Search Results (723)

Tropical evergreen forests represent the richest biodiversity in terrestrial ecosystems, and the fine spatial-temporal resolution mapping of these forests is essential for the study and conservation of this vital natural resource. The current methods for mapping tropical evergreen forests frequently exhibit coarse spatial resolution and lengthy production cycles. This can be attributed to the inherent challenges associated with monitoring diverse surface changes and the persistence of cloudy, rainy conditions in the tropics. We propose a novel approach to automatically map annual 10 m tropical evergreen forest covers from 2017 to 2023 with the Sentinel-2 Dynamic World dataset in the biodiversity-rich and conservation-sensitive Central African Republic (CAR). The Copernicus Global Land Cover Layers (CGLC) and Global Forest Change (GFC) products were used first to track stable evergreen forest samples. Then, initial evergreen forest cover maps were generated by determining the threshold of evergreen forest cover for each of the yearly median forest cover probability maps. From 2017 to 2023, the annual modified 10 m tropical evergreen forest cover maps were finally produced from the initial evergreen forest cover maps and NEFI (Non-Evergreen Forest Index) images with the estimated thresholds. The results produced by the proposed method achieved an overall accuracy of >94.10% and a Cohen’s Kappa of >87.63% across all years (F1-Score > 94.05%), which represents a significant improvement over the performance of previous methods, including the CGLC evergreen forest cover maps and yearly median forest cover probability maps based on Sentinel-2 Dynamic World. Our findings demonstrate that the proposed method provides detailed spatial characteristics of evergreen forests and time-series change in the Central African Republic, with substantial consistency across all years. Full article

Estuarine wetlands are the transition zone between marine, freshwater, and terrestrial ecosystems and are more ecologically fragile. In recent years, the spread of exotic vegetation, specifically Spartina alterniflora, in the Yellow River estuary wetlands has significantly encroached upon the habitats of native species such as Phragmites australis, Suaeda glauca Bunge, and Tamarix chinensis Lour. With advances in land prediction modeling, predicting wetland vegetation distribution can aid management and decision-making for ecological restoration. We selected the core area as the study object and coupled the hydrological model MIKE 21 with the PLUS model to predict the potential future distribution of invasive and dominant species in the region. (1) Based on the fine classification results from satellite images of GF1/G2/G5, we gained an understanding of the changes in wetland vegetation types in the core area of the reserve in 2018 and 2020. (2) Using public data such as ERA5 and GEO as input for basic environmental data, using MIKE 21 to provide high-spatial-resolution hydrodynamic parameters for the PLUS model as an environmental driver, we modeled the spatial distribution of various wetland vegetation in the Yellow River estuary wetland in Dongying under different artificial restoration measures. (3) We predicted the 2022 distribution of typical vegetation in the region, used the classification results of GF6 as the actual distribution, compared the spatial distribution with the actual distribution, and obtained a kappa coefficient of 0.78; the predicted values of the model are highly consistent with the true values. This study combines the fine classification results of vegetation based on hyperspectral remote sensing, the construction of a coupled model, and the prediction effect of typical species, providing a reference for constructing and optimizing the vegetation prediction model of estuarine wetlands. It also allows scientific and effective decision-making for the management of ecological restoration of delta wetlands. Full article

Beach surface moisture (BSM) is crucial to studying coastal aeolian sand transport processes. However, traditional measurement techniques fail to accurately monitor moisture distribution with high spatiotemporal resolution. Remote sensing technologies have garnered widespread attention for providing rapid and non-contact moisture measurements, but a single method has inherent limitations. Passive remote sensing is challenged by complex beach illumination and sediment grain size variability. Active remote sensing represented by LiDAR (light detection and ranging) exhibits high sensitivity to moisture, but requires cumbersome intensity correction and may leave data holes in high-moisture areas. Using machine learning, this research proposes a BSM inversion method that fuses UAV (unmanned aerial vehicle) orthophoto brightness with intensity recorded by TLSs (terrestrial laser scanners). First, a back propagation (BP) network rapidly corrects original intensity with in situ scanning data. Second, beach sand grain size is estimated based on the characteristics of the grain size distribution. Then, by applying nearest point matching, intensity and brightness data are fused at the point cloud level. Finally, a new BP network coupled with the fusion data and grain size information enables automatic brightness correction and BSM inversion. A field experiment at Baicheng Beach in Xiamen, China, confirms that this multi-source data fusion strategy effectively integrates key features from diverse sources, enhancing the BP network predictive performance. This method demonstrates robust predictive accuracy in complex beach environments, with an RMSE of 2.63% across 40 samples, efficiently producing high-resolution BSM maps that offer values in studying aeolian sand transport mechanisms. Full article

Orchids have evolved flowers with unique morphologies through coevolution with pollinators, such as insects. Among the floral organs, the lip (labellum), one of the three petals, exhibits a distinctive shape and plays a crucial role in attracting pollinators and facilitating pollination in many orchids. The lip of the terrestrial orchid Habenaria radiata is shaped like a flying white bird and is believed to attract and provide a platform for nectar-feeding pollinators, such as hawk moths. To elucidate the mechanism of lip morphogenesis, we conducted time-lapse imaging of blooming flowers to observe the extension process of the lip and analyzed the cellular morphology during the generation of serrations. We found that the wing part of the lip folds inward in the bud and fully expands in two hours after blooming. The serrations of the lip were initially formed through cell division and later deepened through polar cell elongation. Transcriptome analysis of floral buds revealed the expression of genes involved in floral organ development, cell division, and meiosis. Additionally, genes involved in serration formation are also expressed in floral buds. This study provides insights into the mechanism underlying the formation of the unique lip morphology in Habenaria radiata. Full article

Variations in vegetation indices derived from multispectral images and digital terrain models from satellite imagery have been successfully used for reclamation and hazard management in former mining areas. However, low spatial resolution and the lack of sufficiently detailed information on surface morphology have restricted such studies to large sites. This study investigates the application of small, unmanned aerial vehicles (UAVs) equipped with multispectral sensors for land cover classification and vegetation monitoring. The application of UAVs bridges the gap between large-scale satellite remote sensing techniques and terrestrial surveys. Photogrammetric terrain models and orthoimages (RGB and multispectral) obtained from repeated mapping flights between November 2023 and May 2024 were combined with an ALS-based reference terrain model for object-based image classification. The collected data enabled differentiation between natural forests and areas affected by former mining activities, as well as the identification of variations in vegetation density and growth rates on former mining areas. The results confirm that small UAVs provide a versatile and efficient platform for classifying and monitoring mining areas and forested landslides. Full article

The continuous development of satellite imagery, coupled with advancements in machine learning technologies, allows detailed mapping of terrestrial landscapes. This study evaluates the classification performance of tree typologies using Sentinel-2 and PRISMA data, focusing on central Italy’s different areas. The purpose is to assess the role of spectral and spatial resolution in land cover classification, contributing to forest management and conservation efforts. Random Forest Classifier was applied to classify tree typologies across two study areas: the Roman Coastal region and the Lake Vico Basin. Ground truth (GT) data, collected from a trial citizen survey campaign, were used for training and validation. PRISMA datasets, particularly when processed with PCA, consistently outperformed Sentinel-2. The PRISMA PCA dataset achieved the highest overall accuracy with 71.09% for the Roman Coastal region and 87.15% for the Lake Vico Basin, emphasizing the value of spectral resolution. However, Sentinel-2 showed comparative strength in spatially heterogeneous areas. Tree typologies with more uniform distribution, such as hazelnut and chestnut, achieved higher classification accuracy compared to mixed-species forests. The study assesses that Sentinel-2 remains a viable alternative where spatial resolution is critical also considering the limited PRISMA images’ availability. Moreover, the work explores the potential of combining satellites and accurate GT for improved land cover mapping. Full article

Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This paper introduces a new pipeline for camera–LiDAR post-registration to produce colorized point clouds. Utilizing deep learning-based matching between 2D spherical projection LiDAR feature layers and camera images, we can map 3D LiDAR coordinates to image grey values. Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. Registration is achieved using collinearity equations and RANSAC to remove false matches. The pipeline’s accuracy is tested using survey-grade terrestrial datasets from the TX5 scanner, as well as datasets from a custom-made, low-cost mobile mapping system (MMS) named Simultaneous Localization And Mapping Multi-sensor roBOT (SLAMM-BOT) across diverse scenes, in which both outperformed their baseline solutions. SuperGlue performed best in high-feature scenes, whereas LoFTR performed best in low-feature or sparse data scenes. The LiDAR intensity layer had the strongest matches, but combining feature layers improved matching and reduced errors. Full article

The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns. Full article

Conventional methodologies often struggle in accurately positioning underwater habitats and elucidating the complex interactions between terrestrial and aquatic environments. This study proposes an innovative methodology to bridge the gap between these domains, enabling integrated 3D mapping and underwater positioning. The method integrates UAV (Uncrewed Aerial Vehicles) photogrammetry for terrestrial areas with underwater photogrammetry performed by a snorkeler. The innovative aspect of the proposed approach relies on detecting the snorkeler positions on orthorectified images as an alternative to the use of GNSS (Global Navigation Satellite System) positioning, thanks to an image processing tool. Underwater camera positions are estimated through precise time synchronization with the UAV frames, producing a georeferenced 3D model that seamlessly joins terrestrial and submerged landscapes. This facilitates the understanding of the spatial context of objects on the seabed and presents a cost-effective and comprehensive tool for 3D coastal mapping, useful for coastal management to support coastal resilience. Full article

In the city of Valencia (Spain), there existed from the Middle Ages until the mid-nineteenth century a building that fulfilled a municipal strategic function: The “new flour-weighing house”. Its purpose was to distribute food to the population and collect the corresponding indirect municipal taxes. Today, the existence of this building is not remembered, neither by scientists nor by citizens, and its importance, location and appearance are unknown. The building investigated, behind which the medieval façade of the “flour-weighing house” is hidden, is the Colomina Palace. In the investigation, its growth phases have been detected, and an idea of its structural organisation has been obtained. Research and investigation have been carried out by consulting historical, cartographic and archival material, together with advanced geomatics techniques, including close-range photogrammetry, terrestrial laser scanning and thermography. The fuse of colour and thermal imagery, together with point clouds and 3D models, help to visualise and check the different spatial transformations of the current “Colomina Palace”, adapting the sequence from medieval times into present. The methodology proposed in this study avoids the need to carry out destructive tests and the processing of permits, which speeds up decision-making and historical architectural reconstruction. Full article

Hyperspectral compressed imaging is a novel imaging detection technology based on compressed sensing theory that can quickly acquire spectral information of terrestrial objects in a single exposure. It combines reconstruction algorithms to recover hyperspectral data from low-dimensional measurement images. However, hyperspectral images from different scenes often exhibit high-frequency data sparsity and existing deep reconstruction algorithms struggle to establish accurate mapping models, leading to issues with detail loss in the reconstruction results. To address this issue, we propose a hyperspectral reconstruction method based on global gradient information and local low-rank priors. First, to improve the prior model’s efficiency in utilizing information of different frequencies, we design a gradient sampling strategy and training framework based on decision trees, leveraging changes in the loss function gradient information to enhance the model’s predictive capability for data of varying frequencies. Second, utilizing the local low-rank prior characteristics of the representative coefficient matrix, we develop a sparse sensing denoising module to effectively improve the local smoothness of point predictions. Finally, by establishing a regularization term for the reconstruction process based on the semantic similarity between the denoised results and prior spectral data, we ensure spatial consistency and spectral fidelity in the reconstruction results. Experimental results indicate that the proposed method achieves better detail recovery across different scenes, demonstrates improved generalization performance for reconstructing information of various frequencies, and yields higher reconstruction quality. Full article

Plant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide objective and rapid information. Various methods rely on camera systems, including RGB, multi-spectral, and hyper-spectral cameras, which offer valuable insights into plant physiology. In recent years, 3D sensing systems such as laser scanners have gained popularity due to their ability to capture structural plant parameters that are difficult to obtain using spectral sensors. Unlike images, point clouds are not structured and require pre-processing steps to extract precise information and handle noise or missing points. One approach is to generate mesh-based surface representations using triangulation. A key challenge in the 3D surface reconstruction of plants is the pre-processing of point clouds, which involves removing non-plant noise from the scene, segmenting point clouds from populations to individual plants, and further dividing individual plants into their respective organs. In this study, we will not focus on the segmentation aspect but rather on the other pre-processing steps, like denoising parameters, which depend on the data type. We present an automated pipeline for converting high-resolution point clouds into surface models of plants. The pipeline incorporates additional pre-processing steps such as outlier removal, denoising, and subsampling to ensure the accuracy and quality of the reconstructed surfaces. Data were collected using three different sensors: a handheld scanner, a terrestrial laser scanner (TLS), and a mobile mapping platform, under varying conditions from controlled laboratory environments to complex field settings. The investigation includes five different plant species, each with distinct characteristics, to demonstrate the potential of the pipeline. In a next step, phenotypic traits such as leaf area, leaf area index (LAI), and leaf angle distribution (LAD) were calculated to further illustrate the pipeline’s potential and effectiveness. The pipeline is based on the Open3D framework and is available open source. Full article

This study aimed to determine whether canopy and air temperature difference (ΔT) as an existing simple normalizing index can be used to detect an increase in canopy temperature induced by soil drought in urban parks, regardless of the unique energy balance and three-dimensional (3D) structure of urban trees. Specifically, we used a thermal infrared camera to measure the canopy temperature of Zelkova serrata trees and compared the temporal variation of ΔT to that of environmental factors, including solar radiation, wind speed, vapor pressure deficit, and soil water content. Normalization based on a 3D energy-balance model was also performed and used for comparison with ΔT. To represent the 3D structure, a terrestrial light detection and ranging-derived 3D tree model was used as the input spatial data. The temporal variation in ΔT was similar to that of the index derived using the energy-balance model, which considered the 3D structure of trees and 3D radiative transfer, with a correlation coefficient of 0.85. In conclusion, the thermal-image-based ΔT performed comparably to an index based on the 3D energy-balance model and detected the increase in canopy temperature because of the reduction in soil water content for Z. serrata trees in an urban environment. Full article

Amidst burgeoning socioeconomic development, anthropogenic activities have exacerbated soil erosion. This erosion, characterized by its brief duration, high frequency, and considerable environmental degradation, presents a major challenge to ecological systems. Therefore, it is imperative to regulate and remediate erosion–prone, anthropogenically disturbed parcels, with dynamic change detection (CD) playing a crucial role in enhancing management efficiency. Currently, traditional methods for change detection, such as field surveys and visual interpretation, suffer from time inefficiencies, complexity, and high resource consumption. Meanwhile, despite advancements in remote sensing technology that have improved the temporal and spatial resolution of images, the complexity and heterogeneity of terrestrial cover types continue to limit large–scale dynamic monitoring of anthropogenically disturbed soil erosion parcels (ADPSE) using remote sensing techniques. To address this, we propose a novel ISegFormer model, which integrates the SegFormer network with a pseudo–residual multilayer perceptron (PR–MLP), cross–scale boundary constraint module (CSBC), and multiscale feature fusion module (MSFF). The PR–MLP module improves feature extraction by capturing spatial contextual information, while the CSBC module enhances boundary prediction through high– and low–level semantic guidance. The MSFF module fuses multiscale features with attention mechanisms, boosting segmentation precision for diverse change types. Model performance is evaluated using metrics, such as precision, recall, F1–score, intersection over union (IOU), and mean intersection over union (mIOU). The results demonstrate that our improved model performs exceptionally well in dynamic monitoring tasks for ADPSE. Compared to five other models, our model achieved an mIOU of 72.34% and a Macro–F1 score of 83.55% across twelve types of ADPSE changes, surpassing the other models by 1.52–2.48% in mIOU and 2.25–3.64% in Macro–F1 score. This work provides a theoretical and methodological foundation for policy–making in soil and water conservation departments. Full article

UAV-SfM techniques are in constant development to address the challenges of accurate and precise mapping in terrains with complex morphologies. In contrast with the traditional photogrammetric processes, where only nadir images were considered, the combination of those with oblique imagery, also called off-nadir, has emerged as an optimal solution to achieve higher accuracy in these kinds of landscapes. UAV flights at a constant height above ground level (AGL) have also been considered a possible alternative to improve the resulting 3D point clouds compared to those obtained from constant height above mean sea level (AMSL) flights. The aim of this study is to evaluate the effect of incorporating oblique images as well as the type of flight on the accuracy and precision of the point clouds generated through UAV-SfM workflows for terrains with complex geometries. For that purpose, 58 scenarios with different camera angles and flight patterns for the oblique images were considered, 29 for each type of flight (AMSL and AGL). The 3D point cloud derived from each of the 58 scenarios was compared with a reference 3D point cloud acquired with a terrestrial laser scanner (TLS). The results obtained confirmed that both incorporating oblique images and using AGL flight mode have a positive effect on the mapping. Combination of nadir image blocks, obtained from an AGL crosshatch flight plan, with supplemental oblique images collected with a camera angle of between 20° and 35° yielded the best accuracy and precision records. Full article