International journal of applied earth observation and geoinformation : ITC journal最新文献_第8页

Mapping hierarchical wetland characteristics by optical-SAR integration with collaborative spatial-spectral-temporal learning

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104395

Linwei Yue , Meiyue Wang , Chengpeng Huang , Qing Cheng , Qiangqiang Yuan , Huanfeng Shen

The learning-based integration of optical and synthetic aperture radar (SAR) satellite imagery is known to be effective in promoting the accuracy of wetland land-cover classification. However, the distribution of wetland categories is characterized as spatially heterogeneous and highly dynamic. It remains a challenge to fuse the inherent characteristics of optical and SAR data by exploiting their discriminative feature representations for delineating wetland landscapes. To fully integrate the complementary information among optical and SAR data, a dual-branch deep network is proposed for mapping hierarchical wetland characteristics, which is referred to as HiWet-DBNet. Within the network, two parallel branches are designed to collaboratively learn the spatial, spectral or polarized, and temporal dependencies in the optical image and SAR image time series, respectively. Inspired by the relationships of deep and shallow features, the intra-layer features are fused across the branches to generate the multi-level wetland mapping results (i.e., general wetland land cover, and wetland vegetation types). The proposed method was tested on the Poyang Lake wetland in China using Sentinel-1 and Sentinel-2 imagery. The evaluation results show that the overall accuracy of HiWet-DBNet reaches 88.51% and 88.61% in the dry and wet seasons, which is superior to the other solutions with only a single data source or insufficient fusion of multi-modal features. For the challenging task of submerged vegetation detection, the producer’s accuracy of HiWet-DBNet is improved by 1.70% to 16.59% compared with the VBI algorithm and state-of-art deep learning-based wetland classification methods.

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引用次数: 0

The pre-processing InSAR feasibility assessment method for wide-area slope displacement monitoring 广域边坡位移监测的预处理InSAR可行性评价方法

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104336

Youdong CHEN , Keren DAI , Ling CHANG , Jin DENG , Guanchen ZHUO , Xiujun DONG , Xianlin LIU , Yu SHAO

The pre-processing feasibility assessment of interferometric synthetic aperture radar (InSAR) is important for predicting the quality of InSAR results and guiding the further accurate slope displacement monitoring. However, such kinds of pre-processing (i.e. before the interferometry data processing) assessment method are few, especially for the slope displacement in wide areas. In this article, a method for assessing the pre-processing feasibility of InSAR displacement monitoring is proposed, which mainly takes into accounts the maximum detectable deformation gradients (MDDG), SAR geometric distortions and vegetation coverage. Based on the above factors, the classification criteria employed for InSAR feasibility assessment are built. The approach is applied over Guangxi Province, China a landslide prone area, and an InSAR feasibility map categorized into four categories: high, moderate, low, and very low over the entire study area for pre-processing analysis is obtained. The feasibility results indicate that approximately 77% of areas are classified as very low in the four categories, while the high and moderate feasibility are mainly distributed in urban areas and regions with a NDVI value of less than 0.5. The feasibility results show great agreement with the time series InSAR monitoring results derived from the real Sentinel-1 data in the further validation. This developed methodology is useful for predicting the quality of time series InSAR measurements and providing important information before the SAR data collection and processing, especially in wide-area slope displacement monitoring.

干涉合成孔径雷达（InSAR）预处理可行性评价对于预测InSAR结果质量和指导进一步精确监测边坡位移具有重要意义。然而，这种预处理（即在干涉测量数据处理之前）的评估方法很少，特别是对于大面积的边坡位移。本文提出了一种主要考虑最大可探测变形梯度（MDDG）、SAR几何畸变和植被覆盖度的InSAR位移监测预处理可行性评估方法。基于上述因素，建立了InSAR可行性评价的分类标准。将该方法应用于中国广西滑坡易发区，获得了整个研究区高、中、低、极低4类InSAR可行性图进行预处理分析。可行性分析结果表明，四类区域中约77%为极低可行性，高、中等可行性主要分布在城市地区和NDVI值小于0.5的区域。可行性结果与进一步验证中Sentinel-1真实数据的InSAR时间序列监测结果吻合较好。该方法可用于预测时间序列InSAR测量的质量，并在SAR数据收集和处理之前提供重要信息，特别是在广域边坡位移监测中。

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引用次数: 0

Plant Phenology Index leveraging over conventional vegetation indices to establish a new remote sensing benchmark of GPP for northern ecosystems 利用植物物候指数取代传统植被指数，建立北方生态系统GPP的新遥感基准

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104289

Hanna Marsh, Hongxiao Jin, Zheng Duan, Jutta Holst, Lars Eklundh, Wenxin Zhang

Northern ecosystems, encompassing boreal forests, tundra, and permafrost areas, are increasingly affected by the amplified impacts of climate change. These ecosystems play a crucial role in determining the global carbon budget. To improve our understanding of carbon uptake in these regions, we evaluate the effectiveness of employing the physically-based Plant Phenology Index (PPI) to estimate gross primary productivity across ten different ecosystems. Based on eddy-covariance measurements from 65 sites, the vegetation index (VI)-driven GPP models (six different algorithms) are calibrated and validated. Our findings highlight that the Michaelis–Menten algorithm has the best performance and PPI is superior to the other five VIs, including NDVI, NIRv, EVI-2, NDPI, and NDGI, at predicting gross primary productivity (GPP) rates on a weekly scale (with an average R

^{2}

of 0.64 and RMSE of 1.70 g C m

^{- 2}

d

^{- 1}

), regardless of short-term environmental constraints on photosynthesis. Through our scaled-up analysis, we estimate the annual GPP of the vast 37 million km

^{2}

study region to be around 22 Pg C yr

^{- 1}

, aligning with other recently developed products such as GOSIF-GPP, FluxSat-GPP, and FLUXCOM-X GPP. Derived from a climate-independent approach, the PPI-GPP product offers distinct advantages in exploring relationships between climate variables and terrestrial ecosystem productivity and phenology. Furthermore, this product holds significant value for assessing forestry and agricultural production in northern regions and for benchmarking terrestrial biosphere models and Earth system models.

包括北方森林、冻土带和永久冻土区在内的北方生态系统日益受到气候变化放大影响的影响。这些生态系统在决定全球碳收支方面起着至关重要的作用。为了提高我们对这些地区碳吸收的认识，我们评估了使用基于物理的植物物候指数（PPI）来估计10个不同生态系统的总初级生产力的有效性。基于65个站点的涡旋协方差测量，对植被指数驱动的GPP模型（6种不同算法）进行了标定和验证。我们的研究结果强调，Michaelis-Menten算法在预测周尺度的总初级生产力（GPP）率方面表现最好，PPI优于其他5种VIs，包括NDVI、NIRv、EVI-2、NDPI和NDGI（平均R2为0.64，RMSE为1.70 g C m -2 d - 1），而不考虑光合作用的短期环境限制。通过我们的放大分析，我们估计3700万平方公里研究区域的年GPP约为22 Pg C yr - 1，与其他最近开发的产品如GOSIF-GPP， FluxSat-GPP和FLUXCOM-X GPP保持一致。基于与气候无关的方法，PPI-GPP产品在探索气候变量与陆地生态系统生产力和物候之间的关系方面具有明显的优势。此外，该产品对于评估北方地区的林业和农业生产以及陆地生物圈模型和地球系统模型的基准具有重要价值。

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引用次数: 0

NeRFOrtho: Orthographic Projection Images Generation based on Neural Radiance Fields nerforth：基于神经辐射场的正射影图像生成

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104378

Dongdong Yue , Xinyi Liu , Yi Wan , Yongjun Zhang , Maoteng Zheng , Weiwei Fan , Jiachen Zhong

The application value of orthographic projection images is substantial, especially in the field of remote sensing for True Digital Orthophoto Map (TDOM) generation. Existing methods for orthographic projection image generation primarily involve geometric correction or explicit projection of photogrammetric mesh models. However, the former suffers from projection differences and stitching lines, while the latter is plagued by poor model quality and high costs. This paper presents NeRFOrtho, a new method for generating orthographic projection images from neural radiance fields at arbitrary angles. By constructing Neural Radiance Fields from multi-view images with known viewpoints and positions, the projection method is altered to render orthographic projection images on a plane where projection rays are parallel to each other. In comparison to existing orthographic projection image generation methods, this approach produces orthographic projection images devoid of projection differences and distortions, while offering superior texture details and higher precision. We also show the applicative potential of the method when rendering TDOM and the texture of building façade.

正射影像具有重要的应用价值，特别是在遥感领域生成真数字正射影像图（TDOM）。现有的正射影图像生成方法主要涉及几何校正或摄影测量网格模型的显式投影。但前者存在投影差异和拼接线问题，后者则存在模型质量差、成本高的问题。本文提出了一种基于神经辐射场任意角度生成正射影图像的新方法NeRFOrtho。通过从已知视点和位置的多视图图像中构建神经辐射场，将投影方法改为在投影光线相互平行的平面上呈现正射影图像。与现有的正交投影图像生成方法相比，该方法产生的正交投影图像没有投影差异和失真，同时具有优越的纹理细节和更高的精度。我们还展示了该方法在渲染TDOM和建筑立面纹理时的应用潜力。

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引用次数: 0

Towards the next generation of Geospatial Artificial Intelligence

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104368

Gengchen Mai , Yiqun Xie , Xiaowei Jia , Ni Lao , Jinmeng Rao , Qing Zhu , Zeping Liu , Yao-Yi Chiang , Junfeng Jiao

Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.

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引用次数: 0

Landslide susceptibility assessment of the Wanzhou district: Merging landslide susceptibility modelling (LSM) with InSAR-derived ground deformation map 万州区滑坡易发性评估：滑坡易发性建模（LSM）与 InSAR 地面变形图的融合

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104365

Chao Zhou , Lulu Gan , Ying Cao , Yue Wang , Samuele Segoni , Xuguo Shi , Mahdi Motagh , Ramesh P Singh

The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The landslide catalog and multiple EML methods are used for the preparation of the preliminary susceptibility map. We have also compared and analyzed the impact of ensemble strategies (homogeneous and heterogeneous ensemble) and base-learners on the modelling performance. Subsequently, Sentinel-1 data from 2018 to 2020, analyzed using MT-InSAR approach, are used to map ground deformation rates. We outline the active slopes and deduce the relationship between the deformation of Matou landslide and triggering factors. The final susceptibility map is generated by coupling catalog-based susceptibility and ground deformation rate maps through an empirical assessment matrix. Our results show that the causal factors of distance to rivers, distance to faults, annual rainfall and distance to roads are basic parameters for landslide spatial development; Heterogeneous EML methods outperform the homogeneous, and the more base-learner types provide better performance. InSAR-acquired deformation rates corrected overestimation and underestimation errors in the landslide susceptibility map produced by catalog-based method. Our proposed method is capable of improving the accuracy and timeliness of susceptibility map, providing a useful instrument to better assess landslide risk scenarios in rapidly changing environments.

流行的基于目录的滑坡敏感性模型（LSM）是在假设未来的滑坡事件反映过去和现在的模式的情况下运行的。由于城市扩张和气候变化，某些滑坡遵循新的发生模式，破坏了基于目录的LSM的基本假设，并导致传统易感性图的有效性受到限制。在这里，为了解决这个问题，我们提出了一种方法，通过耦合先进的集成机器学习（EML）和多时相干涉SAR （MT-InSAR）来生成更准确和动态的滑坡易感性图。以三峡库区万州区为试验场。采用滑坡目录法和多重EML方法编制初步敏感性图。我们还比较和分析了集成策略（同质和异构集成）和基础学习器对建模性能的影响。随后，使用MT-InSAR方法分析2018年至2020年的Sentinel-1数据，用于绘制地面变形率。勾勒出活动边坡，推导出马头滑坡变形与诱发因素的关系。通过经验评估矩阵，将基于目录的敏感性图与地面变形率图耦合生成最终的敏感性图。结果表明：离河距离、离断层距离、年降雨量和离道路距离是影响滑坡空间发展的基本因素；异构EML方法优于同构EML方法，并且基础学习器类型越多，性能越好。insar获取的变形率修正了基于目录法生成的滑坡易感性图的高估和低估误差。该方法能够提高敏感性图的准确性和及时性，为快速变化环境下更好地评估滑坡风险情景提供了一种有用的工具。

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引用次数: 0

Empirical methods to determine surface air temperature from satellite-retrieved data

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104380

Joan Vedrí , Raquel Niclòs , Lluís Pérez-Planells , Enric Valor , Yolanda Luna , María José Estrela

Surface air temperature (SAT) is an essential climate variable (ECV). Models based on remote sensing data allow us to study SAT, without the need for a large network of meteorological stations. Therefore, it allows monitoring the climate in remote and extensive areas. Niclos et al. (2014) proposed parametric equations for the SAT retrieval over the Spanish Mediterranean basins. In this study, we evaluated those equations, but in a larger area and period of study. In addition, we proposed several linear regression models and nonlinear models based on decision tree methods, non-parametric methods and neuronal networks. These models relate SAT to land surface temperature, vegetation indexes and albedo from MODIS data. Moreover, meteorological reanalysis data, from ERA5-Land database, and geographical parameters were used. The accuracy of each model was evaluated against data from meteorological stations operated by AEMET in the Spanish Mediterranean basins, during the period 2021–2022. The equations of Niclos et al. (2014) obtained a robust root mean square error (RRMSE) of 3.1 K at daytime and 1.9 K at nighttime. For the linear regression models, the RRMSE decreased to 2.3 K (1.5 K) at daytime (nighttime). Finally, the nonlinear methods, in particular XGBoost model, showed an RRMSE of 1.5 K for daytime and 1.0 K at nighttime. Therefore, the comparison between methods showed that nonlinear models, in particular those based on decision tree methods, offered the best results in SAT retrieval in our study.

{"title":"Empirical methods to determine surface air temperature from satellite-retrieved data","authors":"Joan Vedrí , Raquel Niclòs , Lluís Pérez-Planells , Enric Valor , Yolanda Luna , María José Estrela","doi":"10.1016/j.jag.2025.104380","DOIUrl":"10.1016/j.jag.2025.104380","url":null,"abstract":"<div><div>Surface air temperature (SAT) is an essential climate variable (ECV). Models based on remote sensing data allow us to study SAT, without the need for a large network of meteorological stations. Therefore, it allows monitoring the climate in remote and extensive areas. Niclos et al. (2014) proposed parametric equations for the SAT retrieval over the Spanish Mediterranean basins. In this study, we evaluated those equations, but in a larger area and period of study. In addition, we proposed several linear regression models and nonlinear models based on decision tree methods, non-parametric methods and neuronal networks. These models relate SAT to land surface temperature, vegetation indexes and albedo from MODIS data. Moreover, meteorological reanalysis data, from ERA5-Land database, and geographical parameters were used. The accuracy of each model was evaluated against data from meteorological stations operated by AEMET in the Spanish Mediterranean basins, during the period 2021–2022. The equations of Niclos et al. (2014) obtained a robust root mean square error (RRMSE) of 3.1 K at daytime and 1.9 K at nighttime. For the linear regression models, the RRMSE decreased to 2.3 K (1.5 K) at daytime (nighttime). Finally, the nonlinear methods, in particular XGBoost model, showed an RRMSE of 1.5 K for daytime and 1.0 K at nighttime. Therefore, the comparison between methods showed that nonlinear models, in particular those based on decision tree methods, offered the best results in SAT retrieval in our study.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104380"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049967","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

Tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104376

Forough Fendereski , Shizhou Ma , Sassan Mohammady , Christopher Spence , Charles G. Trick , Irena F. Creed

Wetlandscapes—hydrologically connected networks of wetlands—vary over time, causing changes in their provision of hydrological, biogeochemical, and ecological functions to landscapes. Here, we developed a method for mapping wetlands and extracting wetlandscape properties from Landsat-derived inundation data and applied this method to the Lake Winnipeg Watershed (LWW). We first mapped the annual (1984–2020) time series of inundated areas using a fusion of two Landsat-derived inundation products, Global Surface Water Extent (GSWE) and Dynamic Surface Water Extent (DSWE), finding that this fusion reduced omission errors from 17 % for GSWE and 18 % for DSWE to 8 % overall. We then used the inundated area maps to identify the topological structure of the wetlandscape, i.e., networks composed of nodes (representing wetlands) and their links (representing hydrological connectivity among wetlands). The time series of the wetlandscape properties (number, size, and connectivity of wetlands) showed coherence with a concurrent increase in precipitation over the watershed. The LWW is transitioning to a more extensive wetland area consisting of a greater number of larger wetlands with increased connections among them (p < 0.1). With Landsat-derived inundation products widely available globally, we suggest using the method developed here to analyze changes in wetlandscape properties in other regions worldwide.

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引用次数: 0

Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104388

Li Zhang , Xiaodong Gao , Shuyi Zhou , Zhibo Zhang , Tianjie Zhao , Yaohui Cai , Xining Zhao

Drought-induced tree mortality has increasingly expanded worldwide under the influence of climate warming, with China’s Loess Plateau (CLP) emerging as a critical hotspot for such impacts. As one of the most active tree-planting regions globally, the CLP primarily aims to achieve soil and water conservation despite facing challenges such as limited rainfall and frequent extreme drought events. However, accurate identification of standing dead trees (SDTs) within plantations using remote sensing techniques remains underexplored, and the spatial distribution patterns of SDTs across the CLP are poorly understood. Therefore, this study leveraged unmanned aerial vehicle (UAV) remote sensing to capture high-resolution RGB images of Robinia pseudoacacia plantations. These images were then integrated with a comprehensive evaluation of multiple detection algorithms, including Faster R-CNN, EfficientDet, YOLOv4, YOLOv5, YOLOv8, YOLOv9, and a novel model, YOLOv9-ECA. Particularly, the YOLOv9-ECA was developed by incorporating the ECA module into key network layers to enhance channel dependency modeling and improve feature representation for SDTs detection. Its merit lies in adaptively reweighting feature channels, enabling efficient detection in resource-constrained environments. As expected, the YOLOv9-ECA model demonstrated significant advancements, achieving a detection speed of 123.5f/s, a mAP of 97.8%, and an F₁ score of 0.97, outperforming other models in both detection efficiency and accuracy. Subsequently, the model was employed to quantify the spatial distribution of SDTs across the CLP by estimating the number of dead trees per unit area. Results revealed an increasing trend in the number of dead trees per unit along decreasing precipitation gradients, emphasizing the vulnerability of Robinia pseudoacacia plantations in drier regions. Additionally, the number of dead trees per unit varied with slope aspect, with sunny slopes exhibiting the highest values and shady slopes the lowest. This study highlights the potential of YOLOv9-ECA as a powerful tool for the efficient detection of SDTs, offering insights for the sustainable management of Robinia pseudoacacia plantations on the CLP and holding potential applicability to similar environments globally.

{"title":"Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models","authors":"Li Zhang , Xiaodong Gao , Shuyi Zhou , Zhibo Zhang , Tianjie Zhao , Yaohui Cai , Xining Zhao","doi":"10.1016/j.jag.2025.104388","DOIUrl":"10.1016/j.jag.2025.104388","url":null,"abstract":"<div><div>Drought-induced tree mortality has increasingly expanded worldwide under the influence of climate warming, with China’s Loess Plateau (CLP) emerging as a critical hotspot for such impacts. As one of the most active tree-planting regions globally, the CLP primarily aims to achieve soil and water conservation despite facing challenges such as limited rainfall and frequent extreme drought events. However, accurate identification of standing dead trees (SDTs) within plantations using remote sensing techniques remains underexplored, and the spatial distribution patterns of SDTs across the CLP are poorly understood. Therefore, this study leveraged unmanned aerial vehicle (UAV) remote sensing to capture high-resolution RGB images of <em>Robinia pseudoacacia</em> plantations. These images were then integrated with a comprehensive evaluation of multiple detection algorithms, including Faster R-CNN, EfficientDet, YOLOv4, YOLOv5, YOLOv8, YOLOv9, and a novel model, YOLOv9-ECA. Particularly, the YOLOv9-ECA was developed by incorporating the ECA module into key network layers to enhance channel dependency modeling and improve feature representation for SDTs detection. Its merit lies in adaptively reweighting feature channels, enabling efficient detection in resource-constrained environments. As expected, the YOLOv9-ECA model demonstrated significant advancements, achieving a detection speed of 123.5f/s, a mAP of 97.8%, and an F<sub>1</sub> score of 0.97, outperforming other models in both detection efficiency and accuracy. Subsequently, the model was employed to quantify the spatial distribution of SDTs across the CLP by estimating the number of dead trees per unit area. Results revealed an increasing trend in the number of dead trees per unit along decreasing precipitation gradients, emphasizing the vulnerability of <em>Robinia pseudoacacia</em> plantations in drier regions. Additionally, the number of dead trees per unit varied with slope aspect, with sunny slopes exhibiting the highest values and shady slopes the lowest. This study highlights the potential of YOLOv9-ECA as a powerful tool for the efficient detection of SDTs, offering insights for the sustainable management of <em>Robinia pseudoacacia</em> plantations on the CLP and holding potential applicability to similar environments globally.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104388"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083302","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

Identification of shrinkage patterns in Japan’s four major metropolitan areas based on nighttime light and population data

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104391

Hao Zheng, Runsen Zhang

Urban shrinkage has become a critical global issue, influencing the sustainable development of cities across social, economic, and environmental dimensions. In Japan, which is characterized by an aging population and low birth rate, this phenomenon has now extended to metropolitan areas, presenting new challenges for urban sustainability. Although many studies have been conducted regarding urban decline in rural regions, the shrinkage dynamics within Japan’s major cities are poorly understood. Addressing this knowledge gap is crucial for devising targeted urban-planning strategies that ensure the long-term viability of urban areas. Here, we integrated Suomi National Polar-orbiting Partnership–Visible Infrared Imager Radiometer Suite nighttime light data with WorldPop population data to examine the patterns of urban shrinkage from 2012 to 2020 in Japan’s four largest metropolitan areas: Tokyo, Osaka, Nagoya, and Fukuoka. Using Theil–Sen median trend analysis and K-means clustering, we developed a method to quantify both shrinking and growing areas within these regions. It was found that Tokyo exhibited the highest urban vitality, with minimal shrinkage, whereas Nagoya and Osaka faced greater declines. Fukuoka displayed a distinct east–west spatial pattern of urban shrinkage. This study introduces the “triple V” theory, which evaluates urban vitality through the lenses of robustness and activity levels. Our analysis highlights the spatial complexities of urban shrinkage, emphasizing the importance of region-specific urban planning. By providing new insights obtained from a data-driven analysis, we offer a framework for policymakers to promote sustainable urban development in the face of demographic and spatial challenges.

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引用次数: 0