Search Results (5,589)

Given global climate change and rapid land cover changes due to human activities, accurately identifying, extracting, and monitoring the long-term evolution of wetland resources is profoundly significant, particularly in areas with fragile ecological conditions. Gansu Province, located in northwest China, contains all wetland types except coastal wetlands. The complexity of its wetland types has resulted in a lack of accurate and comprehensive information on wetland changes. Using Gansu Province as a case study, we employed the GEE platform and Landsat time-series satellite data, combining high-quality sample datasets with feature-optimized multi-source feature sets. The random forest algorithm was utilized to create wetland classification maps for Gansu Province across eight periods from 1987 to 2020 at a 30 m resolution and to quantify changes in wetland area and type. The results showed that the wetland mapping method achieved robust classification results, with an average overall accuracy (OA) of 96.0% and a kappa coefficient of 0.954 across all years. The marsh type exhibited the highest average user accuracy (UA) and producer accuracy (PA), at 96.4% and 95.2%, respectively. Multi-source feature aggregation and feature optimization effectively improve classification accuracy. Topographic and seasonal features were identified as the most important for wetland extraction, while textural features were the least important. By 2020, the total wetland area in Gansu Province was 10,575.49 km², a decrease of 4536.86 km² compared to 1987. The area of marshes decreased the most, primarily converting into grasslands and forests. River, lake, and constructed wetland types generally exhibited an increasing trend with fluctuations. This study provides technical support for wetland ecological protection in Gansu Province and offers a reference for wetland mapping, monitoring, and sustainable development in arid and semi-arid regions. Full article

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPP_HLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPP_MODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPP_EC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide. Full article

Migration-induced land degradation is a challenging environmental issue in Sub-Saharan Africa. The need for expansion due to urban development has raised the question of effective sustainable measures. Understanding migration and land degradation links is paramount for sustainable urban development and resource use. This is particularly true in Nigeria, where elevated migration levels frequently result in accelerated land degradation due to urban expansion. Given the need to understand the impact of migration on land degradation in the Savannah Region of Nigeria (SRN), this study introduces a novel approach by integrating remote sensing data (NDVI, NDBI) with local community perceptions (mixed-methods approach) to assess the impact of migration on land degradation in four migration destination communities located in two local government areas (LGAs) (Sabon Gari East and Sabon Gari West of Fagge LGA; Zuba and Tungamaje of Gwagwalada LGA). We conducted focus group discussions and a semi-structured survey with 360 household heads to obtain a comprehensive view of perceptions. Our findings revealed that 41.1% and 29.5% of the respondents agreed and strongly agreed that migration significantly contributes to land degradation. We analysed the spatiotemporal patterns of the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Built-Up Index (NDBI) acquired from Landsat 8 datasets for 2014 to 2023. While increasing NDBI values were observed in all communities, a slight decrease in NDVI was noted in Sabon Gari East and Tungamaje. Our analyses highlighted activities leading to land degradation such as land pressure due to built-up expansion at Sabon Gari East, Sabon Gari West, and Tungamaje, and deforestation at Zuba. Based on the varying challenges of migration-induced land degradation, we recommend adequate community participation in suggesting targeted interventions and policies to foster various adaptive capacities and sustainable environments within SRN communities and Sub-Saharan Africa. Full article

Agricultural droughts in South Africa, particularly in the Amahlathi Local Municipality (ALM), significantly impact socioeconomic activities, sustainable livelihoods, and ecosystem services, necessitating urgent attention to improved resilience and food security. The study assessed the interdecadal drought severity and duration in Amahlathi’s agricultural potential zone from 1989 to 2019 using various vegetation indicators. Landsat time series data were used to analyse the land surface temperature (LST), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and standardized precipitation index (SPI). The study utilised GIS-based weighted overlay, multiple linear regression models, and Pearson’s correlation analysis to assess the correlations between LST, NDVI, SAVI, and SPI in response to the agricultural drought extent. The results reveal a consistent negative correlation between LST and NDVI in the ALM, with an increase in vegetation (R² = 0.9889) and surface temperature. LST accuracy in dry areas increased to 55.8% in 2019, despite dense vegetation and a high average temperature of 40.12 °C, impacting water availability, agricultural land, and local ecosystems. The regression analysis shows a consistent negative correlation between LST and NDVI in the ALM from 1989 to 2019, with the correlation between vegetation and surface temperature increasing since 2019. The SAVI indicates a slight improvement in overall average vegetation health from 0.18 in 1989 to 0.25 in 2009, but a slight decrease to 0.21 in 2019. The SPI at 12 and 24 months indicates that drought severely impacted vegetation cover from 2014 to 2019, with notable recovery during improved wet periods in 1993, 2000, 2003, 2006, 2008, and 2013, possibly due to temporary drought relief. The findings can guide provincial drought monitoring and early warning programs, enhancing drought resilience, productivity, and sustainable livelihoods, especially in farming communities. Full article

Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this study, a snow cover extraction algorithm integrating cross-scale edge perception and an attention mechanism on the U-net model architecture is proposed. The cross-scale edge perception module replaces the original jump connection of U-net, enhances the low-level image features by introducing edge detection on the shallow feature scale, and enhances the detail perception via branch separation and fusion features on the deep feature scale. Meanwhile, parallel channel and spatial attention mechanisms are introduced in the model encoding stage to adaptively enhance the model’s attention to key features and improve the efficiency of utilizing global semantic information. The method was evaluated on the publicly available CSWV_S6 optical remote sensing dataset, and the accuracy of 98.14% indicates that the method has significant advantages over existing methods. Snow extraction from Landsat 8 OLI images of the upper reaches of the Irtysh River was achieved with satisfactory accuracy rates of 95.57% (using two, three, and four bands) and 96.65% (using two, three, four, and six bands), indicating its strong potential for automated snow cover extraction over larger areas. Full article

Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation. Full article

Coastal wetland ecosystems are critical due to their diverse ecological and economic benefits, yet they have been significantly affected by human activities over the past century. Understanding the spatiotemporal changes and underlying factors influencing these ecosystems is crucial for developing effective ecological protection and restoration strategies. This study examines the Tianjin–Hebei coastal wetlands using topographic maps from the 1940s and Landsat satellite imagery from 1975, 2000, and 2020, supplemented by historical literature and field surveys. The aim is to analyze the distribution and classification of coastal wetlands across various temporal intervals. The findings indicate an expansion of the Tianjin–Hebei coastal wetlands from 7301.34 km² in the 1940s to 8041.73 km² in 2020. However, natural wetlands have declined by approximately 44.36 km²/year, while constructed wetlands have increased by around 53.61 km²/year. The wetlands have also become increasingly fragmented, with higher numbers of patches and densities. The analysis of driving factors points to human activities—such as urban construction, cultivated land reclamation, sea aquaculture, and land reclamation—as the primary contributors to these changes. Furthermore, the study addresses the ecological and environmental issues stemming from wetland changes and proposes strategies for wetland conservation. This research aims to enhance the understanding among researchers and policymakers of the dynamics and drivers of coastal wetland changes, as well as the major challenges in their protection, and to serve as a foundation for developing evidence-based conservation and restoration strategies. Full article

Evapotranspiration (ET) is a critical component of the hydrological cycle, and it has a decisive impact on the ecosystem balance in arid and semi-arid regions. The Yinchuan Plain, located in the Gobi of Northwest China, has a strong surface ET, which has a significant impact on the regional water resource cycle. However, there is a current lack of high-resolution evapotranspiration datasets and a substantial amount of time is required for long-time series remote sensing evapotranspiration estimation. In order to assess the ET pattern in this region, we obtained the actual ET (ET_a) of the Yinchuan Plain between 1987 and 2020 using the Google Earth Engine (GEE) platform. Specifically, we used Landsat TM+/OLI remote sensing imagery and the GEE Surface Energy Balance Model (geeSEBAL) to analyze the spatial distribution pattern of ET over different seasons. We then reproduced the interannual variation in ET from 1987 to 2020, and statistically analyzed the distribution patterns and contributions of ET with regard to different land use types. The results show that (1) the daily ET_a of the Yinchuan Plain is the highest in the central lake wetland area in spring, with a maximum value of 4.32 mm day⁻¹; in summer, it is concentrated around the croplands and water bodies, with a maximum value of 6.90 mm day⁻¹; in autumn and winter, it is mainly concentrated around the water bodies and impervious areas, with maximum values of 3.93 and 1.56 mm day⁻¹, respectively. (2) From 1987 to 2020, the ET of the Yinchuan Plain showed an obvious upward and downward trend in some areas with significant land use changes, but the overall ET of the region remained relatively stable without dramatic fluctuations. (3) The ET_a values for different land use types in the Yinchuan Plain region are ranked as follows: water body > cultivated land > impervious > grassland > bare land. Our results showed that geeSEBAL is highly applicable in the Yinchuan Plain area. It allows for the accurate and detailed inversion of ET and has great potential for evaluating long-term ET in data-scarce areas due to its low meteorological sensitivity, which facilitates the study of the regional hydrological cycle and water governance. Full article

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods. Full article

The real-time monitoring and evaluation of water quality provides a scientific basis for water resource management and promotes regional sustainable development. This study established a database using Landsat-8 satellite data and water quality data from the Ningxia Yellow River basin in China, spanning 2021 to 2023, and this paper proposes a custom residual convolutional neural network model with a hybrid attention mechanism, referred to as PCWA-ResCNN. The accuracy of the model in predicting turbidity, permanganate, ammonia nitrogen, and dissolved oxygen concentration was more than 95%. Compared to convolutional neural networks and long short-term memory models, this model performed better in predicting water quality parameters with significantly improved prediction performance. In terms of spatial distribution, the pollution degree in the middle reaches of the basin is relatively serious. However, the overall water quality is good, being mainly Class I and Class II water quality. The hybrid model established in this paper can better capture the complex nonlinear relationship between the observed values and the surface water reflectance, showing strong robustness. This model can be used for the water quality monitoring of complex inland rivers and lakes, and it can also provide effective support for relevant government departments to formulate scientific and reasonable water quality management policies. Full article

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an increased frequency of drought events, leading to a series of conflicts among water users. However, detailed studies on the impacts of changes in water use due to agriculture expansion, including irrigation, are still lacking. Furthermore, the higher presence of clouds in tropical environments poses challenges for the availability of high-resolution data for vegetation monitoring via satellite images. This study aims to analyze 37 years of agricultural expansion using the Landsat collection and a satellite-based model (geeSEBAL) to assess changes in evapotranspiration resulting from cropland expansion in tropical climates, focusing on the São Marcos River Basin in Brazil. It also used a methodology for estimating daily evapotranspiration on days without satellite images. The results showed a 34% increase in evapotranspiration from rainfed areas, mainly driven by soybean cultivation. In addition, irrigated areas increased their water use, despite not significantly changing water use at the basin scale. Conversely, natural vegetation areas decreased their evapotranspiration rates by 22%, suggesting possible further implications with advancing changes in land use and land cover. Thus, this study underscores the importance of using satellite-based evapotranspiration estimates to enhance our understanding of water use across different land use types and scales, thereby improving water management strategies on a large scale. Full article

The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data and cloud computing provided by Google Earth Engine (GEE), this study examines the effects of various forms of Olympic Games facility urban planning in different historical moments and location typologies, as follows: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games that occurred from Paris 2024 to Montreal 1976. However, this paper focuses specifically on the representative cases of Paris 2024, Tokyo 2020, Rio 2016, Beijing 2008, Sydney 2000, Barcelona 1992, Seoul 1988, and Montreal 1976. The study is not only concerned with obtaining absolute land surface temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, the locally normalized land surface temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it has been determined that Olympic planning has resulted in the development of green spaces, creating “green spots” that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become “hotspots.” Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context. Full article

Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. Earth observation (EO) data make it possible to map crop yield estimations over large areas, although this will remain challenging for specific crops such as sugarcane. Yield data collection is an expensive and time-consuming practice that often limits the number of samples collected. In this study, the sugarcane yield estimation based on a small number of training datasets within smallholder crop systems in the Tha Khan Tho District, Thailand for the year 2022 was assessed. Specifically, multi-temporal satellite datasets from multiple sensors, including Sentinel-2 and Landsat 8/9, were involved. Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R²) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. In this work, the different yield rates at the field level were highlighted, providing a powerful workflow for mapping sugarcane yields across large regions, supporting sugarcane crop management and facilitating decision-making processes. Full article

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R² = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)², consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R² = 0.96; MSE = 0.33 (µg/L)²; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management. Full article

Optimizing urban spatial morphology is one of the most effective methods for improving the urban thermal environment. Some studies have used the local climate zones (LCZ) classification system to examine the relationship between urban spatial morphology and Surface Urban Heat Islands (SUHIs). However, these studies often rely on single-time-point data, failing to consider the changes in urban space and the time-series LCZ mapping relationships. This study utilized remote sensing data from Landsat 5, 7, and 8–9 to retrieve land surface temperatures in Changsha from 2005 to 2020 using the Mono-Window Algorithm. The spatial-temporal evolution of the LCZ and the Surface Urban Heat Island Intensity (SUHII) was then examined and analyzed. This study aims to (1) propose a localized, long-time LCZ mapping method, (2) investigate the spatial-temporal relationship between the LCZ and the SUHII, and (3) develop a more convenient SUHI assessment method for urban planning and design. The results showed that the spatial-temporal evolution of the LCZ reflects the sequence of urban expansion. In terms of quantity, the number of built-type LCZs maintaining their original types is low, with each undergoing at least one type change. The open LCZs increased the most, followed by the sparse and the composite LCZs. Spatially, the LCZs experience reverse transitions due to urban expansion and quality improvements in central urban areas. Seasonal changes in the LCZ types and the SUHI vary, with differences not only among the LCZ types but also in building heights within the same type. The relative importance of the LCZ parameters also differs between seasons. The SUHI model constructed using Boosted Regression Trees (BRT) demonstrated high predictive accuracy, with R² values of 0.911 for summer and 0.777 for winter. In practical case validation, the model explained 97.86% of the data for summer and 96.77% for winter. This study provides evidence-based planning recommendations to mitigate urban heat and create a comfortable built environment. Full article