International journal of applied earth observation and geoinformation : ITC journal最新文献_第2页

A semi-supervised framework for UAV-based individual tree crown segmentation in structurally heterogeneous planted forests 结构异质人工林中基于无人机的树冠分割半监督框架

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-13 DOI: 10.1016/j.jag.2025.105078

Qing Wang , Yihui Zhao , Yingpu Che , Han Shen , Yongbin Qiu , Yixiang Wang

Accurate individual tree crown (ITC) segmentation is essential for quantifying forest fine carbon stocks. However, deep learning segmentation methods face prohibitive annotation costs while unsupervised algorithms struggle in structurally complex forests. This study proposes a semi-supervised framework integrating unsupervised pseudo-label generation, deep instance segmentation, and staged fine-tuning to overcome these limitations. The framework transforms outputs from marker-controlled watershed (MCWS) and region-growing (itcSegment) algorithms into initial training targets to obtain generalizable instance segmentation representations, then refines Mask R-CNN models with minimal manual annotations to enhance structural awareness of instance boundaries and semantic context understanding, significantly reducing labeling dependency while improving robustness. Validated across three structurally heterogeneous Chinese fir stands spanning age (11–67 years), density (450–2500 stems·ha⁻¹), and elevation (192–1047 m) gradients, our UAV RGB-based framework achieved consistent superiority over LiDAR and fused inputs, with spectral-textural features proving dominant for boundary delineation. It attained F1-scores of 0.826 (young, undulating terrain), 0.836 (mature, high-density), and 0.711 (over-mature, occluded) using only 40 % expert annotations (0.6 personnel-hours), representing a 0.42 average improvement over MCWS/itcSegment baselines. The designed staged fine-tuning strategy effectively mitigated pseudo-label error propagation, while expanding this annotation effort to 70–100 % yielded marginal accuracy gains, demonstrating exceptional efficiency in leveraging minimal supervision. Based on the segmented crowns, structural parameters were extracted with high fidelity: crown diameter (R² ≥ 0.76, rRMSE ≤ 11.3 %) and crown area (R² ≥ 0.88, rRMSE ≤ 16.4 %). This approach reduces annotation demands while maintaining robustness across forest heterogeneity, enabling operationally scalable solutions for precision forestry.

准确的单株树冠分割是森林细碳储量量化的关键。然而，深度学习分割方法面临着令人望而却步的标注成本，而非监督算法在结构复杂的森林中挣扎。本研究提出了一种半监督框架，集成了无监督伪标签生成、深度实例分割和阶段微调来克服这些限制。该框架将标记控制分水岭（MCWS）和区域增长（itcSegment）算法的输出转换为初始训练目标，以获得可推广的实例分割表示，然后使用最少的手动注释来改进Mask R-CNN模型，以增强对实例边界的结构感知和语义上下文理解，显著降低标记依赖，同时提高鲁棒性。在三种不同结构杉木林分的年龄（11-67年）、密度（450-2500株·ha - 1）和海拔（192-1047 m）梯度上进行验证，我们基于rgb的无人机框架比激光雷达和融合输入具有一致的优势，光谱纹理特征在边界划分方面占主导地位。仅使用40%的专家注释（0.6人-小时），它就获得了f1分数0.826（年轻，起伏地形），0.836（成熟，高密度）和0.711（过度成熟，闭塞），比MCWS/itcSegment基线平均提高了0.42。设计的分阶段微调策略有效地减轻了伪标签错误传播，同时将注释工作扩展到70 - 100%，产生了边际精度增益，在利用最小监督方面展示了卓越的效率。基于分割的冠提取结构参数：冠直径（R2≥0.76,rRMSE≤11.3%）和冠面积（R2≥0.88,rRMSE≤16.4%）。这种方法减少了注释需求，同时保持了森林异质性的健壮性，为精准林业提供了可操作的可扩展解决方案。

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引用次数: 0

Few-Shot change detection in optical and SAR remote sensing images for disaster response 面向灾害响应的光学和SAR遥感影像少镜头变化检测

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-12 DOI: 10.1016/j.jag.2026.105100

Di Wang , Guorui Ma , Xiao Wang , Ronghao Yang , Yongxian Zhang

Few-shot change detection in optical and Synthetic Aperture Radar images is a critical task for disaster monitoring. offering significant application value in complex scenarios with extremely limited labeled samples. However, the randomness of disasters causes a notable data distribution shift between public datasets and real disaster scenarios. With only a few annotated image pairs, existing methods struggle to effectively fuse features from heterogeneous images, leading to severe performance degradation. To address this challenge, we propose a Dual-Stage Training framework for Change Detection (DSTCD), specifically designed for few-shot scenarios involving fewer than 20 labeled image pairs. DSTCD first undergoes source task pre-training on a heterogeneous image registration dataset. Subsequently, in the target task stage, it leverages task guided feature transfer module to transfer the structural and semantic features of image registration to the change detection task. This mechanism significantly enriches the feature representations under few-shot conditions, enabling accurate identification of affected areas. To validate its performance, we conducted comparative and ablation studies against eleven state-of-the-art methods on four public datasets covering both urban expansion and water expansion scenarios. Experimental results demonstrate that DSTCD achieves a significant performance lead. Its average F1-score surpasses the second-best method by 6.98% in urban expansion scenarios and by 13.09% in water expansion scenarios, proving its superior performance and strong multi-scenario adaptability. Furthermore, robustness analysis of varying training sample sizes and real-world disaster application validation further confirm the method’s practicality and robustness for data-scarce disaster monitoring tasks. The code of the proposed method will be made available at https://github.com/Lucky-DW/DSTCD.

光学和合成孔径雷达图像的少镜头变化检测是灾害监测的关键任务。在极其有限的标记样本的复杂场景中提供重要的应用价值。然而，灾害的随机性导致公共数据集和真实灾害场景之间的数据分布发生了明显的变化。由于只有少数带注释的图像对，现有方法难以有效地融合异构图像的特征，导致性能严重下降。为了解决这一挑战，我们提出了一个双阶段变化检测训练框架（DSTCD），专门为涉及少于20个标记图像对的少量场景设计。DSTCD首先在异构图像配准数据集上进行源任务预训练。随后，在目标任务阶段，利用任务导向特征转移模块将图像配准的结构特征和语义特征转移到变化检测任务中。这一机制大大丰富了少镜头条件下的特征表示，使受影响区域的准确识别成为可能。为了验证其性能，我们在四个公共数据集上对11种最先进的方法进行了比较和消融研究，包括城市扩张和水扩张情景。实验结果表明，DSTCD具有明显的性能领先优势。该方法在城市扩张情景下的平均f1得分比次优方法高6.98%，在水域扩张情景下的平均f1得分比次优方法高13.09%，证明了其优越的性能和较强的多情景适应性。此外，对不同训练样本大小的鲁棒性分析和实际灾难应用验证进一步证实了该方法对于数据稀缺的灾害监测任务的实用性和鲁棒性。建议方法的代码将在https://github.com/Lucky-DW/DSTCD上提供。

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引用次数: 0

Non-negative matrix factorization-assisted independent component analysis for land subsidence feature decomposition and attribution in Lubei Plain, and further integration with deep learning for subsidence prediction 陆北平原非负矩阵分解辅助独立分量分析沉降特征分解与归因，并进一步与深度学习结合进行沉降预测

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-12 DOI: 10.1016/j.jag.2026.105097

Yongkang Wang , Huili Gong , Beibei Chen , Chaofan Zhou , Lin guo , Haotong Wang , Qin Wang , Shuqi Hao , Yabin Yang

Interferometric Synthetic Aperture Radar (InSAR) has become crucial for large-scale land subsidence monitoring, but its results represent complex superpositions of deformation signals from multiple compressible layers driven by various anthropogenic and natural factors. Decomposing subsidence patterns by different driving factors is vital for attribution and prediction. However, existing feature decomposition methods suffer from problems of incomplete decomposition and difficulties in direction determination. This study employs non-negative matrix factorization (NMF) assisted independent component analysis (ICA) to achieve feature decomposition and attribution of land subsidence in the Lubei Plain (LBP) from 2018 to 2023, and integration with deep learning to realize land subsidence prediction. Key contributions include: (1) Using NMF component directions as reference to determine three independent component directions for subsequent attribution and prediction analysis. (2) Groundwater wells in high-spatial-score regions were selected to analyze the relationship between ICs, deformation, and groundwater levels. IC1 showed a high correlation exceeding 0.8 with subsidence, reflecting near-linear patterns from deep confined aquifer over-exploitation and petroleum extraction. IC2 reached maximum correlations of 0.72 with subsidence and 0.56 with groundwater levels (GWL), reflecting inter-annual precipitation-driven variations. Similarly, IC3 exhibited maximum correlations of 0.63 and 0.71, respectively, characterizing seasonal cycles linked to agricultural irrigation and precipitation. (3) The proposed prediction model demonstrated excellent performance with RMSE of 0.06–0.69 and R² of 0.80–0.86 in test datasets. This study provides a new reference perspective for future driving factor analysis and regulation of land subsidence in the Lubei Plain, and offers a new method for land subsidence prediction.

干涉合成孔径雷达（InSAR）已成为大尺度地面沉降监测的重要手段，但其结果是受各种人为和自然因素驱动的多个可压缩层变形信号的复杂叠加。不同驱动因素对沉降模式的分解对沉降成因和预测具有重要意义。然而，现有的特征分解方法存在分解不完全、方向确定困难等问题。本研究采用非负矩阵分解（NMF）辅助独立分量分析（ICA）对鲁北平原2018 - 2023年地面沉降进行特征分解与归因，并结合深度学习实现地面沉降预测。主要贡献包括：(1)以NMF分量方向为参考，确定了3个独立分量方向，用于后续归因和预测分析。(2)选取高空间分值区域的地下水井，分析地表变形与地下水位的关系。IC1与沉降呈高度相关，相关性超过0.8，反映了深部承压含水层过度开采和石油开采的近线性模式。IC2与沉降和地下水位（GWL）的相关性最大，分别为0.72和0.56，反映了降水驱动的年际变化。同样，IC3的最大相关性分别为0.63和0.71，表征了与农业灌溉和降水有关的季节周期。(3)所提出的预测模型在测试数据集上RMSE为0.06 ~ 0.69，R2为0.80 ~ 0.86，具有良好的预测性能。该研究为今后鲁北平原地面沉降驱动因素分析和调控提供了新的参考视角，并为地面沉降预测提供了新的方法。

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引用次数: 0

Satellite assessment of rooftop greenness dynamics and equity in Hong Kong 香港屋顶绿化动态及公平性的卫星评估

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-12 DOI: 10.1016/j.jag.2026.105096

Jing Ling , Shan Wei , Genyun Sun , Liqun Sun , Xiaojun Yang , Hongsheng Zhang

Rooftop greening is increasingly recognized as a pathway to urban sustainability and environmental equity, yet its fine-scale temporal dynamics and distributional fairness remain insufficiently examined. This study develops a building-level framework that constructs annual rooftop greenness time series from satellite-derived NDVI and introduces a Rooftop Greenness Inequality Index (RGII) based on the rooftop green fraction to evaluate distributive fairness. By integrating these measures with a multi-pattern trend classification, the framework captures the co-evolution of temporal dynamics and spatial inequality, and links them to socioeconomic conditions to identify key drivers and vulnerable communities. Using Hong Kong as a case study (2018–2024), we find overall greenness gains and inequality reductions, though unevenly distributed. Citywide, 22.1% of buildings showed significant increases, while 2.1% showed consistent decreases. All 452 communities saw inequality decrease, with the median RGII falling from 0.65 to 0.45. Greenness trajectories varied by land use and development stage: public housing showed a 38.9% rise compared with 21.1% in private housing, and late-stage new towns grew faster but with less stability than older districts. Socioeconomic disparities shaped outcomes: communities with high rent burden, large non-local populations, or many single-person households showed limited improvement, whereas those with more spatial capacity or stronger cultural cohesion performed better. Vulnerable areas were concentrated in older high-density districts where greenness remained low and inequality persisted. These findings demonstrate the framework’s capacity to integrate rooftop greenness change with social dimensions, providing transferable tools for equity-oriented greening strategies in high-density cities.

屋顶绿化越来越被认为是城市可持续发展和环境公平的途径，但其精细尺度的时间动态和分配公平性仍然没有得到充分的研究。本研究开发了一个基于卫星NDVI数据构建年度屋顶绿色度时间序列的建筑级框架，并引入了基于屋顶绿色度比例的屋顶绿色度不平等指数（RGII）来评估分配公平性。通过将这些措施与多模式趋势分类相结合，该框架捕捉了时间动态和空间不平等的共同演变，并将其与社会经济条件联系起来，以确定关键驱动因素和脆弱社区。以香港为例（2018-2024年），我们发现整体绿色增长和不平等减少，尽管分布不均。在全市范围内，22.1%的建筑物显著增加，而2.1%的建筑物持续下降。所有452个社区的不平等程度都有所下降，RGII中位数从0.65降至0.45。绿化轨迹因土地用途和发展阶段而异：公共住房增长38.9%，而私人住房增长21.1%，后期新市镇增长更快，但稳定性不如旧区。社会经济差异影响了结果：租金负担高、非本地人口多或单身家庭多的社区改善有限，而空间容量大或文化凝聚力强的社区表现更好。脆弱地区集中在旧的高密度地区，那里的绿化率仍然很低，不平等现象持续存在。这些发现证明了该框架将屋顶绿化变化与社会维度相结合的能力，为高密度城市的公平导向绿化战略提供了可转移的工具。

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引用次数: 0

A dual-path network for semantic scene completion of single-frame LiDAR point clouds 单帧激光雷达点云语义场景补全的双路径网络

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-12 DOI: 10.1016/j.jag.2025.105020

Wei Liu , Ziwen Kang , Yongtao Yu , Zheng Gong , Yuchao Zheng , Xiaohui Huang , Haiyan Guan , Lingfei Ma , Dedong Zhang

Semantic Scene Completion (SSC) is a fundamental yet challenging task in 3D environment perception, as the sparsity and noise of LiDAR point clouds make it difficult to accurately recover both geometry and semantics. To address these challenges, we propose DPS2CNet, a novel Dual-Path SSC Network that integrates voxel-based and bird’s-eye view (BEV) representations to exploit their complementary strengths. Specifically, DPS2CNet employs a Cylinder3D-enhanced voxel branch to capture fine-grained 3D geometry and a UNet-based BEV branch to model large-scale contextual information. To further boost performance, we incorporate CARAFE for efficient feature upsampling and design a tailored loss function optimized for SSC. Extensive experiments on SemanticKITTI and SSCBench-KITTI-360 demonstrate that DPS2CNet achieves state-of-the-art results. In particular, it ranks first on the SemanticKITTI test set with an IoU of 62.6% among all open-source submissions¹ , highlighting its effectiveness in complex real-world driving scenarios.

语义场景补全（SSC）是3D环境感知中的一项基本但具有挑战性的任务，因为激光雷达点云的稀疏性和噪声使得难以准确恢复几何和语义。为了应对这些挑战，我们提出了一种新的双路径SSC网络DPS2CNet，它集成了基于体素和鸟瞰图（BEV）的表示，以利用它们的互补优势。具体来说，DPS2CNet使用了一个圆柱体3D增强的体素分支来捕获细粒度的3D几何图形，以及一个基于unet的BEV分支来建模大规模的上下文信息。为了进一步提高性能，我们结合CARAFE进行有效的特征上采样，并设计了针对SSC优化的定制损失函数。在SemanticKITTI和sschbench - kitti -360上进行的大量实验表明，DPS2CNet达到了最先进的效果。特别是，它在SemanticKITTI测试集上排名第一，在所有开源提交任务中，IoU为62.6% 11https://codalab.lisn.upsaclay.fr/competitions/7170#results。突出了其在复杂的现实驾驶场景中的有效性。

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引用次数: 0

Federated earth-observation models for collaborative farm-scale soil mapping 协同农场尺度土壤制图的联合地球观测模型

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-12 DOI: 10.1016/j.jag.2025.105067

Giannis Gallios , José A.M. Demattê , Nikolaos Tsakiridis , Matheus Carraco Cardoso , Anastasia Kritharoula , Nikolaos Tziolas

Accurate, privacy-respecting soil information is essential for site-specific nutrient management and carbon accounting, yet the cost of laboratory analyses limits many farms to relatively sparse sampling grids. We propose a collaborative framework that couples a national Sentinel-2 bare-soil composite with FL to produce high-resolution clay and soil organic carbon (SOC) maps while keeping all local data on-premise. A one-dimensional convolutional neural network was first pre-trained on a 53,570-sample Brazilian archive and then fine-tuned across 50 farms through synchronous Federated Averaging. We benchmarked this hybrid configuration against (i) a purely centralized model trained on the same archive and (ii) a fully decentralized FL model initialized at random.

Across farm-level validation sets, pre-trained FL lowered median RMSE by 42% for clay and 31% for SOC relative to the centralized baseline, while increasing median RPIQ by 33% and 25%, respectively. On farms with

\leq 150

samples, the gains remained substantial, confirming that the approach remains effective when local datasets are modest compared with national archives. Error distributions differed significantly between scenarios (Friedman and Wilcoxon tests), and the pre-trained FL maps removed most spatial artefacts observed in the centralized outputs while preserving fine-scale gradients. Because only encrypted weight updates are exchanged, sensitive information never leaves the farm, satisfying GDPR/LGPD-style constraints and allowing late-joining clients to inherit an increasingly mature global model. Taken together, these results indicate that continental pre-training followed by federated fine-tuning reconciles global generality with local specificity and offers a scalable blueprint for privacy-preserving, high-resolution soil mapping in settings where sample densities are often substantially lower than in experimental setups, without compromising data sovereignty.

准确、尊重隐私的土壤信息对于特定地点的养分管理和碳核算至关重要，但实验室分析的成本限制了许多农场相对稀疏的采样网格。我们提出了一个协作框架，将国家Sentinel-2裸土复合材料与FL相结合，生成高分辨率粘土和土壤有机碳（SOC）地图，同时保留所有本地数据。一维卷积神经网络首先在53570个巴西档案样本上进行预训练，然后通过同步联邦平均对50个农场进行微调。我们将这种混合配置与(i)在同一存档上训练的纯集中式模型和（ii）随机初始化的完全分散的FL模型进行基准测试。

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引用次数: 0

Wildfire susceptibility mapping with multiple machine learning algorithms utilizing forest inventory and FIRMS data: A case study in Arsin, Trabzon, Türkiye 利用森林清查和FIRMS数据的多种机器学习算法绘制野火易感性图：以Arsin， Trabzon, t<s:1> rkiye为例

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-10 DOI: 10.1016/j.jag.2026.105091

Sude Gül Yel , Derya Mumcu Küçüker , Esra Tunç Görmüş

Forest fires pose a significant threat to ecosystems, biodiversity, and human settlements. This study focuses on the Arsin Forest Sub-district Directorate, located in Trabzon, Türkiye, with the aim of developing wildfire susceptibility maps using machine learning techniques. To improve the completeness of the wildfire inventory dataset, official fire records from 2013 to 2022 were integrated with active fire pixel data obtained from the Fire Information for Resource Management System (FIRMS) for the period 2001–2012. Five machine learning models Random Forest (RF), Artificial Neural Network (ANN), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Deep Neural Network (DNN) were employed to generate susceptibility maps. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curves, and AUC-ROC metrics. Seventeen wildfire conditioning factors, categorized into four groups (topographic, meteorological, stand-related, and anthropogenic), were used to assess fire risk. Unlike previous studies, this research incorporates a region-specific anthropogenic variable: proximity to hazelnut cultivation. Feature importance scores were computed to determine the influence of each factor on fire occurrence. Additionally, SHapley Additive exPlanations (SHAP), accompanied by graphical analysis, were used to interpret the relationships between predictors and fire events. Areas of very high fire susceptibility covering 4.62% and 4.86% of the study area were identified by the RF and GBM models, both achieving an accuracy of 0.98.

森林火灾对生态系统、生物多样性和人类住区构成重大威胁。本研究的重点是位于 rkiye省Trabzon的Arsin森林分区局，目的是利用机器学习技术开发野火易感性地图。为了提高野火清单数据集的完整性，将2013年至2022年的官方火灾记录与2001年至2012年期间从火灾信息资源管理系统（FIRMS）获得的活火像素数据进行了整合。采用随机森林（Random Forest， RF）、人工神经网络（Artificial Neural Network， ANN）、梯度增强机（Gradient Boosting machine， GBM）、极端梯度增强机（Extreme Gradient Boosting, XGBoost）和深度神经网络（Deep Neural Network， DNN） 5种机器学习模型生成敏感性图。采用准确度、精密度、召回率、f1评分、ROC曲线和AUC-ROC指标评价模型的性能。17个野火调节因子被分为四组（地形、气象、森林相关和人为），用于评估火灾风险。与以前的研究不同，这项研究纳入了一个区域特定的人为变量：接近榛子种植。计算特征重要性分数，以确定每个因素对火灾发生的影响。此外，采用SHapley加性解释（SHAP）和图形分析来解释预测因子与火灾事件之间的关系。RF和GBM模型分别确定了4.62%和4.86%的高火易感性区域，两者的准确率均为0.98。

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引用次数: 0

Climate change weakened the productivity benefits from the forestry ecological engineering projects-induced greening in China 气候变化削弱了中国林业生态工程绿化的生产力效益

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-10 DOI: 10.1016/j.jag.2025.105052

Liang Zheng , Hao Wu , Anqi Lin , Jianzhong Lu , Xiaoling Chen

To enhance ecosystem functioning and terrestrial carbon sink capacity, the Chinese government has implemented a series of regional ecological projects. However, the effectiveness of ecological projects varies considerably due to regional climate differences and diverse implementation strategies. This study analyzed long-term time series of the Normalized Difference Vegetation Index (NDVI) and Gross Primary Productivity (GPP) from 1982 to 2018 across eight Forestry Ecological Engineering Projects (FEEPs) and is the first to combine Ensemble Empirical Mode Decomposition (EEMD) with a modified residual trend analysis to quantify the contributions of these projects to vegetation greenness and productivity. The results indicate that both NDVI and GPP exhibited overall increasing trends in FEEP regions, although GPP exhibited lower magnitude and stability (with a monotonically increasing of about 35 % of pixels) than NDVI (with a monotonically increasing of about 54 % of pixels). GPP was more sensitive to environmental changes, with the proportions of pixels significantly correlated with temperature, precipitation, and sunlight being 74.0 %, 59.0 %, and 55.3 %, respectively, higher than the corresponding values for NDVI (68.6 %, 54.8 %, and 49.2 %). NDVI trend turning points were closely related to the timing of FEEP implementation, whereas GPP turning points showed weaker correlations. Our analysis indicates that FEEPs are the primary driver of NDVI increases, effectively mitigating the negative impacts of climate change on vegetation greenness, while GPP is mainly controlled by climate, with engineering measures insufficient to offset the long-term negative effects of extreme climate events. This study highlights the complex responses of vegetation to environmental changes and the potential vulnerability of ecosystem carbon sequestration under climate change.

为提高生态系统功能和陆地碳汇能力，中国政府实施了一系列区域生态工程。然而，由于区域气候差异和实施策略的不同，生态项目的有效性差异很大。本研究分析了1982 - 2018年8个林业生态工程项目（feep）的归一化植被指数（NDVI）和总初级生产力（GPP）的长期时间序列，并首次将集合经验模态分解（EEMD）与修正残差趋势分析相结合，量化了这些项目对植被绿化率和生产力的贡献。结果表明，在FEEP区域，NDVI和GPP均呈现整体上升趋势，但GPP的幅度和稳定性较NDVI（单调增加约35%像素）低（单调增加约54%像素）。GPP对环境变化更为敏感，与温度、降水和日照相关的像元比例分别为74.0%、59.0%和55.3%，高于NDVI的相应值（68.6%、54.8%和49.2%）。NDVI趋势拐点与FEEP实施时间密切相关，而GPP拐点的相关性较弱。分析表明，feep是NDVI增加的主要驱动因素，有效缓解了气候变化对植被绿度的负面影响，而GPP主要受气候控制，工程措施不足以抵消极端气候事件的长期负面影响。本研究强调了植被对环境变化的复杂响应以及气候变化下生态系统固碳的潜在脆弱性。

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引用次数: 0

Large-scale forest resource mapping with spatial gaps in the training data: Comparison of different modeling approaches 训练数据中存在空间缺口的大尺度森林资源制图：不同建模方法的比较

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-09 DOI: 10.1016/j.jag.2026.105104

Andras Balazs , Jukka Miettinen , Mats Nilsson , Johannes Breidenbach , Timo P. Pitkänen , Mari Myllymäki

Forest attribute maps are essential for supporting local decision-making regarding forest resource use. Such maps are produced by combining remote sensing and field data through various modeling approaches. When mapping across large areas, spatial gaps in field data used for model training are common. Our study evaluates the performance of three methods—

k

-Nearest Neighbor (

k

-NN), Random Forests (RF), and Multi-Layer Perceptron (MLP)—for forest resource mapping across Norway, Sweden, and Finland in an experimental setup with respect to availability of field data around the target area. Models were trained with sample plot sizes (

N

) ranging from 100 to 3000. RF consistently produced the most accurate predictions in terms of relative bias and RMSE. While spatial gaps in the training data (radius: 7–141 km) affected %RMSE of broad-leaved above ground biomass (AGB), they had minimal impact on %RMSE of both local and country-level predictions of total AGB and volume. For RF with

N = 3000

, %RMSE of total AGB ranged between 53%–55% in Finland and Sweden, and 70%–72% in Norway across gap sizes. However, %bias increased for local predictions across the whole study region with larger gaps: RF with

N = 500

showed bias of −12%–12% (7 km gap) and −17%–28% (78 km gap). Similarly, country-level %bias of total AGB for Norway increased from −1.7% to −3.7% with larger gaps. In conclusion, spatial gaps in training data can significantly affect bias in predictions. Therefore, forest attribute maps should always be accompanied by metadata describing the training data used.

森林属性图对于支持当地关于森林资源利用的决策至关重要。这种地图是通过各种建模方法将遥感和实地数据结合起来制作的。当绘制大区域时，用于模型训练的现场数据中的空间差距是常见的。我们的研究评估了三种方法——k-最近邻（k-NN）、随机森林（RF）和多层感知器（MLP）——在挪威、瑞典和芬兰的森林资源映射实验设置中的性能，以及目标区域周围现场数据的可用性。模型的样本量(N)为100 ~ 3000。在相对偏差和均方根误差方面，射频始终产生最准确的预测。虽然训练数据（半径为7 ~ 141 km）的空间差距影响了阔叶地上生物量（AGB）的%RMSE，但它们对地方和国家一级的总AGB和体积预测的%RMSE的影响最小。对于N=3000的RF，芬兰和瑞典总AGB的%RMSE介于53%-55%之间，挪威介于70%-72%之间。然而，在整个研究区域，局部预测的%偏差在较大的差距下增加：N=500的RF显示偏差为- 12%-12% （7 km差距）和- 17%-28% （78 km差距）。同样，挪威总AGB的国家级%偏差从- 1.7%增加到- 3.7%，差距更大。综上所述，训练数据的空间差距会显著影响预测的偏差。因此，森林属性映射应该总是伴随着描述所使用的训练数据的元数据。

{"title":"Large-scale forest resource mapping with spatial gaps in the training data: Comparison of different modeling approaches","authors":"Andras Balazs , Jukka Miettinen , Mats Nilsson , Johannes Breidenbach , Timo P. Pitkänen , Mari Myllymäki","doi":"10.1016/j.jag.2026.105104","DOIUrl":"10.1016/j.jag.2026.105104","url":null,"abstract":"<div><div>Forest attribute maps are essential for supporting local decision-making regarding forest resource use. Such maps are produced by combining remote sensing and field data through various modeling approaches. When mapping across large areas, spatial gaps in field data used for model training are common. Our study evaluates the performance of three methods—<span><math><mi>k</mi></math></span>-Nearest Neighbor (<span><math><mi>k</mi></math></span>-NN), Random Forests (RF), and Multi-Layer Perceptron (MLP)—for forest resource mapping across Norway, Sweden, and Finland in an experimental setup with respect to availability of field data around the target area. Models were trained with sample plot sizes (<span><math><mi>N</mi></math></span>) ranging from 100 to 3000. RF consistently produced the most accurate predictions in terms of relative bias and RMSE. While spatial gaps in the training data (radius: 7–141 km) affected %RMSE of broad-leaved above ground biomass (AGB), they had minimal impact on %RMSE of both local and country-level predictions of total AGB and volume. For RF with <span><math><mrow><mi>N</mi><mo>=</mo><mn>3000</mn></mrow></math></span>, %RMSE of total AGB ranged between 53%–55% in Finland and Sweden, and 70%–72% in Norway across gap sizes. However, %bias increased for local predictions across the whole study region with larger gaps: RF with <span><math><mrow><mi>N</mi><mo>=</mo><mn>500</mn></mrow></math></span> showed bias of −12%–12% (7 km gap) and −17%–28% (78 km gap). Similarly, country-level %bias of total AGB for Norway increased from −1.7% to −3.7% with larger gaps. In conclusion, spatial gaps in training data can significantly affect bias in predictions. Therefore, forest attribute maps should always be accompanied by metadata describing the training data used.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105104"},"PeriodicalIF":8.6,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel two-step method for ratoon rice mapping using Sentinel-1/2 time series 基于Sentinel-1/2时间序列的两步水稻制图方法

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-01-08 DOI: 10.1016/j.jag.2025.105083

Yue Wang , Yuechen Li , Xiaolin Zhu , Jin Chen , Ruyin Cao , Xiong Yao , Wujun Zhang

Ratoon rice plays a vital role in boosting land productivity and contributing to stable food supplies under the context of global climate change. This system offers these advantages by demonstrating the capacity to produce an additional grain yield of 5–6 t ha⁻¹ from the ratoon season, while simultaneously reducing the growth duration by 44–48 days compared to conventional double-season rice cultivation. However, its precise spatial distribution remains unclear under varying climatic and surface conditions, and remote sensing-based monitoring of ratoon rice has received limited attention. Existing methods often struggle to accurately distinguish ratoon rice from other morphologically similar types, such as double-season rice, and are further hampered by frequent cloud cover and rainfall, compromising optical sensing effectiveness. This study proposes a two-step ratoon rice (TSRR) mapping method using multi-source remote sensing data at the parcel scale. The TSRR method integrates Sentinel-1A synthetic aperture radar (SAR) and Sentinel-2 optical imagery, utilizing the distinct growth characteristics of main-season and ratoon rice. It employs object-based segmentation and two novel indices—the SAR-based paddy rice index (SPRI) and the SAR-based ratoon rice index (SRRI)—without relying on detailed phenological information. Results indicate that the TSRR method effectively distinguishes ratoon rice from other paddy rice types, achieving an average overall accuracy (OA) of 0.87, with particularly high performance in separating ratoon rice from double-season rice. The TSRR method demonstrates strong robustness and transferability across different regions, which can provide a reliable solution for large-scale paddy rice mapping, especially in cloud-prone areas with limited optical data availability, and offers valuable support for crop monitoring, yield estimation, and national agricultural inventory initiatives.

在全球气候变化的背景下，大米在提高土地生产力和稳定粮食供应方面发挥着至关重要的作用。该系统具有这些优势，因为它显示了从再生季节开始额外生产5-6吨/公顷粮食的能力，同时与传统的双季稻种植相比，生长期缩短了44-48天。然而，在不同的气候和地表条件下，其精确的空间分布尚不清楚，基于遥感的水稻监测受到的关注有限。现有的方法往往难以准确区分水稻与其他形态相似的水稻，如双季稻，并且由于频繁的云层和降雨，进一步阻碍了光学传感的有效性。本文提出了一种基于多源遥感数据的两步成片水稻（TSRR）制图方法。TSRR方法结合了Sentinel-1A合成孔径雷达（SAR）和Sentinel-2光学图像，利用了主季稻和次季稻不同的生长特征。该方法采用了基于目标的分割和两个新的指数——基于sar的水稻指数（SPRI）和基于sar的生长期水稻指数（SRRI），而不依赖于详细的物候信息。结果表明，TSRR方法能有效区分籼稻和其他水稻类型，平均总体准确率（OA）为0.87，其中在区分籼稻和双季稻方面表现尤为优异。TSRR方法具有较强的鲁棒性和跨区域可转移性，可为大规模水稻制图提供可靠的解决方案，特别是在光学数据可用性有限的多云地区，并为作物监测、产量估计和国家农业清查举措提供有价值的支持。

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引用次数: 0