The information used in water quality remote sensing is primarily concentrated in the low-value regions of imagery， which are highly sensitive to atmospheric absorption and scattering processes， making atmospheric correction a critical component. Although current mainstream atmospheric correction methods exhibit a certain level of general applicability， their inherent atmospheric models fail to efficiently reflect the actual atmospheric conditions and water vapor effects at the time of imaging， thereby limiting their accuracy. To achieve high-precision atmospheric correction for water， this study utilizes the radiative transfer mechanism and Sentinel-2 data， extracting clean water pixels from the imagery as atmospheric control points to retrieve imaging-time atmospheric parameters that account for water vapor effects. Comparisons with FLAASH and Sen2Cor demonstrate the effectiveness of the proposed approach. Specifically： ① The corrected water spectra obtained through this method show high consistency with in situ measurements， achieving correlation coefficients above 0.856 and root mean square errors below 0.017， with reflectance values close to the actual measurements. ② This method not only enables effective extraction of complex natural boundary water， with an extraction rate of 96.78% and a Kappa coefficient of 0.958， but also extracts small area water， with an extraction rate of 89.68% and a Kappa coefficient of 0.871. These results demonstrate that atmospheric correction of Sentinel-2 data using this method is better suited for water quality remote sensing.
Hongze Lake serves as the primary water supply source in northern Jiangsu and functions as the water storage reservoir for the eastern route of the South-to-North Water Diversion Project. The quality of its water is crucial to ensuring safe water supply and the sustainable utilization of water resources. Secchi Disc Depth （SDD） is an important index to measure water environment quality and plays an important role in water ecosystem. In this study， Hongze Lake was taken as the research area， and a remote sensing estimation model suitable for Hongze Lake SDD was constructed based on Landsat 8 OLI remote sensing data by using field measured SDD data and spectral data. The verified results were the Mean Absolute Percentage Error （MAPE） and Root Mean Square Error （RMSE） as 19.8% and 0.07m respectively. The constructed model was applied to Landsat 8 OLI images of Hongze Lake from 2013 to 2022， and the following conclusions were obtained： （1） The average inter-annual variation range of SDD was 0.18~0.25 m， with the highest value and the lowest value appearing in 2019~2020 and 2013~2014， respectively. The overall variation trend of the three lakes was consistent， with the highest SDD in Chengzi Lake Bay and the lowest in Huaihe Lake Bay. The main factors affecting the SDD of the lake area are wind speed， among which the sediment discharge is the main influencing factor of the Huaihe River Bay. （2） The monthly variation of SDD increased from January to August， and decreased month by month after August， with the highest value of 0.36 m in August and the lowest value of 0.20 m in May. The SDD of the lake area is mainly affected by wind speed. Chengzi Lake Bay and Lihe Lake Bay are obviously affected by meteorological factors， and Huaihe Lake Bay is affected by sediment discharge and shipping， and the factors are complicated.
The monitoring of river discharge provides a data basis for the research of climate change， water resources monitoring， soil and water conservation， land water cycle， flood prevention and other aspects. It is of great significance for understanding the water resources and the surrounding ecological environment of rivers. Hydrological gauge stations exhibit a decreasing trend and uneven spatial distribution world-widely， and it is difficult to obtain continuous and complete flow data for many rivers. Therefore， here we present a method to extract river water extent using the Inland Water Monitoring System （SIMS） of Sentinel 1 data， obtain hydraulic parameters through integrating with digital elevation data， and then estimate river discharge using Manning's equation. River sections at Hequ and Fugu on the mainstream of the Yellow River were selected as case studies. Observed discharge at the corresponding gauges was used as the reference for accuracy assessment. The results show that river discharge was successfully retrieved at Hequ， with a Nash efficiency coefficient （NSE） up to 0.89 and a Root Mean Square Error （RMSE） of 37.92 m3/s. However， at Fugu， the accuracy is relatively lower， with a NSE of 0.10 and a RMSE of 135.60 m3/s. The main reason is that the river channel at Fugu has dramatic erosion and deposition， making the cross-sectional morphology unstable. Since all the input data of this method are globally accessible data， this study is expected to provide a new idea for monitoring discharge for ungauged river， and serve related hydrological and water resources studies.
Accurately obtaining information on seasonal wetlands not only helps improve the interdisciplinary wetland science system but also provides scientific basis for wetland restoration projects， which is of great significance for protecting wetlands， stabilizing the Earth's climate， and more. In this study， Landsat remote sensing images were used with the Google Earth Engine cloud platform to automatically extract detailed seasonal wetland information for the past 30 years in the East Dongting Lake area. Firstly， based on the typical characteristics of seasonal water-land alternation in East Dongting Lake wetlands， the random forest classification algorithm was used to obtain the range of seasonal wetlands. Then， a decision tree model was constructed to extract the subcategories of seasonal wetlands. The results show that：（1）the research can effectively extract wetland information， with an average overall classification accuracy and Kappa coefficient of 88.25% and 0.86 for seasonal wetland classification， and 93.28% and 0.92 for subcategories classification of seasonal wetlands. （2） From 1989 to 2020， the area of East Dongting Lake wetlands showed an increasing-decreasing-increasing trend， with a total increase of 154.02 km2 in permanent water bodies and a decrease of 54.11 km2 in seasonal wetland area. The seasonal wetland information extraction method developed in this study based on Landsat long time-series images can provide technical support for related research and decision-making on the dynamic changes of seasonal wetlands.
Since Shenzhen initiated a vigorous campaign to remediate Black-Odorous Water Bodies （BOWBs） in 2016， significant improvements have been achieved in urban aquatic environments. However， newly emerging and recurrent BOWBs persist. The application of remote sensing technology proves instrumental in monitoring emerging BOWBs， supervising remediation processes， and evaluating governance effectiveness， thereby advancing urban water environment management. This study established a BOWB identification model for Shenzhen using two phases of large-scale ground survey data and GaoFen（GF） series high-resolution satellite imagery， extracting spatial distribution patterns of BOWBs within built-up areas from 2013 to 2023. Findings reveal a distinct west-heavy/east-light spatial distribution with gradual eastward expansion from the western region. BOWB quantities showed sustained growth during 2013~2016， followed by progressive decline post-2017 through intensified remediation efforts. Longitudinal analysis of decadal spatiotemporal variations demonstrated significant correlations between BOWB evolution and socioeconomic factors. The three primary determinants influencing BOWB spatial distribution were identified as non-resident population （r=0.68）， secondary industry economic output （r=0.42）， and industrial value-added above designated scale （r=0.41）. These patterns epitomize environmental governance lags during rapid industrialization and urbanization. Subsequent efforts should integrate BOWB remediation with regional industrial upgrading and optimization of public service infrastructure for non-resident populations to achieve source-level pollution reduction.
Domain adaption transfers the source domain knowledge to the target domain to improve the classification accuracy of hyperspectral image classification model for features in different scenes. The development of domain adaptation classification methods for hyperspectral images is rapid， however， there is a lack of comparative analysis for domain adaptation methods. Therefore， the domain adaptation classification methods are classified into four categories： Distribution Adaptation， Feature Selection， Subspace Learning， and Deep Domain Adaptation. In this paper， eight typical methods are selected and three standard hyperspectral datasets from Pavia Center， Pavia University and HyRANK are used to design the comparison experiments. The experimental results show that the deep domain adaptation methods are more advantageous， among which the overall classification effect and computational efficiency of the topological structure and semantic information transfer network method are the best overall.
To comprehensively exploit the rich spectral information and spatial texture features in multispectral remote sensing images， a change detection method based on Gabor-CVA is proposed to extract the change range and identify the change type. For two remote sensing images with different time phases in the same area， on the one hand， Principal Component Analysis （PCA） is applied to reduce dimensionality. Gabor kernel function is used to extract multi-scale and multi-directional spatial texture features and calculate the angle between texture vectors. On the other hand， Change Vector Analysis （CVA） is used to calculate spectral difference vector and spectral change intensity. Subsequently， on the fused feature image incorporating texture vector angles and spectral change magnitudes， thresholds are set to determine the distribution of change extents. Samples of land feature change types be selected， and the Random Forest （RF） method be employed to filter out several important change features from texture differences， spectral differences， and remote sensing index differences. Multiscale segmentation be performed on the corresponding change feature images. A Random Forest model be trained based on the segmented images and samples， and the change types of objects within the change extents be identified， thereby generating a map illustrating the distribution of change types. The experiment demonstrates that the Gabor-CVA method achieves an overall accuracy of 88.77% in detecting change ranges. Compared to the CVA method， under the condition of a 3.06% reduction in omission error， the true positive rate has increased by 21.56%. In comparison to the change detection method after RF classification， there is a reduction of 5.48% in omission error and an increase of 16.25% in true positive rate. In the classification of change types， the Kappa coefficient for the CVA method is 0.38. While the Kappa coefficient for change detection after RF classification is 0.50. Whereas the Gabor-CVA method significantly elevates the Kappa coefficient to 0.75.
Accurately and rapidly assessing soil salinity is crucial for land quality evaluation and agricultural development. Hyperspectral remote sensing， as an effective monitoring technology， offers new avenues. Optimizing hyperspectral data processing to enhance features is critical for accurately estimating soil parameters. However， the impact mechanisms of different spectral combination treatments on soil salinity estimation need further study. This study aims to investigate the potential of hyperspectral data for estimating soil salinity under nonlinear transformation and fractional derivative combination treatments. Based on 60 typical soil samples from the Yulin area， the spectral characteristics of saline soils under different spectral transformation combinations were analyzed. The Competitive Adaptive Reweighted Sampling method （CARS） and Successive Projections Algorithm （SPA） were used to select characteristic bands as input variables. Partial Least Squares Regression （PLSR） and Random Forest （RF） models were established to compare and analyze the estimation capabilities of different spectral processing and modeling strategies for soil salinity. The results indicate that fractional derivatives， compared to integer derivatives， better reflect the absorption characteristics of saline soil spectra. Reciprocal and logarithmic transformations effectively enhance the correlation between spectral data and soil salinity information， especially in the 1905–2078 nm range. The PLSR model generally outperforms the RF model， with the CARS-PLSR achieving optimal modeling accuracy （R²=0.966）. These findings can provide a theoretical basis for the implementation of saline soil prediction and precision agriculture.
Dynamic monitoring of ecological quality is of great significance for achieving regional sustainable management and development. Based on Landsat 5/TM images and Landsat 8/OLI images， a remote sensing ecological index was constructed using the Google Earth Engine platform to achieve a dynamic evaluation of ecological quality in the Daxing'anling Mountains region from 1986 to 2020. （1） Greenness and humidity have a positive impact on ecological quality， while dryness and heat have a negative impact on ecological quality. Heat and greenness have the greatest impact on ecological quality； （2） From 1986 to 2020， the distribution of ecological Mass distribution in the Daxing'anling Mountains changed greatly. Compared with 1986， the phenomenon of low or high ecological quality in large areas decreased， and the distribution was more uniform， and the ecological quality was more stable； （3） From the relevant statistics of the changing areas， it can be seen that the ecological quality change areas in the Daxing'anling Mountains region account for about 96.4% of the total area， with significant changes in ecological quality； （4） The ecological quality of the severely burned areas in the Daxing'anling Mountains shows a trend of first decreasing and then increasing， but the overall ecological quality of the Daxing'anling Mountains region fluctuates greatly. On the whole， the distribution of ecological quality is more stable， there is no phenomenon of local too low or too high， and the level of ecological quality is gradually improved.
The Lijiang River Basin is the largest karst landscape area in the world， with a complex water system and a special ecological status. The health of its ecological environment is of great significance to the development of surrounding areas. Landsat 5/8 images of the Lijiang River Basin in 2005， 2010， 2015， and 2019 were selected， and the Mountain Green Cover Index （MGCI） and Remote Sensing Ecological Index （RSEI） were extracted to build an index evaluation model to evaluate the ecological quality and changes of the watershed. The results show that： （1） In the 14 years， the MGCI of the Lijiang River Basin was 0.428， 0.505， 0.558 and 0.635 respectively， and the RSEI was 0.503， 0.524， 0.606 and 0.643 respectively， showing an overall upward trend； （2） Vegetation coverage and rapid urban development The resulting expansion of construction land are the main positive and negative factors that affect the ecological quality of the Lijiang River Basin respectively； （3） Greenness is the most important indicator among several factors affecting ecological quality， and RSEI is more sensitive to changes in the assessment of ecological quality thanks to its multifactor design， while MGCI can quickly evaluate the trend of ecological changes in the Lijiang River Basin. In the comprehensive ecological environment quality assessment， the fine monitoring capability of RSEI and the efficient assessment function of MGCI can be combined， so as to realize a comprehensive and dynamic mastery of the ecological environment quality of the Lijiang River basin.
Clarifying the consistency and uncertainty of different wildfire data products is the prerequisite and foundation for analyzing and applying the products. FireCCI51 and MOSEV are two internationally widely used wildfire data products， and the pan-Arctic permafrost region is a global concentration of wildfires and an important carbon pool. Analyzing the consistency of wildfire data products in the pan-Arctic permafrost region could be important for enhancement of data accuracy of wildfire products in the future and reduction of carbon flux estimates in this region. This study use spatial analysis to identify the consistency of the FireCCI51 and MOSEV data products in the pan-Arctic permafrost region from the aspects of burned area and spatial location of the burned zones. And then analyzes the geographic characteristics of the consistent zones and inconsistent zones.The results show that： ① The burned area of FireCCI51 product is larger than that of MOSEV from 2001 to 2019， and the percentage difference of burning area of the two products fluctuates irregularly in the range of 7% to 60% The spatial distribution consistency of the burned areas identified by the two products ranges from 27.68 % to 47.14 %. Consistent zones are mainly found in areas where burned areas are concentrated， such as Central Canada， Central and East Siberia in Russia and its southern extension， the Daxinganling region. Inconsistent areas are distributed in the southern Russia and Western Siberia in addition to these areas； ③ Elevation， climate types and vegetation types all have a certain impact on the distribution of the consistent areas. In lower altitude regions and constant wet and cold temperate climate regions， the area of consistent zones of the two products accounted for more， while in relatively higher altitude regions and subboreal monsoon climate regions， the area of inconsistent zones of the two products accounted for more. And both consistent and inconsistent zones are greater in woody savannah and savannah areas. Therefore， future applications of both data products need to be carefully evaluated， especially for regions with a high distribution of inconsistent regions， such as southern Russia， Western Siberia， and subboreal monsoon climate regions.
Burned area is an important parameter for global and regional carbon cycle and climate change research. Satellite remote sensing technology provides an effective way for rapidly obtaining the spatial distribution information of burned areas over large regions. Africa has the highest concentration of burned areas worldwide. Based on the GABAM burned area products from 2014 to 2020， this paper used the non-parametric Theil-Sen trend estimation and the Mann-Kendall （MK） trend significance test method to conduct research on changes of burned area in Africa. The study found that African burned areas are more densely distributed in savannah and sparse woodland areas between 10°N and 10°S. The trend in the area of burned areas in Africa from 2014 to 2020 is not significant， however， there is still a significant difference in the area of burned areas between the different years. Anomalies in meteorological factors （temperature， precipitation） due to extreme global climate events （El Niño， La Niña） can cause inter-annual variability in burned area. The comparative study of GABAM and MCD64 products showed that the MCD64 product was difficult to monitor the burnt areas of small and broken patches， resulting in a serious underestimation of the burned area， with an average underestimation of the burned area by 17.88%. The impacts of burning on vegetation NPP （Net primary productivity） in different vegetation regions （forests， savannas， and woodlands） were analyzed. The results showed that different types of vegetation had significant differences in recovery rate after fire. The research conclusions of this paper can provide reference for accurately monitoring of burned areas in large area and related fields of carbon cycle research.
The evaluation of ecological environment status is of great significance for promoting regional ecological protection， evaluating the effectiveness of ecological civilization construction， and guiding regional development and planning. Using remote sensing data and comprehensive evaluation method to evaluate the ecological environment in the region can provide strong support for regional sustainable development. Based on the Technical Criterion for Ecosystem Status Evaluation， establish an Improved Ecological Index （IEI）， dynamically evaluate the ecological environmental and its evolution characteristics of Jiangxi Province from 2000 to 2020. Furthermore， the ecological environment was comprehensively zoned. The results show that： ①From 2000 to 2020， the average IEI of Jiangxi Province was 69.74， the area of excellent， good and general grade accounted for the largest proportion， all above 40%.②The ecological environment of Jiangxi Province is in a stable state as a whole. The area of significant reduction of ecological environment is small and concentrated， accounting for 1.96% of the province. The area of significant increase of ecological environment is large and scattered， accounting for 9.44% of the province. ③All the 11 cities in Jiangxi Province are in good grade except Nanchang City， and Nanchang City is in general grade. Among them， Jingdezhen， Pingxiang， Ganzhou， Jian and Fuzhou City exceed the average level， and the ecological environment is in good condition. ④According to IEI， Jiangxi Province is divided into 11 eco-geographical units. Among them， Gannan， the eastern foot of Luoxiao Mountain， the western foot of Wuyi Mountain， Raohe River， Fuhe River and Xiushui River Basin have serious soil erosion and high degree of landscape fragmentation. The core area of Poyang Lake and the transitional area of hilly plain in the lower reaches of Ganjiang River have the problems of poor habitat quality and vegetation growth. The research results can provide theoretical basis and scientific reference for the protection and restoration of ecological environment in Jiangxi Province and other geographical units in China.
The interactive patterns of elements in terrestrial vegetation-atmosphere exchange are complex， some are even poorly understood. Linear or general linear methods have been widely used in exploring vegetation dynamic and climatic changes. Yet linear thinking may inhibit our understanding of complex nonlinear systems and the unravelling causality behind linear correlation is difficult to extract directly from observational data. Here， we aimed to quantify the vegetation-climate interactions， using nonlinear dynamical methods based on state-space reconstruction and datasets from Chinese meteorological station and remote sensing data during 1982~2015， in Northeast China （NEC）. Specifically， we detected the causal links between meteorological factors （temperature， precipitation） and vegetation index （NDVI） by reconstructing the state space from historical records. During the study period， vegetation has a strong bi-directional causal relationship with temperature and precipitation across Northeast China. The value of NDVI can be well reconstructed from the state information of meteorological factors （temperature， precipitation）. The strength of the interactions varied across different vegetation types with various meteorological factors， in which coniferous forests， broadleaf forests， and shrublands are more influenced by temperature than causal effects on temperature. The intensity of the driving effect of temperature on vegetation gradually increases from north to south， and the low intensity zones mainly occur in the coniferous forest area in the northern part. The slight effect of precipitation-vegetation cross-mapping skills are found in the north-eastern mountains， eastern plains and mountainous areas. Our results suggest that the balance between positive and negative effects of precipitation on vegetation is influenced by temperature. When temperatures greater than 0℃， the effect of precipitation on vegetation changes from negative to positive. In contrast， the effect of temperature on vegetation was weaker compared to precipitation， but when the precipitation was greater than 800 mm， the increase in temperature showed a roughly negative upward trend on vegetation. Exploring the causality between vegetation and meteorological factors in Northeast China can improve the understanding of climate change and vegetation feedback at mid and high-latitude regions. Our work also suggests that nonlinear exploration may have the potential to discovering new knowledges in earth science.
Accurate monitoring and assessment of vegetation restoration effectiveness are essential for ecological conservation， sustainable development， and environmental management. Previous studies have primarily relied on remote sensing imagery， using two-dimensional monitoring indicators such as Fractional Vegetation Cover （FVC） to assess vegetation restoration effectiveness. However， these studies have focused solely on changes in vegetation coverage area， neglecting structural measures， which limits the precise understanding of ecological governance effects. To address these limitations， this study proposes the integration of LiDAR technology with two-dimensional optical remote sensing for a more comprehensive and accurate monitoring approach in ecological restoration areas. This method combines two-dimensional indicators derived from optical remote sensing imagery with three-dimensional indicators obtained from UAV LiDAR to capture both changes in vegetation coverage and structural differences， enabling comprehensive monitoring and evaluation of vegetation restoration effectiveness. To validate the feasibility of this method， four mine restoration areas in Beijing were selected as examples. Using UAV LiDAR point cloud data in 2022 and Sentinel-2 time series remote sensing imagery data from 2018 to 2022， four vegetation structure indicators （canopy height， canopy cover， leaf area index， and canopy entropy） and two-dimensional plane indicators of FVC were calculated for the study areas. Various analytical methods， including comparative analysis and trend analysis， were employed to evaluate the vegetation restoration situation in the study areas. The results indicate a significant increasing trend in vegetation coverage area in all mining areas from 2018 to 2022. However， when evaluating vegetation structure indicators， only one mining area exhibited consistency between structural indicators and two-dimensional evaluation results. This suggests that both increased vegetation coverage area and improved vegetation structure in this specific mining area. In contrast， other mining areas only showed an increase in vegetation coverage area， emphasizing the need for subsequent efforts to enhance the restoration and management of vegetation structure. The introduction of LiDAR technology alongside optical remote sensing provides a more comprehensive assessment approach， offering more accurate references for ecological restoration effectiveness evaluation and the further implementation of ecological restoration projects.
Under the background of the “Four Million Mu” ecological project and “the community of life is the mountain， water， forest， field， lake， grass and sand”， this paper studies the habitat quality and carbon storage and influencing factors under the land use change of the Aksu River Basin and typical areas in the basin-- the Kekeya greening project， so as to provide a basis for the ecological and environmental protection policy of the Aksu River Basin， provide guidance for land use， carry forward the spirit of Kekeya， and promote the sustainable development of the Aksu River Basin. Based on the land use data from 2000 to 2020， the Habitat quality and Carbon storage module in the InVEST model were used to explore the spatial and temporal variation patterns and influencing factors of Habitat quality and Carbon storage in the Aksu River Basin from 2000 to 2020. The results show that： （1） From 2000 to 2020， the Habitat quality of the Aksu River Basin remained stable on the whole， and there was a slight increase， and the areas with better Habitat quality were distributed in the upper reaches of the basin and oases on both sides of the basin， mainly forest land and grassland land use types. In the past 20 years， the Habitat quality of Kekeya Greening Project Area has shown a steady upward trend. （2） The Carbon storage in the Aksu River Basin showed a stable growth trend during the study period from 2000 to 2020， with a total increase of 13.21×106 t， and the areas with high Carbon storage values showed the distribution characteristics of "northern central cluster and southern band". The Carbon storage in the Kekeya greening project area showed an increasing trend， which was consistent with the overall trend of the Aksu River Basin. （3） Habitat quality and Carbon storage presented a significant synergic relation. The influencing factors of regional Habitat quality and Carbon storage were analyzed by using geographic detectors， land use type was the main influencing factor of Habitat quality and Carbon storage， and the interaction of influencing factors showed two-factor enhancement or nonlinear enhancement.
The water level of reservoirs， especially large reservoirs， is a crucial parameter for flood forecasting and reservoir operation. Taking the Danjiangkou Reservoir as an example， this study utilizes the ATL13 data from the spaceborne photon-counting LiDAR （ICESat-2/ATLAS） from 2018 to 2022. By extracting parameters such as longitude， latitude， geodetic height， and geoid undulation， the water level information is derived and validated against ground-based hydrological station data. This approach enables the acquisition of high-precision water level data even in the absence of direct measurements. Furthermore， considering factors such as water surface area and temperature， stepwise regression and multiple linear regression are applied to construct an area-water level relationship model， enabling the estimation of the Danjiangkou Reservoir’s water levels over multiple years. The results show that： （1） The water level of the Danjiangkou Reservoir exhibits significant seasonal variations； （2） The method's accuracy is validated by comparing the derived water levels with observed data， with an average error of 0.03 m and an R² of 0.999； （3） The area-water level relationship curve established using a cubic polynomial model provides the best fit， with an R² of 0.956 and a Mean Absolute Error （MAE） of 0.009 m. These findings demonstrate that spaceborne photon-counting LiDAR technology offers valuable data support for water level estimation and dynamic change studies of large reservoirs.
Land cover types and their distribution are closely related to terrain factors and time-series NDVI. For research of land cover classification accuracy， terrain factors and time-series NDVI were used to improve the accuracy of land cover classification for the first time. In this study， the MODIS global land cover product MCD12Q1 was selected as a research object， the 1：100 000 land use product was taken as reference data， and the Beijing Tianjin Hebei region was taken as the research area to analyze and improve the target product. Firstly， linear regression models between proportion of land cover area and terrain factors were established to improve accuracy of land cover classification. The overall accuracy and Kappa coefficient of the improved product has increased by 10.96% and 0.12 compared with the original data MCD12Q1， of which the overall accuracy has increased by 0.22% in plain part and by 22.15% in non plain part. Secondly， a time-series NDVI atlas database was established， and the minimum distance method for curve similarity measurement was used to improve the accuracy of land cover classification. The overall accuracy and Kappa coefficient of the improved product has increased by 18.47% and 0.26.Thirdly，the two methods were integrated to improve the accuracy of MCD12Q1 land cover classification. Specifically， two schemes were adopted. The overall accuracy of the two schemes has improved by 11.84% and 26.28% respectively， and the Kappa coefficient has improved by 0.14 and 0.36. The conclusions are as follows： ①both terrain factors and time-series NDVI can effectively improve accuracy of land cover classification， and improvement effect based on time-series NDVI is better than that based on terrain factors. ②The improvement effect based on terrain factors is better in non plain part than that in plain part. ③Integration of terrain factors and time-series NDVI can further improve accuracy of land cover classification.
Based on six indexes of greenness， humidity， heat， dryness， salinity and soil and water conservation factors， a remote sensing ecological index URSEI suitable for arid areas was constructed by using PCA， and the tem-spatial characteristics of ecological quality changes in Shiyang River Basin from 2000 to 2018 were explored by using trend analysis and Hurst persistence analysis. The findings are as follows： （1）URSEI considers salinity and soil and water conservation indicators， and the contribution rate of the first principal component is higher than 80%， integrating the main information of each indicator， which is conducive to improving the comprehensiveness of ecological quality assessment. （2） From 2000 to 2018， the URSEI in Shiyang River Basin increased from 0.288 to 0.316， which tended to improve on the whole， mainly due to the strengthening of ecological management and control policies and the implementation of water transfer projects such as Jingdian Phase II. Among them， the area of ecological quality improvement is about 10 495.75 km2， which is distributed in a belt along Jinchang City， Liangzhou District and Gulang County； The degraded area covers 2 388.50 km2， which is concentrated in the main urban area of Liangzhou District in the middle reaches， the Longshoushan mining and the middle oasis area of Minqin in the lower reaches， and there is a risk of continuous degradation in the future. （3） In the future， the ecological quality of the basin will be mainly unchanged and continuously improved， accounting for about 72.55%， but at the same time， about 2 231.25 km2 of the area will develop from improvement to degradation， distributed in Minquan Township of Gulang County， Huangyang Township of Liangzhou District， Yongfengtan Township， Jiaojiazhuang Township of Yongchang County， etc.， basically corresponding to the surrounding areas of the main urban areas of each district and county. It is the main expansion area of urbanization in the future.
As a major invasive plant in China's coastal areas， Spartina alterniflora has a serious negative impact on the ecological balance of coastal ecosystems， especially posing a great threat to the ecological security of the Tianjin-Hebei coastal area. Based on six periods of Landsat TM/OLI and Sentinel-2 images from 2000 to 2022， four methods， including expansion intensity， centroid， standard deviation ellipse， and fractal dimension， were used to reveal the spatiotemporal characteristics of Spartina alterniflora in the Tianjin-Hebei coastal area and analyze its driving factors. The results showed that the area of Spartina alterniflora in the coastal area of Tianjin-Hebei increased from 10.08 hm2 in 2000 to 425.62 hm2 in 2022， an increase of 41.22 times， and the overall expansion rate showed a trend of first increasing and then decreasing and then increasing. From 2000 to 2015， Tianjin Binhai New District was in a period of rapid expansion， with an expansion rate of 27.28 hm2/a， and the growth rate of Spartina alterniflora in Huanghua was slow. From 2015 to 2022， Huanghua expanded the fastest， at 16.13 hm2/a， and Tangshan first appeared in 2015 and continued to expand rapidly， with an expansion rate of 5.25 hm2/a. The distribution of Spartina alterniflora in the Tianjin-Hebei coastal area is skewed along the northeast-southwest axis， and the centroid moved southward by 19.09 km from 2000 to 2022. The growth rate of Spartina alterniflora in the southern part was faster than that in the northern part. The spatial structure of Spartina alterniflora is becoming more complex， and the spatial complexity of Spartina alterniflora in Huanghua， Cangzhou， Hebei is higher than that in Binhai New District， Tianjin. In addition， the changes and spatiotemporal differentiation characteristics of Spartina alterniflora are influenced by different policy regulations. The research results provide a basis for the control and management of Spartina alterniflora in the Tianjin-Hebei coastal area and are of great significance for the management.
Public health emergencies not only affect the characteristics of urban crowd aggregation but also lead to significant changes in emotional responses. However， current research tends to focus either on crowd aggregation characteristics or emotional responses in isolation， lacking comprehensive analysis. To address this gap， this study proposes an integrated monitoring framework that combines crowd aggregation features with emotional responses to explore the dual impact of public health events on urban psychosocial behavior. The study focuses on the main urban area of Xi’an and utilizes Baidu heat maps to calculate the crowd aggregation index and tidal index. It also integrates social media data for sentiment analysis， employing a social semantic network to reveal the main causes of emotional responses. Data analysis covers both the pandemic control period and the recovery period， comparing crowd aggregation characteristics and emotional responses using multi-source heterogeneous big data. The results indicate that the spatial intensity of crowd aggregation in Xi’an differs significantly between the pandemic control and recovery periods， with higher crowd aggregation during the recovery period. Additionally， the scope and intensity of positive emotions increased during the recovery period compared to the control period， while negative emotions were alleviated. Furthermore， the social semantic network analysis reveals a strong correlation between public sentiment and pandemic control policies. The framework developed in this study not only provides new insights into the analysis of crowd aggregation and emotions under public health events but also offers data support for government decision-making in pandemic control and emotional management. This research holds strong practical value， promoting the integration and application of multi-source spatiotemporal big data in public health and social digital governance.
