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Accurate estimation of grain number per panicle in winter wheat by synergistic use of UAV imagery and meteorological data 无人机影像与气象数据协同精确估算冬小麦穗粒数
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104320
Yapeng Wu, Weiguo Yu, Yangyang Gu, Qi Zhang, Yuan Xiong, Hengbiao Zheng, Chongya Jiang, Xia Yao, Yan Zhu, Weixing Cao, Tao Cheng
Rapid, accurate, and nondestructive estimation of grain number per panicle (GNPP) in winter wheat is crucial to accelerate smart breeding, improve precision crop management, and ensure food security. As two (panicle number per unit ground area and GNPP) of three commonly used yield components, GNPP was much less quantified with remotely sensed data than the former through visual counting. The limited research suffered from either low accuracies with ground canopy spectra or low efficiency with proximal panicle imaging systems. No studies have been reported on estimating GNPP with unmanned aerial vehicle (UAV) imagery, underscoring its strong advantages in high-resolution and efficient monitoring. To address these issues, this study proposed a practical approach for estimating GNPP in winter wheat by integrating UAV imagery and meteorological data with meta-learning ensemble regression. The potential contributions of different variables were examined for understanding the improvement in the spectral estimation of GNPP, including spectral indices (SIs), the optimal canopy height (CH) metric, and absorbed photosynthetic active radiation (APAR).
The results demonstrated that CHP99 (CH at the 99th percentile in the region of interest) derived from red-green-blue (RGB) imagery exhibited the strongest correlation with measured plant height among all RGB- or multispectral (MS)-derived CH metrics. The incorporation of remotely sensed APAR and RGB-derived CHP99 improved the accuracy of GNPP estimation over using merely color indices or SIs. Among all feature combinations, Comb. #6 (SIs + APARMS + CHP99) yielded the highest overall accuracies in estimating GNPP for individual and multiple stages. Compared with the best anthesis models for Combs. #5–7 (Rval2 = 0.52–0.64, RMSE = 2.85–2.47, RRMSE = 6.01–5.21 %), the multi-stage (heading + anthesis) models exhibited higher accuracies in independent validation (Rval2 = 0.60–0.65, RMSE = 2.60–2.42, RRMSE = 5.48–5.10 %). The findings suggest this study has opened a new avenue for estimating GNPP with UAV remote sensing. The proposed method for the synergistic use of UAV imagery and meteorological data has great potential in the prediction of GNPP and grain yield over various regions with satellite imagery and climate datasets.
快速、准确、无损地估算冬小麦每穗粒数(GNPP)对加快智慧育种、提高作物精准管理水平、保障粮食安全具有重要意义。作为3个常用产量要素中的2个要素(单位地面积穗数和GNPP), GNPP的遥感量化效果远不如目测量化。地面冠层光谱的精度较低,近穗成像系统的效率较低。没有关于使用无人机(UAV)图像估计GNPP的研究报道,强调其在高分辨率和高效监测方面的强大优势。为了解决这些问题,本研究提出了一种将无人机图像和气象数据结合元学习集成回归估算冬小麦GNPP的实用方法。研究了光谱指数(SIs)、最佳冠层高度(CH)度量和吸收光合有效辐射(APAR)等不同变量对GNPP光谱估算的潜在贡献。
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引用次数: 0
Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model 基于辐射传输模型的VIIRS数据对活燃料含水率的低方差估计
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104311
Shuai Yang, Rui Chen, Binbin He, Yiru Zhang
The Canopy Live Fuel Moisture Content (LFMC) is a pivotal factor in wildfire risk assessment within the fire triangle model, representing the ratio of canopy moisture content to its dry weight. Against the backdrop of degraded Moderate Resolution Imaging Spectroradiometer (MODIS) performance and the underutilization of Visible Infrared Imaging Radiometer Suite (VIIRS) in LFMC inversion, this study harnessed the coupled radiative transfer models (RTMs) to probe the spectral sensitivity of the VIIRS to LFMC and pinpoint the optimal band combination for LFMC inversion. To tackle the challenge of ill-posed inversion, we leveraged the correlation coefficient matrix to mitigate erroneous combinations of free parameters in the construction of the lookup table. Results showcase that VIIRS-based LFMC inversion yields marginally superior accuracy (R2= 0.57, R2= 0.32) for both grassland and forest types, with VIIRS-based inversion demonstrating a lower relative root mean square error (rRMSE = 5.84%), compared to results from the MODIS. By scrutinizing LFMC trends alongside precipitation (PP) data in four forest fires spanning from 2019 to 2022 in southwest China, varied degrees of LFMC decrease preceding fire outbreaks. Those results substantiated the validity of the proposed method for wildfire warning. Consequently, our study asserts the reliability of VIIRS in LFMC inversion, positioning it as a viable substitute and extension of MODIS. VIIRS offers continuous and effective product support for wildfire warning assessment, enhancing our ability to monitor and mitigate wildfire risks.
林冠活燃料含水率(LFMC)代表林冠含水率与其干重的比值,是火灾三角模型中野火风险评估的关键因子。在中分辨率成像光谱仪(MODIS)性能下降和可见光红外成像辐射计套件(VIIRS)在LFMC反演中利用不足的背景下,利用耦合辐射传输模型(RTMs)探讨了中分辨率成像辐射计(VIIRS)对LFMC的光谱灵敏度,并确定了LFMC反演的最佳波段组合。为了解决不适定反演的挑战,我们利用相关系数矩阵来减轻查找表构造中自由参数的错误组合。结果表明,与MODIS相比,基于viirs的LFMC反演在草地和森林类型上的精度略高(R2= 0.57, R2= 0.32),基于viirs的反演显示出更低的相对均方根误差(rRMSE = 5.84%)。通过分析2019 - 2022年中国西南地区4次森林火灾的LFMC趋势和降水(PP)数据,发现火灾发生前LFMC有不同程度的下降。这些结果证实了所提出的野火预警方法的有效性。因此,我们的研究证实了VIIRS在LFMC反演中的可靠性,将其定位为MODIS的可行替代品和扩展。VIIRS为野火预警评估提供持续有效的产品支持,增强我们监测和减轻野火风险的能力。
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引用次数: 0
Integrating RS data with fuzzy decision systems for innovative crop water needs assessment 遥感数据与模糊决策系统相结合的创新作物需水量评价
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104338
Faezeh Sadat Hashemi , Mohammad Javad Valadan Zoej , Fahimeh Youssefi , Huxiong Li , Sanaz Shafian , Mahdi Farnaghi , Saied Pirasteh
Irrigation is a critical component of global water usage, accounting for approximately 70 % of water consumption. As the world’s population continues to grow, the demand for food will increase, making it essential to improve irrigation management by reducing water waste and increasing efficiency. This study aims to develop and validate a fuzzy decision-making system that determines crop irrigation needs based on parameters that affect plant water requirements. These parameters can be monitored using Remote sensing (RS) satellites, enabling large-scale agricultural irrigation monitoring. The study utilized Landsat-8 satellite data and meteorological data. It also employed a fuzzy decision system with inputs of estimated evapotranspiration, Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST), Crop Water Stress Index (CWSI), Stress Index (SI), and Soil Moisture (SM). The output of the fuzzy model is a map that effectively determines the irrigation requirements for agricultural land relatively. The system was tested on six Landsat images of winter wheat crops in Tehran University’s agricultural fields. The estimated evapotranspiration was compared to Reference Evapotranspiration (ETr) obtained from the FAO-Penman-Monteith equation, resulting in a root mean square error of 0.33 mm. The fuzzy decision system was evaluated by comparing its results with Vegetation Water Content (VWC) measurements during satellite overpass time. The NDVI, CWSI, SI, and SM variables had the highest R2 with VWC data (0.71––0.92) on all six dates. This approach has significant implications for improving irrigation management practices, reducing water waste, and increasing crop yields, which can contribute to global food security. The study highlights the potential of RS technology and fuzzy decision-making systems in promoting sustainable agriculture.
灌溉是全球用水的重要组成部分,约占用水量的70%。随着世界人口的持续增长,对粮食的需求将会增加,因此必须通过减少水浪费和提高效率来改善灌溉管理。本研究旨在开发并验证一个模糊决策系统,该系统可以根据影响植物需水量的参数来确定作物的灌溉需求。这些参数可以利用遥感卫星进行监测,从而实现大规模的农业灌溉监测。该研究利用了Landsat-8卫星数据和气象数据。采用模糊决策系统,以估算蒸散量、归一化植被指数(NDVI)、叶面积指数(LAI)、地表温度(LST)、作物水分胁迫指数(CWSI)、胁迫指数(SI)和土壤湿度(SM)为输入。模糊模型的输出是一幅能有效确定农业用地相对灌溉需求的图。该系统在德黑兰大学农田的六张冬小麦作物的Landsat图像上进行了测试。将估算的蒸散发量与FAO-Penman-Monteith方程获得的参考蒸散发量(ETr)进行比较,结果均方根误差为0.33 mm。通过与卫星立交桥时间植被含水率(VWC)测量结果的比较,对模糊决策系统进行了评价。NDVI、CWSI、SI和SM变量与VWC数据的R2均最高(0.71—0.92)。这种方法对改善灌溉管理做法、减少水浪费和提高作物产量具有重要意义,从而有助于全球粮食安全。该研究强调了遥感技术和模糊决策系统在促进可持续农业方面的潜力。
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引用次数: 0
Quantifying heat-related risks from urban heat island effects: A global urban expansion perspective 从城市热岛效应量化热相关风险:全球城市扩张视角
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104344
Ming Hao , Xue Liu , Xia Li
Quantifying the urban heat island (UHI) effect and its impact on summer heat-related risk is important for both urban environment and human well-being. Existing studies frequently adopt the static (fixed) urban boundary to define urban/rural area in UHI measurement, overlooking the exacerbation of the urbanization-induced warming during long-term urban expansion and the consequent increase in urban heat risks. Here we measured the global surface UHI (SUHI) intensity up to 7,554 urban patches during 2000–2015 using every five-year dynamic urban boundary, followed by a two-stage analysis based on a Distributed Lag Non-linear Model (DLNM) to quantify the additional heat-related risks caused by the urbanization-induced warming. Our results show that the global average SUHI intensity increased by approximately 10 % in 15 years with distinct seasonal and diurnal variations. The global urban expansion from 2000 to 2015 resulted in an average increase of 0.61℃ (95 % CI = 0.56℃-0.66℃) in summer UHI intensity for newly built-up areas. This urbanization-induced warming further leads to a 20 % (95 % CI = 14.8 %-25.2 %) increase in summer heat relative risk (RR) on average, which implied an average increase of 20 % (95 % CI = 14.8 %-25.2 %) in annual heat-related mortality for the newly built-up areas. Furthermore, over 2.3 % of the world population would experience an RR increase greater than 10 %. This study highlights the importance of dynamic urban boundary for long-time span UHI measurements, providing a deeper understanding of the role of urbanization-induced warming on urban heat risk.
量化城市热岛效应及其对夏季热相关风险的影响对城市环境和人类福祉都具有重要意义。现有研究在城市热岛指数测量中经常采用静态(固定)城市边界来定义城市/农村地区,忽视了长期城市扩张过程中城市化引起的变暖加剧及其导致的城市热风险增加。本研究利用每5年动态城市边界测量2000-2015年间全球地表热岛强度达7554个城市斑块,然后基于分布式滞后非线性模型(DLNM)进行两阶段分析,量化城市化引起的增暖带来的额外热相关风险。结果表明,全球平均SUHI强度在15年内增加了约10%,具有明显的季节和日变化。2000 - 2015年全球城市扩张导致新建建成区夏季热岛强度平均升高0.61℃(95% CI = 0.56℃~ 0.66℃)。这种城市化引起的变暖进一步导致夏季热相对危险度(RR)平均增加20% (95% CI = 14.8% - 25.2%),这意味着新建成区的年热相关死亡率平均增加20% (95% CI = 14.8% - 25.2%)。此外,超过2.3%的世界人口将经历超过10%的RR增长。该研究强调了动态城市边界对长跨度热岛指数测量的重要性,为深入了解城市化引起的变暖对城市热风险的作用提供了依据。
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引用次数: 0
Satellite images reveal rapid development of global water-based photovoltaic over the past 20 years 卫星图像显示,近20年来全球水基光伏发电发展迅速
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104354
He Ren , Zhen Yang , Fashuai Li , Maoxin Zhang , Yuwei Chen , Tingting He
Water-based photovoltaics (WPV) have emerged as a promising solution to land-use conflicts associated with solar photovoltaic systems. Accurate monitoring of the spatiotemporal distribution of WPV is essential for evaluating its development potential, environmental impacts, and informing policy decisions. Satellite remote sensing data offer a feasible approach for WPV mapping and monitoring. However, conventional image classification and deep learning methods often limited by sample size requirements, computational costs, and technical complexity, which hinder their widespread applicability. To address these challenges, this study proposes a novel index, the normalized difference photovoltaic index (NDPI), for WPV detection. We generated a global WPV map for the year 2023 using Sentinel-2 MSI imagery and NDPI. Additionally, by integrating NDPI with Landsat time series data, we determined the installation dates of WPV systems and evaluated their development trends from 2000 to 2023. Our results show that: (i) The NDPI demonstrated excellent performance in WPV detection, with overall accuracy for spatial location and installation dates of WPV was 0.935 and 0.927, respectively, and Kappa coefficients of 0.870 and 0.921. (ii) Global WPV coverage in 2023 reached 589.17 km2, with Asia being the primary contributor, accounting for over 97 %. China emerged as the leading country, with a WPV area of 472.92 km2, significantly exceeding other nations (< 50 km2). (iii) WPV experienced significant growth from 2000 to 2023, particularly after 2015. The increase in WPV area (434.57 km2) from 2015 to 2023 was nearly three times the total area covered in the previous 15 years. The proposed NDPI provides a universal approach for global WPV spatiotemporal monitoring and the update of basic information. It also provides potential for assessing the environmental impacts of WPV across its operational lifecycle.
水基光伏(WPV)已成为解决与太阳能光伏系统相关的土地使用冲突的有希望的解决方案。准确监测野生生物多样性的时空分布对于评估其发展潜力、环境影响和为决策提供信息至关重要。卫星遥感数据为WPV制图和监测提供了一种可行的方法。然而,传统的图像分类和深度学习方法往往受到样本量要求、计算成本和技术复杂性的限制,这阻碍了它们的广泛应用。为了解决这些挑战,本研究提出了一种新的指数,即归一化光伏指数(NDPI),用于WPV检测。我们使用Sentinel-2 MSI图像和NDPI生成了2023年的全球WPV地图。此外,通过整合NDPI和Landsat时间序列数据,我们确定了WPV系统的安装日期,并评估了其2000年至2023年的发展趋势。结果表明:(1)NDPI在水样pv检测中表现优异,对水样pv空间位置和安装日期的总体精度分别为0.935和0.927,Kappa系数分别为0.870和0.921。(ii) 2023年全球WPV覆盖面积达到589.17 km2,其中亚洲是主要贡献者,占比超过97%。中国以472.92平方公里的WPV面积,明显超过其他国家,跃居首位。50平方公里)。(iii)从2000年到2023年,特别是在2015年之后,WPV经历了显著增长。2015 - 2023年WPV面积增加了434.57 km2,几乎是前15年总面积的3倍。所提出的npi为全球WPV时空监测和基本信息更新提供了一种通用的方法。它还为评估WPV在整个运行周期中的环境影响提供了潜力。
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引用次数: 0
Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learning
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104390
Stefanie Steinbach , Anna Bartels , Andreas Rienow , Bartholomew Thiong’o Kuria , Sander Jaap Zwart , Andrew Nelson
Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs is often not monitored continuously, with the interlinkages between weather, land, and water remaining unknown. Turbidity is a prime indicator of water quality that can be assessed with remote sensing techniques. Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). Our results suggest that short- and longer-term turbidity prediction can inform reservoir siting and management. However, inter-annual variability prediction could benefit from more knowledge of additional factors that may not be fully captured in commonly available geospatial data. This study contributes to the relatively small body of remote sensing-based research on water quality in small reservoirs and supports improved small-scale water management.
{"title":"Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learning","authors":"Stefanie Steinbach ,&nbsp;Anna Bartels ,&nbsp;Andreas Rienow ,&nbsp;Bartholomew Thiong’o Kuria ,&nbsp;Sander Jaap Zwart ,&nbsp;Andrew Nelson","doi":"10.1016/j.jag.2025.104390","DOIUrl":"10.1016/j.jag.2025.104390","url":null,"abstract":"<div><div>Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs is often not monitored continuously, with the interlinkages between weather, land, and water remaining unknown. Turbidity is a prime indicator of water quality that can be assessed with remote sensing techniques. Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R<sup>2</sup> = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R<sup>2</sup> = 0.88 and 0.72 respectively). Our results suggest that short- and longer-term turbidity prediction can inform reservoir siting and management. However, inter-annual variability prediction could benefit from more knowledge of additional factors that may not be fully captured in commonly available geospatial data. This study contributes to the relatively small body of remote sensing-based research on water quality in small reservoirs and supports improved small-scale water management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104390"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143211648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
RSIT: A waveform retracking method based on reconstructed sea surface height and iterative threshold for coastal altimetry data RSIT:基于重建海面高度和迭代阈值的沿海高程数据波形重跟踪方法
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104346
Zejie Tu , Chuanyin Zhang , Tao Jiang , Fuxi Zhao , Heng Wang , Fanlin Yang
Extending satellite radar altimetry measurements from the open ocean to the coastal zone can improve the accuracy and quality of monitoring coastal sea level. However, radar altimetry waveforms can be distorted by any inhomogeneity in the properties of the surface observed within the footprints, possibly leading to range measurement errors. To address these issues, a coastal retracking method based on reconstructed sea surface height and iterative threshold (RSIT) is proposed in this paper. RSIT involves several steps: First, the sea surface height components are reconstructed as prior information to compute the initial retracking gate. Next, iterate the amplitude scale factor of the entire waveform to identify possible sub-waveforms. After each iteration, continuity between neighboring sub-waveforms is assessed. Eventually, the optimal retracking gate is determined from all identified sub-waveforms. We validated RSIT using Jason-2 data in the coastal regions of Australia and Pakistan. Experimental results show that RSIT can retrieve more available altimetry data and enhance the accuracy by nearly 37.5% and 23.1% compared to ALES within the last few kilometers to the coast, respectively. Moreover, the impact of varied errors in reconstructed sea surface height on RSIT was discussed, with the results reveal that RSIT has strong robustness to errors within 1 m, making it suitable for application in most coastal zones.
将卫星雷达测高范围从公海扩展到海岸带,可以提高海岸带海平面监测的精度和质量。然而,雷达测高波形可能会被足迹内观察到的表面特性的任何不均匀性所扭曲,可能导致距离测量误差。针对这些问题,本文提出了一种基于重建海面高度和迭代阈值(RSIT)的海岸回溯方法。RSIT包括以下几个步骤:首先,重建海面高度分量作为先验信息,计算初始重跟踪门;接下来,迭代整个波形的幅度比例因子,以识别可能的子波形。每次迭代后,评估相邻子波形之间的连续性。最后,从所有已识别的子波形中确定最优的重跟踪门。我们在澳大利亚和巴基斯坦的沿海地区使用Jason-2数据验证了RSIT。实验结果表明,与ALES相比,RSIT可以检索到更多的可用高度数据,在距离海岸最后几公里的范围内,精度分别提高了近37.5%和23.1%。此外,还讨论了重建海面高度误差对RSIT的影响,结果表明,RSIT对1 m以内的误差具有较强的鲁棒性,适用于大多数沿海地区。
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引用次数: 0
Multimodal urban areas of interest generation via remote sensing imagery and geographical prior 通过遥感图像和地理先验生成感兴趣的多模式城市地区
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104326
Chuanji Shi , Yingying Zhang , Jiaotuan Wang , Xin Guo , Qiqi Zhu
Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined polygonal boundaries. The rapid development of urban commerce has led to increasing demands for highly accurate and timely AOI data. However, existing research primarily focuses on coarse-grained functional zones for urban planning or regional economic analysis, and often neglects AOI’s expiration in the real world. They fail to fulfill the precision requirements of Mobile Internet Online-to-Offline (O2O) businesses. These businesses require AOI boundary accuracy down to a specific community, school, or hospital. In this paper, we propose a fully end-to-end multimodal AOI TRansformer (AOITR) model designed for simultaneously detecting accurate AOI boundaries and validating AOI’s reliability by leveraging remote sensing imagery coupled with geographical prior. Unlike conventional AOI generation methods, such as the Road-cut method that segments road networks at various levels, our approach diverges from semantic segmentation algorithms that depend on pixel-level classification. Instead, our AOITR begins by selecting a point-of-interest (POI) of specific category, which can be easily obtained via web crawler, and uses it to retrieve corresponding remote sensing imagery and geographical prior such as entrance POIs and road nodes. This information helps to build a multimodal detection model based on transformer encoder-decoder architecture to regress the accurate AOI polygon. Additionally, we utilize the dynamic features from human mobility, nearby POIs, and logistics addresses for AOI reliability evaluation via a cascaded network module. The experimental results reveal that our algorithm achieves a significant improvement on Intersection over Union (IoU) metric, surpassing previous methods by a large margin. Furthermore, the AOIs produced by AOITR have substantially enriched our AOI library and have been successfully applied on over 10 different O2O scenarios including Alipay’s face scan payment service.
城市兴趣区(AOI)是指具有明确多边形边界的综合城市功能区。城市商业的快速发展导致对高精度和及时性 AOI 数据的需求不断增加。然而,现有的研究主要关注用于城市规划或区域经济分析的粗粒度功能区,往往忽视了 AOI 在现实世界中的应用。它们无法满足移动互联网在线到离线(O2O)业务的精度要求。这些业务要求 AOI 边界精确到具体的社区、学校或医院。在本文中,我们提出了一个完全端到端的多模态 AOI TRansformer(AOITR)模型,旨在同时检测精确的 AOI 边界,并利用遥感图像结合地理先验验证 AOI 的可靠性。与传统的 AOI 生成方法(如在不同层面分割道路网络的道路切割法)不同,我们的方法与依赖于像素级分类的语义分割算法不同。相反,我们的 AOITR 从选择特定类别的兴趣点(POI)开始(可通过网络爬虫轻松获取),并利用它检索相应的遥感图像和地理先验信息,如入口 POI 和道路节点。这些信息有助于建立一个基于变压器编码器-解码器架构的多模态检测模型,以回归精确的 AOI 多边形。此外,我们还通过级联网络模块,利用来自人员流动、附近 POI 和物流地址的动态特征进行 AOI 可靠性评估。实验结果表明,我们的算法在 "交集大于联合"(IoU)指标上取得了显著改进,大大超过了之前的方法。此外,AOITR 生成的 AOI 极大地丰富了我们的 AOI 库,并已成功应用于 10 多个不同的 O2O 场景,包括支付宝的扫脸支付服务。
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引用次数: 0
Assessing differences in work intensity resilience to pandemic outbreaks using large-scale mobile phone data 利用大规模移动电话数据评估工作强度对大流行病爆发的复原力差异
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104343
Xiaorui Yan , Tao Pei , Xi Gong , Zhuoting Fu , Yaxi Liu
Pandemic outbreaks significantly disrupt human work activity, which is a crucial aspect of urban daily life, potentially causing reduced income or unemployment. These disruptions often vary across different population groups and regions. However, most existing studies focus on general human mobility patterns with limited attention specific to work activity, and conduct separate analyses on population groups and regions, overlooking intra-population differences across regions and inter-population variations within the same region. To this end, we first introduce the concept of work intensity to quantify the work activity. Using large-scale mobile phone data, we then estimate an individual’s work intensity, and characterize the changes in work intensity based on the concept of resilience, i.e., the ability to withstand and recover from a disaster. Finally, we propose a novel analytical framework that integrates both population groups and regions to assess differences in resilience. Herein, we take the pandemic outbreak in Beijing after the sudden relaxation of dynamic zero-COVID policy as a case study due to less policy intervention. Results reveal that females and younger people exhibit lower work intensity resilience, respectively. We also find significant regional differences and several negative features for resilience: short distance to city center, long home-to-work distance, high density of high-paying jobs, low road density, and high density of subway stations. By integrating both population group and region perspectives, we identify vulnerable population groups in specific regions. This integrated perspective can help design more targeted response and recovery strategies, and thereby promote health-related urban resilience and sustainability.
大流行病的爆发严重扰乱了人类的工作活动,这是城市日常生活的一个重要方面,可能导致收入减少或失业。这些破坏在不同的人口群体和地区往往有所不同。然而,大多数现有的研究集中于一般的人类流动模式,对工作活动的关注有限,并对人口群体和区域进行单独的分析,忽视了区域间人口内部的差异和同一区域内人口间的差异。为此,我们首先引入工作强度的概念来量化工作活动。利用大规模的移动电话数据,我们估计了一个人的工作强度,并根据弹性的概念描述了工作强度的变化,即承受和从灾难中恢复的能力。最后,我们提出了一个新的分析框架,将人口群体和地区结合起来评估恢复力的差异。本文以突然放松动态零冠政策后由于政策干预较少而导致的北京疫情为例进行研究。结果显示,女性和年轻人的工作强度弹性分别较低。我们还发现,弹性的区域差异显著,且存在几个负向特征:距离市中心较近、家到工作地点距离较长、高收入工作密度较高、道路密度较低、地铁站密度较高。通过整合人口群体和区域视角,我们确定了特定区域的弱势群体。这种综合视角有助于设计更有针对性的应对和恢复战略,从而促进与健康有关的城市复原力和可持续性。
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引用次数: 0
Diffuse attenuation coefficient and bathymetry retrieval in shallow water environments by integrating satellite laser altimetry with optical remote sensing 卫星激光测高与光学遥感相结合的浅水环境散射衰减系数与测深反演
IF 7.6 Q1 REMOTE SENSING Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104318
Changda Liu , Huan Xie , Qi Xu , Jie Li , Yuan Sun , Min Ji , Xiaohua Tong
<div><div>Shallow water environmental information is crucial for the study of marine ecosystems and human activities. There have been numerous satellite remote sensing studies focused on this area. However, accurate information acquisition from remote sensing data remains difficult in this region due to the complexity of the environment and the coupling between benthic reflectance and water column scattering. In this study, we developed a method to retrieve the diffuse attenuation coefficient (<span><math><mrow><msub><mi>K</mi><mi>d</mi></msub></mrow></math></span>), seafloor classification, and bathymetric maps by combining satellite laser altimetry and optical remote sensing imagery in shallow water areas. Firstly, the relationships between remote sensing reflectance (<span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span>), water depth, and <span><math><mrow><msub><mi>K</mi><mi>d</mi></msub></mrow></math></span> were established based on radiative transfer theory. This method allows for the retrieval of <span><math><mrow><msub><mi>K</mi><mi>d</mi></msub></mrow></math></span> in shallow water regions, overcoming the limitations present in previous studies. Secondly, we eliminated the water column attenuation and obtained the bottom reflectance index (BRI). The BRI allowed us to determine the bottom reflectance and classify the seafloor using the Gaussian mixture model clustering method. This approach can effectively reduce the error in bathymetric inversion caused by variations in bottom reflectance. Finally, we developed a neural network model for bathymetric inversion. The model inputs consist of <span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span> data and spectral shape data containing physical constraint information, aiming to achieve a robust estimation performance. We conducted the study in two experimental areas (the Bimini Islands and the Yongle Atoll) and compared the results with validation data to evaluate the algorithm performance. The results indicated an agreement between the estimated <span><math><mrow><msub><mi>K</mi><mi>d</mi></msub></mrow></math></span> and the validation data (inferred <span><math><mrow><msub><mrow><mi>K</mi></mrow><mrow><mi>d</mi></mrow></msub><mn>490</mn></mrow></math></span> values of 0.062<!--> <!-->m<sup>−1</sup> and 0.058<!--> <!-->m<sup>−1</sup>, compared to a validation data range of 0.055–0.087<!--> <!-->m<sup>−1</sup> and 0.059–0.070<!--> <!-->m<sup>−1</sup>, respectively). In addition, the seafloor classification accuracy was 86.74 % for the Yongle Atoll area. Finally, the neural network model accurately predicted the bathymetry in the two regions. The accuracy of the bathymetric maps improved significantly with seafloor classification, as indicated by reductions in root mean square error (RMSE) of 0.12 m and 0.15 m, and in mean absolute percentage error (MAPE) by 2.24 % and 5.87 %, respectively. Overall, the propos
浅水环境信息对于研究海洋生态系统和人类活动至关重要。许多卫星遥感研究都集中在这一区域。然而,由于环境的复杂性以及海底反射率和水柱散射之间的耦合性,在这一区域从遥感数据中准确获取信息仍然十分困难。在这项研究中,我们开发了一种方法,通过结合卫星激光测高和光学遥感图像,获取浅水区的漫反射衰减系数(Kd)、海底分类和水深图。首先,根据辐射传递理论建立了遥感反射率(Rrs)、水深和 Kd 之间的关系。这种方法克服了以往研究中存在的局限性,可用于检索浅水区域的 Kd。其次,我们消除了水体衰减,获得了底部反射率指数(BRI)。利用海底反射率指数,我们可以确定海底反射率,并利用高斯混合模型聚类方法对海底进行分类。这种方法可以有效减少因海底反射率变化而造成的测深反演误差。最后,我们建立了一个用于水深反演的神经网络模型。模型输入包括 Rrs 数据和包含物理约束信息的光谱形状数据,旨在实现稳健的估计性能。我们在两个实验区(比米尼岛和永乐环礁)进行了研究,并将结果与验证数据进行比较,以评估算法性能。结果表明,估计的 Kd 与验证数据一致(推断的 Kd490 值分别为 0.062m-1 和 0.058m-1,而验证数据范围分别为 0.055-0.087m-1 和 0.059-0.070m-1)。此外,永乐环礁区域的海底分类准确率为 86.74%。最后,神经网络模型准确预测了两个区域的水深。海底分类后,测深图的精度显著提高,均方根误差(RMSE)分别降低了 0.12 米和 0.15 米,平均绝对百分比误差(MAPE)分别降低了 2.24% 和 5.87%。总之,所提出的方法可用于有效解耦底栖生物信号和水体信号,并准确获取浅水环境的 Kd、底部反射率和测深信息,为评估和监测生态系统提供前所未有的信息,并促进进一步的研究。
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International journal of applied earth observation and geoinformation : ITC journal
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