. The research results would provide technical references for the work of aerial remote sensing geological survey.
With lower contrast and confidence level, single factor evaluation index is not very effective in the comprehensive evaluation of pixel level image fusion algorithms of Landsat 8 in arid regions. Based on the Landsat 8 image of Juyanze area, 11 single factor indicators and object-oriented classification method were used to compare the following six image fusion algorithms, i.e., Principal Component (PC), Brovey Transform (BT), Hue-Saturation-Value Transform (HSV), Gram-Schmidt Pan Sharpening (G-S), High-pass filtering(HPF) and Wavelet Transform (WT) according to the spatial information quantity, spectral feature and classification accuracy. The results indicate that the spatial resolution and texture features of all fusion images are enhanced in comparison with the original image. HSV is proved to be the best algorithm to highlight the texture features in arid regions, but its spectral fidelity is bad. WT exhibits an excellent capability in maintaining the spectral information, and its capability of revealing spatial details is just next to the HSV method. Therefore, WT is considered the most suitable algorithm for image fusion of Landsat 8 in this study. Taking the spatial information quantity and spectral features into account simultaneously, the authors hold that PC and G-S have moderate performance, and their performance is a little lower than that of HPF, while the performance of BT is the worst. The classification results show that the classification accuracy of WT and HPF is improved to some extent compared with the original image.
Segmenting light detection and ranging (LiDAR) point cloud of building accurately is the important section in the reconstruction of three-dimensional model. In view of the complex roof structure of complex buildings and poor segmentation accuracy of the existing algorithms, the authors put forward a kind of algorithm of region growing with the basic element of triangles to segment the point cloud of the building. First of all, Delaunay triangulation network is constructed, correlation is set up among laser points, unit normal vectors of triangles are calculated, initial partition is conducted on point cloud with the character that vectors in unit vector approach of triangles on the same plane of the building are basically consistent; then, because dispersion and deviation of point cloud could produce many disheveled triangles, dissection is conducted on points that are composed of disheveled triangles; based on good robustness of random sample consensus (RANSAC) algorithm, boundaries of planes of the building combining are obtained with Alpha Shape algorithm, plane and isolated point are combined in over-segmentation. The test result shows that the point cloud segmentation on the roof of the building is ideal in integrity, accuracy and quality with the method put forward in this paper.
With the development of remote sensing technology, the remote sensing has been developed from qualitative application to quantitative application,and atmospheric correction is an important part of quantitative remote sensing research. In this study, the authors used the FLAASH model of ENVI software to conduct atmospheric correction for Worldview3, a satellite image with high spatial resolution and high spectral resolution, and then evaluated the method. Worldview3 data and ASD measurement spectral data of typical ground objects(saline-alkali land and diorite)in Lop Nur were obtained. Firstly, the DN value of Worldview3 was converted into radiation brightness and apparent reflectance, and the atmosphere correction of Worldview3 image was carried out by using FLAASH model. The radiation brightness, apparent reflectance and FLAASH atmospheric corrected reflectance of two typical ground objects (saline-alkali land and diorite) in the study area were comparatively studied, and the measured reflection spectra of saline-alkali and diorite by ASD were also compared after resampling to the response band of Worldview3 by Gaussian filtering function. The results show that it is effective to apply FLAASH model to atmospheric correction of Worldview3 data, and the two methods can obtain high coincidence degree of reflection spectrum, with the correlation coefficient reaching 0.8.
Vegetation is an important nexus connecting atmosphere, pedosphere, hydrosphere and biosphere. Therefore, the relationship between the temporal and spatial variation characteristics of vegetation and its driving factors is of great significance in the study of regional ecological environment changes. Based on multiple data sets such as GIMMS NDVI and CRU, the authors analyzed the temporal and spatial variation characteristics of NDVI in Central Asia from 1991 to 2015, using trend analysis and geo-detector model that included factor detection, risk detection and interaction analysis. The results show that the vegetation activities in Central Asia have remained stable and volatile on the whole in the past 25 years. In detail, NDVI in the middle and low altitude areas of the Kazakh hills has increased significantly, while the NDVI in the southwestern part of Aral Sea has been significantly reduced because of the close diffusion of salt dust in the Aral Sea basin. In addition, because of the contradiction between water resources development and utilization among Central Asian countries, the trend of NDVI in the midstream of the Syr Darya and the downstream has been reversed. The non-irrigated farmland in northern Kazakhstan has a large decline in NDVI, and the results are not significant (P≥0.1) due to the phenomenon of re-cultivation. According to the results of geo-detector model, the water factor dominates the vegetation growth pattern in Central Asia, and the temperature is negatively correlated with the NDVI change. The difference in spatial and temporal variation of NDVI between different terrains, elevations, soil types and land use types is also significant. In terms of the interaction factor, the bi-factor interaction has enhanced the interpretation of spatial distribution and temporal and spatial variation of NDVI. The synergistic effect of potential evapotranspiration and elevation on the spatial distribution of NDVI is over 64%.
Hyperspectral data are characterized by many bands, but the strong correlation between the adjacent bands makes it redundant and ineffective, which becomes a challenge for information extraction. To solve this problem, this paper proposes a method combining wavelet packet transform and weight SAM. First, radiometric calibration, reflectivity calculation and wavelet denoising are conducted on the raw hyperspectral data. Second, by using daubechies4 as the wavelet basis, the target and reference spectra are both processed with an 8-layer wavelet packet decomposition, and information entropy feature vector is based on the decomposition coefficient to characterize the features of the raw spectra. Third, efforts are made to find the feature range, in which the difference in information entropy feature vector between the target and reference spectra is huge, and a weight in this range is set. Finally, alteration minerals are mapped according to the SAM principle. In this study, experiments were conducted to testify the feasibility and effectiveness of the method by using the HySpex SWIR hyperspectral core data of a volcanic type uranium deposit in southern China. The results indicate that the new algorithm can efficiently characterize the raw spectral information by using the wavelet packet information entropy feature vector. Furthermore, the local characteristic information can be extruded by setting difference feature range, and different minerals can be more easily distinguished by this method with a total classification accuracy of 75.33% and a kappa coefficient of 0.706 3. The method proposed by the authors performs better than the traditional SAM algorithm and has a great application potential.
At present, the extraction accuracy of the forest fire area by synthetic aperture Radar (SAR) is mainly limited to the analysis of single pixel. However, the application study of object-oriented technology based on pixel sets as the analysis unit is less in dealing with SAR images. In this paper, a multi-scale segmentation algorithm based on fractal net evolution approach (FNEA) was applied to the span of ALOS PALSAR images. Through the application research, the forest fire region, which happened in 2009 and was located in the Middle East of Alaska, USA, was extracted. The application validation of the algorithm was verified by comparing the experiment results with the auxiliary data of monitoring trends in burn severity (MTBS) data. The experiment results show that the classification accuracies of one-static span and two-static spans based on object-oriented analysis are improved by 12.7% and 15.8% respectively, compared with precious research. The researches show that object-oriented technology can be effectively applied to the information extraction form SAR image, and SAR technology has potential application in forest fire monitoring.
Scientific measurement is the basis for the rational planning of urban green space, and indicators of green space are vital references in the construction of urban green space for urban planners. Up till now, the greening indicators widely used in urban green space construction have all been two-dimensional, which are too rough to reflect the stereoscopic landscape of green space and its ecological benefits. Therefore, three-dimensional index (three-dimensional green index, TGI) should be constructed to evaluate the spatial landscape quality of green space construction more precisely. First, the vegetation and its shadow information are extracted through the object-oriented classification; then, the vegetation heights are retrieved according to the geometric relation model between the shadow lengths and the vegetation heights; at last, TGI is constructed and compared with the traditional index green coverage rate for analysis. A case study of Shatou Street in Futian District of Shenzhen City was carried out, and the result showed that, compared with green coverage rate, TGI is capable of evaluating the three-dimensional landscape of green space more objectively and meticulously and can reflect the eco-benefits realistically. So it can provide scientific basis for planning, decision-making and management in the construction of urban green space.
This study is aimed to discuss the temporal and spatial evolution of landscape pattern under the impact of mining work. With the support of moving window analysis and geographic information system (GIS) technology, the authors extracted the spatial distribution map of landscape fragmentation and diversity index from three remote sensing image data in one open-air mining site in Kunming from 2007 to 2017. On the basis of obtaining landscape types and determining appropriate window scales, the spatial distribution maps of landscape fragmentation and diversity index were extracted, and then mining site cores were connected to build transects for spatial overlay analysis. The result shows that, under the most suitable window range of 950 m, the high value center of landscape fragmentation and diversity index spread from the regional edge to the center from 2007 to 2017. The effect of patch aggregation in open mining area was remarkable, and the fragmentation and diversity effect of surrounding area in nearly 1~2 km were obvious year by year. At the same time, the area beyond 2.5 km of the mining center was on the whole not affected by mining activities. The result visually shows the rule for the temporal and spatial evolution of landscape pattern affected by mining activities, and the result can provide data support for follow-up work.
Oil slick thickness is a key parameter in estimating oil spill volume. In order to determine the feasibility of detecting oil slick thickness on water surface by Landsat TM/ETM remote sensing, the authors used heavy crude oil, light crude oil, diesel oil and gasoline as experimental oils, quartz brine as a simulated solar light source, ASD Field Spec 3 portable spectrometer as a detection instrument, and carried out oil film simulation with different thicknesses and pectral measurement experiments. By calculating the correlation coefficient of oil slick thickness and its reflectance in the range from 350 to 2 500 nm, four characteristic response spectra for heavy crude oil, seven characteristic response spectra for light crude oil, six characteristic response spectra for diesel oil and four characteristic response spectra for gasoline were determined. For Landsat TM/ETM data,characteristic multispectral indices were found by scatter plot of oil slicks thickness-multispectral indices (band reflectance and band ratio), and oil slick thickness estimation model was established. For heavy crude oil, band B4 and band ratio B4/B5 are better spectral indices; for light oil, band ratio B1/B2 and B1/B3 are good spectral indicators; for diesel fuel, band B1, B2 and band ratio B1/B2, B1/B3, B1/B4, B2/B3, B2/B4 and B3/B4 are good spectral indicators; for gasoline, all spectral indicators have segmentation characteristics, with no good spectral indicators. The results show that the Landsat TM/ETM has the capability of detecting the oil slick thickness of heavy crude oil, light crude oil and diesel oil on the water surface, and thus can be used to estimate the oil spill volume on the water surface.
In the previous study of the Anhui segment of Tanlu fault zone, it is generally believed that the Anhui segment of Tanlu fault zone is composed of the Wuhe-Hefei fault, the Shimenshan fault, the Chihe-Taihu fault and the Jiashan-Lujiang fault. However, there is no comprehensive and systematic research on the exact boundary position of the fault zone, especially the boundary of the Dabie Mountain in the southern part of the Anhui segment of the Tanlu fault zone. In view of such a situation, based on the multi-source data such as Landsat 8 OLI, ZY-3 and DEM data, the authors comprehensively utilized GIS spatial analysis and spatial statistics to perform remote sensing interpretation of the Anhui segment of Tanlu fault zone and statistical analysis of the interpretation results. The analysis shows the more accurate boundary range of the Anhui segment of Tanlu fault zone. The existing geological data prove that the boundary range is reasonable and reliable to some extent. The study shows that the spatial distribution characteristics of the north and south sections of the Anhui segment of Tanlu fault zone are different. The average trend of the northern section is about N23.5°E, the average width is about 25.99 km, the average trend of the southern section is about N34.9°E, and the average width is about 38.38 km, the overall trend of the whole Anhui segment of Tanlu fault zone is about N33.3°E, with an average width about 30.35 km. The fault zone presents a spatial distribution pattern of “short in the north and long in the south”, “ wide in the south and narrow in the north” and “the fault trend progressively moving northward from south to north”. The research results can provide an important reference for urban planning, engineering construction and geological disaster prevention within the Anhui segment of Tanlu fault zone.
The complex climatic environment and topographical structure of the Tibetan Plateau region have caused great troubles for the observation of hydrometeorological data. The lack of effective high-temporal resolution observation data has become an important obstacle to regional meteorological forecasting and prediction. Based on the 2001—2015 tropical rainfall measurement mission (TRMM) precipitation product, the authors used the 1 km resolution enhanced vegetation index (EVI) spatial data to calculate the downscaling based on the geographically weighted regression(GWR) model. The downscaling results at the annual and monthly scales were tested and compared with the measured data from the ground stations. The results show that the spatial distribution characteristics of TRMM products before and after downscaling are generally consistent, but the accuracy of the results after downscaling is significantly higher than that of the original TRMM products. From 2001 to 2015, the correlation coefficient R 2 of the precipitation of TRMM products after downscaling and the actual ground precipitation was higher than that of original TRMM, and the RMSE and MAE decreased by 21.652 mm and 16.379 mm, respectively. During these years, the accuracy of the original precipitation of TRMM products was relatively low, and hence further correction is required in utilization. The degree of fitting of the TRMM precipitation with the measured precipitation was significantly improved, except for June, August and November, R 2 in other months was 0.65 or even higher, showing good consistency and applicability.
In order to explore the feasibility of estimating the heavy metal cadmium (Cd) content in soil by hyperspectral data, the authors chose the cinnamon soil of Shijiazhuang water conservation area as the research object. Based on the multiple spectral transformation indexes corresponding to the sensitive bands of soil organic matter, the authors established the hyperspectral indirect inversion model of soil heavy metal Cd by partial least squares regression method. Some conclusions have been reached: the average Cd content of soil samples in the study area is 0.220 mg/kg, which is at the serious pollution level. There exists a significant correlation between organic matter content and Cd content, and there is a certain adsorption relationship. The sensitive band corresponding to the original spectral reflectance of organic matter is 797 nm. The correlation coefficient between the absorbance transform first derivative (ATFD) and the organic matter content is the largest among the various spectral transformations. The first derivative (FD) has the largest positive correlation with the organic matter. The modeling and verification sample analysis show that the multivariate partial least squares model is better than the univariate partial least squares model and multivariate linear stepwise regression model. The model explanatory variables are the absorbance transform second derivative (ATSD) of 1 409 nm and the FD of 1 396 nm, and the modeling and verification samples R 2 were 0.83 and 0.80. The research shows that it is feasible to estimate heavy metal Cd content indirectly by establishing multiple spectral transformation indexes estimation model based on spectral diagnostic features of organic matter. The optimal model can provide a reference for the rapid monitoring of heavy metal Cd in this area.
The fractal dimension of water system is one of the quantitative representation methods for determining geomorphic development degree. The study of water system fractal dimension is of great significance for the investigation of the sedimentation mechanism of karst dam basin landform. Advanced space borne thermal emission and reflection radio mater global digital elevation model (ASTER-GDEM) was used as a data source for extracting water system and 30 m resolution ASTER-GDEM. Based on the ArcGIS 10.2, Horton-Strahler theory, water grid method and fishing net method, the authors estimated the water system fractal dimension of Longchangqiao watershed in the dam construction area of Yuzhong, and explore the influence of landform development on the hydrological characteristics of the basin in dam construction. Some conclusions have been reached: The fractal dimensions of the water system estimated by different methods and different data sources under the complex geomorphic structure of the karst area are quite different. The fractal dimension values of the extracted water system of 1.50 million topographic maps estimated by the Horton-Strahler method, the water grid method and the fishing net method are 1.69, 1, 53, 1.54 respectively. The fractal dimensions estimated by 30 m resolution ASTER-GDEM extraction water system are 0.66, 1.59, 1.60. Among them, the fractal dimension values estimated by the Horton-Strahler method are significantly different, with the difference reaching 1.03. Comprehensive analysis of Horton-Strahler theory, water grid method and fishing net method for estimating the relationship between the fractal dimension of different data source water systems and actual landform development in karst dam construction area shows that the water system fractal dimension estimated by the fishing net method and the actual landform status of the study area are most consistent with each other. According to the estimation of the water system estimated by the fishing net, the fractal dimension of the extracted water system estimated by the fishing net method is 1.54, and the fractal dimension estimated by the 30 m resolution ASTER-GDEM extraction system is about 1.60, which suggests that the study area is at the late stage of the young period and the early stage of mature period in geomorphological development, and the results coincide with the actual development of the study area. In addition, three methods were used to estimate the fractal dimension accuracy of the water system in the karst dam basin, and the results show the following order: fishnet method>water grid method>Horton-Strahler method.
For the reasons that images of Picea schrenkiana var. tianschanica in the western tianshan forest were round or suborbicular, crown information extraction was conducted with the space geometric features. According to the workflow of “investigating the features of Picea schrenkiana var. tianschanica in satellite images - extracting tree crown information-estimating tree crown width with remote sensing images”, a method for estimating tree crown width in Central Asia mountain forests based on remote sensing images was proposed and evaluated, with the purpose of challenging the problems that it is difficult to set the marks for marking watershed transform target ground objects and the active contour model evolution results are limited by the original positions of contour lines. Multi-scale blob detection, marking watershed transform and GVF Snake active contour model were orderly combined for tree crown information extraction. This technical process integrated and optimized the process of tree crown information extraction, and gained the tree crown contour distribution map of Picea schrenkiana var. tianschanica from images. A comparison with the measured tree crown width of each tree in the investigated sample ground shows that this method well estimates the tree crown width of Picea schrenkiana var. Tianschanica with high, medium or low canopy density, with the mean error being 10.8%, 4.5% and 6.4%, respectively. The research results provide a better solution for the key technical problem of tree crown interpretation for high-resolution remote sensing data in forest resource monitoring.
Anomaly information caused by hydrocarbon seepage in oil-gas fields can be detected by remote sensing technology. Compared with traditional oil and gas exploration methods,remote sensing technology has many advantages in getting information from long range, large-area mapping, high efficiency and low cost, especially in areas with complex terrain and geomorphological environment. Based on the hydrocarbon microseepage theory, the mineral alteration information such as clays, carbonates, ferrous ion and brightness temperature were respectively extracted by methods of crosstalk correction, atmospheric correction, band ratio, principal component analysis and mono-window algorithm with the ASTER data in Salamat Basin of Central Africa. The results show that the above-mentioned several types of strong mineral alteration information and high temperature anomaly information are mainly distributed in the central and southern part of the study area, namely, the central uplift zone and the southern depression zone are highly likely to contain hydrocarbons. Combined with existing geological, seismic, geophysical and geochemical data, five oil-gas prospecting areas were delineated, which can provide theoretical direction for the further oil-gas exploration.
In order to further understand the glacier change in Ulugh Muztagh under the background of climate change, historical topographic map data, Landsat TM, SRTM DEM and TerraSAR-X/TanDEM-X data with bi-static mode were employed to obtain the detail change of the glacier area and mass balance between 1972 and 2011 around Muztagh peak. The results indicated that reduction rate of annual glacier area was 0.02±0.06% between 1972 and 2011. Among all glaciers, 47 glaciers showed retreat while 2 glaciers advanced for some distance. And the mass change showed a slight negative balance (-0.06±0.01 m w.e./a) for the whole region. From 1972 to 1999, the mass balance was -0.11±0.02 m w.e./a, caused probably by the temperature rising; From 1999 to 2011, the mass change was close to balance (0.02±0.04 m w.e./a), caused by the precipitation increasing. Glacier advance in Muztagh was different from that of normal advanced glacier. For this kind of Polar type glaciers, it was probably caused by the inner melting or liquid water occurrence due to more precipitation that made some glaciers moving forward or surging. In the background of the current climate, most glaciers showed retreat but two glaciers advanced and one glacier surged (avalanche); overall, the glaciers in the region showed slightly negative mass balance.
Forests have an important impact on global environmental change, especially in the carbon cycle. The demand of sensing monitoring for meteorological disasters, especially typhoon and flood, has become increasingly important. Traditional sensing monitoring of low and medium resolution can hardly meet the requirement. High resolution satellite has the advantage of high spatial resolution in vegetation monitoring. In this paper, the characteristics and pretreatment methods of GF-1 satellite images were studied in detail. The methods of radiation calibrater, atmospheric correction, ortho-rectification and calculating vegetation coverage were described in this paper. Finally, there was a case study of vegetation eco-environmental monitoring in Xiamen City. Researches show that most part of Xiamen belongs to high or higher vegetation coverage area, and the vegetation coverage in Haicang, Jimei, Xiangan and Tongan inland areas is significantly better than that in coastal areas. In the island of Xiangan, the vegetation coverage is better in the southern part than in the northern part.
With the popularity of the idea that clear waters and green mountains are as valuable as mountains of gold and silver, rehabilitation of mine environment has become an important part of national ecological civilization. The eastern coast of Hainan Island is one of the most potential areas for uhligite exploitation. However, long-term mining activities have destructed the original ecological environment of that area, leading to the fact that the local ecological environment has become poorer and needs to be rebuilt urgently. By using high resolution remote sensing data, exploitation and recovery conditions of beach placers from 2016 to 2017 in the eastern coast of Hainan island were monitored. The results indicate that, from 2016 to 2017, damaged areas of zirconium-titanium sand mine grew by 40.65 hm 2 and the number of exploitation surfaces increased by 15. The exploitation presents a tendency that the scale was reduced and distributed. Besides, during 2016 to 2017, recovery areas of mine ecological environment grew to some extent and the mine environment conditions showed a tendency of improvement. Through annual comparison and analysis, the authors proposed treatment advice for mining wasteland of zirconium-titanium sand mine as follows: Casuarina equisetifolia could be used as the main species, combined with vatican hainanensis, acacia confuse, and slash pine as accessory species, to restore the forest ecosystem of the mining area; in addition, watermelons, sweet potatoes, peanuts and some other crops could be also applied to conducting restoration experiments in some representative regions; furthermore, damaged mining areas may also be rebuilt into seawater farms.
Based on the Himawari-8 Level3 aerosol optical depth data (Once per hour) and NDVI from MOIDS data, the authors constructed a new RSAI, which can be used to analyzed the seasonal variation of Fujian Province. The results indicate that AOD along the coast line of Fujian Province stand high all the seasons, while the AOD value reaches the bottom in western Fujian. In autumn, the AOD value is the lowest; furthermore, AOD values of Fujian Province are lower than those of any other provinces in China’s mainland. Contrary to things of Yangtze River Delta, Pearl River Delta and Nanchang urban agglomerations, in such main cities of Fujian as Fuzhou, Xiamen and Quanzhou, the vegetation coverage is pretty good. As a result, according to the new construction RSAI, Fujian ranks above ‘fresh’ level throughout all the seasons, the RSAI statistics are higher than those of other neighborhood in China’s mainland, implying privilege ecosystems of Fujian Province. These data show that Fujian Province is of good air quality, high atmospheric transparency, good vegetation coverage and high-level RSAI, as shown by comprehensively analysis of the three indexes. It is therefore held that Fujian Province is favorable not only for tourism but also for improving health, poverty alleviation, reducing pollution and improving the environment.
The national project of land use monitoring via remote sensing has created millions of samples of new construction land. Based on these data, the authors conducted a preliminary research on applying the deep learning technology to automatically detect new constructions in comparison with the results generated by specialists. This study demonstrates that the deep learning technology has a great potential for completing the task of monitoring land use via remote sensing. It is believed that the efficiency of the project would be increased dramatically with minor manual assistance when a recall accuracy reaches 80%.
Lake water reserves are often estimated according to water level observation and manually-measured underwater topography data. As for the lakes which lack data, it is really difficult to obtain the information of lake water reserves. In order to explore the solution of this problem, the authors chose the Nam Co Lake in Tibet as a study case. Based on the features of topography similarities between the above lake level and the under lake level, the authors made use of SRTM DEM of above the lake level to construct the relationship between the elevation and the area, the area and the volume increment. In turn, the elevation-area-volume increment of the lake's underwater was recalculated. Finally, the authors constructed the area-volume model to calculate the lake water reserves. The result shows that the calculation is of high relative accuracy. According to the area of the lake by using the DEM of the Nam Co Lake basin,the authors calculated the lake water reserves, which reach 111.570 billion m 3. This result is compared with the calculated water reserves of 101.950 billion m 3 in the Nam Co Lake underwater terrain digital elevation model established based on measured water depth data, and its absolute error is 9.620 billion m 3 whereas its relative error is 9.40%. The results obtained by the authors provide a reference for the estimation of natural lake water reserves with consistent water and underwater topographic parameters in data-absent areas.
Based on the digital landslide technology, the authors employed revised 10 high-resolution remote sensing images between 2002 and 2016 as remote sensing image information source, densified ASTER DEM data as landslide elevation information source, and 1:200 000 Luoyang City regional geological maps as the geological environment source and, in combination with Dongmiaojia landslide, analyzed the creation conditions and active characteristics of the landslide, and predicted the present stability situation of landslide. The result shows that the active process of Dongmiaojia landslide can be divided into four stages, i.e., the first landslide activity, the subsequent traction activity, the front landslide activity and the follow-up activity, which formed topography of four level terraces with seven platforms and the front steep slope. The estimation of the total size of the Dongmiaojia landslide is 425.4×10 4 m 3. The comparison of ten high-resolution images indicates that there has been no obvious deformation and displacement in the Dongmiaojia landslide in recent years.
Different spatial resolutions, spectral resolutions and radiation resolutions influence the accurate estimation of remotely sensed chlorophyll a concentration of water. In this study, GF-1 WFV and Landsat8 OLI imagery was used as objects, and the cooperative methods of single-band substitution, single-band fusion and three-band fusion were respectively used to analyze dominant characteristics of spatial resolution and spectral resolution for improving the precision of chlorophyll a concentration inversion in multi-source remote sensing data. On such a basis, the optimal combination of GF-1 WFV and Landsat8 OLI data was further explored so as to improve the inversion accuracy of chlorophyll a concentration and promote the application of domestic high-resolution satellite GF-1 imagery. The results show that, in the GF-1 WFV and Landsat8 OLI cooperative inversion process, the spectral resolution and radiation resolution of near infrared band dominate the characteristics, and the influence of the near infrared band spectrum resolution enhancement is more favorable for improving the inversion accuracy of chlorophyll a concentration, whereas in the blue and red bands, the higher the spatial resolution, the higher the accuracy of chlorophyll a concentration inversion. The combination factors of GF-1 WFV and Landsat8 OLI optimal chlorophyll a concentration synergistic inversion spectral index are as follows: Landsat8 OLI near infrared band, GF-1 WFV and Landsat8 OLI fused red band, GF-1 WFV and Landsat8 OLI fused blue band. The GF-1 WFV and Landsat8 OLI separate inversion accuracy with average relative errors of 41.93% and 38.37%, respectively. After optimization, the average relative error of synergistic inversion is reduced to 17.35%. This study preliminarily explored the spectral resolution and spatial resolution of GF-1 WFV and Landsat8 OLI imagery of water chlorophyll a concentration cooperative inversion dominant characteristics and the optimal coordinated way. The authors are in the hope of providing reference for the channel design of the following domestic satellites and the cooperative inversion of multi-source satellites.
Bare land is considered to be an important source causing urban heat island (UHI) in the urban underlying surface. However, quantitative description of the contribution of bare land to UHI in different periods of urbanization remains vague. Taking three phase Landsat TM/OLI remote sensing images from 2005 to 2017 of Mianyang, a mountain city in southwest China, as the research area and based on inverting the thermal environment response characteristics of land use/land cover change, the authors constructed the contribution index of bare land to UHI effect and analyzed the spatial and temporal changes of the surface thermal environment of bare land in the process of urbanization. The results are as follows: (1) In 2005—2017, bare land accounted for 4.73% (53.98 km 2 in 2005) to 6.34% (72.28 km 2 in 2011) in the study area, showing a trend of “increasing first and then decreasing” with total area (5.54 km 2) decreasing. Bare land was mainly distributed along new roads, urban development zones and urban-rural boundaries. (2) In 2005—2017, with the spatial agglomeration of bare land patches, the surface temperature of bare land in high-density area was significantly higher than that in low-density area, but the influence of bare land topography (elevation, slope, aspect), patch area and shape on the surface temperature of bare land was not significant. (3) In 2005—2017, the absolute difference of surface temperature between bare land and rural areas increased from 1.73 ℃ to 2.12 ℃, which was lower than the temperature of urban impermeable surface and rural area (3.07~3.23 ℃), and the contribution of bare land to urban heat island effect increased from 34% (2005) to 37% (2011) and finally decreased to 20% (2017). This study can provide a scientific basis for evaluating the spatial and temporal changes of urban bare land elements and mitigating the urban heat island effect.
The strained human-land relationship in habitats which are isolated like islands inland, is an indicator sensitive to regional environmental changes as well as human activities. Based on image data obtained from Landsat MSS/TM/OLI (1982—2016), average annual precipitation and temperature data derived from meteorological stations (1982—2012), and DEM acquired by ASTER, the authors carried out time series analysis and correlation analysis of climate change factors (annual precipitation and temperature), human activity factors (area of cultivated and construction land) and specific remote sensing indexes including NDVI (normalized difference vegetation index) and NDBI (normalized difference building index) in Culai-Lianhua area. Some conclusions have been reached: The trend of vegetation degradation has been obvious in the past 35 years. It is clear that woodland and shrub have been transformed into grassland and sparse grassland, whereas grassland and sparse grassland have been transformed into bare land. The landscape pattern has changed. Woodland is surrounded by cultivated land and construction land. The woodland has been mixed with cultivated and construction land as well. The construction of the core area has made positive effect. In the core area, the growth rate of NDVI is 0.006 9/10 a and the NDBI has decreased at a rate of 0.014/10 a. On the contrary, around the core area, its NDVI has been speeding down at a rate of 0.018/10 a and the growth rate of NDBI is 0.003 5/10 a. The cultivated land has been greatly reduced, and mostly has been transformed into construction land and woodland. There exists a significant negative correlation between the change of woodland area and the change of cultivated and construction land area. In general, both the ecological structure and the landscape pattern have undergone adverse changes in Culai-Lianhua area.
Based on an analysis of geological structure and environmental geological conditions in the Luojishan area, western Sichuan, the authors comprehensively interpreted and analyzed the basic geological elements such as formation and structure, hydrogeological elements such as surface water and groundwater, geological hazards, vegetation, cultivated land, residential land, surface temperature and other environmental geological factors by using RapidEye data, Landsat8 OLI data and network high-definition data images. Combined with the field survey data, the authors evaluated the hydrogeological environment of the study area and, based on remote sensing information, graded the water richness of water-bearing rocks, and divided them into abundant water resource area, medium water resource area and poor water resource area, which provides basic remote sensing data for carrying out further hydrogeological survey work in this area.
The study of the spatio-temporal evolution of urban agglomeration is of great importance for optimizing the spatial structure of urban agglomeration and promoting the coordinated development of cities. Taking the circum-Bohai urban agglomeration as a study case, the authors revealed the characteristics and objective law of urban evolution process from spatial and temporal dimensions. On the basis of DMSP/OLS night-time light data, the spatial distribution and area of urban from 1992 to 2013 were obtained for the circum-Bohai urban agglomeration. Using the rank-size rule model, the authors determined the size characteristics of urban area. Combined with urban spatial expansion speed index and standard deviation ellipsoid method, the intensity and spatial dynamics of urban area change were clarified in the circum-Bohai urban agglomeration. Some conclusions have been reached: (1) from 1992 to 2013, there was a significant increase of the total night-time lights in the circum-Bohai urban agglomeration, with the overall growth rate being 135.89%. (2) The effect of radiative driving effect of the central cities of the urban agglomeration on the surrounding areas gradually enhanced, and the distribution scale of the urban agglomeration changed from the unbalanced state to the equilibrium state with time. (3) The spatial expansion of the urban agglomeration was characterized by the ring stratification around central cities. The evolution pattern of the urban agglomeration was from west to east and from north to south, and its gravity center shifted to the southwest. It can be inferred that the driving force of the circum-Bohai urban agglomeration development was mainly concentrated in the coastal cities. The study can provide data supports and references for the coordinated development of the circum-Bohai urban agglomeration and even the whole country.
Recently, the utilization of nighttime light data and optical remote sensing images to extract urban built-up areas has become a research hotspot, and the vegetation adjusted nighttime light data (NTL) urban index (VANUI) is widely used. However, it may easily lead to confusion of buildings and water bodies at the edge of the city, and the spatial resolution is relatively low. Therefore, some improvements were made on this index in this paper, and the building adjusted NTL urban index was proposed. The means was used to extract urban built-up areas in Baotou City in this paper. Firstly, normalized difference build-up index (NDBI) was extracted from Sentinel-2A image data and it was combined with NTL to obtain building adjusted NTL urban index BANUI with the spatial resolution of 20 m, which has higher spatial resolution and more information about the building. Finally, the watershed segmentation algorithm was applied to the extraction of urban built-up area of Baotou City from BANUI, VANUI and NTL, and the results were comparatively studied. The extraction results show that the overall precision of the urban built-up area extracted by BANUI could reach 93.61%, the Kappa coefficient is 0.793 4, the user accuracy is 81.34%, and the producer accuracy is 85.34%. The extraction results are consistent with the distribution of actual urban built-up area, and the accuracy is high. The result is better than the area extracted by the other two kinds of data. This method could provide some reference for the study of the extraction of urban built-up area from NTL, and could also be used to monitor the development of urban planning.
Monitoring the spatial pattern and dynamic change of Karst rocky desertification has an important significance in Karst areas. In this study, the Karst rocky desertification evaluation model was established in Guizhou Province, which based on vegetation fractional coverage and degree of exposed bedrock using multi-temporal MODIS data, slope and population density as evaluation factors. The contribution of four factors was compared to accomplish the judgment matrix and calculate the weight by analytic hierarchy process. Karst rocky desertification evaluation model was established through consistency check. By this evaluation model, the spatial patterns of Karst rocky desertification and characteristics of conversion in different degree of desertification were acquired, the spatial-temporal evolution and dynamic change of Karst rocky desertification were analyzed in the period of 2007—2016. Some conclusions have been reached: ①Karst rocky desertification was improved dramatically from 2007 to 2016. The proportion of moderate rocky desertification and severe rocky desertification was 55.67% and 40.53% respectively in 2010 but was 79.71% and 17.35% in 2016. ②Moderate rocky desertification was the intermediary process of severe rocky desertification transform to light rocky desertification. It can firstly transform severe rocky desertification to moderate rocky desertification and then transform to light rocky desertification. ③Moderate rocky desertification and light rocky desertification were active, whereas severe rocky desertification was stable and the conversion rate was low.
In this paper, a topographic factor extraction algorithm based on the accumulation of the multiple flow direction up-slope considering the effect of land use/vegetation on the confluence is proposed, which improves the topographic factor extraction algorithm in revised university soil loss equation (RUSLE) and improves the accuracy of soil erosion extraction. The spatial distribution of soil erosion intensity and its relationship with environmental factors in Shangcheng County of Huaihe River basin were studied by using geographic information system (GIS) and remote sensing (RS) techniques. The results show that the average annual soil erosion modulus is 28.16 t·hm -2·a -1, suggesting moderate erosion. The total erosion area reaches 905.95 km 2, soil erosion intensity and erosion modulus also increase significantly with the increase of slope. The study provides a technical example for the application of the RUSLE model to soil erosion assessment in the ecological functional region and provides an effective basis for soil erosion control and environmental sustainable development in this region.
Geological Cloud 1.0 is the first comprehensive integrated application platform for cloud computing in China’s geological survey. As one of its distributed nodes, geological environment sub-node provides data sharing and product socialization services in the geological environment. In order to better serve the construction of Geological Cloud 1.0, this paper describes the specific technical implementation of the Geological Cloud 1.0 geological environment sub-node in four aspects: infrastructure construction, data sharing service, geological information product service and service portal construction. The construction effect of this node is discussed. The results show that geological environment sub-node supplies data and products resources in the geological environment, and provides technical support for the operation and upgrade of Geological Cloud 1.0 in the future.
Mathematical precision, correctness of attributes and logical consistency are the main contents of current quality inspection of geographic information products. The inspection of precision and attributes mostly uses manual field inspection methods. This method has several problems: First, the acquisition of test data is discrete; second, the property correctness check is greatly affected by human factors; third, the inspection work has high labor intensity, high cost and low efficiency; fourth, it is difficult to do check and implementation work in special areas. In this paper, the technology of laser point cloud, image, video, POS (position and orientation system) and other data acquired by low-altitude drones for the third-party quality inspection of geographic information products was studied. The attribute evaluation of geographic information products based on multi-source low-altitude remote sensing data is proposed, and the method of mathematical precision classification detection is put forward. The experiment shows that the method proposed in this paper can be applied to the quality inspection of geographic information products.