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Seagrasses are in danger due to anthropogenic activities causing a reduction in seagrass area and global damage. It is crucial to map seagrass distribution, as there is limited information on its existence. Sentinel-2A satellite provides high-resolution multispectral data with a 10-m resolution. The study aimed to evaluate the ability of Sentinel-2A imagery from the Google Earth Engine (GEE) platform to classify with the random forest (RF) algorithm in mapping seagrass in shallow water and various other objects in the study area. The results showed the area’s presence of seagrass, coral, sand, sand seagrass, and rubble. Also, the photo transect method was used for collecting field data. The random forest algorithm had an accuracy of 76% in classifying each of the five classes. The combination of Sentinel-2A imagery and random forest algorithms can provide insight into the status and distribution of seagrass. [ABSTRACT FROM AUTHOR]

As a renewable energy source, ocean wind energy plays an important role in addressing challenges such as global energy shortages and climate warming. In the past decade, the offshore wind power industry has developed rapidly. However, its development has also inevitably affected local social, economic and environmental aspects. Therefore, a timely understanding of offshore wind power dynamics development is crucial for its healthy and sustainable development. Based on this, this study designs and develops a more economical, reliable and real-time offshore wind turbine (OWT) extraction method by combining deep learning and the Google Earth Engine (GEE) cloud computing platform. The method consists of two main steps. The first part utilizes multiple semantic segmentation models to construct a multi-model detection method to initially detect OWTs. The second part utilizes the GEE cloud computing platform for installation time detection and secondary purification processing of the preliminary results. The results show that the number of global OWTs reached 13,609 by 2023, and the accuracy of the detection results reached 99.93%. China has been the fastest-growing country in offshore wind power in the last decade, from installing only 4 units in 2015 to installing 6,775 units in 2023 and surpassing the UK in 2020 and becoming the country building the most OWTs worldwide. Currently, 85% of the world's OWTs are located in China and European North Sea waters. Additionally, other regions have great potential for offshore wind development. Finally, this study provides the world's most up-to-date and complete OWT dataset, which can provide data support for research on marine ecological and environmental protection, marine spatial planning, and socioeconomic benefits. [ABSTRACT FROM AUTHOR]

Shuozhou is a typical coal mining city, and the Pingshuo Antaibao open-pit coal mine in its area is one of the largest open-pit coal mines in China. The mining of coal resources is an important part of ensuring national energy security, and at the same time, it inevitably has a certain impact on the ecology, such as coal dust generated by open-pit mining will affect air quality, soil, water and vegetation. It is of great significance to explore the temporal and spatial variation of ecological environment quality in coal mining cities for ecological protection and sustainable social and economic development. Based on the Google Earth Engine (GEE) platform, this paper combines the index-based coal dust index (ICDI) and Remote Sensing Ecological Index (RSEI) models to construct an improved RSEI (IRSEI) that can reflect coal mining cities. This paper explores the spatial–temporal evolution characteristics and spatial correlation of ecological environment quality in Shuozhou from 2000 to 2020. The results showed that the average value of IRSEI in Shuozhou was between 0.262 and 0.418, and the overall change showed an upward trend. The growth areas of ecological environment quality are mainly located in the eastern and southwestern areas with good vegetation growth, and these regions have vigorously implemented the Northern Shelter Forest Project, afforestation and greening projects, implemented the forest resource management and protection responsibility system, promoted the construction of ecological civilization, and significantly improved the ecological environment. While the declining areas are mainly located in the central and southern regions where mining activities and human activities are more intensive. The IRSEI in the study area showed a significant spatial positive correlation, and the agglomeration types of the spatial pattern were mainly high-high and low-low agglomeration types, with the high-high agglomeration types mainly distributed in the eastern and southwestern regions, and the low-low agglomeration types distributed in the northern and south-central regions of the study area. The trend of low and low agglomeration has decreased, which further proves that the ecological restoration measures taken by the government, such as returning farmland to forests, integrating protection and restoration of mountains, waters, forests, fields, lakes, grasslands, and sands, controlling soil erosion, and stage wise reclamation of coal mining subsidence areas, have improved the ecological environment quality of Shuozhou. This study provides a reference for understanding the spatiotemporal changes of the ecological environment of coal mining cities, and is conducive to formulating appropriate ecological protection strategies. [ABSTRACT FROM AUTHOR]

Rice is a crucial agricultural product for Thailand's economy, as the majority of the country's agricultural sector primarily cultivates rice for both domestic consumption and international demand. This research focuses on land use analysis, specifically on rice cultivation areas, and the classification of different rice varieties. The study significantly contributes to the achievement of the Sustainable Development Goals (SDGs) concerning food security. This research utilizes Sentinel-2 Spectral Instrument, Level 2A, and Sentinel-1 polarization VV and VH satellite images from 2023, covering the planting season (June to November). The data processing and analysis are conducted on the cloud platform Google Earth Engine (GEE), employing Support Vector Machine (SVM) and Random Forest (RF) classification methods for both land use analysis and rice variety classification. The analysis results indicate that the RF classification method has higher accuracy than the SVM method. Specifically, for land use analysis, the RF and SVM classification methods achieved accuracy values of 0.91 and 0.87, and kappa values of 0.89 and 0.85, respectively. For rice variety classification, the RF and SVM methods achieved accuracy values of 0.88 and 0.83, and kappa values of 0.73 and 0.61, respectively. [ABSTRACT FROM AUTHOR]

River temperature is a critical parameter influencing aquatic ecosystems and water quality. However, it can be changed by natural (e.g., flow and depth conditions) and human factors (e.g., waste and industrial water drainage). Satellite-based monitoring offers a valuable tool for assessing river temperature on a large scale, elucidating the impacts of various factors. This study aims to analyze the spatiotemporal dynamics of surface water temperature (SWT) in the medium-sized Tisza River in response to natural and anthropogenic influences, employing Landsat satellites and in situ water temperature data. The validity of the Landsat-based SWT estimates was assessed across different channel sections with varying sizes. The longitudinal thermal profile of the Tisza was analyzed by mosaicking, monthly, four Landsat 9 images, covering the entire 962 km length of the Tisza in 2023. The impact of climate change was evaluated by analyzing SWT trends at a specific site from 1984 to 2024, utilizing 483 Landsat 4–9 images. The findings indicated elevated accuracy for Landsat-based SWT estimation (R2 = 0.94; RMSE = 3.66 °C), particularly for channel sizes covering ≥ 3 pixels. Discharge, microclimatic conditions, and channel morphology significantly influence SWT, demonstrating a general increasing trend downstream with occasional decreases during the summer months. Dams were observed to lower the SWT downstream due to cooler bottom reservoir water discharge, with more pronounced differences during the summer months (1–3 °C). Tributaries predominantly (75%) elevated the SWT in the Tisza River, albeit with varying magnitudes across different months. Over the 40-year study period, an increasing trend in SWT was discerned, with an annual rise rate of 0.0684 °C. While the thermal band of Landsat satellites proved valuable for investigating the Tisza River's thermal profile at a broad scale, finer spatial resolution bands are necessary for detecting small-scale phenomena such as thermal plumes and localized temperature variations in rivers. [ABSTRACT FROM AUTHOR]

Carbon storage estimates are essential for sustainable urban planning and development. This study examines the spatio-temporal effects of land use and land cover changes on the provision and monetary value of above- and below-ground carbon sequestration and storage during 2011, 2019, and the simulated year 2027 in Noida. The Google Earth Engine-Random Forests (GEE-RF) classifier, the Cellular Automata Artificial Neural Network (CA-ANN) model, and the InVEST-CCS model are some of the software tools applied for the analysis. The findings demonstrate that the above- and below-ground carbon storage for Noida is 23.95 t/ha. Carbon storage in the city increased between 2011 and 2019 by approximately 67%. For the predicted year 2027, a loss in carbon storage is recorded. The simulated land cover for the year 2027 indicates that if the current pattern continues for the next decade, the majority of the land will be transformed into either built-up or barren land. This predicted decline in agriculture and vegetation would further lead to a slump in the potential for terrestrial carbon sequestration. Urban carbon storage estimates provide past records to serve as a baseline and a precursor to study future changes, and therefore more such city-scale analyses are required for overall urban sustainability. [ABSTRACT FROM AUTHOR]

Background: Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration. Objectives: The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2. Methods: A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations. Results: The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45–89%. During 32 years (1990–2022), about 1280.47 km2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96). Conclusions: This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover. [ABSTRACT FROM AUTHOR]

Shoreline detection and estimation of changes is a well-established concept in the field of coastal zone management. Recent technological advancements in the form of machine learning (ML) have transformed shoreline detection methodologies. However, due to the constantly changing nature of coasts, identification of the boundary between land and ocean has become an intricate process. In this particular investigation, long-term changes in shoreline along the lower segment of the Indian Sundarbans Delta (ISD) are estimated by employing Landsat sensor's optical imageries spanning 28 years from 1995 to 2023. A fully automated approach involving support vector machine (SVM) classification algorithm with a 99.5% accuracy and zero-crossing edge detection algorithm for shoreline extraction from optical imagery has been proposed. The implementation stage of shoreline extraction utilizes Google Earth Engine's cloud platform. In contrast, subsequent analysis to calculate shoreline changes conforms to the Digital Shoreline Analysis System (DSAS v.5), an extension of ArcGIS Desktop's functionality. This research article examines the long-term shoreline changes in the Indian Sundarban Delta. The study uses three statistical measures: end point rate (EPR), linear regression rate (LRR), and net shoreline movement (NSM). EPR analysis shows significant erosion on Kanak Island and Bhangaduni Island up to 88.21 m. LRR statistics reveal negative trends on the eastern side. NSM analysis highlights maximum accretion in the northwestern part of the delta. This study offers valuable insights into dynamic coastal processes in the area. [ABSTRACT FROM AUTHOR]

Abstract Shuozhou is a typical coal mining city, and the Pingshuo Antaibao open-pit coal mine in its area is one of the largest open-pit coal mines in China. The mining of coal resources is an important part of ensuring national energy security, and at the same time, it inevitably has a certain impact on the ecology, such as coal dust generated by open-pit mining will affect air quality, soil, water and vegetation. It is of great significance to explore the temporal and spatial variation of ecological environment quality in coal mining cities for ecological protection and sustainable social and economic development. Based on the Google Earth Engine (GEE) platform, this paper combines the index-based coal dust index (ICDI) and Remote Sensing Ecological Index (RSEI) models to construct an improved RSEI (IRSEI) that can reflect coal mining cities. This paper explores the spatial–temporal evolution characteristics and spatial correlation of ecological environment quality in Shuozhou from 2000 to 2020. The results showed that the average value of IRSEI in Shuozhou was between 0.262 and 0.418, and the overall change showed an upward trend. The growth areas of ecological environment quality are mainly located in the eastern and southwestern areas with good vegetation growth, and these regions have vigorously implemented the Northern Shelter Forest Project, afforestation and greening projects, implemented the forest resource management and protection responsibility system, promoted the construction of ecological civilization, and significantly improved the ecological environment. While the declining areas are mainly located in the central and southern regions where mining activities and human activities are more intensive. The IRSEI in the study area showed a significant spatial positive correlation, and the agglomeration types of the spatial pattern were mainly high-high and low-low agglomeration types, with the high-high agglomeration types mainly distributed in the eastern and southwestern regions, and the low-low agglomeration types distributed in the northern and south-central regions of the study area. The trend of low and low agglomeration has decreased, which further proves that the ecological restoration measures taken by the government, such as returning farmland to forests, integrating protection and restoration of mountains, waters, forests, fields, lakes, grasslands, and sands, controlling soil erosion, and stage wise reclamation of coal mining subsidence areas, have improved the ecological environment quality of Shuozhou. This study provides a reference for understanding the spatiotemporal changes of the ecological environment of coal mining cities, and is conducive to formulating appropriate ecological protection strategies.

Abstract The non-coordination between the socio-economic systems and ecosystems of a region is a crucial obstacle to sustainable development. To reveal the relationships between complex urban systems and achieve the goal of sustainable and coordinated urban development, we constructed a coupling coordination degree model (CCDM) and coupling angle model (CAM) and analyzed the degree of coupling coordination and evolution process among the population, water resources, economy, and ecology (PWEE) system of the Tuha region for 2005–2020. The results indicated that: (1) During 2005–2020, the comprehensive development index (CDI) of the population, water resources and economy subsystems was 0.21–0.65, with the three subsystems portraying an overall increase; the average values of the RSEI at five-year intervals were 0.29, 0.28, 0.28, and 0.26, indicating a downward trend in the environmental quality. (2) The coupling coordination effect of the PWEE system portrayed a low level; the coupling coordination degree (CCD) values were 0.28–0.58, portraying a fluctuating upward trend. The level of CCD increased from low disorder to marginal coordination. (3) The PWEE system’s scissor difference reflects large evolutionary characteristics. The ecological support capacity was not observed until the late stage. We conclude that the PWEE composite system of the region is in a stage of disordered development. These findings significantly bolster the theoretical underpinnings of sustainable development studies, offering essential scientific theories and methodological frameworks for crafting sustainable development policies tailored to urban systems in the Tuha region.

Historic maps showing the temporal distribution of rice fields are important for precision agriculture, irrigation optimisation, forecasting crop yields, land use management and formulating policies. However, mapping rice fields using traditional ground surveys is impractical when high cost, time and labour requirements are considered, and the availability of such detailed records is limited. Although satellite remote sensing appears to be a viable solution, conventional segmentation and classification methods with spectral bands are often unable to contrast the distinct characteristics between rice fields and other vegetation classes. To this end, we explored a novel, Google Earth Engine (GEE) based multi-index random forest (RF) classification approach to map rice fields over two decades. Landsat images from 2000 to 2020 of two Sri Lankan rice cultivation districts were extracted from GEE and a multi-index RF classification algorithm was applied to distinguish the rice fields. The results showed above 80% accuracy for both training and validation, when compared against high spatial resolution Google Earth imagery. In essence, multi-index sampling and RF together synergised the compelling classification accuracy by effectively capturing vegetation, water (ponding) and soil characteristics unique to the rice fields using a single-click approach. The maps developed in this study were further compared against the MODIS land cover type product (MCD12Q1) and the corresponding superior statistics on rice fields demonstrated the robustness of the proposed approach. Future work seeking effective index combinations is recommended, and this approach can potentially be extended to other crop analyses elsewhere.

This study aimed at delineating the shoreline by using the Digital Shoreline Analysis System (DSAS 5.0) tool and detected the changes of land use/land cover (LULC) by Google Earth Engine (GEE) platform. The shoreline is divided into two zones, whereas Zone I covered 87.12km and Zone II possessed 168.05km. According to End Point Rate (EPR), the mean shoreline change rate of Zone I is 3.55m/year and Zone II is −6.84m/year. Likewise, based on Linear Regression Rate (LRR), the mean shoreline change rate of Zone I is 5.46m/year and Zone II is −4.71m/year, respectively. Apart from that, the Net Shoreline Movement (NSM) recorded in Zone I is 109.42m as well as Zone II is −213.25m, which also revealed how much the shoreline has changed during the last 32 years. This study also used the Kalman filter model to forecast the shoreline positions for 20 years. The most destructive signal is that more than 70% of the coastline is vulnerable due to erosion, whereas 6% is highly vulnerable. By contrast, the results of LULC changes demonstrated the increasing trend of water bodies, built up, and agricultural land while vegetation along with bare land is reduced continuously. The overall accuracy is recorded above 88%, and the kappa co-efficient is found above 0.87 for all three years. The outcome of this study will provide fruitful insight into coastal land use management and adaptation measures against the ongoing along with future threats of shoreline changes to coastal ecosystems and livelihoods. [ABSTRACT FROM AUTHOR]

The upper reaches of the Yellow River are critical ecological barriers within the Yellow River Basin (YRB) that are crucial for source conservation. However, environmental challenges in this area, from Longyangxia to Lijiaxia, have emerged in recent years. To assess the ecological environment quality (EEQ) evolution from 1991 to 2021, we utilized remote sensing ecological indices (RSEIs) on the Google Earth Engine (GEE) platform. Spatial autocorrelation and heterogeneity impacting EEQ changes were examined. The results of this study show that the mean value of the RSEIs fluctuated over time (1991: 0.70, 1996: 0.77, 2001: 0.67, 2006: 0.71, 2011: 0.68, 2016: 0.65, and 2021: 0.66) showing an upward, downward, and then upward trend. The mean values of the overall RSEI are all at 0.65 and above. Most regions showed no significant EEQ change during 1991–2021 (68.59%, 59.23%, and 55.78%, respectively). Global Moran's I values (1991–2021) ranged from 0.627 to 0.412, indicating significant positive correlation between EEQ and spatial clustering, and the LISA clustering map (1991–2021) shows that the area near Longyangxia Reservoir shows a pattern of aggregation, dispersion, and then aggregation again. The factor detection results showed that heat was the most influential factor, and the interaction detection results showed that greenness and heat had a significant effect on regional ecosystem distribution. Our study integrates spatial autocorrelation and spatial heterogeneity and combines them with reality to provide an in-depth discussion and analysis of the Longyangxia to Lijiaxia Basin. These findings offer guidance for ecological governance, vegetation restoration, monitoring, and safeguarding the upper Yellow River's ecological integrity. [ABSTRACT FROM AUTHOR]

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for major crops in China, and the visual interpretation process is usually time-consuming and laborious. The sample size also makes it difficult to support the model to learn enough ground features, resulting in poor generalisation ability of the model, which in turn makes the model difficult to apply in fine extraction tasks of large-area crops. In this study, a method to establish a pseudo-label sample set based on the random forest algorithm to train a semantic segmentation model (U-Net) was proposed to perform winter wheat extraction. With the help of the GEE platform, Winter Wheat Canopy Index (WCI) indicators were employed in this method to initially extract winter wheat, and training samples (i.e., pseudo labels) were built for the semantic segmentation model through the iterative process of "generating random sample points—random forest model training—winter wheat extraction"; on this basis, the U-net model was trained with multi-time series remote sensing images; finally, the U-Net model was employed to obtain the spatial distribution map of winter wheat in Henan Province in 2022. The results illustrated that: (1) Pseudo-label data were constructed using the random forest model in typical regions, achieving an overall accuracy of 97.53% under validation with manual samples, proving that its accuracy meets the requirements for U-Net model training. (2) Utilizing the U-Net model, U-Net++ model, and random forest model constructed based on pseudo-label data for 2022, winter wheat mapping was conducted in Henan Province. The extraction accuracy of the three models is in the order of U-Net model > U-Net++ model > random forest model. (3) Using the U-Net model to predict the winter wheat planting areas in Henan Province in 2019, although the extraction accuracy decreased compared to 2022, it still exceeded that of the random forest model. Additionally, the U-Net++ model did not achieve higher classification accuracy. (4) Experimental results demonstrate that deep learning models constructed based on pseudo-labels exhibit higher classification accuracy. Compared to traditional machine learning models like random forest, they have higher spatiotemporal adaptability and robustness, further validating the scientific and practical feasibility of pseudo-labels and their generation strategies, which are expected to provide a feasible technical pathway for intelligent extraction of winter wheat spatial distribution information in the future. [ABSTRACT FROM AUTHOR]

Savanna ecosystems across the globe have experienced substantial changes in their vegetation composition. These changes can be attributed to three main processes: (1) encroachment, which refers to the expansion of woody plants into open areas, (2) thicketization, which is characterized by the growth of sub-canopy woody plants, and (3) disturbance, defined here as the removal of woodland cover due to both natural forces and human activities. In this study, we utilized Landsat surface reflectance data and Sentinel-1 SAR data to track the progression of these process from 1996 to 2022 in the significantly modified Post Oak Savannah ecoregion of Central Texas. Our methodology employs an ensemble classification algorithm, which combines the results of multiple models, to develop a more precise predictive model, along with the spectral-temporal segmentation algorithm LandTrendr in Google Engine (GEE). Our ensemble classification algorithms demonstrated high overall accuracies of 94.3 and 96.5% for 1996 and 2022, respectively, while our LandTrendr vegetation map exhibited an overall accuracy of 80.4%. The findings of our study reveal that 9.7% of the overall area experienced encroachment of woody plants into open area, while an additional 6.8% of the overall area has transitioned into a thicketized state due to the growth of sub-canopy woody plants. Furthermore, 5.7% of the overall area encountered woodland disturbance leading to open areas. Our findings suggest that these processes advanced unevenly throughout the region, resulting in the coexistence of three prominent plant communities that appear to have long-term stability: a dense deciduous shrubland in the southern region, as well as a thicketized oak woodland and open area mosaic in the central and northern regions. The successional divergence observed in these plant communities attests to the substantial influence of human modification on the landscape. This study demonstrates the potential of integrating passive optical multispectral data and active SAR data to accurately map large-scale ecological processes. [ABSTRACT FROM AUTHOR]

Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection. [ABSTRACT FROM AUTHOR]

Real-time assessment of ecological environment quality in arid and semi-arid regions is crucial for the sustainable development of ecological environments in China. In this study, we constructed a topsoil remote sensing ecological index (TRSEI) by coupling five indicators, greenness, wetness, dryness, topsoil grain size, and heat, with the Google Earth Engine (GEE). With the index, we evaluated the ecological environment quality of Wuchuan County from 1990 to 2020, and examined the spatio-temporal variations of ecological environment quality and its driving factors by using univariate linear regression, multiple regression residual analysis, and Hurst index. Results showed that the first principal component of the TRSEI in the study area contributed over 70%, with a mean eigenvalue of 0.148, indicating the effective integration of various ecological indicators by TRSEI. The topsoil grain size index was essential for the assessment of ecological environment quality in arid and semi-arid regions. From 1990 to 2020, the fluctuation range of TRSEI in the study area was between 0.289 and 0.458, showing an overall slight deterioration trend. The ecological environment quality of cropland and de-farming region had improved, with the improved area accounting for 47.9% of the total area. The grassland, barren land, and construction land areas had deteriorated, with the deteriorated area accounting for 52.1% of the total area. In the future, 36.9% of the regions would experience continuous improvement in ecological environment quality, while 41.4% might continue to deteriorate. Human activities were the primary driving factor for the changes in ecological environment quality in arid and semi-arid regions, accounting for 88.6% of the total area. Climate change also had a significant impact, accounting for 11.4% of the total area. The TRSEI could effectively assess the ecological environment quality of arid and semi-arid regions, providing a scientific basis for ecological conservation and construction in these areas. [ABSTRACT FROM AUTHOR]

Propelled by rapid economic growth, the southwestern Shandong urban agglomeration (SSUA) in China has become a crucial industrial hub, but this process has somewhat hindered vegetation growth and environmental quality. Leveraging the functionalities of the Google Earth Engine (GEE) platform, we derived the fractional vegetation coverage (FVC) through the Normalized Difference Vegetation Index (NDVI) and assessed the eco-environmental quality using the Remote Sensing Ecological Index (RSEI). To examine the patterns and shifts in the SSUA, we employed the Theil–Sen median slope estimation, which provided robust estimates of linear trends, the Mann–Kendall trend test to determine the statistical significance of these trends, and the Hurst exponent analysis to evaluate the long-term persistence and predict future changes in the vegetation coverage and eco-environmental quality. Furthermore, to explore the interdependencies between vegetation coverage (VC) and environmental quality, we applied an improved coupling coordination degree model (ICCDM). This model allowed us to assess the co-evolution and synergy between these two factors over the study period, providing comprehensive insights for sustainable urban and ecological planning in the region. The VC and eco-environmental quality improved consistently across most of the SSUA from 2000 to 2020. The dominance of VC had transitioned from being predominantly characterized by relatively high VC to being mainly characterized by high VC. A substantial portion of the SSUA is predicted to experience improvements in its VC and environmental quality moving forward. Furthermore, the coupling coordination relationship between VC and environmental conditions in the southwest of Shandong Province generally exhibited a state of orderly coordinated development. With the passage of time, there was a clear tendency toward expansion in the coupled uncoordinated areas distributed in a network within each regional economic center. Our research unveils the dynamics and spatial-temporal patterns of VC and ecological quality in the southwestern Shandong urban agglomeration (SSUA) and elucidates the coupling and coordination mechanism between these two aspects, which provides theoretical support for understanding the healthy development of vegetation and ecology in urban agglomerations in an industrial context. [ABSTRACT FROM AUTHOR]

The study of Land Use and Landscape Patterns (LULPs) changes and their ecological quality effects in Haikou city under the background of the Hainan Free Trade Port Plan (HFTPP) helps to promote coordinated development between cities and the environment. To date, most research on ecological quality has focused on areas with extremely fragile ecology and/or is related to LULP analysis. There are few studies in the literature focusing on the impact of high-intensity human activities caused by relevant policies on urban LULPs. The purpose of this research was to design a framework that monitors urban ecological security by considering the effect of the developing free trade port. The proposed framework was constructed by integrating multi-temporal Sentinel-2 remote sensing images, night light remote sensing data, digital elevation model (DEM) data, and spectral index features such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), bare soil index (BSI), and normalized intertidal mangrove index (NIMI), as well as analytical approaches such as the land use transfer matrix, land use dynamic degree, land use degree and transfer matrix, land use gravity center measurement, and landscape pattern index. The framework takes advantage of the Google Earth Engine (GEE) cloud platform and was applied to a highly developed Haikou city, the capital of Hainan province. Maps of brightness (SBI), greenness (GVI), and humidity (WET) were created annually from 2016 to 2021, enabling detailed ecological environment quality evaluation and analysis. The advantages of this study are (1) reliable land cover results obtained automatically and quickly; (2) the strong objectivity of the quantitative research on landscape patterns and land use; and (3) deep integration with free trade port policies. Through the research on the ecological quality problems caused by the change in LULP in the study area, the research results show that, from 2016 to 2021, the spatial distribution of land use and landscape pattern in Haikou city had been constantly changing; the area of construction land has decreased, with most of it having been converted into forest land and farmland; the gravity center of the building land has moved to the northwest; the degree of landscape fragmentation has decreased and the heterogeneity of landscape distribution has increased; the free trade port policies have promoted Haikou's economic development and ecological civilization construction; and finally, Haikou's ecological environmental quality has improved significantly. [ABSTRACT FROM AUTHOR]

Alpine grasslands, a crucial component of the Qinghai–Tibet Plateau, play a vital role in maintaining ecological barriers and facilitating sustainable development, and the exact stability change is also the key to coping with climate change and implementing ecological protection projects. The purpose of this study was to identify the spatial and temporal distribution of multi-stage alpine grassland and explore its inter-annual distribution and growth stability. The Guoluo Tibetan Autonomous Prefecture, China (hereinafter referred to as Guoluo), where alpine grassland is widely distributed, was selected as the research area. Long-term stable grassland samples constructed using the Mann–Kendall–Sneyers mutation test method were analyzed alongside random forest classification to identify multi-stage grassland distribution trends from 1990 to 2020. Based on the Fractional Vegetation Cover (FVC) and coefficient of variation (Cv), spatial and temporal changes in grassland quality and their driving factors were discussed. The results show the following: (1) Remote sensing grassland extraction, based on the establishment of long-term stable grassland samples and random forest classification, demonstrated high accuracy and reliability, with OA and Kappa coefficients consistently above 0.89 and 0.77, and PA and UA maintained consistently at approximately 0.9. (2) The distribution of grassland in Guoluo corresponded to the spatial patterns determined by the natural geographical environment, showing a gradual trend from high-cover grassland in the southeast to low-cover grassland in the northwest. The proportion of medium and high-cover grasslands slightly increased, indicating an improvement in grassland quality. However, the encroachment and degradation caused by human activities and climate change resulted in a slight decrease in the proportion of grassland area compared with 1990. (3) Despite the overall grassland ecosystem still having relative stability, local grassland quality changes dramatically, mainly in the north of Maduo County. And significant fluctuations in the area of grassland quality were noted over the last two decades, suggesting potential degradation in ecosystem stability. Climate change and human activities were identified as primary drivers of these changes. Climate change is dominant in the alpine region. The low-warming region is dominated by human activities. These findings offer essential insights for the planning and implementation of alpine grassland ecosystem protection and restoration initiatives and also have important value for exploring the evolution law of alpine grassland ecosystems. [ABSTRACT FROM AUTHOR]

Introduction: Landslides are known to be one of the most frequent types of geological disasters. However, there is not an established method for large- scale, rapid, and high-precision landslide extraction. The quantitative impact of environmental changes on landslide development is also not well understood, which hinders accurate assessments and decision-making in environmental and disaster response. The polar regions, including the Antarctic, the Arctic, and the Tibetan plateau (TP), sensitive to global environmental changes, are significantly affected by global warming. This leads to extensive landslide development, particularly in the southern TP. This research focuses on new landslides in the southern TP, exploring extraction methods and the relationship between landslides and environmental factors. Methods: Utilizing the Google Earth Engine (GEE) and an improved Otsu threshold segmentation algorithm, we processed remote sensing images with 10 m resolution to identify landslide areas. The proposed Normalized Landslide Bare-soil Separation Index (NDLBSI) achieved an 87% pre-extraction accuracy in extracting landslides from Sentinel-2 images from 2019 to 2023. For the pre-extraction results, manual interpretation and correction were carried out, and a model correlating annual landslide changes with environmental factors was established based on least squares multivariate statistical methods. Results: Results show that a significant increase in landslide areas in the southern TP over the past 5 years, correlating with the watershed-wide increase in annual average temperature and vegetation cover, along with a decrease in snow cover area. Discussion: These changes could affect soil and rock moisture, influencing soil stability and landslide occurrence. The study provides valuable insights for large-scale landslide detection and understanding the environmental factors influencing landslides, which is of some significance for landslide hazards early warning. [ABSTRACT FROM AUTHOR]

Robust Satellite Technique (RST) was applied to detect small-scale landslides along electrical lines in Sicily, Italy. To this end, electrical poles were selected as targets within the study area. The methodology, implemented in Google Earth Engine (GEE) environment, exploits the Copernicus Sentinel-2 platform to identify anomalous land cover variation, in terms of Normalized Difference Vegetation Index (NDVI), possibly related to small displacements affecting electric poles. Since the applied methodology is based on land cover change, dense vegetation plays an important role in detecting small-scale landslides. Therefore, we targeted months with the highest vegetation density, such as February, March, and April from 2016–2023. The results obtained reveal that out of the five targeted electrical poles, four of them exhibited anomalies > 2-sigma indicating significant changes in land cover possibly related to local ground movement as confirmed by aerial photos collected in the period 2015–2023. Our findings reveal anomalies of −2.17 and −2.36 on 7/17/2017 and 9/05/2017 for pole 1. For pole 2, the results show an anomaly of −2.02 on 8/11/2018. The results also indicate anomalies of −4.40 and −2.99 on 7/09/2021 and 9/27/2022 for pole 3. For pole 4, the findings show an anomaly of −3.10 on 1/18/2019.

Flooding is considered a significant natural hazard in Malaysia. climatic conditions and anthropogenic activities are gradually triggering floods in different parts of Malaysia including the study area, i.e. Shah Alam municipality. In the southern region of Shah Alam, where the Klang River runs, November/December and March/April saw the most flooding. To create a mapping of Shah Alam’s flood potential, the Analytic Hierarchy Process (AHP) and Fuzzy-AHP methods were applied with the twelve criteria. The Sentinel 1 Synthetic Aperture Radar (SAR) datasets and the Google Earth Engine (GEE) platform were used to compute the flood inundation area. Based on the twelve criteria, flood potential zones were divided into five categories such as very high potential (11.58 km2 – AHP and 10.35 km2 – F-AHP) to very low potential (6.49 km2 – AHP and 39.21 km2 – F-AHP), respectively. The most affected areas are the southern part (near Klang River), the central part, and some parts of the northern zone in Shah Alam. The near real-time flood mapping used for previous flood-affected area identification in Shah Alam, Malaysia. Local government and relevant stakeholders can benefit from using this flood potential mapping to reduce the flood effects at Shah Alam via appropriate planning.

Energy demand fluctuations due to low probability high impact (LPHI) micro-climatic events such as urban heat island effect (UHI) and heatwaves, pose significant challenges for urban infrastructure, particularly within urban built-clusters. Mapping short term load forecasting (STLF) of buildings in urban micro-climatic setting (UMS) is obscured by the complex interplay of surrounding morphology, micro-climate and inter-building energy dynamics. Conventional urban building energy modelling (UBEM) approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale. Reduced order modelling, unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization, limit the inter-building energy dynamics consideration into UBEMs. In addition, mismatch of resolutions of spatio–temporal datasets (meso to micro scale transition), LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations. This review aims to direct attention towards an integrated-UBEM (i-UBEM) framework to capture the building load fluctuation over multi-scale spatio–temporal scenario. It highlights usage of emerging data-driven hybrid approaches, after systematically analysing developments and limitations of recent physical, data-driven artificial intelligence and machine learning (AI-ML) based modelling approaches. It also discusses the potential integration of google earth engine (GEE)-cloud computing platform in UBEM input organization step to (i) map the land surface temperature (LST) data (quantitative attribute implying LPHI event occurrence), (ii) manage and pre-process high-resolution spatio–temporal UBEM input-datasets. Further the potential of digital twin, central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored. It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a crucial link in water circulation. It plays an important role in the global water cycle and surface energy balance, and has significant impacts on climate, ecosystems, and water resources management. Therefore, the quality of evapotranspiration data is crucial for the precise management of global water resources. This study conducted accuracy validation and spatiotemporal comparison of three ET products in the Northern Hemisphere, selecting the ET products that is more suitable for the Northern Hemisphere, providing suggestions for strengthening the combination of remote sensing and ground observation research. Using the monthly average measured data from FluxNet2015 flux sites to verify three ET products, it was found that PML_ V2 product has the highest accuracy in the Northern Hemisphere, followed by GLDAS, and finally MOD16A2, with correlation coefficients R of 0.66, 0.57, and 0.56, respectively; The root mean square error (RMSE) is 2.46, 5.68, and 12.42 mm/month, respectively; The average biases are 14.36%, 16.86%, and 35.02%, respectively. The GLDAS ET product has the ability to monitor daily scale ET, and the consistency between the daily average estimated value and the measured value at the flux tower site is high. The correlation coefficient R is 0.74, and the RMSE and Bias are 1.62mm/day and 27.90%, respectively. Overall, on the time scale, all three ET products can simulate the seasonal changes in the Northern Hemisphere, with higher summer evapotranspiration and lower winter evapotranspiration. The three ET products in summer all have overestimation phenomena on different land cover types, and the simulation results in other seasons are better than the ground observation values. Among them, MOD16A2 performs the worst, and the overestimation phenomenon is the most obvious. In addition, during the period from 2001 to 2020, except for arid areas, the spatial distribution of the three ET products was relatively consistent in most regions, with a correlation coefficient R greater than 0.6. This study provides scientific recommendations for selecting suitable ET data sources for conducting evapotranspiration studies in the Northern Hemisphere by evaluating the uncertainty and product quality of different ET products. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal variations and driving factors of regional vegetation coverage is crucial for developing scientific plans for ecological environment protection and maintaining regional ecological balance. Based on the Google Earth Engine ( GEE) platform and using Landsat Collection 2 data, we investigated the spatio-temporal variation and driving factors of vegetation coverage in Shanxi Province, China, from 1990 to 2020, by employing methods such as pixel-based binary model, trend analysis, zonal statistics, and geodetector. The results showed that vegetation coverage in Shanxi Province showed a fluctuating upward trend from 1990 to 2020. Vegetation coverage in 44.4% of this region had been significantly improved, and the area with significant degradation accounted for 7.4%. Vegetation coverage in Shanxi Province was positively correlated with elevation, slope, and mountain terrain relief. The area proportion of vegetation coverage growth was the highest in the plateau and hilly regions. Factor detection results showed that land use type, landform type, annual average precipitation, and soil type were the main influencing factors of the spatial differentiation of vegetation coverage in Shanxi Province. Results of the interaction detection showed that the interaction between driving factors all showed enhancement. The interaction between natural factors showed a downward trend, while the interaction results of social factors showed an upward trend, reflecting that the impacts of human activities on vegetation coverage in Shanxi Province were gradually increasing. [ABSTRACT FROM AUTHOR]

Highly accurate data on urban and rural settlement (URS) are essential for urban planning and decision-making in response to climate and environmental changes. This study developed an optimal random forest classification model for URSs based on spectral–topographic–radar polarization features using Landsat 8, NASA DEM, and Sentinel-1 SAR as the remote-sensing data sources. An optimal urban and rural settlement boundary (URSB) extraction technique based on morphological and pixel-level statistical methods was established to link discontinuous URSs and improve the accuracy of URSB extraction. An optimal random forest classification model for URSs was developed, as well as a technique to optimize URSB, using the Google Earth Engine (GEE) platform. The URSB of Xining, China, in 2020 was then extracted at a spatial resolution of 30 m, achieving an overall accuracy and Kappa coefficient of 96.21% and 0.92, respectively. Compared to using a single spectral feature, these corresponding metrics improved by 16.21% and 0.35, respectively. This research also demonstrated that the newly constructed Blue Roof Index (BRI), with enhanced blue roof features, is highly indicative of URSs and that the URSB was best extracted when the window size of the structural elements was 13 × 13. These results can be used to provide technical support for obtaining highly accurate information on URSs. [ABSTRACT FROM AUTHOR]

The present study attempts to identify the flood footprint of river Keleghai, a tributary of river Haldi, in West Bengal, India. Keleghai basin is known for recurrent flooding that causes severe damage to the socioeconomic infrastructure. So far, no attempt has been made for the identification of flood inundation footprints and flood risk zones in Keleghai river basin through remote sensing and multi-criteria decision-making process. Initially, a flood susceptibility or vulnerability map has been prepared, and secondly, flood footprints have been identified in the said river basin. For the preparation of flood vulnerability map with the help of the analytical hierarchy process (AHP), the elevation, slope, rainfall, normalised difference vegetation index (NDVI), land use and land cover (LULC) and distance from river and topographical wetness index (TWI) of the concerned river basin have been used. To prepare the flood footprints synthetic aperture radar (SAR), data have been processed on Google Earth Engine (GEE) platform. The result shows that more than 50% of the basin area belongs to high risk zone, and the other 40% comes under the moderate risk category. The central, northern and eastern parts of the basin present the highest susceptibility to flood hazard. This area is characterised by moderate-to-low elevation, gentle slope, moderate rainfall and less vegetative cover. This outcome can effectively be utilised in hazard management purpose for Keleghai as well as other river basins. This study will help in identifying the most vulnerable zones of the basin in terms of flood hazard assessment. On the other hand, correlating the empirical model with the real world data will provide excellent opportunity to testify the applicability of the model in decision-making purpose that could lead to a way of resilience. [ABSTRACT FROM AUTHOR]

Cuba is struggling with a growing environmental problem: the uncontrolled spread of the allochthonous weed species marabou (Dichrostachys cinerea) throughout the country. Over the last 70 years, marabou has become a formidable invasive species that poses a threat to Cuban biodiversity and agricultural productivity. In this paper, we present a free and affordable method for regularly mapping the spatial distribution of the marabou based on the Google Earth Engine platform and ecological surveys. To test its accuracy, we develop an 18-year remote sensing analysis (2000–2018) of marabou dynamics using the Valle de los Ingenios, a Cuban UNESCO World Heritage Site, as an experimental model. Our spatial analysis reveals clear patterns of marabou distribution and highlights areas of concentrated growth. Temporal trends illustrate the aggressive nature of the species, identifying periods of expansion and decline. In addition, our system is able to detect specific, large-scale human interventions against the marabou plague in the area. The results highlight the urgent need for remedial strategies to maintain the fragile ecological balance in the region. [ABSTRACT FROM AUTHOR]

Eucalyptus plantations are expanding rapidly in southern China owing to their short rotation periods and high wood yields. Determining the plantation dynamics of eucalyptus plantations facilitates accurate operational planning, maximizes benefits, and allows the scientific management and sustainable development of eucalyptus plantations. This study proposes a sliding-time-window change detection (STWCD) approach for the holistic characterization and analysis of eucalyptus plantation dynamics between 1990 and 2019 through dense Landsat time-series data. To achieve this, pre-processing was first conducted to obtain high-quality reflectance data and the monthly composite maximum normalized-difference vegetation index (NDVI) time series was determined for each Landsat pixel. Second, a sliding time window was used to segment the time series and obtain the NDVI change characteristics of the subsequent segments, and a sliding time window-based LandTrendr change detection algorithm was applied to detect the crucial growth or harvesting phases of the eucalyptus plantations. Third, pattern-matching technology was adopted based on the change detection results to determine the characteristics of the eucalyptus planting dynamics. Finally, we identified the management history of the eucalyptus plantations, including planting times, generations, and rotation cycles. The overall accuracy of eucalyptus identification was 90.08%, and the planting years of the validation samples and the planting years estimated by our algorithm revealed an apparent correlation of R2 = 0.98. The results showed that successive generations were mainly first- and second-generations, accounting for 75.79% and 19.83% of the total eucalyptus area, respectively. The rotation cycles of the eucalyptus plantations were predominantly in the range of 4–8 years. This study provides an effective approach for identifying eucalyptus plantation dynamics that can be applied to other short-rotation plantations. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the trend in land surface temperature (LST) over time using the entire archive of the available cloud-free Landsat images from 1986 to 2022 for Jordan and its nine local climate zones (LCZs). Two primary datasets were used (i) Landsat-5; -8 imagery, and (ii) map of LCZs of Jordan. All LST images were clipped, preprocessed, and checked for cloud contamination and bad pixels using the quality control bands. Then, time-series of monthly LST images were generated through compositing and mosaicking processes using cloud computing functions and Java scripts in Google Earth Engine (GEE). The Mann–Kendall (MK) test and Sen's slope estimator (SSE) were used to detect and quantify the magnitude of LST trends. Results showed a warming trend in the maximum LST values for all LCZs while there was annual fluctuation in the trend line of the minimum LST values in the nine zones. The monthly average LST values showed a consistent upward trajectory, indicating a warming condition, but with variations in the magnitude. The annual rate of change in LST for the LCZs showed that the three Saharan zones are experiencing the highest rate of increase at 0.0184 K/year for Saharan Mediterranean Warm (SMW), 0.0185 K/year for Saharan Mediterranean Cool (SMC), and 0.0169 K/year for Saharan Mediterranean very Warm (SMvW), indicating rapid warming in these regions. The three arid zones came in the middle, with values of 0.0156 K/year for Arid Mediterranean Warm (AMW), 0.0151 for Arid Mediterranean very Warm (AMvW), and 0.0139 for Arid Mediterranean Cool (AMC), suggesting a slower warming trend. The two semi-arid zones and the sub-humid zone showed lower values at 0.0138, 0.0127, and 0.0117 K/year for the Semi-arid Mediterranean Cool (SaMC), Semi-arid Mediterranean Warm (SaMW) zones, and Semi-humid Mediterranean (ShM) zones, respectively, suggesting the lowest rate of change compared to other zones. These findings would provide an overall understanding of LST change and its impact in Jordan's LCZs for sustainable development and water resources demand and management. [ABSTRACT FROM AUTHOR]

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Savanna ecosystems across the globe have experienced substantial changes in their vegetation composition. These changes can be attributed to three main processes: (1) encroachment, which refers to the expansion of woody plants into open areas, (2) thicketization, which is characterized by the growth of sub-canopy woody plants, and (3) disturbance, defined here as the removal of woodland cover due to both natural forces and human activities. In this study, we utilized Landsat surface reflectance data and Sentinel-1 SAR data to track the progression of these process from 1996 to 2022 in the significantly modified Post Oak Savannah ecoregion of Central Texas. Our methodology employs an ensemble classification algorithm, which combines the results of multiple models, to develop a more precise predictive model, along with the spectral–temporal segmentation algorithm LandTrendr in Google Engine (GEE). Our ensemble classification algorithms demonstrated high overall accuracies of 94.3 and 96.5% for 1996 and 2022, respectively, while our LandTrendr vegetation map exhibited an overall accuracy of 80.4%. The findings of our study reveal that 9.7% of the overall area experienced encroachment of woody plants into open area, while an additional 6.8% of the overall area has transitioned into a thicketized state due to the growth of sub-canopy woody plants. Furthermore, 5.7% of the overall area encountered woodland disturbance leading to open areas. Our findings suggest that these processes advanced unevenly throughout the region, resulting in the coexistence of three prominent plant communities that appear to have long-term stability: a dense deciduous shrubland in the southern region, as well as a thicketized oak woodland and open area mosaic in the central and northern regions. The successional divergence observed in these plant communities attests to the substantial influence of human modification on the landscape. This study demonstrates the potential of integrating passive optical multispectral data and active SAR data to accurately map large-scale ecological processes.

The grassland ecosystems of Xilingol, China, characteristically part of the vast Eurasian steppe, are currently facing two challenges: natural variations and anthropogenic stress, which are leading to significant degradation. This article harnesses a sequence of high-resolution (30 m) land cover and greenness trend maps derived from multiyear Landsat imagery to describe these ecologically critical shifts over a landscape spanning more than 200 000 km2. By leveraging random forest models complemented with phenological patterns, we streamlined the generation of land cover maps, securing overall accuracies upwards of 94% across eight categorical classifications, as substantiated by rigorous validation. Between 1985 and 2000, there were significant changes in the landscape, such as an increase in farmland of about 4.0 × 103 km2, mostly at the expense of natural grasslands and wetlands. Throughout the study period, an ongoing trend is the noticeable shrinkage of water bodies with the biggest reduction of wetlands reported between 1995 and 2015. Open-pit mining regions began to increase with the start of the 21st century, and from 1985 to the present, urbanization drove the growth of impervious surfaces. These maps offer powerful visual representations of major land use changes, capturing the expansion of surface mining, the retreat of wetland areas, and the growth of urban areas. Therefore, our findings compose an essential part in the documentation and comprehension of the details of wetland reduction, cropland intensification, surface water decline, and rapid urban growth, providing crucial information to conservationists and policymakers working toward sustainable ecosystem management.

Eucalyptus, valued for its rapid growth and economic potential, has been widely introduced in China to address timber demands while conserving natural forests. Precisely estimating the spatiotemporal expansion of short-rotation eucalyptus plantations is crucial for evaluating their ecological and social value and formulating effective sustainable forestry policies. Medium-resolution satellite images, such as Landsat data, offer a cost-effective tool for large-scale forest mapping compared with the traditional forest inventories. This study used pixel-level time-series analysis to identify annual eucalyptus plantation distributions across Guangxi, China, from 2004 to 2019, based on the standard temporal vegetation index curves derived from the characteristics of short-rotation and fast-growing eucalyptus. Furthermore, an image segmentation method, coupled with an empirical relationship linking patch-level landscape indices to optimal thresholds, was employed to eliminate isolated pixels and reduce omission errors arising from the above time-series analysis. The established thresholds increased the accurate identification of eucalyptus patches within segments. Our proposed eucalyptus detection algorithm achieved an overall accuracy exceeding 80%, demonstrating its effectiveness. The analysis revealed eucalyptus plantations increased from 0.42 × 106 ha in 2004 to 2.47 × 106 ha in 2019, exhibiting a pronounced northward expansion. Initially concentrated in upland areas, plantations subsequently expanded into flatter terrains, raising concerns about potential agricultural conflicts. Annual eucalyptus plantation maps offer critical information for sustainable forest management and policymaking. This study highlights the potential of medium-resolution satellite data and time-series analysis for robust and cost-effective monitoring of annual short-rotation timber forest expansion dynamics over large scales.

Owing to the rapid urbanization combined with global climate change, dramatic land-use change in coastal watersheds is occurred, which, in turn, cause the evolution of landscape patterns and threaten the valuable but fragile ecosystem. The coastal zone is characterized by severe cloud cover, frequent changes in land type, and fragmented landscape, so it is challenging to carry out the accurate landscape patterns analysis. To address this problem, this study employed the Google Earth engine cloud platform, Landsat time series, and landscape metrics in the Fragstats model to develop a comprehensive framework that integrates landscape pattern metrics and spatial analysis methods, considering both type level and landscape level. The Hangzhou Bay region was selected for conducting land-use classification and landscape patterns analysis. The results indicate that, during nearly four decades, with the continuous expansion of the urban, the urbanization process has accelerated, and the construction land has expanded by 6.93 times. By analyzing the evolution of landscape patterns, Hangzhou Bay heightened landscape fragmentation and patch shapes became more irregular caused by a trend toward intensified urbanization. The Shannon's diversity index continuously increased from 1.14 to 1.51, while the contagion index consistently decreased from 59.83% to 42.21%, suggesting an increase in land-use diversity, reduced aggregation, and extension tendencies between land patches, along with a decrease in the proportion of highly connected patches within the landscape. This study is anticipated to provide robust evidence for the rational planning of future development directions and the deployment of landscape ecological spatial services.

Changes in land use and land cover (LULC) are critical for evaluating global spatiotemporal trends, especially regarding climate change and urbanization. This study investigates the dynamics of Landsat surface temperature (LST) in response to LULC changes and their effects on the seasonal microclimate in Kasur District, Pakistan. Using the Google Earth Engine platform, we employed a random forest algorithm to detect LULC changes (cropland, forest, built-up, fallow, barren, and water) and analyze seasonal spectral indices from Landsat imagery for 1988, 2002, and 2022. Significant LULC changes were observed, including a 9.8% increase in built-up areas, a 4.2% decrease in cropland, and a 1.4% decrease in forested areas, linked to urban heat island effects and population growth. Additionally, there was a 2.7% increase in fallow and open land, contributing to the district's impervious surface area. Significant correlations (p < 0.001) were found between LST and spectral indices—normalized difference vegetation index, enhanced vegetation index, and normalized difference built index (NDBI)—ranging from 0.7 to 0.8 in both winter and summer. In summer, the maximum LST increased from 43 °C in 1988 to 44 °C in 2002, with a linear correlation (R²) increase from 0.57 to 0.75 and a polynomial correlation (R²) increase from 0.63 to 0.76 with NDBI from 1988 to 2022. Understanding these dynamics is crucial as LULC changes and the resulting temperature variations have significant implications for local climate, agriculture, and human health. This study underscores the need for effective LULC policies to mitigate impacts, protect vegetation cover, and ensure sustainable land management. These findings provide valuable insights for policymakers and urban planners aiming to balance development with environmental sustainability.

As a key part of coastal wetlands, the accurate distribution data of tidal flats is of great significance for the protection and management of coastal ecosystems. Current techniques for extracting tidal flats from remote sensing imagery predominantly focus on detecting the distribution range of temporary water bodies. This requires a higher degree of synchronicity between high and low tides and satellite transit time, so the existing extraction methods often result in low spatial accuracy in irregular tidal areas. To address these limitations, we selected the Yellow River Delta (YRD) as our study area and utilized the Google Earth Engine (GEE) cloud computing platform combined with time-series Landsat images to propose a new method for extracting tidal flats affected by irregular tides based on surface humidity differences. This method successfully identified the tidal flats of the YRD and was validated temporally within the study area. Additionally, promotion verification was carried out in other coastal areas affected by irregular tides, revealing strong levels of universality and robustness. Notably, in the study area, the extraction method’s accuracy improved by about 25% compared with other methods. Consequently, tidal flat mapping based on this method can provide critical data support for ecological protection of coastal zones and assist in the formulation of sustainable coastal zone management policies.

Riparian habitats are the most crucial yet the most fragile ecosystems, focal to safeguarding both the aquatic and terrestrial regions. Technology such as remote sensing, now powered by cloud-based server-side processing of high-resolution satellite imagery, and Big Data analytics, such as Google Earth Engine (GEE) combined with uncrewed aerial vehicles (UAVs), have accentuated ecological monitoring of natural habitats. This study leverages a nested approach to remote sensing, combining satellite data and UAV imagery to evaluate the present condition of riparian habitats along the Ganga River in the Upper Gangetic Plains. We used GEE to analyze Sentinel data and identify critical habitats, encompassing wetlands, grasslands, scrublands, plantations, river islands, and riparian forests in the study area. Strategic locations covering 291 km2 area were delineated, and over 1000 patches of 1 ha were isolated, with the largest patch of 23.99 km2 in Haiderpur. Furthermore, UAV-based data were collected for key identified regions. The status of a total of 284 field surveyed points was categorized as 29 intact grassland patches, 87 good habitat patches, 25 patches recently converted to agriculture, and 60 patches being converted to agriculture, remaining plantations, and waterbodies. UAV-based raster thematic maps of four key habitat regions generated using object-based image analysis classification found a promising approach for high-precision riparian habitat mapping, monitoring, and management, offering data quality, cost optimization, and time savings with an overall accuracy of 98% and kappa coefficient 0.97. UAV are, thus, effective tools for reach-level assessment of freshwater habitats, especially of smaller stream networks, retaining fine-scale riverscape information of the mosaic of land use and vegetation types.

The frequency of wildfires is increasing worldwide, contributing to a third of forest loss over the last two decades. Tracking burned area progression using traditional optical remote sensing is hindered by cloud and smoke coverage. Therefore, this research employs multitemporal synthetic aperture radar (SAR) satellite data, which are not susceptible to atmospheric effects. Focusing on four Greek wildfires in 2021, the research utilizes unsupervised k-means clustering on bitemporal and multitemporal SAR polarimetric features. The impact of input feature smoothing with varying moving kernel window sizes was assessed to improve accuracy. The use of these smoothed features led to a substantial improvement in accuracy across all four areas examined, while a window size of 19 × 19 was chosen as the right balance between preserving fine details and minimizing speckle. Furthermore, adding a filter after clustering to remove areas smaller than 2 ha led to additional improvements in accuracy, especially in commission error. The results using the defined settings revealed F1 scores of 0.75–0.88, overall accuracy of 81%–94%, and omission/commission errors of 33%–16% and 14%–3%, respectively. Challenges were observed in regions characterized by a substantial share of agricultural areas, while terrain effects revealed no substantial effects on the results. The assumption that the SAR will be sensitive mainly to bigger structural changes was proved in the visual validation using high-resolution imagery. In addition, a Google Earth Engine toolbox “Sentinel-1 Burned Area Progression” was developed using the presented methodology and is freely available for the scientific community on GitHub.

The composition and dynamics of wetland plant communities play a critical role in maintaining the functionality of wetland ecosystems and serve as important indicators of wetland degradation and restoration. Accurately identifying wetland plant communities using remote sensing techniques remains challenging due to the complex environment and cloud contamination. Here, we applied a sample migration method based on change vector analysis and a random forest (RF) classifier incorporating SHapley Additive exPlanations (SHAP) to explore the spatiotemporal changes of wetland plant communities in the western Songnen Plain of China between 2016 and 2022, and to better understand the decision logic of the RF model. Our work achieved accurate annual wetland classification at the community scale, with an average overall accuracy of 89.5% and an average kappa coefficient of 0.87. Our analysis revealed different spatiotemporal change characteristics of wetland plant communities in the western Songnen Plain and three national nature reserves. The SHAP model showed that MOS_IRECI is the most important feature determining the prediction results of the RF model, and the importance of the features differs at global and local levels. This study confirms the feasibility of annual dynamic monitoring of wetland plant communities at a regional scale. The results are expected to provide a reference for the fine and sustainable management of wetland resources in the western Songnen Plain, as well as valuable data support for related wetland ecology research.

The United Nations has proposed Sustainable Development Goal 15 (SDG15), which emphasizes the importance of sustainable land development. This study aims to use remote sensing data to build a spectral index feature dataset based on Google Earth Engine (GEE) platform, dig deep spectral features of ground objects, and use a random forest (RF) algorithm to extract land use type distribution data in the Bohai Rim region from 2000 to 2020. Meanwhile, combined with a land use transfer map and landscape pattern index, the spatio-temporal pattern of land use was quantitatively analyzed. Finally, the sustainable development level of land was evaluated quantitatively from three aspects: forest resource sustainability, wetland resource sustainability, and land system sustainability. The results show that the overall accuracy and kappa coefficient of land use classification achieved by GEE and the RF algorithm were 0.94 and 0.92, respectively. From 2000 to 2020, the main land use type in the study area was cropland, accounting for 33% of the total, and the impervious has significantly expanded, increasing by 9588.01 km2, mainly from cropland, barren, and water. In relation to SDG15, forest resources exhibited poor stability, wetland resources demonstrated a steady recovery, SDG15.3.1 revealed that the problems of land degradation exist in various provinces and cities around the Bohai Sea, and the stability of the land system was poor between 2000 and 2020. This article can provide a valuable reference for land use management and ecological remediation in the Bohai Rim region.

Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area. HIGHLIGHTS The combination of Sentinel-1 dual-polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests is shown to provide better classification results of mangroves.; Machine learning algorithms such as random forest and support vector machine are used for classification, with a comparison of their performance.; This study emphasizes the importance of feature selection for the accurate classification of mangrove forests.;

The different land uses in the irrigation water area of the eleven streams of Khansar city during 1969, 1995, 2014, and 2019 have been identified and their area has been determined by analysis of the aerial photos as well as the satellite images of QuickBird, and Landsat in the Google Earth Engine (GEE) environment. Then, the net and gross areas of land under irrigation water, area of non-agricultural land uses, location and area of agricultural land uses under irrigation of the streams are separated according to the type of agricultural activity (orchard or farmland) for each stream. Aerial photos of the study area dated 1969 are the basis for the assessment of agricultural conditions before the law of Fair Water Allocation. The results showed that non-agricultural and particularly urban and residential land uses have increased since 1969. In other words, land use of part of the agricultural lands has been changed to residential and urban land uses. Despite the decreasing trend of agricultural land uses in the last 50 years, these changes have not been the same between the farm and orchard land uses and the area under orchard plantation showed an increasing trend. These changes have dramatically influenced on water demand of the streams. Land use has not significantly changed from 2014 to 2019 and no noticeable change was observed in the area of the agricultural and green agricultural lands as well as the percentage of the orchard and farming lands during these years. The results of this study confirmed the significant changes in agricultural land use and consequently water consumption in the district of the eleven streams of Khansar in recent decades. This study also highlighted the high efficiency of the combined use of aerial photos, spectral satellite images with medium spatial resolution, and visible spectral satellite data with high spectral resolution, as well as using cloud system capabilities of the Google Earth Engine to study changes in agricultural land uses during last decades.

With the emergence of multisource data and the development of cloud computing platforms, accurate prediction of event-scale dust source regions based on machine learning (ML) methods should be considered, especially accounting for the temporal variability in sample and predictor variables. Arid Central Asia (ACA) is recognized as one of the world’s primary potential sand and dust storm (SDS) sources. In this study, based on the Google Earth Engine (GEE) platform, four ML methods were used for SDS source prediction in ACA. Fourteen meteorological and terrestrial factors were selected as influencing factors controlling SDS source susceptibility and applied in the modeling process. Generally, the results revealed that the random forest (RF) algorithm performed best, followed by the gradient boosting tree (GBT), maximum entropy (MaxEnt) model and support vector machine (SVM). The Gini impurity index results of the RF model indicated that the wind speed played the most important role in SDS source prediction, followed by the normalized difference vegetation index (NDVI). This study could facilitate the development of programs to reduce SDS risks in arid and semiarid regions, particularly in ACA.