搜索结果: 1-15 共查到“知识库 Sentinel-1”相关记录80条 . 查询时间(0.062 秒)
Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data
tree species classification Sentinel-2 multi-temporal Wienerwald biosphere reserve
2024/1/23
Detailed knowledge about tree species composition is of great importance for?forest?management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented...
Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery
Mangroves Aboveground biomass UAV-LiDAR Sentinel-2 Random forest
2024/1/23
The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former ...
Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data
Space-borne imagery Stereogrammetry Boreal forest structure Biomass mapping Open data
2024/1/22
Boreal forests constitute a large portion of the global forest area, yet they are undersampled through field surveys, and only a few remotely sensed data sources provide structural information wall-to...
Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine
multisensor tree species large areas cloud-computing machine learning
2023/12/6
Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tre...
The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape
reforestation ecosystem services carbon stock random forest
2023/12/5
The transformation of the natural landscape into an impervious surface due to urbanization has often been considered an important driver of environmental change, affecting essential urban ecological p...
Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data
urban green space above-ground biomass classification Sentinel-2 Sentinel-1
2023/12/1
High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban gre...
Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series
Synthetic Aperture Radar Sentinel-1 forest mapping deforestation monitoring deep learning convolutional neural networks
2023/12/1
The monitoring of land cover and land use patterns is pivotal in the joint effort to fight deforestation in the Amazon and study its relation to climate change effects with respect to anthropogenic ac...
RSE: 基于理论散射模型的区域尺度Sentinel-1A卫星观测模拟与半经验水云模型标定方法研究（图）
理论散射模型 区域尺度 Sentinel-1A卫星观测模拟 半经验水云模型标定方法
2023/1/16
土壤水分及其冻融变化在控制陆气间水热交换方面发挥着非常关键的作用，尤其是在青藏高原的寒区。合成孔径雷达（SAR）具有高空间分辨率以及对土壤水分高敏感性等特点，是当前获取区域尺度高分辨率土壤水分及冻融变化产品最重要的手段。如何在区域尺度上对微波散射模型进行有效标定，准确模拟卫星观测的后向散射系数，对于提升基于SAR的土壤水分及冻融变化反演精度具有重要意义。
多时相InSAR技术具有探测大范围毫米级地表形变的能力，已被广泛应用于地面沉降监测。近几年，应用多时相InSAR技术监测以高铁为代表的大尺度人造线状地物形变备受关注。本文将C波段SAR数据用于高铁沿线路基形变监测，应用相位稳定性分析和改进的StaMPS技术来增加相干性点的密度和形变参数解算的稳定性。采用研究区时间跨度为21个月的47景Sentinel-1A数据，对连（云港）盐（城）高铁及其沿线区域...
基于Sentinel-1A微波遥感数据的森林蓄积量估测
Sentinel-1A 森林蓄积量 微波遥感数据 纹理特征 随机森林法 偏最小二乘法 估测
2021/7/13
Sentinel-1A作为开源的集微波和光学数据一体的卫星数据源,给森林资源调查和监测提供了重要的数据源。为了探索Sentinel-1A数据在森林资源调查中的可用性,以Sentinel-1A为遥感数据源,结合地面样地森林蓄积量调查数据,采用随机森林法和偏最小二乘法两种模型对云南省普洱市思茅区的森林蓄积量进行预测以及遥感反演。通过对遥感影像进行一系列预处理,提取微波遥感数据VV和VH极化下的后向散射...
时序Sentinel-1TOPS模式SAR数据精配准
TOPS 配准 相干性 Sentinel-1 SAR 增强谱分集
2021/6/30
Sentinel-1卫星星座采用TOPS作为默认成像模式，要求方位向配准精度达到0.001像素以便确保相邻burst干涉图的相位偏差小于3°。ESD技术是消除几何配准系统误差、纠正方位向配准偏移量的有效方法，但在低相干性场景或快速去相关的条件下，该方法配准精度会随着相干的降低而衰减，并导致配准误差的传播，造成干涉图出现相位跳变。为解决这一问题，本文提出一种TOPS模式数据时间序列配准方法。该方法在...
Sentinel-1卫星TOPS模式影像通常在几何配准的基础上，再次利用Burst重叠区域进行增强谱分集处理以实现高精度配准。几何配准主要依赖卫星轨道参数，难以进一步提高其配准精度，因此Sentinel-1影像配准的关键则是通过增强谱分集准确估计出几何配准后的残余偏移量。然而，增强谱分集易受失相干噪声等因素的影响，低相干条件下难以满足0.001像素的配准精度要求。因此本文首先针对单基线条件下的增强...
基于Sentinel-1A数据的多种机器学习算法识别冰山的比较
冰山 机器学习 Sentinel-1A SAR
2020/6/16
冰山识别对于海洋环境监测和船只安全运行等具有重要的意义，是北极航道开通和北极开发过程中的重要内容。采用合成孔径雷达（SAR）影像进行冰山识别具有独特的优势，多种机器学习算法均可用于SAR影像的冰山识别中。
湖泊变化是气候变化的指示器.为探索利用单一短时间尺度的卫星水位数据源估算长时间序列的湖泊水量变化的可行性，本文利用短时间尺度（2016—2018年） Sentinel-3A合成孔径雷达高度计（SRAL）作为唯一卫星水位数据源，以藏北高原内陆湖泊当惹雍错为例，结合基于Landsat光学遥感数据提取的1988—2018年的湖泊面积，综合分析2016—2018年间的非结冰期遥感湖泊面积与遥感湖泊水位变化...
基于Sentinel-1A雷达影像的思茅松林蓄积量估测
森林蓄积量 Sentinel鄄1A 随机森林 纹理 特征选择 思茅松
2019/9/19
探讨C波段雷达影像估测森林蓄积量的应用潜力，建立思茅松林蓄积量遥感估测模型，为利用遥感技术快速、准确、大面积的估测森林蓄积量提供参考。以云南省普洱市思茅区思茅松林为研究对象，采用C波段双极化合成孔径雷达Sentinel-1A影像为数据源，提取影像不同极化方式下的后向散射系数。