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In this article, a three-axis stable platform control method based on a lightweight, lowprecision global navigation satellite system (GNSS), inertial measurement unit (IMU) system, and proportion integration and differentiation (PID) algorithm is proposed to solve the stable imaging issues of a low-altitude unmanned airship aerial remote sensing system. The system utilizes a lightweight GNSS/IMU, which work with a dual global positioning system (GPS), connecting the camera system and flight platform through a three-axis stabilization platform, employing a PID control method with an open-loop control approach, and implementing stabilization control using a system-on-chip microcontroller for 32-bit (STM32) control chip circuit to address the camera attitude stabilization issue in the unmanned airship remote sensing system. The stability of the system is verified by a flight test, and the experimental results also show that the method can effectively isolate the effect of the instability of the unmanned airship attitude on the imaging system and effectively improve the imaging quality, which is of considerable significance for improving the accuracy of the unmanned airship aerial survey system. [ABSTRACT FROM AUTHOR]

Tree height and vertical forest structure are important attributes in forestry, but their traditional measurement or assessment in the field is expensive, time-consuming, and often inaccurate. One of the main advantages of using remote sensing data to estimate vertical forest structure is the ability to obtain accurate data for larger areas in a more time- and cost-efficient manner. Temporal changes are also important for estimating and analysing tree heights, and in many countries, national airborne laser scanning (ALS) surveys have been conducted either only once or at specific, longer intervals, whereas aerial surveys are more often arranged in cycles with shorter intervals. In this study, we reviewed all freely available national airborne remote sensing data describing three-dimensional forest structures in Slovenia and compared them with traditional field measurements in an area dominated by uneven-aged forests. The comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data, but they are still useful due to their temporally dense data. Up-to-date data are very important for forest management and the study of forest resilience and resistance to disturbance. Based on field measurements (2013 and 2023) and all remote sensing data, dominant and maximum heights are statistically significantly higher in uneven-aged forests than in mature, even-aged forests. Canopy height diversity (CHD) information, derived from lidar ALS and DAP data, has also proven to be suitable for distinguishing between even-aged and uneven-aged forests. The CHDALS 2023 was 1.64, and the CHDCAS 2022 was 1.38 in uneven-aged stands, which were statistically significantly higher than in even-aged forest stands. [ABSTRACT FROM AUTHOR]

Understanding of the formation and evolution of supraglacial lakes in high mountain regions is crucial for accurately assessing their impact on glacier behaviour, hydrology, and potential hazards such as outburst floods. This article examines the annual spatio-temporal evolution of supraglacial lakes on the Belvedere Glacier between 2000 and 2023. Very high-resolution aerial photography and high-resolution satellite imagery were used to identify supraglacial lakes as small as 37 m² and narrow bands of ice-marginal lakes. The mapping revealed that the well-known Lake Effimero is stable in its position but unstable in size, with variations from 428 m² to 99.7 x 10³ m². These changes are potentially due to snowmelt or glacier dynamics. In 2002, the area of Effimero was at its largest extent observed during the study period. The first appearance of the Lake Effimero was revelated by the Landsat imagery on 27 May 2001, which differed from the findings of other studies. New lakes were observed to form in a manner independent of Effimero formation, exhibiting a consistent annual occurrence with nearly linear area growth up to 9.7 x 10³ m² in 2023. The formation of the lakes is shown to be influenced by their morphological characteristics. [ABSTRACT FROM AUTHOR]

[Objective] This study aims to explore the application of traditional mixed pixel decomposition method in drone remote sensing technology, and to propose a fast estimation method for vegetation cover and management factor (C factor) at a small scale. [Methods] Drone aerial photography was used to capture remote sensing imagery of land use in the Guli area of Jiangning, Nanjing. An object-oriented classification method was utilized to extract the coverage of various land types. The C value of the research area was computed based on the mixed pixel decomposition C factor model, and the method′s accuracy was evaluated by comparing it with previous research results. [Results] The object classification results (vegetation, bare land, and non-photosynthetic land) have an overall accuracy of over 95%. Based on the mixed pixel decomposition C factor model, the estimated C values for forestland, cultivated land, and grassland in the Guli area are 0.057, 0.176, and 0.043, respectively, which are close to existing research results. [Conclusion] Estimating C factor based on UAV remote sensing is feasible and more efficient compared to the traditional method of runoff plot observation. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office 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.)

Mining is an environmentally destructive human activity. Consequently, community expectations and legislation require minimisation of impacts and rehabilitation once mining ceases. Rehabilitation standards now include restoration of structural and functional attributes of pre-disturbed landscapes. However, insufficient baseline data before, and during, mining often makes it difficult to assess impacts and develop rehabilitation objectives. Techniques that retrospectively document the pre-impact condition and environmental history of wetlands affected by mines can provide this information. We demonstrate how this can be achieved using data from palaeoecology and remote sensing, to understand mine impact on Fishermans Wetland, North Stradbroke Island (Minjerribah), by inferring its environmental history from formation to present. Fishermans Wetland is a small, clear, open water, wetland with extensive macrophyte growth. A lack of information about the wetland's pre-mine condition created uncertainty about the effects of upstream sand mining. Contrary to local community concerns that Fishermans Wetland was ancient and hydrologically modified by mining, it only formed in the 1950s. Moreover, changes to site hydrology predated mining. Consequently, ongoing supplementation of water is unnecessary for maintaining the wetland's ecological character. Similar techniques could be used elsewhere where mine impacts are poorly understood or contested. [ABSTRACT FROM AUTHOR]

Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale. [ABSTRACT FROM AUTHOR]

Remote sensing was used to map the invasion of yellow-flowered legumes on the Central Plateau of New Zealand to inform weed management strategy. The distributions of Cytisus scoparius (broom), Ulex europaeus (gorse) and Lupinus arboreus (tree lupin) were captured with high-resolution RGB photographs of the plants while flowering. The outcomes of herbicide operations to control C. scoparius and U. europaeus over time were also assessed through repeat photography and change mapping. A grid-square sampling tool previously developed by Manaaki Whenua—Landcare Research was used to help transfer data rapidly from photography to maps using manual classification. Artificial intelligence was trialled and ruled out because the number of false positives could not be tolerated. Future actions to protect the natural values and vistas of the Central Plateau from legume invasion were identified. While previous control operations have mostly targeted large, highly visible legume patches, the importance of removing outlying plants to prevent the establishment of new seed banks and slow spread has been underestimated. Outliers not only establish new, large, long-lived seed banks in previously seed-free areas, but they also contribute more to range expansion than larger patches. Our C. scoparius and U. europaeus change mapping confirms and helps to visualise the establishment and expansion of uncontrolled outliers. The power of visualizing weed control strategies through remote sensing has supported recommendations to improve outlier control to achieve long-term, sustainable landscape-scale suppression of invasive legumes. [ABSTRACT FROM AUTHOR]

In response to the challenges of remote sensing imagery, such as unmanned aerial vehicle (UAV) aerial imagery, including differences in target dimensions, the dominance of small targets, and dense clutter and occlusion in complex environments, this paper optimizes the YOLOv8n model and proposes an innovative small-object-detection model called DDSC-YOLO. First, a DualC2f structure is introduced to improve the feature-extraction capabilities of the model. This structure uses dual-convolutions and group convolution techniques to effectively address the issues of cross-channel communication and preserving information in the original input feature mappings. Next, a new attention mechanism, DCNv3LKA, was developed. This mechanism uses adaptive and fine-grained information-extraction methods to simulate receptive fields similar to self-attention, allowing adaptation to a wide range of target size variations. To address the problem of false and missed detection of small targets in aerial photography, we designed a Semantics and Detail Infusion Feature Pyramid Network (SDI-FPN) and added a dedicated detection scale specifically for small targets, effectively mitigating the loss of contextual information in the model. In addition, the coordinate adaptive spatial feature fusion (CASFF) mechanism is used to optimize the original detection head, effectively overcoming multi-scale information conflicts while significantly improving small target localization accuracy and long-range dependency perception. Testing on the VisDrone2019 dataset shows that the DDSC-YOLO model improves the mAP0.5 by 9.3% over YOLOv8n, and its performance on the SSDD and RSOD datasets also confirms its superior generalization capabilities. These results confirm the effectiveness and significant progress of our novel approach to small target detection. [ABSTRACT FROM AUTHOR]

The introduction of airborne laser scanning (ALS) technology in the Mediterranean region over the past decade has significantly increased opportunities for archaeological research. Archaeological remote sensing has proven to be a versatile tool with numerous applications beyond simple site mapping. One approach is the large-scale interpretation of ALS data, which allows for the analysis of the stratigraphic information contained within the data. This is particularly useful for analysing the rich remains of past land use in the karst landscapes of coastal Croatia. The Roman dry stone walls of the Kvarner islands of Krk and Cres serve as an example. These structures outline the framework backbone of Roman surveying; however, due to their poor state of preservation, many remains can only be identified through ALS data rather than aerial photography. An absolute chronology for these features was established using the OSL profiling and dating method (OSL-PD), providing a date range of AD 200 ± 100. These results can be considered the first clear evidence of surveying municipal lands on the Croatian islands. [ABSTRACT FROM AUTHOR]

Tree detection and canopy area measurement are important and difficult tasks in forest inventory, which are important for understanding forest stand structure. This study utilized remotely piloted aircraft (RPA) aerial photography technology to collect remote sensing images of forests in Xiong County, China, creating a dataset comprising 1200 images of six tree species. Based on this dataset, the paper proposes an optimized model, Att-Mask R-CNN, for canopy detection and segmentation. Att-Mask R-CNN outperforms the original models (Mask R-CNN and MS R-CNN) by achieving 65.29% mean average precision for detection, 80.44% mean intersection over union for segmentation, and 90.67% overall recognition rate for the six tree species. In addition, a pixel statistics method based on segmentation masks is introduced for estimating the vertical projected area of individual tree crowns, and comparisons between the measured and predicted vertical projected area of the crowns of six tree species (100 trees of each class) show an overall goodness-of-fit R2 of 85% and a relative root-mean-square error rRMSE of 12.81%. By using remote sensing images from RPAs and optimizing existing deep learning models, the detection and segmentation of individual tree canopies can be achieved, resulting in a more accurate understanding of forest structure, which provides scientific support for forest management and resource monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Virtual Archaeology Review is the property of Virtual Archaeology Review 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.)

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture 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.)

Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image‐derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R2 (p < 0.026). Currently, BaMGRID is best‐suited for site‐by‐site analysis to support practical applications at the reach scale; continuous, basin‐wide mapping of river bathymetry will require additional research. Key Points: Combine depth measurements made during site visits to gaging stations with archived images to enable remote sensing of river bathymetryMultispectral satellite images acquired daily can yield highly accurate depth estimates, but high resolution air photos were less accurateBathymetric Mapping using Gage Records and Image Databases (BaMGRID) is well suited to site‐by‐site analysis for reach‐scale applications [ABSTRACT FROM AUTHOR]

The advancement of UAV technologies has enabled the automated capture of photos and videos, eliminating the need for manual intervention in flight control. (Research purpose) This research aims to conduct a retrospective analysis of the evolution offlight control systems and the development of aerial photography equipment for agricultural land, covering the period from the mid-19th century to present. (Materials and methods) A systematic literature review was conducted using the historical-analytical method. The paper examines original works by both domestic and international authors, including monographs, scientific journals, conference proceedings, museum exhibitions, photographic archives, and open-source software code. (Results and discussion) The paper identifies six distinct phases in the development of aerial photography and flight control systems. The classification is based on key innovations in camera types, control systems, and aircraft designs. Each phase highlights the predominant cameras, control systems, and aircraft utilized for agricultural applications. (Conclusions) Over the past 165 years, notable changes have occurred in aerial photography parameters, including the type of photographic material, image spatial and spectral resolution, camera weight and mounting, shutter types and their mechanisms, inertial control units, integrated GPS/GLONASS receivers, and light sensors. In terms of flight control systems for UAVs, significant developments over the last 106 years include variations in flight control types, the number offlight-stabilizing sensors, obstacle detection systems, size of the flight control units, flight modes, and takeoff/landing techniques, along with interfaces for attachments. It is anticipated that future intellectualization and miniaturization of flight control systems will not only boost UAV performance but also reduce the economic costs associated with the aerial monitoring of agricultural biological assets.

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git. [ABSTRACT FROM AUTHOR]

Barrier islands are highly dynamic coastal landforms that are economically, ecologically, and societally important. Woody vegetation located within barrier island interiors can alter patterns of overwash, leading to periods of periodic‐barrier island retreat. Due to the interplay between island interior vegetation and patterns of barrier island migration, it is critical to better understand the factors controlling the presence of woody vegetation on barrier islands. To provide new insight into this topic, we use remote sensing data collected by LiDAR, LANDSAT, and aerial photography to measure shrub presence, coastal dune metrics, and island characteristics (e.g., beach width, island width) for an undeveloped mixed‐energy barrier island system in Virginia along the US mid‐Atlantic coast. We apply decision tree and random forest machine learning methods to identify new empirical relationships between island geomorphology and shrub presence. We find that shrubs are highly likely (90% likelihood) to be present in areas where dune elevations are above ∼1.9 m and island interior widths are greater than ∼160 m and that shrubs are unlikely (10% likelihood) to be present in areas where island interior widths are less than ∼160 m regardless of dune elevation. Our machine learning predictions are 90% accurate for the Virginia Barrier Islands, with almost half of our incorrect predictions (5% of total transects) being attributable to system hysteresis; shrubs require time to adapt to changing conditions and therefore their growth and removal lags changes in island geomorphology, which can occur more rapidly. Plain Language Summary: In this study, we present two machine learning models for predicting the presence of shrubs on barrier islands. We use data derived from satellites, LiDAR, and aerial imagery to create machine learning models. Using these models, we find that whether or not shrubs are present on barrier islands depends on dune elevation and the width of island interior sustained over time; sufficiently high dune elevations and sufficiently wide widths are necessary to support shrubs. Additionally, in certain areas, we observe a lag between predicted and observed behavior. We attribute this lag to the different time scales over which shrub and barrier island geomorphology processes operate; barrier island geomorphology can change rapidly, but it can take several years for shrubs to respond to these changes. Key Points: Decision tree analysis and random forest modeling can predict shrub presence on barrier islands in Virginia with ∼90% accuracyShrub presence on barrier islands correlates with dune elevations >1.9 m and maintenance of island interior widths >160 m over a ∼6‐year periodShrub establishment and removal lags changes in geomorphic conditions, indicating hysteresis [ABSTRACT FROM AUTHOR]

A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on "space-sky-earth" remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have become a popular and cost-effective technology in urban scenarios, encompassing applications such as material transportation, aerial photography, remote sensing, and disaster relief. However, the execution of prolonged tasks poses a heightened challenge owing to the constrained endurance of UAVs. This paper proposes a model to accurately represent urban scenarios and an unmanned system. Restricted zones, no-fly zones, and building obstructions to the detection range are introduced to make sure the model is realistic enough. We also introduced an unmanned ground vehicle (UGV) into the model to solve the endurance of the UAVs in this long-time task scenario. The UGV and UAVs constituted a heterogeneous unmanned system to collaboratively solve the path-planning problem in the model. Building upon this model, this paper designs a Three-stage Alternating Optimization Algorithm (TAOA), involving two crucial steps of prediction and rolling optimization. A three-stage scheme is introduced to rolling optimization to effectively address the complex optimization process for the unmanned system. Finally, the TAOA was experimentally validated in both synthetic scenarios and scenarios modeled based on a real-world location to demonstrate their reliability. The experiments conducted in the synthetic scenarios aimed to assess the algorithm under hypothetical conditions, while the experiments in the scenarios based on real-world locations provided a practical evaluation of the proposed methods in more complex and authentic environments. The consistent performance observed across these experiments underscores the robustness and effectiveness of the proposed approaches, supporting their potential applicability in various real-world scenarios. [ABSTRACT FROM AUTHOR]

The article explores the use of remote sensing techniques to study the Boynton Mounds Complex in Palm Beach County, Florida. The site, which contains earthworks and mounds, has been investigated since the 1970s and is believed to have a long history of habitation. The research aims to analyze the site's design and its relationship with other sites in the region. The article emphasizes the significance of remote sensing in archaeology, as it provides valuable information about ancient earthworks that may have been lost due to development or agriculture. The findings from the analysis of the Boynton Mounds Complex contribute to a better understanding of the site's layout and reveal similarities to other earthwork sites in the area. The article also highlights the importance of using new survey technologies to re-examine previously investigated sites. [Extracted from the article]

Landslides represent significant risks to human activity, leading to infrastructure damage and loss of life. This study focuses on assessing landslide hazards in Utmah Region, Yemen. The evaluation involves comparing the effectiveness of the relative frequency ratio model with five machine learning algorithms (MLAs) for hazard mapping. Field surveys, high-resolution satellite imagery, and aerial photography were utilized in the study. The inventory map was generated after identifying and mapping 100 landslides. The inventory was then divided randomly into a training dataset (70 landslides) and a validation dataset (30 landslides), with an equal number of non-landslide pixels. Eleven additional landslide conditioning factors were collected from various sources, and the frequency ratio (FR) approach was employed to identify the most crucial variables for modeling. The models were rigorously tested and assessed using statistical metrics, including the Friedman and Wilcoxon signed-rank tests, as well as the area under the receiver operating characteristics (AUROC) curve. The findings based on the training and validation datasets revealed that the RF algorithm (AUROC, 0.992) outperformed the other models in generating hazard maps. The XGBoost model (AUROC, 0.991), NB model (AUROC, 0.970), ANN model (AUROC, 0.922), KNN model (AUROC, 0.877), and FR (AUROC, 0.674) were found to be less effective. Consequently, the study highlights that the Random forest (RF) model shows promise as an effective approach for predicting landslides spatially on a global scale. [ABSTRACT FROM AUTHOR]

Introduction: Land use classification plays a critical role in analyzing land use/cover change (LUCC). Remote sensing land use classification based on machine learning algorithm is one of the hot spots in current remote sensing technology research. The diversity of surface objects and the complexity of their distribution in mixed mining and agricultural areas have brought challenges to the classification of traditional remote sensing images, and the rich information contained in remote sensing images has not been fully utilized. Methods: A quantitative difference index was proposed quantify and select the texture features of easily confused land types, and a random forest (RF) classification method with multi-feature combination classification schemes for remote sensing images was developed, and land use information of the mine-agriculture compound area of Peixian in Xuzhou, China was extracted. Results: The quantitative difference index proved effective in reducing the dimensionality of feature parameters and resulted in a reduction of the optimal feature scheme dimension from 57 to 22. Among the four classification methods based on the optimal feature classification scheme, the RF algorithm emerged as the most efficient with a classification accuracy of 92.38% and a Kappa coefficient of 0.90, which outperformed the support vector machine (SVM), classification and regression tree (CART), and neural network (NN) algorithm. Conclusion: The findings indicate that the quantitative differential index is a novel and effective approach for discerning distinct texture features among various land types. It plays a crucial role in the selection and optimization of texture features in multispectral remote sensing imagery. Random forest (RF) classification method, leveraging a multi-feature combination, provides a fresh method support for the precise classification of intricate ground objects within the mine-agriculture compound area. [ABSTRACT FROM AUTHOR]

Visible-infrared person re-identification (VI-ReID) aims at matching pedestrian images with the same identity between different modalities. Existing methods ignore the problems of detailed information loss and the difficulty in capturing global features during the feature extraction process. To solve these issues, we propose a Transformer-based Feature Compensation Network (TFCNet). Firstly, we design a Hierarchical Feature Aggregation (HFA) module, which recursively aggregates the hierarchical features to help the model preserve detailed information. Secondly, we design the Global Feature Compensation (GFC) module, which exploits Transformer's ability to capture long-range dependencies in sequences to extract global features. Extensive results show that the rank-1/mAP of our method on the SYSU-MM01 and RegDB datasets reaches 60.87%/58.87% and 91.02%/75.06%, respectively, which is better than most existing excellent methods. Meanwhile, to demonstrate our method's transferability, we also conduct related experiments on two aerial photography datasets. [ABSTRACT FROM AUTHOR]

Aerial photography is becoming an integral part of remote sensing in digital agriculture. The first aerial photographs were taken in the mid-19th century. (Research purpose) The paper aims to retrospectively analyze the evolution of aerial photography equipment for capturing agricultural lands, beginning with the creation of the first aerial photograph up to the present day. (Materials and methods) A historical-analytical approach was employed to examine the existing literature. Within this study, the development of agricultural aerial photography equipment was categorized into four distinct time periods: 1885-1908, 1909-1945, 1946-1979, and from 1980 to the present day. (Results and discussion) In the initial phase of experimental aerial photographic equipment development, significant advancements were achieved, encompassing the emergence of the first photograph, the creation of portable cameras and their adaptation for use with hot air balloons and kites, rockets, and birds. Technological growth in the first half of the 20th century contributed to elevating aerial photography to a versatile tool applied for a wide range of intelligence operations, including agricultural tasks. The evolution of space technologies in the second half of the 20th century resulted in the rapid development of both aerial photography equipment and their carriers. This progress facilitated the use of color aerial photography for the examination of the Earth's surface. The advancements of digital technologies at the end of the 20th century and the beginning of the 21st century facilitated the use of high-resolution digital aerial cameras mounted on various carrier platforms, ranging from unmanned aircraft to artificial Earth satellites. (Conclusions) A retrospective analysis reveals that the development and creation of equipment for aerial photography of agricultural lands unfolded in a sporadic fashion. This progression was closely intertwined with global political, social, and economic situation, as well as the state of technological advancement in related areas. Over the coming decade, the sustained application of aerial photography in agriculture is poised to enhance the efficiency of unmanned aircraft, reduce the production costs associated with aerial photography, and facilitate the widespread adoption of digital remote sensing technology within the agricultural sector.

This report presents an analysis of the potential of using the information product ATL08 derived from the ATLAS/ICESat-2 (Advanced Topographic Laser Altimeter System/Ice, Cloud, and land Elevation Satellite) satellite lidar to determine the height of protective forest stands. Height measurements, corresponding to vegetation based on lidar data from 2019 to 2022, were compared with the results of aerial photography processing conducted in Volgograd oblast in 2022. A significant strong correlation was found between the mean and maximum canopy heights determined from aerial survey and laser scanning data for 20 × 14-m segments with a woody and shrub vegetation cover exceeding 50%. For the mean canopy height, the root mean square error (RMSE) is ±0.7 m and coefficient of determination R2 = 0.85; for the maximum canopy height, RMSE = 2.2 m and R2 = 0.83. Comparison of projective cover using lidar data, calculated as the ratio of the number of photons above a certain threshold height to the total number of photons in the segment, with aerial photography data showed insufficient accuracy of this approach. The results suggest that ATL08 lidar data holds promise for evaluating the height of protective forest stands, although it may not be suitable for determining projective cover. [ABSTRACT FROM AUTHOR]

Remote sensing applications have a long history in treeline research. Recent reviews have examined the topic mainly from a methodological point of view. Here, we propose a question‐oriented review of remote sensing in treeline ecology to relate remote sensing methodologies to key ecological metrics and identify knowledge gaps and promising areas for future research. We performed a meta‐analysis to assess the role of remote sensing as a tool for measuring spatial patterns and dynamics of alpine and Arctic treeline ecotone globally. We assessed the geographic distribution, scale of analysis, and relationships between remote sensing techniques and treeline ecological metrics through co‐occurrence mapping and multivariate statistics. Our analysis revealed that only 10% of treeline ecology studies applied remote sensing tools, often associated with the keyword 'climate change'. Monitoring studies adopted coarser spatial resolutions over longer temporal extents in comparison with other treeline studies. A multiscale and multi‐sensor spatial approach was implemented in just 19% of papers. Long‐term research commonly relied on aerial and oblique photography to measure treeline shifts through photointerpretation within a multidisciplinary framework. More recent treeline dynamics were often quantified using greenness trends derived from the pixel‐based classification of satellite images. Many recent short‐term studies focused on delineating tree scale metrics derived from the object‐based classification of uncrewed aerial vehicle (UAV) images or LiDAR data. Over the past decade, high‐resolution and low‐cost UAV remote sensing has emerged as an interesting opportunity to fill the gap between local‐scale ecological patterns and coarse‐resolution satellite sensors. Additionally, treeline remote sensing applications would strongly benefit from multidisciplinary frameworks that integrate field studies in ecology and environmental science. The multi‐dimensional structural complexity of treelines typically responds to environmental drivers over multiple scales and thus is best described with multiscale and multi‐sensor approaches. [ABSTRACT FROM AUTHOR]

Moreover, resources such as Copernicus not only make access to imagery simple, but also provide analysis-ready data and even data products so that specialist knowledge on extracting information from the remotely sensed data is sometimes no longer necessary. At that time, researchers were essentially constrained to data acquired by airborne systems such as aerial photography and multispectral scanners together with only limited options for satellite sensor imagery, notably from Landsat sensors and the NOAA AVHRR. Many articles have made use of Landsat sensor data (Moris et al. [32]; Hopkins et al. [23]) notably taking advantage of the relatively long time series of data that has now been formed (Zhao et al. [57]; Bost et al. [5]; Jung et al. [24]; Fisher et al. [16]; Yu et al. [55]). Graph The Allerton Park workshop essentially defined landscape ecology as a field of study that cuts across multiple natural and social sciences (Risser et al. [42]). [Extracted from the article]

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Geospatial technologies have become an essential component of archaeological research, aiding in the identification, mapping, and analysis of archaeological sites. Several journals have published existing narratives on the development and impact of geospatial technologies in the study of archaeology and cultural heritage. However, this has not been supported by a systematic review of articles and papers, where meticulously collected evidence is methodically analysed. This article systematically reviews the trends in the use of geospatial technologies in archaeology and cultural heritage through the search for keywords or terms associated with geospatial technologies used in the two fields on the Scopus database from 1990 to 2022. Bibliometric analysis using the Scopus Analyze tool and analysis of bibliometric networks using VOSviewer visualisations reveals how modern archaeological studies are now a significant discipline of spatial sciences and how the discipline enjoys the tools of geomatic engineering for establishing temporal and spatial controls on the material being studied and observing patterns in the archaeological records. The key concepts or themes or distinct knowledge domains that shape research in the use of geospatial technologies in archaeology and cultural heritage, according to the Scopus database (1990–2022), are cultural heritage, archaeology, geographic information systems, remote sensing, virtual reality, and spatial analysis. Augmented reality, 3D scanning, 3D modelling, 3D reconstruction, lidar, digital elevation modelling, artificial intelligence, spatiotemporal analysis, ground penetrating radar, optical radar, aerial photography, and unmanned aerial vehicles (UAVs) are some of the geospatial technology tools and research themes that are less explored or less interconnected concepts that have potential gaps in research or underexplored topics that might be worth investigating in archaeology and cultural heritage. [ABSTRACT FROM AUTHOR]

The extensive application of unmanned aerial vehicle (UAV) technology has increased academic interest in object detection algorithms for UAV images. Nevertheless, these algorithms present issues such as low accuracy, inadequate stability, and insufficient pre-training model utilization. Therefore, a high-quality object detection method based on a performance-improved object detection baseline and pretraining algorithm is proposed. To fully extract global and local feature information, a hybrid backbone based on the combination of convolutional neural network (CNN) and vision transformer (ViT) is constructed using an excellent object detection method as the baseline network for feature extraction. This backbone is then combined with a more stable and generalizable optimizer to obtain high-quality object detection results. Because the domain gap between natural and UAV aerial photography scenes hinders the application of mainstream pre-training models to downstream UAV image object detection tasks, this study applies the masked image modeling (MIM) method to aerospace remote sensing datasets with a lower volume than mainstream natural scene datasets to produce a pre-training model for the proposed method and further improve UAV image object detection accuracy. Experimental results for two UAV imagery datasets show that the proposed method achieves better object detection performance compared to state-of-the-art (SOTA) methods with fewer pre-training datasets and parameters. [ABSTRACT FROM AUTHOR]

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey's (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD. [ABSTRACT FROM AUTHOR]

The official establishment of China's national parks marks a new stage in the construction of China's ecological civilization system. National parks systematically protect the areas with the richest biodiversity and the most complete ecosystem processes in China. This is beneficial not only for China's natural conservation work, but also for the world's response to environmental issues, such as climate change. Based on remote sensing images of land use in the four periods 1990, 2000, 2010, and 2020, this study calculated the land use changes in each national park during the corresponding period. Using the Plus model LEAS module, the driving factors of land use change in the national parks were studied and explored. In addition, the study used the InVEST model carbon storage module, using remote sensing images from different periods and the corresponding carbon pools of each national park as the basic data for model operation, to obtain the carbon storage changes in each national park over the past 30 years. Based on the hotspot analysis function, the hotspot areas of carbon storage changes in the national parks in the past 30 years were determined. Consequently, based on the CARS module of the PLUS model, the carbon storage in Northeast Tiger and Leopard National Park in 2030 was estimated under different scenarios. Research suggested that, except for Sanjiangyuan National Park where grassland is the main land use type, the other four national parks are all dominated by forests, and the expansion and changes in the main land use types were due to human activities. In the past 30 years, the carbon storage in China's national park ecosystem has mainly shown a trend of first increasing and then gradually decreasing. Based on the changes in carbon storage in the national park, restoration scenarios were simulated for the core protected and generally controlled areas of Northeast Tiger and Leopard National Park. Under the ideal scenario, the highest value of carbon storage would be achieved by 2030, which would be 7,468,250 t higher than that in 2020. The present study provides a reference for the regional management of China's national parks and further confirms that the implementation of the national park system can enhance China's ability to achieve carbon peaking and neutrality goals. [ABSTRACT FROM AUTHOR]

Accurate maps of subsurface tile drainage lines are needed for agronomic and environmental research studies and the maintenance of current tile drainage systems. In this study, tile lines at the Purdue University Agronomy Center for Research and Education near West Lafayette, Indiana were located using a combination of visual aerial photo interpretation, expert knowledge, and paper construction drawings. The mapping accuracy was assessed at 27 locations where tile lines were located physically using a tile probe. Tile lines were correctly predicted 89% of the time with an average spatial accuracy of ±1.23 m of the true tile locations. This approach was better than a previous tile line location map prepared using an automated remote sensing method, which had an average spatial accuracy of ±2.12 m. Core Ideas: Tile lines were located based on visual aerial photo interpretation, paper maps, and expert knowledge.Photo interpretation was a useful method to map unknown tile lines and provided better results than remote sensing.Accurate location of tile lines is vital for agronomic and environmental research studies. [ABSTRACT FROM AUTHOR]

The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges' rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties. [ABSTRACT FROM AUTHOR]

Approaches to aerial photography and remote sensing have become increasingly complex, can rely on opaque workflows, and have the potential to be published with inaccessible language. Conversely, aerial capture has become increasingly accessible with affordable, user-friendly unmanned aerial systems (UAS) now being commonplace in the field-archaeology toolkit. This means that considerable amounts of data are being produced by diverse projects, yet only a limited quantity are subject to advanced processing techniques. This paper aims to address this imbalance through a low-cost, accessible workflow that pairs frequent (multi-temporal) surveys with straightforward, out of the box processing. The results are comparable to more complex methodologies without the need to invest in expensive hardware (although a fast computer will make processing quicker) or abstract workflows. The detail and depth are still available if needed, but the aim is to make the interpretation of a wide range of imagery easier, rather than focus on the mechanics of the phenomena. The results demonstrate an effective, inexpensive and user-friendly workflow that requires only limited computational skills, but which offers robust, highly interpretable results. [ABSTRACT FROM AUTHOR]

Multi-temporal land use/land cover (LULC) change analysis is essential for environmental planning and recourses management. Various global LULC datasets are available now. However, they do not show strong consistency on a regional scale and are mainly time limited. Therefore, high-quality multi-temporal LULC mapping with reasonable consistency on a regional scale is still demanding. In this study, using the Landsat 7, Landsat 8, and the NASA digital elevation model (DEM), LULC mapping of the Tigris and Euphrates rivers basin (TEB) was performed by random forest (RF) classifier in the Google Earth Engine platform during 2000–2022. The spectral bands, spectral indices, morphological, and textural features were applied in the developed procedure. The results indicated that the proposed approach had accurate performance (accuracy = 0.893 and an F score = 0.820) with a good consistency with previous studies. The feature importance evaluation was carried out using Gini index, and spectral indices were identified as the most important features in LULC mapping. Overall, severe LULC change has happened in the TEB during the last two decades. Our results revealed the expansion of water and built-up classes while trees class has experienced a decreasing trend. From a regional perspective, three main areas in the east and south-east of Iraq, north-west of Iraq, and east of Syria were identified where LULC change was intense. These areas are prone to land degradation and dust storms emission problems, and it is necessary to take steps to prevent severe LULC changes in them. [ABSTRACT FROM AUTHOR]

Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex, making them challenging for end-users, such as those who use or create WUI maps, to apply. Aims: We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs and Woolsey. Methods: We evaluated a CNN-based building dataset and a CNN model from a separate commercial vendor to detect buildings from high-resolution imagery. This dataset and model represent to end-users the state of the art of what is readily available for potential WUI mapping. Key results: We found moderate accuracies for the building dataset and the CNN model and a severe underestimation of buildings and their destruction rates where trees occluded buildings. The CNN model performed best post-fire with accuracies ≥73%. Conclusions: Existing CNNs may be used with moderate accuracy for identifying individual buildings post-fire and mapping the extent of the WUI. The implications are, however, that CNNs are too inaccurate for post-fire damage assessments or building counts in the WUI. Wildland–urban interface (WUI) maps identify communities at risk from wildfires. However, WUI maps are often outdated. We evaluated a pre-trained convolutional neural network (CNN) model and CNN-based building dataset and found that they were too inaccurate to estimate building counts and destruction, but sufficient to map where WUI is. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of remote sensing and GIS techniques applied on aerial photographs for forests spatio-temporal dynamics analysis. An assessment of the changes in the distribution and extension of Kiadi cork oak forest was carried out using historical imagery, covering a period of 35 years. The results indicate that, roadways and building surfaces in Kiadi forest have increased by 9.71 and 3.86% respectively, while the surface initially covered by vegetation decreased by 13.57%, as a result of anthropogenic disturbance. Digital processing of historical aerial photographs proved to be a powerful tool for quantitative analysis of forest dynamics.

Proper management of the earth's natural resources is imperative to combat further degradation of the natural environment. However, the environmental datasets necessary for informed resource planning and conservation can be costly to collect and annotate. Consequently, there is a lack of publicly available datasets, particularly annotated image datasets relevant for environmental conservation, that can be used for the evaluation of machine learning algorithms to determine their applicability in real-world scenarios. To address this, the Time-evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) project in New Zealand aims to provide a collection of datasets and accompanying example notebooks for their analysis. This paper showcases three New Zealand-based annotated image datasets that form part of the collection. The first dataset contains annotated images of various predator species, mainly small invasive mammals, taken using low-light camera traps predominantly at night. The second provides aerial photography of the Waikato region in New Zealand, in which stands of Kahikatea (a native New Zealand tree) have been marked up using manual segmentation. The third is a dataset containing orthorectified high-resolution aerial photography, paired with satellite imagery taken by Sentinel-2. Additionally, the TAIAO web platform also contains a collated list of other datasets provided and licensed by our data partners that may be of interest to other researchers. [ABSTRACT FROM AUTHOR]

Flooding events have been negatively affecting the Republic of Kazakhstan, with higher occurrence in flat parts of the country during spring snowmelt in snow-fed rivers. The current project aims to assess the flood hazard reduction capacity of Alva irrigation system, which is located in the interfluve area of Yesil and Nura Rivers. The assessment is performed by simulating spring floods using HEC-RAS 2D and controlling the gates of the existing system. A digital elevation model of the study domain was generated by integration of Sentinel-1 radar images with the data obtained from bathymetrical survey and aerial photography. Comparison of the simulated inundation area with a remote sensing image of spring flood in April 2019 indicated that the main reason for differences was due to local snowmelt in the study domain. Exclusion of areas flooded by local snowmelt, which were identified using the updated DEM, from comparison increased the model similarity to 70%. Further simulations of different exceedance probability hydrographs enabled classification of the study area according to maximum flood depth and flood duration. Theoretical changes on the dam crest as well as additional gates were proposed to improve the system capacity by flooding agriculturally important areas, which were not flooded during the simulation of the current system. The developed model could be used by local authorities for further development of flood mitigation measures and assessment of different development plans of the irrigation system. [ABSTRACT FROM AUTHOR]

This research article presents an overview of the role of carbon farming in mitigating climate change by sequestering carbon in soil and vegetation. The article highlights the potential of remote sensing technology for monitoring carbon farming practices and CO2 absorption. Carbon farming practices, such as conservation tillage, cover cropping, crop rotation, and agroforestry, are discussed. The article explains the application of remote sensing technology, including satellite-based remote sensing, aerial photography, and ground-based sensors, in monitoring changes in carbon sequestration and CO2 absorption. The article concludes that remote sensing technology provides a powerful tool for monitoring carbon farming and CO2 absorption and is likely to become even more effective in the future as technology continues to advance. [ABSTRACT FROM AUTHOR]