Science of Remote Sensing最新文献_第6页

Joint analysis and modeling of the hot spot effect from the diurnal reflectance and temperature cycles observed by SEVIRI SEVIRI观测的日反射率和温度周期对热点效应的联合分析和模拟

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-10-17 DOI: 10.1016/j.srs.2025.100309

Chandrika Pinnepalli , Roujean Jean-Louis , Eswar Rajasekaran , Thomas Vidal , Zunjian Bian , Tian Hu , Mark Irvine , Biao Cao , Philippe Gamet

This study evaluates the influence of the hot spot effect, i.e. when the solar and viewing angles coincide, producing a radiance peak on the diurnal reflectance and temperature cycles (DRC and DTC, respectively) observed by the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) sensor aboard the Meteosat Second Generation (MSG) satellite. Focusing on clear-sky conditions and multiple land cover types, we assess the directional impact on both spectral brightness temperature (Tb) and land surface temperature (LST). A four-parameter DTC model is coupled with a directional kernel-driven model (KDM), including a hot spot term, to create Time-Evolving KDMs. The models are applied to six diverse sites to evaluate whether optical BRDF characteristics can inform thermal BRDF (Bidirectional Reflectance Distribution Function) behavior, and to what extent directional effects distort DTC profiles. Findings indicate a clear hot spot signature in the DRC, while in the DTC, it subtly alters the bell-shaped curve, resulting in Tb deviations up to 3 K and LST differences up to 4 °C. The results underscore the need to correct for angular effects when comparing DTCs across sites or seasons. Moreover, visual inspections show that optical BRDF peaks align closely with cosine peaks for two satellites, whereas thermal peaks diverge—highlighting mismatches and the challenges of modeling mixed land cover. Present findings underscore the need for improved models and multi-sensor validation to support a full exploitation of thermal remote sensing.

本研究评估了热点效应的影响，即当太阳和视角重合时，由气象卫星第二代（MSG）卫星上的SEVIRI（旋转增强型可见光和红外成像仪）传感器观测到的日反射率和温度周期（分别为DRC和DTC）产生一个辐射峰值。以晴空条件和多种土地覆盖类型为研究对象，评估了光谱亮度温度（Tb）和地表温度（LST）的方向性影响。将四参数DTC模型与包含热点项的定向核驱动模型（KDM）相结合，形成时间演化的KDM模型。该模型应用于六个不同的站点，以评估光学BRDF特征是否可以影响热BRDF（双向反射分布函数）行为，以及方向效应在多大程度上扭曲了DTC剖面。研究结果表明，在刚果民主共和国有明显的热点特征，而在DTC，它微妙地改变了钟形曲线，导致Tb偏差高达3 K， LST差异高达4°C。结果强调，在比较不同地点或季节的dtc时，需要纠正角度效应。此外，目视检测显示，两颗卫星的光学BRDF峰与余弦峰紧密对齐，而热峰则偏离——突出了不匹配和混合土地覆盖建模的挑战。目前的研究结果强调需要改进模型和多传感器验证，以支持热遥感的充分利用。

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引用次数: 0

GNSS-IR real-time water level retrieval method based on hybrid sliding window and LSTM 基于混合滑动窗口和LSTM的GNSS-IR实时水位检索方法

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-10-28 DOI: 10.1016/j.srs.2025.100321

Peiyuan Wang , Fang Cheng , Junqiang Han , Zhen Jiang , Yang Liu , Rui Tu , Xiaolei Wang , Weisheng Wang , Bayin Dalai , Gulayozov Majid Shonazarovich , Yaoming Li , Xiaochun Lu

Real-time water level monitoring is of critical significance in flood disaster mitigation and water resource management. This paper proposes a real-time Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) water level retrieval method based on the hybrid integration of sliding window and Long Short-Term Memory (LSTM). By dynamically updating input sequences through the sliding window mechanism, an LSTM model captures both temporal and nonlinear characteristics of water level variations, enabling high-precision real-time prediction. Experimental results demonstrate that during non-typhoon seasons, the predicted sea level achieves a correlation coefficient of 99.78 % and a root mean square error (RMSE) of 10.81 cm compared to tide gauge measurements. The system still formulates stable predictions for near-real-time sea level monitoring even with 1.31 % data gaps caused by missing values, which satisfies the requirements. During storm surge, the correlation coefficient between predicted and measured data reaches 96.18 %, with a RMSE of 16.55 cm. Notably, the method maintains robust real-time predictive capability even under extreme conditions where wind speeds exceed 30 m/s and retrieval values significantly decrease. These results demonstrate that the proposed method achieves high accuracy under both normal and extreme hydrological conditions, providing an efficient, cost-effective technical pathway for nearshore real-time water level monitoring and disaster early warning.

实时水位监测在防洪减灾和水资源管理中具有重要意义。提出了一种基于滑动窗口和长短期记忆混合集成的全球导航卫星系统干涉反射（GNSS-IR）实时水位反演方法。LSTM模型通过滑动窗口机制动态更新输入序列，同时捕捉水位变化的时间和非线性特征，实现高精度的实时预测。实验结果表明，在非台风季节，预测海平面与验潮仪测量值的相关系数为99.78%，均方根误差（RMSE）为10.81 cm。对于近实时的海平面监测，即使存在1.31%的数据缺失导致的数据缺口，该系统仍能给出稳定的预测结果，满足要求。在风暴潮期间，预测值与实测值的相关系数达到96.18%，RMSE为16.55 cm。值得注意的是，即使在风速超过30米/秒且检索值显著下降的极端条件下，该方法也保持了强大的实时预测能力。结果表明，该方法在正常和极端水文条件下均具有较高的精度，为近岸实时水位监测和灾害预警提供了一条高效、经济的技术途径。

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引用次数: 0

Improving soil moisture estimation in wet soils using L-band Synthetic Aperture Radar (SAR) through polarization and filtering optimization 通过极化和滤波优化，改进l波段合成孔径雷达（SAR）在湿润土壤中的土壤水分估算

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-09-21 DOI: 10.1016/j.srs.2025.100290

Naoto Sato , Shinsuke Aoki , Daiki Kobayashi , Yuichi Maruo , Shunsuke Kodaira , Kosuke Noborio

Accurately mapping soil water distribution is crucial for effective irrigation management and landslide risk assessment. Microwave remote sensing is an effective method for assessing soil water content over extensive areas; however, its efficacy tends to diminish in regions with high soil water content. This study aimed to evaluate the sensitivity of soil moisture estimation in areas with volumetric water content exceeding 0.3 m³/m³, focusing on methodologies of optimizing polarization, despeckling filter application, and ground truth data determination. Among the polarizations of electromagnetic wave analyzed—vertical and horizontal polarization (VH), vertical and vertical polarization (VV), horizontal and horizontal polarization (HH), and horizontal and vertical polarization (HV)—HH and VH polarization exhibited the favorable performance. We evaluated the effectiveness of various despeckling filters—Boxcar, Lee, Frost, Gamma-Map, Refined Lee, and Lee Sigma—on the accuracy of soil moisture estimation. The combination of HH polarization with the Frost filter yielded high correlation coefficients, low root mean square error, and satisfactory sensitivity. While the smallest window size (3 × 3) provided sufficient noise reduction, larger window sizes degraded estimation accuracy. A notable correlation was observed when the ground-truth data were derived from averaging soil water content across an area matching the despeckling filter window size. This suggests that SAR image pixel values effectively represent the mean soil water content within an equivalent spatial extent of the despeckling filter.These findings highlight that accurate soil moisture estimation using SAR backscatter requires careful consideration of polarization, filter type, window size, and the spatial scale of in-situ measurements.

准确的土壤水分分布图对有效的灌溉管理和滑坡风险评估至关重要。微波遥感是评估大面积土壤含水量的有效方法；但在土壤含水量高的地区，其效果趋于减弱。本研究旨在评价体积含水量超过0.3 m3/m3地区土壤水分估算的敏感性，重点研究极化优化方法、消斑滤波器应用方法和地面真值数据确定方法。在分析的电磁波极化中，垂直和水平极化（VH）、垂直和垂直极化（VV）、水平和水平极化（HH）和水平和垂直极化（HV），其中HH和VH极化表现较好。我们评估了各种消斑滤波器（boxcar、Lee、Frost、Gamma-Map、Refined Lee和Lee sigma）对土壤水分估计精度的有效性。HH偏振与Frost滤波器的结合获得了高的相关系数、低的均方根误差和令人满意的灵敏度。虽然最小的窗口尺寸（3 × 3）提供了足够的降噪，但较大的窗口尺寸会降低估计精度。当从匹配消斑滤波器窗口大小的区域的平均土壤含水量获得地面真实数据时，观察到显着的相关性。这表明，SAR图像像素值有效地代表了去斑滤波器等效空间范围内的平均土壤含水量。这些发现表明，利用SAR后向散射准确估算土壤水分需要仔细考虑极化、滤光片类型、窗口大小和原位测量的空间尺度。

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引用次数: 0

Three-dimensional models of coral microatolls using structure-from-motion photogrammetry and iPhone LiDAR scanning: A fast, reproducible method for collecting relative sea-level data in the field 利用运动摄影测量和iPhone激光雷达扫描的珊瑚微环礁三维模型：一种快速、可重复的方法，用于收集现场相对海平面数据

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1016/j.srs.2025.100288

Nurul Syafiqah Tan , Rohan Gautam , Fangyi Tan , Gina M. Sarkawi , Jędrzej M. Majewski , Junki Komori , Shi Jun Wee , Khai Ken Leoh , Lucas D. Koh , Adam D. Switzer , Aron J. Meltzner

Coral microatolls, geological proxies commonly used for reconstructing relative sea-level (RSL) in low-latitude regions, are valued for their precision and ability to continuously track RSL changes through the elevation of successive concentric surface rings. The brief low-tide window prevents rigorous methods for replicating field observations, limiting opportunities for reinterpretation of coral morphology. Additionally, while the extraction of a physical coral slab remains the preferred method for RSL reconstruction, logistical constraints can render it non-viable. When slabbing is possible, the reliability of the reconstructed RSL might be questionable. This study introduces three-dimensional models created using structure-from-motion photogrammetry and iPhone LiDAR scans to facilitate rigorous analysis of coral microatolls. These methods result in accurate and high-resolution documentation of the coral surface, enabling comprehensive and simultaneous analysis of ring structures of multiple microatolls while ensuring results are representative and replicable. Where slabbing is feasible, this method guides the selection of optimal corals that contain the most complete record of RSL change and validates slabbing results. Where slabbing is not viable, this approach provides an alternative means to obtaining RSL histories. Integrating this model-based approach into conventional fieldwork enables extensive data interpretation off-site. Furthermore, the user-friendly nature of these methods enhances accessibility for researchers with limited resources. The benefits and limitations of each technique are also discussed. While photogrammetry-derived point clouds are denser, they necessitate additional georeferencing steps to ensure accurate scale and orientation. Conversely, iPhone-derived models possess inherent scale, though they require additional processing steps, carrying a potential risk of data loss.

珊瑚微环礁是低纬度地区重建相对海平面（RSL）的常用地质指标，其精度和通过连续同心表面环的高程连续跟踪相对海平面变化的能力受到重视。短暂的低潮窗口妨碍了复制实地观察的严格方法，限制了重新解释珊瑚形态的机会。此外，虽然提取物理珊瑚板仍然是重建RSL的首选方法，但后勤限制可能使其不可行。当有可能发生板裂时，重建的RSL的可靠性可能会受到质疑。本研究介绍了利用运动摄影测量和iPhone激光雷达扫描技术创建的三维模型，以促进对珊瑚微环礁的严格分析。这些方法可以精确和高分辨率地记录珊瑚表面，从而可以对多个微环礁的环状结构进行全面和同时的分析，同时确保结果具有代表性和可复制性。在可行的情况下，这种方法指导选择包含最完整的RSL变化记录的最佳珊瑚，并验证slab结果。在不可行的情况下，这种方法提供了一种获取RSL历史的替代方法。将这种基于模型的方法集成到常规的现场工作中，可以在现场之外进行广泛的数据解释。此外，这些方法的用户友好性提高了资源有限的研究人员的可及性。还讨论了每种技术的优点和局限性。虽然摄影测量衍生的点云密度更大，但它们需要额外的地理参考步骤来确保精确的尺度和方向。相反，iphone衍生的机型具有固有的规模，尽管它们需要额外的处理步骤，存在数据丢失的潜在风险。

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引用次数: 0

Improving ICESat-2 photon classification and tree height estimation using Moran's I and machine learning 利用Moran’s I和机器学习改进ICESat-2光子分类和树高估计

IF 5.7 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-06-28 DOI: 10.1016/j.srs.2025.100251

Mei-Kuei Lu , Sorin Popescu , Lonesome Malambo

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides valuable data for vegetation mapping using photon counting lidar (PCL) technology. However, its ATL08 data product, designed for canopy height and terrain classification, exhibits classification inaccuracies due to algorithm limitations and noise contamination. This study aimed to address these challenges by leveraging local spatial autocorrelation, Moran's I, as a feature input in machine learning methods to enhance photon classification accuracy. Random Forest models were developed and compared, with one model incorporating Moran's I to capture spatial patterns. The study covered 12 diverse ecoregions across the United States, including conifer forests, broadleaf forests, and savannas. Canopy heights derived from different models were validated against Airborne Laser Scanning (ALS) data. The results demonstrated that the model incorporating Moran's I improved classification accuracy, with R² values ranging from 0.30 to 0.76 across ecoregions. Top of Canopy (TOC) classifications in dense forests, such as those in South Carolina, achieved higher agreement with ALS data, whereas sparse environments like Louisiana savannas exhibited lower accuracy. This study highlights the importance of incorporating spatial autocorrelation measures in machine learning workflows to improve vegetation classification, which can be beneficial for more accurate ecological assessments using ICESat-2 data in diverse landscapes.

冰、云和陆地高程卫星2号（ICESat-2）为利用光子计数激光雷达（PCL）技术进行植被测绘提供了有价值的数据。然而，其为冠层高度和地形分类设计的ATL08数据产品，由于算法限制和噪声污染，表现出分类不准确。本研究旨在通过利用局部空间自相关（Moran’s I）作为机器学习方法的特征输入来提高光子分类的准确性，从而解决这些挑战。开发了随机森林模型并进行了比较，其中一个模型结合了Moran的I来捕捉空间模式。这项研究涵盖了美国12个不同的生态区域，包括针叶林、阔叶林和稀树草原。利用机载激光扫描（机载激光扫描）数据对不同模型的冠层高度进行了验证。结果表明，纳入Moran’s I的模型提高了分类精度，各生态区域的R2值在0.30 ~ 0.76之间。冠层顶部（TOC）分类在茂密的森林中，如南卡罗来纳州，与ALS数据的一致性较高，而在路易斯安那州稀树草原等稀疏环境中，准确性较低。该研究强调了将空间自相关措施纳入机器学习工作流程以改进植被分类的重要性，这有助于在不同景观中使用ICESat-2数据进行更准确的生态评估。

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引用次数: 0

An optimised land-use land-cover classification approach for general application in deserts and arid regions 一种适用于沙漠和干旱地区的最佳土地利用土地覆盖分类方法

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-11-12 DOI: 10.1016/j.srs.2025.100334

João Carlos Campos , André Vicente Liz , László Patkó , Ayman Abdulkarem , Lourens Van Essen , Magdy El-Bana , Ahmed Al-Ansari , Omar Al-Attas , José Carlos Brito

Land-use/land-cover (LULC) is a critical driver of ecosystem dynamics globally. Arid regions are particularly vulnerable to global change factors, and comprehensive LULC assessments are crucial for evaluating the environmental stability of these areas. Despite considerable developments of remote sensing (RS) data/products and classification algorithms, the available multi-scale maps fail to represent the complex heterogeneity of LULC in these areas. The major limitation resides not on the improvements in data resolutions or the complexity of algorithmic decisions, but rather on the lack of approaches prioritizing detailed categorization of LULC in arid regions. Therefore, we propose an integrative multi-classification approach of LULC based on RS techniques and in-situ data for general application in arid regions, using the north-western Saudi Arabia as pilot area. Using in-situ data (N = 7523) and a Landsat-8 time-series, we applied a supervised classification of non-dynamic classes representing regional geodiversity, combined with a clustering analysis of dynamic harmonic regression model coefficients to categorize ecologically dynamic classes. The map was obtained with high accuracy for non-dynamic classes (Kappa≈0.84; overall accuracy≈0.87; overall producer's accuracy≈0.86; overall user's accuracy≈0.89) and dynamic classes (combined overall accuracy≈0.76). The final map presents a total of 15 classes, considerably improving the available categorical resolution for the study area. The approach is transferable to other arid regions, having the potential to integrate other finer-scale RS data and classification algorithms. We urge for increased efforts in data collection and the implementation of approaches considering the prominent diversity of LULC in arid regions.

土地利用/土地覆盖（LULC）是全球生态系统动态的关键驱动因素。干旱区特别容易受到全球变化因素的影响，综合的LULC评估对于评价干旱区的环境稳定性至关重要。尽管遥感数据/产品和分类算法有了长足的发展，但现有的多比例尺地图无法反映这些地区土地利用变化的复杂异质性。主要的限制不在于数据分辨率的提高或算法决策的复杂性，而在于缺乏对干旱地区LULC进行优先详细分类的方法。基于此，本文以沙特阿拉伯西北部地区为试点，提出了一种基于遥感技术和原位数据的综合多分类方法，该方法在干旱区具有普遍应用价值。利用实测数据（N = 7523）和Landsat-8时间序列，对代表区域地质多样性的非动态类别进行了监督分类，并结合动态调和回归模型系数的聚类分析对生态动态类别进行了分类。对于非动态类（Kappa≈0.84；总体精度≈0.87；总体生产者精度≈0.86；总体用户精度≈0.89）和动态类（综合总体精度≈0.76），获得了较高的精度。最终的地图共有15个类别，大大提高了研究区域的分类分辨率。该方法可推广到其他干旱地区，具有整合其他更精细尺度遥感数据和分类算法的潜力。我们敦促在数据收集方面加大努力，并采取考虑到干旱地区土地利用变化显著多样性的方法。

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引用次数: 0

A spectral-preserving resampling for spatial upscaling of hyperspectral imagery 高光谱图像空间升级的保谱重采样方法

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-11-06 DOI: 10.1016/j.srs.2025.100330

Yuxin Tian, Zhenghai Wang

This paper proposes a spectral-preserving hyperspectral image resampling method (Spectral-Preserving Resampling, SpePR) designed to effectively retain the diagnostic spectral features of minerals during spatial upscaling. In this method, the band correlation structure of hyperspectral imagery is utilized as an intrinsic representation of spectral features, and Tikhonov regularized pseudo-inversion is introduced to mitigate spectral distortion induced by resampling. Within the proposed framework, spectral structural information is initially characterized by band correlation matrices. Subsequently, during the spatial resampling stage, the spectral preservation and spatial resampling terms are jointly optimized to ensure coordinated preservation of spectral and spatial information. The performance of the method was validated by analyzing multi-scale hyperspectral imagery data, with flight altitudes ranging from 30m to 150m, acquired using unmanned aerial vehicles. The results indicate that as spatial resolution decreases, mineral spectral features exhibit a corresponding decrease in absorption depth and absorption area, while maintaining stable absorption positions. Compared with seven conventional interpolation algorithms, SpePR reduces errors by 8.2 %–15.7 % in spectral angular mapping (SAM) and by 23.6 %–27.9 % in spectral correlation relative to the best conventional method. The proposed method also demonstrated significant advantages in metrics such as spectral gradient angle (SGA) and spectral correlation, while also more accurately preserving key mineral absorption features. Concurrently, SpePR demonstrated superior spatial information retention compared to conventional methods, as its resulting spatial features more closely approximated the actual observational imagery. The research findings confirm that the SpePR approach effectively preserves diagnostic spectral features of minerals, thereby providing reliable technical support for multi-scale hyperspectral mineral mapping.

本文提出了一种保留光谱的高光谱图像重采样方法（spectral-preserving resampling, SpePR），旨在有效地保留矿物在空间上尺度的诊断光谱特征。该方法利用高光谱图像的波段相关结构作为光谱特征的内在表征，并引入Tikhonov正则化伪反演来缓解重采样引起的光谱畸变。在该框架内，光谱结构信息首先由波段相关矩阵表征。随后，在空间重采样阶段，对光谱保存项和空间重采样项进行联合优化，保证光谱信息和空间信息的协调保存。通过分析无人机获取的飞行高度在30m ~ 150m之间的多尺度高光谱图像数据，验证了该方法的性能。结果表明：随着空间分辨率的降低，矿物光谱特征的吸收深度和吸收面积相应减小，但吸收位置保持稳定；与传统插值方法相比，SpePR在光谱角映射（SAM）上的误差降低了8.2% ~ 15.7%，在光谱相关上的误差降低了23.6% ~ 27.9%。该方法在光谱梯度角（SGA）和光谱相关性等指标上也具有显著的优势，同时也能更准确地保留关键的矿物吸收特征。与传统方法相比，SpePR显示出更好的空间信息保留能力，因为其得到的空间特征更接近实际观测图像。研究结果证实，SpePR方法有效地保留了矿物的诊断光谱特征，为多尺度高光谱矿物填图提供了可靠的技术支持。

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引用次数: 0

Corrigendum to “Transferability of country-wide airborne laser scanning-based models for individual-tree attributes” [Sci. Rem. Sens. 12 (2025) 100310] “基于单个树属性的全国机载激光扫描模型的可移植性”的勘误表[Sci]。[j] .上院学报，12(2025)100310。

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-11-12 DOI: 10.1016/j.srs.2025.100329

Valtteri Soininen, Xiaowei Yu, Matti Hyyppä, Juha Hyyppä

引用次数: 0

Mobile laser scanning in support of national and regional forest inventories 移动激光扫描，支持国家和区域森林清查

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-10-17 DOI: 10.1016/j.srs.2025.100316

Justin Holvoet , Nicolas Latte , Jérôme Perin , Jean-François Bastin , Hugo de Lame , Daniel Kükenbrink , Philippe Lejeune

In the context of a growing need to diversify forest information, national and regional forest inventories (NFI and RFI) could benefit from mobile Light Detection and Ranging (LiDAR) technologies. Ground-based mobile laser scanning (MLS) and unmanned aerial laser scanning (ULS) can potentially retrieve a large panel of forest attributes quickly, efficiently, and accurately. In this study, conducted in Wallonia (southern Belgium), we aimed to evaluate, in the context of an NFI, the accuracy of MLS at tree, plot, and inventory levels and the potential benefits of fusing ULS with MLS. In total, 60 circular forest plots of 0.1 ha containing 2497 trees were measured by traditional inventory means and scanned using MLS. Among them, 27 were additionally scanned by ULS, and ULS and MLS scans were fused to produce an enhanced point cloud. We then evaluated the accuracy of MLS considering, at tree level, the diameter at breast height, total height, merchantable wood volume, and crown projected area and volume; at plot level, the total merchantable wood volume, number of trees, and total basal area; and for the whole inventory, the total volume and number of trees. Tree, plot, and inventory metrics were accurately acquired with a strong correlation to field measurements (r² ranging from 0.83 to 0.98). Out of all estimated metrics, height has a potential accurately estimated by MLS than by field measurements. The fusion of ULS and MLS allowed for a more accurate crown measurement, but height estimation was not significantly better than with MLS scan alone. The accuracy of soft- and hardwood forest plot estimations differed considering total plot wood volume, number of trees, and individual tree height. In this study, we explored the possibility and limitations of MLS in undertaking large-scale inventory in terms of accuracy, time, and reliability.

在日益需要多样化森林信息的背景下，国家和区域森林清单（NFI和RFI）可以受益于移动光探测和测距（LiDAR）技术。地面移动激光扫描（MLS）和无人机激光扫描（ULS）可以快速、高效、准确地检索大量森林属性。本研究在比利时南部的瓦隆尼亚进行，目的是在NFI的背景下，评估树木、地块和库存水平上MLS的准确性，以及将ULS与MLS融合的潜在好处。采用传统清查方法对60个面积为0.1 ha的圆形森林样地进行了测量，共包含2497棵树木。其中27例追加ULS扫描，融合ULS和MLS扫描形成增强点云。然后，我们评估了MLS的准确性，考虑树木水平，胸径高度，总高度，可销售木材体积，树冠投影面积和体积；在样地水平上，总可售木材量、乔木数和总基面积；对于整个库存，树木的总体积和数量。树、样地和库存指标准确获得，与现场测量结果有很强的相关性（r2范围为0.83至0.98）。在所有的估计指标中，MLS比现场测量更能准确地估计出身高。ULS和MLS的融合可以更准确地测量冠，但高度估计并不比单独MLS扫描好得多。软硬木样地和阔叶林样地估算的精度在样地木材总量、树木数量和单株树高的影响下存在差异。在本研究中，我们从准确性、时间和可靠性方面探讨了MLS进行大规模库存的可能性和局限性。

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引用次数: 0

PSO-optimized dual-channel BP neural network for bathymetric prediction using multisource marine geodetic data: A case study of the gulf of Mexico 基于pso优化的双通道BP神经网络用于多源海洋大地测量数据的水深预测：以墨西哥湾为例

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2025-12-01 Epub Date: 2025-08-21 DOI: 10.1016/j.srs.2025.100274

Jiajia Yuan , Haoran Liu , Jianli Chen , Chen Yang

Accurate seafloor topography is essential for marine scientific research, resource exploration, and understanding geological processes. Traditional bathymetric surveying methods are constrained by limited spatial coverage and high operational costs, particularly in deep-sea environments. To overcome these challenges, we developed a Particle Swarm Optimization (PSO)-optimized dual-channel BP neural network (PSO_BP), integrating shipborne bathymetric data with satellite altimetry-derived gravity anomalies. These gravity anomalies were further decomposed into long-wavelength, short-wavelength, and residual components to enhance bathymetric prediction accuracy. We systematically evaluate the impact of different gravity data combinations, including gravity anomalies, gravity gradients, and vertical deflections, used individually, in pairs, or as a three-component combination, on bathymetric prediction accuracy. Results show that PSO_BP consistently outperforms existing models (GEBCO_2024, Topo_25.1, DTU18_BAT, and SRTM15 + V2.6), achieving the lowest RMSE (25.45 m), MAE (9.95 m), MAPE (3.70 %), and highest R² (99.96 %) across various depth ranges and shoreline distances. The decomposition of gravity anomalies into long- and short-wavelength components and their residuals proves to be the most effective approach for improving bathymetric prediction accuracy, while PSO optimization enhances model convergence and reduces prediction errors. This study highlights the importance of integrating diverse gravity datasets and advanced optimization techniques to improve the accuracy and robustness of seafloor depth prediction, offering a reliable solution for global bathymetric mapping in deep and remote ocean regions.

准确的海底地形对于海洋科学研究、资源勘探和了解地质过程至关重要。传统的水深测量方法受到空间覆盖范围有限和操作成本高的限制，特别是在深海环境中。为了克服这些挑战，我们开发了一个粒子群优化（PSO）优化的双通道BP神经网络（PSO_BP），将船载测深数据与卫星测高得出的重力异常相结合。将重力异常进一步分解为长波分量、短波分量和残差分量，提高水深预测精度。我们系统地评估了不同重力数据组合，包括重力异常、重力梯度和垂直偏转，单独使用、成对使用或作为三分量组合使用，对水深预测精度的影响。结果表明，PSO_BP持续优于现有模型（GEBCO_2024、Topo_25.1、DTU18_BAT和SRTM15 + V2.6），在不同深度范围和岸线距离上实现了最低RMSE （25.45 m）、MAE （9.95 m）、MAPE（3.70%）和最高R2（99.96%）。将重力异常分解为长短波长分量及其残差是提高水深预测精度的最有效方法，而粒子群优化提高了模型的收敛性，减小了预测误差。该研究强调了整合不同重力数据集和先进优化技术对提高海底深度预测精度和鲁棒性的重要性，为深海和偏远海洋区域的全球水深测绘提供了可靠的解决方案。

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