Google Earth Engine is a cloud-based, global-scale geospatial analysis platform that makes full use of Google Earth’s rich data resources and cloud computing power to store and process petabyte-level data, being an effective and convenient tool for remote sensing research. Based on the introduction of Google Earth Engine system architecture, the authors firstly sorted out the research fields of Google Earth Engine. 291 related articles on CNKI and Web of Science published from 2011 to 2019 were analyzed, and some results were concluded such as publication time, research field, research area, the first author’s institution and journal of the article. Then the authors analyzed Google Earth Engine’s application and research trends of land use and land cover. The authors found that Google Earth Engine is widely used in the field of land cover remote sensing information extraction and has advantages in global or large-scale study. Based on the advantages of Google Earth Engine in remote sensing information extraction, the authors divided the study fields into agricultural remote sensing mapping, vegetation extent mapping and dynamic monitoring, building extraction, hydrological information extraction and land cover classification mapping. The research and application progress of Google Earth Engine was elaborated from two aspects: large-area mapping and multi-temporal dynamic monitoring. Finally, the authors discussed the Google Earth Engine’s problems and the development potential in land use and land cover. This paper is intended to serve as a basis for further understanding the advantages, application status, trends and potential of Google Earth Engine as well as for further understanding and using Google Earth Engine in the future.
Deep learning technology provides technical means for hyperspectral image classification due to its unique advantages in deep mining of features. However, in the pixel-level feature classification of hyperspectral images, the number of deep learning layers is limited due to the influence of the sample input size, and the depth features in the hyperspectral images cannot be fully mined. The classification of hyperspectral image based on feature fusion of residual network is proposed in this paper. First, the principal component analysis (PCA) method is used to extract the first principal component in the original hyperspectral image, and the residual network is used to effectively extract the spatial spectrum features of the ground objects; then the feature map is expanded by the deconvolution algorithm, and after deconvolution, features of different dimensions are fused with multi-scale features to fully mine the depth feature information in the hyperspectral image, thus further improving the classification accuracy of the hyperspectral image. The ground feature classification experiment was conducted on the two areas of Taihu Lake in Jiangsu and Chaohu Lake in Anhui captured by the “Zhuhai-1” satellite. The results show that, compared with other methods, this method can effectively solve the problem of insufficient depth feature extraction in hyperspectral image classification, thus showing better classification performance.
In order to further study the method of obtaining high-resolution soil moisture by downscaling FY-3B soil moisture and make it more suitable for agricultural and hydrological simulation, the authors constructed a comprehensive ATI and TVI by using MODIS data in Naqu area. Combined with low resolution FY-3B soil moisture products, the coefficients of soil moisture inversion model under high resolution were obtained by using soil moisture downscaling method, and the high-resolution soil moisture was obtained. Compared with the ground observation data, the R2 of the downscaling soil moisture and the measured data is above 0.4, and the RMSE is between 0.055 and 0.103 cm3/cm3, indicating that the downscaling soil moisture can better reflect the spatial distribution and change of soil moisture.
Aiming at tackling the problem of the low efficiency of common detection methods and the dynamic background of UAV-based video sequences imagery, this paper proposes a fast detection method for moving targets in UAV video based on the feature extraction. The method mainly includes six steps, i.e., preprocessing, ORB feature extraction, PROSAC feature fine matching, global motion estimation and global motion compensation, initial detection of moving target and morphological post-processing. The results of two video experiments carried by UAVs show that the moving target detection results of the proposed method in this paper are better, the computational efficiency is the highest, and hence this method can meet the requirements of real-time processing.
Black soil is a valuable land resource, and the content of organic matter is an important index reflecting soil fertility, state and degradation degree. In order to estimate black soil organic matter content more accurately, this paper proposes a hyperspectral estimation method based on wavelet transform and successive projections algorithm. In this paper,the soil samples collected in the typical black soil region were used as the research object,and the Vis-NIR spectral data of the soil obtained from analytical spectral deviees (ASD) spectrometer and the organic matter content through chemical analysis were used as the data sources.Firstly, wavelet transform was used to extract the wavelet coefficients of 1 to 7 levels, and then successive projections algorithm was used to select the variables from soil original spectrum and the wavelet coefficients of 1 to 7 levels respectively. Finally, based on the soil original spectrum, the wavelet coefficients of 1 to 7 levels and the selected variables based on successive projections algorithm respectively, partial least squares and support vector machine were used to build the estimation models. The results show that, by using wavelet transform and successive projections algorithm, not only the number of variables is reduced greatly, but also the accuracies of the models are improved. When using partial least squares method,R2 increases from 0.79 of the soil original spectrum to 0.93 of the wavelet coefficient of the sixth level, and RMSE decreases from 6.06 g·kg-1 to 3.48 g·kg-1. When support vector machine method is used, R2 increases from 0.75 of the soil original spectrum to 0.91 of the wavelet coefficient of the third level, and RMSE decreases from 7.46 g·kg-1 to 4.12 g·kg-1. The results indicate that the proposed method can be effectively used for the hyperspectral estimation of black soil organic matter content.
The extraction of impervious surface (IS) is very important for the development of cities, and linear spectral mixture analysis is commonly adopted to calculate the fraction of IS in the mixed pixel to improve the extraction of the urban IS at the subpixel scale. Owing to errors in the spectra of pure pixels selected from remote sensing images, incorrect fractions of different land cover types often emerge after unmixing. In this paper, the modified endmember selection was proposed to improve the accuracy of the spectral information of endmembers. Sentinel-2A images were applied to selected endmembers to get the spectral, which was used to modify the spectral information of the endmembers from Landsat8. In addition, the optimization scheme of LSMA results in which the normalized differential vegetation index (NDVI) and dry bare-soil index (DBSI) thresholds are used to optimize the mixing results was applied to improve the accuracy of LSMA results. With the WorldView-2 remote sensing image for sample verification, the results showed that the accuracy of IS fraction extracted by the method in this paper was 20% higher than that of the traditional method, providing reliable theoretical support for endmember selection and IS extraction.
ing at tackling the problem that some edge features of buildings are easily blurred or lost in the extraction of buildings with high resolution image by U-Net, this paper proposes an optimized building extraction method, which firstly enhances the edge of buildings with high resolution image and simultaneously improves the partial convolution process of U-Net. Specific process is as follows: Firstly, the domain change recursive filtering method is used to enhance the edge of the building, and the enhanced image is input into U-Net neural network results for training. To make full use of the rich details characteristics of the buildings on the GF-2 images, the authors tried to extract pairs from training images and label patch on the basis of the original U-Net structure and in the process of coding decoding, so as to increase the training data. These patches further strengthened the positive and negative deep learning of high-dimensional feature for buildings, thus successfully realizing building image segmentation. In this paper, the experimental results of the extraction of GF-2 image buildings in Panjin City of Liaoning Province adjacent to Bohai Bay on September 29, 2017 show that the overall classification accuracy of the buildings detected by U-Net is 75.99% for the shaded and unsatisfied area sample data, and the maximum overall classification accuracy of this method can reach 83.12%, which is 7.13 percentage higher than that of the original U-Net network. It is proved that the U-NET model combined with domain change recursive filtering is effective.
Shadow is a common interference factor in remote sensing image interpretation in mountainous and hilly areas. The study of shadow detection in hyperspectral remote sensing images is helpful to removing shadow and giving full play to its advantage of hyperspectral resolution. Taking the multi-angle hyperspectral image PROBA/CHRIS as the data source, this paper tries to increase the spectral differences among three typical ground objects, namely, bright area vegetation, shadow area vegetation and water area, selects the characteristic bands by using the sequential projection algorithm (SPA), and analyzes the spectral characteristics of typical ground objects in the original band of CHRIS image and normalized difference vegetation index. Therefore, the normalized shaded vegetation index (NSVI) of the image is constructed. The reasonable threshold is set based on the step-size method, and the images are classified. The ability of NSVI to detect CHRIS shadow is evaluated from two aspects of classification accuracy and spectral difference enhancement effect. The results show that B9 and B15 can be used as the characteristic bands for constructing NSVI of CHRIS images by using SPA to select the band subset with the smallest root-mean-square error (RMSE) and discard the edge bands. CHRIS multi-angle images are classified based on NSVI threshold method. The classification accuracy of three kinds of land in each angle image is above 94%, and the total Kappa is higher than 0.89. The classification effect of 0° image is the best. The sub-images of the three classified land objects are obtained through the mask, and the spectral mean values of the sub-images are different. However, considering the standard deviation, it is found that the spectral overlap phenomenon is obvious, which indicates that NSVI can enhance the spectral differences among typical land objects and improve the separability between spectral confusion pixels. By further comparing the shadow detection effects of NSVI with NDUI and SI, it also proves the shadow detection ability of NSVI, which shows that the constructed NSVI can be applied to shadow detection of PROBA/CHRIS hyperspectral image and can provide important support for shadow removal and shadow information restoration of this image.
The addition of meteorological factors to the estimation of near-ground atmospheric particulate concentration based on AOD is one of the most popular techniques nowadays. In this paper, AOD (Aerosol Optical Depth), FMF (Fine-Mode Fraction) and PM2.5mass concentration data from March 2014 to February 2019 in Nanjing were obtained, and the mass concentration of PM2.5 in Nanjing was retrieved in combination with the meteorological simulation data from WRF (Weather Research and Forecast) model. The results show that, compared with correlation between AOD and PM2.5, the correlation analysis of fine aerosol optical depth AODf and PM2.5 obtained by FMF correction can obtain a higher fitting coefficient, and the maximum R2 reaches 0.40. By adding meteorological factors on different heights into random forest model to establish an inversion model for PM2.5 mass concentration, the obtained fitting coefficients and various error indicators are better than those from models with only near-surface meteorological factors, which indicates that the PM2.5 mass concentration is affected by the combined effect of multiple factors, thus the result can provide a basis and reference for inversion of PM2.5 mass concentration by using multi-source data.
The task of extracting buildings with high-resolution remote sensing image plays an important role in urban planning and urbanization. In view of the problems of existing deep learning extraction methods, for example, the shallow features can’t been used effectively and small target information is easily lost, this paper proposes a multi-level perceptual network. This network uses dense connection mechanism to fully extract feature information, and constructs parallel structure to retain spatial information of different feature resolution and enhance feature information of different depth and scale in order to reduce the loss of detail feature. At the same time, the ASPP module is used to obtain the information of different receptive fields and extract the deep architectural features at different scales. The experimental results show that the overall accuracy of the proposed method is 97.19%, intersection over union is 74.33% and theF1 score is 85.43% in the buildings extraction of GF-2 remote sensing image, all of which are higher than those of the traditional method and other deep learning methods. In addition, buildings with multi-source remote sensing images still have good extraction effect, which reflects the practicability of the method presented in this paper.
When a satellite is in transit, the presence of clouds or fog will cause shadows on some remote sensing images, and this accordingly directly affects the quality of image and the extraction, interpretation and recognition of the feature information. The authors firstly counted the data of 2017 MODIS11A1 in Gansu Province, and found that the data pixels values of 2017 MODIS11A1 are void to a large extent. Mainly because it is difficult for the remote sensing image to penetrate the cloud to obtain the feature information, the image pixel value is 0. Then the authors explored and compensated the missing value based on the phenological solar term as the time period, proposing the method of historical average value. After using the historical average method to compensate the data, the authors found that the effective utilization ratio of pixels could be greatly improved. The image information basically reflects the real feature information, and the compensation result can meet the demand of remote sensing images.
Pollutants from thermal power plants are discharged into the air, posing a great threat to urban ecological environments and resident health. However, currently, there is no effective method for power plant detection and working state determination. This paper proposes a thermal power plant cooling tower detection method with cooling towers as the key target and then further captures the production state information from water vapor discharge from the top of cooling towers based on the Faster R-CNN deep learning network. 8 typical thermal power plants are provided to verify the proposed method. The ideal results have been achieved in these test scenarios, which implies that this method can be effectively applied to the working state detection of thermal power plants. In addition, the applicability of this method can be broadened to similar industrial targets, such as steel mills and nuclear power plants. Creatively applying a deep learning network to determining the target working state is the authors' theoretical contribution that develops an innovative orientation, and this method could provide practical guidance for the governance of urban industrial gas emissions.
Automatic extraction of buildings from satellite remote sensing images has a wide range of applications in the development of economy and society. Due to the influence of mutual occlusion, illumination, background environment and other factors in satellite remote sensing images, it is difficult for traditional methods to achieve high-precision building extraction. This paper proposes an attention enhanced feature pyramid network (FPN-SENet) and constructs a large-scale pixel-wise building dataset (SCRS dataset) by using multi-source high-resolution satellite images and vector data to realize the automatic extraction of buildings from multi-source satellite images, and compares it with the other full convolution neural networks. The results show that the accuracy of building extracted from SCRS dataset is close to the world’s leading open source satellite image dataset, and the accuracy of Pseudo color data is higher than that of true color data The accuracy of FPN-SENet is better than that of other full convolution neural networks. The extraction of building can also be improved by using the sum of cross entropy and Dice coefficient as the loss function. The overall accuracy of the best classification model is 95.2%, Kappa coefficient is 79.0%, and F1-score and IoU are 81.7% and 69.1% respectively. This study can provide a reference for building automatic extraction from high-resolution satellite images.
Secchi disk depth (Zsd) is an important parameter for describing the optical properties of water bodies. With high spatial and temporal resolution, satellite remote sensing technology has become an important method of Zsd observation. Using the in-situ measured data and GOCI images of Jiaozhou Bay (JZB) on May 16, 2017, the authors used semi-analytical algorithms Doron11 and Lee15 to retrieve the Zsd. It is shown that the Lee15 performed better than Doron11, with the decision coefficient of 0.976 and the root mean square error of 0.02 m between the estimated values and in-situ measured values. Selecting eight GOCI images from 8: 16 to 15: 16, the authors used Lee15 algorithm to get the spatial and temporal distribution characteristics of the diurnal variation ofZsd on the JZB. On the spatial distribution, the overall Zsd level of the JZB is low (0~4 m), and gradually increases from the inside to the outside of the Bay. On the time variations, the Zsd at the Bay mouth is obviously affected by the tides. The changes between the Bay mouth and the Bay outside are dominated by the solar zenith angle (SOLZ). The change of averageZsd of the JZB is mainly caused by the joint effect of the SOLZ and the tide. According to the respectively statistical analysis between the in-situ Zsd at each sampling station and simultaneously measured other environmental factors, the change in the Zsd of the JZB is the result of the joint action of multiple environmental factors, and has a strong positive correlation with the water depth, with correlation coefficient reaching 0.84, but it is negatively correlated with other environmental factors.
As an important indicator reflecting the surface ecological environment, vegetation is widely used in the study of regional resources and environmental carrying capacity. Taking Erhai Lake basin as an example and based on the Google Earth Engine remote sensing big data cloud computing platform, the authors obtained the annual maximum normalized difference vegetation index (NDVI) value of Erhai Lake basin in 1988—2018 by using nearly 455 Landsat series images with 30 m resolution. The pixel binary model was used for quantitative estimation of fractional vegetation cover (FVC), and the spatial-temporal change characteristics of FVC in Erhai Lake basin were comprehensively analyzed through the linear regression model. Additionally, the internal relationship between FVC and geological lithology was investigated. The results are as follows: (1) From 1988 to 2018, the vegetation coverage of Erhai Lake basin showed a trend of continuous fluctuation growth, with a growth rate of 0.38%/a. (2) The basin was dominated by high vegetation coverage, of which 82.54% of the regional vegetation coverage continued to be improved, whereas the area of continuous degradation accounted for only 3.27%, which was mainly distributed in the area with significant urbanization. (3) the FVC varied in different types of lithological areas, among which the highest was metamorphic rock, and the lowest was dolomite and volcanic rock.
Soil salinization is one of the important factors that affect the soil health in the arid area, so it is very important to obtain the information of soil salinity and monitor the change of soil salinity for the rational use of land resources and soil restoration in the arid area. Based on 52 soil samples collected in the field and Landsat 8 OLI remote sensing images obtained at the same time, the correlation and curve regression analysis were used to quantitatively analyze the correlation and fitting degree between the soil salinization evaluation index based on multispectral remote sensing data and the measured soil Electrical Conductivity (EC). The results are as follows: ① The soil salinity in the study area is relatively light, and the total proportion of non-salinized and slightly salinized soil samples is 82.68%; ② The correlation between salinity index and soil EC is higher than that of vegetation index. The correlation between salinity index S3 (S3), salinity index S5 (S5), salinity index S6 (salinity index, S6) and salinity index Si (salinity index, SI) is above 0.50; ③ Salinity indexes S2 (S2), S3, S5 and Si have the highest fitting degree with soil EC in the whole sample, among which S5 has the best performance (R2 = 0.41). The fitting degree of index and soil EC increases significantly with the increase of soil salinity under different salinity levels. The highest fitting degree of salinity index and soil EC is S1 (R2 = 0.73) and S2 (R2 = 0.72); ④ In the fitting model, the evaluation index and soil EC calculated based on cubic model, quadratic model and S model has a high fitting degree. This study has analyzed the applicability of various soil salinization evaluation indexes in soil salinity monitoring of Yinbei irrigation area, and the preliminary conclusions can provide reference for remote sensing monitoring of soil salinity in Yinbei irrigation area of Ningxia.
The ZY1-02D satellite is the first hyperspectral operational satellite in China. To test the application ability of ZY1-02D hyperspectral loading data in geological and mineral survey, the authors identified lithologic and mineral information on the basis of data pre-processing, and the results were compared with GF-5 data. The application ability of the data was analyzed effectively in combination with the results of field survey. The results are as follows: the coincidence degree of ZY1-02D hyperspectral data reflectivity spectrum curve and geological body spectrum curve is high in shape, which can meet the requirements of rock and mineral information identification; through the identification of rock and mineral information in combination with the geological and mineral data of the study area, it is shown that the lithological information of marble, monzogranite, calcite and dolomite and alteration mineral information of chlorite and limonite are consistent with the measured results. The results show that the data has good recognition effect on the information of rocks and minerals, and can provide data guarantee for the application of hyperspectral technology in the field of geology and mineral exploration.
With a special geographical location and abundant marine resources, Zhoushan is the first prefecture-level city composed of islands in China. Therefore, the acquisition of dynamic information on the coastline is of great significance to this area. However, the large amount of suspended sediments, the tortuous coastline, the numerous tidal flats and some other factors have brought a lot of challenges to coastline extraction and the analysis of the spatial-temporal dynamics in Zhoushan Islands. In order to solve this problem, the authors have developed a method for extracting coastline remote sensing information based on the tasseled cap transformation and used long time series satellite remote sensing data to carry out the analysis of the temporal and spatial evolution of the coastline. The experimental results show that the proposed method can effectively remove the influence of suspended sediments, winding coastline and shoals on the extraction of coastline information, and make its position accurate. From 2000 to 2018, the total length of the coastline of Zhoushan Islands increased by about 327.36 km, the average growth length was 18.19 km, the average growth rate was 0.72%, the total area of Zhoushan Islands increased by about 112.26 km2, the average growth area was 6.24 km2, and the average growth rate was 0.49%. The constructions of reclamation and marine projects seem to have been the main reasons for Zhoushan’s coastline changes. This study is of great significance for improving the accuracy of coastline remote sensing information extraction as well as coastal development and protection in complex marine environments.
At present, the commonly used partition modeling of population can reflect the spatial differences and dynamic changes of population distribution. Nevertheless, due to the limitations of methods and data, the population distribution indicators in multi-partition also need to be specifically optimized according to regional characteristics to improve the accuracy of population spatialization. Based on the geographical characteristics of the developing countries along the “Belt and Road”, the authors proposed four geographic partition of high-light plain area, high-light hilly area, low-light plain area and low-light hilly area, and optimized the modeling index of multi-divisional partition through the adjustment of population distribution indicators, fusion of functional area population index and some other means. Finally, Tajikistan was used as the study area to draw a 30 m population distribution map (TJK_POP), and TJK_POP was compared with modeling results of using a single index for each district (NTL_POP and HSI_POP) for verification. The results show that the mean relative error (MRE) of TJK_POP is 22.57%, of which the MRE of the four partition are 28.01%, 19.33%, 17.99%, and 24.97%, respectively. The accuracy is better than that of NTL_POP and HSI_POP. At the same time, TJK_POP reduces the interference of the flowing population of commercial land such as airports and factories on the actual population distribution. The optimization of population distribution indicators for multi-divisional partition in this paper also provides a reference for the study of population spatialization in other similar areas along the “Belt and Road”.
The ancient city of Pingtao, Zhengzhou City, Henan Province, was an important city in the Eastern Zhou Dynasty and has important historical value. Due to the problems of time-consuming, heavy investment and heavy workload in traditional archaeological investigations, the settlement layout and relic distribution of the old city of Pingtao are still unclear. In this study, the authors selected Corona, Google Earth historical images and aerial thermal infrared images, comparatively analyzed the tonal and texture features on images of different loads, phases and scales, and extracted the archaeological anomalous areas of the Pingtao City site and Dianjuntai site. Suspected ruins such as city walls, gates, corner platforms and rectangular building foundations were discovered, and the spatial structure of the ruins was initially reconstructed based on the identification results. The results of the study show that Corona imagery helps to identify the early appearance of the site, Google Earth historical imagery provides assistance for the detection and extraction of tiny suspected relic features, and aerial thermal infrared imagery can reveal such archeological features as indistinct burial on the ground or optical image. The research proves that the comprehensive utilization of multi-source high-score data can investigate, predict and reconstruct the distribution and spatial structure of the relics, thus providing a reference for further archaeological research and site protection.
The information value model (IVM) is a statistical prediction method derived from information theory, which is widely used in natural hazard risk assessment. The problem as to how to formulate a suitable factor classification method to maximize the advantages of pre- single factor statistical analysis remains a key issue. In order to solve this problem, the authors processed a method of factor classification by combining symmetrical intervals. Statistical knowledge related to normal distribution was referred, the factors was pre-segmented by 1/2 standard deviation, and the intervals were merged symmetrically from outside to inside. After that, factors approximately fitting normal distribution, such as slope angel and topographic wetness index (TWI), were classified based on this method, and IVM was built, which was later used in landslide hazard susceptibility analysis in Wenchuan area. Meanwhile, 5 standard classification methods were selected and tested as comparative experiments for rationality verification, namely equal quantile (EQ) classification method, natural break (NB) classification method, geometric break (GB) classification method and standard deviation (SD) classification method. The results show that the IVM using symmetrical method as factor classification method stands out among the rests. The actual landslide area ratio in the high and extremely high-risk areas in the susceptibility map reached 80.87%, higher than that obtained by other standard classification methods. This proves that the symmetrical classification method performs well.
Mineral resources are an important part of natural resources and constitute an important material basis for the development of human society. With the rapid development of economic construction, the demand for mineral resources has become increasingly urgent, and the ecological environment destruction caused by mineral development has also become increasingly prominent. The construction of green mines and green development is the inevitable trend of mine development. In the construction of green mines, the site selection of mines is particularly important. With the Duolong ore concentration area as the study area and through the good grades ii and Landsat8 remote sensing satellite image preprocessing, the authors extracted information of a series of important environmental factors such as fault location, vegetation coverage, drilling and mining area and exploratory trench, village, river, road, slope and elevation difference for quantitative interpretation and normalized processing; finally the analytic hierarchy process (AHP) was used to calculate weight coefficient of each factor and construct the green mining location model in the study area. Specific layout planning was carried out for the mining stopes, waste rock sites, administrative living areas, mineral processing plants and tailings ponds of some mineral deposits in the Duolong ore concentration area of Tibet so as to provide basic data and reference suggestions for the development and construction of green mines.
Hongyashan Reservoir is located in northwestern China where water resources are lacking. Reservoirs are an important support for the ecosystem in this region. Analyzing the changes in the area of the reservoir can effectively help Minqin County Government to make overall plans for water ecological protection and restoration as well as rational use of water resources and can also provide support for its decision-making. Through the extraction and analysis of the water area and vegetation coverage of the Landsat series data and GF-2 data from 2000 to 2019 and in combination with the surrounding meteorological data and the collection of local data, the authors comprehensively analyzed the influencing factors of the water area change and explored the spatial and temporal changes of the water area as well as the driving force. The results show that, on the whole, the water area of Hongyashan Reservoir has continued to increase in the past 20 years, the total area has increased by 8.98 km2, and the area change rate is as high as 42.6%, and that, in terms of monthly changes, the change in water area has an inverted “normal distribution” curve. The trend is that the wet season is mainly concentrated in March and September-October in the spring and autumn seasons, and the dry season is mainly concentrated in June in the summer. In terms of interannual variability, the water area is greatly affected by the seasons, so it is divided into spring, summer, autumn and winter. Interannual analysis shows that the water area in spring and winter continues to rise, with average annual growth rates of 5.03% and 5.22%, the lowest average annual growth rate in autumn is only 2.42%, and the average annual growth rate of summer water area is 22.19%, which is the season with the largest variation amplitude, exhibiting “V” fluctuation and rising. According to the meteorological data such as temperature, precipitation and evaporation, the correlation analysis of vegetation coverage and water area, and the analysis of related hydrological data, the following conclusions can be drawn: the direct driving forces are the change in precipitation, the increasing project expansion, and the change of runoff into the reservoir, whereas the indirect driving forces include changes in temperature, changes in vegetation coverage, the industrial, agricultural and domestic water use, and the restoration of the ecological environment.
The clarification of the dynamic change trend of cultivated land and its driving factors is an important basis for ensuring national food security, rationally developing and utilizing soil and water resources and adjusting land use structure. Taking Alar reclamation area in southern Xinjiang as an example and based on Landsat satellite remote sensing images, population, GDP and other data of seven important periods from 1990 to 2019, the authors selected the best algorithm to interpret remote sensing images by comparing the accuracy of five classification algorithms comprising SAM-CRF, ANN-CRF, MDC-CRF, MLC-CRF and SVM-CRF. Next, the characteristics of cultivated land area change, type transformation and spatial dynamic change were analyzed by using the interpretation results, and then the main driving factors, action path and intensity of cultivated land area change were discussed. The results show that the SVM-CRF algorithm has the highest classification accuracy among the five classification algorithms, with the overall accuracy of 0.95 and the Kappa coefficient of 0.94. The overall accuracy of the other four algorithms is between 0.65 and 0.89, and the Kappa coefficient is between 0.58 and 0.86. The area of cultivated land in the study area has continued to increase in the past three decades, and the net increase in cultivated land area is 729.97 km 2 (312.21%). Cultivated land transfer-in and transfer-out has shown a trend of outward expansion and inward contraction, respectively. Total population, GDP, Total Investment in Fixed Assets, gross agricultural product and cotton price are the main driving factors for the change of cultivated land area,among which GDP has the greatest direct impact on the change of cultivated land area, while cotton price has the least impact. Except that GDP has a negative effect on cultivated land area, the other four factors have a positive effect on cultivated land area, and the overall performance of the five factors is a positive effect.
Based on satellite images of two different wavelengths acquired by ALOS-2 and Sentinel-1, the authors used DInSAR two-track method and stacking technology to analyze and delineate the active landslide that is undergoing surface deformation in the central part of Maoxian County, Sichuan Province. First, in view of the long time baseline of the ALOS-2 satellite and the small amount of image data, the two-track DInSAR method was used to detect landslide deformation. Secondly, on the basis of the short-time baseline multi-scene Sentinel-1 data, the stacking technology was used to detect landslide deformation. Finally, comprehensive analysis of the deformation rate results of the two data sets was conducted, and the potential landslide area in the middle of Maoxian County of Sichuan was delineated. The results show that the suspected active landslides in Baibu Village of Maoxian County and other places have obvious surface deformation, and the maximum absolute amount of radar line-of-sight deformation rate reaches 200 mm/a; combined with optical image characteristics and existing historical survey data, 8 active landslides were delineated, and the exploration result of two kinds of data shows that the detection results of the data can correspond to each other in the spatial distribution of 7 active landslides. Field surveys were conducted on 6 active landslides, and signs of ground deformation were found. The work in this paper shows that using a small number of long-band ALOS-2 images and the DInSAR two-track method can detect more obvious landslide surface deformation in mountainous areas with certain vegetation coverage; when C-band Sentinel-1 images are applied, accumulation of continuous multi-scene data is required; in addition, by applying time series analysis method, the detection effect is better than the two-track method using ALOS-2 images.
Based on the MODIS13Q1,MODIS11Q2 data and national meteorological station monitoring data and using the methods of maximum value synthesis, the average two pixel model and partial correlation analysis, the authors analyzed the temporal and spatial variation trend of vegetation coverage in the growing season and the interaction of land surface temperature and soil moisture on vegetation growth. The results are as follows: ① From 2001 to 2018, the vegetation coverage of Liupanshan poverty-stricken area increased from 0.28 to 0.45, and showed a decreasing pattern from southeast to northwest. ② During the research period, there existed a trend of overall improvement and local degradation: the improved area accounted for 51.91%, the area without significant change accounted for 44.22%, and the degraded area accounted for 3.87%. ③ The growth of vegetation is closely related to the annual change of land surface temperature and soil moisture. There are three types of spatial correlation: positive correlation, negative correlation and reverse correlation, but the positive correlation is dominant. ④ The interaction analysis shows that the influence of soil moisture on vegetation growth is greater than that of land surface temperature. Soil moisture condition is the dominant factor affecting the vegetation growth in this area. The improvement of soil moisture condition is very important for the construction and restoration of ecological environment in the study area.
In order to better carry out the ecological protection and restoration of the wetland in the lower reaches of the Yangtze River, the authors selected the Landsat images of Shengjin Lake in different seasons from 2000 to 2019 as the research data with the support of Google Earth Engine (GEE) cloud platform. The land surface temperature (LST) was retrieved by a batch program using radiative transfer equation method. The spatio-temporal variation of LST and its responses to land cover in Shengjin Lake during the past 20 years were comprehensively analyzed. The results are as follows: ① From the perspective of space, the spatial distribution of different temperature grades has shown obvious differences with the seasonal changes. The high-temperature region is dispersed in spring, generally located in the northwest in summer and autumn, and mostly in the south in winter. The area of the lake varies with the seasons, but its temperature belongs to very low or low temperature grade at all seasons. ② From the perspective of time, in the past 20 years, affected by forest and water, the Shengjin Lake wetland has always been dominated by the medium and low temperature grades that account for a large proportion of 70%-85% or so. The area proportion of temperature grades varies with the time trend such as seasons and years. ③ There exist seasonal differences in the responses of LST to land cover. It is basically presented in the form of a descending order of artificial surface> cultivated land> forest and mudflat> water. ④ Non-urbanization factors have a certain impact on the surface temperature of natural wetland. The research results are certainly significant for the reasonable development of Shengjin Lake.
With the implementation of the National Marine Strategy and the deepening of coastal zone development in coastal areas, it is necessary to study the coastal zone evolution as well as monitor and protect the coastal zone, which will provide a reasonable basis for coastal zone development. In this paper, remote sensing (RS) and (geographic information system, GIS) technology, Landsat, (digital elevation model, DEM) and tidal data were used to extract coastal zone data of Rizhao City in 1988, 1998, 2008 and 2018, and analyze the coastline distribution characteristics, the spatio-temporal distribution and land use status of coastal zone and dynamic evolution of estuary. The results are as follows: Firstly, the coastline of Rizhao showed an overall growth trend from 1988 to 2018, with a total increase of 52.7 km; The period of 1998—2008 experienced the fastest coastline growing, with the growth rate being 0.68 km/a. The distribution of coastline was dominated by sandy coastline and artificial coastline. Secondly, the land use change in the coastal zone was manifested in the continuous increase of the construction land area, with its proportion from 213.77 km2 to 413.93 km2, while the farmland/grassland area and its proportion decreased from 445.50 km2 to 287.03 km2. The overall trend was that a large amount of cultivated land/grassland was converted to construction land. Thirdly, the estuary was a place where the change of coastal erosion and deposition was the most prominent. The estuary was eroded and the estuarine shoreline retreated from 1988 to 1998. The estuary remained relatively stable from 1998 to 2008. The estuary silted up to the sea and the coastline grew seaward from 2008 to 2018. In general, changes in the landward direction of the coastal zone are affected by geomorphic types, sea level rise, sediment discharge, artificial sand mining and some other factors. Changes in the seaward direction are related to sediment accumulation, establishment of breeding areas and ports, reclamation and other coastal development activities. The conclusion of this paper can provide reference for the planning and management of Rizhao coastal zone.
In order to facilitate the low-cost, ultra-light weight and easier operation, the authors constructed a smartphone based unmanned aerial vehicle (UAV) low altitude oblique photogrammetric system by integrating DJI Phantom 4 UAV flight platform with good flight traits and Nokia 808 PureView mobile phones with good image-taking functions. In this system, relative functions of multi-camera imaging system with mobile phones were optimized, and the module design method was adopted for the system which includes the measurement of improving image quality, and the design of flight control module used for automatically image-taking control developed by open source flight control system, the design of the POS module and some other means. The integrating mode by the multi-camera system adopted as payload and flight platform was discussed, and then the working flow of the integrated system was concluded. The system was used for different applicable fields, i.e., real estate surveying, open-pit mine monitoring, and 3D reconstruction of urban buildings. The application results assessed by check points measured with field work and manual vision inspect indicate that the real-world 3D model has better texture quality, and the digital survey and mapping products, real-world 3D model and digital linear graph as well as some other means have higher geometric accuracy with centimeter level. The proposed system will be very important for boosting the development of UAV low altitude oblique photogrammetry in terms of practical demands.
A farmland drought remote sensing dynamic monitoring system has been established on the Android mobile platform in order to meet the actual needs of users for observation of agricultural conditions such as farmland drought. For the problem of inefficiency in traditional manual field recording by users, the system combines the advantages of portable mobile devices and global positioning system (GPS) to realize the digital management of farmland data, and completes a set of processing flow from field data entry, processing to export. With the purpose of real-time drought dynamic monitoring, the system uses the massive remote sensing data management and powerful calculating ability advantages provided by the Google Earth Engine remote sensing cloud computing platform, utilizes multi-source remote sensing data such as Landsat, MODIS and Sentinel, applies the Flask framework to implement the Google Earth Engine platform Python service interface access scheme, and completes the function of dynamic drought monitoring for farmland, which provides users with a technical application platform for selecting the remote sensing data source, calculating the drought monitoring model and finally generating the grade thematic map of drought.
In view of the problems of manual numbering of new mine patchs in the compilation of existing mine remote sensing monitoring data, such as time-consuming and error-prone nature and unclear specification, the authors, based on the analysis of the technical requirements for the submission of achievement data, realized the whole process automation of new mine patchs numbering. By using ArcPy site package, automatic operations such as dividing vectors, sorting, numbering and writing attribute table were realized. By using the customization function of ArcToolbox, the numbering function of the whole process was encapsulated into the toolbox and visualized to improve its interactivity and effectiveness. In view of the imperfection of the original spatial sorting method in ArcPy, an improved method of mine patchs sorting method was put forward and realized. By verifying the numbering of dozens to hundreds of patches in different counties and cities, the speed of automatic numbering could reach dozens per second, and the numbering efficiency increased with the increase of the number of samples. Experimental results show that this function can provide effective support for the compilation of mine remote sensing monitoring data, significantly reduce the workload of the numbering process and improve work efficiency. In addition, this method is also applicable to other similar large and repetitive remote sensing monitoring patch numbering.
