Search Results (449)

Drought, a natural phenomenon intricately intertwined with the broader canvas of climate change, exacts a heavy toll by ushering in acute terrestrial water scarcity. Its ramifications reverberate most acutely within the agricultural heartlands, particularly those nestled in arid regions. To address this pressing issue, this study harnesses the temperature vegetation dryness index (TVDI) as a robust drought indicator, enabling a granular estimation of land water content trends. This endeavor unfolds through the sophisticated integration of geographic information systems (GISs) and remote sensing technologies (RSTs). The methodology bedrock lies in the judicious utilization of 72 high-resolution satellite images captured by the Landsat 7 and 8 platforms. These images serve as the foundational building blocks for computing TVDI values, a key metric that encapsulates the dynamic interplay between the normalized difference vegetation index (NDVI) and the land surface temperature (LST). The findings resonate with significance, unveiling a conspicuous and statistically significant uptick in the TVDI time series. This shift, observed at a confidence level of 0.05 (Z_S = 1.648), raises a crucial alarm. Remarkably, this notable surge in the TVDI exists in tandem with relatively insignificant upticks in short-term precipitation rates and LST, at statistically comparable significance levels. The implications are both pivotal and starkly clear: this profound upswing in the TVDI within agricultural domains harbors tangible environmental threats, particularly to groundwater resources, which form the lifeblood of these regions. The call to action resounds strongly, imploring judicious water management practices and a conscientious reduction in water withdrawal from reservoirs. These measures, embraced in unison, represent the imperative steps needed to defuse the looming crisis. Full article

This study presents a comparative analysis of two advanced classification techniques applied to Landsat 8 and Sentinel-2 imagery. The first technique is based on the combined use of Tversky’s fuzzy similarity and Mamdani-type fuzzy inference, specifically designed to handle transition zones—areas characterized by gradual shifts in land cover, such as from vegetation to suburban environments. The second approach is based on the Random Forest algorithm. After performing the ranking of spectral, textural, and geometric features using the fuzzy approach, a fuzzy system based on Tversky’s fuzzy similarity was developed. This system enables a more adaptive and nuanced classification of different land cover classes, including water bodies, forests, and cultivated areas. The results indicate that the proposed fuzzy approach slightly outperforms the Random Forest method in handling mixed land cover regions and reducing classification uncertainties, achieving overall accuracies of 98.5% for Sentinel-2 and 96.7% for Landsat 8. Full article

Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R² ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time. Full article

Changes in climate and land use significantly impact downstream water availability. Quantifying these effects in the Ethiopian Highlands is crucial, as 85% of the transboundary water in Egypt and Sudan originates from these highlands. While the impact of climate change on water availability has been widely studied, few experimental studies have examined how it is affected by eucalyptus reforestation. Therefore, the objective was to investigate how eucalyptus expansion impairs water availability in the Ethiopian Highlands. The study was conducted in the 39 km² Amen watershed, located in the upper reaches of the Blue Nile. Rainfall data were collected from local agencies from 1990 to 2024, while streamflow data were available only for 2002–2009 and 2015–2018. Actual evapotranspiration was obtained using the WaPOR portal, and land use was derived from Landsat 5 TM and Landsat 8 OLI. The satellite images showed that the eucalyptus acreage increased from 238 ha in 2001 to 799 ha in 2024, or 24 ha y⁻¹. The actual evapotranspiration of eucalyptus was up to 30% greater than that of other land uses during the dry monsoon phase (January to March), resulting in decreased water storage in the watershed over a 23-year period. Since runoff is generated by saturation excess runoff, it takes longer for the valley bottoms to become saturated. In the 2002–2009 period, it took an average of around 160 mm of cumulative effective rain for significant runoff to start, and from 2015 to 2018, 274 mm was needed. Additionally, base flow decreased significantly. The annual runoff trended upward when the annual rainfall was more than the additional amount of water evaporated by eucalyptus, but decreased otherwise. Full article

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018–2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python’s Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy. Full article

The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity. Full article

Modern cities are rapidly evolving in terms of urban morphology, driven by exponential population growth that accelerates the urbanisation process. The changes in land use have increased urban area and density, intensifying the urban heat island (UHI) effect, which poses one of the biggest threats to human health and well-being, especially in metropolitan regions. One of the most effective strategies to counter urban heat is the implementation of green infrastructure and the use of suitable building materials that help reduce heat stress. However, access to green spaces and the affordability of efficient building materials are not the same among citizens. This paper aims to identify the socio-economic characteristics of communities in Melbourne, Australia, that contribute to their vulnerability to urban heat under local conditions. This study employs remote sensing and geographical information systems (GIS) to conduct a macro-scale analysis, to investigate the correlation between urban heat patterns and socio-economic characteristics, taking into account factors such as vegetation cover, built-up areas, and land use types. The results from the satellite images and the geospatial data reveal that Deer Park, located in the western suburbs of Melbourne, has the highest land surface temperature (LST) at 32.54 °C, a UHI intensity of 1.84 °C, a normalised difference vegetation index (NDVI) of 0.11, and a normalised difference moisture index (NDMI) of −0.081. The LST and UHI intensity indicate a strong negative correlation with the NDVI (r = −0.42) and NDMI (r = −0.6). In contrast, the NDVI and NDMI have a positive correlation with the index of economic resources (IER) with r values of 0.29 and 0.24, indicating that the areas with better finance resources tend to have better vegetation coverage or plant health with less water stress, leading to lower LST and UHI intensity. This study helps to identify the most critical areas in the Greater Melbourne region that are vulnerable to the risk of urban heat and extreme heat events, providing insights for the local city councils to develop effective mitigation strategies and urban development policies that promote a more sustainable and liveable community. Full article

Over the past quarter-century, the enhanced availability of satellite imagery, characterized by improved temporal, spectral, radiometric, and spatial resolutions, has enabled valuable insights into the spatial soil variability of annual croplands and orchards. This study investigates the impact of spatial resolution on classifying three-year, multi-temporal vegetation indices derived from satellites with coarse (30 m, Landsat 8), medium (10 m, Sentinel-2), and fine spatial resolutions (3.7 m, PlanetScope). The classification was performed using the fuzzy c-means algorithm, with the fuzziness performance index (FPI) and normalized classification entropy (NCE), which were used to determine the optimal number of management zones (MZs). Our results revealed that the Landsat 8-based NDVI images produced the highest number of clusters (nine for annual cropland and six for orchards), while the finer resolutions from PlanetScope reduced this to three clusters for both cultivation types, more accurately capturing the intra-parcel variability. Except for Landsat 8, the NDVI means of MZs generated based on Sentinel-2 and PlanetScope using the fuzzy c-means algorithm showed statistically significant differences from each other, as determined by a one-way and Welch’s ANOVA (p < 0.05). The use of PlanetScope imagery demonstrated its superiority in generating zones that reflect inherent variability, offering farmers actionable insights at a reconnaissance scale. Multi-temporal satellite imagery has proved effective in monitoring plant growth responses to edaphological soil properties. In our study, the PlanetScope satellites, which offer the highest spatial resolution, consistently produced effective zones for orchard areas. These zones have the potential to enhance farmers’ discovery of knowledge at a reconnaissance scale. With the increasing spatial resolution and enhanced spectral resolution of newer satellite sensors, using cluster analysis with insights from soil scientists promise to help farmers better understand and manage the fertility of their fields in a cost-effective manner. Full article

Water temperature, a key environmental factor in river ecosystems, plays an important role in understanding the health of river ecosystems and addressing climate change. The Tibetan Plateau is sensitive to global climate change, and owing to its unique geographic and climatic conditions, the spatiotemporal distribution of water temperature in plateau rivers is highly heterogeneous. However, owing to the complex terrain and harsh climate, traditional water temperature monitoring methods struggle to provide comprehensive coverage. This study focuses on the downstream section of the Yarlung Tsangpo River and uses Landsat 7 and 8 images from 2004–2022. Considering the high water vapor content in the region and the satellite’s inherent system errors, a remote sensing-based model for interpreting water temperature in plateau rivers was developed. This model aims to address the limitations of traditional monitoring methods and provide a new technological approach for studying the spatiotemporal variations in water temperature in plateau rivers. The results show that the model has high accuracy (RMSE ranging from 1.00 °C to 1.85 °C), and regression correction can reduce the relative error by 1.6% to 22.2%. The water temperature downstream of the Yarlung Tsangpo River is influenced by a combination of climate, topography, and runoff inputs, resulting in clear spatiotemporal variation characteristics. Air temperature is the most important factor affecting water temperature, and both the intra-annual variations and spatial distributions of water temperature show significant regional differences. This study provides important data support and technical methods for long-term monitoring and ecological research on water temperature in plateau rivers, as well as scientific evidence for water resource management in plateau regions. Full article

In spaced crop systems, understanding the interactions between different types of vegetation in the agroecosystem and solar radiation is essential for understanding surface radiation dynamics. This study aimed to both seasonally and spatially quantify and characterize the components of the solar radiation balance in the cultivation of green dwarf coconut. The experiment was conducted in Santa Izabel do Pará, Brazil, and monitored the following meteorological parameters: rainfall, incident global radiation (R_g), and net radiation (R_n). Landsat 8 satellite images were obtained between 2021 and 2023, and the estimates for global and net radiation were subsequently calculated. The resulting data were subjected to mean tests and performance index analysis. The dry season showed higher values of R_g and R_n due to reduced cloud cover. In contrast, the rainy season exhibited lower R_g and R_n totals, with reductions of 21% and 23%, respectively. In the irrigated area, a higher R_n/R_g fraction was observed compared to the non-irrigated area, with no significant differences between the row and inter-row zones. In the non-irrigated system, there were no seasonal differences, but a spatial difference between row and inter-row was noted, with the row having higher net radiation (9.95 MJ m⁻² day⁻¹) than the inter-row (8.36 MJ m⁻² day⁻¹), which could result in distinct energy balances at a micrometeorological scale. Spatially, the eastern portion of the study area showed higher global radiation totals, with the radiation balance predominantly ranging between 400 and 700 W m⁻². Based on the performance indices obtained, satellite-based estimates proved to be a viable alternative for characterizing the components of the radiation balance in the region, provided that the images have low cloud cover. Full article

Water is an essential necessity for maintaining the life cycle on Earth. These resources are continuously changing because of human activities and climate-related factors. Hence, adherence to effective water management and consistent water policy is vital for the optimal utilization of water resources. Water resource monitoring can be achieved by precisely delineating the borders of water surfaces and quantifying the variations in their areas. Since Lake Van is the largest lake in Turkey, the largest alkaline lake in the world, and the fourth largest terminal lake in the world, it is very important to determine the changes in water surface boundaries and water surface areas. In this context, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automatic Water Extraction Index (AWEI) were calculated from Landsat-8 satellite images of 2014, 2017, 2020 and 2023 in June, July, and August using the Google Earth Engine (GEE) platform. Water pixels were separated from other details using the Canny edge detection algorithm based on the calculated indices. The Otsu thresholding method was employed to determine water surfaces, as it is the most favored technique for calculating NDWI, AWEI, and MNDWI indices from Landsat 8 images. Utilizing the Canny edge detection algorithm and Otsu threshold detection approaches yielded favorable outcomes in accurately identifying water surfaces. The AWEI demonstrated superior performance compared to the NDWI and MNDWI across all three measures. When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector Machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. In 2017, the highest producer accuracy, user accuracy, overall accuracy, kappa, and f score evaluation criteria were all 100% with the SVM algorithm and AWEI. In 2020, the SVM algorithm and NDWI produced the highest evaluation criteria values of 100% for producer accuracy, user accuracy, overall accuracy, kappa, and f score. In 2023, using the SVM and CART algorithms as well as the AWEI, the highest evaluation criteria values for producer accuracy, user accuracy, overall accuracy, kappa, and f score were 100%. This study is a case study demonstrating the successful application of machine learning with Canny edge detection and the Otsu water surfaces thresholding method. Full article

Water quality monitoring is essential to assess and manage anthropogenic eutrophication, especially for coastal estuaries in heavily populated areas. Current monitoring techniques rely on in situ sampling, which can be expensive and limited in spatial and temporal coverage. Satellite remote sensing, using the Landsat 8 (Operational Land Imager, OLI) platform, has the potential to provide more extensive coverage than traditional methods. Coastal waters are optically more complex and often shallower and more enclosed than the open ocean, presenting conditions that pose challenges to remote sensing approaches. Here, we compared in situ data from 18 stations around Pleasant Bay, Massachusetts, USA from the years 2014–2021 to contemporaneous observations with Landsat 8 OLI. Satellite-derived estimates of chlorophyll-a and Secchi depth were acquired using various algorithms including the “Case-2 Regional/Coast Color” (C2RCC), “Case-2 Extreme” (C2X), l2gen processor, and a random forest machine learning algorithm. Based on our results, predictions of water quality indices from both C2RCC and random forest techniques can be a useful addition to existing water quality monitoring efforts, potentially expanding both spatial and temporal coverage of monitoring efforts. Full article

This paper implements an unsupervised single-image sharpening method for multispectral images, focusing on Sentinel-2 and Landsat 8 imagery. Our method combines traditional model-based methods with neural network optimization techniques. Our method solves the same optimization problem as traditional model-based methods while leveraging neural network optimization techniques through a customized U-Net architecture and specialized loss function. The key innovation lies in simultaneously optimizing a low-rank approximation of the target image and a linear transformation from the subspace to the sharpened image within an unsupervised training framework. Our method offers several distinct advantages: it requires no external training data beyond the image being processed, it provides fast training speeds through a compact, interpretable network model, and most importantly, it adapts to different input images without requiring extensive parameter tuning—a common limitation of traditional methods. The method was developed with a focus on sharpening Sentinel-2 imagery. The Copernicus Sentinel-2 satellite constellation captures images at three different spatial resolutions, 10, 20, and 60 m, and many applications benefit from a unified 10 m resolution. Still, the method’s effectiveness extends to other remote sensing tasks, achieving competitive results in both sharpening and multisensor fusion scenarios. It is evaluated using both real and simulated data, and its versatility is shown through successful applications to Sentinel-2 sharpening and Sentinel-2/Landsat 8 fusion. In comparison with leading methods, it is shown to give excellent results. Full article

Leveraging satellite monitoring and machine learning (ML) techniques for water clarity assessment addresses the critical need for sustainable water management. This study aims to assess water clarity by predicting the Secchi disk depth (SDD) using satellite images and ML techniques. The primary methods involve data preparation and SSD inference. During data preparation, AquaSat samples, originally from the L1TP collection, were updated with the Landsat 8 satellite’s latest postprocessing, L2SP, which includes atmospheric corrections, resulting in 33,261 multispectral observations and corresponding SSD measurements. For inferring the SSD, regressors such as SVR, NN, and XGB, along with an ensemble of them, were trained. The ensemble demonstrated performance with an average determination coefficient of $R^2$ of around 0.76 and a standard deviation of around 0.03. Field data validation achieved an $R^2$ of 0.80. Furthermore, we show that the regressors trained with L1TP imagery for predicting SSD result in a favorable performance with respect to their counterparts trained on the L2SP collection. This document contributes to the transition from semi-analytical to data-driven methods in water clarity research, using an ML ensemble to assess the clarity of water bodies through satellite imagery. Full article

This study focuses on remote sensing and monitoring of asparagus crops in the provinces of Ica and Trujillo, highlighting their importance in global food security. Using satellite images and temperature data, productivity was compared using the NDWI, NDVI, and EVI indices. The Grad-CAM technique was used to analyze the AlexNet Convolutional Neural Network (CNN) model, seeking to improve productivity. Although AlexNet validated the satellite images, it showed some confusion in regions of medium and low productivity. The model, supported by Grad-CAM, will contribute to the monitoring of optimal climatic conditions. Full article