Remote Sensing Applications-Society and Environment最新文献

{"title":"Tracing Morphological Transformations and Braiding Dynamics in the Himalayan Rivers of Nepal","authors":"Bishwas Bhatta ,&nbsp;Umesh Singh ,&nbsp;Basanta Raj Adhikari ,&nbsp;Saroj Karki ,&nbsp;Astha Bhatta","doi":"10.1016/j.rsase.2025.101705","DOIUrl":"10.1016/j.rsase.2025.101705","url":null,"abstract":"<div><div>This study quantifies multi-decadal (1990–2022) planform change and braiding behavior in three Himalayan rivers of Nepal: The Koshi, Narayani, and Karnali using a unified geospatial workflow. Multi-temporal Landsat and Sentinel-2 imagery were processed with water-detection indices (MNDWI, NDWI) to extract channel boundaries and map erosion, accretion, and persistence. High-frequency gauge records were used to derive discharge and examine functional relations between wetted-area ratio and flow, and 180 Sentinel-2 scenes (2017–2022) supported braiding-intensity (BI_T3) estimation and bar-scale assessment at sub-reach level. Results show strong river-specific contrasts: the Koshi exhibits the greatest adjustment, with only 32.5 % channel persistence and a pronounced westward lateral migration, whereas the Narayani and Karnali are comparatively stable, with 64.8 % and 54.5 % unchanged areas, respectively. Functional analyses indicate distinct sensitivities of wetted-area ratio to daily maximum discharge, and braiding intensity peaks at intermediate flows before attenuating at higher discharges. Focused sandbar analysis (2017–2022) in the Koshi reach reveals persistent bifurcation asymmetry and directional bar migration, consistent with post-monsoon redistribution of flow and sediment. By linking satellite-derived morphology to real-time discharge within a consistent, transferable framework, the study provides robust, repeatable indicators for monitoring Himalayan braided rivers and establishes a basis for comparative evaluation of planform stability and braiding across data-sparse systems.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101705"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven prediction of Visual Range under changing climate conditions over complex terrain using AOD and CMIP6 climate simulations","authors":"Sadaf Javed ,&nbsp;Muhammad Imran Shahzad ,&nbsp;Muhammad Zeeshaan Shahid ,&nbsp;Jun Wang ,&nbsp;Imran Shahid","doi":"10.1016/j.rsase.2025.101712","DOIUrl":"10.1016/j.rsase.2025.101712","url":null,"abstract":"<div><div>Visibility through the atmosphere, or Visual Range (VR), is a key indicator of ambient air quality, especially in areas with complex topography and vulnerability to climate change. The specific aims of this study were to (1) evaluate the ability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model outputs and satellite Aerosol Optical Depth (AOD) to predict VR across diverse topography; (2) select important meteorological parameters for VR; and (3) design an ensemble Machine Learning (ML) model with high accuracy using Bagged Extreme Gradient Boosting (BG-XG) for long-term VR trends under future climate scenarios. This study contributes to the significant gap in regional visibility prediction by combining climate model projections, remotely sensed AOD, and ML to project future VR through 2100 across Pakistan. The BG-XG model was trained using in situ meteorological data, AOD, and six CMIP6 models (Euro-Mediterranean Centre on Climate Change Climate Model 2 High Resolution – version SR5 (CMCCCM2-SR5 (Italy))was the most consistently accurate model across all the topography). For the results computed at Lahore (LHR), the BG-XG model achieved the highest correlation coefficient of R = 0.98 and Root Mean Square Error (RMSE) = 0.24 km for the validation dataset. It is expected that the region will observe an average VR of 5.88 km with a standard deviation of 1.66 km by the end of 2100. The predictive strength of climate model parameters for VR was high (&gt;90 %), with significant dependencies on sea-level pressure (SLP), relative humidity (RH), eastward wind (EW), and AOD. The region is expected to witness a significant decrease in average VR at a rate of −281.3 m/year due to an increase in AOD at a rate of 0.14/year from 2003 to 2100. Among the regions, Karachi (KHI) is anticipated to experience the most substantial reduction in VR by 2100, followed by Sindh and the northwestern areas. This study provides the first long-term, region-specific VR forecasts for Pakistan by integrating ML with CMIP6 climate projections. These findings can guide climate adaptation strategies, particularly for regions at considerable risk of declining air quality due to reduced visibility.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101712"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supra-glacier debris cover expansion and recession of glaciers across Himalaya and Karakoram range","authors":"Arvind Chandra Pandey ,&nbsp;Mrityunjoy Kumbhakar ,&nbsp;Chandra Shekhar Dwivedi ,&nbsp;Rakesh Bhambri","doi":"10.1016/j.rsase.2025.101674","DOIUrl":"10.1016/j.rsase.2025.101674","url":null,"abstract":"<div><div>The Himalaya and the Karakoram Range are intensely glacierized, with large parts covered by debris cover that strongly influences ablation rates. Therefore, monitoring for Supra Glacial Debris (SGD) coverage is highly essential. The current study focuses on 24 debris-covered glaciers (DCG) across these extensive regions, mapping their spatial distribution, morphometric variations, and frontal retreat. We modified the RGI outlines using Standard False Colour Composite satellite imagery and ASTER GDEM data to assess the uncertainty for the mapped outlines. In order to map and monitor SGD, the highly accurate image classification technique of Random Forest was implemented through Google Earth Engine (GEE) - a cloud computing platform. It integrated Landsat TM, OLI, and also OLI-2 high-resolution imagery of the years 1992 and 2022 during peak ablation periods. The result exhibited increasing upward expansion and spatial coverage of SGD across all studied glaciers. All morphometric parameters showed significant changes in dimensions where most glaciers retreated. Our study also analyzed the relationship between various morphometric parameters and established a relationship between the coverage by SGD and the retreat of glaciers. Although the relation is not strong, it establishes a fact that increased coverage by SGD influences the rate of melting. Significantly, glaciers in the Central and Western Himalaya are melting at a faster rate than those in the Karakoram Ranges.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101674"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated terrestrial and airborne LiDAR systems to monitor stand structure variations in dryland forests affected by thinning treatments","authors":"Guy Sadot ,&nbsp;Moshe (Vladislav) Dubinin ,&nbsp;Yagil Osem ,&nbsp;José Marc Grünzweig ,&nbsp;Tarin Paz-Kagan","doi":"10.1016/j.rsase.2025.101725","DOIUrl":"10.1016/j.rsase.2025.101725","url":null,"abstract":"<div><div>Light Detection and Ranging (LiDAR) technologies have become useful tools for forest monitoring, enabling precise evaluation of structural attributes that inform management decisions. However, the potential of integrating Mobile LiDAR Scanning (MLS) and Airborne LiDAR Scanning (ALS) for monitoring forest structure across stands with varying management remains underexplored. This study assessed the capabilities of MLS, ALS, and their fusion for quantifying tree- and stand-level structural characteristics in two dryland pine forests in Israel: HaKedoshim (semi-arid) and Yatir (arid). In HaKedoshim, both MLS and ALS data were collected and integrated; in Yatir, ALS alone was used. ALS-MLS fusion models demonstrated strong agreement with traditional field inventory measurements, achieving high correlations for diameter at breast height (DBH, R² = 0.88), stem basal area (BA, R² = 0.86), crown projection area (CP, R² = 0.88), and canopy volume (CV, R² = 0.85). Stand-level attributes such as tree density (TD, R² = 0.98), average tree canopy projection (R² = 0.84), and stand canopy cover (CC, R² = 0.91) were also reliably estimated. However, canopy top height (CTH) was predicted with lower precision (R² = 0.68), reflecting challenges in vertical segmentation using LiDAR and field measurement errors. As detected by the ALS-MLS models, thinning treatments reduced TD and CC at the stand level while average tree CP and CV increased. ALS-derived estimates of Plant Area Index (PAI) demonstrated high accuracy for understory (R² = 0.82), overstory (R² = 0.90), and ecosystem PAI (R²= 0.85). PAI_Ecosystem values ranged from 1.57 to 3.22 m²/m² in HaKedoshim and from 0.65 to 0.98 m²/m² in Yatir, highlighting the role of climatic aridity in shaping forest structure. The analysis of vertical PAI profiles from ALS data revealed that thinning treatments (applied 10 years ago) consistently reduced overstory PAI, while understory PAI increased due to thinning only in the more humid HaKedoshim site. Overall, the MLS–ALS fusion approach enhanced multi-scale assessments of forest structural properties. Our results offer a scalable framework for monitoring forest structure, including vertical canopy partitioning as affected by climate and thinning treatments, with direct implications for dryland forest management and ecosystem modeling.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101725"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated aeromagnetic and remote sensing analysis for structural mapping in the El-Barramiya-Dungash region, Eastern Desert, Egypt","authors":"Mohamed Abdelrady ,&nbsp;Luan Thanh Pham ,&nbsp;Ferenc Molnár ,&nbsp;Ali Shebl","doi":"10.1016/j.rsase.2025.101716","DOIUrl":"10.1016/j.rsase.2025.101716","url":null,"abstract":"<div><div>The El-Barramiya-Dungash region in Egypt's Central Eastern Desert, is very rich in minerals and metals that has a significant part posting the economy of the country. This study integrates aeromagnetic data with textural analysis of ALOS PALSAR data to map structural features of the El-Barramiya-Dungash region. We apply some edge detecting techniques like the modified theta map (MTM), tilt angle of the horizontal gradient (TAHG), horizontal gradient of the second tilt derivative (HGSTDR), fast sigmoid detector (FS), enhanced horizontal gradient amplitude (EHGA), and balanced gradient amplitude (BHG) to aeromagnetic data to identify the underlying structures that are concealed beneath geological formations of the area. The findings show that all methods are able to give high resolution results, but utilizing the TAHG, FS, EHGA and BHG can avoid creating false edges. Four main tectonic trends including NNE-SSW, NE-SW, NW-SE, N-S and E-W are identified by analyzing the lineaments obtained from filtered aeromagnetic maps statistically. In addition, we also apply the Euler deconvolution (EUL) method to aeromagnetic anomalies of the area, where the results show that most magnetic structures are located between 123 and 850 m under the surface. Textural analysis, employing techniques such as Second Moment and Dissimilarity, effectively differentiated lithologies and delineated prominent structural trends, coinciding with aeromagnetic data findings. The findings also show that the determined lineaments according to Egypt's tectonic structure, correlate to the Gulf of Aqaba, Najd fault system, and Nubian trend (East African trend). These findings demonstrate the significant potential of the applied techniques for enhancing geological understanding and guiding future mineral exploration efforts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101716"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remotely sensed mapping of plant diversity in Earth's largest mangrove forests: Developing a spectral diversity metric with DESIS hyperspectral data and the ‘spectral species’ concept","authors":"Subham Banerjee ,&nbsp;Swapan Kumar Sarker ,&nbsp;Bryan Pijanowski","doi":"10.1016/j.rsase.2025.101676","DOIUrl":"10.1016/j.rsase.2025.101676","url":null,"abstract":"<div><div>Global biodiversity monitoring faces significant challenges, yet recent advancements in spaceborne remote sensing, particularly through hyperspectral sensors, are opening new avenues for cost-effective and scalable plant diversity mapping. The high spectral resolution of these sensors enables precise identification of plant traits and community compositions. Employing the “Spectral Species” concept, which categorizes spectral imagery pixels into distinct spectral types, we have developed a novel semi-discrete “Spectral Species Diversity” (SSD) metric. This metric has proven effective in modeling plant diversity, as demonstrated by our study in the mangrove forests of Bangladesh's Sundarbans using DESIS (DLR Earth Sensing Imaging Spectrometer) hyperspectral imagery.</div><div>In this study, we analyzed data from 110 Permanent Sampling Plots in the Sundarbans, calculated traditional plant diversity indices (Species Richness, Shannon and Simpson Diversity), and compared these with our spectral diversity metric. The comparison revealed robust correlations between field-measured plant diversity and our DESIS-derived SSD (<em>R</em>^<em>2</em> = 0.473 for Shannon diversity and <em>R</em>^<em>2</em> = 0.468 for Simpson diversity). However, species richness showed poor correlation with the newly developed SSD metric. Conversely, the continuous conventional Coefficient of Variation (CV) spectral diversity metric, also computed using the same hyperspectral dataset, underperformed relative to the SSD metric. Furthermore, when assessing the performance of our SSD metric using multispectral imagery from Sentinel-2 and Landsat 8, the metrics derived from Sentinel-2 exhibited weaker relationships with plant diversity (<em>R</em>^<em>2</em> = 0.152 for Shannon Diversity and <em>R</em>^<em>2</em> = 0.144 for Simpson Diversity), and those from Landsat 8 were less effective.</div><div>Upon examining different spectral space sizes, we determined that the optimal size for computing spectral diversity metrics was 150 m × 150 m. This size most effectively captured plant diversity in the vegetation survey plots. While the SSD metric within these spectral spaces mirrored the plant diversity trend across the three salinity zones of the Sundarbans, the observed differences were not statistically significant. Nonetheless, the alignment in pattern highlights the ecological relevance of the SSD metric.</div><div>This study underscores that the newly developed SSD metric, utilizing hyperspectral imaging and adapting the Spectral Species concept, can accurately map plant diversity in ecologically diverse ecosystems like the Sundarbans mangroves. Future enhancements, such as aligning spectral space with vegetation survey plot dimensions and incorporating data from SWIR sensors, SAR, or LiDAR, could further refine the metric's robustness and global applicability. These improvements will provide crucial insights for biodiversity co","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101676"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep hyperspectral clustering using attention-enhanced 3D-2D convolutional autoencoder for mineral mapping","authors":"Sima Peyghambari,&nbsp;Yun Zhang","doi":"10.1016/j.rsase.2025.101700","DOIUrl":"10.1016/j.rsase.2025.101700","url":null,"abstract":"<div><div>Remotely sensed hyperspectral images (HSIs) provide valuable compositional information on the surface target materials crucial for Earth observation and geoscience applications. The necessity of labelled data and the high dimensionality of HSI hinder efficient hyperspectral data processing. Hyperspectral data clustering can help to address this challenge. Conventional clustering approaches mainly use shallow spectral absorption features. Deep-learning-based methods, such as autoencoder models, can extract deep HSI's spectral and spatial features. However, the most commonly used 3D-convolutional autoencoder (3D-CAE) models have several disadvantages, including intensive computational costs and the potential to lose spatial information. To avoid losing important information and reduce computational costs, this research proposes an attention-enhanced hybrid 3D-2D-CAE spectral-spatial model for clustering HSIs in mineral mapping. The proposed model enables the capture of non-linear relationships between data points in an unsupervised manner. The network utilizes spectral and spatial attention layers in the 3D and 2D convolutions to capture spectral and spatial information, reducing spectral complexities and enhancing spatial features. The captured feature representations are fed to an agglomerative Gaussian mixture model (AGMM) to cluster HSI. Experimenting with different autoencoder-based clustering methods and comparing their results with those of conventional clustering algorithms, the proposed model achieved an overall accuracy of 88.14 %. It consistently demonstrated the superior performance of the hybrid attention-enhanced 3D-2D-CAE-based clustering method, reinforcing its potential for accurate mineral mapping. Furthermore, the less computationally expensive, attention-enhanced 3D-2D-CAE structure outperforms the extensive GPU usage and processing time of the 3D-CAE method.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101700"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of EOS-07/ millimetre-wave humidity sounder (MHS) retrieved specific humidity using in-situ and satellite observations","authors":"V. Varaprasad,&nbsp;Ghouse Basha,&nbsp;M. Venkat Ratnam","doi":"10.1016/j.rsase.2025.101718","DOIUrl":"10.1016/j.rsase.2025.101718","url":null,"abstract":"<div><div>To better understand precipitation patterns over the Indian region, ISRO launched the Earth Observation Satellite (EOS-07) carrying a millimetre-Wave Humidity Sounder (MHS) instrument on 10 February 2023. This MHS is designed to provide humidity data under all-weather conditions. This study evaluates the performance of EOS-07/MHS specific humidity (SH) retrievals over India using radiosonde data and Constellation Observation System for Meteorology, Ionosphere and Climate -2 (COSMIC-2) satellite observations. The EOS-07/MHS instrument offers improved spatial coverage over the Indian region compared to other satellite platforms. Strong agreement is observed with high-resolution radiosonde data from Gadanki, with correlation coefficients exceeding 0.5 and relative percentage differences (RPD) consistently below 20 % across all pressure levels. Similar agreement is found with India Meteorological Department (IMD) radiosonde observations across diverse regions of India. EOS-07/MHS SH retrievals also exhibit good agreement with COSMIC-2 SH data, except in the upper troposphere, with enhanced consistency observed over oceanic regions within the boundary layer. The diurnal variation suggests that EOS-07/MHS retrievals align more closely with COSMIC-2 SH during the daytime than at night. These findings establish EOS-07/MHS as a valuable tool for monitoring atmospheric moisture variability over the Indian region and for supporting a wide range of meteorological and climatological applications.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101718"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring cyanobacteria temporal dynamics in a hypereutrophic lake using remote sensing: From multispectral to hyperspectral","authors":"Samantha L. Sharp ,&nbsp;Alicia Cortés ,&nbsp;Alexander L. Forrest ,&nbsp;Carl J. Legleiter ,&nbsp;Liane S. Guild ,&nbsp;Yufang Jin ,&nbsp;S. Geoffrey Schladow","doi":"10.1016/j.rsase.2025.101704","DOIUrl":"10.1016/j.rsase.2025.101704","url":null,"abstract":"<div><div>Cyanobacterial harmful algal blooms (cyanoHABs) and associated cyanotoxins are a concern for inland waters. Due to the extensive spatial coverage and frequent availability of satellite images, multispectral remote sensing tools demonstrate utility for monitoring these blooms. The next frontier for remote sensing of cyanoHABs in inland waters is hyperspectral data. Recent and upcoming hyperspectral satellite missions using narrow wavelength imaging spectrometers could have a major impact on advancing our ability to detect, quantify, and characterize cyanobacterial blooms. This study compares multispectral and hyperspectral remote sensing capabilities and processing tools for monitoring cyanoHAB dynamics. We evaluated the temporal trends of cyanoHABs in Clear Lake, California, a hypereutrophic lake with diverse cyanobacteria genera based on 38 sampling events over a five-year monitoring period (2019–2023). We validated the Sentinel-3 Ocean and Land Color Instrument (multispectral) Cyanobacteria Index algorithm for Clear Lake using in situ cyanobacteria measurements, which complemented our field-based evaluation of cyanobacteria trends in Clear Lake. We then demonstrate the advantages of hyperspectral data from both in situ spectroradiometer measurements and full-lake hyperspectral satellite images. We apply the Spectral Mixture Analysis for Surveillance of HABs (SMASH) workflow, a Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm, to the hyperspectral images to assess the potential of satellite imaging spectrometer data to identify cyanobacteria genera – the first study to test this tool outside its original study sites. We developed a Clear Lake-specific cyanobacteria spectral library using our field spectroradiometer measurements to improve SMASH performance in Clear Lake, which supports the continued development of this tool.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101704"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discrimination of the intertidal goose barnacle Pollicipes pollicipes from rocky shore invertebrates and macroalgae using in situ hyperspectral signatures","authors":"Marta Román ,&nbsp;BedeF.R. Davies ,&nbsp;Simon Oiry ,&nbsp;Philippe Rosa ,&nbsp;Pierre Gernez ,&nbsp;Celia Olabarria ,&nbsp;Laurent Barillé","doi":"10.1016/j.rsase.2025.101697","DOIUrl":"10.1016/j.rsase.2025.101697","url":null,"abstract":"<div><div>Using remote sensing techniques based on spectral reflectance for mapping intertidal species, offers advantages over traditional surveys. However, the effect of the spectral resolution of the sensors, hyper- or multi-spectral, for the detection of intertidal sessile goose barnacles has not been assessed yet. We described the reflectance spectra of the species <em>Pollicipes pollicipes</em> and tested if it could be discriminated from mussels (adults and spat) from the <em>Mytilus</em> genus, other barnacles of the genus <em>Chthamalus</em> and <em>Semibalanus</em> and red, brown and green macroalgae. We measured their hyperspectral signatures in 337 samples, at three sites along the French and Spanish NE Atlantic coast and in the laboratory. From these hyperspectral signatures, composed of 491 narrow bands, we tested the ability of a lower resolution multispectral sensor of 10 bands to capture key spectral features for classification. Dissimilarity between classification targets was assessed and Random Forest classifications were trained with hyper and multispectral resolutions. There was a higher accuracy with hyperspectral data, but classifications with multispectral resolution showed a satisfactory overall accuracy of 0.88. Barnacles, mussels and macroalgae, showed discernible dissimilarities. User accuracy of the goose barnacle class at a multispectral resolution was 0.89 showing only a small confusion with other barnacles. Spectral bands belonging to red edge and green wavelengths were the most important features for discrimination between the two classes of barnacles. This highlights the necessity of near infrared bands for goose barnacle identification and suggests a potential for mapping these populations with a visible near infra-red sensor mounted on a drone.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101697"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}