Water storage is a critical component of the global and regional hydrological cycle， which can be used to analyze the spatio-temporal evolution of regional water resources and drought. Traditional methods to monitor water storage are usually based on in-situ groundwater level data. However， challenges arise due to the limited placement and distribution of monitoring stations in large-scale research and exploration. The Gravity Recovery and Climate Experiment （GRACE） satellite have provided large-scale monthly data on Earth's gravity field variation. Several scholars have applied the water storage anomalies data retrieved by GRACE in hydrology research， which has facilitated the progress and development of hydrology. However， the current systematic elaboration of research on inversion of water storage based on GRACE data is not comprehensive enough， and few studies have systematically summarized the status of monitoring drought， interpolation， and reconstruction based on GRACE data. Firstly， this study briefly introduces the application fields of GRACE data， and discusses the advantages and disadvantages of the two data processing methods. Then， the application status and existing problems of GRACE data in the verification and uncertainty of inversion results， terrestrial water storage anomalies， groundwater storage anomalies， drought evolution and response， and interpolation and reconstruction were analyzed and summarized. Finally， further research about GRACE was suggested to carry out in the aspects of exploring the impact of changing environments on water storage anomalies， reducing the uncertainty of GRACE data， constructing a suitable drought index for drought monitoring， improving the accuracy of interpolation and reconstruction， and improving spatio-temporal resolution. The study is aiming to provide reference and insight for related research using GRACE data.
The Bistatic SAR system has no temporal decoherence， and combined with the powerful penetration capability of long wave， it has great prospects for application in estimating vegetation structure parameters. Using polarimetric interferometric SAR decomposition technique to study the scattering process of vegetation area in bistatic SAR system is of great significance for revealing the interaction between signal and ground object and constructing the inversion model of vegetation structure parameters. Considering the applicability of the model and the non-negligible decoherence in bistatic SAR system， the polarization interference matrix is expressed as the sum of the product of the polarization azimuth-extended generalized surface scattering matrix， the generalized quadratic scattering matrix and the Neumann adaptive volume scattering matrix with their corresponding coherent components. Solving all model parameters simultaneously using nonlinear least squares optimization technique based on residual least squares criterion. The method is tested using L-band fully polarimetric airborne data from the BioSAR 2008 project. The coherent components， phase distribution and energy information of different scattering mechanisms in the experimental area are obtained and analyzed with airborne lidar data. The results show that the decomposition method can well distinguish different scattering mechanisms in vegetation area， effectively suppress the overestimation of volume scattering power， and better fit with the actual data. The vertical distribution of surface scattering in the vegetation area is related to the vegetation height and penetration degree. The height of the volume scattering phase center is close to and the trend is consistent with the vegetation height of the airborne lidar. The coherence of the scattering mechanism is effectively estimated.
Surface deformation is a geological phenomenon caused by natural or artificial factors， and its disaster-causing process is slow and irreversible. It is also a geological disaster with destructive solid power. Therefore， real-time and high-precision surface deformation monitoring is one of the most critical tasks in maintaining urban safety. However， due to the complex causes， long duration， wide range， and many triggering factors of surface deformation， there are many difficulties in monitoring surface deformation using single technology such as leveling， GNSS， INSAR， and optical remote sensing. Considering the characteristics and complementarities of InSAR and GNSS， the combination of InSAR and BeiDou/GNSS can improve the surface deformation monitoring capability in space and time at the same time. Unluckily， the traditional GNSS-InSAR data fusion method is simple to fuse and cannot dynamically reflect surface deformation characteristics， leading to insufficient data use and low accuracy of deformation features. A new fusion method is proposed based on the Kalman filter algorithm GNSS-InSAR correction values. The method mainly consists of two sequential processes， i.e.， the a priori processing of GNSS and INSAR data and the fusion process of GNSS-InSAR correction values based on the Kalman filter algorithm. The a priori processing of GNSS and INSAR data is to obtain the a priori deformation results using the fitted estimation model to correct the systematic errors in the InSAR observations. The fusion process of GNSS-InSAR correction values based on the Kalman filtering algorithm is to fuse the two data through Kalman filtering based on the spatial and temporal correlation between the time-series GNSS observations and the InSAR correction observations. The experiment was processed using 103 views of sentinel-1A data from November 15， 2018， to June 3， 2022， and 13 GNSS point data during the same period. The experimental results show that the fusion result of the corrected InSAR observations and GNSS observations by the Kalman filter is 45% more accurate than the fusion result of the uncorrected InSAR observations and the GNSS observations， which is 45% 57% higher than the accuracy of InSAR observations. Therefore， the fusion method model based on the Kalman filter algorithm of GNSS-InSAR corrected values proposed in this paper improves the accuracy of InSAR deformation monitoring and expands the breadth and depth of InSAR applications.
Namcha Barwa region is located in the core tectonic deformation zone of the Eastern Himalayan Syntaxis with a complicated geological-tectonic environment and frequent geohazards. Therefore， it is of great significance to strengthen the research of surface deformation monitoring in this area for local disaster prevention and mitigation and sustainable economic development. This study aims to monitor surface deformation using Sentinel-1 SAR images in this region. Using PS-InSAR technique， the surface deformation rates distribution and deformation time series on LOS （Line-Of-Sight） were acquired. Then the status of surface deformation distribution and coseismic deformation caused by Mainling M6.9 earthquake in 2017 were discussed. It is revealed that the deformation in Namcha Barwa is greatly affected by Cenozoic tectonic deformation. Tectonic deformation in the study area mainly included coseismic deformation， postseismic relaxation deformation and thrust deformation in plate boundary. The deformations were quite different on both sides of the Yarlung Zangbo River. A slow negative deformation trend is shown on the north side， while the south side is positive deformed at a high rate caused by thrust faults. The coseismic deformation of Mainling earthquake showed a spatial distribution trait of negative deformation on the southeast side of the epicenter， positive deformation on the northeast side， positive and a larger deformation on the southwest side. This study demonstrated that， InSAR can provide high spatial and temporal resolution surface deformation data for hazard monitoring and scientific research on Qinghai-Tibet Plateau.
Baihetan Hydropower station is the second largest hydropower station in China after the Three Gorges Hydropower Station. With the rapid change of water level during the reservoir impoundment period， the geoenvironment of the reservoir area is changed， which is easy to cause sudden geological disasters. In addition， the maximum reservoir level of the transformed hydropower station is 825m， which has a large storage capacity and higher sudden-onset. In order to ensure the normal impoundment of the hydropower station and the safety of residents' life and property， it is necessary to quickly and dynamically identify geological hazards in the reservoir area. Therefore， based on the short baseline DInSAR method， this paper carried out the identification of geological hazards in the key reservoir bank section of Baihetan Reservoir area （Hulukou-Xiangbi Ling section） during the water storage period of the Jinsha River Basin. The sentinel-1A /B data were used for joint monitoring， and the re-entry period was increased to one scene every 6 days. Finally， a total of 66 scenes of data were obtained during the study area's water storage period from April 2021 to November 2021. DInSAR processing based on the same main image was performed combined with SRTM DEM data. The fast and dynamic deformation monitoring can be achieved by combination analysis of interference pair. Don't out of the 92 consensus exists significant deformation of strong deformation zone， are distributed in Jinsha River two sides， including eight strong deformation area have been inundated by the highest water level 800 m， 38 strong deformation area near the water， part of the small bank collapse， at the same time has chosen three continuous deformation area in combination with the actual topography analysis， found signs of deformation is more obvious， The location of strong deformation area is consistent with the monitoring results of DInSAR method， which proves the feasibility of using DInSAR method to quickly and dynamically discover new hidden points. The disaster analysis and monitoring and early warning during water storage period is of great significance to ensure the normal water storage and power generation of Baihetan Hydropower Station. The method provided in this paper can be used for large-scale， efficient， rapid and dynamic geological disaster monitoring， and the hidden danger points can be found in the first time and qualitative analysis can be carried out， which provides a new idea for the identification of potential geological disaster caused by the change of water level during the reservoir bank impoundment period.
Vegetation cover plays an important role in maintaining the stability of the ecosystem and preventing the loss of water and soil. Hainan has developed in a rapid pace since its establishment as a province in 1988， which has led to tremendous changes in the vegetation cover across the Hainan Island. To shed light on the impact of topographic factors on the vegetation cover on the Hainan Island and provide a basis for a more reasonable ecological and environmental protection strategy for the island， this study targets the Hainan Island for research， and applies the normalized differential vegetation index and the dimidiate pixel model for the vegetation cover extraction based on the Landsat TM/OLI multispectral images of 1988， 1998， 2008 2017 and 2020， and provides a linear trend analysis in the characteristics of the vegetation cover changes on the Hainan Island in the last 30 years. Combined with the altitude， slope and aspect data obtained through 30m_DEM， it offers further explorations over the characteristics of the spatial distribution of vegetation cover on the Hainan Island with different topographic factors. The results show that： （1） The average vegetation cover on the Hainan Island from 1988 to 2020 is between 0.58~0.88， and the general trend is first downward and then upward； （2） High vegetation cover is mainly distributed in the central， southwestern and southeastern parts of the Hainan Island， while low vegetation cover mainly appears in areas of the island with man-made interference such as residential and coastal areas. （3） With the increase of altitude， the vegetation cover of various levels in the Hainan Island decreases， and the areas with an altitude of less than 100 meters have the largest vegetation cover； at 0~5° of the slope， the vegetation cover reaches the maximum， and with the increase of slope， the vegetation cover shrinks； and there is little difference in the vegetation cover between the shady and sunny slopes that are primarily characterized by high vegetation cover.
To reveal the long-term variation trend of vegetation cover and further determine the main climatic driving factors affecting vegetation variation in Hainan Island. Also， to provide scientific evidence related to the impact of climate change on vegetation and scientific basis for achieving vegetation optimum development in island regions. The spatiotemporal variation trend of vegetation in Hainan Island from 1982 to 2015 was explored by applying trend analysis method to the GIMMS NDVI data. The effects of temperature， precipitation， and solar radiation on vegetation variability in Hainan Island were investigated by partial correlation analysis and principal component regression analysis over the 34 years. Results show that： ①Spatially， vegetation exhibited a significant increasing trend in the northern and coastal regions of Hainan Island while displayed a degeneration trend in Sanya city and its surrounding areas. ②Temporally， we found the vegetation in Hainan Island showed a slowly increased trend in most areas with a speed of 0.019/10 a and its intra-annual variability was obvious. ③In general， temperature and solar radiation jointly dominate the vegetation growth in 88% areas of Hainan Island in a significant way. Solar radiation was the most important climate driving factor to control vegetation variability in Hainan Island， followed by temperature， and precipitation had a small impact on the vegetation variability. ④Temperature dominated the vegetation variability in the northern and western areas of the Hainan Island. By contrast， solar radiation dominated the vegetation variability in the southern areas of the Hainan Island. Precipitation was the dominant climate driving factor to explain the variability of forests in the middle of the Hainan Island. Overall， this study found that temperature and solar radiation were two major climate driving factors which affected vegetation growth in the Hainan Island.
Remote sensing images have low detection accuracy due to the characteristics of different object angles， generally arranged densely， high proportion of small objects and complex background. In view of the inapplicability of the horizontal detection algorithm for remote sensing rotating object detection， and the periodicity and edge interchangeability of angle in the mainstream five-parameter method， a VR-CenterNet is proposed， which used the vector representation to detect the rotating box and design the loss function to avoid the problem of angle regression， and to optimize the high displacement sensitive problem of slender objects. For the high redundancy problem of shallow feature fusion， self-adaptive channel activation is introduced to automatically filter impurity information. In order to strengthen the key point information， an improved global contextual self-adaptive layer activation attention block is introduced in the output of backbone. First， the performance of different algorithms is compared on HRSC2016 and UCAS-AOD data sets. Then， the module ablation experiment is conducted on the two data sets to verify the effectiveness of each improved method. Experimental results show that： 88.48% and 90.35% accuracy are obtained on HRSC2016 and UCAS-AOD data sets respectively. The improved algorithm can improve the detection accuracy of remote sensing rotating objects， and provide another problem-solving idea for the accurate detection of remote sensing rotating objects.
The color matching function can map the information of any band to the three primary color reference values by constructing the relationship between the spectral distribution of the reflected light of the ground object and the color three stimulus values， and reconstruct the multispectral remote sensing image after the conversion of the color space. In view of the limited number of bands in visible light， narrow band channels and uneven band intervals of most multispectral sensors， direct interpolation between adjacent bands will lead to large errors in the color matching integration process， combined with remote sensing data simulation and back propagation （ BP ） neural network， the R， G and B three stimulation values obtained by the color matching function and color space conversion of the Hyperspectral Imager for the Coastal Ocean （ HICO ） in the visible range channel are used as the output value of the network， and the simulated band of the target sensor after band reconstruction is used as the input value. The true color image synthesis model suitable for Landsat 8 OLI， Terra MODIS and Himawari-8 AHI sensors was trained. By calculating the four objective evaluation parameters of mean， standard deviation， mean gradient and information entropy， and combining with the subjective analysis of true color images and histograms， the results show that the proposed method can enrich and expand the limited band information， improve the clarity， color saturation and the amount of information contained in the image， correct the color deviation existing in the three-band composite image， and solve the problem of integral calculation error by inserting the color matching function into the interpolation band under the condition of limited band number of original data.
Compared to the change detection of homologous remote sensing images， heterogeneous images can integrate the advantages of different satellite sensor data features and temporal relevance， better satisfying application requirements. To address the issues of spectral differences and inconsistent feature spaces in change detection of heterogeneous remote sensing images， this study proposes an aligned generative adversarial network for high-precision change detection of heterogeneous images. Considering the differences in channels and data types between heterogeneous images， it is difficult to maintain the consistency of spatial structures before and after reconstruction. The study incorporates autoencoders and constructs alignment loss to constrain the spatial structure changes of encoder output features， ensuring consistency in spatial structures between the reconstructed images and reducing information loss effectively. In the cross-domain mapping process， to minimize the color differences between source and target domain images， a cycle-consistent adversarial generative network is used for color transfer in the absence of paired images， enabling mutual cross-domain mapping between two temporally distinct heterogeneous images， generating color-preserving reconstructed images that can be directly compared with the original images. By utilizing designed change probability weights， the network automatically selects samples during the training process， effectively extracting land cover change information. Experimental results demonstrate that compared to methods such as CGAN and SCCN， the proposed method can more fully extract image features and reduce the randomness of cross-domain mapping functions. The detection accuracies on four publicly available datasets reach 0.93， 0.96， 0.97， and 0.88， with the highest accuracy achieved. The consistency between the change detection results and the reference maps， as well as the quality of the difference maps， is optimal. This method enables high-precision change detection in heterogeneous remote sensing images.
Speckle is an inherent property of SAR image， but its existence seriously interferes with the quality of SAR image and affects the high-quality application based on SAR image， so it is urgent to suppress it. The accuracy of the edge detection model of the traditional AD （Anisotropic Diffusion） filter still has room for improvement， and the noise suppression effect is often limited by the problem that it is difficult to accurately estimate the diffusion threshold. To solve the above problems， a novel AD filter based on Multidirectional Sobel （MSAD） is proposed. MSAD filter is an improved algorithm of SRAD. It builds a new edge detection model based on Multidirectional Sobel templates. Based on this， a new AD diffusion coefficient is established by integrating Gaussian kernel function， which can effectively solve the limitation of traditional AD diffusion coefficient by parameter estimation and improve the accuracy of speckle anisotropy suppression. Three real SAR images are selected for filtering experiments. In experiments， SRAD， DPAD， EnLee， and PPB filters are selected as the comparison algorithms； ENL， SSI， ESI， and M-Index are selected to evaluate the performance of proposed algorithms. Experiments show that MSAD filter can effectively improve the edge detection ability and obtain better speckle suppression effect.
The existing detection research of abnormal water bodies is usually carried out for specific regions， specific data sources and specific time phases. Anomaly recognition algorithm testing is often a backtracking of the water body anomaly events that have occurred， rather than real-time monitoring of the anomaly events， which cannot serve the requirements of rapid detection and identification of water body anomalies. In this paper， a method of extracting water body abnormal information based on unsupervised isolated forest plus decision rule （U-IForest-SD） is proposed. We selected Landsat and Sentinel as the test data， and tested the accuracy of U-IForest-SD with the black and smelly water body of Qingdao Enteromorpha， Songya lake and the oil spill in the Gulf of Mexico as research cases. We also compared U-IForest-SD with SVM and supervised isolated forests. The results show that the overall accuracy of the proposed method for the three types of anomalies is above 90%， and the kappa coefficient is above 0.8. The overall accuracy is higher than that of supervised isolated forest but slightly lower than that of SVM. This algorithm only needs to input single phase images， and does not need training samples. It has the advantages of good portability， strong universality and high automation. In addition， it can effectively avoid the occurrence of "wrong alarm" and "false alarm". Therefore， the newly proposed method has a good application prospect in the rapid detection and identification of abnormal water bodies.
The Tibetan Plateau （TP），with its unique climate characteristics and geographical pattern， plays an important role in global climate change. As an important part of the earth atmosphere system， cloud is key to affecting climate change. Cloud cover can more directly reflect the change of cloud. Therefore， it is of great significance to reconstruct a cloud cover product with longer time series and higher accuracy in the TP. In this paper， Considering the complex underlying surface types and geographical elevations in the TP， We select the cloud cover of MOD06， ERA5 and CRA40 from 2001 to 2020. We take the cloud cover of MOD06 from March to November as the true value and evaluate the applicability of the two reanalysis data in the TP through methods such as climate tendency rate and correlation coefficient.Based on ERA5 and MOD06， the improved auto-encoder model is used to reconstruct the cloud cover of the plateau from March to November of 1950 to 2020. The results show that the cloud cover of ERA5 is higher than that of MOD06， while that of CRA40 is lower than that of MOD06， and the correlation between ERA5 and MOD06 is obviously better than that between CRA40 and MOD06；The improved auto-encoder model evaluated by four evaluation indicators of correlation coefficient（R）， bias， Mean Absolute Error（MAE） and Root Mean Square Error（RMSE） has a good effect on cloud amount reconstruction. The correlation coefficient between cloud amount reconstructed by the improved autoencoder model and MOD06 cloud amount data increases by more than 20% on average from March to November， and can simulate the change trend of cloud amount over the TP. The results provide reliable long time series data for studying the temporal and spatial evolution of cloud cover over the TP.
As one of the important trace gases in the atmosphere， nitrogen dioxide （NO2） is an important weather vane to measure the state of air pollution and have been associated with human health. Compared with traditional ground-based observations， space-borne sensors can provide large-scale and long-term observational data， overcoming the limitations of the number and location of observation sites. This paper reviewed the development of the space-borne hyper-spectral sensors at home and abroad， the retrieval algorithms of tropospheric NO2 vertical column density， and also discussed the accuracy of the tropospheric NO2 products.With the development of technology， the resolution of the time， space and spectral are getting higher and the selection of the retrieval model and algorithm of the NO2 column density product are getting more reasonable，so the NO2 products are more accurate.
The concentration of suspended matter in water body is an important parameter to describe the optical characteristics of water body. Satellite remote sensing has the advantages of a large range， fast and high-frequency word dynamic monitoring， which helps to strengthen the monitoring of water environment quality of Qinghai Lake and reduce the monitoring cost. And ZY1-02D satellite hyperspectral camera with high spatial resolution and high spectral resolution provides the possibility of high-precision monitoring of water quality in Qinghai Lake. In order to verify the applicability of the ZY1-02D hyperspectral camera in the application of remote sensing monitoring of water quality， this paper uses the ZY1-02D hyperspectral camera as the remote sensing data source， and also assists the actual measurement data to construct an inversion model of the suspended matter concentration in Qinghai Lake， and conducts accuracy verification to evaluate the accuracy of the inversion results. The results show that the average relative error of the Qinghai Lake suspended concentration inversion model is 21.1%， and the root mean square error is 0.296 mg/L. The accuracy is good， and the inversion results of Qinghai Lake suspended concentration show the characteristics of low in the center of the lake and high on the shore， compared with the retrieval results of Sentinel-2 and Landsat-8 in the same period， the retrieval results of Sentinel-2 and Landsat-8 in the same period， the inversion results remain consistent， results remain consistent， which indicates that the ZY1-02D hyperspectral image can retrieve the water quality parameters.
As an important part of terrestrial ecosystems， vegetation is often used as an indicator to assess the effectiveness of climate change and ecological restoration. In this study， the Shiyang River Basin is taken as an example， Theil-Sen and Mann-Kendall models， and the Hurst index were used to analyze the change characteristics of vegetation cover. The correlation analysis， residual analysis and Geodetector were used to explore the influencing factors of vegetation cover change. The results showed that the vegetation NDVI demonstrated a fluctuating but upward trend from 2001 to 2020， with a rate of increase of 0.023/10 a. Areas with significant increased and significant decreased accounted for 72.32% and 2.4%， respectively. Areas with sustainability （Hurst>0.5） accounted for 63.84 % of the entire area， among which 47.37% showed continuously significant increasing trend. The correlation results between NDVI and climatic factors indicated that the impact of precipitation was particularly significant， and the impacts of temperature， solar radiation and saturated vapor pressure deficit were relatively weak. The area of NDVIpre showed a significant increase trend accounted for 21.59%， while the area of NDVIres showed a significant increase trend accounted for 60.07%， so interannual variation of NDVI in Shiyang River Basin was greatly affected by human activities. Geodetector results showed that the spatial distributation characteristics of water-heat conditions. It is noted that the spatial distribution of NDVI of cultivated land is greatly affected by population density. The results of this study are helpful to deepen the understanding of the driving factors of vegetation change and provide scientific reference for ecological protection and restoration of Shiyang River Basin.
Landslides represent a prevalent geological hazard with serious consequences for the safety of human lives and properties. Therefore， the efficient and accurate extraction of landslides is of great significance for the timely development of emergency response plans aimed at reducing losses. Current studies typically focus on single or a few events， often under relatively simple background conditions. To address these limitations， we propose using high-resolution remote sensing images to extract multiple landslides under complex background conditions， with the precision of the approach being verified. Specifically， we construct a landslide extraction model that utilizes the result of the minimization of energy equation using Markov random field as a feature. To evaluate the effectiveness of the model， we compare it with features commonly used in landslide extraction research. We select multi-temporal Planet 3M resolution remote sensing images to extract landslides in Hokkaido on September 6， 2018. Our results demonstrate that the proposed feature improves the accuracy of landslide extraction by 2%， while also improving the integrity of the extraction to a certain extent. This approach offers valuable assistance for accurately extracting landslides in large areas.
Based on Sentinel-2 remote sensing data， we selected three methods， including Supervised Classification （Maximum Likelihood Classification）， Machine Learning Classification （Random Forest Classification） and Phenological Feature Classification based on time-series NDVI， to extract Potamogeton crispus L.
UAV remote sensing technology can quickly obtain the Canopy Height Model（CHM） of the survey area. How to identify tree vertices more accurately from CHM is key to tree height extraction. This paper discusses the influence of different window types， window sizes， and stand canopy density on the extraction of tree vertices. Using the university campus as the study area， two local areas of dense and sparse forest land were selected based on canopy density. GIS rectangular neighborhood analysis， GIS circular neighborhood analysis， and local maximum algorithm are used to extract tree vertices. The results show that the accuracy of tree vertex extraction is not only affected by the window size and canopy density， but also closely related to the window type， and the result of GIS rectangular neighborhood analysis to extract tree vertices is more stable and accurate， and the highest F-Measure value is 78.13% in dense forest， and 96.94% in sparse forest. Comparing the extracted tree heights corresponding to the tree vertices obtained based on this result with the tree height values measured in the field， the RMSE is 37cm for dense forest and 39cm for sparse forest. The results proved the feasibility of extracting tree heights of broad-leaved forests with higher canopy density based on the visible light remote sensing technology of small UAVs， which provided a reference for the subsequent identification of tree vertices based on the canopy height model and improved the accuracy of tree height extraction.
Change detection from heterogeneous remote sensing images is an important and challenging research topic with a wide range of applications in disaster assessment， urban planning and environmental monitoring. However， the direct comparison of heterogeneous data for change detection always has a poor detection accuracy. To address this issue， a multioutput adaptive regression and association-based feature fusion method for heterogeneous remote sensing change detection is proposed. Firstly， the proposed method determines the adaptive regression direction according to the information entropy， which utilizes the difference of information between heterogeneous data. To transform heterogeneous data into a common feature space， the regression image will be obtained via a multioutput multilayer perceptron image regression algorithm. Then， the fuzzy local information C-means algorithm is used to identify the fuzzy region in the difference image， which further ensures the reliability of significant sample pairs. Finally， an association-based fusion method was applied to the heterogeneous remote sensing change detection dataset by simultaneously exploiting the high-order information of heterogeneous data and the association information between features. The binary change map is obtained via training a classification model with the boosting dataset. Experiments conducted on three real datasets （Sardinia， Yellow River and Texas） show the effectiveness of the proposed method by comparing it with seven related change detection methods. Experimental results indicate that the proposed method owns the best change detection results on both three datasets， which proves its effectiveness， and it can suppress the influence of noise and improve the accuracy of change detection.
Studying the topographic differentiation characteristics and driving mechanism of land use landscape pattern is of great significance for land use optimization and landscape dynamic management. Yongding District of Zhangjiajie City， with complex terrain， various types of coverage and tourism interference， was selected as the research object. The landscape type maps of multiple years in the study area were superimposed one by one with elevation， slope and aspect classification maps， and classified and counted. Eight landscape indices such as Patch Density（PD）， Aggregation Index（AI）， and contagion index（CONTAG） were selected from the landscape level index and type level index to calculate the annual change of the index and explore its topographic differentiation law and driving mechanism. The results show that ： （1） The land use landscape types in the study area have obvious altitude gradient characteristics. More than 80 % of the land area is concentrated in the area with an altitude of 300 ~ 800 m and a slope of 6 ° ~ 35 °. （2） Whether the landscape level index or the type level index， the topographic differentiation characteristics are obvious， and the differentiation of elevation and slope is significantly higher than that of slope direction. （3） The evolution of land use landscape pattern in the area with large terrain gradient （ high altitude steep slope area ） is dominated by natural ecological evolution， while the evolution of the area with small terrain gradient （ low altitude gentle slope area ） is obviously disturbed by social and economic factors.
The Leaf Area Index（LAI） is one of the important parameters to study the global and regional carbon cycle， hydrological cycle， and regional response to climate change. Studying the temporal and spatial consistency of different products can provide suggestions and references for the use of LAI products in this region. This study made statistical analysis on the changes of the average value， frequency and difference frequency of GLOBMAP，GLOBALBNU and GLASS LAIs across different river basins，DEM and land use types. （1）The three products can clearly capture the spatial distribution， monthly and annual variation characteristics of LAI in China. Annual average GLOBMAP started to decline in 2001 since data sources changed. （2）There were differences among the three products under the nine major basins， different DEMs， and different land use types. In the Haihe River Basin， the Yellow River Basin and the Inland River Basin， the correlations of the three products were good， but in the Yangtze River basin， the Southeast River Basin and the Pearl River basin the differences between the three products were more than 2.00. There were obvious differences in the annual average change trends of the three products in the 2 000~4 000 m area. Compared with other products， GLASS was underestimated in grassland areas， GLOBMAP was underestimated compared in urban and rural industrial land areas， and GLOBALBNU was overestimated in forest land areas. The temporal and spatial differences of three sets of domestic LAI products were quantitatively analyzed， and the results could provide scientific reference for the application of domestic LAI products in China.
