Archive

  • 2019 Volume 21 Issue 12
    Published: 25 December 2019
      

  • Select all
    |
  • LI Deren
    Download PDF ( ) HTML ( )   Knowledge map   Save

    The intelligent processing and service of spatiotemporal big data is an important application and development opportunity of Geo Spatial Information Science, which is centered on surveying and mapping, remote sensing and geographic information technology. The development, main characteristics and mining methods of spatiotemporal big data are comprehensively discussed in this paper; Then automatic matching, change detection and intelligent decision-making of intelligent processing technologies based on spatiotemporal big data are introduced; On this basis, the "3S" socialized applications from earth observation to human observation are discussed; Finally, the current situation, development goal, key technologies, and application prospects of PNTRC based on spatiotemporal big data are introduced. Many practices have proved that in the age of big data and artificial intelligence,facing on the massive multi-source and heterogeneous spatiotemporal big data, focusing on the construction of automation, real-timized, intelligence, popularization and socialization, the innovation and development of Geo Spatial Information Science will have a bright future!

  • LI Xiang, XIAO Guirong, CAI Shengzhun
    Download PDF ( ) HTML ( )   Knowledge map   Save

    Water environment monitoring data plays a key role in water environmental sensitivity assessment. However, insufficiencies remains due to influences of factors such as terrain, environment and site layout. Therefore, this paper took Fujian Province as the experimental area, and used web text collected from key platforms of Fujian Province from April to June 2017 as the data source. Thirteen evaluation factors were selected from three aspects: web text sensitivity, pollution sensitivity and the protection sensitivity of the water environment. Based on the fuzzy analytic hierarchy process combined with the web text, a water environmental sensitivity evaluation model was constructed, and classification validated the rationality of the evaluation results. Results show: (1) In terms of web text sensitivity, the eastern and central-northern region of the province was higher than the western and central-southern region, and the highly sensitive areas were concentrated in Gulou District, Jin'an District and Minhou County in the lower reaches of the Min River. (2) In terms of pollution sensitivity, the southern part of the province (especially the southwest and southeast) was higher than the northern, and the highly sensitive areas were mainly distributed in the middle and lower reaches of Ting River, the estuary of the Jiulong River, and the lower reaches of Jin River and Long River. (3) In terms of protection sensitivity, the northwest-central-south part of the province was higher than the northeast-southwest, and the highly sensitive areas were mainly located in Jiangle County and Mingxi County in the upper reaches of Min River, and Wuyishan City in Jian River, Xianyou County in Mulan River and Hanjiang District in Qiulu River. (4) The water environmental sensitivity of the whole province decreased by the order of the southeast, southwest, north, central, north, and the east, and the economically developed areas along the eastern coast and estuaries of rivers showed high sensitivity, especially in Min River Estuary and Jin River Estuary. (5) The water environmental sensitivity assessment combined with the web text was more accurate in the investigation of highly water environment sensitive areas with pollution risks. Our findings suggest that the proposed method is more reasonable for water environmental sensitivity assessment, and has practical significance for predicting or troubleshooting highly-sensitive pollution-risk areas, important protected areas, and areas of public concern.

  • LI Siyu, XIANG Longgang, ZHANG Caili, Gong Jianya
    Download PDF ( ) HTML ( )   Knowledge map   Save

    Taxi GPS trajectory data are of low acquisition cost, short cycle, large coverage, large-scale, and real-time. Moreover, taxi trajectory data contain a large amount of driving record information for extracting digital road information. Thus, taxi GPS trajectory data are suitable for obtaining and rapidly updating the information of large-scale urban traffic road networks. The extraction of urban road network intersections based on GPS trajectory data is currently a research hotspot. However, most of existing methods, which are applicable to the high-frequency GPS data, are difficult to adapt to taxi trajectories with low sampling frequency, low positioning accuracy, many noise points, and uneven data distribution. Therefore, existing methods are not readily applied to extract the intersections of suburb areas where taxi trajectory data are sparse or low-frequency. To extract road intersection information as accurately and comprehensively as possible, this paper proposed an integrated methodology to identify the intersections of urban road networks based on dense and sparse trajectory data. In this paper, the density peak clustering method was adopted in the vector space. Meanwhile, the mathematical morphology processing method was adopted in the grid space, where multiple resolution images were generated in the trajectory data rasterization stage. The extraction results were finally fused to achieve the purpose of extracting the road intersections of suburb areas with low traffic (i.e., sparse sampled data). Further, a fusion mechanism was designed to detect these intersections by fusing multiple results in both spaces. Finally, this paper used principal component analysis to determine the authenticity of the intersections, which was used to identify real intersections and remove pseudo intersections that were incorrectly extracted. In so doing, we obtained the urban road intersections based on the low-frequency taxi trajectory. Compared with existing methods, this method extracted more intersections and showed considerable consistency with remote sensing imagery. Besides, the accuracy evaluation shows that the extraction accuracy was 92.23%, the recall rate was 77.26% and the F-value was 84.08%. Our findings suggest that the proposed methodology can ensure the integrity and accuracy of urban road network intersections and be applied in intelligent transportation systems.

  • MA Xiaoyan, BAI Yulong, TANG Lihong, WANG Yue, LI Shanshan
    Download PDF ( ) HTML ( )   Knowledge map   Save

    In numerical simulations of the earth system, ensemble data assimilation methods are commonly used to study the various observation errors in predicting geological variables. In this regard, the widely used Ensemble Kalman Filter (EnKF) may suffer from a series of problems such as undersampling, covariance underestimation, filter divergence, and distanced spurious correlations, when the ensemble size is small. In particular, implementing the traditional Local Analysis (LA) method by the distance-based Gaspari and Cohn (GC) function can reduce the underestimation of background error covariance to some extent, but cannot completely eliminate the spurious correlation problem. In this study, a Fuzzy Analysis (FA) localization method coupled with the fuzzy logic control algorithm was proposed in the framework of the EnKF assimilation algorithm. In the design of the fuzzy logic controller, the distance between observation points and the corresponding status update points is taken as the fuzzy inputs. Through a series of fuzzy inference, more accurate fuzzy weight coefficients can be obtained as the control outputs, so as to reduce the observation error and improve the assimilation accuracy. Based on the Lorenz-96 model, the effectiveness of LA and FA under different model errors was compared; and the robustness of the two methods under ensemble numbers, observation numbers, and observation space, covariance inflation factor, and forced parameter change was discussed and analyzed. Meanwhile, Root Mean Square Error (RMSE) and Power Spectral Density (PSD) were used as performance indexes to evaluate the performance of the two algorithms. The experimental results show that the new localization method can correct the background error covariance matrix by constructing the corresponding equivalent weight of observation position to update local coefficient based on EnKF. To some extent, it can effectively eliminate the remote correlation between observations and state, and the observation data can be effectively utilized in the local scope. The FA algorithm can reduce the observation error, and the effectiveness and robustness of the new method was proved in nonlinear chaotic systems. These schemes illustrate that the new localization method based on Fuzzy Analysis of observation information can lead to a systematic improvement of the data assimilation performance. However, the determination of fuzzy distance and the calculation of fuzzy equivalent weight coefficient take extra long time; how to combine parallel computation with fuzzy control for improving assimilation efficiency remains to be further studied.

  • CHEN Xiaoyan, PAN Jun, XING Lixin, JIANG Lijun, SUN Yehan, ZHONG Weijing, FAN Bowen
    Download PDF ( ) HTML ( )   Knowledge map   Save

    Topographic and geomorphological features are important for remote sensing lithologic interpretation, but there still lacks quantitative analysis of the geological correlation between topography and lithology. Digital Elevation Model (DEM) is a digital representation and simulation of topographic and geomorphological features in space. Topographic factors derived from DEM can describe the characteristics of concave and convex changes of different topographic slopes and undulations, thus quantitatively describing the characteristics of different topographic and geomorphological features. This paper focuses on studying the mathematical and geological significance of topographic factors, by establishing the quantitative correlation between lithology and topographic factors combination for classifying rock types. Based on ASTER GDEM data, this paper extracted 12 topographic factors, such as slope, profile curvature, maximum curvature, topographic relief, and elevation coefficient of variation. Based on analysis of the geological significance of each topographic factor, the characteristics and correlation of each lithologic topographic factor were studied by cluster analysis and variance analysis, and the quantitative difference between lithologies in the study area was established. In addition, the factor analysis method was used to study the dominant factors in the process of lithological classification, and to determine the appropriate lithological classification method and achieve quantitative lithological classification. Quantitative research was carried out on the two basic issues: whether topographic factors can be used for lithologic classification and how to use topographic factors to identify lithology. Experimental results show: (1) There was a significant correlation between lithology and topographic factors and the topographic factors of different lithologies could be distinguished significantly. (2) There were significant differences among the topographic factors (combinations) of different lithology and topography. The macro-topographic complexity index (MTI) and micro-curvature index (MCI) based on factor analysis had a relatively high classification accuracy of 77.36% for the rock types in the study area, highly consistent with the actual lithologic types. Our findings suggest that topographic factors such as slope and topographic complexity can be used for lithological classification, and that factor analysis can be used to obtain quantitative indicators reflecting macro-topographic complexity and micro-topographic curvature characteristics. This study can serve as a methodological reference for quantitative classification of lithology.

  • DING Xiaotong, YU Zhuoyuan, SONG Haihui, XIE Yunpeng, LV Kejing
    Download PDF ( ) HTML ( )   Knowledge map   Save

    To date, there have been 43 types of natural focus diseases reported in China, 14 of which are officially-recognized infectious diseases including plague, human-avian influenza, malaria, and dengue fever. Most natural focus diseases are characterized by strong pathogenicity, serious clinical behavior, high mortality rate, and high incidence rate. In 2008, the fever with thrombocytopenia syndrome emerged in China, and dengue fever broke out in Guangdong province in 2014. Natural focus diseases are great threats to Chinese, epsically in the context that there is currently no comprehensive method for acquiring the distribution characteristics of multiple diseases. The equilibrium degree in a region reflects the structure of the diseases in that region, and the distribution of the degree can help understand the distribution of multiple diseases. The paper used the quantity information of 14 natural focus diseases in China from 2004 to 2015, and applied Shannon information entropy theory to explore the spatial distribution pattern of the equilibrium degree of multiple natural focus diseases. Spatial autocorrelation analysis was adopted to detect the high incidence areas and low incidence areas. Finally, based on Pearson correlation coefficient analysis, the correlations among elevation, temperature, precipitation, NDVI, population, density of population, GDP, and information entropy were quantified. Results show that: (1) Anhui Province and Inner Mongolia Autonomous Region had the highest number of natural focus diseases. The information entropy of natural focus diseases in mainland China showed obvious northwest-southeast differentiation characteristics. The high-value aggregation areas and low-value aggregation areas were mainly distributed on the two sides of the boundary line of the mountains from Hebei Province to Yunnan Province. (2) Compared with social factors, natural factors were the main factors affecting the equilibrium degree of natural focus diseases. It was more prone to a variety of diseases in warm and humid areas with appropriate temperatures and adequate moisture. Single disease was more likely to occur in specific livestock or specific mosquitoes areas. (3) Areas with a high total number of cases usually resulted from a large number of cases of one disease, and these areas were less equilibrated, while areas with high information entropy usually resulted from many concentrated outbreaks of diseases. Our findings help understand the distribution characteristics of natural focus diseases in China, and demonstrate the potential of applying information entropy to analyze the prevention and control measures of natural focus diseases.

  • HUANG Kui, LU Yimin, WEI Zheng, CHEN He, ZHANG Baozhong, MA Wenjin
    Download PDF ( ) HTML ( )   Knowledge map   Save

    Evapotranspiration (ET) is the key element linking surface water balance and hydrological energy cycle. Studying the spatiotemporal variations of ET and its driving climatic factors is of great significance for clarifying the relationship between water resources and climate change. Based on the MOD16/ET dataset, this paper quantitatively analyzed the temporal and spatial variations of ET in the Haihe River Basin from 2000 to 2014. Based on the long-term observation data of air temperature and precipitation, the correlation analysis method was used to quantitatively explore the relationships between ET and various climatic factors. Results show: (1) The ET of the Haihe River Basin from 2000 to 2014 showed a relatively significant spatial distribution pattern, with higher values in the north and south, and lower values in the northwest and middle east. Distribution of the inter-annual ETs of different land use types showed a decreasing pattern for forest land, grassland, cultivated land, and other types. (2) The annual average ET of the Haihe River Basin from 2000 to 2014 ranged from 371.96 mm/a to 441.29 mm/a, with an average of 398.69 mm/a. The average relative change rate was -0.41%, which showed a downward trend. (3) The ET as well as climatic factors of air temperature and precipitation in 2000-2014 showed a uni-modal periodic variation trend, and the monthly ET showed a single peak trend within the year. (4) The correlation between ET and precipitation/air temperature in spring and autumn was significantly higher than other seasons, with their average correlation coefficients being -0.17 and 0.37, respectively. This indicates that ET is positively related with precipitation and negatively related with air temperature. (5) The majority types of ET changes in the Haihe River Basin were precipitation driven as well as precipitation and air temperature combination driven. (6) The climate factor driving mode of cultivated land ET change in Haihe River Basin was mainly precipitation and temperature; the driving mode of forest land and grassland was mainly air temperature and precipitation, while the drivers of other land-use types were mainly other factors. Our findings provide scientific guidance for water resource development management and regional climate regulation in the Haihe River Basin.

  • CHEN Kexin, CONG Pifu, LU Weizhi, QU Limei
    Download PDF ( ) HTML ( )   Knowledge map   Save

    The aim of this paper is to select the optimization model of the region and understand the future quantity and spatial variation trend of the wetland landscape types in the Yellow River Delta. We used the classified maps of the three periods of 1996, 2006 and 2016, of which the 1996 and 2006 maps were modeled for predicting 2016; we then compared the classified and simulated maps of 2016 to assess the model performances. The best model were used to take the classified 2006 and 2016 maps to simulate the landscape of the Yellow River Delta in 2026. We found that: ① For the simulation of the landscape types of the Yellow River Delta, under the influence of the same driving force factors, the LCM (Land Change Modeler) model performed better than the CA-Markov model in terms of spatial error, while CA-Markov was more suitable for the actual wetland change trend modeling than the LCM model in terms of numerical error. For the areas of larger landscape changes, the advantages of the two models should be combined to best simulate the change trend of wetlands. ② The interference of some human factors and the impact of natural disasters on the landscape types cannot be considered the model, it would cause some interference to simulation accuracy. For the LCM model, the number of transition sub-models had an effect on the simulation results with the same driving force factor, the more transition sub-models were used to generate suitable images, the higher the simulation accuracy. For CA-Markov model, the setting of proportional error coefficient was suitable for improving the accuracy of numerical simulation. ③ Assuming the continuation of the landscape dynamics trend during 2006-2016, and by simulation via combining the two simulation methods up to year 2026, the simulated natural wetlands area was 1252.69 km 2, the human-made wetlands area was 1265.00 km 2, and the non-wetlands area was 924.51 km 2. The simulated results suggest that natural wetlands and non-wetlands area will likely reduce, and human-made wetlands area will increase and expand to even shallow sea areas. Our findings can provide a scientific basis for the rational layout planning of the regional development space and the rational and effective utilization and management of wetland resources.

  • GONG Meng, WU Xiaoqing, YU Lu
    Download PDF ( ) HTML ( )   Knowledge map   Save

    With the rapid socioeconomic development, human activities continue to expand from mainland toward the sea. In this context, reclamation has become an important way to solve the shortage of land resources in coastal areas. However, high-intensity and unreasonable reclamation has caused tremendous pressure on the eco-environment in the coastal zone. Timely and accurately monitoring regional reclamation is of great significance for the protection of coastal resources and the promotion of regional sustainable development. Shandong Province is a major marine province with a long history of reclamation, with prominent coastal eco-environment problems caused by reclamation. By summarizing the existing literature, we found that there are few studies focused on unraveling the human-environmetal laws of reclamation, especially in Shandong province. So we extracted the vector data of reclamation using remote sensing images, and analyzed the spatiotemporal dynamics along the mainland coast of Shandong Province from 1974 to 2017. The present study was based on RS/GIS technology, combined with a variety of data sources and aiming to inform reclamation management and related planning of the coastal zone in the study area. Results showed that: (1) The reclamation area of the mainland coast of Shandong Province had reached 4649.26 km 2 by 2017, presenting a sustained growth trend during the research period. The main type of utilization was sea-based reclamation. (2) Sea-based reclamation was mainly distributed in the Yellow River delta and the tidal flat areas of Laizhou Bay, while land-based reclamation was concentrated in the coastal areas of major ports and cities. The gravity center of reclamation moved from southeast to northwest during 1974-2017. (3) Frequent conversion from sea-based reclamation to land-based reclamation was prominent, and reclamation utilization types changed from being single to more diversified. The proportion of harbors, towns, industries, and other utilization types in reclamation development had increased rapidly, and the development of aquaculture and salt industry was no longer the main way. The reclamation utilization turned to be more comprehensive, diversified, and intensified. (4) Coastal reclamation in the whole province presented obvious multi-stage characteristics. Before 2000, reclamation in Shandong Province was mainly used to develop aquaculture and salt industry. While after 2000, especially from 2007 to 2017, the land-based reclamation area had increased rapidly, mainly used for port construction, urban and tourism infrastructure construction, and industrial development of coastal ports, due to the impact of the coastal development strategy, urbanization and industrialization. (5) The types of reclamation had been transformed between each other frequently. The new land in Shandong Province came not only from land reclamation to the sea, but also from the transformation of original reclamation types such as aquaculture ponds and salt fields.

  • SHI Guoping, HE Yongjian, ZHANG Mei, QIU Xinfa, ZENG Yan
    Download PDF ( ) HTML ( )   Knowledge map   Save

    The temperature and humidity index is one of the climate comfort evaluation models, which reflects the heat exchange between human body and the surrounding environment through the combination of temperature and humidity.Based on the GridMet model, the spatial distribution of temperature and humidity indices(THI)in ZhejiangProvince(100 m×100 m)weresimulated by using monthly average air temperature, relative humidity from 71 meteorological stations, and MODIS 05over Zhejiang and surrounding areas during 2003-2018. Furthermore, we analyzed the distribution characteristics of THI in Zhejiang with the terrain factors (elevation, gradient, slope direction). The influence of terrain factors on the spatial distribution of the THIwas discussed. The results show that most montane areas in Zhejiang were more comfortable in summer due to the topographic influence, and thus, are suitable for establishing summer tourism projects. Specifically, among the three topographic factors of elevation, slope, and aspect, the THI in January changed the most with the slope direction and the least in July.On the same slope, the change of slope degree had a greater impact on the THI in January, while the change of altitude had the greatest impact in July. The THI in January on the south slope increased slightly with the increase of elevation and slope degree, while in other months on the south slope and on the north slopeTHI increased slightly with the elevation, and the sum of the THI decreased with the increase of slope gradient. Compared with the south slope, the influences of elevation and slope gradient on THIwere more obvious on the north slope.

  • XU Xuan, LI Junli, BAO Anming, WANG Baoshan, LI Changchun
    Download PDF ( ) HTML ( )   Knowledge map   Save

    The assessment of desert vegetation damage in large-scale strip coal mining areas is a research hotspot in environment restoration studies in the recent years. The Wucaiwan mining area is located in the western part of the Xinjiang Zhundong coal mining base, most of which is open-pit coal mining. Since the construction of the mining area in 2006, coal mining and coal chemical industry have had a serious impact on the surrounding eco-environment. With 90 scenes of Landsat imagery, the present paper analyzed the spatiotemporal characteristics of Wucaiwan and its surrounding desert vegetation before and after the open-pit coal mining. The aim was to study the disturbance effects of open-pit mineral exploitation from 1990 to 2017, and to quantify the response of vegetation growth to climate change and mining area expansion. Results show that: (1) The vegetation change during 2006-2013 in the mining area presented a trend opposite to that of surrounding areas, with similar trends during 1990-2006 and 2014-2017. Vegetation in the mining area was severely disturbed and experienced an obvious degradation during 2006-2013. (2) Vegetation of the mining area was most flourishing in May or June and fell into decay in July or August, as evidenced by the monthly NDVI during the growing season. This illustrates the typical phenological characteristics of desert ephemeral plants, and it can be seen clearly that vegetation in the central sample area of the mining area has undergone successively degradation and restoration. (3) The disturbance distances of the development of surrounding areas on the growth of surrounding vegetation were -17~21 and -13~23 km along the “west-east” and “south-north” directions, respectively. Besides, the disturbance distance in 2013 was the largest. (4) The precipitation in winter and spring was the main influencing factor on the growth of the Wucaiwan mining area and the surrounding vegetation. Although the mining area has been in an expanding state, the vegetation in the mining area has improved since 2014 (consistent with the surrounding vegetation), thanks to the increase of precipitation and the strengthening of dust protection measures. Our findings provide accurate data for the dynamic monitoring of vegetation changes in open pit coal mining and surrounding deserts in arid areas, informs managers of mining areas to take more effective measures for environmental protection and pollution control.

  • WANG Liying, WANG Sheng, LI Yu
    Download PDF ( ) HTML ( )   Knowledge map   Save

    The existing binary voxel primitive based 3-Dimensional (3D) filtering algorithms for airborne Light Detection And Ranging (LiDAR) data, which use only elevation features, cannot distinguish between connected ground and non-ground objects. As a result, an airborne LiDAR 3D filtering algorithm based on intensity voxel primitive was proposed in the present study. First, airborne LiDAR data were regularized into intensity voxel structure based on computational geometry theory, in which intensity value of the voxel corresponds to the quantized intensity of the LiDAR point(s) within the voxel. Second, based on the theory of 3D connected region construction, the non-zero voxels with the lowest local elevation was selected as ground seeds, and then ground seeds and their connected regions where the voxels are 3D connected and have similar grayscales and slope with seeds were labelled as ground voxels. The proposed algorithm makes comprehensive use of the features of elevation, reflection intensity, and slope, supports 3D filtering in areas where the ground are adjacent to non-ground objects but with different intensities, and provides more effective information for the accurate distinction between the connected ground and non-ground objects. The proposed algorithm is helpful to improve the filtering accuracy and extend voxel primitive based 3D filtering algorithm for more complex scenes. The International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark dataset, which contains a variety of features that is expected to be difficult for automatic filtering, were used to analyze the sensitivity of “spatial adjacency size” parameter in the proposed algorithm and to assess the accuracy of the proposed algorithm quantitatively. Results show: (1) The 51-adjacency was the optimal spatial adjacent size. (2) The average Kappa coefficient of the proposed algorithm was 0.9380, 0.7749, and 0.6866 in relatively flat, steep slope, and discontinuous terrain areas, respectively. (3) In terms of total error, the proposed algorithm improved the accuracy of 7 out of 15 samples and had a higher accuracy than all other binary voxel primitive based 3D filtering algorith-ms on average.

  • LI Yang, WANG Jie, HUANG Chunlin
    Download PDF ( ) HTML ( )   Knowledge map   Save

    Snow and vegetation cover fractions are important for studying climate change, water resource balance, and eco-environmental conditions. Yet, it is difficult to acquire accurate cover products due to the high spatiotemporal variability of snow and vegetation cover fractions. The enemember variability can result from complex terrains, atmospheric influences, and the intrinsic variability of features such as the chlorophyll concentration in plants, snow particle size, and snow contamination. Supervised spectral unmixing algorithms often assume that the endmembers are known exactly. However, in practice, the endmembers are extracted from real spectral images that may be affected by measurement noises or errors. In addition, available knowledge of the endmembers might not exactly match the actual endmembers of the spectral image at hand, since the spectral signature of the same material may be slightly altered in different images or because distinct but confusingly similar spectral signatures may be mixed up. To solve this problem, this paper proposed a perturbed mixing model (PMM) based on spectral normalization. The PMM attempted to reduce the errors caused by spectral changes and noises by introducing disturbed factors (both image and endmember matrix are perturbed). The PMM model could capture the small noise and endmember variations, yet its accuracy would decrease when the spectrum changed enormously. To solve the problem, the spectral normalization was used to reduce the differences between the endmembers and the spectrum matrix. Spectral normalization did not change the correlation between endmembers and the relative position of high dimensional feature space, but aggregated spectral features of higher correlation coefficients and decreases spectral changes. Then, the PMM was used to quantify the spectral variation and measurement noise/error in order to improve the accuracy of the snow and vegetation cover mapping. Finally, three different areas (snow-dominated region, vegetation-dominated region, and region where snow and vegetation are mixed) were selected to validate the feasibility of the proposed framework. Results show: (1) The root mean square error (RMSE) of snow-dominated region was 0.172, the RMSE of vegetation-dominated region was 0.223, and the RMSE of snow and vegetation mixed region were 0.185 and 0.249, respectively. Relatively high accuracy was achieved in the three types of areas. (2) After normalizing the endmembers and images, the spectral heterogeneity was obviously decreased and the overall accuracy of the three algorithms was better than before normalization. (3) The snow coverage fraction obtained by the framework had higher accuracy than the vegetation coverage fraction.

  • LEI Zhibin, MENG Qingyan, TIAN Shufang, ZHANG Linlin, MA Jianwei
    Download PDF ( ) HTML ( )   Knowledge map   Save

    As an important component of soil, soil moisture plays an important role in crop growth. The GaoFen-3(GF-3) satellite, as the first C-band full-polarization Synthetic-Aperture Radar (SAR) satellite of China, provides a valuable data source for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed over densely-vegetated areas based on GF-3 and Landsat8 data. To improve the accuracy of the soil moisture retrieval, this paper firstly analyzed the correlation between eight vegetation indices and Vegetation Canopy Water Content (VCWC) based on the PROSAIL model, measured vegetation parameters and the Landsat8 optical data. The Normalized Difference Water Index (NDWI5), which was identified as the optimal index from these indexes, was used to obtain the VCWC. The inversion model of Vegetation Water Content (VWC) was established by analyzing the relationship between measured VWC and the VCWC. Secondly, the model was integrated with simplified Michigan Microwave Canopy Scattering (MIMICS) model to correct the effects of vegetation on the radar backscattering coefficient. Finally, the backscattering coefficient simulation dataset of bare soil was established based on the Advanced Integrated Equation Model (AIEM) for developing the soil moisture retrieval model over densely-vegetated areas by combining active microwave and optical remote sensing data. The soil moisture retrieval algorithm was validated in a region of corn in Yucheng city, Shandong province, with soil moisture retrievals obtained at HH, VV and HH+VV combination, respectively. Results show: ① NDWI5 had the best fit with measured VCWC values among the eight vegetation indices, with the coefficient of determination (R 2) reaching 0.7433, and the Root Mean Square Error (RMSE) being 0.5146 kg/m 2. Thus, it was adopted to correct the effects of vegetation. ② The proposed algorithm based on GF-3 and Landsat8 satellite data performed well in soil moisture retrieval that resulted in improved accuracy in soil moisture monitoring. ③ Compared with the HH and VV polarization, the HH+VV dual-channel mode exhibited the highest accuracy, with a R 2 of 0.4037 and a RMSE of 0.0667 m 3m -3, followed by the HH polarization (R 2=0.2894, RMSE=0.0692 m 3m -3) and the VV polarization (R 2=0.3577, RMSE=0.0675 m 3m -3). Our findings suggest that the proposed algorithm has good potential for operationally estimating soil moisture from the new GF-3 satellite data with high accuracy.

  • JIANG Hong, YUAN Yawei, WANG Sen
    Download PDF ( ) HTML ( )   Knowledge map   Save

    Topographic correction is a crucial step in the pre-processing of remote sensing imagery of rugged terrain areas. Recently, a Shadow-Eliminated Vegetation Index (SEVI) was proposed to eliminate the influence of the self and cast terrain shadows. To further evaluate the SEVI performance for reducing the terrain shadow effect on regional scales, here we compared the SEVI with classic topographic correction models, including the C model, Sun-Canopy-Sensor (SCS)+C model, and Second Simulation of the Satellite Signal in the Solar Spectrum (6S)+C model via the case study of Fuzhou city, China. Landsat 5 TM satellite data and associated 30-meter Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 (ASTER GDEM V2) were used for the comparison. The satellite imagery were first corrected using the C, SCS+C, and 6S+C models, followed by the calculation of the Normalized Difference Vegetation Index (NDVI) and the Ratio Vegetation Index (RVI). Then, the calculated vegetation indices were evaluated in different ways, including visual comparison, statistical analysis, and linear correlation analysis of the cosine of solar incidence angle ( cos i ) versus vegetation indices. The C and SCS+C models showed accurate correction results over the self shadow areas but less accurate results over the cast shadow areas. Using adjacent sunny slopes as a reference, the relative errors of the NDVI and RVI over the self shadow areas were reduced from 71.64% and 52.57% to 4.80% and 6.43% (C model) and 0.50% and 9.94% (SCS+C model), respectively; the relative errors over cast shadow areas were reduced from 62.01% and 47.57% to 31.05% and 24.40% (C model) and 33.42% and 16.01% (SCS+C model), respectively. The 6S+C model showed better correction results over the cast shadow areas than the C model and the SCS+C model did. The relative errors of the NDVI were 8.63% and 14.27% over self shadow and cast shadow areas, respectively, if the 6S+C model was used. The SEVI seemed the most accurate among these models for corrections of self and cast shadows. The relative errors of the SEVI were 9.86% and 10.53% over the self and cast shadows, respectively. Finally, the SEVI was used to study the vegetation cover change in Fuzhou city from 1999 to 2014. Results show: (1) the vegetation cover in Fuzhou city increased from 1999 to 2014 in general, particularly over the areas with elevation ranging from 250 to 1250 meters; (2) The highest SEVI mean was located on the slope of about 40 degrees.