Remote-sensing-based time series data of vegetation have been increasingly available with the accumulation of remote sensing data. These data are vital for ascertaining the changes in an ecosystem and analyzing relevant driving factors. However, some factors (e.g., cloud cover and instrument errors) restrict the observation quality of the vegetation products of remote sensing, creating data gaps in continuous and high-quality observation data. The data gaps can be filled based on the spatio-temporal dependence of the earth’s surface characteristics, which is called the spatio-temporal reconstruction of time series data. High-quality spatio-temporal reconstruction of time series data is an important prerequisite for the accurate extraction of changes in time series data. Taking the remote-sensing-based time series data of vegetation indices as examples, this study briefly reviewed the widely used reconstruction methods of time series data firstly. These methods generally include two steps: interpolation and smoothing. The interpolation can be divided into three major types, namely time-based, space-based, and spatio-temporal interpolation. Then, taking the simulated normalized vegetation index (NDVI) time series and actual GIMMS NDVI time series as examples, different proportions of data gaps in the two time series were created. Then, this study compared the effects of four types of data reconstruction methods (i.e., linear interpolation, singular spectrum analysis (SSA), Whittaker, and time series harmonic analysis (HANTS)) on the reconstruction results of the two time series. The results show that the four methods have their own advantages and disadvantages, and the Whittaker method showed relatively good performance overall. However, the performance of interpolation methods might vary within different regions, and thereby the data reconstruction methods need to be further verified.

Total suspended matter concentration (TSMC) is one of the important factors influencing water bodies in coastal gulfs and lagoons. The rapid and accurate TSMC inversion can be obtained using remote sensing data. However, it is scarce to conduct TSMC inversion using two different data sources at the same time. This study conducted the inversion of TSMC in Maowei Sea and its estuary based on two data sources. Specifically, this study carried out image segmentation and object extraction using the dual-band ratio algorithm and the Cloude-Pottier target decomposition algorithm, respectively based on GF-1C optical images and GF-3 SAR data of September 2019. Meanwhile, contemporaneous field sample data were utilized. Then, the TSMC inversion was performed using the cubic polynomial regression algorithm. As revealed by the accuracy analysis, the fitting degree (R²), root mean square error, and mean relative percentage error of the GF-1C-based inversion model were 0.88, 130.25 mg/L, and 9.65%, respectively, while those of the GF-3-based inversion model were 0.61, 230.87 mg/L, and 15.13%, respectively. These indicate that the GF-1C-based TSMC inversion had a higher inversion accuracy (90.35%) than the GF-3-based TSMC inversion (84.87%). However, the inversion results of the two models showed highly similar distribution patterns. This further indicates that the inversion models established using two different data sources in this study can serve as references for TSMC inversion of Maowei Sea and its estuary and for the environmental monitoring in coastal zones.

At present, the common methods for extracting tidal flats using remote sensing images tend to estimate tidal flat boundaries. Therefore, it is difficult to ensure high extraction accuracy. This study combined remote sensing cloud computing platform Google Earth Engine with the geographic information system (GIS) technology and selected 77 Landsat images during 1990—2021. Meanwhile, the mean high-tide line was set to the artificial coastline obtained through visual interpretation, and the mean low-tide line was determined through the fitting of the shoreline. Based on these, this study extracted the tidal flat in the Cixi section on the south bank of the Hangzhou Bay and estimated its area. Furthermore, this study analyzed the spatio-temporal changes in the area of the tidal flat. The results are as follows. During 1990—2021, the area of the tidal flat in the Cixi section on the south bank of the Hangzhou Bay was roughly maintained in the range of 20 000~24 000 hm², and the tidal flat migrated from south to north at a speed of 286.9 m·a^-1. The main driving force behind the spatial and area changes of the tidal flat was local policies.

Mastering the subaqueous physical state of artificial reefs (ARs) is critical for assessing the benefits of ARs. Tradition survey methods suffer shortcomings such as low efficiency and incomplete information and cannot meet the requirements for the precise monitoring of ARs. This study established a high-precision DEM (digital elevation model) of ARs using a multibeam sonar system. Meanwhile, this study conducted a quantitative analysis of the distribution, height, volume, and complex topographic features of ARs using the GIS (geographic information system) spatial analysis method. The high-precision DEM was tested in an offshore AR area in Shandong Province, obtaining the following results. The AR area has a water depth of -9.92~-6.73 m. The ARs in the area are stacked in piles with different distances, with a total reef volume of 5 458.49 m³. Meanwhile, 50% of the ARs have a height of 1.48~1.82 m. The terrain characteristic variables such as slope, curvature, rugosity, and topographic relief intensively show high values in the reef distribution area. Affected by their own gravity and local hydrodynamic force, the ARs have a subsidence depth of about 0.5 m, leading to the formation of the special erosion and silting terrain around the ARs. This study can provide technical and data support for the monitoring and assessing the physical stability of ARs and thus is practically significant.

Accurate, detailed, and three-dimensional land type statistical data with an appropriate resolution is greatly significant for the natural resources monitoring, supervision, and ecological protection in coastal zones. A land type statistical model needs the support of DEM. However, there is little studies on the adaptability between the DEM resolution and the statistical model. Given this, this study proposed an optimization method of DEM resolution for land type statistical model of coastal zones. Specifically, this study systematically explored the impacts of DEM resolution on land type statistical model, selected indices and constructed an assessment model from four aspects, namely statistical accuracy, generality, information amount, and calculation efficiency. Then, this study determined the index weight using the entropy weight method and obtained the optimal DEM resolution through weighted calculation. The results are as follows. ①An increase in the DEM resolution led to the increasingly apparent negative impacts on the statistical accuracy and information amount and the increasingly significant positive effects on the generalization of the model. ②To meet the requirements of statistical accuracy, the DEM resolution should not exceed 30 m. Meanwhile, as required by the landform generalization, the DEM resolution should not be less than 10 m. ③There is a linear positive correlation between the calculation time of spatial operations and the number of DEM grids. ④Based on the comprehensive assessment using the weights calculated by the entropy weight method, the optimal DEM resolution was 10 m. The method of DEM resolution developed in this paper is universal and can be expanded in the natural resource statistics of coastal zones and in the land type statistics of other surveys and monitoring.

To address the problem that image details are liable to be lost in the process of hyperspectral super-resolution, this study proposed a hyperspectral super-resolution algorithm that combines multi-receptive field features and spectral-spatial attention. By fully using the high- and low-frequency information in hyperspectral images, this algorithm reduces the loss of image details and improves the hyperspectral super-resolution effects. First, in the feature extraction stage, convolution with different sizes of convolutional kernels is used to obtain multi-scale receptive field features. This assists in extracting more high- and low-frequency information from low-resolution images, thus retaining the features of original images. Then, the acquired image features are enhanced by the spatial-spectral attention mechanism, and the reconstruction of spatial-dimension features is conducted using spectral-dimension information. Finally, the features of various groups are fused, and the checkerboard pattern is relieved by applying the pixel deconvolution layer. As a result, clear and high-resolution images can be produced. The proposed super-resolution algorithm that combines multi-receptive field features with spectral-spatial attention was applied to two public datasets Chikusei and Pavia Center Scene, achieving peak signal-to-noise ratios of 39.869 7 and 31.942 2, respectively and structural similarity of 0.937 6 and 0.878 6, respectively. Therefore, the super-resolution algorithm enjoys obvious performance advantages compared to the latest super-resolution algorithms. Overall, the algorithm proposed in this study integrates the advantages of the multi-receptive field feature extraction module and the spatial-spectral attention module and can significantly improve image details.

Image segmentation is a key step in the analysis of high-resolution remote sensing images and plays an important role in improving information extraction accuracy. To improve the performance of traditional pixel-based image segmentation methods, this study proposed a new algorithm based on Parzen window density estimation of super-pixel blocks. The new algorithm includes three main steps, namely super-pixel initial segmentation, feature measurement, and density estimation and re-clustering. In the first step, an image is coarsely divided using the simple linear iterative clustering (SLIC) algorithm, and each super-pixel block is marked as a node in the graph structure of the image. In the second step, the Gabor texture features of each super-pixel block are measured to construct high-dimension feature vectors. Meanwhile, the similarity of the image textures is calculated as the weight of the edge connecting two nodes in the graph. Then, the distance between the two nodes is calculated on the minimum spanning tree (MST) of the graph. In the third step, the calculated distance is used for Parzen window density estimation of each node, and re-clustering of the density values is conducted to obtain the final results. In the experiments, multiple multispectral high-resolution remote sensing images were adopted to verify the algorithm proposed in this study. Using visual discrimination and the quantitative evaluation based on precesion rate and recall rate, the segmentation results of the algorithm proposed in this study were compared with those of other algorithms. The experiments verified that the algorithm proposed in this study is effective.

With the improvement of the spatial resolution of remote sensing images, the imaging features of ground objects have become increasingly complex. As a result, the change detection methods of remote sensing images based on texture expression and local semantics are difficult to meet the demand. To improve the change detection accuracy of high-resolution remote sensing images, this study constructed a large-scale remote sensing-based human activity change detection dataset (HRHCD-1.0) with a high resolution of 0.8~2 m. Moreover, this study designed an attention-based Siamese change detection network with a strong capability to extract contextual semantic features by introducing spatial attention and channel attention mechanisms. In the model comparative experiment, the attention-based Siamese change detection network proposed in this study increased the mean intersection over union on the validation set by 24% and showed more complete detection results compared to the models using non-attention mechanisms, effectively alleviating the problems of poor boundary, local omission, and holes of models using non-attention mechanisms. The post-processing method allows for small polygon removal, hole filling, and graphic smoothing of the detection results, improving the processing graphic effects of polygons. Furthermore, the increase in the sample size in the training of change detection significantly improves the application accuracy and generalization ability of the attention-based Siamese change detection network proposed in this study.

Surface change detection is an important component of the applications of remote sensing big data. However, it is essentially subject to manual interactive interpretation in actual production. With this regard, this paper developed an application method and software for the automatic detection of changes in vegetation regions on a polygon scale using correlation coefficients and feature analysis. The details are as follows. Correlation coefficients of surface features were constructed using spectral and textural features, and then the changes in vegetation regions were detected using the similarity measurement method. According to the analysis of spectral differences between the vegetation and other types of surface features, the red band ratio was selected to remove spurious changes. Finally, the change detection software was designed and developed using the.NET framework and the ArcGIS Engine component library for secondary development. Test data were imported into the software for change detection. The test results show the accuracy rate and omission rate of the software in the change detection were 94.3% and 8.5%, respectively. Furthermore, the software has a higher automatic level compared to manual interactive interpretation. In conclusion, the method and software developed in this study can be widely applied.

Existing registration algorithms suffer low efficiency and accuracy in the registration of synthetic aperture Radar (SAR) and optical images. This study proposed a stepwise refinement-based automatic registration algorithm of images with special textures by coupling marker-controlled watershed segmentation and mathematical morphology. Firstly, the improved marker-controlled watershed algorithm was used to extract the features of water bodies from images, and then binarization and mathematical morphology were applied to accurately extract the water regions. Secondly, the centroids of water bodies were extracted for rough registration between images to improve the search efficiency of the subsequent algorithm. Finally, using the optimization algorithm, the optimal transformation parameters when the similarity measure was maximized were obtained and were used to carry out the spatial transformation of SAR images for image registration. In this manner, the precise registration of SAR and optical images was completed. The experimental results show that the algorithm proposed in this study that couples image segmentation with registration reduced calculation amount while ensuring the registration accuracy. Meanwhile, this algorithm effectively solved the difficulty in the automatic registration of SAR and optical high-resolution images that have large differences in gray levels and spatial resolution.

To overcome the shortcomings such as poor adaptability, low efficiency, and the demand for prior knowledge in the 3D reconstruction using UAV images, this study proposed a method for extracting match pairs of UAV images considering geospatial information. The steps of this method are stated as follows. Firstly, reduce high-dimensional features of the images to low-dimensional features using the principal component analysis (PCA) method to improve the construction efficiency of the retrieval vocabulary. Secondly, construct a comprehensive retrieval factor by calculating the inverse distance weighting factor between query images to improve the distinguishability between similar images. Finally, discard invalid match pairs by calculating the retrieval threshold to improve the query precision of images. The experimental results show that, compared to the traditional footprint map method and 128-dimensional feature retrieval method, this method enjoys higher processing efficiency and more comprehensive sparse reconstruction results, especially for the massive UAV data.

Buildings are important urban components because they serve as the carrier and represent the image of a city. Establishing the 3D models of buildings is a critical base for constructing digital, virtual, and smart cities. However, existing 3D modeling methods of buildings suffer the shortcomings such as high cost, tedious and complex operations, and high labor intensity. Given this, this study proposed a method for 3D modeling of urban buildings based on multi-source data integration. Meanwhile, this study achieved the automatic construction of the 3D models of buildings using the GIS modeling technology. The main principles and operations of the modeling method are as follows. First, high-resolution satellite remote sensing images, electronic maps of building contours, and panoramic images were integrated and preprocessed on a remote sensing and GIS integration platform to extract buildings’ spatial and attribute information such as geometric boundaries, height, floor number, and roof type. Next, this study proposed a scheme for modeling the main structures of buildings based on the constructive solid geometry (CSG) method. Then, the automatic construction of the 3D models of buildings was achieved using the GIS modeling technology, as well as multiple tools in the ArctoolBox window, such as combined data processing, file conversion, spatial analysis, 3D analysis, and scripts and programs. Afterward, the 3D models of buildings were visualized using the texture mapping technology. Finally, the north campus of Huaiyin Normal University was selected to verify the modeling method proposed in this study. As indicated by the analysis of modeling process and visualization effects, the modeling method proposed in this study is characterized by low cost, simple operations, and high automatic degree and can meet the high requirements of accuracy. Meanwhile, this method has great visualization effects and can provide reliable technical solutions for the 3D modeling and visualization of large-scale urban buildings.

To monitor and evaluate maize seedlings in Northeast China and estimate their number in time, this study provided effective support for the rapid estimation of the maize seedling number using unmanned aerial vehicle (UAV) remote sensing images. Using the multispectral UAV data, the color indexes ExG, GBDI, ExG-ExR, NGRDI, and GLI were compared to segment maize seedlings from the soil background. Then, the optimal threshold was determined using the Otsu algorithm, and ExG was selected as the optimal color index. According to optimization, the best combination of morphological parameters consists of area (A), perimeter (B), rectangle length (D), rectangle perimeter (G), ellipse long axis length (H), and shape factor (Q). Then, the number of maize seedlings was predicted using the support vector regression (SVR) model and the prediction accuracy was assessed. Finally, the spatial distribution map of the local maize seedling number was developed. Tests revealed that the accuracy and the statistical error of the SVR model were 96.54% and 0.6%, respectively. These results allow the number and growth trends of maize seedlings to be predicted quickly and accurately in a short time.

It is significant for the market management and regulation of local government to accurately extract wolfberry planting areas in the Qaidam Basin using remote sensing technology. Taking the Nuomuhong Farm, a typical wolfberry planting area, as an example, this study selected Landsat8 OLI and GF-1 WFV images to construct the time-series NDVI/EVI data of the crop growth period. Then, this study employed four novel ensemble learning classifiers (i.e., LightGBM, GBDT, XGBoost, and RF) and two widely used machine learning classifiers (SVM and MLPC) to classify wolfberry planting areas. The results show that: ① Relatively high classification accuracy were obtained using LightGBM (90.4%), GBDT (90.4%), XGBoost (89.31%), and RF (86.96%). Most especially, LightGBM-EVI yielded the highest overall classification accuracy (91.67%), with a Kappa coefficient of 0.90; ② Enhanced vegetation index (EVI) is more sensitive in the middle-late stage of the wolfberry growth period. For the same classifier, better mapping effects of wolfberry planting areas can be obtained when time-series EVI data were used; ③ Data redundancy can be further reduced while obtaining high classification accuracy by determining the optimal temporal features of NDVI/EVI classification using the feature importance scores of the GBDT, XGBoost, and RF classifiers.

Urban land use (ULU) reflects urban functions and structures, and the study of ULU classification can provide guidance for the sustainable development of cities. This study conducted the ULU classification of the main urban area of Harbin City using the object-oriented and random forest methods by integrating multi-source geospatial data including Sentinel-2A remote sensing images, OpenStreetMap (OSM) data, point of interest (POI) data, and nighttime light data from the Luojia-1 satellite. The results are as follows. The overall accuracy of the first-level land use type was 86.0%, with a Kappa coefficient of 0.75. The overall accuracy of the second-level land use types was 73.9%, with a Kappa coefficient of 0.69. The introduction of POI data can significantly improve the classification accuracy of residential land, industrial and mining storage land, and educational land. Meanwhile, night light data can effectively improve the classification accuracy of commercial office land and business land. This study shows that the combination of remote sensing images with multi-source geographic data is effective for ULU classification.

The study on many years’ land cover plays a crucial role in promoting the high-quality development of the Yellow River basin. Meanwhile, high-frequency and high-precision land cover data are vital for land cover monitoring. This study took the basin’s geometric center that has been stable for many years to sample and quickly selected a set of sample points that can be used for annual image supervised classification. Then, cloudless images were screened out from nearly one thousand Landsat images on average of the Yellow River basin of each year from 2000 to 2020 and were spliced by year using Google Earth Engine. Then, the random forest classification method was used to conduct the supervised classification of the cloudless images, producing the annual land cover data of the Yellow River basin in the recent 20 years. Finally, the land cover data of 2010 of the basin were compared with well-known annual land cover data at home and abroad. The results are as follows. ① The selection method of sample points used in this study is reasonable and reliable, with a selection accuracy of more than 94.7%, meeting the requirements of sample accuracy for supervised classification. ② The overall accuracy of the annual land cover data created based on Google Earth Engine is 0.82±0.03, with an average Kappa coefficient of 0.82. The classification accuracy and the overall and local classification results are better than the MCD12Q1 and ESA-CCI datasets. ③ Using the method for creating annual land cover data using Google Earth Engine, the frequency and accuracy of large-scale land cover data can be considered at the same time to a certain extent.

Soil moisture is an important indicator affecting dynamic climate changes, vegetation ecological recovery, and land desertification control in arid regions. Using Landsat8 OLI/TIRS multispectral remote sensing images, this study determined the optimal spectral indices by introducing thermal infrared (b10) band to improve nine traditional spectral indices and through significance tests and multiple covariance tests. Then, with the improved spectral indices as the modeling factors and based on the terrain data, this study constructed multispectral comprehensive inversion models of desert soil moisture using the multivariate linear regression (MLR) and random forest (RF) algorithms. Finally, the spatial distribution characteristics of soil moisture and their driving factors were analyzed using the optimal model. The results are as follows: ① The correlation coefficients of the improved spectral indices EBSI, ECI, ECal, ENDVI, and EPDI increased by 0.02~0.11; ② For the prediction datasets of linear and non-linear models, their R ² increased by 0.12 and 0.05, respectively and their RPD values increased by 0.35 and 0.49, respectively after the spectral indices were improved. Moreover, the RPD value of model RF-II was up to 3.12, and thus this model can accurately predict soil moisture. ③ The accuracy of the non-linear models was significantly higher than that of the linear models. The R ² of the prediction datasets of MLR-based linear models was only 0.59 and 0.71, while that of the RF-based non-linear models reached 0.86 and 0.91. ④ The distribution of soil moisture was influenced by both natural and artificial factors. The soil moisture content is [0, 5)% and [5, 12)% in the northeastern desert and shows cross-distribution in the southern farmland. Soil moisture is difficult to evaporate in the northern and central desert-oasis transition zones due to inhibiting factors such as the vegetation coverage and surface salt crust, with the content of [15, 20)% and [20, 40)% mostly.

An improved and comprehensive drought monitoring model was developed in this study. Given multi-genetic types such as the interaction of atmospheric precipitation, vegetation growth, and elevation, multiple data sources were selected for the model, including EOS-MODIS data, TRMM precipitation data, and the region SRTM-DEM(digital elevation model) data from 2001 to 2019 in Xinjiang. The parameters including precipitation concentration index (PCI), temperature and vegetation drought index (TVDI), and DEM were calculated, and the principal component analysis (PCA) method was employed to establish the model. Then, the model was used to analyze the spatio-temporal characteristics of drought in the study area. The analytical results show that the annual occurrence frequency of drought in the study area from 2001 to 2019 was high in the middle part and low in the surrounding areas. In addition, drought struck 47.7% of the study area, and the occurrence frequency of drought reached 60% in 32.3% of the drought regions. Meanwhile, drought was concentrated in the Tarim and Turpan basins. The changing trends of drought in the study area differed greatly. For the linear regression slope of drought from March and September, the absolute values of the positive slope were far greater than those of the negative slope. Based on this, it can be predicted that the drought in the study area mainly included spring and summer droughts in 2020.

With the rapid development of China’s photovoltaic industry in recent years, the assessment of the impacts of the large-scale solar parks on the environment is greatly significant for guiding the healthy development of the photovoltaic industry. The changes in the local thermal environment induced by solar parks have attracted the attention of researchers at home and abroad. Floating solar parks (also known as floating-on-water solar parks) serve as a new development mode of photovoltaic power generation in recent years. However, their impacts on the spatial pattern of land surface temperature(LST) are currently unclear. Using the single-channel algorithm, this study extracted the LST dataset of the floating solar park in Huainan City and its adjacent areas from Landsat8 time-series remote sensing data. Then, this study determined the differences between monthly LST and air temperature of the corresponding month (LST_s-a) and analyzed the influencing mode and scopes of floating solar parks on the spatial pattern of LST, as well as their seasonal differences. Finally, this study ascertained the influencing degrees of different construction stages on LST in the construction area. The results are as follows. ① The construction of the floating solar park significantly changed the thermal environment of the construction area, and warming effect occurred during both summer and winter when the temperature changes the most appearantly. Moreover, the warming effect mainly concentrated with 200 m of the construction area, while being very weak in typical surrounding land cover. ② During the construction and the completion phases of the floating solar park, the average monthly LST in the construction area was generally higher than that of the water body and was close to that in the forest. The average annual LST increased by 3.26 ℃ and 4.50 ℃, respectively in the construction and the completion phases. ③ This study can serve as a reference for the related research on assessing the impacts of the floating solar parks on the local environment. The authors recommended conducting an in-depth study from the aspects of the construction of cloudless time-series LST datasets, the separation of the increased/decreased amplitude of the temperature induced by floating solar parks, and the influencing scope and degrees and the genesis analysis of the distribution pattern of LST on the different types of land cover in a floating solar park and its adjacent areas.

This study analyzed the spatio-temporal evolutionary characteristics of the Ningxia urban agglomeration along the Yellow River during 1998—2018 from the aspects of morphology, scale, and structure. To this end, the urban built-up area in the study area during the 20 years was extracted from five stages of DMSP/OLS and NPP/VIIRS nighttime light data using a high-resolution data comparison method. The results are as follows: ①The Ningxia urban agglomeration along the Yellow River expanded rapidly during 1998—2018, and the increment and growth rate peaked during 2008—2013. ②The constantly decreased fragmentation degree indicates that the inner morphology of the urban agglomeration became increasingly compact and the built-up patches were relatively concentrated. The fractal dimension continuously decreased, and the geometric shape of the urban agglomeration tended to be regular. Moreover, the urban agglomeration mainly expanded in the means of internal filling. ③The center of the urban agglomeration migrated to the southwest and approached its geometric center, and the urban development accelerated in the south. ④The primacy ratio and Gini coefficient of the urban agglomeration first decreased and then increased, indicating that the gap between cities in the urban agglomeration first narrowed and then widened.

The Jinchuan mining area is the largest nickel production base in China. However, the surface deformation in the mining area has not been monitored since 2018 when the plan for restoring mining was proposed. Based on the Sentinel-1A data of three orbits (ascending orbit 128 and descending orbits 33 and 135), this study obtained the 3D surface deformation rates and time-series displacement by applying the small baseline subset InSAR (SBAS-InSAR) and the least-squares iterative method combining prior conditions. The results are as follows. Three large deformation areas have formed in three mining areas (i.e., the Longshou, Xi’er, and Dongsan mines). The deformation in these areas is primarily present as surface subsidence, with the maximum vertical subsidence and subsidence rate (i.e., -408.9 mm and -162.8 mm/a, respectively) occurring in the Xi’er Mine. For the Longshou Mine, the southwestern and northeastern slopes contract toward ore veins. For the Xi’er and Dongsan mines, the deformation areas show similar displacement directions, that is, the eastern and western sides of subsidence funnels contract toward ore veins. The surface deformation in the Jinchuan mining area is closely related to man-machine mining, geological faults, and lithologic structures. Among them, man-machine mining is the main cause for the surface deformation, while faults and lithologic structures serve as the controlling factors of the surface deformation. The results of this study will provide theoretical support for safe production and mining planning in the Jinchuan mining area.

Low-cost microsatellites and their constellations are important directions in the development of satellite remote sensing in recent years. This is because they can effectively alleviate the questions such as the low transit frequency of a single satellite and the high networking cost of satellites. Monitoring using remote sensing satellites is an important means to obtain bare land information in the ecological field owing to its wide coverage area and immunity to man-made interference. This study carried out exploratory research on urban bare land identification using the remote sensing images of low-cost micro-satellites of MV-1 Constellation. The identification results were compared to those obtained using Landsat8 images to explore the reliability of the implication of domestic low-cost micro-satellite images in urban bare land identification. To this end, this study selected Donggang District, Rizhao City, Shandong Province as an example and developed the extraction method that combines unsupervised vegetation indices-excess green and excess red (ExG-ExR)-with the maximum likelihood method. The results are as follows. ① The panchromatic images with a resolution of 5 m that were shot by Micro-satellite No. 02 of MV-1 Constellation can clearly reflect the current status of Donggang District. They have higher resolution and perform better in capturing details of ground features. However, they lack wave band advantages over Landsat8 images. ② The images of Micro-satellite No. 02 had an overall classification accuracy of 93.3% and a Kappa coefficient of up to 0.85. Therefore, the micro-satellites of MV-1 Constellation are reliable in bare land identification to some extent. ③ The difference between the bare land area in the Donggang urban area identified using Micro-satellite No. 02 images and Landsat8 images was 1.5 percentage points. This indicates that micro-satellites of MV-1 Constellation have equivalent inversion capacity in urban bare land identification to mainstream satellites under the conditions of proper algorithms, close shooting time, and consistent geo-coordinate correction.

This study aims to explore the land use change and its future development trend in the Aral reclamation area, a typical artificial oasis in the arid region in northwest China and to provide a reference for the regulation and management of land use change in similar areas. After the multi-temporal synthesis of monthly images of each year, annual land use classification maps were obtained using the support vector machine method. Then, the land use change was analyzed from the aspects of area change, type transformation, and spatial dynamic change. Finally, the cellular automaton (CA)-Markov model was used to simulate the land use change in 2050 and 2080, and the sudden changes and their driving factors were explored using the cumulative departure method and the path analysis. The results of this study are as follows. During 1990—2019, the area of arable land, garden land, water bodies, and construction land in the Aral reclamation area showed an increasing trend. Among them, the arable land and garden land increased in area mainly due to the conversion of unused land outside the areas along the Tarim River. By 2080, the unused land in the northeastern and southeastern parts of the reclamation area will be gradually reclaimed. As a result, arable land, garden land, and construction land will significantly increase. The area of various types of land use in the Aral reclamation reached a turning point in 2005, showing a sharp increase in the area of arable land, garden land, and building land. This was mainly driven by total population, gross agricultural product, and cotton prices. It can be concluded that it is necessary to develop policies on the sustainable development of arable land, to strictly control the area of construction land, and to construct a reasonable land use structure in future land development and utilization.

To analyze the phenological characteristics of the rubber forest in Hainan Island and to explore the phenological change characteristics of tropical forest vegetation, this study reconstructed the 2001—2015 MODIS NDVI time series using the Savitzky-Golay (S-G) filtering method based on the MODIS normalized difference vegetation index (NDVI) data. Then, this study extracted the phenological parameters of the rubber forest using the dynamic threshold method and typical sampling areas. Finally, this study analyzed the spatio-temporal changes in the phenological characteristics of the rubber forest. The results are as follows. During 2001—2015, the rubber forest started its foliation season mainly from mid-January to late March in spring and started its defoliation season from mid-November to late December in autumn, with the growing season lasting for about 7~10 months. On the time scale, the phenological characteristics did not significantly changed in the 15 years. Specially, the spring phenology occured about 0.94 days earlier, the autumn phenology showed an about 0.84 days delay, and the growing season was prolonged for about 1.79 days every year. On a spatial scale, the regions where the spring phenology occurred significantly earlier in the 15 years primarily included Baisha Li Autonomous County, Tunchang County, Qiongzhong Li-Miao Autonomous County, Wanning City, and Qionghai City, with a changing rate of -1.8~-0.1 d/a. The areas with a significant delay in autumn phenology included Danzhou City, Baisha Li Autonomous County, Tunchang County, Qiongzhong Li-Miao Autonomous County, Qionghai City, Wanning City, Ledong Li Autonomous County, Sanya City, and Baoting Li-Miao Autonomous County, with a changing rate of 0.5~2.7 d/a. The areas where the growing season was significantly prolonged mainly included Danzhou City and Baisha Li Autonomous County, with a changing rate of 0.2~0.8 d/a. The main characteristic of phenological changes of the rubber forest is the significant delay in the start date of the defoliation season.

Major crops tend to receive far more attention in current remote sensing (RS) monitoring of vegetation than minor tree species with ecological and economic benefits. Zanthoxylum bungeanum Maxim (ZBM) is an important but niche ecological tree, and its fruits are common oil and medicinal materials. It is vital for the sustainable development of local economy, ecology, and society to obtain accurate information of planting area and spatial distribution ZBM in time. Using the GF-2 PMS images and the random forest algorithm, this study discussed the feasibility of RS monitoring of ZBM planting. Three classification schemes were designed using four classification features, namely spectral bands, normalized difference vegetation index (NDVI), textural features, and digital elevation model (DEM). Furthermore, this study explored the role of different classification features in identifying ZBM by analyzing the classification accuracy of the schemes. Results show that it is difficult to obtain satisfactory classification accuracy when only spectral band characteristics were used (overall accuracy: 65.90%). Combining NDVI and DEM with the spectral band characteristics can slightly improve the classification effect (overall accuracy: 67.67%). After textural features were further combined, the overall accuracy was greatly increased (74.43%). This indicates that textural features play an important role in monitoring ZBM planting. As revealed by the results of the optimal classification scheme, ZBM in Linxia, Gansu Province is mainly distributed along the Yellow River and around the Liujiaxia Reservoir, with a total area of 231.59 km², which accounts for 22.56% of the total area of the study area. The area of ZBM planted in the patterns of single cropping and mixed cropping is 189.06 km² and 42.53 km², respectively. More than 90% of ZBM grows at an elevation of [1 683, 2 300) m and its number tends to decrease, increase, and decrease successively with an increase in the elevation. Moreover, 58% of ZBM are planted in regions with a slope of [8, 25)°. Overall, GF-2 PMS images have great potential in monitoring ZBM planting. The development of RS-based identification methods of ZBM will assist in the regulation of the local ecological industry and the layout of subsequent ecological engineering. Furthermore, it will provide a strong reference for the remote sensing monitoring of ecological tree species or a minority of vegetation species in other regions.

Urban expansion is the main characteristic of Yibin City in recent years, and the study of its impacts on ecology is significant for urban development and ecological protection. To assess the impacts of urban expansion on the ecology more accurately, this study established an improved remote sensing ecological index (IRSEI) by using the impervious surface area index as the dryness index to replace the original building index. The IRSEI coupled the improved dryness index and the indices greatly influencing the ecology, such as greenness, humidity, and temperature. This study analyzed the IRSEI using principal component analysis and correlation and established an IRSEI-based ecological assessment model of the three-river (i.e., the Jinsha River, Minjiang River, and Yangtze River) confluence in Yibin City. Then, this study analyzed and assessed the ecological environment of the confluence in 2013—2020. The results are as follows. The IRSEI can more accurately reflect the negative impacts of the dryness index on the ecology of the confluence. It can concentrate more useful information in the first principal component than the RSEI and can better apply to the quality assessment of urban ecological environment. In 2013, the IRSEI of the confluence was 0.54, indicating the moderate ecological status overall. The reason is that the original vegetation was destroyed by serious urban expansion. In 2017, the IRSEI was 0.67. The greenness was significantly improved by the continuous advancement of returning farmland to forests and the restoration of urban ecology, which is the reason that the ecology has greatly improved in 2017 compared to 2013. In 2020, the IRSEI was 0.63. The greenness, humidity, and dryness in 2020 were roughly the same as those in 2013, while the temperature rose in 2020 compared to 2017 due to the heat island effect induced by urban expansion. This is the reason for the slight decline in the ecological level in 2020.

This study aims to assist Jiangsu Province in selecting the regional development modes suitable for its local conditions. To this end, this study identified the driving factors in the evolution of the land use pattern in Jiangsu Province by comprehensively using three regression methods (i.e., Logistic, Auto-Logistic, and SBS-Logistic). Then, this study simulated the spatial pattern of ecology-agriculture-construction land in Jiangsu Province in 2030 under four scenarios using the Markov-CLUES model. The results are as follows. ①The accuracy of the three regression methods was in the order of Auto-Logistic > SBS-Logistic > Logistic. The ROC values of the three land use types obtained using the Auto-Logistic regression method were all over 0.75. The Markov-CLUES model performed well in the verification simulation of the land use pattern in Jiangsu Province during 2005—2018, with a Kappa coefficient of 0.758 according to the accuracy evaluation. ②The rapid development in the “Three Circles” and “Four Lines” areas will be guaranteed under the scenario of natural growth in 2030. However, some rivers will narrow, threatening food security and leading to weak development sustainability. For the scenario of ecological protection, the environment of river canals and areas surrounding lakes will be greatly improved by controlling and managing ecology. For the scenario of cultivated land protection, high-quality cultivated land in the main grain-producing areas will be effectively protected, and the cultivated land area in northern and central Jiangsu will obviously increase. The dual protection of both ecology and cultivated land will allow for strong sustainable development and the effective restriction of the disorderly expansion of construction land in 2030. However, the contradiction between demand and supply of construction land will be prominent in a short term. ③It is advisable to adopt the development mode of protecting ecology in the Taihu Lake, the Huai River basin, the conservation areas of Yangtze River wetland, and coastal areas, to adopt the development mode of protecting cultivated land in the Subei and Suzhong plains, and to adopt the development mode of protecting both ecology and cultivated land in the five cities in southern Jiangsu. Given the further implementation of the development strategy along the Yangtze River, the development mode of natural growth can be adopted in Yangzhou and Taizhou cities in central Jiangsu Province. This study revealed the spatio-temporal evolutionary laws of the spatial pattern of ecology-agriculture-construction land in Jiangsu Province. The simulation of the spatial pattern under various scenarios will provide decision support for Jiangsu Province to achieve regional development modes suitable for local conditions and assist Jiangsu Province in forming the spatial pattern of land where ecology-agriculture-construction land couples mutually and develops in a coordinated manner.

Google Earth Engine enables some limitations in the remote sensing monitoring of vegetation to be overcome, including difficult data acquisition, large local storage capacity, and low processing efficiency. Using GEE, the data of satellites Landsat and MODIS (spatial resolution: 30 m and 250 m, respectively), and temperature and precipitation data, this study investigated the spatio-temporal change trends and sustainability of the vegetation in the Nuomuhong alluvial fan in Qinghai Province during 2000—2017. Moreover, this study analyzed the relationships of vegetation between wolfberry plantations and salinized areas on the alluvial fans of different eras and the future changes of the relationships. The results are as follows: ① The average annual maximum synthetic normalized difference vegetation index (NDVI) increased from 0.029 to 0.054 during 2000—2017, with an increased amplitude of 0.025. The average annual enhanced vegetation index (EVI) increased from 0.633 to 0.771 during these years, with an increased amplitude of 0.138. The multiyear average maximum EVI showed that EVI peaks occurred from May to October each year. ② A correlation analysis and a partial correlation analysis were conducted between the average maximum EVI and the temperature and precipitation data. According to the analytical results, the correlation coefficient between the average maximum EVI and temperature was 0.839, indicating a strong positive correlation. Meanwhile, the correlation coefficient between the average maximum EVI and precipitation amount was 0.457, indicating a weak positive correlation. ③ Over 18 years, the vegetation in wolfberry plantations was rapidly improved, while the vegetation in the salinized area degenerated. ④ The future changes in the vegetation in wolfberry planting areas and salinized areas will have strong sustainability. The vegetation growth in wolfberry planting areas will continuously restrict the vegetation in the salinized area to a certain extent in a period in the future.

As an important indicator reflecting the geological sensitivity of slopes, relief amplitude is significant for assessing hidden risks of glacial lake outburst floods. This study obtained information on the current status of glacial lakes in the glacial lake concentrated area in the eastern Shishapangma, southern Tibet through interpretation of the GF-2 and ASTER GDEM V3 data on a scale of 1∶50 000. The feature extraction of relief amplitude was conducted using the mean change point method. Then, this study conducted a correlation analysis, achieving the hidden risk rating of glacial lake outburst floods. The results are as follows: ① On a scale of 1∶50 000, the optimal sampling unit of relief amplitude is 21×21 (0.39 km²). ② The relief amplitude characteristics of glacial lakes include the single, combined, and cross-level types, of the which the risk grade increases successively. ③ Among 1 020 glacial lakes in the study area, the number of glacial lakes with low-, medium-, and high-grade hidden risks of outburst floods accounts for 97.35%, 1.77%, and 0.88%, respectively. This study revealed the practical significance of relief amplitude as an assessment index for the hidden risks of glacial lake outburst floods. Furthermore, this study can provide theoretical references for related studies of similar areas.

Glaciers are widely distributed in southeast Tibet. Glacier instability is prominent in this region against the backdrop of global warming. Surveys of the potential hazards of ice avalanches using optical remote sensing are practically significant for disaster prevention and mitigation in the region. According to the hue, morphology, texture, and shadow characteristics of the potential hazards of ice avalanches on remote sensing images, this study established the symbols of remote sensing interpretation of potential hazards of ice avalanches in the study area. Based on this, a total of 232 potential hazards of ice avalanches were interpreted in southeast Tibet, including 47 large, 147 super large, and 38 giant ones. Then, this study analyzed the essential characteristics and spatial distribution of the potential hazards based on the characteristics of terrain, landform, and regional geological environment. Consequently, four concentrated distribution areas and two concentrated distribution zones were determined. The potential hazards of ice avalanches in the study area show distinct chain characteristics. According to the spatio-temporal relationships between the potential hazards and their possible secondary disasters, the ice avalanche disaster chains in southeast Tibet can be divided into three types, namely, ice avalanche - glacial lake outburst flood - debris flow disaster chains, ice avalanche - debris flow - barrier lake - flood disaster chains, and ice avalanche - debris flow disaster chains. Taking the potential hazard chains of ice avalanches in Miduigou, Jianmupuqu, and Zelongnonggou as examples, this study analyzed the dynamic change characteristics and chain structure of these potential hazard chains using optical remote sensing technology. The purpose is to provide basic data for an in-depth study on potential hazards of ice avalanches in southeast Tibet.

This study extracted and analyzed the altered mineral anomalies in the Suolake area in the Altyn metallogenic belt, Xinjiang using the airborne hyperspectral remote sensing data (CASI/SASI). Based on this, the distribution pattern and genesis of the altered mineral anomalies in the area were summarized. Then, the spectral curve characteristics of altered minerals from different geologic bodies were analyzed and summarized according to the ground spectrum measurement of typical rocks and minerals. Meanwhile, the spectral measurement and analysis of the geologic profiles of altered minerals in the Suolake copper-gold deposit were conducted. Then, representative altered mineral assemblages were determined, and the hyperspectral remote sensing prospecting model based on gold deposits in this area was established. Base on metallogenic geological setting and geochemical anomaly characteristics, this study explored the application of hyperspectral remote sensing data-based anomaly extraction in metallogenic prediction. The results verified that the anomaly areas delineated using hyperspectral remote sensing data have favorable gold mineralization, suggesting that hyperspectral remote sensing can provide accurate and reliable information for ore prospecting.

UAV airborne geophysical exploration has become an emerging branch of airborne geophysical exploration technology. To obtain high-quality measured data in UAV airborne geophysical exploration, it is necessary to plan UAV flight trajectory according to the application characteristics of airborne geophysical exploration. Focusing on the demand for 3D planning of UAV flight trajectory and autonomous obstacle avoidance, this paper studied the 3D information extraction technology of ground obstacles based on point cloud data of UAV LiDAR and extracted ground points and non-ground points (e.g., transmission towers, power line points, and vegetation points). The construction of terrain information and the 3D reconstruction of transmission towers and power lines will provide important primary data for UAV 3D flight trajectory planning software.

The vector data of mines in China obtained from remote sensing-based monitoring are characterized by wide coverage, high complexity, and high accuracy of area statistics. However, existing software suffers low calculation efficiency and low accuracy. This study proposed a solution based on the feature manipulation engine (FME) platform. This solution consists of the following steps. Firstly, the vector data of mines were divided into zones according to the locations of polygons and mineral rights. Secondly, the positions of the polygons relative to mineral rights were analyzed, obtaining four types of polygons, namely being separated from, containing, being contained in, and covering mineral rights. Finally, relevant area of mines was calculated, including the development area (KFZDMJ), the area covered by mining right (KQNMJ), and the area uncovered by mining right (KQWMJ) according to the types of relative position relationships. The solution was verified using the vector data of mines in a medium-sized province. According to the verification results, the solution proposed in this study can greatly improve calculation efficiency and accuracy and its operation is feasible and straightforward. The results show that the consistency checking of the map attributes can provide effective support for the compilation of remote sensing-based monitoring data of mines and can be widely applied.