Large-scale 3D scene reconstruction based on remote sensing images provides critical support for smart city development,map navigation,virtual reality,and digital twin systems.Existing 3D reconstruction algorithms predominantly rely on feature matching techniques and demonstrate satisfactory performance in small-scale or structurally simple scenes.Given the intricate terrain features and noise interference in complex or large-scale environments,significant challenges,such as suboptimal reconstruction accuracy and incomplete modeling,exist.These challenges hinder the effectiveness of these methods.Therefore,this study proposes a diffusion-constrained multiview stereo network comprising a multiscale feature enhancement feature pyramid network(MFE-FPN),an adaptive feature aggregation module(AFA),and a diffusion-constrained module(DCM)to address the issues of low-feature matching accuracy,high noise in predicted depth maps,and incomplete edge reconstruction in multiview stereo for remote sensing images. The proposed method consists of several steps.First,the network takes N multiview remote sensing images as input,with the first image serving as the reference and the remaining N-1 as source images.It adopts a three-stage coarse-to-fine strategy to predict depth maps progressively.The network utilizes the MFE-FPN module to extract multiscale features from the input images,thereby generating hierarchical feature representations.Second,the top-level features from the FPN are mapped through an edge-aware network to compute edge-aware features,which are subsequently fused with the multiscale features.Third,an AFA is designed to aggregate the multiscale features,thereby forming a matching cost volume.Fourth,a diffusion constraint module is introduced to integrate cost volume features with edge-aware features.Fifth,an edge-guided transformer is employed to enhance the representation of edge details during the denoising stage.Sixth,the cost volume features are regularized and regressed to estimate depth,resulting in the final reconstructed depth map.Seventh,an edge-aware loss function is constructed during training to preserve the edge information in the predicted depth maps effectively. Experimental results show that compared with other methods,the DiffusionMVS network shows an improved mean absolute error metric on the WHU-TLC and LuoJia-MVS datasets by 28.11%and 3.37%,respectively,thereby demonstrating superior reconstruction performance.However,in terms of inference time,the proposed method does not achieve the best performance because of the relatively low operational efficiency of the diffusion constraint module.Nevertheless,it achieves an optimal balance between accuracy and efficiency,thereby making it highly suitable for remote sensing stereo reconstruction tasks.The results on the self-constructed dataset of oil and gas stations verify the model's capability to reconstruct detailed geometric features.This capability benefits from the model's excellent performance in edge preservation and generalization in unseen scenarios.Moreover,ablation experiment results confirm that the proposed MFE-FPN,AFA,and DCM modules can effectively enhance the accuracy of depth map reconstruction. The proposed diffusion-constrained multiview stereo network significantly improves edge-processing capability and overall reconstruction accuracy through a multiscale feature enhancement module and a diffusion constraint module.Results indicate the model is well-suited for reconstructing mountains,forests,and buildings,because of its superior performance on weak-texture regions and depth map denoising challenges.It effectively addresses the reduced reconstruction accuracy of remote sensing images under noise interference.Future work will explore incorporating the Segment Anything Model into the MVS framework to leverage its rich semantic information,thereby refining the matching process and further improving reconstruction efficiency and accuracy.
As an important part of the terrestrial ecosystem,vegetation exerts a profound influence on global climate variation.Accurately characterizing vegetation dynamics is crucial for capturing and predicting changes in ecosystem structure and function in the context of global warming.On the basis of existing long-time series and multisource vegetation remote sensing products,this study uses the spectrum analysis method to characterize the spatial pattern of global continental surface vegetation on timescales and analyzes the dynamic trend characteristics of vegetation from the perspective of multiple timescales. Using long-term,multi-source remote sensing vegetation products,this study decomposes vegetation dynamics into three key timescales:interannual,annual,and intra-annual through by using spectral analysis.The amplitude obtained from Fourier analysis is used to characterize the spatial pattern of vegetation dynamics,and the phase value is used to evaluate the coupling relationship with phenological parameters.In addition,the temporal trend of vegetation indices is evaluated to explore its relationship with the observed"global greening"phenomenon. (1)Vegetation growth dynamics in most areas of the global land surface(78.4%)are largely dominated by the annual scale.The areas dominated by the intra-annual scale account for 16.1%and are mostly distributed in tropical areas.Areas dominated by the interannual scale constitute the smallest proportion(5.5%)and are mostly located in semiarid shrublands.(2)A significant correlation exists between the annual scale amplitude of vegetation and the peak value of vegetation growth.This correlation is mostly distributed in regions with more significant characteristics of vegetation seasonal growth.Only 28.19%of the global vegetation cover areas exhibited a significant positive correlation between the phase values at the annual scale and the phenological parameters in the time domain(P<0.1).Among these,the correlation between the peak vegetation period and annual-scale phase values is the strongest.(3)The amplitude temporal trend of the vegetation index on multiple timescales(Low frequency,Middle frequency,High frequency)exhibits a large-scale growth trend,which is generally consistent with the phenomenon of"global vegetation greening"(i.e.,increasing annual mean vegetation index values).However,the trend characteristics exhibit considerable spatial heterogeneity across scales.Notably,amplitude dynamics at the three scales explain only 66%of the annual mean dynamic characteristics,highlighting the temporal scale dependence of the"global vegetation greening"phenomenon. This study employs spectral analysis to conduct a multi-temporal scale analysis of global vegetation dynamics,offering a novel perspective for related research.The findings contribute significantly to a deeper scientific understanding of global vegetation greening and its response mechanisms within broader ecosystem functions.
1 定 义 在植被定量遥感研究中,通常需要将传感器接收到的电磁辐射信号转化为具有明确物理意义、可比较的辐射或反射量,以便开展后续分析与参数反演.单一波段的遥感信号虽然可对植被的某些生物化学或物理属性敏感,但常常会受冠层结构、土壤背景、大气散射与太阳—观测几何影响,使其难以与植被参数建立稳定、唯一的对应关系.而植被指数VI(Vegetation Index)是通过多波段组合构建的综合性遥感指标,很大程度上能克服上述问题,因此植被指数成为量化植被覆盖、叶面积指数、生物量等植被参数,实现植被信息增强与量化的重要遥感分析工具.
Climate change has intensified precipitation variability in arid regions,disrupting hydrological regimes and increasing flood risk.Flood control systems in these regions are frequently designed primarily for drought mitigation and water storage,limiting their capability to withstand extreme rainfall.In late July 2025,the Tumochuan Plain in Inner Mongolia experienced basin-wide flooding triggered by an unprecedented prolonged rainfall event.Multiple embankment breaches subsequently occurred along the Hasuhai drainage canal,causing severe damage to agricultural production and infrastructure.Accordingly,this study aims to provide timely and reliable information on flood evolution and quantify flood effects on croplands,particularly maize inundation and associated yield loss risk.A refined flood mapping method was developed by integrating a composite water index with morphological operations to enable the efficient automated extraction of surface water information.To reduce misclassification caused by terrain and building shadows,a rule-based discrimination scheme was implemented during post-processing.The proposed method was applied to Sentinel-2 imagery to extract surface water across the Tumochuan Plain for 2024 and 2025.By using water occurrence frequency in 2024,permanent and seasonal water bodies were distinguished to establish a baseline water distribution.This baseline was then used as a reference to delineate the 2025 flood extent and monitor its temporal evolution.To investigate maize growth responses to inundation duration,a dynamic time warping(DTW)-k-means model was constructed by combining DTW with the k-means algorithm to cluster the normalized difference vegetation index(NDVI)phenological trajectories.Cluster centroids represent NDVI recovery trajectories under different inundation durations.The trained model was applied across the study area to assess maize yield loss risk.The proposed automated water detection method outperformed the optimal threshold-based approach derived from Sentinel-1 data,achieving an overall classification accuracy of 97.4%and a kappa coefficient of 0.947.Time-series analysis indicated that flood extent peaked around August 25,with the total water area reaching 880.01 km2,approximately 2.2 times the normal extent in 2024.Flood recession was slow,with the inundated area decreasing by approximately 53%over the following month.Among major crops,maize was the most severely affected,with an inundated area of 192.6 km2.Approximately 39.4%of croplands remained waterlogged for more than 30 days,mostly in low-lying lands and along river systems.The DTW-k-means model clustered NDVI recovery trajectories into three types associated with flood duration.The subsequent yield loss risk assessment indicated high-risk areas that covered 238.9 km2,primarily corresponding to prolonged inundation.Maize exposed to flooding for more than 2 weeks faces an elevated risk of lodging and potential mortality,indicating limited resilience to prolonged inundation.These findings demonstrate the capability of remote sensing to link flood dynamics with abnormal crop growth responses in arid regions.The proposed framework enables rapid flood extent mapping and maize yield loss risk assessment,and it can be transferred to other regions affected by similar flood events.
