Sentinel时序影像的长江流域地表水体提取

传统水体提取算法大多基于某一时期单景遥感影像,无法表现出水体随着时间和空间高度可变的特性,虽然国内外已出现部分时序水体数据产品,但其空间分辨率及水体边界的精度仍无法满足一些研究和应用的需要.本文以地表环境复杂的长江流域为研究区,基于GEE (Google Earth Engine)云平台,使用Sentinel-2 MS...     

Sentinel-2密集时间序列数据和Google Earth Engine的潮间带湿地快速自动分类

潮间带湿地是滨海湿地的重要组成部分,具有维持生物多样性、促进碳汇等重要生态功能.及时、准确地掌握潮间带湿地现状是实现潮间带湿地可持续管理目标的基础.先前的潮间带湿地分类研究依赖于训练样本、人工设定阈值或后处理等,本研究基于GEE (Google Earth Engine)平台开发一种自动、快速、高精度的潮间带湿地分类方...     

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

ABSTRACT

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop a high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, 10 time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the three time-periods over 12 months (monsoon: Days of the Year (DOY) 151–300; winter: DOY 301–365 plus 1–60; and summer: DOY 61–150), taking the every 8-day data from Landsat-8 and 7 for the years 2013–2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the five agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N = 2179) in five AEZs. The classification was performed on GEE for each of the five AEZs using well-established knowledge-base and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N = 1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full-resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/.     

时序Sentinel-1A数据支持的长江中下游汛情动态监测

合成孔径雷达（SAR）因其对地观测全天候、全天时优势,成为多云多雨天气限制下洪水动态监测中不可或缺的数据来源之一。由于GEE（Google Earth Engine）云计算平台的兴起和短重访Sentinel-1数据的可获取性,洪水监测与灾害评估目前正面向动态化、广域化快速发展。顾及洪水淹没区土地覆盖变化的复杂性和发生时间的不确定性,基于时序Sentinel-1A卫星数据提出了针对大尺度范围、连续长期的汛情自动检测及动态监测方法。该方法首先,利用图像二值化分割时序SAR数据实现水体时空分布粗制图,逐像素计算时间序列中被识别为水体候选点的频率。然后,利用Sentinel-2光学影像对精度较粗的初期SAR水体提取结果进行校正,得到精细的水体分布图。最后,针对不同频率区间的淹没特点,采用差异化的时序异常检测策略识别淹没范围：对低频覆水区利用欧氏距离检测时序断点,以提取扰动强度大、淹没时间短的洪涝灾害区;对高频覆水区利用标准分数（Z-Score）检测时序断点,以提取季节性水体覆盖区。在GEE平台上利用该方法,实现了2020-05—10长江中下游地区全域洪水淹没范围时空信息的自动、快速、有效监测,揭示了不同区域汛情发展模式的差异性。本文提出的洪水快速监测方法对大尺度下的汛情动态监测、灾害定量评估和快速预警响应具有重要的现实意义。     

基于Google Earth影像的黄河上游干流地区滑坡特征分析

黄河上游干流地区由于特殊的地形地貌和地质构造使得滑坡灾害频发,对其开展滑坡灾害监测、分析研究,具有十分重要的意义。本文利用2015年间Google Earth遥感数据,提取并分析了该地区的滑坡灾害分布信息,取得了如下成果及认识:1)研究区的空间展布形态主要有7种,滑体性质类型有6种,岩质滑坡数量最多。2)从空间分布特征看,共发现研究区有各类滑坡162处,滑坡主要集中分布在群科-尖扎盆地;从滑坡类型看,研究区滑坡主要为大型滑坡和巨型滑坡。3)滑坡体长、宽主要集中在0~1 500 m和500~1 500 m之间,且长、宽呈两极化方向延伸,滑坡体面积分布不均,滑坡数量随着方量的增大呈现减少的趋势,发生的滑坡主要是滑坡体厚度在25~50 m的深层滑坡。4)滑坡数量在0°~90°之间有峰值出现,然后向两端逐渐减少。     

Land change attribution based on Landsat time series and integration of ancillary disturbance data in the Athabasca oil sands region of Canada

The Alberta Oil Sands (AOS) is a unique area in Canada undergoing significant disturbance and recovery due to a variety of anthropogenic and natural factors. Accurately quantifying these changes in space and time is important for assessing ecosystem status and trends. In this research, we implemented an approach to combine Landsat time series for the period 1984–2012 with ancillary change datasets to derive detailed change attribution in the AOS. Detected changes were attributed to causes including fire, forest harvest, surface mining, insect damage, flooding, regeneration, and several generic change classes (abrupt/gradual, with/without regeneration) with accuracies ranging from 74% to 100% for classes that occurred frequently. Lower accuracies were found for the generic gradual change classes which accounted for less than 3% of the affected area. Timing of abrupt change events were generally well captured to within ±1 year. For gradual changes timing was less accurate and variable by change type. A land-cover time series was also created to provide information on “from-to” change. A basic accuracy assessment of the land cover showed it to be of moderate accuracy, approximately 69%. Results show that fire was the major cause of change in the region. As expected, surface mine development and related activities have increased since 2000. Insect damage has become a more significant agent of change in the region. Further investigation is required to determine if insect damage is greater than past historical events and to determine if industrial development is linked to the increasing trend observed.