Egyptian Journal of Remote Sensing and Space Sciences最新文献

Spectral–Spatial Adaptive Weighted Fusion and Residual Dense Network for hyperspectral image classification

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-11-30 DOI: 10.1016/j.ejrs.2024.11.001

Junding Sun , Hongyuan Zhang , Xiaoxiao Ma , Ruinan Wang , Haifeng Sima , Jianlong Wang

The dense and nearly continuous spectral bands in hyperspectral images result in strong inter-band correlations, which can diminish performance of the model in classification tasks. Moreover, most convolutional neural network-based methods for hyperspectral image classification typically depend on a fixed scale to extract spectral–spatial features, which ignore the detail features of some objects. To address the above issues, a novelty Spectral Spatial Adaptive Weighted Fusion and Residual Dense Network (S

^{2}

AWF-RDN) is proposed for Hyperspectral image classification. Specifically, the proposed S

^{2}

AWF-RDN consists of spectral–spatial adaptive weighted fusion module, multi-channel feature concatenation residual dense module, and spatial feature fusion module. Firstly, the spectral information optimization branch is developed to adjust the weights assigned to various spectral channels. Similarly, the spatial information optimization branch is developed to adjust the weights for different spatial regions. Secondly, to obtain rich spectral spatial information from different levels, multi-channel feature concatenation residual dense module has been proposed. In addition, a multi-channel feature concatenation block is designed guiding the model to extract spectral spatial information at different scales. Finally, spatial feature fusion module is introduced to retain more spatial information. The experimental outcomes illustrate that the proposed network model exhibits superior classification performance on three renowned hyperspectral image datasets. Furthermore, the efficacy of the proposed network model is further corroborated through comparative and ablation studies.

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引用次数: 0

New radio-seismic indicator for ELF seismic precursors detectability

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-11-29 DOI: 10.1016/j.ejrs.2024.10.003

Andrea Mariscotti , Renato Romero

This work considers the effectiveness of earthquakes (EQs) radio precursors mainly in the Extremely Low Frequency (ELF) range and below, and carries out an analysis based on a comprehensive set of EQ events documented in past publications and provided by the Opera 2015 project (six stations located in Italy). A new Radio-Seismic Indicator (RSI) is proposed, with the magnitude-distance relationship physically justified by path-loss expressions of the transverse magnetic mode. Classification performances of past and proposed RSIs are assessed calculating confusion matrices and on those the balanced accuracy and Matthews’ coefficient: the RSI performs significantly better reducing fall-outs and increasing precision for both classes, positive and negative precursors. Performance improvement is inherently limited by the overlap of the classes.

引用次数: 0

Estimation of above ground biomass of mangrove forest plot using terrestrial laser scanner 利用陆地激光扫描仪估算红树林地块的地上生物量

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-11-23 DOI: 10.1016/j.ejrs.2024.11.002

Yeshwanth Kumar Adimoolam , Nithin D. Pillai , Gnanappazham Lakshmanan , Deepak Mishra , Vinay Kumar Dadhwal

Above-Ground Biomass (AGB) is an important parameter in the conservation of mangrove ecosystem owing to their ecological and economic benefits. LiDAR technologies in forest studies have become popular, due to its highly accurate 3D spatial data acquisition. In this study, we propose an end-to-end framework for estimating AGB of mangroves from Terrestrial Laser Scanner (TLS) point clouds. The framework includes pre-processing of data, segmenting the wood and foliage at tree level using Weighted Random Forest (WRF) classifier and constructing Quantitative Structure Model (QSM) of the wooden components to estimate its biomass. The flow was extended to AGB estimation of 33 x 33 m plot by integrating tree level framework. The study also finds a unique solution to estimate the contribution of pneumatophores in the AGB. Segmentation of wood/foliage of tree point cloud using WRF yielded better results with an increment of 15.27 % in Balanced accuracy, 0.2 of Cohen’s Kappa coefficient, and 7.45 % in F1score than RF classifier. AGB estimation of mangroves using our approach using TLS data is 47.54 T/ha which has a mean bias of 0.0044 T/ha and RMS variation of 0.026 T/ ha when compared with the allometric methods. A Breadth-first graph-search segmentation approach was used to count the pneumatophores, aerial roots seen in few mangrove species (R² = 0.94 with manual counting) and estimate its contribution to AGB of mangroves which is first of its kind using TLS point cloud. This outcome could also aid future studies in modeling the underlying root network and estimating the below-ground biomass.

由于红树林的生态和经济效益，地上生物量（AGB）是保护红树林生态系统的一个重要参数。由于能获取高精度的三维空间数据，激光雷达技术在森林研究中得到了广泛应用。在本研究中，我们提出了一个端到端框架，用于从地面激光扫描仪（TLS）点云估算红树林的 AGB。该框架包括数据预处理、使用加权随机森林（WRF）分类器分割树木层面的木质和叶片，以及构建木质成分的定量结构模型（QSM）以估算其生物量。通过整合树级框架，该流程扩展到 33 x 33 米地块的 AGB 估算。该研究还找到了一个独特的解决方案来估算气生植物在 AGB 中的贡献。与射频分类器相比，使用 WRF 对树木点云的木材/叶片进行分类的结果更好，平衡精度提高了 15.27%，科恩卡帕系数提高了 0.2，F1 分数提高了 7.45%。利用 TLS 数据，采用我们的方法估算出的红树林 AGB 为 47.54 吨/公顷，与其他计量方法相比，平均偏差为 0.0044 吨/公顷，均方根变异为 0.026 吨/公顷。利用广度优先图搜索分割方法计算了少数红树林物种的气生根（人工计算的 R2 = 0.94），并估算了其对红树林 AGB 的贡献，这是首次利用 TLS 点云进行此类估算。这一结果也有助于今后的研究建立底层根系网络模型和估算地下生物量。

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引用次数: 0

Efficient bundle optimization for accurate camera pose estimation in mobile augmented reality systems 在移动增强现实系统中进行高效的捆绑优化，以实现精确的相机姿态估计

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-11-16 DOI: 10.1016/j.ejrs.2024.10.006

Shanglin Li , Yalan Li , Yulin Lan , Anping Lin

Augmented reality has a long research history in computer vision and computer graphics communities. It aims to enhance the user experience for real scenes via overlapping virtual objects. Nowadays, mobile augmented reality has attracted much attention from researchers and developers due to the development of hardware techniques. Modern mobile devices such as mobile phones have a powerful computational ability for augmented reality applications. As a result, many researchers have paid attention to mobile augmented reality. From the technical viewpoint of augmented reality, mobile augmented reality largely depends on camera pose estimation. However, existing methods make it difficult to achieve the best balance between accuracy and efficiency, according to our investigation, and this may handicap the performance of mobile augmented reality systems. To overcome the problem, in this paper, we propose a novel approach to camera pose estimation based on bundle optimization. Our proposed method is evaluated on real-world datasets and is also tested in the mobile augmented reality system. Both experiments demonstrate that our proposed method has fast speed and high accuracy.

增强现实技术在计算机视觉和计算机图形学领域有着悠久的研究历史。其目的是通过重叠虚拟对象来增强用户对真实场景的体验。如今，由于硬件技术的发展，移动增强现实技术已经引起了研究人员和开发人员的广泛关注。手机等现代移动设备具有强大的计算能力，可用于增强现实应用。因此，许多研究人员开始关注移动增强现实技术。从增强现实的技术角度来看，移动增强现实在很大程度上取决于相机姿态估计。然而，根据我们的调查，现有的方法很难在精度和效率之间取得最佳平衡，这可能会影响移动增强现实系统的性能。为了解决这个问题，我们在本文中提出了一种基于捆绑优化的新型摄像机姿态估计方法。我们提出的方法在真实世界数据集上进行了评估，并在移动增强现实系统中进行了测试。这两项实验都证明了我们提出的方法速度快、精度高。

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引用次数: 0

Revealing Potential Mineralization Zones Utilizing Landsat-9, ASTER and Airborne Radiometric Data at Elkharaza-Dara Area, North Eastern Desert, Egypt 利用 Landsat-9、ASTER 和机载辐射测量数据揭示埃及东北部沙漠 Elkharaza-Dara 地区的潜在成矿带

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-11-10 DOI: 10.1016/j.ejrs.2024.10.005

Mahmoud Abd El-Rahman Hegab, Islam Abou El Magd, Kareem Hamed Abd El Wahid

The present work enhances mineral exploration in Egypt’s Eastern Desert by mapping lithological units and identifying hydrothermal alteration zones, potentially leading to the discovery of economically viable mineral deposits. This study employs a comprehensive approach of integrating multispectral bands from Landsat-9 and ASTER images with airborne radiometric data. Various image enhancement techniques such as False Color Composite (FCC), Minimum Noise Fraction (MNF), and Principal Component Analysis (PCA) are utilized to map enhanced lithological units. Additionally, image classification techniques, including Spectral Angle Mapper (SAM) and Crosta Principal Component (CROSTA PC), are applied to emphasize hydrothermal alteration minerals like alunite, calcite, hematite, illite, chlorite, epidote, kaolinite, montmorillonite, and sericite. Furthermore, radioelement ratios (eU/eTh, eU/K, eTh/K, and eU-(eTh/3.5)) and the F-parameter (K*(eU/eTh)) are utilized. Mineral percentages are determined using Scanning Electron Microscope (SEM), allowing for the observation of ore minerals from the Elkharaza-Dara area deposits, which exhibit varying compositions. Maximum values are recorded for specific elements: aluminum (10.48 wt% Al), silicon (65.38 wt% Si), silver (0.32 wt% Ag), copper (2.65 wt% Cu), gold (5.25 wt% Au), potassium (4.32 wt% K), hafnium (3.84 wt% Hf), calcium (26.94 wt% Ca), carbon (56.92 wt% C), and oxygen (53.71 wt% O). These findings offer valuable insights into the elemental composition of the mineralized deposits in the study area. The multi-algorithm integration approach has been confirmed through various methods, including comparison with existing geological maps, fieldwork, and microscopic analysis of selected samples from alteration zones across the study area.

本研究通过绘制岩性单元图和确定热液蚀变区，加强了埃及东部沙漠的矿产勘探，从而有可能发现具有经济价值的矿藏。这项研究采用了一种综合方法，将 Landsat-9 和 ASTER 图像的多光谱波段与机载辐射测量数据整合在一起。利用各种图像增强技术，如假色合成（FCC）、最小噪声分数（MNF）和主成分分析（PCA）来绘制增强岩性单元图。此外，图像分类技术，包括光谱角度绘图仪（SAM）和 CROSTA 主成分分析（CROSTA PC），被用于强调热液蚀变矿物，如白云石、方解石、赤铁矿、伊利石、绿泥石、绿帘石、绿泥石、高岭石、蒙脱石和绢云母。此外，还利用了放射性元素比（eU/eTh、eU/K、eTh/K 和 eU-（eTh/3.5））和 F 参数（K*（eU/eTh））。使用扫描电子显微镜（SEM）测定矿物百分比，以便观察埃尔克哈拉扎-达拉地区矿床中呈现不同成分的矿石矿物。特定元素的最大值为：铝（10.48 wt% Al）、硅（65.38 wt% Si）、银（0.32 wt% Ag）、铜（2.65 wt% Cu）、金（5.25 wt% Au）、钾（4.32 wt% K）、铪（3.84 wt% Hf）、钙（26.94 wt% Ca）、碳（56.92 wt% C）和氧（53.71 wt% O）。这些发现为了解研究区域矿化矿床的元素组成提供了宝贵的信息。多算法整合方法已通过多种方法得到证实，包括与现有地质图的对比、实地考察以及对研究区域内蚀变带的部分样本进行显微分析。

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引用次数: 0

Potential of temporal satellite data analysis for detection of weed infestation in rice crop 时空卫星数据分析在检测水稻作物杂草侵扰方面的潜力

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-11-08 DOI: 10.1016/j.ejrs.2024.10.002

Manju Tiwari , Prasun Kumar Gupta , Nitish Tiwari , Shrikant Chitale

Weeds are unwanted vegetation that compete with main crops for essential resources like light, water, and nutrients, leading to significant reductions in food crop yield and economic losses. Addressing this issue is crucial, particularly during the Kharif cropping season when cloud cover interferes with remote sensing capabilities. This study is an attempt to investigate the potential of satellite-based temporal analysis in weed detection from agricultural fields. The research focused on rice cultivation at the Research cum Instructional farms of Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh. The study explored the utility of satellite imagery for assessing crop health, demonstrating how weed infestation influences vegetative indices. The study utilized satellite images from PlanetScope and Sentinel-2 to examine the temporal variation in vegetation indices across two treatments: pure rice and rice with weeds. NDVI analysis revealed a significant decline in treatments affected by weeds (upto 41% less), suggesting that time-series satellite data can serve as an early indicator of weed infestation in standing rice crops. These findings were further verified by backscatter values from the Sentinel-1 dataset, which indicated a reduction in backscatter (upto 18% less) due to the suboptimal growth conditions in weed-infested treatments compared to weed-free rice. While the technology has shown efficacy at a preliminary stage, there is significant potential for its broader application and scalability in operational contexts.

杂草是与主要作物争夺光照、水分和养分等必要资源的无用植被，导致粮食作物大幅减产和经济损失。解决这一问题至关重要，尤其是在云层干扰遥感能力的 Kharif 耕种季节。本研究试图调查基于卫星的时间分析在农田杂草探测中的潜力。研究的重点是恰蒂斯加尔邦赖布尔英迪拉-甘地-克里希-维希瓦维亚学院研究与教学农场的水稻种植。该研究探索了卫星图像在评估作物健康方面的效用，展示了杂草侵扰如何影响植被指数。该研究利用 PlanetScope 和哨兵-2 的卫星图像，研究了两种处理中植被指数的时间变化：纯水稻和杂草丛生的水稻。NDVI分析表明，受杂草影响的处理植被指数明显下降（降幅高达41%），这表明时间序列卫星数据可作为水稻作物杂草侵染的早期指标。哨兵-1 数据集的反向散射值进一步验证了这些发现，该数据集显示，与无杂草水稻相比，受杂草影响的处理区生长条件较差，导致反向散射减少（最多减少 18%）。虽然该技术在初步阶段显示出了功效，但其在业务环境中的更广泛应用和可扩展性还有很大潜力。

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引用次数: 0

Visualization of humpback whale tracking on edge device using space-borne remote sensing data for Indian Ocean 利用印度洋空间遥感数据实现座头鲸在边缘装置上的可视化追踪

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-11-07 DOI: 10.1016/j.ejrs.2024.10.004

S. Vasavi, Vasanthi Sripathi, Chandra Mouli Simma

The conservation of humpback whale populations faces ongoing challenges, including human-induced mortality, despite the ban on commercial whaling. Recent advancements in high-resolution satellite imagery offer promise for estimating whale populations, particularly in remote and inaccessible regions. However, significant research gaps persist, necessitating innovative approaches for effective monitoring and conservation efforts. This paper presents a novel methodology that integrates high- resolution satellite imagery with state-of-the-art deep learning techniques to monitor and conserve humpback whale populations, with a focus on the Indian Ocean region. Specifically, application of cutting-edge deep learning models such as YOLO for object detection and EfficientNet for classification to automate the detection, classification, and tracking of humpback whales in satellite images is explored. By leveraging deep convolutional neural networks (CNNs), the proposed ensemble system offers a robust and generalizable approach for automatically detecting, classifying, and tracking whales in space-borne satellite imagery, thereby addressing the challenge of uncertain whale populations in the world’s oceans. The results demonstrate promising accuracy and performance metrics: the Segment Anything Model(SAM) achieves an accuracy of 89.2%, YOLO achieves an accuracy of 99.2%, EfficientNet achieves an accuracy of 99% across various tasks.

尽管禁止商业捕鲸，座头鲸种群的保护仍面临着持续的挑战，包括人类造成的死亡。高分辨率卫星图像的最新进展为估算鲸鱼种群数量带来了希望，尤其是在偏远和人迹罕至的地区。然而，研究方面仍然存在巨大的差距，因此需要创新的方法来进行有效的监测和保护工作。本文介绍了一种新颖的方法，该方法将高分辨率卫星图像与最先进的深度学习技术相结合，用于监测和保护座头鲸种群，重点关注印度洋地区。具体来说，该研究探讨了如何应用最先进的深度学习模型（如用于对象检测的 YOLO 和用于分类的 EfficientNet）来自动检测、分类和跟踪卫星图像中的座头鲸。通过利用深度卷积神经网络（CNNs），所提出的集合系统为自动检测、分类和跟踪天基卫星图像中的鲸鱼提供了一种稳健且可推广的方法，从而解决了世界海洋中鲸鱼数量不确定的难题。研究结果表明，该系统的准确率和性能指标都很不错：Segment Anything Model（SAM）的准确率达到了 89.2%，YOLO 的准确率达到了 99.2%，EfficientNet 在各种任务中的准确率达到了 99%。

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引用次数: 0

Phenology-based winter wheat classification for crop growth monitoring using multi-temporal sentinel-2 satellite data 利用多时区哨兵-2 号卫星数据进行基于物候学的冬小麦分类以监测作物生长情况

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-10-18 DOI: 10.1016/j.ejrs.2024.10.001

Solomon W. Newete , Khaled Abutaleb , George J Chirima , Katarzyna Dabrowska-Zielinska , Radoslaw Gurdak

Wheat is one of the most important staple crops consumed by more than four billion people in the world. However, its production is challenged by the impact of climate change which accounts for a 5.5 % reduction in wheat yield and it is predicted to dwindle further by about 30 % in 2050, due to trends in temperature, precipitation, and carbon dioxide. An effective annual crop estimate is necessary not only to inform governments the status of national food security, but also to determine the benchmark on which agricultural commodities are priced in the market. Thus, annual crop monitoring and yield estimate is paramount to determine the amount of wheat imports required to make up for the shortfalls in the national wheat production in South Africa, which has been a net importer of wheat since 1998. This study aimed at investigating the most distinguishable crop phenology for accurate winter wheat classification during the growing season from August – December 2020 using Sentinel-2 imageries and Random Forest algorithm. The winter wheat crop was more accurately identified during the crop ‘heading’ stage in October yielding the highest user’s (75.56 %) and producer’s (92.52 %) accuracies, despite the relatively lower overall accuracy (78.14 %) compared to that of December with overall accuracy of 83.58 % obtained during the maturity stage. This study, therefore, found that the extraction of NDVI values of the winter wheat crop over the period of the growing season using the Sentinel-2 NDVI series method and grouping these values into distinct classes using the K-means unsupervised clustering techniques assist to identify the different crop phenologies based on which the winter wheat crop could be detected and mapped accurately. The phenology-based classification of the winter wheat crop during the heading stage, reduce the ambiguity of spectral confusion created with surrounding grass and maize crops.

小麦是全世界 40 多亿人消费的最重要的主食作物之一。然而，气候变化的影响使小麦减产 5.5%，预计到 2050 年，由于气温、降水和二氧化碳的变化趋势，小麦产量将进一步减少约 30%。有效的年度作物估产不仅是政府了解国家粮食安全状况的必要条件，也是确定农产品市场定价基准的必要条件。因此，年度作物监测和产量估算对于确定南非小麦进口量以弥补国家小麦产量不足至关重要，因为南非自 1998 年以来一直是小麦净进口国。本研究旨在利用哨兵-2 成像和随机森林算法，调查在 2020 年 8 月至 12 月的生长季节中最易区分的作物物候，以便对冬小麦进行准确分类。尽管总体准确率（78.14%）相对较低，但与 12 月份成熟期 83.58% 的总体准确率相比，10 月份作物 "打顶 "阶段对冬小麦作物的识别更为准确，用户准确率（75.56%）和生产者准确率（92.52%）最高。因此，本研究发现，使用 Sentinel-2 NDVI 系列方法提取冬小麦作物生长季节的 NDVI 值，并使用 K-means 无监督聚类技术将这些值分成不同的类别，有助于识别不同的作物物候，从而准确地检测和绘制冬小麦作物图。基于物候的冬小麦打顶期作物分类减少了与周围草地和玉米作物光谱混淆造成的模糊性。

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引用次数: 0

Space-based mid-wavelength infrared camera module for peatland fires and volcanic activities of Andesite rock 用于泥炭地火灾和安第斯岩火山活动的天基中波红外摄像模块

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-09-30 DOI: 10.1016/j.ejrs.2024.09.001

Bustanul Arifin , Irwan Priyanto , Ahmad Fauzi , Andi Mukhtar Tahir , Moedji Soedjarwo

Two major perenial disasters are prevalent in Indonesia, namely, peatland fires and volcanic activities associated with Andesite rock. Thus, the Indonesian Government has prioritized the prevention and mitigation of both disasters. Indonesia’s Research Center for Satellite Technology-National Research and Innovation Agency then implemented the program as a satellite payload project. In this study, we describe the design of a space-based mid-wavelength infrared (SMWIR) camera module to monitor peatland fires and volcanic activities associated with Andesite rock. Using the spectral range as the basis of design and the iteration process of general steps in designing a camera, a SMWIR camera module was successfully designed. First, the spectral range was obtained by an intersection of four methods of determining spectral bands. Subsequently, the optical section, was conducted using Zemax by applying three criteria to analyze the optical performance, such as the spot diagram, encircled energy, and modulation transfer function (MTF). Thereafter, the mechanical design was achieved through the SOLIDWORKS software. The fourth step, namely, the structure or thermal design, was achieved by both Thermal Desktop/SINDA FLUINT and Zemax. In the electronic section, both the camera and detector were developed. Finally, a calibration system was specified over the module. Results in the form of graphs, pictures, and tables indicate that all established conditions, including those of the technical side, were achieved. Therefore, high performance in terms of the image, durability, transmission, and thermal stability can easily be achieved; additionally, the module is feasible, lightweight, and compact.

印度尼西亚普遍存在两种主要的周边灾害，即泥炭地火灾和与安山岩有关的火山活动。因此，印尼政府将预防和减轻这两种灾害列为优先事项。印尼卫星技术研究中心-国家研究与创新局随后将该计划作为卫星有效载荷项目实施。在本研究中，我们介绍了天基中波红外（SMWIR）照相机模块的设计，该模块用于监测泥炭地火灾和与安山岩相关的火山活动。利用光谱范围作为设计基础，并采用设计照相机一般步骤的迭代过程，成功设计了 SMWIR 照相机模块。首先，通过四种确定光谱带方法的交叉获得了光谱范围。随后，使用 Zemax 对光学部分进行了设计，采用了三个标准来分析光学性能，如光斑图、包围能量和调制传递函数（MTF）。之后，通过 SOLIDWORKS 软件实现了机械设计。第四步是结构或热设计，由 Thermal Desktop/SINDA FLUINT 和 Zemax 完成。在电子部分，开发了照相机和探测器。最后，在模块上指定了一个校准系统。图表、图片和表格形式的结果表明，所有既定条件，包括技术方面的条件，均已达到。因此，该模块在图像、耐用性、传输和热稳定性方面都能轻松实现高性能；此外，该模块还具有可行性、重量轻和结构紧凑等特点。

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引用次数: 0

Gap filling of missing satellite data from MODIS and CMEMS for chlorophyll-a in the waters of Aceh, Indonesia 填补 MODIS 和 CMEMS 提供的印度尼西亚亚齐水域叶绿素-a 卫星数据的空白

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2024-09-27 DOI: 10.1016/j.ejrs.2024.08.004

M.N. Hidayat , R. Wafdan , M. Ramli , Z.A. Muchlisin , S. Rizal

The motivation behind our study is to identify a robust method to enhance the accuracy of missing data, particularly chlorophyll-a data, which often goes undetected due to various factors. This study analyzes chlorophyll-a concentrations and sea level changes due to tides using three methods: Linear Interpolation, Fillgaps, and Modified Fillgaps. Two experiments were conducted: Experiment I involved random data removal (60% and 70%), and Experiment II combined sequential and random data removal (25% sequentially on the right, 35% and 45% randomly on the left). In Experiment I, the Modified Fillgaps method showed high correlation coefficients (up to 0.96) between original and reconstructed data, demonstrating its effectiveness in accurately filling significant data gaps. This method also exhibited low Root Mean Square Error and Mean Absolute Error values, confirming its predictive precision. In Experiment II, despite structured and realistic data loss patterns, the method maintained high correlation and low prediction errors, with low Normalized Root Mean Squared Error and Mean Absolute Percentage Error values, further validating its reliability. Additionally, the method excelled in two-dimensional chlorophyll-a maps, outperforming Linear Interpolation and Fillgaps methods in scenarios with 50% and 60% data loss, achieving higher correlation and lower prediction errors. These findings are crucial for environmental and climatological studies relying on satellite-derived data, confirming the Modified Fillgaps method as the most reliable and effective for handling significant data loss in chlorophyll-a map analyses. Future research should explore its application to other environmental data types and more complex data loss patterns.

我们研究的动机是找出一种稳健的方法来提高缺失数据的准确性，特别是叶绿素-a 数据，因为这些数据经常由于各种因素而未被检测到。本研究使用三种方法分析了叶绿素-a 浓度和潮汐引起的海平面变化：线性插值法、填充法和修正填充法。共进行了两次实验：实验 I 涉及随机数据移除（60% 和 70%），实验 II 结合了顺序和随机数据移除（右侧顺序移除 25%，左侧随机移除 35% 和 45%）。在实验 I 中，"修正填充间隙 "方法在原始数据和重建数据之间显示出较高的相关系数（高达 0.96），证明了该方法在准确填补重要数据间隙方面的有效性。该方法还显示出较低的均方根误差和平均绝对误差，证实了其预测精度。在实验 II 中，尽管出现了结构化和现实的数据丢失模式，但该方法仍保持了高相关性和低预测误差，归一化均方根误差和平均绝对百分比误差值都很低，进一步验证了其可靠性。此外，该方法在二维叶绿素-a 地图中表现出色，在数据丢失 50% 和 60% 的情况下，其相关性更高，预测误差更小，优于线性插值法和 Fillgaps 法。这些发现对于依赖卫星数据的环境和气候学研究至关重要，证实了修正的 Fillgaps 方法是处理叶绿素-a 地图分析中大量数据丢失的最可靠、最有效的方法。未来的研究应探索该方法在其他环境数据类型和更复杂的数据丢失模式中的应用。

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