Shangluo, as the only prefecture-level city in China entirely located in the heart of the Qinling Mountains, boasts a unique geographical position and ecological resources. It serves as an important ecological barrier and eco-tourism area in southeastern Shaanxi Province. Moreover, the traditional remote sensing environment index (RSEI) model has problems such as single indicators and insufficient representation of biodiversity when evaluating the ecological environment of mountainous cities. It is difficult to comprehensively reflect the complexity of the Qinling Mountain ecosystem. Research on its ecological environment quality and influencing factors holds significant guiding value for ecological security in the Qinling region and the overall ecological civilization construction of Shaanxi Province. Taking the administrative region of Shangluo City as the study area, this research selects MODIS datasets from 2001, 2005, 2010, 2015, and 2023 available on the Google Earth Engine (GEE) platform. An Improved Remote Sensing Ecological Index (IRSEI) is constructed to comprehensively evaluate the ecological environment quality of Shangluo City using the entropy weight method. Through Mann-Kendall-Sen trend analysis and Hurst index calculation, the study investigates the spatiotemporal distribution characteristics and trends of ecological environment quality in Shangluo over the past 23 years. It reveals long-term evolution patterns in ecological quality over time and showcases spatial differentiation patterns in ecological quality. Additionally, a geographically weighted detector model with optimized parameters is employed, using IRSEI as the dependent variable. Ten potential influencing factors are selected, including NDVI, LST, NDBSI, WET, human activity intensity(HAI), land use types, nighttime light intensity, precipitation and elevation. The study quantitatively analyzes the impact intensity and interaction characteristics of these factors on IRSEI.The results show that: (1) The ecological environment quality of Shangluo City has been good over the past 23 years, with the IRSEI values remaining above 0.650, presenting a spatial pattern of "low in the middle and high around". (2) The ecological environment quality of Shangluo City has shown an obvious improvement trend. The ecological environment quality of 78.19% of the area has been improved, among which the significantly improved area accounts for 52.61%, mainly concentrated in the central region. It is predicted that the ecological environment quality of Shangluo City will show signs of degradation in the future, with 76.16% of the area showing a shift from improvement to degradation.(3) The geodetector analysis shows that greenness and human activity intensity are the main driving factors, and the interaction between greenness and dryness has the strongest explanatory power for the spatial differentiation of ecological quality.

The long-term burning of coal fires not only leads to resource loss in mining areas but also may trigger geological disasters and environmental pollution. Therefore, accurate monitoring of coal fire regions is crucial. This study utilizes SDGSAT-1 thermal infrared data to invert surface temperature in coal fire regions and compares the results with Landsat 9 Land Surface Temperature (LST) Product (LC2L2ST) and drone thermal infrared imagery to assess the reliability of SDGSAT-1 in coal fire monitoring within mining areas. First, the surface temperature is inverted from SDGSAT-1 imagery using the single-window algorithm (SW). The results show that the surface temperature in the study area reaches a maximum of 13.33°C and a minimum of -9.46°C, with the temperature distribution highly consistent with known coal fire areas. Profile analysis indicates that the temperature variation trends of SDGSAT-1 and Landsat 9 are similar, both showing distinct temperature peaks in coal fire areas. However, due to SDGSAT-1 TIS's higher spatial resolution (30m), it can more accurately capture small-scale temperature variations. Furthermore, SDGSAT-1 TIS has an imaging swath of 300 km, significantly larger than the 185 km of Landsat 9 TIRS, making it more suitable for large-scale coal fire monitoring. By using natural breaks classification to analyze drone thermal infrared imagery and extract thermal anomaly areas, a comparison with the SDGSAT-1 inversion results shows a high spatial consistency in the thermal anomaly areas. Notably, in several coal mine regions, SDGSAT-1 and drone imagery both successfully identify significant thermal anomalies associated with coal fires. This suggests that SDGSAT-1 is feasible for coal fire monitoring and can overcome the limitations of drones related to terrain, climate, and transportation, enabling larger-scale coal fire detection and dynamic monitoring. Overall, SDGSAT-1 demonstrates high application potential in coal fire monitoring due to its high resolution, large swath, and complete thermal infrared data. Compared to Landsat 9, SDGSAT-1 offers more precise temperature characterization, while compared to drones, SDGSAT-1 provides a broader monitoring range. Therefore, SDGSAT-1 can offer new data support for coal fire disaster assessment, early warning, and management, as well as serve as an important remote sensing monitoring tool for mining area ecological environment protection.

Chlorophyll content, as a key factor influencing photosynthetic efficiency, serves as a vital indicator for vegetation health assessment. Traditional ground-based measurement methods are limited by time-consuming procedures and destructive sampling, whereas remote sensing technology enables non-destructive and efficient monitoring of leaf chlorophyll content. The establishment of a high-accuracy, strongly generalizable remote sensing estimation model for chlorophyll content is crucial for vegetation physiological monitoring and ecosystem health evaluation. Eleven field-measured datasets and one PROSPECT simulated dataset, covering multiple vegetation types and ecosystems, were employed to systematically compare the accuracy, universality, and cross-scenario transferability of chlorophyll estimation models developed using nine spectral indices and seven machine learning algorithms. Among spectral indices, the red-edge chlorophyll index (CIred-edge) demonstrated superior performance in both measured (=0.7567, nRMSE=13.73 μg/cm2, MAE=11.83 μg/cm2) and simulated datasets (=0.9578, nRMSE=5.31 μg/cm2, MAE=4.93 μg/cm2), while showing the strongest generalization capability in simulated-to-measured transfer (=0.7567, nRMSE=15.27 μg/cm2, MAE=14.36 μg/cm2). For machine learning approaches, GBRT achieved optimal accuracy and universality (=0.8172, nRMSE=11.58 μg/cm2, MAE=3.93 μg/cm2), with SVM exhibiting the best transfer learning performance between simulated and measured data (=0.8113, nRMSE=11.81 μg/cm2, MAE=4.14 μg/cm2). These results provide methodological guidance for selecting appropriate remote sensing inversion models, confirm the application potential of simulated data, and offer a practical transfer learning solution for chlorophyll monitoring in data-scarce scenarios, thereby supporting precision agriculture applications and global carbon cycle studies with cost-effective, high-precision estimation techniques.

Qinghai Lake is an important water resource for maintaining the ecological security of the northeastern part of the Tibetan Plateau, but in recent years, Cladophora has been distributed in the western and northern shores of the lake. Timely and accurate acquisition of spatial distribution data of Cladophora blooms is the key to effectively protect the water environment of Qinghai Lake and scientifically manage the ecological problems of Cladophora blooms. Sentinel-2 MSI remote sensing imagery has a spatial resolution of 10 m, which can be used as an effective source of data for the monitoring of Cladophora blooms in Qinghai Lake. Based on the 2019 Sentinel-2 MSI remote sensing images and UAV aerial orthophotos, we used the Buha estuary and the surrounding lakeshore neighborhood as the sample region, and combined the Single Band (SB), Three Bands (TB), Band Difference (BD), Band Ratio (BR), Normalized Difference Vegetation Index (NDVI), The Maximum Chlorophyll Index (MCI), Floating Algae Index (FAI), Surface Algae Bloom Index (SALI), and the Normalized Difference Vegetation Index (NDVI). The nine spectral indices, namely, the Maximum Chlorophyll Index (MCI), the Floating Algae Index (FAI), the Surface Algal Bloom Index (SABI), and the Virtual-Baseline Floating Macroalgae Height (VB-FAH), were used to extract the distribution information of the Cladophora blooms. The results of the extraction and application of the nine indices were compared. Six spectral indices (SB, BD, NDVI, MCI, SABI and VB-FAH) with Kappa coefficients greater than 0.9 were selected to extract information on the distribution of Cladophora blooms in the whole lake region of Qinghai Lake, and the results showed that: ① The VB-FAH index was the most effective in the whole lake region, followed by the NDVI index. ② The total area of the Cladophora blooms in Qinghai Lake in 2019 was 4.10 km2.The area with the largest distribution of Cladophora blooms was the north side of the mouth of the Buha River, followed by the south side of the mouth of the Buha River, and the area of Cladophora blooms in the other regions was relatively small. The area of each distribution region of Qinghai Lake showed different fluctuation characteristics, and the time of occurrence of the largest bloom area in each region was also different. ③ Spatially, the Cladophora blooms migrated outward with the expansion of the Qinghai Lake area by wind force, and was distributed near the lake shore in the form of strips and patches. The results of this paper can provide a reference basis for the extraction of information on the distribution of long time-series Cladophora blooms.

Surface deformation such as subsidence, ground fissures and landslides induced by coal mining in northern Shaanxi have significantly impacted the ecological environment and safety of residents in mining areas. In order to comprehensively understand the characteristics of surface deformation and corresponding geological hazards in the mining area, Shennan mining area in Shenmu was selected as study area. The characteristics and formation processes of surface deformation were analyzed by employing the surface deformation monitoring results obtained from SBAS-InSAR from March 12th, 2017 to September 18th, 2022. Additionally, the development process and influencing factors of surface deformation were investigated based on the cumulative subsidence value along the profile and the curve of time-series cumulative subsidence. Furthermore, drone surveys confirmed that surface subsidence has caused other geological hazards, such as ground fissures and landslides. The development trends and related factors of ground fissures and landslides were discussed by utilizing the displacement monitoring data from ten monitoring points between December 2021 and December 2022. The results indicate that surface deformation is primarily resulted from mining activities and influenced by the thickness of overlying strata. Strong spatial extensibility and temporal continuity exists between surface deformation and mining activities, which also exhibit a spatiotemporal correlation. The study provides a reference for understanding the influence of mining on surface deformation and their spatiotemporal correlation.

Vegetation cover (FVC), as an indispensable climate parameter, and the spatial and temporal evolution characteristics of long time series FVC can provide data reference for assessing the surface vegetation condition. MODIS-NDVI data were used to estimate FVC using the image element dichotomous model, and the spatial and temporal evolution characteristics of vegetation cover in Shenyang from 2000 to 2020 were explored by using trend analysis and deviation analysis, while multi-scenario simulation prediction of vegetation cover in Shenyang in 2030 was carried out based on land use data in 2010, 2015 and 2020 combined with PLUS model. The results show that (1) in time, the annual average FVC in Shenyang City increases at a rate of 3.14%/10a, the high and medium-high vegetation cover shows an increasing trend, and the proportion of vegetation improvement area is higher than that of deterioration. (2) Spatially, the high value areas of FVC in Shenyang are mainly distributed in Shenyei New District, Hunnan District and Sujiatun District, while the low value areas are distributed in the five districts and the central part of districts and counties in the city. (3) The simulation results found that: in the historical trend scenario, the area of arable land, forest land, grassland and water area decreased; in the arable land protection scenario, the area of arable land increased and forest land decreased; in the low-carbon development scenario, forest land increased significantly. The results of the study provide a theoretical basis for the future formulation of environmental management policies in Shenyang.

The spatiotemporal big data of urban agglomerations has the characteristics of dynamic and real-time, across time and space, and across administrative regions. The method of traditional small sample data construction brings challenges to the technical aspects of data storage management, integrated analysis, information mining and knowledge discovery. This research is oriented to the major needs of the construction and management of urban agglomerations for key services of spatial information. It focuses on the characteristics of event models and related elements triggered by application subject areas. Based on elements-events-themes, we have designed a spatiotemporal big data framework system for urban agglomeration construction and management with the examples of four subjects. The spatiotemporal big data information resource framework system proposed in this research is helpful to promote the consistency process of spatiotemporal big data in time, space, attributes and scales. This research result will be possible to provide fast data support services for the spatiotemporal information integration application system of urban agglomerations.

Oil palm is a major economic crop and the area of land converted to oil palm cultivation in the tropics has expanded rapidly. Oil palm has become the world's largest source of vegetable oil and it provides tremendous regional economic benefits. However, the expansion of oil palm cultivation has led to the loss of forests, arable land, and peatland, which has caused severe ecological and environmental problems. Application of 3S (RS, GIS, GNSS) technology is useful for the collection, analysis, and management of spatial information, and is essential for both optimizations of the spatial distribution of land use and sustainable development. This paper analyzes the progress of 3S technology application in oil palm research on the basis of a literature review and scientometric analysis. The factors affecting the precision of oil palm mapping are also discussed. We established that papers describing 3S technology application in oil palm research are based primarily on the study of land cover change, and that scientific institutions and researchers in Malaysia, the United States, China, Indonesia, and the United Kingdom are the major contributors. Currently, the application of 3S technology in oil palm research includes oil palm mapping, oil palm land change monitoring, oil palm tree counting, tree age estimation, aboveground biomass and carbon storage estimation, suitability analysis, yield estimation, pest and disease monitoring, and plantation management. The accuracy of mapping is not correlated significantly with the year of publication of specific literature but is correlated with RS data sources and classification methods. The use of 3S technology in oil palm research is currently dominated by RS, which has been used in diverse fields of oil palm research. GIS technology is used mainly for oil palm land change mapping, suitability analysis, plantation management, and pest and disease monitoring, while GNSS is used largely as an additional tool in pest and disease monitoring and plantation management.

Based on a large number of forest litter produciton data and NDVI data, a random forest model was constructed to simulate the spatial and temporal variation characteristics of forest litterfall carbon density in China from 1982 to 2020. The nonlinear relationship between litterfall carbon density and NDVI proves the feasibility of the research method. The simulated results showed a significantly higher positive correlation with the observed values (r=0.65, P <0.001,n=4882), and a smaller error percentage (0.96%), indicating that the simulation accuracy of the random forest model was higher. The temporal and spatial variation characteristics of the carbon density of litters in China during 1982-2020 showed a significant increasing trend (r=0.81, P <0.001), and the carbon density of litters in evergreen coniferous forest, deciduous broad-leaved forest and deciduous coniferous forest increased significantly. It can be seen that long-term continuous NDVI data can effectively simulate and monitor the temporal and spatial dynamics of forest litter carbon density at the national scale, driving advanced random forest models. This provides a new technique for dynamic monitoring of large scale forest litter using remote sensing observation data, and also expands the application scenarios of remote sensing data.

In the background of rapid urbanization and rural revitalization, there is a great significance to optimizing the urban-rural land use structure of metropolises and urban-rural spatial integration development by master the change characteristics of construction land and impervious surface in urban expansion period. Based on the remote sensing monitoring dataset and the internal impervious surface dataset of urban-rural construction land since the 21st century, this research analyzes the structure and impermeable land proportion of urban and rural construction land in Harbin from 2000 to 2015. The purpose is to explore the urban expansion patterns, regional differences, construction land use intensity, and urban-rural differences. The results show that: ①From 2000 to 2015, the urban-rural construction land expanded by 158.32km2 rapidly, the trend of annual gradient and dynamic degree were firstly increased and then decreased. In the same period, from the core area of the city to the far suburbs, the scale of expansion increased in turn, and the construction focus continues move towards to the urban periphery, which shows a spatial heterogeneity obviously. ②The area and proportion of urban construction land and independent industrial and mining land increased year by year, and the sources of expansion were mainly cultivated land. The proportions of rural residential areas decreased by 13.14% from 2000 to 2015, while the structural characteristics of urban-rural construction land changed significantly. ③From 2000 to 2015, the area and proportion of impervious surface in urban-rural construction land increased by 145.32km2 and 10.04% respectively. The land use intensity of urban construction reached a high level, because of the land use intensity of rural residential areas increased rapidly, and the gap between urban and rural areas is narrowing. The proportion of impervious surface was decreasing continuously along the direction of the urban core area to the far suburbs, but the potential for development and utilization was greater in the same direction, because the increment, proportional increment, proportional growth rate and expansion intensity of impermeable surface area was generally increasing. In general, there is a similar trend between the area of impermeable surface and the scale of urban and rural construction land, which can reveal the urban expansion track to a certain extent.