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A process-based framework for validating forest landscape modeling outcomes 用于验证森林景观建模结果的基于过程的框架
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106327
Mia M. Wu , Yu Liang , Hong S. He , Jian Yang , Bo Liu , Tianxiao Ma
Forest landscape models (FLMs) simulate forest dynamics by integrating stand- and landscape-scale processes. Thus, evaluating FLMs simulations necessitates including both processes. Thus far, stand-scale processes were evaluated in some FLMs, whereas landscape-scale processes were rarely evaluated. This study presents a framework that evaluates both stand- and landscape-scale processes. For the stand-scale processes, we proposed using stand density management diagrams to evaluate the simulated stand development trajectories that encapsulate the interplay of tree growth, competition, and mortality. For the landscape-scale processes, we evaluated seed dispersal, the basic spatial process driving forest landscape dynamics and not evaluated previously, through comparing simulated tree species colonization pattern against tree age distribution data from inventory data. We demonstrated the applicability of the framework to a 300-year historical forest landscape reconstructed using LANDIS. Given the common features, the framework is applicable to other FLMs or terrestrial ecosystem models operating at large scales.
森林景观模型(FLMs)通过整合林分尺度和景观尺度过程来模拟森林动态。因此,评估flm模拟需要包括这两个过程。迄今为止,林分尺度的过程在一些生态系统中得到了评价,而景观尺度的过程很少得到评价。本研究提出了一个评估林分尺度和景观尺度过程的框架。对于林分尺度的过程,我们建议使用林分密度管理图来评估包含树木生长、竞争和死亡相互作用的模拟林分发展轨迹。对于景观尺度的过程,我们通过比较模拟树种定植模式和清查数据中的树龄分布数据,评估了种子传播这一驱动森林景观动态的基本空间过程。我们展示了该框架对使用LANDIS重建的300年历史森林景观的适用性。鉴于这些共同特征,该框架适用于其他大尺度的陆地生态系统模型或陆地生态系统模型。
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引用次数: 0
EarthObsNet: A comprehensive Benchmark dataset for data-driven earth observation image synthesis EarthObsNet:用于数据驱动的地球观测图像合成的综合基准数据集
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106292
Zhouyayan Li , Yusuf Sermet , Ibrahim Demir
Recently, there are attempts to expand the current usage of satellite Earth surface observation images to forward-looking applications to support decision-making and fast response against future natural hazards. Specifically, deep learning techniques were employed to synthesize Earth surface images at the pixel level. Those studies found that precipitation and soil moisture play non-trivial roles in Earth surface condition prediction tasks. However, unlike many well-defined and well-studied topics, such as change detection, for which many benchmark datasets are openly available, there are limited public datasets for the abovementioned topic for fast prototyping and comparison. To close this gap, we introduced a comprehensive dataset containing SAR images, precipitation, soil moisture, land cover, Height Above Nearest Drainage (HAND), DEM, and slope data collected during the 2019 Central US Flooding events. Deep-learning-based SAR image synthesis and flood mapping with the synthesized images were presented as sample use cases of the dataset.
最近,有人试图将卫星地球表面观测图像的现有用途扩展到前瞻性应用,以支持决策和对未来自然灾害的快速反应。具体而言,深度学习技术被用来合成像素级的地球表面图像。这些研究发现,降水和土壤湿度在地球表面状况预测任务中发挥着非同小可的作用。然而,与许多定义明确、研究深入的课题不同,例如变化检测,许多基准数据集都是公开的,但上述课题用于快速原型开发和比较的公开数据集却很有限。为了填补这一空白,我们引入了一个综合数据集,其中包含在 2019 年美国中部洪灾事件中收集的合成孔径雷达图像、降水、土壤水分、土地覆盖、最近排水沟以上高度(HAND)、DEM 和坡度数据。作为数据集的示例用例,介绍了基于深度学习的合成孔径雷达图像合成和使用合成图像绘制洪水地图。
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引用次数: 0
AI-driven forecasting of harmful algal blooms in Persian Gulf and Gulf of Oman using remote sensing 人工智能驱动的波斯湾和阿曼湾有害藻华遥感预测
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106311
Amirreza Shahmiri, Mohamad Hosein Seyed-Djawadi, Seyed Mostafa Siadatmousavi
This study develops an artificial intelligence (AI) model to forecast harmful algal blooms (HABs) in the Persian Gulf and Gulf of Oman using freely available remote sensing data, including chlorophyll-a (Chl-a), sea surface temperature (SST), salinity, and wind. The model introduces novel features such as spatial and temporal standard deviations of Chl-a concentration and a derived gradient feature. Correlation analysis indicated that these features enhance predictive capability. A multi-layer artificial neural network (ANN) was trained using a 66%/34% data split for training and testing, achieving 88.7% accuracy in binary classification (bloom/non-bloom) with an area under the ROC curve (AUC) of 90.1%. Overfitting was mitigated by monitoring training and validation loss, both of which consistently decreased over epochs, confirming robust model generalization. The use of standard deviation in SST and salinity highlights their influence on bloom dynamics, providing key insights into algal bloom drivers. The focus on freely available data enables stakeholders to better manage the environmental challenges posed by HABs.
本研究开发了一个人工智能(AI)模型,利用可免费获得的遥感数据,包括叶绿素-a (Chl-a)、海面温度(SST)、盐度和风,预测波斯湾和阿曼湾的有害藻华(HABs)。该模型引入了新的特征,如Chl-a浓度的时空标准偏差和衍生的梯度特征。相关分析表明,这些特征增强了预测能力。采用66%/34%的数据分割率训练多层人工神经网络(ANN)进行训练和测试,二元分类(开花/不开花)准确率达到88.7%,ROC曲线下面积(AUC)为90.1%。通过监测训练和验证损失来减轻过拟合,两者都随着时间的推移而持续下降,证实了模型的鲁棒泛化。使用海温和盐度的标准差强调了它们对水华动态的影响,为了解藻华驱动因素提供了关键见解。将重点放在可免费获得的数据上,使利益攸关方能够更好地管理有害藻华带来的环境挑战。
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引用次数: 0
Machine learning-based prediction of belowground biomass from aboveground biomass and soil properties 基于机器学习的地下生物量从地上生物量和土壤性质预测
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106313
Yuquan Zhao , Lu Zhang , Shilong Lei , Lirong Liao , Chao Zhang
Precise and accurate quantification of belowground biomass (BGB) is essential for understanding terrestrial carbon dynamics. Traditional methods for estimating BGB suffer from a number of disadvantages, including inability to resolve differences among plant species, high dependence on Diameter at Breast Height, and destructive sampling. To address these issues, we developed a novel machine learning framework to estimate grassland BGB by integrating vegetation and soil data from 294 plots on China's Loess Plateau. An ensemble model combining XGBoost regression, Gradient boosting regression, Ridge regression, and ElasticNet regression outperformed the individual models, achieving a training R2 of 0.623 and a testing R2 of 0.502, highlighting its superior ability to identify the complex dependencies of BGB. Integration of key features, including soil organic carbon, plant height, and aboveground biomass, significantly improved the predictive accuracy. Nonlinear BGB–environment interactions are commonly underrecognized in traditional models. The model presented herein advances our ability to assess underground carbon stocks and offers insights into the ecological strategies of grassland species under competitive light conditions. By revealing the multifaceted influences of soil and vegetation on BGB, our research refines the understanding of grassland carbon dynamics. This study marks a precedent for harnessing advanced machine learning in ecological modeling to facilitate more accurate predictions of global change.
地下生物量(BGB)的精确定量对理解陆地碳动态至关重要。传统的BGB估算方法存在许多缺点,包括无法解决植物物种之间的差异,高度依赖于胸围直径,以及破坏性采样。为了解决这些问题,我们开发了一个新的机器学习框架,通过整合中国黄土高原294个样地的植被和土壤数据来估计草地BGB。结合XGBoost回归、Gradient boosting回归、Ridge回归和ElasticNet回归的集成模型优于单个模型,训练R2为0.623,测试R2为0.502,突出了其识别BGB复杂依赖关系的卓越能力。整合土壤有机碳、植物高度和地上生物量等关键特征显著提高了预测精度。在传统模型中,bgb环境的非线性相互作用通常被低估。本文提出的模型提高了我们评估地下碳储量的能力,并为了解竞争光照条件下草地物种的生态策略提供了见解。通过揭示土壤和植被对BGB的多方面影响,我们的研究完善了对草地碳动态的认识。这项研究标志着在生态建模中利用先进的机器学习来促进更准确地预测全球变化的先例。
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引用次数: 0
A simple method for the enhancement of river bathymetry in LiDAR DEM
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106354
Gabriele Farina , Marco Pilotti , Luca Milanesi , Giulia Valerio
The preparation of an accurate bathymetry is crucial for flood modeling and is usually done using a LiDAR-derived Digital Elevation Model (DEM). However, a recurrent flaw of LiDAR DEM is the presence of water along rivers, that prevents a careful reproduction of the river bed and channel conveyance. This paper provides a simple and effective algorithm to tackle this problem when ground surveyed cross sections are available to complement DEM data. In contrast to most interpolation approaches, the algorithm is physically-based, using a 2D Shallow Water Equations solver in the identification of the wetted river bed perimeter. The method was applied to a 37 km long stretch of the Mella River (Northern Italy) providing satisfactory results. Further examples show the potential of the method in cases of increasing complexity of riverbed bathymetry. The procedure is explained step by step in the supplementary material, using two widely used freeware software.
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引用次数: 0
Synthetic random environmental time series generation with similarity control, preserving original signal’s statistical characteristics 采用相似度控制合成随机环境时间序列,保持原始信号的统计特征
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106283
Ofek Aloni , Gal Perelman , Barak Fishbain
Synthetic datasets are widely used in applications like missing data imputation, simulations, training data-driven models, and system robustness analysis. Typically based on historical data, these datasets need to represent specific system behaviors while being diverse enough to challenge the system with a broad range of inputs. This paper introduces a method using discrete Fourier transform to generate synthetic time series with similar statistical moments to any given signal. The method allows control over the similarity level between the original and synthetic signals. Analytical proof shows that this method preserves the first two statistical moments and the autocorrelation function of the input signal. It is compared to ARMA, GAN, and CoSMoS methods using various environmental datasets with different temporal resolutions and domains, demonstrating its generality and flexibility. A Python library implementing this method is available as open-source software.
合成数据集广泛应用于缺失数据输入、模拟、训练数据驱动模型和系统鲁棒性分析等领域。通常基于历史数据,这些数据集需要表示特定的系统行为,同时具有足够的多样性,以广泛的输入挑战系统。本文介绍了一种利用离散傅立叶变换生成与任意给定信号具有相似统计矩的合成时间序列的方法。该方法允许控制原始信号和合成信号之间的相似度。分析证明,该方法保留了输入信号的前两个统计矩和自相关函数。将其与ARMA、GAN和CoSMoS方法进行了比较,这些方法使用了具有不同时间分辨率和域的各种环境数据集,证明了其通用性和灵活性。实现此方法的Python库可以作为开源软件获得。
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引用次数: 0
Community-enabled life-cycle assessment Stormwater Infrastructure Costs (CLASIC) tool 社区支持的生命周期评估雨水基础设施成本(classic)工具
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106279
Mazdak Arabi , Tyler Dell , Mahshid Mohammad Zadeh , Christine A. Pomeroy , Jennifer M. Egan , Tyler Wible , Sybil Sharvelle
Urbanization, land use change, and climate change have profound effects on urban stormwater. This study develops the Community-enabled Life-cycle Analysis of Stormwater Infrastructure Costs (CLASIC) software to support decisions about stormwater control infrastructure over a range of alternative scenarios at the neighborhood to municipal scales. The tool quantifies hydrologic and stormwater quality performance, life-cycle costs, and triple-bottom-line social, economic, and environmental co-benefits of green, gray, and hybrid green-gray stormwater practices. CLASIC is deployed as a cloud-based web-tool, with a geographical information system (GIS) enabled interface, and built-in computing services to characterize terrain, soil, land use, and climatic conditions using publicly available datasets, and to parameterize and execute the modeling modules. Three community level case studies in the United States illustrate the utility of CLASIC for climate change assessments, green infrastructure implementation for community redevelopment, and assessment of the effects of changes in rainfall characteristics on the performance of stormwater practices.
城市化、土地利用变化和气候变化对城市雨水有着深远的影响。本研究开发了社区支持的雨水基础设施成本生命周期分析(classic)软件,以支持在社区到市政规模的一系列备选方案中有关雨水控制基础设施的决策。该工具量化了水文和雨水质量表现、生命周期成本,以及绿色、灰色和混合绿灰雨水处理的三重底线社会、经济和环境共同效益。classic作为基于云的网络工具部署,具有地理信息系统(GIS)支持的界面,内置计算服务,可以使用公开可用的数据集描述地形、土壤、土地利用和气候条件,并参数化和执行建模模块。美国的三个社区层面的案例研究说明了classic在气候变化评估、社区重建的绿色基础设施实施以及评估降雨特征变化对雨水处理效果的影响方面的效用。
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引用次数: 0
Predicting massive floating macroalgal blooms in a regional sea 预测区域海洋中大量漂浮的大型藻华
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106310
Fucang Zhou , Zhi Chen , Zaiyang Zhou , Bing Cao , Lili Xu , Dongyan Liu , Ruishan Chen , Karline Soetaert , Jianzhong Ge
Increasingly frequent and severe floating macroalgal blooms present significant challenges to coastal and ocean environments. Here a short-term forecast system of floating macroalgal blooms was developed to predict the physical-biogeochemical environment and macroalgal ecodynamic processes in a regional ocean. Predictions of macroalgal ecodynamic processes are influenced by oceanic conditions (hydrodynamics, temperature, and nutrients), as well as atmospheric conditions (wind). The system's effectiveness is demonstrated by successfully hindcasting the June 2021 green tide bloom event in the Yellow Sea and using real-time satellite data to make reliable and robust continuous short-term predictions for 2022 and 2023. The prediction accuracy of coverage reaches 87.5%, and the minimum transport error of the green tide center of mass is 6.09 nautical miles over an 7-day prediction duration. Supported by regional marine physics and biogeochemistry and macroalgal physiological characteristic datasets, this system may serve as a crucial cornerstone for similar floating macroalgal disaster prevention.
日益频繁和严重的浮藻华对沿海和海洋环境构成了重大挑战。为了预测区域海洋浮游藻华的物理-生物地球化学环境和生态动力学过程,建立了浮游藻华短期预报系统。大藻生态动力学过程的预测受到海洋条件(水动力学、温度和营养物)以及大气条件(风)的影响。该系统的有效性通过成功预测2021年6月黄海绿潮事件以及使用实时卫星数据对2022年和2023年进行可靠和稳健的连续短期预测来证明。覆盖预报精度达到87.5%,7 d预报周期内绿潮质心最小输运误差为6.09海里。在区域海洋物理、生物地球化学和大型藻生理特征数据集的支持下,该系统可作为类似大型藻漂浮灾害预防的重要基石。
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引用次数: 0
Solving the Master Equation on river networks: A computer algebra approach 求解河网主方程:一种计算机代数方法
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106288
Samuele De Bartolo , Gaetano Napoli , Stefano Rizzello , Raffaele Vitolo
We describe the algorithms and the software that have been used in a new computational method based on the use of Master Equations. Our computer algebra procedures simulate the diffusion of a pollutant in river networks. The representation of river networks as trees makes the derivation of governing equations for pollutant transport an easy task. This includes mass balance equations that account for the sources, sinks, and transport of pollutants in the river network. In two previous papers we described the model and some simulations obtained from our software. In this paper we describe two software libraries, respectively for the Reduce and the Mathematica computer algebra systems, that have been developed on the basis of our model. The libraries can be found in our GitHub repository.
我们描述了基于主方程的一种新的计算方法所使用的算法和软件。我们的计算机代数程序模拟了污染物在河网中的扩散。将河网表示为树使得推导污染物运移的控制方程变得容易。这包括解释河网中污染物的源、汇和输送的质量平衡方程。在之前的两篇论文中,我们描述了该模型以及用我们的软件得到的一些模拟结果。本文描述了基于该模型开发的Reduce和Mathematica计算机代数系统的两个软件库。这些库可以在我们的GitHub库中找到。
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引用次数: 0
A spatiotemporal autoregressive neural network interpolation method for discrete environmental factors 离散环境因素的时空自回归神经网络插值法
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106289
Jin Qi , Wenting Lv , Junxia Zhu , Minyu Wang , Zhe Zhang , Guangyuan Zhang , Sensen Wu , Zhenhong Du
The spatiotemporal interpolation model is necessary for generating continuous distributions for spatiotemporally discrete sampling points. However, there remain challenges in spatiotemporal interpolation due to the complex spatiotemporal effect and the imprecise kernel functions. Here, we proposed a spatiotemporal autoregressive neural network interpolation model (STARNN) that incorporates adaptive spatiotemporal distance quantification and supervised learning. The 10-fold cross-validation modelling on sea surface temperature and coastal nutrients demonstrated that the STARNN model performs better than baseline models and can well depict reasonable spatiotemporal distributions for environmental factors. By proposing two stacked neural networks, the STARNN model can accurately integrate spatial and temporal distances and avoids subjective selection of the kernel function. This study developed a novel interpolation model for processing discrete spatiotemporal points by following the data-driven paradigm, which can offer decision support for simulating the spread of sea temperature anomalies and optimizing the distribution of water quality measurement stations.
时空插值模型是产生时空离散采样点连续分布的必要条件。然而,由于时空效应的复杂性和核函数的不精确性,在时空插值方面仍然存在挑战。本文提出了一种结合自适应时空距离量化和监督学习的时空自回归神经网络插值模型(STARNN)。对海表温度和海岸营养物的10倍交叉验证模型表明,STARNN模型优于基线模型,能较好地描述环境因子的合理时空分布。通过提出两个堆叠神经网络,STARNN模型可以准确地整合时空距离,避免了核函数的主观选择。本研究建立了一种基于数据驱动的离散时空点插值模型,为模拟海温异常扩散和优化水质测量站分布提供决策支持。
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引用次数: 0
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Environmental Modelling & Software
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