Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(4), P. 871 - 871

Published: Feb. 11, 2022

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance data sources change detection (CD). CD is a technique recognizing dissimilarities in acquired at distinct intervals and used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been tremendously processes, where it has achieved great success because their practical applications. Some researchers even claimed that DL approaches outperform traditional accuracy. Therefore, this review focuses on techniques, supervised, unsupervised, semi-supervised datasets, SAR, multispectral, hyperspectral, VHR, heterogeneous images, advantages disadvantages will be highlighted. end, some significant challenges discussed understand context improvements datasets models. Overall, beneficial future methods.

Language: Английский

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A Comprehensive Description of Multi-Term LSM for Applying Multiple a Priori Constraints in Problems of Atmospheric Remote Sensing: GRASP Algorithm, Concept, and Applications

Frontiers in Remote Sensing, Journal Year: 2021, Volume and Issue: 2

Published: Oct. 19, 2021

Advanced inversion Multi-term approach utilizing multiple a priori constraints is proposed. The used as base for the first unified algorithm GRASP that applicable to diverse remote sensing observations and retrieving variety of atmospheric properties. utilization demonstrated.

Language: Английский

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Benthic habitat mapping: A review of three decades of mapping biological patterns on the seafloor

Estuarine Coastal and Shelf Science, Journal Year: 2023, Volume and Issue: 296, P. 108599 - 108599

Published: Dec. 12, 2023

What is benthic habitat mapping, how it accomplished, and has that changed over time? We query the published literature to answer these questions synthesize results quantitatively provide a comprehensive review of field past three decades. Categories maps are differentiated unambiguously by response variable (i.e., subject being mapped) rather than approaches used produce map. Additional terminology in clarified defined based on provenance, statistical criteria, common usage. Mapping approaches, models, data sets, technologies, range other attributes reviewed their application, we document changes ways components have been integrated map habitats time. found use acoustic remote sensing surpassed optical methods for obtaining environmental data. Although wide variety employed ground truth maps, underwater imagery become most validation tool – surpassing physical sampling. The empirical machine learning models process increased dramatically 10 years, superseded expert manual interpretation. discuss products derived from address ecological emerging seascape ecology, technologies survey logistics pose different challenges this research across ecosystems intertidal shallow sublittoral regions deep ocean. Outstanding identified discussed context with trajectory field.

Language: Английский

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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 715 - 715

Published: Feb. 19, 2025

Forest attributes, such as standing stock, diameter at breast height (DBH), tree height, and basal area, are critical for effective forest management; yet, traditional estimation methods remain labor-intensive often lack the spatial detail required contemporary decision-making. This study addresses these challenges by integrating machine learning algorithms with high-resolution remotely sensed data rigorously collected ground truth measurements to produce accurate, national-scale maps of attributes in Romania. To ensure reliability model predictions, extensive field campaigns were conducted across representative Romanian forests. During campaigns, detailed recorded every within selected plots. For each tree, DBH was measured directly, heights obtained either direct measurement—using hypsometers or clinometers—or, when not feasible, applying well-established DBH—height allometric relationships that have been calibrated local types. comprehensive approach collection, supplemented an independent dataset from Brasov County using same protocols, allowed robust training validation models. evaluates performance three algorithms—Random (RF), Classification Regression Trees (CART), Gradient Boosting Tree Algorithm (GBTA)—in predicting Sentinel-2 satellite imagery. While Random consistently delivered high R2 values low root mean square errors (RMSE) all GBTA showed particular strength CART excelled area but less reliable other attributes. A sensitivity analysis multiple resolutions revealed varied significantly changes resolution, emphasizing importance selecting appropriate scale accurate mapping. By focusing on both methodological advancements applications rigorous, empirical this provides a clear solution problem obtaining reliable, spatially attribute maps.

Language: Английский

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Estimating grassland vegetation cover with remote sensing: A comparison between Landsat-8, Sentinel-2 and PlanetScope imagery

Ecological Indicators, Journal Year: 2022, Volume and Issue: 141, P. 109102 - 109102

Published: June 29, 2022

Grassland fractional vegetation cover (FVC) accurate mapping on a large scale is crucial, since degraded grasslands contribute less to provisioning services, carbon storage, water purification, erosion control and biodiversity conservation. The spatial temporal resolution of Sentinel-2 (S2) PlanetScope (PS) data has never been explored for grassland FVC estimation so far will enable researchers agencies quantify map timelier more precisely processes. In this paper we compare models developed from Landsat-8 (L8), S2 PS imagery. reference dataset was obtained the Paganella ski runs (46.15°N, 11.01°E, Italy) applying unsupervised classification nadir RGB photographs taken 1.35 m above soil. Fractional Response Models between 18 indices (VIs) extracted satellite imagery were fitted analysed. Then, leave-one-out cross validation spatiotemporal change analysis also performed. Our study confirms robustness commonly used VIs based difference NIR red wavelength region (R2 = 0.91 EVI using imagery) indicate that red-edge spectral are best performing 0.89 RECI). Only medium high (S2 PS) mapped patterns at site, varies fine scale. Previously available low (e.g., L8) may still be interesting requiring long time-series data.

Language: Английский

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Aerosol optical and radiative properties and their environmental effects in China: A review

Earth-Science Reviews, Journal Year: 2023, Volume and Issue: 248, P. 104634 - 104634

Published: Nov. 26, 2023

Language: Английский

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Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102234 - 102234

Published: July 26, 2023

Language: Английский

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Towards a Sustainable Urban Future: A Comprehensive Review of Urban Heat Island Research Technologies and Machine Learning Approaches

Sustainability, Journal Year: 2024, Volume and Issue: 16(11), P. 4609 - 4609

Published: May 29, 2024

The urban heat island (UHI) is a crucial factor in developing sustainable cities and societies. Appropriate data collection, analysis, prediction are essential first steps studying the effects of UHI. This research systematically reviewed papers related to UHI that have used on-site collection United States Canada predicting analyzing this effect these regions. To achieve goal, study extracted 330 articles from Scopus Web Science and, after selecting papers, 30 detail 1998 2023. findings paper indicated methodological shift traditional sensors loggers towards more innovative customized technologies. Concurrently, reveals growing trend using machine learning, moving supportive direct predictive roles techniques like neural networks Bayesian networks. Despite maturation due developments, they also present challenges technology complexity integration. review emphasizes need for future focus on accessible, accurate Moreover, interdisciplinary approaches addressing an era climate change.

Language: Английский

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Carbon Storage and Sequestration Analysis by Urban Park Grid Using i-Tree Eco and Drone-Based Modeling

Forests, Journal Year: 2024, Volume and Issue: 15(4), P. 683 - 683

Published: April 10, 2024

Urban areas play a crucial role in carbon absorption, while also producing considerable amount of emissions. However, there has been lack research that systematically examined the storage and sequestration green spaces located within urban environments, at spatial scale. This study analyzes Yurim Park, Daejeon, South Korea on grid basis to fill gap. The compares variation capacity across different grids provides insights into development sustainable parks planning. classification is based specific site characteristics, such as land cover, tree distribution, type, density. results total seven distinct types. employs combination I-tree eco model, drone-based modeling, on-site surveys estimate parks. show average per unit area entire park was 15.3 tons hectare, ranging from minimum 5.0 maximum 21.4 hectare. For planted area, 8.6 Grids with dominated by broad-leaved trees closed canopy cover had highest values. planting ratio type were found directly influence stands out previous conducting detailed area-based comparison analysis using sophisticated measurement techniques. findings offer direct strategies policies for securing future sinks can be practical use this regard.

Language: Английский

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ForestSplat: Proof-of-Concept for a Scalable and High-Fidelity Forestry Mapping Tool Using 3D Gaussian Splatting

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 993 - 993

Published: March 12, 2025

Accurate, scalable forestry insights are critical for implementing carbon credit-based reforestation initiatives and data-driven ecosystem management. However, existing forest quantification methods face significant challenges: hand measurement is labor-intensive, time-consuming, difficult to trust; satellite imagery not accurate enough; airborne LiDAR remains prohibitively expensive at scale. In this work, we introduce ForestSplat: an monitoring, reporting, verification (MRV) system built from consumer-grade drone footage 3D Gaussian Splatting. To evaluate the performance of our approach, map reconstruct a 200-acre mangrove restoration project in Jobos Bay National Estuarine Research Reserve. ForestSplat produces average mean absolute error (MAE) 0.17 m (ME) 0.007 compared canopy height maps derived scans, using 100× cheaper hardware. We hope that proposed framework can support advancement modeling with drones computer vision, facilitating new gold standard MRV.

Language: Английский

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