Search

Redd surveys estimate spawning population size for many salmonid species. Studies of field‐based redd counting methods highlight observer bias caused by redd density, observer experience, and environmental factors. Researchers have begun using remotely piloted vehicles (RPVs, drones) to count redds; yet, no studies have quantified bias and variability in counts. This study aimed to quantify the influence of redd density, observer experience, and environmental factors (namely, water clarity) on redd counting bias and variability when using RPVs. We found that technological and procedural improvements from our previous study increased precision and reduced variability among observers (coefficient of variation, сυ = 11% compared to сυ = 42%). Redd density was the leading covariate causing differences between RPV and both "best counts" (p < 0.05) and field counts (p < 0.05). We found a reduction in variability with experience level (no experience сυ = 78%; semi‐experienced сυ = 33%; experienced сυ = 20%), with no directional bias in counting. Our paper is the first to quantify observer bias in RPV‐based redd counts. This study describes RPV methods and can help agencies decide how to use RPVs in redd counting and incorporate RPV methods into long‐term datasets. [ABSTRACT FROM AUTHOR]

Utilizing long‐term and high‐resolution pollution remote sensing data, this study examines the dynamic impact of export expansion on the local environment in China from 2000 to 2020. Regions facing larger export expansion experience more pronounced initial deterioration in local air quality, while followed by a more prominent subsequent environmental improvement, indicating that the environmental impact of trade varies over time. Further analysis reveals that a key driver behind this dynamic change is the adjustment in environmental policies, particularly when local government faces higher pressure in achieving the environmental goals and when the central government takes over environmental monitoring powers. Additionally, under more stringent environmental regulations, regional export growth leads to increased environmental expenditures, concurrently driving localities to reduce heavily polluting engergy inputs such as coal. Our study suggests that environmental policies can facilitate the synergistic development of trade and the environment. [ABSTRACT FROM AUTHOR]

This paper proposes an evaluation study of the impact of mini-grid electrification projects on SDG7 (ensure access to affordable, reliable, sustainable and modern energy for all). Questioning the effectiveness of mini-grid development programmes on electricity access is essential because we have observed many failed cases in which the mini-grids stopped delivering on their promises a few years after commissioning the equipment. Our approach is based on night-time light (NTL) data. The methodology is designed and tested using mini-grid projects precisely localised and documented in published papers. We compare NTL data for each project before and after commissioning. We show that in about half the projects, NTL detects significant positive changes after commissioning. We build a test of impact, which confirms the causal nature of the observed effects of mini-grid projects on progress being made in SDG7, based on a comparison between non-treated localities matched with treated localities. But the domain of validity of this conclusion has limitations, related in particular to remoteness, extreme initial energy poverty and insufficient inclusiveness. These factors should be considered in the design of mini-grid development programmes. [ABSTRACT FROM AUTHOR]

Remote sensing data prove to be an effective resource for constructing a data-driven predictive model of mineral prospectivity. Nonetheless, existing deep learning models predominantly rely on neural networks that necessitate a substantial number of samples, posing a challenge during the early stages of exploration. In order to predict mineral prospectivity using remotely sensed data, this study introduced deep forest (DF), a non-neural network deep learning model. Mainly based on ASTER multispectral imagery supplemented by Sentinel-2 and geological data, gold ore in Hamissana area, NE Sudan was used to test the DF predictive model capability. In addition to four geological-based evidential layers, 20 remote sensing-based evidential layers were generated using remote sensing enhancing techniques, forming the predictor variables of the proposed model. The applicability of the DF was thoroughly examined including its accuracy for delineating prospective areas, sensitivity to amount of training samples, and adjustment of hyperparameters. The results demonstrate that DF model outperformed conventional machine learning models (i.e., support vector machine, artificial neural network, and random forest) with AUC of 0.964 and classification accuracy of 93.3%. Moreover, the sensitivity analysis demonstrated that the DF model can be trained with a limited number (i.e., < 15) of mineral occurrences. Therefore, the DF algorithm has great potential and proves to be a viable solution for data-driven prospectivity mapping, particularly in scenarios with data availability constraints. [ABSTRACT FROM AUTHOR]

High-quality precipitation data input and the selection of reasonable and applicable hydrological models are the main ways to improve the accuracy of runoff simulation, and are crucial for flood control, drought resistance and comprehensive water resource management in the basin. This study takes the Jingle Basin as the research area, establishing a transformer model that integrates rainfall data from multiple sources considering environmental factors. It combines six types of remote sensing data with rainfall data, which are then used as inputs for the XAJ model, LSTM model, and Prophet model, respectively. The output results are further separately using the ensemble mean method and the Bayesian mean method for ensemble forecasting. The results show that: Compared with a single precipitation product, the fusion model considering environmental factors significantly enhances the correlation between the predicted rainfall and the observed rainfall, with the CC value reaching 0.72; Compared with the other two models, the LSTM model has the NSE value of 0.89, showing a better runoff prediction effect; Compared with the LSTM model with the NSE value of 0.85 and the ensemble average method with the NSE value of 0.76, the Bayesian model averaging method demonstrates the best runoff prediction and simulation effect, with the NSE value of 0.88. [ABSTRACT FROM AUTHOR]

Poland as well as other countries keep extensive collections of 20th and 21st-century aerial photos, which are underexploited compared to such other archival materials as satellite imagery. Meanwhile, they offer significant research potential in various areas, including urban development, land use changes, and long-term environmental monitoring. Archival photographs are detailed, often obtained every five to ten years, and feature high resolution, from 20 cm to 1 m. Their overlap can facilitate creating precise digital models that illustrate topography and land cover, which are essential variables in many scientific contexts. However, rapidly transforming these photographs into geographically accurate measurements of the Earth's surface poses challenges. This article explores the obstacles in automating the processing of historical photographs and presents the main scientific research directions associated with these images. Recent advancements in enhancing work˚ows, including the development of modern digital photogrammetry tools, algorithms, and machine learning techniques are also discussed. These developments are crucial for unlocking the full potential of aerial photographs, making them easier accessible and valuable for a broader range of scientific fields. These underutilized photographs are increasingly recognized as vital in various research domains due to technological advancements. Integrating new methods with these historical images offers unprecedented opportunities for scientific discovery and historical understanding, bridging the past with the future through innovative research techniques. [ABSTRACT FROM AUTHOR]

Aiming at the low tracking accuracy and longtime tracking problems of the traditional automatic mobile human body tracking methods, this research proposes an automatic tracking method of the moving human body based on remote sensing spatial information. The proposed automatic tracking method for the moving human body first utilizes remote sensing technology to obtain the spatial information of the moving human body and then constructs a human body target motion model. By segmenting and fusing human body motion images, automatic matching and tracking of human body motion is finally realized. The experimental results showed that the auto-tracking time of the moving human body was only 0.3 s, while the auto-tracking accuracy rate was as high as 98.29%. In summary, the method used in this research has a high human motion tracking recognition effect. In addition, the study still has shortcomings, such as how to maintain high accuracy tracking in low light and bad weather conditions, still need to be studied. [ABSTRACT FROM AUTHOR]

The Amazon Basin is experiencing large-scale land use conversion from primary forest to pasture. While several land cover datasets map cleared areas in the Amazon, the percent cover of woody vegetation (trees and shrubs) in cleared areas and its association with clearing age, soil type, and geology is poorly understood, despite its importance for carbon emissions, biodiversity, land–atmosphere interactions, and monitoring of pasture condition. We used temporal mixture analysis on Sentinel-2 imagery from 2019 to map woody vegetation cover on cleared areas in Rondônia, Brazil. Binary woody vegetation masks were generated at 10-m resolution using a threshold of the evergreen endmember, with an overall accuracy of 84%. The age of clearing for each pixel was calculated from MapBiomas (Sousa and Davis, Remote Sens Environ 247, 2020) with a 2019 base year. We find little evidence of large-scale abandonment of pasture: most (53%) of the cleared area in 2019 was “clean pasture” (< 10% woody vegetation cover), 34% was “dirty pasture” (10–90% woody vegetation cover), 10% was forest (90–100% woody vegetation cover), and 3% was early stage clearing (> 10% woody vegetation cover, cleared 1–5 years ago). Recently cleared areas (1–2 years) had high (60%) woody vegetation cover, woody vegetation cover decreased with pasture age, and older pastures (20–34 years) had consistently low woody vegetation cover (25% on average). The commonly observed decrease in greenness with increasing clearing age, which is sometimes interpreted as decreasing grass health, was due in part to decreasing woody vegetation cover as pastures were gradually cleared over a decade. These results suggest modifications to existing conceptual models that describe clearing as a rapid process with high rates of secondary growth. We found a gradual and semi-permanent clearing of woody vegetation and proposed a revised conceptual model of deforestation dynamics. [ABSTRACT FROM AUTHOR]

The Ganges–Brahmaputra–Meghna delta is one of the most populous deltas in the world, threatened by diverse environmental stressors, including salinity intrusion, causing adverse consequences on livelihood adaptation. Shrimp farming has been recognized as one of the most practiced livelihoods among various livelihood opportunities. Growing global demand and higher economic returns encouraged many farmers to convert their agricultural land to shrimp farms by intruding saline water into the farmlands to produce shrimps. In this study, we investigated whether the growing trend of shrimp farming influences the migration aspirations of the vulnerable coastal communities in Bangladesh. The study employed qualitative interviews with individuals in four villages in southwest coastal Bangladesh. Satellite images were analyzed to detect land-use changes and changes in soil salinity in the last 28 years, confirming a rapid increase in shrimp farms. Results from the qualitative interviews indicate that poor people with limited resources cannot produce paddy on their land because of increased soil salinity caused by shrimp farming. Such a situation leads them to aspire to migrate. Thus, our analysis contributes to the discourse on “aspiration and capability” in environmental migration by adding the impacts of land-use changes on livelihoods. [ABSTRACT FROM AUTHOR]

The tropical Andes, the highest global biodiversity hotspot, is highly threatened by habitat loss and intense land use. Forest loss rates have remained persistent throughout the region during this century, resulting in a spread fragmented landscape composed of small forest fragments. However, the spatial distribution and magnitude of the proximate causes of associated land-cover change across the tropical Andes remain unclear at regional scales (e.g., provinces). Here, we combined climate, socioeconomic, and remote sensing data with machine-learning methods to model the proximate causes of forest loss across Antioquia, Colombia, where mountains dominate the landscape. We found that temperature is one of the main proximate causes of deforestation because it defines the distribution of crops and cattle (i.e., livestock) along an elevational gradient. We also detected that vegetation features and preceding deforestation predict current deforestation increasing our ability to identify proximate causes of deforestation. Additionally, there is a high influence of local-scale socioeconomic factors driving deforestation trends over the last decade, providing evidence that implementing control measures and economic alternatives tailored to each region can reduce deforestation rates. Our model would be used as a valuable tool to inform decisions aimed at reducing pressure on forests. [ABSTRACT FROM AUTHOR]

Satellite remote sensing (SRS) is a technique that can provide effective method on surface water quality assessment at large spatial scale studie. The analysis research involves: (1) analysis of changes in surface water quality in the Vam Co Tay River, Long An province, Vietnam in the period 2015–2020, (2) selection a model to estimate water quality assessment index from remote sensing data based on Bayesian Model Averaging (BMA); and (3) quantitative assessment of surface water pollution risks in the study area. The results show that the predictive coefficients of determination (R2) for water quality (BOD5, COD, and TSS) are higher than 0.70 for all three parameters. In particular, the upstream of Vam Co Tay river with "very high risk" in 2015 tended to decrease to "high risk" in 2020. Besides, the results also show an increasing risk in downstream from "low risk" in 2015 to "moderate risk" in 2020. The study demonstrated the potential of SRS for providing an overall assessment of the spatial distribution of risks associated with surface water pollution and forecasting the concentration changing trends in the future, and supporting to overcome data shortage in water monitoring. [ABSTRACT FROM AUTHOR]

In recent years, drought has become a global problem. Undoubtedly, drought monitoring is an important step for combating and reducing the resultant damage. Soil moisture, namely its spatial and temporal variability, is one of the most important environmental variables. Due to the difficulty cost, and timeliness of field measurements, this parameter has not been used widely in drought indexes. The recent development of global databases based on satellite imagery as well as rapid progress in hardware and software for modeling complex processes governing the water balance at the land surface employ these new tools to reduce the limitations in this field. The purpose of this research is to provide a comprehensive drought monitoring approach by integrating remote sensing data and the variable infiltration capacity (VIC) model with the Palmer Index (PDSI) in central Iran. In this study, the components of water and energy balance in the Central Iran region were simulated using the VIC land surface model. The output components of this model, especially soil moisture after evaluation, were used as inputs in the drought index based on Palmer's water balance. The integrated index of the VIC-PDSI in comparison with conventional Palmer indices and the SPI index at the 3, 6, 12, 24, and 48-month intervals was fitted with increments in moisture data and variations in the storage of water extracted from GRACE satellite data. Results showed that the combination of VIC-PDSI had the highest correlation coefficient of 0.87 with groundwater level change compared with other drought indices. [ABSTRACT FROM AUTHOR]

In this paper we propose a scalable framework for large-scale farm scene modeling that utilizes remote sensing data, specifically satellite images. Our approach begins by accurately extracting and categorizing the distributions of various scene elements from satellite images into four distinct layers: fields, trees, roads, and grasslands. For each layer, we introduce a set of controllable Parametric Layout Models (PLMs). These models are capable of learning layout parameters from satellite images, enabling them to generate complex, large-scale farm scenes that closely reproduce reality across multiple scales. Additionally, our framework provides intuitive control for users to adjust layout parameters to simulate different stages of crop growth and planting patterns. This adaptability makes our model an excellent tool for graphics and virtual reality applications. Experimental results demonstrate that our approach can rapidly generate a variety of realistic and highly detailed farm scenes with minimal inputs. [ABSTRACT FROM AUTHOR]

The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentation. Recently developed fully reversible or fully invertible networks can mostly avoid memory limitations by recomputing the states during the backward pass through the network. This results in a low and fixed memory requirement for storing network states, as opposed to the typical linear memory growth with network depth. This work focuses on a fully invertible network based on the telegraph equation. While reversibility saves the major amount of memory used in deep networks by the data, the convolutional kernels can take up most memory if fully invertible networks contain multiple invertible pooling/coarsening layers. We address the explosion of the number of convolutional kernels by combining fully invertible networks with layers that contain the convolutional kernels in a compressed form directly. A second challenge is that invertible networks output a tensor the same size as its input. This property prevents the straightforward application of invertible networks to applications that map between different input-output dimensions, need to map to outputs with more channels than present in the input data, or desire outputs that decrease/increase the resolution compared to the input data. However, we show that by employing invertible networks in a non-standard fashion, we can still use them for these tasks. Examples in hyperspectral land-use classification, airborne geophysical surveying, and seismic imaging illustrate that we can input large data volumes in one chunk and do not need to work on small patches, use dimensionality reduction, or employ methods that classify a patch to a single central pixel. [ABSTRACT FROM AUTHOR]

Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral exploration. In this investigation, the Visible Near-Infra-Red (VNIR), Short-Wave Infra-Red (SWIR), and Thermal Infra-Red (TIR) bands of the ASTER satellite sensor derived layers were fused to detect alteration minerals and silicification in east the Kerdous inlier for cupriferous mineralization exploration. Several image processing techniques were executed in the present investigation, namely, Band Ratio (BR), Selective Principal Component Analysis (SPCA) and Constrained Energy Minimization (CEM) techniques. Initially, the BR and SPCA processing results revealed several alteration zones, including argillic, phyllic, dolomitization and silicification as well as iron oxides and hydroxides. Then, these zones were mapped at sub-pixel level using the CEM technique. Pyrophyllite, kaolinite, dolomite, illite, muscovite, montmorillonite, topaz and hematite were revealed displaying a significant distribution in relation with the eastern Amlen region lithological units and previously detected mineral potential zones using HyMap imaging spectroscopy. Mainly, a close spatial association between iron oxides and hydroxide minerals, argillic, and phyllic alteration was detected, as well as a strong silicification was detected around doleritic dykes unit in Jbel Lkest area. A weighted overlay approach was used in the integration of hydrothermal alteration minerals and silicification, which allowed the elaboration of a new mineral alteration map of study area with five alteration intensities. ASTER and the various employed processing techniques allowed a practical and cost effective mapping of alteration features, which corroborates well with field survey and X-ray diffraction analysis. Therefore, ASTER data and the employed processing techniques offers a practical approach for mineral prospection in comparable settings. [ABSTRACT FROM AUTHOR]

Central Europe experienced severe droughts from 2018 to 2020, which led to bark beetle infestations in Norway spruce (Picea abies L.). While recent studies have identified these droughts as a major factor in the dieback of spruces, the exact mechanism through which drought stress influences spruce vitality and increases vulnerability to infestations is not well understood. To gain insights into the spatial and temporal relations between infestations and drought stress, this study compared soil moisture index (SMI) data for the entire soil layer from the German Drought Monitor and Sentinel-2 satellite time series for healthy and bark beetle infested spruce stands preceding the infestation. The study used t-tests to compare pre-infestation monthly sums of SMI and vegetation indices (VIs) for healthy and infested stands. Yearly trajectories of six VIs were used to compare the separability between infested and healthy plots. The results showed that the monthly sum values of SMI for healthy spruce plots were statistically significantly higher than the infested plots from May 2017 to January 2018, while the monthly sum values of Inverted Red Edge Chlorophyll Index of healthy spruce plots were significantly higher than infested plots during growing seasons. With these findings, the study provides useful information for the understanding of the relation of soil moisture and remote sensing VIs patterns in relation to bark beetle infestations. [ABSTRACT FROM AUTHOR]

This study aims to assess the spatio-temporal defoliation dynamics of box tree, one of the few evergreen species of the Hyrcanian Forests. For this, we integrated multi-temporal leaf-off optical Sentinel-2 and radar Sentinel-1 data from 2017 to 2021 with elevation data. A state-of-the-art sample migration approach was used to generate annual reference samples of two categories (defoliated and healthy box tree) for a set of target years 2017–2020. This approach is based on field samples of the reference year 2021 and two similarity measures, the Euclidean distance and the spectral angle distance. The analysis of spectral and radar profiles showed that the migrated samples were well representative of both defoliated and healthy box trees categories. The migrated samples were then used for spatially mapping the two classes using support vector machine classification. The results of support vector machine classification indicated a large extent of box tree mortality. The most significant changes from healthy box trees to defoliated ones, or vice versa, occurred during the years 2017 and 2018. In the consecutive years of 2019, 2020, and 2021, no significant changes in the distribution of healthy or defoliated box trees were observed. The statistical assessment also revealed that mortality of evergreen understory tree species can be mapped with practically sufficient overall accuracies reaching from 84% (in 2017) to 91%–92% (in 2020 and 2021) using spaceborne remote sensing data. This information using freely accessible satellite data can benefit forest managers responsible for monitoring landscapes affected by the box moth and facilitates the identification of optimal control programs. [ABSTRACT FROM AUTHOR]

Geophysical and remote sensing products that rely on Poisson‐distributed measurement signals, such as cosmic‐ray neutron sensing (CRNS) and gamma spectrometry, often face challenges due to inherent Poisson noise. Common techniques to enhance signal stability include data aggregation or smoothing (e.g., moving averages and interpolation). However, these methods typically reduce the ability to resolve detailed temporal (stationary data) and spatial (mobile data) features. In this study, we introduced a method for contextual noise suppression tailored to Poisson‐distributed data, utilizing a discrete score attribution system. This score filter evaluates each observation against eight different criteria to assess its consistency with surrounding values, assigning a score between 0 (very unlikely) and 8 (very likely) to indicate whether the observation is likely to act as noise. These scores can then be used to flag or remove data points based on user‐defined thresholds. We tested the score filter's effectiveness on both stationary and mobile CRNS data, as well as on gamma‐ray spectrometry and electromagnetic induction (EMI) recordings. In our examples, the score filter consistently outperformed established filters, for example Savitzky–Golay and Kalman, in direct competition when applied to CRNS time series data. Additionally, the score filter substantially reduced Poisson noise in mobile CRNS, gamma‐ray spectrometry and EMI data. The scoring system also provides a context‐sensitive evaluation of individual observations or aggregates, assessing their conformity within the dataset. Given its general applicability, customizable criteria and very low computational demands, the proposed filter is easy to implement and holds promise as a valuable tool for denoising geophysical data and applications in other fields. [ABSTRACT FROM AUTHOR]

Grasslands and savannas are experiencing transformation and degradation due to bush encroachment (BE). BE has been monitored using restrictive traditional techniques that include field surveys and manual long-term observations. Owing to the limitations of traditional techniques, remote sensing (RS) is an attractive alternative to assess BE because of its generally high precision and return interval, cost-effectiveness, and availability of historical data archives. Furthermore, RS has an added advantage in its ability of acquiring global coherent data in near-real time compared to the snapshot acquisition mode with traditional surveying techniques. Despite its extensive application and vast possibilities, a critical synthesis for RS successes, shortcomings, and best practices in mapping BE in savannas and grasslands is lacking. Thus, broadly, the direction, which this type of investigation has taken over the years is largely unknown. This study sought to connect and measure the progress RS has made in mapping BE in grassland and savanna ecosystems through bibliometric analysis. One hundred and twenty-three peer-reviewed English written documents from the Web of Science and Scopus databases were evaluated. The study revealed 13.05% average annual publication growth, indicating that RS and BE mapping research in grasslands and savannas has been increasing over the survey period. Most published studies came from the USA, while the rest came from South Africa, China, and Australia. The results indicate that BE has been extensively mapped in grasslands and savannas using coarse to medium resolution data. As a result, there is a weak relationship (r² = 0.324) between the dependent variable (aerial images) and the independent variable (percentage of woody cover). This connotes the need to improve BE assessments in grasslands and savannas by integrating recent high-resolution data, machine learning algorithms and artificial intelligence. [ABSTRACT FROM AUTHOR]

Jordan is severely affected by climate change, it suffers from significance fluctuation and decrease in the amounts of the annual precipitation basically during the last decade which had dire consequences for farmers and the provision of fresh water. In this study, the impact of climate change on the Al-Wala basin was analyzed during the period 2013 to 2024 using Geomatics techniques, Google Earth Engine (GEE) and machine learning codes. Soil and Water Assessment Tool (SWAT) model was used to simulate the hydrological process up to year 2064. Moreover, the Meteorological Research Institute Earth System Model (MRI-ESM2-0) was used to predict the change of water surface area of the Al-Wala dam lake in the future. Annual satellite images: Lanadsat and sentinel, covering the period of the study area were downloaded and enhanced. They permit to provide the necessary information to carry out this study. As result, an important fluctuation of the amount of annual rainfall quantity was observed as well as, the amounts of annual rainfall expected to increase and decrease wobbly for several years in the future. Overall the average annual runoff will increase by 10% compared to the baseline scenario. The minimum temperature is expected to be higher than their rates throughout the year by 0.09°- 0.11o C, this will increase the evaporation rates with about 0.03%. The analysis of the sensitivity using the SWAT model was identified by 6 parameters out of 17. The regression coefficient (R2), Nash and Sutcliffe efficiency (NSE), on monthly basis, were above 0.60 for both of them which indicates satisfactory model results. [ABSTRACT FROM AUTHOR]

Existing methods for dehazing remote sensing (RS) images using deep learning have typically relied on convolutional frameworks. However, the limitations inherent in convolution, such as local receptive fields and independent input elements, hinder the network's ability to grasp long-range dependencies and nonuniform distributions. Consequently, the network is constrained in its capacity to learn these aspects. In response to this challenge, a proficient architecture for enhancing the clarity of remote sensing images through transformation, labeled RSDformer, has been designed. The architecture is structured to tackle the non-regular formations and varied spreads of hazing commonly found inside pictures of Remote Sensing. Emphasizing the importance of acquiring features from both nearby and distant areas, the design incorporates a novel detail-compensated transposed attention (DCTA) mechanism. This mechanism aims to get both localized and globalized dependency throughout channel. Furthermore, for enhancing model's capability towards learning from aspects that have undergone degradation and direct processes of restoring effectively, the DFBA or dualized frequencies blocks (adaptive) with filters of dynamic type has been developed. In the end DGBF or blocks of fusion that are dynamic has been devised to facilitate the effective fusion along with exchanging of aspects over differing levels. Through these innovations, these frameworks demonstrate robustness in their capability in capturing dependency in local regions and regions that are globalized, thereby enhancing restoration of visual information within the image. Wide-ranging experimental evaluations confirm superiorities of the proposed methodology over other competitive approaches. [ABSTRACT FROM AUTHOR]

Remote sensing scene classification is a critical task in the processing and analysis of remote sensing images. Traditional methods typically use standard convolutional kernels to extract feature information. Although these methods have seen improvements, they still struggle to fully capture unique local details, thus affecting classification accuracy. Each category within remote sensing scenes has its unique local details, such as the rectangular features of buildings in schools or industrial areas, as well as bridges and roads in parks or squares. The most important features are often these rectangular structures and their spatial positions, which standard convolutional kernels find challenging to capture effectively.To address this issue, we propose a remote sensing scene classification method based on a Rectangle Convolution Self-Attention Fusion Network (RCSFN) architecture. In the RCSFN network, the Rectangle Convolution Maximum Fusion (RCMF) module operates in parallel with the first 4 × 4 convolutional layer of VanillaNet-5. The RCMF module uses two different rectangular convolutional kernels to extract different receptive fields, enhancing the extraction of shallow local features through addition and fusion. This process, combined with the concatenation of the original input features, results in richer local detail information.Additionally, we introduce an Area Selection (AS) module that focuses on selecting feature information within local regions. The Sequential Polarisation Self-Attention (SPS) mechanism, integrated with the Mini Region Convolution (MRC) module through feature multiplication, enhances important features and improves spatial positional relationships, thereby increasing the accuracy of recognising categories with rectangular or elongated features. Experiments were carried out on AID and NWPU-RESISC45 data sets, and the overall classification accuracy was 96.56% and 92.46%, respectively. This shows that the RCSFN network model proposed in this paper is feasible and effective for class classification problems with unique local detail features. [ABSTRACT FROM AUTHOR]

Analysing land transformation and its driving factors is of utmost importance for the optimal land use planning for development at grassroots level. The south-western zone of Punjab, India is vulnerable in terms of adverse climatic conditions, problem of sand dunes, water logging and brackish ground water for irrigation. Therefore, it was felt imperative to understand the long term land use land cover dynamics in the intensively cultivated region of south-western Punjab having more than 80% area under agriculture. The objective was to study and analyse the conversion of agricultural land to urbanization, industrialization, and other classes, which is of significant concern to food production and security. Change detection for last two decades (2000, 2010 and 2020) was carried out using Landsat archive data to observe distinct land transformation. An increase has been observed in the agricultural land from 86.60 to 88.96% during 2000–2010 whereas it decreased to 87.38% during 2020. In contrast, the area under settlement shows a gradual increase i.e. 4.43, 6.22, 7.57% for 2000, 2010 to 2020, respectively indicating major conversion of agricultural and wastelands to this category. Further, reduction of wastelands from 6.82 to 2.03 to 1.4% can be attributed to ameliorative measures taken up by the government and farmers. The waterlogged areas showed the extent as 1.24%, 1.09%, and 2.0% for 2000, 2010 and 2020, respectively. The results of this study can be effectively utilised to alter the land use practices and policies for south-western region of Punjab, as the study area represents the similar ecological conditions of the entire region. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a key component of the hydrological cycle, encompassing evaporation processes from soil and water surfaces and plant transpiration (Sun et al., 2017). Accurate estimation of ET is vital for effective water resource management, agricultural planning, and environmental monitoring (Gowda et al., 2008). However, the complex interactions between land surface conditions, vegetation, and atmospheric factors make direct measurement of ET challenging, leading to the development of various estimation methods. Remote sensing has become a widely used approach for estimating ET over large areas because it provides spatially comprehensive data (Xiao et al., 2024). Methods like the Surface Energy Balance Algorithm for Land and the Surface Energy Balance System utilise satellite-derived thermal imagery and meteorological inputs to calculate ET by analysing the energy exchanges between the land surface and the atmosphere. These methods are advantageous for their broad spatial coverage, making them particularly useful for regional to global scale studies. However, they require careful calibration and validation, and their accuracy can be affected by the spatial resolution of the satellite data and the quality of meteorological inputs. In addition to remote sensing, several other ET estimation methods are commonly employed. The Penman-Monteith equation is one of the most widely accepted methods, integrating meteorological data—such as air temperature, humidity, wind speed, and solar radiation— with biophysical properties of vegetation to estimate ET. This method has been validated extensively, making it a standard reference in ET studies. Empirical methods like the Hargreaves-Samani equation provide simpler alternatives that require fewer data inputs, making them suitable for regions with limited meteorological information but with a trade-off in accuracy. Direct measurement techniques offer highly accurate ET data, including lysimeters and eddy covariance systems. Lysimeters measure water loss directly from a soil column, while eddy covariance systems assess the exchange of water vapour and energy between the surface and the atmosphere. Despite their precision, these methods are limited by high costs, maintenance requirements, and their applicability to small-scale, homogeneous areas (Howell, 2005). Choosing the appropriate ET estimation method depends on the scale of the study, data availability, and the specific application. Remote sensing and models like Penman-Monteith offer scalability and broad applicability, while direct measurements provide precise data at localised scales. Integrating these methods can improve the reliability of ET estimates, enhance water resource management, and aid in climate adaptation efforts. [ABSTRACT FROM AUTHOR]

This study employed remote sensing data, geographic information systems, and statistical methods to analyze the morphometric features of the Antsokia watershed and its sub-watersheds. It assessed drainage network, watershed geometry, drainage texture, and relief characteristics. The Antsokia watershed is drained mainly by a sixth-order river with a dendritic pattern. The mean bifurcation ratio (Rb) was 3.9, indicating a typical branching pattern, while sub-watersheds showed higher Rb values (>5), suggesting steep terrain. The longest flow path is 42.5 km, marked by knickpoints due to lithological changes and major faults. The watershed's elongated shape indicates longer peak flows, aiding flood management. Drainage texture analysis revealed fine drainage, implying soft rock prone to erosion prevails. Most of the watershed comprises high relief and steep slopes (78 %), including hills, breaks, and low mountains. The S-shaped hypsometric curve with a hypsometric integral of 0.4 suggests the watershed is in a mature stage of geomorphic evolution and equilibrium. Sub-watershed morphometric parameters varied spatially, categorized into low, moderate, and high clusters. Overall, this study enhances understanding of Antsokia watershed's characteristics, aiding in sustainable resource management and decision-making. [ABSTRACT FROM AUTHOR]

Copyright of Methods in Ecology & Evolution is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We introduce a new "ecosystem‐scale" experiment at the Cedar Creek Ecosystem Science Reserve in central Minnesota, USA to test long‐term ecosystem consequences of tree diversity and composition. The experiment—the largest of its kind in North America—was designed to provide guidance on forest restoration efforts that will advance carbon sequestration goals and contribute to biodiversity conservation and sustainability.The new Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long‐term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of 12 species was planted in 16 m2 plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie‐grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes.Within the first 6 years, mortality in 400 m2 monoculture plots was higher than in 100 m2 plots. The highest mortality occurred in Tilia americana and Acer negundo monocultures, but mortality for both species decreased with increasing plot diversity. These results demonstrate the importance of forest diversity in reducing mortality in some species and point to potential mechanisms, including light and drought stress, that cause tree mortality in vulnerable monocultures. The experiment highlights challenges to maintaining monoculture and low‐diversity treatments in tree mixture experiments of large extent.FAB2 provides a long‐term platform to test the mechanisms and processes that contribute to forest stability and ecosystem productivity in changing environments. Its ecosystem‐scale design, and accompanying R package, are designed to discern species and lineage effects and multiple dimensions of diversity to inform restoration of ecosystem functions and services from forests. It also provides a platform for improving remote sensing approaches, including Uncrewed Aerial Vehicles (UAVs) equipped with LiDAR, multispectral and hyperspectral sensors, to complement ground‐based monitoring. We aim for the experiment to contribute to international efforts to monitor and manage forests in the face of global change. [ABSTRACT FROM AUTHOR]

Soil degradation in Guinea has become a subject of national concern in general, and in Lower Guinea in particular. We set ourselves the objective of understanding the source of this phenomenon, evaluating its extent and proposing perspectives for its mitigation. To do this, we conducted about 30 surveys in the Boké region (Lower Guinea), combined with a very thorough geoscientific analysis (Teledetection, GIS, Cartography and Geology). Our results show that between 1982 and 1992, fresh vegetation cover was 67% and about 11% for each of the other classes: dry vegetation, barren soil and sand mineral soil. Between 1992 and 2002, the trend was 70% for fresh vegetation cover; 23% for dry vegetation; 2% for barren soil and 5% for sand mineral soil. Between 2002 and 2012, the same entities each and respectively increased to: 61%; 16%; 7% and 16% respectively. The period between 2012 and 2021 was marked by: 73% fresh vegetation cover; 22% dry vegetation; 2% barren soils and 3% sand mineral soil combination. Domestic needs, deforestation, meteorological variations and mining activity (bauxite mining affects the health of the local and surrounding population in several ways: by polluting the environment with charged air masses that move over several kilometres or by destroying its physical state; by the uncontrolled use of chemical explosives that impact human and animal wastewater in rivers and lakes) play a key role in this temporal and geographical fluctuation. In addition, to providing a more stable land management policy, we demonstrate here the natures of the negative impacts of all these factors and their magnitudes. The locations of the main pollution units in the region have been displayed. Degradation over time and in space was tracked, and then position the main rivers in relation to the high-risk areas. [ABSTRACT FROM AUTHOR]

Hyperspectral-multispectral image fusion (HSI-MSI Fusion) for enhancing resolution of hyperspectral images is a hot topic in remote sensing. An important category of approaches for HSI-MSI Fusion is based on deep learning. The main challenges in deep learning based fusion methods include the lack of training data, poor generalization to various datasets, and high computational costs. This paper suggests a new approach to tackle these difficulties by introducing an innovative technique for HSI-MSI fusion. The proposed method involves training a tiny deep neural network that can reconstruct high-resolution hyperspectral images through spectral super-resolution of high-resolution multispectral images. This method does not require high resolution training data and they are artificially generated based on the spatial degradation model of the input observation images. Therefore, the problems of data scarcity and poor generalization are addressed, and also the computational burden is significantly reduced. After conducting thorough experiments, it was found that the proposed method provides promising results. The source code of this method is available at https://github.com/rajaei-arash/SSSR-HSI-MSI-Fusion. [ABSTRACT FROM AUTHOR]

Thick clouds reduce the useful information in visible remote sensing and restrict the usefulness of remote sensing images. Many scholars have proposed time-based strategies to solve these restrictions. However, these approaches still suffered from difficulties, such as requiring paired cloud mask pictures as additional auxiliary images, having artefacts or image distortions, and model training difffculties. For thick clouds in multitemporal remote sensing, this research offers an implicit multitemporal remote sensing thick cloud removal network (IRNet). As the name suggests, IRNet does not need cloud masks as external auxiliary data inputs to the network. To attenuate the temporal difference problem between multitemporal remote sensing images with model training difffculties, multiscale feature extraction block (MFEB) is designed in IRNet. In addition, the designed upsampling and convolution blocks are utilized in the designed high-fidelity reconstruction block (HFRB) to reconstruct the missing information under the cloud to generate highly preserved cloud-free images. By utilizing two readily datasets for removing multitemporal thick clouds, we have evaluated our method against the most effective existing techniques. Our findings consistently demonstrate that our method improves the restoration of texture detail and spatial information. [ABSTRACT FROM AUTHOR]

Forest and land fires cause substantial economic, social, and environmental devastation. Interagency forest and land fire management has succeeded in decreasing the impact of these fires, particularly in Indonesia. Having comprehensive information on fire locations and frequencies will benefit national forest and land fire management programmes. This study describes nearly a decade of satellite-based burned area (BA) monitoring conducted by the Indonesian government. We discuss (1) the history of BA mapping in Indonesia, (2) the most recent techniques for producing monthly BA maps, (3) an evaluation of product accuracy, (4) advantages and disadvantages, and (5) recommendations for future research. The most recent approach combines manual and digital classification, primarily using Landsat images, but has been supplemented with Sentinel data since 2020. The digital analysis, named the normalized burn ratio difference index threshold, was used to distinguish between burned and unburned pixels, guiding the interpreter’s manual digitization. BA confidence levels were determined using active fire products and ground truth data. We engaged provincial and local agency stakeholders to verify the products and provide quality assurance. We also assessed product performance by examining high-resolution images captured at three different locations to ascertain the relative advantages and disadvantages, which varied depending on each region’s fire regime. Small fires and cloud cover reduced the accuracy of the national monthly BA product. However, the omission error decreased by 65% when all fires throughout the entire year were considered. The inclusion of all available Sentinel-2 (A and B) images yielded higher accuracy than using only Landsat 8 data. We conclude that Indonesia’s cloud coverage constraint requires additional observational data to obtain clear-sky imagery when using optical sensors, in addition to the adjusted technique. We urge the introduction of reliable digital classifications for all Indonesian fire regimes to simplify resource deployment and reduce manual labour operations. [ABSTRACT FROM AUTHOR]

Multi‐source data merging via weighted average (WA) is widely applied to enhance large‐scale precipitation estimates. However, these data sets usually contain substantial conditional biases with respect to extreme precipitation (EP) events—undermining their utility for extreme event analysis. Nevertheless, the main source of such EP biases remains unknown. Here, we demonstrate that WA algorithms are responsible for less than 1% of total EP biases. Instead, EP biases originate from the multi‐source precipitation inputs, which are not adequately adjusted prior to WA. Specifically, current data‐merging frameworks only correct the monthly means or statistical distributions of the remote sensing/reanalysis precipitation inputs prior to WA. Such procedures are insufficient for adjusting EP timing uncertainties, which eventually propagate into the WA‐based merged data set as an EP bias. Therefore, developing algorithms that iteratively adjust EP timing and intensity errors should be prioritized in future precipitation merging frameworks. Plain Language Summary: Remote sensing (RS) and reanalysis systems are crucial for estimating large‐scale precipitation. Weighted averaging (WA) of different data sets can enhance overall precipitation estimation accuracy and has been widely applied for generating global precipitation data sets. However, WA algorithms often lead to biases for extreme precipitation (EP). Such issues undermine the usefulness of WA‐based precipitation data sets for flood forecasting. This study investigates the sources of EP biases in WA frameworks, based on surface precipitation gauge observations and numerical experiments. Results show that the WA algorithms themselves contribute less than 1% to EP biases. Instead, most EP bias is related to RS/reanalysis data correction procedures. Specifically, current WA methods only adjust the monthly means or general statistical distributions of the input data. However, EP occurrence errors are often neglected during the precipitation correction. This means that the timing and location of EP as estimated by different data sets are not entirely consistent, leading to substantial biases when they are averaged. Therefore, to improve the accuracy of EP estimates, it is important to develop preprocessing methods that better account for both the timing and intensity errors of extreme events. Key Points: We investigate sources of bias in extreme precipitation (EP) estimates provided by commonly used data merging frameworksWe demonstrate that EP biases arise from the neglect of EP timing error correction and not the merging algorithmAlgorithms that iteratively adjust the EP intensities and timing errors should be prioritized in future merging frameworks [ABSTRACT FROM AUTHOR]

Submarine groundwater discharge (SGD) in volcanic areas commonly exhibits high temperatures, concentrations of metals and CO2, and acidity, all of which could affect sensitive coastal ecosystems. Identifying and quantifying volcanic SGD is crucial yet challenging because the SGD might be both discrete, through fractured volcanic rock, and diffuse. At a volcanic area in the Philippines, the novel combination of satellite and drone‐based thermal infrared remote sensing, ground‐based fiber‐optic distributed temperature sensing, and in situ thermal profiling in coastal sediment identified the multi‐scale nature of SGD and quantified fluxes. We identified SGD across ∼30 km of coastline. The different approaches revealed numerous SGD signals from the intertidal zone to about a hundred meters offshore. In active seepage areas, temperatures peaked at 80°C, and Darcy fluxes were as high as 150 cm/d. SGD is therefore locally prominent and regionally important across the study area. Plain Language Summary: Submarine groundwater discharge (SGD) is the flow of groundwater from land to sea. SGD in volcanic areas can have high temperatures, high concentrations of heavy metals, high CO2, and can be acidic, all of which impact sensitive coastal ecosystems. Quantifying volcanic SGD is important yet challenging because the flow can be diffuse and broadly distributed. At a volcanic area in the Philippines, the unique combination of satellite and drone‐based thermal infrared remote sensing, ground‐based fiber‐optic distributed temperature sensing, and in situ thermal profiling in coastal sediment identified the multi‐scale nature of SGD and quantified flow rates. We identified SGD across ∼30 km of coastline. The different approaches revealed numerous SGD signals from the intertidal zone (the region between high and low tide) to about a hundred meters offshore. At some locations, active seepage areas reached temperatures up to 80°C, and we calculated groundwater flow rates to be as high as 150 cm/day. SGD is therefore locally prominent and regionally important across the study area. Key Points: Multi‐scale thermal submarine groundwater discharge (SGD) was mapped in a volcanic area with coral reefs and marine protected areasThermal SGD was prevalent in the area with both broadly diffuse signals and pronounced discrete dischargeLocal SGD reached 150 cm/d and 80°C [ABSTRACT FROM AUTHOR]

Beginning in March 2022, the Antarctic Peninsula's Hektoria Glacier experienced an unprecedented retreat of ∼23 km over 1.5 years, one of the fastest observed glacier retreats on record. Improving constraints on the drivers of such extreme events is key to understanding glacier change around the continent and future sea‐level rise. We use satellite remote sensing and reanalysis data to characterize changes in Hektoria, a former Larsen B Ice Shelf tributary, over the last ∼20 years and document a period of retreat from 2002 to 2011, and readvancement from 2011 to 2022. We find that the long‐term ice front and velocity response (2002–2022) correlated more strongly with changes in modeled ocean temperatures compared to surface air temperatures. However, the acute loss of buttressing support following fast ice collapse paired with a near‐contemporaneous extreme atmospheric river in the region likely catalyzed the unprecedented 2022–2023 retreat. Plain Language Summary: The Antarctic Ice Sheet is one of the largest sources for future sea level rise, yet how much and how fast ice is lost to the ocean here remains relatively unknown. Ice shelves can buttress glaciers from flowing quickly into the ocean, stabilizing their movement and limiting mass discharge. As ice shelves retreat or break up, glaciers accelerate, adding mass to the ocean. In this study, we use imagery and elevation data collected from airborne studies and satellites to characterize how Hektoria Glacier—a marine‐terminating glacier located on the Eastern Antarctic Peninsula that was previously a Larsen B Ice Shelf tributary—has changed over the past 20 years. We compare these changes with available ocean and air temperatures in the region to determine how they influenced the observed fluctuations over time. We find that Hektoria retreated from 2002 to 2011 and readvanced from 2011 to 2022, followed by an unprecedented retreat of ∼23 km between March 2022 and August 2023. We find that abrupt changes in stress following buttressing loss drives glacier change, while modeled ocean temperatures wield influence on Hektoria's long‐term fluctuations and atmospheric temperatures drive shorter term changes in glacier response. Key Points: Hektoria Glacier retreated ∼23 km between March 2022 and August 2023—one of the fastest observed marine‐terminating glacier retreatsChanges in buttressing support and mid‐depth ocean temperatures served as primary drivers for change at Hektoria between 2002 and 2022Understanding long‐ and short‐term glacier response to ocean and atmospheric variability is key to improved sea level rise predictions [ABSTRACT FROM AUTHOR]

ABSTRACT Since its inception, remote sensing has evolved from informing military and natural resource management decisions to addressing a broad spectrum of information deficits about environmental phenomena in support of myriad sciences, industries, and management priorities. Driven by changing information requirements and the advancement of technology, geographers, physicists, statisticians, and many others developed a shared understanding of the problem of extracting reliable information from the patterns of electromagnetic radiation observed by radiometers and other electromagnetic sensors, a shared understanding that is now manifest in professional societies, academic journals, and specialized training programs around the world. The discipline we call remote sensing is now uniquely positioned to play a principal role in a new convergent project oriented around the vexing problem of informing humanity's response to a rapidly shifting climate and the 6th mass extinction. Remote sensing, artificial intelligence, computer vision, and robotics each make substantial contributions to this problem, but language, standards, and practices vary widely. The proliferation of sensors now deployed in the environment as part of the internet‐of‐things (e.g., modern cars, satellite constellations, smartphones, CCTV, and trail cameras), coupled with rapid advancements in machine learning and the proliferation of communication networks, makes the potential for persistent, robust, and largely autonomous environmental monitoring systems clear, but also points to the pressing need to converge around a shared understanding of the problem. [ABSTRACT FROM AUTHOR]

This study investigated the historical and future trends of urban expansion and its subsequent impact on agricultural land-use in Hawassa city, Ethiopia. A time-series of remote-sensing imageries from Landsat Thematic Mapper for the years 1984, 1990, 2000, and 2010 and Operational Land Imager for 2021 were used to extract the LULC information from the study area. Seven major land-cover classes' waterbody, built-up, agricultural land, wetland, grassland, woody vegetation, and agroforestry were identified with visual image interpretation along with supervised image classification techniques using the maximum-likelihood algorithm for the study years. The urban and agricultural lands were then extracted from the original LULC data to quantify the extent, rates, and number of area conversion between the two. The Land Change Modeler module of TerrSet software was used to predict the spatial extents of built-up and agricultural lands in 2030 and 2050. The results showed that there have been significant changes between the LULC types in Hawassa city within the past 37 years, from which built-up and agricultural land have shown the most prevalent changes. It showed that built-up land has increased from 584.73 ha in 1,984–3,939.03 ha in 2021, representing a 573.65% increase at an annual growth rate of 15.50%. However, agricultural land decreased from 8,324.64 ha to 3,595.68 ha in the respective years, with a 56.81% decrease at a rate of −1.54% each year. A total of 3,148.74 ha (37.82%) of agricultural land was converted into built-up land within the past 37 years (85.10 ha per year, a rate of 1.02%. The built-up land is projected to increase to 5,009.85 ha and 6,794.73 ha from 2021 to 2030 and 2050, with annual growth rates of 3.02% and 2.50%, respectively. In the same years, agricultural land will decrease to 2,849.58 ha and 2033.46 ha by 2.31% and 1.50% annually, respectively, from which 64.76 ha (1.80%) and 48.41 ha (1.35%) will be converted into built-up land, respectively. Future planning and development in the city should consider the rapid increase in built-up land toward agricultural land areas and develop appropriate adaptation mechanisms for the local community, which is highly dependent on agriculture. [ABSTRACT FROM AUTHOR]

The ecological environmental quality (EEQ) of the Yongding-Luan River Basin (YLRB) is pivotal to the ecological security of the Beijing-Tianjin-Hebei (JJJ) region's core area. Evaluating the EEQ and analyzing its changes are essential for regional ecological management. However, long-term ecological changes in the YLRB remain uncovered. In this study, we constructed a seamless Remote Sensing Ecological Index (RSEI) for the YLRB from 1986 to 2022 using time-series Landsat imagery on the Google Earth Engine (GEE) platform. The Sen + Mann-Kendall method was employed to analyze the spatiotemporal trends of EEQ, and the Geodetector was used to quantitatively assess the driving factors and their interactions. The results show that: 1) The mean RSEI of the YLRB increased from 0.486 in 1986 to 0.532 in 2022, marking a 9.5% rise and indicating a fluctuating upward trend. 2) The EEQ of the YLRB experienced three distinct phases: improvement, deterioration, and re-improvement. Improvements were predominantly in the western YLRB, while deterioration was mainly in the northern Xilinguole region and the southern urban expansion areas of Beijing, Langfang, Tianjin, and Tangshan. 3) The driving factor detection indicates that land use type and annual average precipitation are the primary driving factors of RSEI change in the YLRB. Furthermore, their interaction results in a significant effect on RSEI, with a maximum of 0.691. These findings align with the historical urban expansion in the YLRB and the environmental policies implemented by the Chinese government. The ecological evolution and driving factors identified in this study offer a scientific basis for regional ecological decision-making and management. [ABSTRACT FROM AUTHOR]

In the past, the use of remote sensing for winter wheat growth monitoring mainly relied on the relative growth assessment of a single vegetation index, such as normalized Vegetation index (NDVI). This study advanced the methodology by integrating field-measured data with Sentinel-2 data. In addition to NDVI, it innovatively incorporated two parameters, aboveground biomass (AGB) and leaf area index (LAI), for a more comprehensive relative growth assessment. Furthermore, the study employed the agricultural production systems simulator (APSIM) model to use LAI and AGB for absolute growth monitoring. The results showed that the simulated LAI and AGB closely match the field-measured values throughout the entire growth period of winter wheat under various conditions (R2 > 0.9). For relative growth monitoring, NDVI showed significant linear positive correlations (r > 0.74 and P< 0.05) with both LAI and AGB simulated by the APSIM model. Overall, this research shows that LAI and AGB obtained from the APSIM model provide a more detailed and accurate approach to monitoring of winter wheat growth. This improved monitoring capability can support effective land management arable and provide technical guidance to advance precision agriculture practices. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technologies can potentially revolutionize the aerospace industry with applications such as remote sensing data refinement, autonomous landing, and drone-based agriculture. However, safety concerns have prevented the widespread adoption of AI in commercial aviation. Currently, commercial aircraft do not incorporate AI components, even in entertainment or ground systems. This paper explores the intersection of AI and aerospace, focusing on the challenges of certifying AI for airborne use, which may require a new certification approach. We conducted a comprehensive literature review to identify common AI-enabled aerospace applications, classifying them by the criticality of the application and the complexity of the AI method. An applicability analysis was conducted to assess how existing aerospace standards - for system safety, software, and hardware - apply to machine learning technologies. In addition, we conducted a gap analysis of machine learning development methodologies to meet the stringent aspects of aviation certification. We evaluate current efforts in AI certification by applying the EASA concept paper and Overarching Properties (OPs) to a case study of an automated peripheral detection system (ADIMA). Aerospace applications are expected to use a range of methods tailored to different levels of criticality. Current aerospace standards are not directly applicable due to the manner in which the behavior is specified by the data, the uncertainty of the models, and the limitations of white box verification. From a machine learning perspective, open research questions were identified that address validation of intent and data-driven requirements, sufficiency of verification, uncertainty quantification, generalization, and mitigation of unintended behavior. For the ADIMA system, we demonstrated compliance with EASA development processes and achieved key certification objectives. However, many of the objectives are not applicable due to the human-centric design. OPs helped us to identify and uncover several defeaters in the applied ML technology. The results highlight the need for updated certification standards that take into account the unique nature of AI and its failure types. Furthermore, certification processes need to support the continuous evolution of AI technologies. Key challenges remain in ensuring the safety and reliability of AI systems, which calls for new methodologies in the machine learning community. [ABSTRACT FROM AUTHOR]

Greater Dhaka area is home to large industrial clusters that are driving economic growth and the poverty reduction efforts of Bangladesh. These clusters are located around peripheral rivers- Turag, Buriganga, Dhaleswari, Balu, Shitalakhya, Bangshi, and Tongi-Khal, which are important for water transport, environment, and eco-systems where flooding of floodplains in monsoon is an integral part. The urban and industrial growth stressing natural resources has led to severe degradation of the rivers and floodplains, affecting the livelihoods, health, and well-being of the people. Monsoon-time exposure to polluted water is yet to be studied and addressed scientifically. This study looked into the water quality and flooding situation of Greater Dhaka for two successive monsoons through extensive river sampling coupled with the estimation of flooded area and exposed population using remote sensing tools. Sentinel 1’s Synthetic Aperture Radar (SAR) images are used for flood mapping considering cost-effectiveness and its advantages for data-scarce regions. The estimated population exposed to flooding was over 668,000 in 2019, and this number increased by 1.53 times in 2020, totaling over one million. During the monsoon and post-monsoon periods of 2019 and 2020, Buriganga, Tongi Khal, and Balu were consistently in poor condition. The lowest water quality index (WQI) was observed in Balu during the monsoon of 2019 (32.28), post-monsoon of 2019 (35.71), and post-monsoon of 2020 (29.58). The lowest WQI during the monsoon of 2020 was recorded in Tongi Khal (35.75). Among the four districts, Dhaka and Gazipur were the most affected by floods in terms of inundation area and exposed population. Our study indicates that most rivers remain in poor condition during the monsoon when exposure is also high. This highlights the need for policymakers to take monsoon exposure seriously and design appropriate interventions. [ABSTRACT FROM AUTHOR]

India's rapid economic development, marked by industrial growth, expanding transportation networks and urbanization has brought many benefits but also poses a significant threat to the nation's cultural and natural heritage. A recent NIAS Policy Brief highlights the urgent need for clear regulatory boundaries and effective enforcement to protect cultural heritage sites from urban sprawl and degradation. Citing examples such as Nalanda, Halebeedu, Srirangapatna and Bodh Gaya, it emphasizes that unchecked development can erode the historical integrity of heritage landscapes. It advocates for a multi-tiered approach using modern technologies like geographic information system (GIS) and remote sensing, along with community engagement and stricter land-use controls, to ensure sustainable preservation. Thotlakonda and Bavikonda, two significant Buddhist heritage sites in Andhra Pradesh, were declared protected by the Government of Andhra Pradesh on 2nd May 1978. However, imprecise descriptions of the protected area have led to conflicting interpretations, fuelling litigation between parties aiming to preserve the sites and parties pushing for development. This study utilizes remote sensing and GIS to examine these sites and their surrounding landscapes, identifying potential archaeological features and assessing the impact of recent land-use changes in preserving these historically important areas. [ABSTRACT FROM AUTHOR]

Remote sensing object detection remains a challenge under complex conditions such as low light, adverse weather, modality attacks or losses. Previous approaches typically alleviate this problem by enhancing visible images or leveraging multi‐modal fusion technologies. In view of this, the authors propose a unified framework based on YOLO‐World that combines the advantages of both schemes, achieving more adaptable and robust remote sensing object detection in complex real‐world scenarios. This framework introduces a unified modality modelling strategy, allowing the model to learn abundant object features from multiple remote sensing datasets. Additionally, a U‐fusion neck based on the diffusion method is designed to effectively remove modality‐specific noise and generate missing complementary features. Extensive experiments were conducted on four remote sensing image datasets: Multimodal VEDAI, DroneVehicle, unimodal VisDrone and UAVDT. This approach achieves average precision scores of 50.5%$\%$, 55.3%$\%$, 25.1%$\%$, and 20.7%$\%$, which outperforms advanced multimodal remote sensing object detection methods and low‐light image enhancement techniques. [ABSTRACT FROM AUTHOR]

Fast and robust vehicle recognition from remote sensing images (RSIs) has excellent economic analysis, emergency management, and traffic surveillance applications. Additionally, vehicle density and location data are vital for intelligent transportation systems. However, correct and robust vehicle recognition in RSIs has been complex. Conventional vehicle recognition approaches depend on handcrafted extracted features in sliding windows with distinct scales. Recently, the convolutional neural network can be executed for aerial image object recognition, and it has accomplished promising outcomes. This research projects an automatic vehicle detection and classification utilizing an imperialist competitive algorithm with a deep convolutional neural network (VDC-ICADCNN) technique. The primary purpose of the VDC-ICADCNN technique is to develop the RSI and apply deep learning (DL) models for the recognition and identification of vehicles. Three main procedures were involved in the presented VDC-ICADCNN technique. At the primary stage, the VDC-ICADCNN technique employs the EfficientNetB7 approach for the feature extractor. Then, the hyperparameter fine-tuning of the EfficientNet approach takes place utilizing ICA, which aids in attaining improved performance in the classification process. The VDC-ICADCNN technique utilizes a variational autoencoder (VAE) model for the vehicle recognition method. Extensive experiments can be implemented to establish the superior solution of the VDC-ICADCNN technique. The obtained outcomes of the VDC-ICADCNN technique highlighted a superior accuracy value of 96.77% and 98.59% with other DL approaches under Vehicle Detection in Aerial Imagery (VEDAI) and Potsdam datasets. [ABSTRACT FROM AUTHOR]

Cloud coverage poses a prevalent challenge in optical remote sensing image processing, significantly hindering the visibility and interpretability of ground‐level information. The application of deep learning technology in remote sensing image cloud detection has witnessed a remarkable advancement, rendering it an increasingly pervasive and potent solution to the challenge of cloud coverage. To thoroughly investigate the effectiveness of diverse deep learning architectures in cloud detection tasks, this research selected five seminal models: AlexNet, VGG16, GoogLeNet, ResNet34, and the cutting‐edge Swin Transformer, and performed a comprehensive comparative analysis of their application in identifying clouds within medium‐resolution Landsat 8 satellite imagery. During the training phase, each model distinctly demonstrated unique strengths in enhancing accuracy, refining loss functions, optimizing resource utilization, and accelerating training processes. In the test phase, the models' performance was rigorously evaluated using overall accuracy (OA) and other quantitative metrics, providing solid empirical evidence for the variations in their performance. Furthermore, an insightful analysis of the visualization outcomes and a meticulous comparison of the nuanced differences between model predictions and actual cloud images underscored the individual models' exceptional abilities in capturing intricate cloud details and executing precise edge processing. To alleviate the cumbersome process of pixel‐level labeling, this study adopts the effective block‐level labeling approach. This approach drastically streamlines annotation processes, ultimately translating into substantial cost savings and a marked reduction in time required for data preparation. The experimental results demonstrate that the Swin Transformer network performs particularly well in the cloud detection task of Landsat 8 images, achieving an OA of 90.73% under block‐level annotation, significantly higher than other comparison models, showcasing excellent cloud detection capabilities. Furthermore, the study also found that the size of input image blocks has a significant impact on detection results. Under the same network architecture, using 32 × 32 image blocks for cloud detection improves the OA by at least 10 percentage points compared to using 64 × 64 image blocks. This finding provides important insights for optimizing cloud detection algorithms. In summary, this study not only verifies the superiority of Swin Transformer in cloud detection in remote sensing images but also reveals the impact of image block size on detection performance, providing new ideas and directions for future advancements in remote sensing image processing and cloud detection technology. [ABSTRACT FROM AUTHOR]

Monitoring vegetation response to valley‐scale floodplain restoration to evaluate effectiveness can be costly and time‐consuming. We used publicly available National Agriculture Imagery Program (NAIP) data and commonly used ArcGIS software to assess land cover change over time at five study sites located in semi‐arid environments of eastern Oregon and north‐central California. Accuracy assessments of our unsupervised classifications were used to evaluate effectiveness. Overall accuracy across sites and years ranged from 64.2% to 89.2% with mean and median accuracy of 79.1% and 80.6%, respectively. Further, we compared our classifications with high‐resolution uncrewed aerial systems (UAS)‐based data collected in the same timeframe. Restored areas classified as dense vegetation were within 4% of the UAS study, water was within 6%, and post‐restoration classifications of sparse vegetation and bare ground classes were within 6% and 4% of the UAS study, respectively. This comparison demonstrates that our unsupervised NAIP data classification of land cover change across entire valley‐scale restoration projects can be used to monitor riparian vegetation change over time as accurately as UAS‐based methods, but at lower cost. Additionally, our methods leverage existing fine‐resolution, pre‐restoration vegetation density data that were not collected as part of project planning. [ABSTRACT FROM AUTHOR]

Australia’s interaction with forest fires highlights the interplay of geography, climate, and ecosystems. This research explores the evolution of fire management practices, from ancient Indigenous ‘firestick farming’ to modern strategies using remote sensing and predictive modelling. For millennia, Indigenous Australians used controlled burns to manage landscapes, promoting ecological health and reducing wildfire risks. The disruption of these practices by European settlers in the late 18th century, through land clearing and the introduction of non-native species, led to increased fuel loads and more severe wildfires. The 20th century brought significant technological advancements to fire management, including motorised fire trucks, aircraft, and improved communication systems. However, the real revolution came with the introduction of remote sensing and early warning systems. These technologies enabled real-time monitoring and predictive insights, significantly enhancing fire management. Today, predictive modelling and machine learning further improve fire management by enabling precise forecasting and efficient resource allocation. This research underscores the crucial role of integrating Indigenous knowledge and community engagement with modern strategies for sustainable fire management. As climate change increases wildfire risks, adapting to these challenges is more important than ever. Australia’s approach, which blends traditional wisdom with modern science, serves as a global model for effective wildfire management. [ABSTRACT FROM AUTHOR]

In the process of remote sensing data processing, cloud data will have a considerable negative impact on data processing. Therefore, a matrix optimization cloud detection algorithm based on multiple receptive fields is proposed in this paper. First, the algorithm adopts a block reshaping model to improve the efficiency of the algorithm, while reducing the need for hardware configuration. Second, the cloud region adaptive sparse attention matrix is used to improve the characteristics of cloud regions with different concentrations. Finally, the multi-receptive field scaling module is used to improve the ability to segment cloud regions at different scales. The experimental results show that the accuracy of the proposed algorithm is 85.5%, and the recall rate is 86.4%. The proposed algorithm can basically accurately screen the location of a cloud region and provide technical support for the subsequent image application processing. [ABSTRACT FROM AUTHOR]

The use of bi-temporal remote sensing images for detecting changes in land cover is an important means of obtaining surface change information, thus contributing to urban governance and ecological environment monitoring. In this article, we propose a deep learning model named Siam-UNet3+ for high-resolution remote sensing image change detection. This model integrates the full-scale skip connections and full-scale deep supervision of the network UNet3+, which can achieve the multi-scale feature fusion of remote sensing images, effectively avoiding the locality disadvantage of convolution operations. Different from UNet3+, Siam-UNet3+ has made major improvements, including the following: (1) incorporating a Siamese network in the encoder, which can process bi-temporal remote sensing images in parallel; (2) leveraging the residual module as the backbone, which can avoid gradient vanishing (or exploding) and model degradation problems; (3) adding a Triplet Attention module to the decoder, which can avoid information redundancy that may occur in full-scale skip connections and increase the ability to focus on changing patterns; and (4) designing a hybrid loss function consisting of focal loss and dice loss, which is more suitable for remote sensing image change detection tasks. In this study, we conducted change detection experiments using the publicly available LEVIR-CD dataset, as well as two local datasets in Beijing. Through comparative experiments with five other models and ablation experiments, the proposed model Siam-UNet3+ in this article demonstrated significant advantages and improvements in four evaluation metrics, namely, precision, recall, F1-score, and overall accuracy (OA), proving to have great potential in the application to highresolution remote sensing image change detection tasks. [ABSTRACT FROM AUTHOR]

In this article, we address the practical needs for the rapid processing and application of aerial photogrammetry remote sensing images, proposing a method based on high-performance computing technology to achieve the real-time, dynamic orthorectified visualization of aerial remote sensing images. This method differs from traditional orthorectification processing and application workflows, utilizing high-performance parallel computing capabilities to directly achieve orthorectification based on raw aerial images. In this paper, we also compare and analyze four different real-time orthorectified visualization schemes to meet various application needs, evaluating the efficiency and accuracy of the proposed method. Finally, experimental results with unmanned aerial vehicle (UAV) flight image data indicate that this method is feasible, with real-time computational results meeting practical application requirements in terms of effect and performance. [ABSTRACT FROM AUTHOR]

Accurate instance segmentation of individual crops is crucial for field management and crop monitoring in smart agriculture. To address the limitations of traditional remote sensing methods in individual crop analysis, this study proposes a novel instance segmentation approach combining UAVs with the YOLOv8-Seg model. The YOLOv8-Seg model supports independent segmentation masks and detection at different scales, utilizing Path Aggregation Feature Pyramid Networks (PAFPN) for multi-scale feature integration and optimizing sample matching through the Task-Aligned Assigner. We collected multispectral data of Chinese cabbage using UAVs and constructed a high-quality dataset via semi-automatic annotation with the Segment Anything Model (SAM). Using mAP as the evaluation metric, we compared YOLO series algorithms with other mainstream instance segmentation methods and analyzed model performance under different spectral band combinations and spatial resolutions. The results show that YOLOv8-Seg achieved 86.3% mAP under the RGB band and maintained high segmentation accuracy at lower spatial resolutions (1.33 ~ 1.14 cm/pixel), successfully extracting key metrics such as cabbage count and average leaf area. These findings highlight the potential of integrating UAV technology with advanced segmentation models for individual crop monitoring, supporting precision agriculture applications. [ABSTRACT FROM AUTHOR]