制作一个盛放内容的盒子,在BlogRoot/themes/butterfly/layout/includes/head.pug最后一行加入如下代码:
为了应对当前HTML格式和js脚本对老人机的不友好,我们需要处理一下某些小说网站,让它回到十几年前的样子,剔除大量CSS和JS,精简HTML标签,这样就能够让老人机快速、友好地看小说了。
摘要:本篇文章主要介绍下载遥感卫星影像数据常用的几种的获取方法。适合刚接触遥感这个领域不久却需要下载和使用遥感影像的人群。
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本文介绍基于Python中的gdal模块,对大量长时间序列的栅格遥感影像文件,绘制其每一个波段中、若干随机指定的像元的时间序列曲线图的方法。
目前WiFi已经推出了6G频段,Android源码中也增加了相关的功能,这里总结一下。
The MCD43A1 V6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is a 500 meter daily 16-day product. The Julian date represents the 9th day of the 16-day retrieval period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The MCD43A1 algorithm, as is with all combined products, chooses the best representative pixel from a pool that includes all the acquisitions from both the Terra and Aqua sensors from the retrieval period.
The MCD43A3 V6 Albedo Model dataset is a daily 16-day product. It provides both directional hemispherical reflectance (black sky albedo) and bihemispherical reflectance (white sky albedo) for each of the MODIS surface reflectance bands (band 1 through band 7) as well as 3 broad spectrum bands (visible, near infrared, and shortwave). Each 500m/pixel daily image is generated using 16 days of data, centered on the given day. A quality band is also provided for each of the 10 albedo bands.
Landsat 7 Collection 1 Tier 1 and Real-Time data DN values, representing scaled, calibrated at-sensor radiance.
Landsat 8陆地资源卫星于2013年2月11日成功发射,日前发表过一篇博文关于利用Landsatlook Viewer进行Landsat 8影像数据的下载(http://blog.csdn.net/arcgis_all/article/details/9136493),今天咱来谈谈卫星的各种波段组合情况,为具体应用奠定基础。我们都知道Landsat8同以前的Landsat系列卫星相比,在保留已有波段的基础上,又新增了一些波段,比如沿海蓝波段(coastal blue band),可以进行水体普及率/气溶胶检测;卷云波段(cirrus cloud band)用于云去除及其它应用。下面是Landsat 8应用过程中常见的波段组合形式,影像将显示为RGB形式:
Landsat 7 Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
该文介绍了如何基于客户分段进行精细运营,包括客户分段的定义和意义、基于客户分段设计运营指标和运营模型、运营指标和运营模型的值域和意义以及基于客户分段的运营流程和运营指标体系。同时,文章还介绍了如何结合客户分段运营指标体系进行运营设计,并通过实例进行了说明。
Landsat 3 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat 5 TM Collection 1 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.
Landsat 8 Collection 1 Tier 1 and Real-Time data DN values, representing scaled, calibrated at-sensor radiance.
Hyperion is a high resolution hyperspectral imager producing 220 unique spectral channels ranging from 0.357 to 2.576 micrometers with a 10-nm bandwidth. The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for all bands and a standard scene width of 7.7 kilometers.
Landsat 4 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat 5 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
The MCD43A2 V6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Quality dataset is a 500 meter daily 16-day product. It contains all the quality information for the corresponding 16-day MCD43A3 Albedo and the MCD43A4 Nadir-BRDF (NBAR) products.
本文作为码农学ODL系列的实战篇,首先介绍OpenFlow相关理论及其在ODL中的具体实现,接着讲述了基于ODL的应用程序总体开发流程,进而实现了流量限速的基础功能,最后验证了流量限速的基础功能。 一、理论基础 SDN中的流量限速可以基于OpenFlow协议来实现,其原理大致为:OpenFlow通过用户定义的流表(Flow Table)来匹配和处理报文,计量表项被流表项所引用,并为引用该计量表项的流表项提供报文限速的功能。由于大家对OpenFlow流表相对比较熟悉,而对计量表相对陌生。下面首先对计量表的基础
The mosaics are created by using the MODIS 8-Day 500 meter BRDF-Albedo Quality product. Data Quality flags are used to select the best observations from the MODIS 8-day 500 meter Nadir BRDF-Adjusted Reflectance imagery product. This specific mosaic picks observations from the three highest quality categories over a 1-year period of MODIS data.
Landsat 7 Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation.
The MCD43A4 V6 Nadir Bidirectional Reflectance Distribution Function Adjusted Reflectance (NBAR) product provides 500 meter reflectance data of the MODIS "land" bands 1-7. These are adjusted using a bidirectional reflectance distribution function to model the values as if they were collected from a nadir view. The data are produced daily based on a 16-day retrieval period, with the image's date occurring on the 9th day. This product combines data from both the Terra and Aqua spacecrafts, choosing the best representative pixel from the 16-day period.
The MCD43C3 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Albedo dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. MCD43C3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) at local solar noon. Black-sky albedo and white-sky albedo values are available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 20 albedo layers are ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty. See dataset user guide for more information.
有时候,我们需要执行数据分箱操作,而pandas提供了一个方便的方法cut可以实现。
NV(NonVoliatile) 参数就是保存在终端上的非易失参数,可以通过 view 中的 NV Browser 来进行查看和修改。这些信息由厂家固化在终端内部,一般不允许用户修改。同时,可以通过NV Browser对终端进行 Offline(掉电重启)操作。
Landsat 1 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
Landsat 2 MSS Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
这需要两次遍历表:一次用于分母,一次用于百分比。对于针对大型表的 BI 查询(即:对于大多数 BI 查询),更多的表传递会显著降低性能。
Landsat 4 TM Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
2021年之前,手机支持700M的带宽普遍是20MHz,但基站侧已开始引入30MHz和40MHz信道带宽。对于新增加的基站信道带宽,尤其是40MHz,需要考虑一些射频要求。
美国NASA的陆地卫星(Landsat)计划从1972年7月23日以来,已发射8颗(第6颗发射失败)。目前Landsat1-4均相继失效,Landsat-5于2013年6月退役。Landsat-7于1999年4月15日发射 升空。Landsat-8于2013年2月11日发射升空,经过100天测试运行后开始获取影像。
Landsat 5 TM Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation.
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最近遇到一个新需求,成品如下,就是WiFi扫描界面增加一个选项,可以只扫描2.4G或者5G的AP。
Landsat 8 Collection 1 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.
下面的例子打开一副GeoTIFF影像,输出了影像的一些信息,然后遍历了所有波段,输出波段的一些信息
本文介绍Landsat 7遥感影像数据中B61、B62两个热红外波段的区别,以及研究应用时二者选择的依据。
Landsat 4 TM Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation.
第一个 "和 "最后一个 "波段是大地遥感卫星光谱波段(红、近红外、SWIR1 和 SWIR2)的第一个和最后一个可用年份的参考多光谱图像。参考复合图像代表了这些波段中每个波段的生长季节质量评估观测数据集的中值观测数据。
一直在学习新的可视化工具,因为这有助于找到适合手头任务的正确工具。在数据可视化方面,d3通常是首选,最近一直在用Vega。
#%% #载入数据 、查看相关信息 import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder print('第一步:加载、查看数据') file_path = r'D:\train\201905data\liwang.csv' band_data = pd.read_csv(file_path,encoding='UTF-8') band_data.info() band_data.
截至2017年5月1日,美国地质调查局不再生产预收集的Landsat,因此这个收集已经完成。请切换到基于集合1的数据集。更多信息请参见该文档页面。
Landsat 8 Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation.
FREF = FREF-Offs +ΔFGlobal (NREF –NREF-Offs)
注意读取数据的数组下标不要越界!GDAL并不会自动帮你处理下标越界的问题,它只会报错。因此特别当你想用部分读取的方式处理一个很大的文件时,对边界的处理需要你特别的注意,必须正好读完不能越界也不能少读。
版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。
This dataset is the atmospherically corrected surface reflectance from the Landsat 8 OLI/TIRS sensors. These images contain 5 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature
根据植被的光谱特性,将卫星可见光和近红外波段进行组合,形成了各种植被指数。植被指数是对地表植被状况的简单、有效和经验的度量,目前已经定义了40多种植被指数,广泛地应用在全球与区域土地覆盖、植被分类和环境变化,第一性生产力分析,作物和牧草估产、干旱监测等方面;并已经作为全球气候模式的一部分被集成到交互式生物圈模式和生产效率模式中;且被广泛地用于诸如饥荒早期警告系统等方面的陆地应用;植被指数还可以转换成叶冠生物物理学参数。
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