基于中分辨率遥感数据的秸秆露天焚烧碳排放估算——以呼和浩特市为例

秸秆露天焚烧是农业生产的主要碳排放活动。为全面掌握秸秆露天焚烧的时空分布,准确获取秸秆露天焚烧的碳排放量,以2014-2020年Satellite See Fire数据为数据源,提取呼和浩特市的秸秆火点,分析其数量的年际、月际和区域变化规律;基于2020年Sentinel-2遥感影像,使用归一化燃烧指数(NBR)、燃烧面积指数(BAI)以及改进的燃烧指数(NBRSWIR11、BAI_(Red Edge5)、BAI_(Red Edge6)、BAI_(Red Edge7))提取研究区主要月份的火烧迹地,通过筛选燃烧指数、确定估算阈值,进而得到秸秆露天焚烧的碳排放量。结果表明:2014-2020年呼和浩特市秸秆火点主要集中在土默特左旗、托克托县、和林格尔县以及市区南部,火点数量整体呈下降趋势;春季(3、4月)和秋季(10、11月)为秸秆露天焚烧的主要月份,其中3、10、11月提取火烧迹地的最佳燃烧指数和阈值分别为BAI和78、120与110,4月提取火烧迹地的最佳燃烧指数和阈值分别为BAIRed Edg e5和78;2020年秸秆露天焚烧面积为16万hm^(2),秸秆露天焚烧总量为114.8万t,CH4、CO、CO_(2)和碳排放总量分别为0.25万t、11.73万t、159.62万t和48.75万t。

中分卫星遥感技术在森林资源动态监测中的应用

以青海省8个县为试点区,利用Sentinel-2A和Landsat-8中分辨率遥感数据,结合2018—2020年试点单位的森林督查成果及森林资源管理“一张图”年度更新数据,协同运用LSMA光谱混合分析模型、Li-Strahler几何光学模型,对试点县2020年森林资源变化情况进行动态监测和实证研究。结果显示:在15个月的时间跨度中,共监测疑似林地变化图斑693个,总面积1286.3827 hm2,主要发生在公益林地范围内,占疑似变化图斑总面积97.48%,总体准确率达到87.30%,满足森林资源动态监管要求;10m分辨率的中分卫星影像能检测到的最小图斑面积达到0.1 hm2,但主要集中在郁闭度较大的林区;在计算机自动检测的基础上,加入人工识别,能进一步提高变化检测的准确率。

CHEOS Major Special Project Promotes Development of Chinese Commercial Remote Sensing Cameras

High resolution remote sensing data has been applied in many fields such as national security, economic construction and in the daily life of the general public around the world, creating a huge market. Commercial remote sensing cameras have been developed vigorously throughout the world over the last few decades, resulting in resolutions down to 0.31 m. In 2010, the Chinese government approved the implementation of the China High-resolution Earth Observation System(CHEOS) Major Special Project, giving priority to development of high resolution remote sensing satellites. More than half of CHEOS has been constructed to date and 5 satellites operate in orbit. These cameras have different characteristics. A number of innovative technologies have been adopted, which have led to camera performance increasing in leaps and bounds. The products and the production capability enables the remote sensing technical level to increase making it on a par with Europe and the US.

Using Support Vector Regression to Analyze Urban Atmosphere Pollution with Optical Remote Sensing Data

The concentration of absorbable particulate matter less than 10 μm termed as PM10 is the most important urban air pollution index for air quality monitoring. This paper presents a space based PM10 monitoring algorithm based on QUAC （QUick atmosphere correction） for optical remote sensing data and SVR （support vector regression）. PM 10 concentration measurements from nine ground based stations in Hangzhou, China and the MODIS （moderate-resolution imaging spectroradiometer） images were analyzed. Experimental result indicates that the correlation between CD （correction differences） with actual measured data is better than correlation between AOD （aerosol optical depth） with measured data. In addition, the fitting performance of the SVR model established with CD and measured data is better than traditional regression models.

Retrieval algorithm for microwave surface emissivities based on multi-source, remote-sensing data: An assessment on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau plays a very important role in studying severe weather in China and around the globe because of its unique characteristics. Moreover, the surface emissivities of the Qinghai-Tibet Plateau are also important for retrieving surface and atmospheric parameters. In the current study, a retrieval algorithm was developed to retrieve the surface emissivities of the Qinghai-Tibet Plateau. The developed algorithm was derived from the radiative transfer model and was first validated using simulated data from a one-dimensional microwave simulator. The simulated results show good precision. Then, the surface emissivities of the Qinghai-Tibet Plateau were retrieved using brightness temperatures from the advanced microwave-scanning radiometer and atmospheric profile data from the moderate resolution imaging spectroradiometer. Finally, the features of the time and space distribution of the retrieved results were analyzed. In terms of spatial characteristics, a spatial distribution con- sistency was found between the retrieved results and surface coverage types of the Qinghai-Tibet Plateau. In terms of time characteristics, the changes in emissivity, which were within 0.01 for every day, were not evident within a one-month time scale. In addition, surface emissivities are sensitive to rainfall. The reasonability of the retrieved results indicates that the algorithm is feasible. A time-series surface emissivity database on the Qinghai-Tibet Plateau can be built using the developed algorithm, and then other surface or atmospheric parameters would have high retrieval precision to support related geological re- search on the Qinghai-Tibet Plateau.