Search Results (71)

Understanding and mapping ephemeral gullies (EGs) are vital for enhancing agricultural productivity and achieving food security. This study proposes an upscaling-based strategy to refine the predictive mapping of EGs, utilizing high-resolution Pléiades Neo (0.6 m) and medium-resolution Sentinel-2 (10 m) satellite imagery, alongside ground-truth EGs mapping in Niagara Region, Canada. The research involved generating spectral feature maps using Blue, Green, Red, and Near-infrared spectral bands, complemented by indices indicative of surface wetness, vegetation, color, and soil texture. Employing the Random Forest (RF) algorithm, this study executed three distinct strategies for EGs identification. The first strategy involved direct calibration using Sentinel-2 spectral features for 10 m resolution mapping. The second strategy utilized high-resolution Pléiades Neo data for model calibration, enabling EGs mapping at resolutions of 0.6, 2, 4, 6, and 8 m. The third, or upscaling strategy, applied the high-resolution calibrated model to medium-resolution Sentinel-2 imagery, producing 10 m resolution EGs maps. The accuracy of these maps was evaluated against actual data and compared across strategies. The findings highlight the Variable Importance Measure (VIM) of different spectral features in EGs identification, with normalized near-infrared (Norm NIR) and normalized red reflectance (Norm Red) exhibiting the highest and lowest VIM, respectively. Vegetation-related indices demonstrated a higher VIM compared to surface wetness indices. The overall classification error of the upscaling strategy at spatial resolutions of 0.6, 2, 4, 6, 8, and 10 m (Upscaled), as well as that of the direct Sentinel-2 model, were 7.9%, 8.2%, 9.1%, 10.3%, 11.2%, 12.5%, and 14.5%, respectively. The errors for EGs maps at various resolutions revealed an increase in identification error with higher spatial resolution. However, the upscaling strategy significantly improved the accuracy of EGs identification in medium spatial resolution scenarios. This study not only advances the methodology for EGs mapping but also contributes to the broader field of precision agriculture and environmental management. By providing a scalable and accessible approach to EGs mapping, this research supports enhanced soil conservation practices and sustainable land management, addressing key challenges in agricultural sustainability and environmental stewardship. Full article

In rapidly urbanizing regions, encroachment on native green spaces has exacerbated ecological issues such as urban heat islands and flooding. Accurate mapping of tree species distribution is therefore vital for sustainable urban management. However, the high heterogeneity of urban landscapes, resulting from the coexistence of diverse land covers, built infrastructure, and anthropogenic activities, often leads to reduced robustness and transferability of remote sensing classification methods across different images and regions. In this study, we used very high–resolution Pléiades imagery and field-verified samples of eight common urban trees and background land covers. By employing transfer learning with advanced segmentation networks, we evaluated each model’s accuracy, robustness, and efficiency. The best-performing network delivered markedly superior classification consistency and required substantially less training time than a model trained from scratch. These findings offer concise, practical guidance for selecting and deploying deep learning methods in urban tree species mapping, supporting improved ecological monitoring and planning. Full article

In the context of sea levels rising, monitoring spatial and temporal topographic changes along coastal dunes is crucial to understand their dynamics since they represent natural barriers against coastal flooding and large sources of sediment that can mitigate coastal erosion. Different technologies are currently used to monitor coastal dune topographic changes (GNSS, UAV, airborne LiDAR, etc.). Satellites recently emerged as a new source of topographic data by providing high-resolution images with a rather short revisit time at the global scale. Stereoscopic or tri-stereoscopic acquisition of some of these images enables the creation of 3D models using stereophotogrammetry methods. Here, the Ames Stereo Pipeline was used to produce digital elevation models (DEMs) from tri-stereo panchromatic and high-resolution Pleiades images along three 19 km long stretches of coastal dunes in SW France. The vertical errors of the Pleiades-derived DEMs were assessed by comparing them with DEMs produced from airborne LiDAR data collected a few months apart from the Pleiades images in 2017 and 2021 at the same three study sites. Results showed that the Pleiades-derived DEMs could reproduce the overall dune topography well, with averaged root mean square errors that ranged from 0.5 to 1.1 m for the six sets of tri-stereo images. The differences between DEMs also showed that Pleiades images can be used to monitor multi-annual coastal dune morphological changes. Strong erosion and accretion patterns over spatial scales ranging from hundreds of meters (e.g., blowouts) to tens of kilometers (e.g., dune retreat) were captured well, and allowed to quantify changes with reasonable errors (30%). Furthermore, relatively small averaged root mean square errors (0.63 m) can be obtained with a limited number of field-collected elevation points (five ground control points) to perform a simple vertical correction on the generated Pleiades DEMs. Among different potential sources of errors, shadow areas due to the steepness of the dune stoss slope and crest, along with planimetric errors that can also occur due to the steepness of the terrain, remain the major causes of errors still limiting accurate enough volumetric change assessment. However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography. Full article

Satellite imagery is a widely used source of spatial information in many applications, such as land use/land cover, object detection, agricultural monitoring, and urban area monitoring. Numerous factors, including projection, tilt angle, scanner, atmospheric conditions, terrain curvature, and fluctuations, can cause satellite images to become distorted. Eliminating systematic errors caused by the sensor and platform is a crucial step to obtaining reliable information from satellite images. To utilize satellite images directly in applications requiring high accuracy, the errors in the images should be removed by geometric correction. In this study, geometric correction was applied to the Pléiades 1A (PHR) image using non-parametric methods, and the effects of different transformation models and digital elevation models (DEMs) were investigated. Ground control points (GCPs) were obtained from orthophotos created by the photogrammetric method using precise positioning. The effect of photogrammetric DEMs with various spatial resolutions on geometric correction was investigated. Additionally, the effect of DEMs obtained using the photogrammetric method was compared with those from open-source DEMs, including SRTM, ASTER GDEM, COP30, AW3D30, and NASADEM. Two-dimensional polynomial transformation, the thin plate spline (TPS), and the rational function model (RFM) were applied as transformation methods. Our results showed that a higher-accuracy geometric correction process could be achieved with orthophotos and DEMs created using precise positioning techniques such as RTK and PPK. According to the results obtained, an RMSE of 0.633 m was achieved with RFM using RTK-DEM, while an RMSE of 0.615 m was achieved with RFM using PPK-DEM. Full article

Urbanization poses significant challenges to ecosystems, resources, and human well-being, necessitating sustainable planning. Urban vegetation, particularly trees, provides critical ecosystem services such as carbon sequestration, air quality improvement, and biodiversity conservation. Traditional tree assessments are resource-intensive and time-consuming. Recent advances in remote sensing, especially high-resolution multispectral imagery and object-based image analysis (OBIA), offer efficient alternatives for mapping urban vegetation. This study evaluates and compares the efficacy of Sentinel-2 and Pléiades satellite imagery in classifying tree species within historic urban parks in Rome—Villa Borghese, Villa Ada Savoia, and Villa Doria Pamphilj. Pléiades imagery demonstrated superior classification accuracy, achieving an overall accuracy (OA) of 89% and a Kappa index of 0.84 in Villa Ada Savoia, compared to Sentinel-2’s OA of 66% and Kappa index of 0.47. Specific tree species, such as Pinus pinea (Stone Pine), reached a user accuracy (UA) of 84% with Pléiades versus 53% with Sentinel-2. These insights underscore the potential of integrating high-resolution remote sensing data into urban forestry practices to support sustainable urban management and planning. Full article

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of Aedes aegypti larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana. The identified environmental and meteorological factors were used to generate dynamic maps with high spatial and temporal resolution. The study collected larval data from 261 different surveyed houses, with each house being surveyed between one and three times. Of the observations, 41% were positive for the presence of Aedes aegypti larvae. We modeled the Aedes larvae risk within a radius of 50 to 200 m around houses using six explanatory variables and extrapolated the findings to other urban municipalities during the 2020 dengue epidemic in French Guiana. This study highlights the potential of spatiotemporal modeling approaches to predict and monitor the evolution of vector-borne disease transmission risk, representing a major opportunity to monitor the evolution of vector risk and provide valuable information for public health authorities. Full article

Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea’s eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data and manual, time-consuming expert analysis. This study introduces an efficient deep learning model designed to improve sinkhole mapping using accessible satellite imagery, which could enhance management practices related to sinkholes and other geohazards in evaporite karst regions. The developed AI system is centered around the U-Net architecture. The model was initially trained on a high-resolution drone dataset (0.1 m GSD, phase I), covering 250 sinkhole instances. Subsequently, it was additionally fine-tuned on a larger dataset from a Pleiades Neo satellite image (0.3 m GSD, phase II) with 1038 instances. The training process involved an automated image-processing workflow and strategic layer freezing and unfreezing to adapt the model to different input scales and resolutions. We show the usefulness of initial layer features learned on drone data, for the coarser, more readily-available satellite inputs. The validation revealed high detection accuracy for sinkholes, with phase I achieving a recall of 96.79% and an F1 score of 97.08%, and phase II reaching a recall of 92.06% and an F1 score of 91.23%. These results confirm the model’s accuracy and its capability to maintain high performance across varying resolutions. Our findings highlight the potential of using RGB visual bands for sinkhole detection across different karst environments. This approach provides a scalable, cost-effective solution for continuous mapping, monitoring, and risk mitigation related to sinkhole hazards. The developed system is not limited only to sinkholes however, and can be naturally extended to other geohazards as well. Moreover, since it currently uses U-Net as a backbone, the system can be extended to incorporate super-resolution techniques, leveraging U-Net based latent diffusion models to address the smaller-scale, ambiguous geo-structures that are often found in geoscientific data. Full article

Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the feature level and the decision level. At the feature level, LiDAR 2D attributes were derived and combined with multispectral imagery vegetation indices. At the decision level, LiDAR data were used for 3D tree crown delimitation, providing unique trees or groups of trees. The segmented tree crowns were used as a support for an object-based species classification at tree level. Data augmentation techniques were used to improve the training process, and classification was carried out with a random forest classifier. The workflow was entirely automated using a Python script, which allowed the assessment of four different fusion configurations. The best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a kappa of 0.66, thanks to red edge-based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages, helping to discriminate the species. Full article

Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial resolution and frequent revisit times, could serve as an alternative data source for phenotyping. In this study, we separately evaluated pan-sharpened Pléiades satellite imagery (50 cm) and UAV imagery (2.5 cm) to phenotype durum wheat in small-plot (12 m × 1.10 m) breeding trials. The Gaussian process regression (GPR) algorithm, which provides predictions with uncertainty estimates, was trained with spectral bands and а selected set of vegetation indexes (VIs) as independent variables. Grain protein content (GPC) was better predicted with Pléiades data at the growth stage of 20% of inflorescence emerged but with only moderate accuracy (validation R²: 0.58). The grain yield (GY) and protein yield (PY) were better predicted using UAV data at the late milk and watery ripe growth stages, respectively (validation: R² 0.67 and 0.62, respectively). The cumulative VIs (the sum of VIs over the available images within the growing season) did not increase the accuracy of the models for either sensor. When mapping the estimated parameters, the spatial resolution of Pléiades revealed certain limitations. Nevertheless, our findings regarding GPC suggested that the usefulness of pan-sharpened Pléiades images for phenotyping should not be dismissed and warrants further exploration, particularly for breeding experiments with larger plot sizes. Full article

Satellite-derived bathymetry is increasingly attracting stakeholders’ attention tasked with remote and/or shallow depths, given its affordability compared to airborne lidar and waterborne sonar surveys. The 6-band 1.2 m Pléiades Neo (PNEO) multispectral imagery has not yet been evaluated for such a purpose. The contribution of the novel PNEO bands to the depth retrieval was assessed over unclear coastal seawaters (0.2 m⁻¹ of vertical light attenuation in the bay of Saint-Malo, France). The relevance of the radiometric level was also tested: top-of-atmosphere (TOA) digital number (DN), TOA radiance, TOA reflectance, bottom-of-atmosphere (BOA) maritime-modeled reflectance, and BOA tropospheric-modeled reflectance. The lidar response, ranging from 0 to 20 m depth, was stratified by 90 random samples per bathymetric slice of 1 m. The model was based on an easy-to-transfer neural network (one hidden layer and three neurons). The best predictions, reaching R²_test of 0.81, were equally obtained for the full PNEO dataset at TOA DN, radiance, and reflectance. For both BOA full-dataset products, the results were slightly less satisfactory: R²_test of 0.75 (maritime) and 0.76 (tropospheric). Full article

Buildings are important components of urban areas, and the construction of rooftop photovoltaic systems plays a critical role in the transition to renewable energy generation. With rooftop solar photovoltaics receiving increased attention, the problem of how to estimate rooftop photovoltaics is under discussion; building detection from remote sensing images is one way to address it. In this study, we presented an available approach to estimate a building’s rooftop solar photovoltaic potential. A rapid and accurate rooftop extraction method was developed using object-based image classification combining normalized difference vegetation index (NDVI) and digital surface models (DSMs), and a method for the identification of suitable rooftops for solar panel installation by analysing the geographical restrictions was proposed. The approach was validated using six scenes from Beijing that were taken using Chinese Gaofen-2 (GF-2) satellite imagery and Pleiades imagery. A total of 176 roofs in six scenarios were suitable for PV installation, and the estimated photovoltaic panel area was 205,827 m². The rooftop photovoltaic potential was estimated to total 22,551 GWh. The results indicated that the rooftop photovoltaic potential estimation method performs well. Full article

In recent years, the most popular methods for determining coastline course are geodetic, satellite, and tacheometric techniques. None of the above-mentioned measurement methods allows marking out the shoreline both in an accurate way and with high coverage of the terrain with surveys. For this reason, intensive works are currently underway to find alternative solutions that could accurately, extensively, and quickly determine coastline course. Based on a review of the literature regarding shoreline measurements, it can be concluded that the photogrammetric method, based on low-altitude images taken by an Unmanned Aerial Vehicle (UAV), has the greatest potential. The aim of this publication is to present and validate a method for determining coastline course based on low-altitude photos taken by a drone. Shoreline measurements were carried out using the DJI Matrice 300 RTK UAV in the coastal zone at the public beach in Gdynia (Poland) in 2023. In addition, the coastline course was marked out using high-resolution satellite imagery (0.3–0.5 m). In order to calculate the accuracy of determining the shoreline by photogrammetric and satellite methods, it was decided to relate them to the coastline marked out using a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) receiver with an accuracy of 2.4 cm Distance Root Mean Square (DRMS). Studies have shown that accuracies of determining coastline course using a UAV are 0.47 m (p = 0.95) for the orthophotomosaic method and 0.70 m (p = 0.95) for the Digital Surface Model (DSM), and are much more accurate than the satellite method, which amounted to 6.37 m (p = 0.95) for the Pléiades Neo satellite and 9.24 m (p = 0.95) for the Hexagon Europe satellite. Based on the obtained test results, it can be stated that the photogrammetric method using a UAV meets the accuracy requirements laid down for the most stringent International Hydrographic Organization (IHO) order, i.e., Exclusive Order (Total Horizontal Uncertainty (THU) of 5 m with a confidence level of 95%), which they relate to coastline measurements. Full article

The mountainous region of southwest China has the largest karst geomorphology in China and in the world. Quantifying the forest aboveground biomass in this karst region is of great significance for the investigation of carbon storage and carbon cycling in terrestrial ecosystems. In this study, the actual measured aboveground biomass was calculated based on the allometric functions of 106 quadrats from 2012 to 2015. A backpropagation artificial neural network (BPANN) inversion model was constructed by combining very high-resolution satellite imagery, field inventory data, and land use/land cover data to estimate the forest aboveground biomass in the Banzhai watershed, a typical peak–cluster karst basin in southern Guizhou Province. We used 70% of the actual measured aboveground biomass for training the BPANN model, 20% for accuracy verification, and 10% to prevent overtraining. The results show that the absolute root mean square error of the BPANN model was 11.80 t/ha, which accounted for 9.92% of the mean value of aboveground biomass. Based on the BPANN inversion model, the average value of the forests’ aboveground biomass was 135.63 t/ha. The results showed that our study presented a quick, easy, and relatively high-precision method for estimating forest aboveground biomass in the Banzhai watershed. This indicates that the Pléiades image-based BPANN model displayed satisfactory results for estimating the forests’ aboveground biomass in a typical peak–cluster karst basin. This method can be applied to the estimation of forest AGB in the karst mountainous areas of southwest China. Full article

In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R² coefficient of determination of 0.92. Full article

Many issues arise from the recession of sea cliffs, including threats to coastal communities and infrastructure. The best proxy to study cliff instability processes is the cliff face evolution. Unfortunately, due to its verticality, this proxy is difficult to observe and measure. This study proposed and compared three remote sensing methods based on structure-from-motion (SfM) photogrammetry or stereorestitution: boat-based SfM photogrammetry with smartphones, unmanned aerial system (UAS) or unmanned aerial vehicle (UAV) photogrammetry with centimetric positioning and Pléiades tri-stereo imagery. An inter-comparison showed that the mean distance between the point clouds produced by the different methods was about 2 m. The satellite approach had the advantage of covering greater distances. The SfM photogrammetry approach from a boat allowed for a better reconstruction of the cliff foot (especially in the case of overhangs). However, over long distances, significant geometric distortions affected the method. The UAS with centimetric positioning offered a good compromise, but flight autonomy limited the extent of the monitored area. SfM photogrammetry from a boat can be used as an initial estimate for risk management services following a localized emergency. For long-term monitoring of the coastline and its evolution, satellite photogrammetry is recommended. Full article