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Geotechnological multicriteria analysis applied to identify optimal locations for the installation of sanitary landfills 应用土工多标准分析确定卫生填埋场的最佳安装位置
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-15 DOI: 10.1016/j.rsase.2024.101398
Kassiel Trajano da Luz , Antonio Henrique Cordeiro Ramalho , Edna Santos de Souza , Cristiano Bento da Silva
The Urban Solid Waste sector is one of the main contributors to methane emissions. Despite specific legislation, many Brazilian municipalities still maintain outdated waste dumps. Geotechnological tools, such as Fuzzy logic, can provide a viable and efficient solution. This research aimed to evaluate the current location and identify optimal sites for the implementation of sanitary landfills, using Fuzzy logic. We considered were slope, proximity to water bodies, urban areas, roads, and airports, land use and occupation, geology, and pedology. The results showed that the current dump location is inadequate due to its proximity to the airport, roads, and urban center. The suitability map revealed that 35.38% of the studied area has high to very high suitability. The new selected location to landfill having sufficient area, being distant from the airport and urban center, and complying with operational and logistical standards of proximity to highways and water bodies. The research confirms that the current Urban Solid Waste structure is not in compliance with regulations and that Fuzzy logic is effective in selecting sites for new sanitary landfills. This model can serve as a reference for other municipalities, contributing to more efficient and responsible waste management.
城市固体废物部门是甲烷排放的主要贡献者之一。尽管有专门的法律规定,但巴西许多城市仍然保留着过时的垃圾堆放场。模糊逻辑等土工技术工具可以提供可行且高效的解决方案。这项研究旨在利用模糊逻辑评估当前位置,并确定实施卫生填埋场的最佳地点。我们考虑的因素包括坡度、与水体、城区、道路和机场的距离、土地使用和占用、地质和土壤学。结果表明,由于靠近机场、公路和城市中心,目前的垃圾堆放地点并不合适。适宜性地图显示,35.38% 的研究区域具有较高或非常高的适宜性。新选定的垃圾填埋场具有足够的面积,远离机场和城市中心,并符合靠近公路和水体的操作和物流标准。研究证实,目前的城市固体废弃物结构不符合法规要求,而模糊逻辑在选择新的卫生填埋场地点方面是有效的。该模型可作为其他城市的参考,有助于提高废物管理的效率和责任感。
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引用次数: 0
A novel machine learning automated change detection tool for monitoring disturbances and threats to archaeological sites 用于监测考古遗址所受干扰和威胁的新型机器学习自动变化检测工具
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-13 DOI: 10.1016/j.rsase.2024.101396
Ahmed Mutasim Abdalla Mahmoud , Nichole Sheldrick , Muftah Ahmed
Archaeological sites across the globe are facing significant threats and heritage managers are under increasing pressure to monitor and preserve these sites. Since 2015, the EAMENA project has documented more than 200,000 archaeological sites and the disturbances and threats affecting them across the Middle East and North Africa (MENA) region, using a combination of remote sensing, digitization, and fieldwork methodologies. The large number of sites and their often remote or otherwise difficult to access locations makes consistent and regular monitoring of these sites for disturbances and threats a daunting task. Combined with the increasing frequency and severity of threats to archaeological sites, the need to develop novel tools and methods that can rapidly monitor the changes at and around archaeological sites and provide accurate and consistent monitoring has never been more urgent. In this paper, we introduce the EAMENA Machine Learning Automated Change Detection tool (EAMENA MLACD). This newly-developed online tool uses bespoke machine learning algorithms to process sequential satellite images and create land classification maps to detect and identify disturbances and threats in the vicinity of known archaeological sites for the purposes of heritage monitoring and preservation. Initial testing and validation of results from the EAMENA MLACD in a case study in Bani Walid, Libya, demonstrate how it can be used to identify disturbances and potential threats to heritage sites, and increase the speed and efficiency of monitoring activities undertaken by heritage professionals.
全球各地的考古遗址正面临着重大威胁,遗产管理者在监测和保护这些遗址方面面临着越来越大的压力。自 2015 年以来,EAMENA 项目采用遥感、数字化和实地考察相结合的方法,记录了中东和北非(MENA)地区 20 多万个考古遗址及其受到的干扰和威胁。由于遗址数量众多,而且往往地处偏远或交通不便,对这些遗址进行持续、定期的干扰和威胁监测是一项艰巨的任务。加之考古遗址面临的威胁日益频繁和严重,开发新型工具和方法以快速监测考古遗址及其周围的变化,并提供准确、一致的监测已成为当务之急。在本文中,我们将介绍 EAMENA 机器学习自动变化检测工具(EAMENA MLACD)。这款新开发的在线工具使用定制的机器学习算法来处理连续的卫星图像并创建土地分类图,以检测和识别已知考古遗址附近的干扰和威胁,从而达到遗产监测和保护的目的。在利比亚巴尼瓦利德进行的一项案例研究中,对 EAMENA MLACD 的结果进行了初步测试和验证,展示了如何利用该工具识别考古遗址受到的干扰和潜在威胁,以及如何提高遗产专业人员开展监测活动的速度和效率。
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引用次数: 0
Multisensor Integrated Drought Severity Index (IDSI) for assessing agricultural drought in Odisha, India 用于评估印度奥迪沙农业干旱的多传感器综合干旱严重程度指数(IDSI)
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-13 DOI: 10.1016/j.rsase.2024.101399
Rajkumar Guria , Manoranjan Mishra , Richarde Marques da Silva , Carlos Antonio Costa dos Santos , Celso Augusto Guimarães Santos
Recurrent droughts in India have severely impacted the economy and the quality of life. The agricultural drought from June to October 2023 in Odisha (the Kharif season), India, highlighted the urgent need for precise monitoring and assessment due to its significant effects on crop yield and food security. This study develops and validates the multisensor Integrated Drought Severity Index (IDSI) to accurately assess agricultural drought severity using multiple remote sensing indices, including optical, thermal, and microwave sensors. Ten indices were computed and combined using the Analytic Hierarchy Process (AHP) to assign weights, aiming to establish a new agricultural drought index that can monitor severity, identify critical indices, and assess uncertainties in affected areas. Validation results from ROC-AUC indicate that the IDSI model achieved a precision exceeding 85% using empirical weights. The study area's mapping shows that approximately 8.91% experience extreme drought conditions, with significant impacts in specific districts of Odisha. This comprehensive tool provides critical insights for policymakers and farmers, enhancing global drought preparedness and response strategies through its adaptable methodology.
印度一再发生的干旱严重影响了经济和生活质量。2023 年 6 月至 10 月,印度奥迪沙邦(哈里发季节)发生农业干旱,由于干旱对作物产量和粮食安全造成重大影响,因此迫切需要进行精确的监测和评估。本研究开发并验证了多传感器综合干旱严重程度指数(IDSI),以利用光学、热学和微波传感器等多种遥感指数准确评估农业干旱严重程度。利用层次分析法(AHP)对十个指数进行计算和组合以分配权重,旨在建立一个新的农业干旱指数,该指数可监测严重程度、识别关键指数并评估受灾地区的不确定性。ROC-AUC 验证结果表明,利用经验权重,IDSI 模型的精确度超过了 85%。研究地区的地图显示,约有 8.91% 的地区经历了极端干旱,在奥迪沙的特定地区造成了严重影响。这一综合工具为政策制定者和农民提供了重要的见解,并通过其适应性强的方法加强了全球干旱准备和应对战略。
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引用次数: 0
Mapping coastal wetland changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat time series and national wetland inventories 利用大地遥感卫星时间序列和国家湿地清单绘制 1985 年至 2022 年美国大西洋和海湾沿岸湿地变化图
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-10 DOI: 10.1016/j.rsase.2024.101392
Courtney A. Di Vittorio , Melita Wiles , Yasin W. Rabby , Saeed Movahedi , Jacob Louie , Lily Hezrony , Esteban Coyoy Cifuentes , Wes Hinchman , Alex Schluter
The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing reflectance with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The algorithm uses data and tools within the Google Earth Engine platform, making it accessible and scalable. The average overall accuracy is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsatderived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the new products’ severe change magnitude matches that of the existing US inventory and the moderate change magnitude matches that of the Northeastern Coast product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.
全世界沿海湿地的面积正在迅速减少,科学家和土地管理者需要能显示随时间变化的幅度和严重程度的土地覆被图,以评估影响并制定有效的保护策略。在美国,广泛使用的大陆尺度湿地土地覆被数据产品要么在时间上是静态的(美国国家湿地名录),要么时间分辨率较低,不能区分不同类型的变化(美国国家海洋和大气管理局沿海变化分析计划,C-CAP)。本研究提出了一种新的沿岸湿地地理空间数据产品,它利用 Landsat 数据库,绘制了 1985 年至 2022 年美国大西洋和墨西哥湾沿岸的年度土地覆盖图。该算法在现有的美国湿地清单上进行了训练,以使最终地图与用于业务管理的产品相兼容。设计了一种多阶段分类方法,使用连续变化检测和分类 (CCDC) 算法,利用拟合谐波函数描述遥感反射率时间序列的特征,并识别可能发生变化的时间。然后,将拟合的时间序列模型输入随机森林分类器,进行分类预测。同时进行年度规模的随机森林分类,并将两种算法的结果结合起来进行分析,以检测渐变和突变,并识别过渡时间序列段。随后应用时间序列平滑程序,以确保类别过渡的逻辑性和一致性,并提取显示变化严重程度和空间密度的总变化特征图。最终的地图区分为四个同质类别和六个混合类别,代表了在类别之间过渡的区域以及类别之间边界不稳定的区域。该算法使用了谷歌地球引擎平台中的数据和工具,使其具有可访问性和可扩展性。平均总体准确率为 93.7%,平均类别遗漏误差为 6.7%,误差率为 6.4%。我们使用现有的湿地清单(该清单采用了一种根本不同的变化检测方法)和一种更具可比性的年度尺度、Landsat 导出的产品进行了各种变化检测比较,该产品估计了整个东北大西洋沿岸的变化。这些比较表明,新产品的严重变化幅度与美国现有清单相符,而中度变化幅度与东北海岸产品相符。据《2019 年湿地现状和趋势报告》估计,从 2010 年到 2019 年,萌生湿地的净损失率为 1.7%,而新地图显示的损失率相当于 1.6%,两者再次显示出密切的一致性。
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引用次数: 0
Assessment of Dry Microburst Index over India derived from INSAT-3DR satellite INSAT-3DR 卫星得出的印度上空干微爆指数评估
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-04 DOI: 10.1016/j.rsase.2024.101393
Priyanshu Gupta, Neeti Singh, R.K. Giri, A.K. Mitra
Dry microbursts can generate severe meteorological conditions including turbulence and strong winds even in the absence of precipitation. Present study evaluate the performance of Indian geostationary satellite, INSAT-3DR in capturing Dry Microburst Index (DMI) and validated against the radiosonde dataset. Data is validated across 14 selected stations across the India for 3 year (2020–2022). However, radiosonde data is very limited but spatial and temporal resolution of INSAT-3DR is good to analyse and predict the atmospheric phenomena. Different statistics have been used to validate INSAT-3DR against radiosonde observation. A Taylor plot confirm strong correlation and low RMSE between INSAT-3DR and radiosonde data. Spatial distribution depicts annual mean DMI values, it is influence by diurnal variation, regional weather pattern, and seasonal factors. Seasonal analysis indicates lower DMI during winter (5–45) due to reduced instability and moisture, while post-monsoon season witness increased DMI owing to warmer, humid conditions. The pre-monsoon season shows rising DMI as temperature increase. Study also analyses the co-occurrence of thunderstorm during DMI events, revealing a Probability of Detection (POD) of 0.75 for the INSAT-3DR DMI product, indicating 75% correct identification of thunderstorms. However, the False Alarm Rate (FAR) suggest false alarms occurred in approximately 55.2% of cases. Overall, study underscores the importance of considering local factors and conditions in interpreting INSAT-3DR satellite-based DMI data. Understanding and accurately predicting dry microbursts are crucial for enhancing aviation safety and improving the resilience of infrastructure in regions prone to these phenomena.
即使在没有降水的情况下,干燥微爆也会产生包括湍流和强风在内的恶劣气象条件。本研究评估了印度地球静止卫星 INSAT-3DR 在捕捉干燥微爆指数(DMI)方面的性能,并与无线电探空仪数据集进行了验证。对印度 14 个选定站点 3 年(2020-2022 年)的数据进行了验证。然而,无线电探空仪的数据非常有限,但 INSAT-3DR 的空间和时间分辨率很高,可用于分析和预测大气现象。INSAT-3DR 与无线电探空仪观测数据采用了不同的统计方法进行验证。泰勒图证实 INSAT-3DR 和无线电探空仪数据之间具有很强的相关性和较低的 RMSE。空间分布描述了 DMI 的年平均值,它受到昼夜变化、区域天气模式和季节因素的影响。季节分析表明,冬季(5-45 月)由于不稳定性和湿度降低,DMI 值较低,而季风后季节由于温暖潮湿,DMI 值增加。季风季节前,随着气温的升高,DMI 有所上升。研究还分析了 DMI 事件期间雷暴的共现情况,结果显示 INSAT-3DR DMI 产品的检测概率 (POD) 为 0.75,表明雷暴的正确识别率为 75%。然而,误报率(FAR)表明约 55.2% 的情况下会出现误报。总之,研究强调了在解释 INSAT-3DR 星基 DMI 数据时考虑当地因素和条件的重要性。了解和准确预测干微暴对加强航空安全和提高易受这些现象影响地区的基础设施的抗灾能力至关重要。
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引用次数: 0
Analysis of radiative heat flux using ASTER thermal images: Climatological and volcanological factors on Java Island, Indonesia 利用 ASTER 热图像分析辐射热通量:印度尼西亚爪哇岛的气候和火山因素
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-01 DOI: 10.1016/j.rsase.2024.101376
Dini Andriani , Supriyadi , Muhammad Aufaristama , Asep Saepuloh , Alamta Singarimbun , Wahyu Srigutomo
This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m2 and 115.13 W/m2, indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.
本研究的重点是分析自然辐射热通量(RHF)异常,以绘制爪哇岛的热量分布图。利用遥感技术,我们使用从高级星载热发射和反射辐射计(ASTER)图像中获得的陆地表面温度(LST)和陆地表面发射率(LSE)数据计算了自然辐射热通量异常。我们的方法的一个关键方面是通过将 LST 地图与爪哇岛的土地利用土地覆盖(LULC)地图相互参照,区分自然热源和人为热源。该研究通过考察非火山地区的区域趋势和火山地区的局部趋势,并考虑气候和火山因素,解释了自然 RHF 异常。与气候因素的关系包括评估土壤水分主动被动数据(SMAP)中的土壤水分参数、全球降水量月度测量数据(GPM)中的降水量,以及根据柯本-盖革气候模式进行的分类。我们的区域分析显示,西爪哇北部地区、中爪哇部分地区和东爪哇大部分地区的自然 RHF 异常值较高,这归因于热带稀树草原和季风气候的低土壤湿度和低降水量。在更局部的范围内,火山地区的 RHF 值明显偏高,尤其是在中爪哇和东爪哇的火山周围,如默拉皮火山、斯拉梅特火山、塞默鲁火山、锡多阿茹泥火山和伊真火山。除帕图哈火山外,西爪哇火山的自然 RHF 异常值并不高。这些地区的平均自然 RHF 异常值介于 32.22 W/m2 和 115.13 W/m2 之间,表明火山活动强烈。这些发现解释了爪哇岛的总体热特征,并突出了与火山活动和地热潜力相关的地下热区的存在。
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引用次数: 0
Effective cooling networks: Optimizing corridors for Urban Heat Island mitigation 有效的冷却网络:优化城市热岛减缓走廊
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-01 DOI: 10.1016/j.rsase.2024.101372
Teimour Rezaei, Xinyuan Shen, Rattanawat Chaiyarat, Nathsuda Pumijumnong
The detrimental impacts of the Urban Heat Island (UHI) effect are widely recognized in cities globally. Despite the natural cooling capacity of urban cold islands (UCIs), their fragmented state diminishes overall effectiveness. Previous research focused on identifying corridors to connect these isolated UCIs, aiming to enhance cooling networks. However, optimal connection strategies remained elusive. This study introduces a novel framework to address this gap. Utilizing ArcGIS Pro's optimal region connection tools alongside Morphological Spatial Pattern Analysis (MSPA) and ecological parameters, corridors in Ghaemshahr, Iran were meticulously planned and assessed. Through minimum cumulative resistance and gravity models, 63 potential corridors totaling 153 km were identified. Optimization procedures then refined this selection to 27 key corridors spanning 22 km, with 67% measuring less than 0.5 km and strategically positioned near UCIs. This prioritizes adjacency, maximizing corridor protection and construction likelihood. This cost-effective approach fosters stronger connectivity between adjacent UCIs, ultimately linking all UCIs within the region. This innovative methodology provides a holistic solution for mitigating UHI effects, promoting sustainable urban development.
城市热岛效应(UHI)的有害影响在全球城市中已得到广泛认可。尽管城市冷岛(UCIs)具有天然降温能力,但其分散状态削弱了整体效果。以往的研究侧重于确定连接这些孤立的 UCI 的走廊,旨在加强冷却网络。然而,最佳的连接策略仍然难以捉摸。本研究引入了一个新颖的框架来填补这一空白。利用 ArcGIS Pro 的最佳区域连接工具以及形态空间模式分析 (MSPA) 和生态参数,对伊朗盖姆沙赫尔的走廊进行了细致的规划和评估。通过最小累积阻力和重力模型,确定了 63 条潜在走廊,总长 153 公里。随后,优化程序将这一选择细化为 27 条主要走廊,总长 22 千米,其中 67% 的走廊长度小于 0.5 千米,并战略性地位于 UCI 附近。这优先考虑了邻近性,最大限度地提高了走廊保护和建设的可能性。这种具有成本效益的方法加强了相邻 UCI 之间的连接,最终将区域内所有 UCI 连接起来。这种创新方法提供了缓解 UHI 影响的整体解决方案,促进了城市的可持续发展。
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引用次数: 0
Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data 利用 PALSAR-2 数据进行基于 Naïve Bayes 高斯混合模型和合成孔径雷达偏振测量法的自动淹没植被研究
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-01 DOI: 10.1016/j.rsase.2024.101361
Samvedya Surampudi, Vijay Kumar
Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.
使用合成孔径雷达(SAR)数据绘制洪水地图在完全区分植被下的洪水方面存在局限性,原因是被洪水淹没的树干会产生虚假的双反弹返回,而不断变化的土地覆盖环境又会产生季节性的异质性。此外,在近乎实时的应用中,快速绘制洪水植被图具有挑战性。本文提出了一种名为 "极坐标奈维贝叶斯"(Polarimetric Naïve Bayes)的全自动新型监督分类方法,该方法将极坐标信息与奈维贝叶斯框架中的一系列高斯混合模型相结合,以检测各种淹没植被类别。该方法还允许用户选择类别配置,并无需为监督训练创建感兴趣区域(ROI)。建议的方法在训练步骤中使用季风前 PolSAR 数据集的散射信息,为建筑物和其他特征创建 ROI。在下一步中,一系列高斯混合物被用于贝叶斯多类问题中不同特征的密度估计。新开发的分类器应用于 2016 年阿萨姆邦洪水事件,精确绘制了洪水中至少三种不同的植被类别,如淹没植被、湿地和漂浮植被。在最佳类别配置下,与应用于相同数据集的其他监督技术(如 MLE、Mahalanobis、最小欧氏距离和 SVM 分类)相比,该方法在划分洪水、淹没植被、湿地和漂浮植被方面表现出更好的性能,生产者准确率分别为 98.6%、81.1%、94% 和 51.5%,洪水植被类别的综合准确率为 95.5%。与同类方法相比,该方法检测多个植被类别的准确率也更高。
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引用次数: 0
Unveiling soil coherence patterns along Etihad Rail using Sentinel-1 and Sentinel-2 data and machine learning in arid region 利用 Sentinel-1 和 Sentinel-2 数据及机器学习揭示干旱地区伊蒂哈德铁路沿线的土壤一致性模式
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-01 DOI: 10.1016/j.rsase.2024.101374
Sona Alyounis , Delal E. Al Momani , Fahim Abdul Gafoor , Zaineb AlAnsari , Hamed Al Hashemi , Maryam R. AlShehhi
This research applies machine learning to predict soil coherence for Etihad Rail, marking the first comprehensive study in the United Arab Emirates (UAE)'s arid regions. By integrating Sentinel-1 SAR and Sentinel-2 data with MODIS Aerosol Optical Depth (AOD) observations, the study develops detailed models that depict complex soil coherence patterns crucial for urban planning and risk assessment. Findings show variations in soil coherence between operational and under-construction phases, influenced by seasonal changes in aerosol dynamics and sand dust levels. Higher soil coherence is linked with lower annual sand dust deposition and AOD measurements, emphasizing the importance of this data for informed decision-making. The study employs a unique combination of data sources and machine learning algorithms to predict soil coherence, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBOOST), Gaussian Process Regression (GPR), Random Forest (RF), and 1D Convolutional Neural Network (CNN), with the Random Forest model achieving the lowest root mean squared error (RMSE) of 0.0826. These contributions enhance our understanding and provide a valuable framework for infrastructure development in similar environments.
这项研究将机器学习应用于预测阿提哈德铁路的土壤连贯性,这是在阿拉伯联合酋长国(UAE)干旱地区进行的首次全面研究。通过将 Sentinel-1 SAR 和 Sentinel-2 数据与 MODIS 气溶胶光学深度 (AOD) 观测数据相结合,该研究建立了详细的模型,描述了对城市规划和风险评估至关重要的复杂土壤连贯性模式。研究结果表明,受气溶胶动态和沙尘水平季节性变化的影响,运行阶段和施工阶段的土壤连贯性存在差异。较高的土壤相干性与较低的沙尘年沉积量和 AOD 测量值相关,强调了这些数据对知情决策的重要性。该研究采用了独特的数据源和机器学习算法组合来预测土壤连贯性,包括支持向量机(SVM)、极梯度提升(XGBOOST)、高斯过程回归(GPR)、随机森林(RF)和一维卷积神经网络(CNN),其中随机森林模型的均方根误差(RMSE)最低,为 0.0826。这些贡献加深了我们的理解,并为类似环境下的基础设施开发提供了宝贵的框架。
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引用次数: 0
Recent trends in moisture conditions across European peatlands 欧洲泥炭地湿度条件的最新趋势
IF 3.8 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-10-30 DOI: 10.1016/j.rsase.2024.101385
Laura Giese , Maiken Baumberger , Marvin Ludwig , Henning Schneidereit , Emilio Sánchez , Bjorn J.M. Robroek , Mariusz Lamentowicz , Jan R.K. Lehmann , Norbert Hölzel , Klaus-Holger Knorr , Hanna Meyer
Peatlands play a key role in climate change mitigation strategies and provide multiple ecosystem services, presuming near natural, waterlogged conditions. However, there is a lack of knowledge on how spatially heterogeneous changes in climate across Europe, such as the predicted increase in drought frequency in Central Europe, might affect these ecosystem services and peatland functioning. While analysis of peat cores and moisture sensors provide high-quality insights into past or present hydrological conditions, this information is usually only available for a limited number of locations. Satellite remote sensing is an effective method to overcome this limitation, providing spatially continuous and temporally highly resolved environmental information.
This study proposes to use freely available data from the Landsat Mission to analyze trends in proxies of surface moisture of European peatlands over the last four decades. Based on a large random sample of peatland sites across Europe, we performed a pixel-wise trend analysis on monthly time-series dating back to 1984 using the Normalized Difference Water Index as a moisture indicator.
The satellite-derived moisture changes indicated a pronounced shift towards wetter conditions in the boreal and oceanic region of Europe, whereas in the temperate, continental region, a high proportion of peatlands experienced drying. Small-scale patterns of selected sites revealed a high spatial heterogeneity, the complexity of hydro-ecological interactions, and locally important environmental and anthropogenic drivers affecting the moisture signal. Overall, our results support the expected effects of current climate trends of increasing precipitation in boreal northern and oceanic north-western Europe and increasing frequency of drought in continental Europe.
Our fully reproducible approach provided new insights on continental and local scales, relevant not only to a better understanding of moisture trends in general, but also to practitioners and stakeholders in ecosystem management. It may thus contribute to developing a cost-effective long-term monitoring strategy for European peatlands.
泥炭地在气候变化减缓战略中发挥着关键作用,并提供多种生态系统服务,前提是泥炭地接近自然的水涝条件。然而,对于欧洲各地气候在空间上的异质性变化(如预测中欧地区干旱频率会增加)可能会如何影响这些生态系统服务和泥炭地功能,人们还缺乏了解。虽然泥炭岩芯和湿度传感器分析可提供有关过去或现在水文条件的高质量见解,但这些信息通常只能在有限的几个地点获得。卫星遥感是克服这一局限性的有效方法,可提供空间上连续、时间上高度分辨的环境信息。本研究拟利用可免费获取的大地遥感卫星任务数据,分析过去四十年欧洲泥炭地表面湿度代用指标的变化趋势。基于欧洲泥炭地的大量随机样本,我们使用归一化差异水指数作为湿度指标,对可追溯到1984年的月度时间序列进行了像素趋势分析。所选地点的小尺度模式显示了高度的空间异质性、水文-生态相互作用的复杂性以及影响湿度信号的局部重要环境和人为因素。总体而言,我们的研究结果支持当前气候趋势的预期影响,即欧洲北部北方和西北部海洋性气候降水量增加,欧洲大陆干旱发生频率增加。因此,它可能有助于为欧洲泥炭地制定一项具有成本效益的长期监测战略。
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引用次数: 0
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Remote Sensing Applications-Society and Environment
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