Bamboo resources are widely distributed in tropical, subtropical and temperate regions. They are good substitutes for many non-renewable resources. Their rapid growth and wide distribution play an important role in mitigating climate change and developing countries to lift poverty and reduce poverty. Remote sensing technology is widely used in resource monitoring and quantitative mapping of forest structures, and has the advantages of wide monitoring range and high precision in space and time. This paper systematically sorts out the application of remote sensing data sources in bamboo resource monitoring, the temporal and spatial dynamic change monitoring of bamboo resources and the bamboo resource monitoring and classification method. Focus on the data sources and classification methods of monitoring and mapping, and statistical analysis of the accuracy of various methods. It is proposed that the remote sensing monitoring of bamboo resources should pay attention to use the multi-source data and classification methods, strengthen the special growth stages of bamboo species and bamboo, and monitor the quantity and quality of bamboo as the future research focus, in order to protect the endangered wild animals, poverty alleviation in poverty-stricken areas, and bamboo industry. Provide technical support for development and utilization, improvement of people's livelihood in developing countries.
Accurate and efficient extraction of road information based on remote sensing image is a great significance for the establishment and maintenance of basic geographic databases. Due to the complex background information of high-resolution remote sensing images, existing algorithms cannot extract road information very well. U-Net network has good experimental results in image segmentation, but the accuracy of road segmentation results is not good. For this reason, this paper proposes a high-resolution image road extraction method based on improved U-Net network. Firstly, the U-Net-based network structure is designed and implemented. The network uses VGG16 as the network coding structure, which can extract feature semantic information better. Secondly, the use of Batch Normalization and Dropout solves the phenomenon of over-fitting that occurs during the network training process. Finally, the training data is expanded by rotation and mirror transformation, and the ELU activation function is used to improve the network training speed. The experimental results show that the method can extract road information more accurately and efficiently.
Based on convolutional neural networks and five different spatial resolution remote sensing images, the land use/land cover classification study was carried out on a small area in the eastern part of Xining City, aiming at exploring the differences of image classification by CNN with different spatial resolutions and CNN’s ability to extract different features. In order to improve the selection efficiency of the samples, a window sliding method was introduced to assist the samples selection. The research shows that the overall classification accuracy of the five different spatial resolution images is above 89%, the Kappa coefficient is above 0.86. The result further shows that within the resolution scale the higher the resolution, the performance of the CNN classification results for the details is better, and can maintain high classification accuracy, indicating that CNN is more suitable for high spatial resolution images; at the same time, the image spatial resolution is too high, the ground objects exhibit high intra-class variability and low inter-class variability, the classification accuracy tends to decrease. In comparison, CNN has the best classification effect on SPOT 6 images in this study, and window sliding is an effective sample-assisted selection method. This research has certain reference significance for similar research in the future.
Geospatial data sharing as a significant component of geoscience research. It is also an important means to exploit geoscience data and avoid repeated collection of geospatial data. It is an essential foundation for modern data-intensive geoscience research. Therefore, how to break the geospatial data “island” and realize different geoscience data portable access, secure data security, and promote geospatial data author copyright are urgent problems in geospatial data sharing. The rise of blockchain technology has brought new possibilities for the expansion of the value of geospatial data, the enhancement of data security and the improvement of the protection of the rights of stakeholders in geospatial data. Based on the systematic review of the status quo of data sharing, existing problems, basic principles of blockchain, underlying architecture, characteristics and application status, this paper discusses the application feasibility of blockchain technology in geospatial data sharing, and expounds the application challenges of blockchain technology in geospatial data sharing, in order to provide reference for the research and application of blockchain in geospatial data sharing.
High-resolution remote sensing images have precise geometric structure and spatial layout, but the spectral information is limited, which increases the difficulty of classifying similar features of spectral features. Aiming at the problem of high resolution remote sensing image classification, a U-Net convolutional neural network classification method based on deep learning is proposed. Based on the remote sensing image of the Ejina Oasis GF-2 in the lower reaches of the Heihe River, the U-Net model was used to extract the five types of land cover types of Populus euphratica, Tamarix chinensis, cultivated land, grassland and bare land. The overall classification accuracy and Kappa coefficient were 85.024% and 0.795 6 respectively. Compared with the traditional Support Vector Machine(SVM) and object-oriented method, the results show that compared with SVM and object-oriented method, the U-Net model is used to classify the high-resolution remote sensing, and the classification effect is better. The ground extracts the essential features of the features to meet the accuracy requirements.
The spatial pattern and density of residential areas directly reflect the intensity of regional human activities, and affect the evolution of a regional human-land system and the sustainable development of ecological environment. In this study, we proposed an objected-oriented automatic extraction method, which based on the high spatial resolution satellite remote sensing image data in the surrounding area of Qinghai lake watershed. Firstly, multi-scale segmentation of high-resolution satellite remote sensing image was carried out by using the scale sets theory to obtain segmentation objects in different scales. Secondly, the custom, spectral, geometric and texture features of the sample attributes were trained through the sets of machine learning algorithms, and the optimal automatic classification algorithm was selected. Finally, the optimal automatic classification algorithm was used to extract the information of urban and rural residential areas in the surrounding area of Qinghai lake watershed. The average recall rate, accuracy rate and F value were used to evaluate the classification results. Accuracy evaluation indexes of urban residential areas were more than 93%, and those of rural residential areas were more than 86%. The results show that this method has higher overall precision when extracting urban residential areas and rural residential areas, and has better scientific significance and application value in fine monitoring of human activities in large areas.
The measurement and analysis of the dielectric constant of soil samples with different moisture and salinity are achieved based control experiment. The saturation is introduced to the dielectric model of salinity soil to improve simulation accuracy by taking the Stogryn model and the influence of soil solution ion concentration, conductivity, moisture content, and salt content for dielectric constant imaginary part into consideration. The results indicate that the soil salt content has little influence on both real part and imaginary part of dielectric constant when soil volumetric moisture content is low, while soil volumetric moisture content is high, the real part of the dielectric constant decreases with the increase of soil salt content, and the imaginary part of the dielectric constant increases. The improved dielectric model of salinity soil can well reveal the changes of dielectric constant, and it is also having a great effect in Baiyin soil samples. That is to say, the improved dielectric model can apply to different soil types.
To obtain Aerosol Optical Depth (AOD) products with high spatial and temporal resolution and high precision in China. We enhance the AOD retrieval algorithm by applying the MODIS red and blue surface re?ectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOD over both dark and bright surfaces., we retrieve the 10-minute AOD of China from April 2018 to April 2019. The AOD retrievals from the enhanced red-blue ratio algorithm (RB AOD) were validated by the Level 1.5 AERONET (Aerosol Robotic Network) sunphotometer measurements and MOD04_3K AOD , and the retrieval of AOD were compared with the latest AOD product (version 020) released by Himawari-8.The results show that :(1) The AOD retrievals from the enhanced algorithm agreed well with those from the AERONET. Except Baotou station, the correlation coefficient R of the other five stations is between 0.728-0.863, and the percentage of sample points within the range of EE (Expectation of Error) is between 47.7% and 68.6%, which has great advantages over Himawari-8 AOD products.(2) The RB AOD are basically consistent with AERONET AOD in time series trend. the RB AOD results are higher than those of AERONET AOD when AOD > 1.The spring and summer trend of Himawari-8 AOD is relatively consistent with that of AERONET AOD. However, due to the obvious diurnal change of Himawari-8 AOD in autumn and winter, and the diurnal change is relatively large, its trend deviates greatly from AERONET AOD.(3) The spatial distribution of RB AOD is basically consistent with that of MODIS AOD products, and the retrieved results are slightly lower than those of MODIS AOD.In winter, the inversion range of red-blue ratio method is wider than that of MOD04_3K AOD.(4) the red-blue ratio retrieval algorithm has great advantages over Himawari-8 AOD and MOD04_3K in precision and range of retrieval results of high-reflectance surface area in north China in winter and spring.
Sand emission process of sandstorm is a fundamental part of sand-dust cycle. Sand emission process simulating accuracy plays a crucial role in correctly simulating sand transporting and settling process. As one of the most widely used sandstorm models, WRF-Chem (Weather Research and Forecasting with Chemistry) is used to simulate the sandstorm happened during March 26 and March 28, 2018 in northern China in this study. It is reported that uncertainties in underlying surface and soil moisture initial status in WRF-Chem can lead to great bias in its simulating results. Remote sensing products like land cover and soil moisture products have been widely accepted for their higher accuracy, which provides an opportunity for WRF-Chem simulating sandstorms better. Therefore, to examine the effects of initial field uncertainties on sandstorm simulating, we simulated a sandstorm using WRF-Chem by replacing the underlying surface and soil moisture initial field with new version soil database, MODIS (Moderate Resolution Imaging Spectroradiometer) land cover products and AMSR2 (the Advanced Microwave Scanning Radiometer 2) soil moisture products. Four experiments were carried out, including a control experiment and three contrast experiments. The three contrast experiments are organized by only replacing the soil moisture initial field, only replacing land cover and soil texture, and replacing both. After replacing traditional initial field with remote sensing data, the simulation accuracy all has improved. Among the three contrast experiments, replacing all three parameters (land cover, soil texture and soil moisture) has the greatest improvement: the correlation coefficient of PM10 increases by 0.30, the average deviation reduces by 31.18 μg/m3, the root mean square error reduces by 21.7 μg/m3, the correlation coefficient of AOD (Aerosol Optical Depth) improves by 0.14, the average deviation reduces by 0.29, the root mean square error reduces by 0.18. The contrast experiment which only replacing soil moisture performs the second, followed by only replacing land cover and soil texture which does not improve the simulation results much. In conclusion, the simulation accuracy of sandstorm is improved by introducing the remote sensing products.
Housing Vacancy Rate (HVR) is an important index in assessing the healthiness of residential real estate market. Due to lack of clear and effectively evaluation criterion, the understanding of housing vacancy in China is then rather limited. This paper quantitatively analyzed spatial identification and difference pattern of house vacancy at different scale in China by using nighttime light data and micro-blog check-in data, in order to make up the deficiency of traditional methods in the aspects of data missing and differential approach. The nighttime light intensity for non-vacancy area is estimated after removing the nighttime light intensity from non-residential sources of NPP-VIIRS light data and difference of nighttime light caused by the different urban area ratio. Then, the HVR is calculated for the spatial pattern analysis. This paper analyzed the spatial strength of residents activities by using micro-blog check-in data, based on density-based spatial clustering of applications with noise and heat map. The 30 sample cities were selected to identify house vacancy from 100 cities which ghost city index were high. The following conclusions were drawn through the study: The HVR of eastern coastal cities and regions with rapid development of economy were lower, while the phenomenon of house vacancy in central and western regions were more obvious. The HVR increased from eastern coastal regions to inland areas. What’s more, the phenomenon of house vacancy in middle and small cities were more distinct from the aspect of urban scale. The house vacancy of China were divided into five types: industry or resources driven, government planned, epitaxy expansionary, environmental constraint and speculative activate by taking the factors of natural environment, social economic development level, and population density into consideration. This may shed light on policy implications for Chinese urban development.
Drought is the first disaster affecting agricultural production. The annual precipitation in Xinjiang of China is scarce and the climate is dry. This is one of the major obstacles to the agricultural transformation and rural revitalization in Xinjiang. Therefore, timely and accurate monitoring of agricultural drought in Xinjiang is of great significance for safeguarding agricultural production. Yanqi Basin in Xinjiang was took as an example. Landsat8 and MODIS data were used. The Spatio Temporal Adaptive Reflectivity Fusion Model (STARFM), the Enhanced STARFM (Enhanced STARFM, ESTARFM) Model and Flexible Spatio Temporal Data Fusion (FSDAF) model were used to construct the Temperature Vegetation Dryness Index (TVDI). At the same time, the Relative Soil Moisture (RSM) was used to verify the TVDI inversion results. The results show that coefficient of determination (R2) and root mean square error (RMSE) of the drought factors(NDVI and surface temperature) simulated by the ESTARFM model were better than that by the other two models. And the R2 and RMSE of NDVI simulated by the ESTARFM model reached 0.924 and 0.076. In addition, the R2 and RMSE of surface temperature simulated by the ESTARFM model reached 0.877 and 2.799. Comparing with TVDI of the real Landsat8 data inversion and RSM data, it was found that the TVDI simulated by the ESTARFM model is better than the other two models, with 0.873 of R2 and 0.248 of RMSE. The ESTARFM model can more accurately simulate the TVDI distribution of the Landsat8 images in the same period, so as to monitor the drought degree of the farmland in Xinjiang.
Air pollution characterized by PM2.5 pollutants poses severe challenges to the sustainable development of society and human health. Therefore, it is of great significance to clarify the spatial-temporal distribution and evolution of PM2.5 pollutants in China for regional joint prevention and control of PM2.5 pollutants. Based on the MODIS satellite aerosol products, meteorological basic data and PM2.5 pollutant monitoring site monitoring data, a geographically weighted regression model was established to simulate and estimate PM2.5 pollutant concentration in China in 2015 on the basis of aerosol and meteorological data pre-processing. In addition, the spatial distribution pattern, the seasonal evolution characteristics of PM2.5 pollutant concentration were analyzed. The results showed that: (1) the PM2.5 concentration values in China in 2015 as a whole showed obvious spatial zonal differentiation characteristics. The concentration of pollutants in the north is significantly higher than that in the south, and the areas with high PM2.5 concentrations are mainly concentrated in the Beijing-Tianjin-Hebei region, the Jianghuai plain, the Sichuan basin, and the Takaramalkan desert. The area has a wide spatial distribution and significant continuity; (2) The PM2.5 concentration in the fourth quarter showed obvious seasonal adaptive evolution characteristics. The PM2.5 concentration changed significantly in the season. PM2.5 pollution was the heaviest in the fourth quarter, followed by the first quarter of the third quarter and the lowest in the second quarter. The maximum occurred in the fourth quarter (165 μg/m3). The minimum appeared in the second quarter (4.3 μg/m3). Seasonal changes in PM2.5 concentrations were influenced by meteorological factors and human social activities; and (3) The accuracy of the inversion of PM2.5concentration by a multi-factorial, geographically weighted regression model was higher, with relative errors in the four quarters being 10.2%, 7.0%, 9.3%, and 8.6%, respectively.
Picea crassifolia and Sabina przewalskii are the dominant species in Liancheng Nature Reserve. Extracting the spatial distribution of two types of trees is of great significance for the management and monitoring of forest resources in the reserve. Based on the method of random forest,22 feature variables in eight combinations from Sentinel-2A (S2),Sentinel-1A (S1),Landsat-8 (L8) three remote sensing images and digital elevation model of SRTM DEM to classify Picea crassifolia and Sabina przewalskii in Liancheng Nature Reserve of Gansu Province.The results demonstrated that the accuracy of integrating VV and VH backscattering information of sentinels-1A (S1) was the highest，reaching 92.85%,which is 11.64% higher than that of single image Landsat-8. Experiments showed that combining different bands of multi-source remote sensing images is an effective means to improve the classification accuracy of forest types,which is beneficial to forest resource survey and vegetation information extraction in complex mountainous areas.
Through taking 97 Sentinel-1A SAR images from October 2014 to May 2019 covered most parts of Eastern Gansu province as experimental data, we monitored the surface deformation applying PS-InSAR technique for superimposed data processing based on ISCE and StaMPS to obtain the annual mean LOS rate of surface deformation field. Moreover, we filtered the LOS velocity field using two-dimensional mesh filtering to obtain the variation characteristics of the subsidence center. Our results reveal that there have two patterns of surface deformation. (1) Ground deformation caused by tectonic activity mainly locates around Haiyuan fault, whose mean annual LOS rate of deformation is ~1mm/a. However, there is no obvious deformation near the Liupanshan fault. Meanwhile, the internal deformation of the Ordos Block is subtle. (2) Another surficial deformation caused by mining activity occurs in the regions of Huating Mining Area and Ningzheng Mining Area, in which a ground subsidence funnel has been found. According to the time series deformation characteristic analysis of the settlement center, we know the mean annual LOS rate of deformation in the Huating Mining Area and Ningzheng Mining Area are ~8 mm/a and ~30 mm/a, respectively.
Taking Minqin Oasis in the downstream area of the Shiyang River Basin which is located in the east of Hexi Corridor as an example, the Landsat 8 OLI image was chosen as the data source. Under the consideration of the basic concept of the artificial oasis and natural oasis in this paper, combining with the information of the spectrum, texture, shape and context basing on the image data preprocessing and multi-scale segmentation, we introduce a series of indexes such as NDVI、maximum difference, compactness, shape index, the space adjacency relation and so on to construct a rule set for distinguish between natural oasis and artificial oasis. The obtained results were further compared with the results based on the maximum likelihood method. As a result, the total accuracy of using the object-oriented image analysis method to distinguishing between natural oasis and artificial oasis is 91.75%, and the Kappa coefficient is 0.65 by using the rule set established in this paper. Compared with the results based on the maximum likelihood method, the overall accuracy is improved by 10.40% and the Kappa coefficient is 0.13. The Kappa coefficient of the artificial oasis is increased by 0.19, and the Kappa coefficient of the natural oasis condition is increased by 0.30. The results showed that the object-oriented image analysis method can overcome the limitations of the classification method that only using spectral feature to a certain extent, avoid the confusion caused by the phenomenon of “same object with different spectrums” and “same spectrum with different objects”, and increase the accuracy of distinguishing between the artificial oasis and natural oasis.
Lakes in the Qinghai-Tibet Plateau are numerous and widely distributed, accounting for 41% and 57% of the total number and area of lakes in China, which are very important for the study of lakes in the whole country and even in the whole world. Remote sensing has been used to monitor the lake distribution for a long time, but optical remote sensing images are often obscured by clouds, from which it’s impossible to automatically extract complete lake boundaries. An automatic interpolation algorithm for lake boundary generation based on cloudy Landsat TM/OLI image and Shuttle Radar Topography Mission (SRTM) 30 m resolution Digital Elevation Model (DEM) is proposed. Firstly, supported by the platform of Google Earth Engine, the tier1 data of Landsat TM/OLI images are used to eliminate the effects of cloud, cloud shadow, snow and mountain area, based on the Pixel Quality Assessment (pixel_qa) attribute and SRTM 30 m DEM. Then, the Modified Normalized Difference Water Index (MNDWI) is calculated, and the Canny edge detection algorithm are used to obtain the known part of the lake boundary (L) in cloud-free areas. The possible lake areas are obtained by range filtering of DEM locally. At the same time, DEM is used to generate contours with an isometric interval of 1 m, and a series of contours surrounding the possible lake area are automatically screened out. The tree structure is established according to the inclusion relationship between contours. The leaf nodes are the innermost contours, which are recorded as inner contours (C1). Because the acquisition time of Landsat and DEM is different, with the lake expanding or shrinking, the lake water surface will rise or fall relative to the inner contour. Different methods of determining the outer contour (C2) are adopted. Subsequently, the slope-aspect relationship between the inner contour C1 and the outer contour C2 and the known part of the lake boundary L is established, and the unknown lake boundary points are interpolated. Finally, the nearest neighbor method is used to connect the known lake boundary points with the interpolated Lake boundary points to form a complete lake boundary. The extracted lake boundaries were validated by visual digitized lake boundaries from ZiYuan-3 image or cloud-free Landsat image on the near date. It is found that they are basically coincided, and the percentage of differences in length and area are -6.81%~9.4% and -2.11%~2.7% respectively. It shows that this method is very effective for automatic extraction of Lake boundary from cloudy Landsat TM/OLI images, and provides a new method for automatic extraction of long time series Lake boundary and its temporal and spatial variation analysis in the Qinghai-Tibet Plateau on GEE and other big data platforms.
Aiming at the problems of spectral information loss and spectral distortion in traditional Brovey Transform (BT) fusion, the adaptive weighted average is introduced to improve it. Taking EO-1 ALI multispectral imagery as an example, a new multispectral image fusion algorithm based on improved BT is proposed. Information entropy, average gradient, correlation coefficient and root mean square error are used to comprehensively evaluate and compare the fusion effects of this algorithm, so as to verify the effectiveness and superiority of the multispectral image fusion algorithm based on improved BT. Experimental results show the multispectral fusion image using this improved algorithm has better spectral information and spatial resolution, and its visual effects and spatial texture features have been significantly improved, and the color information of the source image has been well extended, and the brightness is relatively moderate; this improved algorithm can reduce the loss and distortion of spectral information in the fusion process, and has obvious advantages in maintaining spectral information and clarity compared with the traditional BT fusion method.
Using measurements with the Microwave Temperature and Humidity Sounder (MWHTS) onboard the Chinese Fengyun-3C satellite, real-time and high resolution sea surface pressure information can be retrived. Based on the three-dimensional variational assimilation (3DVAR) method, the retrieved sea pressure fields from FY-3C/MWHTS observations are assimilated into the Weather Research and Forecasting (WRF) model. The influence of the retrieved pressure fields on typhoon forecasting is discussed through the comparison between control experiment and assimilation experiment. Sensitivity experiments of typhoon Maria and Noru show that the assimilation of sea surface pressure fields makes the central pressure and central location closer to the actual value, and adjusts the structure and distribution of initial temperature fields and wind fields. The numerical prediction results show that the assimilation of the sea surface pressure fields can improve the accuracy of typhoon track and intensity prediction.
Currently urban renewal and transformation monitoring is a relatively new remote sensing application field. In view of current status and deficiencies in remote sensing of urban renewal, this paper introduces the basic concepts, methods and applications of remote sensing in this field. Through brief review of recent advances in remote sensing of urban monitoring, this paper interpreted the remote sensing data types suitable for urban renewal; examples of applications have been presented; for the purpose of illustrating the value of remote sensing in this field. Hopefully this paper can fill the knowledge gaps in the applications of remote sensing technology to urban renewal, and can inspire new thinking in this field. Possible future directions of remote sensing in this field are then pointed out.
The Net Primary Productivity (NPP) of vegetation and its response to climate change is one of the key areas in research of global change. The study on spatial and temporal changes of NPP in central Asia is important to understand the mechanism of vegetation-environment action and to cope with global change. Therefore, based on the MOD17A3 dataset and meteorological data and GIS analysis method, this paper is intended to analyze the spatial pattern, temporal variation and the driving factors to NPP in Central Asia during 2000~2014. The results shows that: ①the spatial variation of NPP in Central Asia is ranged from 0 to 874 gC/m2·a, with an average of 151.90 gC/m2·a. The average annual total NPP is 482.41TgC (1 Tg=1012 g), and both the average NPP and total NPP showed a decrease trend. ②The average NPP was higher in southeastern alpine regions and high latitudes areas than in central and southern desert areas in Central Asia. ③From 2000 to 2014, the annual NPP in central Asia showed a decrease trend with a rate of -2.05 gC/m2·a2, covering 39.89% of the region with significant reduction. The areas in which NPP decreased were mainly distributed in Kazakhstan, with typical steppe zone being the most significant in five ecological zones. ④The effect of precipitation on NPP in Central Asia was stronger than that of temperature. Precipitation influenced NPP of typical steppe，desert and desert steppe more seriously, while alpine meadow and alpine forest were jointly affected by precipitation and temperature.
In order to improve the separability of oil film and other targets, a new polarization feature G based on eigenvalue and eigenvector decomposition is proposed. The new feature can not only reflect the polarization states between different targets in the corresponding set, but also has the ability to describe the statistical information impurities of the different scattering types. If the depolarization state was weaker, the impurities were smaller, then the value of the new polarimetric feature G in the specific region would be lower. Two sets of Radarsat-2 fully Pol-SAR (Polarimetric Synthetic Aperture Radar) data are used to verify the validity of the new feature G. The results show that there is a small eigenvalue in the seawater, a large eigenvalue in the oil film, the eigenvalue of the biofilm is between the oil film and seawater. In addition, the new feature G have better performance than span,
Gross Primary Production (GPP) of vegetation refers to the assimilation of all organic matter produced by green plants through photosynthesis and fixed carbon dioxide per unit time and unit area. Accurate estimation of GPP is helpful for the study of carbon cycle. In order to improve the estimation accuracy of GPP, this study combines machine learning technology and remote sensing technology. First, the remote sensing data under the GEE platform and the flux tower measurement data of the China Terrestrial Ecosystem Flux Observation Research Network are used to establish a data set. Then use random forest as the estimation model, and adjust the model according to the data characteristics after modeling. Finally, the prediction results of the model are obtained, the determination coefficient R2 is 0.87, and the root mean square error RMSE is 1.132 gC·m-2·d-1. This shows that the random forest model can estimate GPP more accurately.From the results of this study, we can see that the rapid development of computer technology represented by big data and artificial intelligence will inject new vitality into remote sensing technology and make remote sensing technology enter a more mature stage of development and application.
The absorbed and utilized Fraction of Photosynthetically Active Radiation(FPAR) reflects the capacity of carbon fixation and oxygen release by vegetation, which may vary over space and time in large scale. Analysis of spatial-temporal variation in FPAR is an important topic of plant ecology. Based on GIMMS NDVI3g (1982~2015) and MODIS NDVI (2001~2015) data in the Tibetan plateau, here we used the nonlinear, semi-theoretical and semi-empirical models to inverse and analyze the spatial and temporal variation in FPAR. The results showed that (1) The spatial distributions of FPAR derived from GIMMS NDVI3g and MODIS NDVI were highly consistent, with the correlation coefficient being 0.82 (P<0.01). The area in which the trends of inter-annual change in the two inversion data were consistent for at least 6 years made up 80% of the studying area. (2) FPAR in Tibetan Plateau was greatly affected by slope and altitude. Changes in FPAR were fastest at slopes of 15~35 degrees and highest at altitude of 700~2 100 m. The effect of slope direction on FPAR was limited. There was little difference in FPAR among different slope directions except for the south where the FPAR was relatively lower. (3)The FPAR data from 1982 to 2015 demonstrated seasonal variation. The inter-annual variation in FPAR was most significant in winter, in which FPAR in about 78.5% of the area increased. FPAR declined most significantly in the fall. (4) FPAR derived from both the MODIS NDVI and GIMMS NDVI data demonstrated a small, temporary decline in 2012. The trend of inter-annual variation in FPAR was largely different among different vegetation types. In conclusion, the FPAR data from 1982 to 2015 in the Tibetan plateau demonstrated both spatial and seasonal variation, which may have important implications for further studies concerning climate and environmental changes in the region.
The method of linear spectral mixture analysis combined with V-I-S model(Vegetation-Imperious surface-Soil) is used to estimate the impervious surface abundance of Urumqi city, which is in the core area of the Silk Road Economic Belt, using Landsat OLI and TM multi-spectral data. Because the red color steel shed has low brightness on the impervious surface brightness image, an improvement was proposed, and then verify the accuracy through the interpretation of high-resolution satellite imagery. The results show that: the impervious surface area in Urumqi displayed a significant expansion from 140.41 km2 to 462.62 km2 during past 24 years (1994 to 2018). It expanded slowly during 1994 to 2005, and increased rapidly since 2005. The expansion intensity increased during 1994~2015 and decreased after 2015; and the spatial expansion of urban impervious surface is significantly different, with the largest expansion area in the west and northeast direction. The comprehensive analyses suggested that the expansion of the impervious surface of Urumqi city is limited by the surrounding mountain topography and coal mining, and the “Urumqi-Changji integration” policy is the major driving factor for urban expansion in the past 24 years.
