Cover Data Research Articles

Simplifying Land Cover-Geoprocessing-Model Migration with a PAMC-LC Containerization Strategy in the Open Web Environment

Land cover and its changes over time are significant for better understanding the Earth’s fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs.

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. Soil loss estimation is a critical component of sustainable land management practices because it provides important information about soil erosion hotspot areas and prioritizes areas that require immediate management interventions. This study integrates the Revised Universal Soil Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate soil erosion rates and map soil erosion in the Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections and land use land cover (LULC) data were accessed and processed in the GEE Platform. LULC was classified using Random forest (RF) classification algorithm in the GEE platform. Landsat surface reflectance images from Landsat 8 Operational land imager (OLI) sensors (2021) was used for LULC classification. Besides, different auxiliary data were utilized to improve the classification accuracy. Using the RUSLE-GEE framework, we analyzed the soil loss rate in different agroecologies and LULC types in the upper Tekeze basin in Waghimra zone. The results showed that the average soil loss rate in the Upper Tekeze basin is 25.5 t ha−1 yr−1. About 63 % of the basin is experiencing soil erosion above the maximum tolerable rate, which should be targeted for land management interventions. Specifically, 55 % of the study area, which is covered by unprotected shrubland is experiencing mean annual soil loss of 34.75 t ha−1 yr−1 indicating the need for immediate soil conservation intervention. The study also revealed evidence that this high mean soil loss rate of the basin can be reduced to a tolerable rate by implementing integrative watershed management and exclosures. Furthermore, this study demonstrated that GEE could be a good source of datasets and a computing platform for RUSLE, in particular for data scarce semi-arid and arid environments. The results from this study are reliable for decision-making for rapid soil erosion assessment and intervention prioritization.

Distribution of Coral Life Forms Based on Sea Current Velocity in Kei Besar Island, Maluku, and Mare Island, North Maluku

Abstract Diverse life forms found on coral reefs indicate resilience to environmental change, sustaining marine biodiversity and ensuring the stability of the coastal community’s ecosystems. This study investigates the relationship between sea currents and the life forms of coral reefs in Indonesia and focuses on two primary locations: Kei Besar Island, Maluku, and Mare Island in North Maluku. Sea current data from the current real-time global forecasting and coral cover data were analysed to assess the influence of current velocities on the percentage of hard coral cover and the morphological composition of corals. The dominant bottom substrate in both locations is hard coral, with varying percentages of cover. Thirteen distinct life forms of coral were identified in both study sites, with massive and sub-massive forms being the most prevalent on Kei Besar Island (41%) having average current velocities of 0.159 m/s, and branching forms (40%) being dominant on Mare Island with average current velocities were 0.103 m/s. The massive form (CM) is strongly positively (negatively) correlated with faster (slower) currents, with a correlation coefficient of 0.81 (-0.69). On the other hand, several life form of fragile coral reefs particularly Acropora branching (ACB); Acropora sub-massive (ACS); and coral Tubipora (CTU) were found to be positively (negatively) correlated with slower (faster) currents, with a correlation coefficient of 0.55 (-0.63); 0.77 (-0.75); and 0.58 (-0.67).

Interpretation of spatial and temporal changes and drivers of ecological source regions based on LightGBM-SHAP

Ecological source areas play a pivotal role in nature preservation and soil conservation. These areas are critical in optimizing land-use patterns and fostering the establishment facilitating the establishment of protected ecological source areas. In this study, two types of state layers of ecological source area change were extracted based on land cover data and integrated with an intelligent algorithm to analyze the role of ecological factors contributing to land change. Recommendations are proposed to enhance the optimization of the ecological spatial pattern within the Yellow River Basin. The results indicate key findings: (1) The ecological source areas in the Daxia River Basin experienced Fluctuations between 1990 and 2020 due to the interconversion of grassland and unused land. The change was relatively smooth during 1990–2000, with the ecological source areas increasing slightly by 16.016 km2. From 2000 to 2010, the proportion of unutilized land transferred to grassland was 6.3%, which is significantly higher than the annual transfer average of 1.6%. This is mainly due to the ecological protection measures in the cities of Linxia and Hezuo. Between 2010 and 2020, economic construction resulted in the loss of grassland, and the percentage of grassland turned out was 7.3%, which was significantly higher than the annual average of 1.8%. (2) In the Daxia River Basin, rainfall, sunlight intensity, temperature, and elevation had a greater impact on land change during the period from 1990 to 2020, with contributions of −0.4, 0.09, 0.08, and −0.26, respectively.

NeonPlantEcology: An R package for preparing NEON plant data for use in ecological research

The National Ecological Observatory Network (NEON) is a continental-scale endeavor of ecological data collection for 30 years. We created a software package, neonPlantEcology that automatically arranges the raw data from the plant presence and percent cover (DP1.10058.001) data product from NEON into tables familiar to plant ecologists. Because of the broad scale of the observatory, it is necessary to tailor the data collection to the idiosyncrasies of each of 47 different ecosystems. Furthermore, data collection practices are occasionally modified for various reasons. These complexities, along with the volume and multiscalar nature of the data, need to be understood and accounted for in order to correctly process the data. This is particularly true for the plant diversity data product. We present three case studies using the package, centered around the three primary functions of neonPlantEcology. By automating the process of preparing NEON's plant diversity data, neonPlantEcology makes it more accessible to a wide range of users.

Knowledge-Based Identification of Urban Green Spaces: Allotments

Abstract. Urban Green Spaces (UGS) play a crucial role in enhancing the quality of life in cities by providing numerous environmental, social, and health benefits. Among these green spaces, allotment gardens stand out as a unique type that contributes to ecological services, preservation of biodiversity, and the overall well-being of urban dwellers. Unfortunately, the significance of allotment gardens as a specific type of UGS is still disregarded and they are not recognized as a separate category in land use / land cover maps or city maps of green spaces. This is mainly due to the mixed use of allotment areas, their small size and absence of tailored identification or mapping workflows. In this research, we address the latter one by proposing an approach that utilizes various semantic characteristics of allotment gardens to create distinctive spatial representations. The semantic characteristics we consider include the presence, density, and height of garden huts, proximity to water bodies and railroads, as well as the presence of pathways within the allotment gardens. Allotments are delineated using a three-step procedure. This involves utilizing a Random Forest machine learning classifier to create maps of the distribution of green spaces, extracting garden huts employing a threshold, and demarcating the area using a density based clustering technique. Furthermore, we repeat the same workflow in a new study area to assess the applicability of the proposed workflow. With the established workflow, we are able to accurately identify 78% of allotments in Augsburg and 88% in Wuerzburg respectively. Our results demonstrate that the proposed workflow can be a useful approach to validate and extend existing land use and land cover data sets while remaining time and cost effective.

Spatial Study of Maleo Bird Habitat Area Change in Sulawesi

Maleo (Macrocephalon maleo), a unique Sulawesi endemic bird, is threatened by habitat loss. This study aims to analyse changes in maleo habitat areas in Sulawesi using spatial data and propose effective conservation measures. Spatial data in the form of maleo home range maps (IUCN) were used for overlay analysis. The results of the overlay analysis were compared with land cover data to identify changes in maleo habitat area. Of the total area of approximately 884,040 hectares, 14.13% or 124,887.63 hectares have been converted to agricultural land. The construction of new settlements has affected 0.46% or 4,087.5 hectares of the habitat. In addition, plantations have occupied 2.82% or 24,965.92 hectares of the maleo habitat area. Changes to maleo habitat areas in Gorontalo are significant, with agricultural land conversion, settlement development, and plantations being the main threats. Effective conservation efforts should focus on habitat restoration, law enforcement, community education, and the development of environmentally friendly economic alternatives.

Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia

Crop modeling is a powerful tool for predicting yield and water productivity. The aim of the study was to calibrate and validate the AquaCrop model for tomato under staged deficit drip irrigation in Ethiopia. The AquaCrop model was calibrated and validated by using the observed data of canopy cover, biomass, dry yield, and soil water content. The results showed that the model was accurate in predicting canopy cover, biomass, and dry yield under different water levels. The overall performance in simulating canopy cover of AquaCrop, biomass, and soil water content showed a good match between measured and simulated data. The calibration results indicated good performance on canopy cover with 0.96 ≤ r ≤ 1.00, 2.9% ≤ RMSE ≤ 8.0%, 7.5% ≤ CV (RMSE) ≤ 21.1%, 0.91 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. On biomass, the model calibration values were 0.99 ≤ r ≤ 1.00, 0.3 t/ha ≤ RMSE ≤ 0.8 t/ha, 8.3% ≤ CV (RMSE) ≤ 25.2%, 0.92 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. Soil water content displayed poor performance on calibration, with performance values of 0.54 ≤ r ≤ 0.82, 3.6 mm ≤ RMSE ≤ 24.4 mm, 1.30% ≤ CV (RMSE) ≤ 9.00%, −4.18 ≤ EF ≤ 0.00, and 0.66 ≤ d ≤ 0.81. The validation results demonstrated that the model performed well on canopy cover with 0.96 ≤ r ≤ 1.00, 3.1% ≤ RMSE ≤ 7.7%, 8.2% ≤ CV (RMSE) ≤ 20.8%, EF ≥ 0.91, and d ≥ 0.9. The model validation correctly predicted biomass with r ≥ 0.98, 0.3 t/ha ≤ RMSE ≤ 0.7 t/ha, 8.9% ≤ CV (RMSE) ≤ 21.8%, EF ≥ 0.94, and d ≥ 0.98. The model validation poorly performed in forecasting soil water content, 0.20 ≤ r ≤ 0.80, 10.2 t/ha ≤ RMSE ≤ 24.0 t/ha, 3.5% ≤ CV (RMSE) ≤ 9.0%, −22.76 ≤ EF ≤ 0.63, and 0.40 ≤ d ≤ 0.87. The AquaCrop model is easy to use, requires less input data, and has high simulation accuracy, making it a useful tool for predicting crop yields and water productivity under staged deficit irrigation in areas with limited data.

Assessing the impact of urban expansion on carbon emission

Urban expansion is not only a driving force for economic growth and development but also a perilous element for anthropogenic carbon emission coinciding with population growth, urban expansion, land use pattern, urban sprawl. The study compares urban expansion and its impact on the carbon emission pattern for Rajshahi, a city of Bangladesh where land cover data is an important determinant. At first, the study aims to evaluate the pattern of urban expansion and the second step quantifies the carbon emission trend. The satellite images are used for land cover detection of urban expansion and Support Vector Machine (SVM) algorithm is applied for its classification. Similar carbon emission data are examined to relate carbon emission quantification rate by remote sensing algorithm. It indicates that urban expansion significantly explains carbon emission by comparing with similar secondary data to extract carbon emission and energy consumption. After accentuating urban expansion and carbon emission rate, the spatiotemporal pattern is analyzed in 16 different directions revealing that the urban expansion and emission trend expands to north-western and north-eastern directions of Rajshahi, turning it into a low carbon absorbed city. The responsible landmarks are real estate housing complex, educational institutions, student dormitories, industrial areas, brickfields and mango wholesale business. In order to understand the effects of uncontrolled urban expansion, this study will assist policymakers and urban planners in developing a comprehensive and well-organized strategy.

An integrated GEE and machine learning framework for detecting ecological stability under land use/land cover changes

Ecological stability (ES) is recognized as a crucial factor for sustainable development at global and regional scales. However, the importance of this factor was not considered significant. Hence, the main aim of this study was to introduce a new approach that focuses on detecting ES over the Maharloo watershed in Iran. To achieve this goal, we extracted land use and land cover (LULC) data from the Google Earth Engine (GEE) platform by applying the random forest (RF) machine learning method, which obtained Kappa statistics of 0.85, 0.86, and 0.87 for the years 2002, 2013, and 2023, respectively. We identified both stable and unstable regions based on LULC changes and employed them using machine learning to forecast the ES. The most important predictors of ES were elevation, soil organic carbon index, precipitation, and salinity. The results of this research revealed that certain areas within the Maharloo watershed have experienced ecological instability in recent years, with gardens showing the highest percentage (60.65 %) of instability among all land-use categories. The performance and validation of our model suggest that the study results are reliable (AUC = 0.86). This study offers detailed maps of ES and trends, offering valuable insights for decision makers to support landscape conservation and restoration efforts. Overall, the findings contribute to a more comprehensive understanding of the ecological dynamics of the Maharloo watershed and provide valuable insights for sustainable development and conservation efforts in other regions.

Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops

Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.

Exploring the Response of Ecosystem Services to Socioecological Factors in the Yangtze River Economic Belt, China

Understanding the response of the mechanisms driving ecosystem services (ESs) to socioecological factors is imperative for regional sustainable ecosystem management. However, previous studies of the mechanisms driving ESs have focused more on the degree and direction (positive and negative) of effects on ES supply or the supply–demand balance, while their nonlinear response processes have not been fully considered. In this study, a theoretical framework was developed through integrating land use/land cover data and supply and demand matrices with random forest models to assess response processes, including the relative importance and marginal effects, of essential factors that drive ES demand, supply, and supply–demand balance. Using the Yangtze River Economic Belt (YREB) as an example, our results indicated that the ES deficit regions (332 of 1070 counties or 14.45% of the area) of the YREB were located mainly in the three national urban agglomerations. Moreover, this study indicated that natural environmental factors (such as slope and precipitation) significantly influence the supply and supply–demand balance of ESs, while socioeconomic factors (such as cropland ratios and population density) profoundly influence the demand for ESs. However, cropland ratios were the most important drivers of ES supply, demand, and supply–demand balance in the YREB. Moreover, three types of response processes were identified in this study: logarithmic increase, logarithmic decrease, and volatility increase. Specific driving factors (e.g., proportion of cropland area, precipitation, population density, and slope) had significant threshold effects on the supply–demand balance of ESs. The turning points that can be extracted from these response processes should be recommended for ecosystem restoration projects to maintain regional sustainable ecosystem management.

Assessment of environmental and spatial factors influencing the establishment of Anopheles gambiae larval habitats in the malaria endemic province of Woleu-Ntem, northern Gabon

BackgroundThis study aimed to assess the spatial distribution of Anopheles mosquito larval habitats and the environmental factors associated with them, as a prerequisite for the implementation of larviciding.MethodsThe study was conducted in December 2021, during the transition period between the end of the short rainy season (September–November) and the short dry season (December-February). Physical, biological, and land cover data were integrated with entomological observations to collect Anopheles larvae in three major towns: Mitzic, Oyem, and Bitam, using the "dipping" method during the transition from rainy to dry season. The collected larvae were then reared in a field laboratory established for the study period. After the Anopheles mosquitoes had emerged, their species were identified using appropriate morphological taxonomic keys. To determine the influence of environmental factors on the breeding of Anopheles mosquitoes, multiple-factor analysis (MFA) and a binomial generalized linear model were used.ResultsAccording to the study, only 33.1% out of the 284 larval habitats examined were found to be positive for Anopheles larvae, which were primarily identified as belonging to the Anopheles gambiae complex. The findings of the research suggested that the presence of An. gambiae complex larvae in larval habitats was associated with various significant factors such as higher urbanization, the size and type of the larval habitats (pools and puddles), co-occurrence with Culex and Aedes larvae, hot spots in ambient temperature, moderate rainfall, and land use patterns.ConclusionsThe results of this research mark the initiation of a focused vector control plan that aims to eradicate or lessen the larval habitats of An. gambiae mosquitoes in Gabon's Woleu Ntem province. This approach deals with the root causes of malaria transmission through larvae and is consistent with the World Health Organization's (WHO) worldwide objective to decrease malaria prevalence in regions where it is endemic.

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.

Evaluating Urban Green Space Inequity to Promote Distributional Justice in Portland, Oregon

Access and exposure to urban green space—the combination of parks and vegetative cover in cities—are associated with various health benefits. As urban green space is often unequally distributed throughout cities, understanding how it is allocated across socio-demographic populations can help city planners and policy makers identify and address urban environmental justice and health equity issues. To our knowledge, no studies have yet combined assessments of park quality, park availability, and green cover to inform equitable urban green space planning. To this end, we developed a comprehensive methodology to identify urban green space inequities at the city scale and applied it in Portland, OR, USA. After auditing all public parks in Portland and gathering green cover data from publicly accessible repositories, we used a suite of statistical tests to evaluate distribution of parks and green cover across Census block groups, comprising race, ethnicity, income, and educational attainment characteristics. Right-of-way tree canopy cover was the most significant urban green space inequity identified in bivariate analysis (rs = −0.73). Spatial autoregressive models identified that right-of-way, private, and overall tree canopy cover (Nagelkerke pseudo-R2 = 0.66, 0.77, and 0.67, respectively) significantly decreased with the proportion of minoritized racial population and increased with median income. The results were then used to identify priority locations for specific urban green space investments. This research establishes a process to assess intra-urban green space inequities, as well as identify data-informed and spatially explicit planning priorities to promote health equity and environmental justice.

No lasting effects of a severe drought event on plant community composition in experimental grassland

The effect of severe drought in the summer 2018 on the plant community composition and overall diversity was investigated in a replicated long-term grassland experiment where nitrogen availability was manipulated. The possible plant community response to the drought and the possible role of selected plant traits were investigated using model-based ordination techniques, which were tailored to the sampled pin-point cover data. Contrary to our prior expectation, that the severe drought had lasting effects on the plant community, we could not detect any significant effects of the severe drought on the plant community composition or Hill–Shannon diversity the following year and 3 years later. Furthermore, we did not detect any significant mediating effect of nitrogen on the possible effects of severe drought. The possible reasons for the observed high resistance of the grassland community to the severe drought event are discussed. Lasting effects of drought on plant communities cannot be predicted from observations during or immediately after the drought.

Estimation of Spatiotemporal Changes of Land Cover in Llapi River Catchment (Northeastern Part of Kosova)

Aim of the studyIn recent decades, Kosova in general and the Llapi River catchment have experienced landscape transformation which resulted in Land Use and Land Cover (LULC) changes, mainly in flat terrains where most of country’s population is concentrated. Other landscapes have experienced changes too, but on a small scale. The aim of the study was to identify landscape transformation and driving forces behind it.Material and methodsSpatiotemporal changes of Land Use and Land Cover (LULC) were made using GIS techniques. Land Cover data from Copernicus Land Monitoring Service (CLMS) with spatial resolution 100 m for three different years: 2000, 2006 and 2018 were analyzed. DEM with 10 m spatial resolution was used to estimate slope steepness and catchments’ altitude. Population data for settlements inside the Llapi River catchment represented in features were downloaded from the Statistical Agency of Kosova, and those data include population numbers for official censuses between 1948 until 2011, while ArcMap 10.8 environment was used for analysis.Results and conclusionsWith GIS technique in analyzing Land Use and Land Cover data (LULC) and other open source data, we found that agricultural land have decreased in area by 9.53%, while the increase of artificial surface is 160.87%, or expressing by absolute values, total agricultural areas in 2000 were 37,827 ha, while in 2018 were 34,334 ha, meaning 0.3ha per capita, less than European level. In order to protect agricultural land, zoning and land use plans should be implemented.

Study on Spatialization and Spatial Pattern of Population Based on Multi-Source Data—A Case Study of the Urban Agglomeration on the North Slope of Tianshan Mountain in Xinjiang, China

Study on Spatialization and Spatial Pattern of Population Based on Multi-Source Data—A Case Study of the Urban Agglomeration on the North Slope of Tianshan Mountain in Xinjiang, China

Restore polder and aquaculture enclosure to the lake: Balancing environmental protection and economic growth for sustainable development

The restoration of lakes and their buffer zones is crucial for understanding the intricate interplay between human activities and natural ecosystems resulting from the implementation of environmental policies. In this study, we investigated the ecological restoration of shallow lakes and buffer zones in the Yangtze-Huaihe River Basin, specifically focusing on the removal of polder and aquaculture enclosure areas within the lakes. By examining data from eight shallow lakes and their corresponding buffer zones, encompassing lake morphology, water quality parameters, and land use/land cover (LULC) data spanning from 2008 to 2022, which shed light on the complex relationships involved. During the process of restoring polder and aquaculture enclosure areas, we observed a general decrease in the extent of polders and aquaculture enclosures within the lakes. Notably, the removal of aquaculture enclosures had a more pronounced effect (reduction rate of 83.37 %) compared to the withdrawal of polders (reduction rate of 48.76 %). Linear regression analysis revealed a significant decrease in the concentrations of seven water quality parameters, including COD, CODMn, TN, TP, NH3-N, Chl-a, and F, while pH and DO factors exhibit a distinct increasing trend. The results of redundancy analysis and Pearson correlation analysis demonstrated significant correlations between the area of polders and aquaculture enclosures and the changes in lake water quality. Encouragingly, the withdrawal of polders and the removal of aquaculture enclosures had a positive impact on the lake water quality improvement. In contrast, the LULC in the buffer zones of the lakes experienced a gradual decline owing to land degradation, resulting in a reduction in ecosystem service value (ESV). These results offer valuable support for policymakers in their endeavors to restore lake water quality, mitigate the degradation of buffer zones land, and promote the sustainable development of land and water resources.

Technical Framework and Preliminary Practices of Global Geographic Information Resource Construction

Abstract. High-precision and real-time global geographic information data are fundamental and strategic resources in various fields such as safeguarding global strategic interests, studying global environmental changes, and planning for sustainable development. However, due to challenges related to ground control and obtaining reference information, the development of global geographic information resources faces significant hurdles in terms of geometric positioning, information extraction, and data mining. This paper starts with the characteristics of domestically produced remote sensing images and proposes a comprehensive technical framework centered around "uncontrolled geometric positioning, intelligent interpretation of typical elements, mining of multi-source data from abroad, and intelligent hybrid collection and compilation of Digital Elevation Models (DEMs)." The paper elaborates on the key technical challenges that need to be overcome and their corresponding solutions. It also outlines the development of relevant data products and production technical specifications. Multiple production-oriented software tools were developed, leading to the creation of a variety of data products in multiple types and scales, including global 30-meter land cover data, DEM data, core vector data, and more.