AUTOCAD技术在绘制矿图中的应用

利用 CAD 技术 强 大的 计算 功 能及 图形 处 理能 力,绘制 采 掘矿 图。 如 建立 通用 座标 系 ,绘 制 方 格 网;设 计 绘制新 矿图 ,修 改 已有 矿图 ;合 理利 用 图层 、块 的功 能 进行 复杂 矿 图的 制作 。 使用 AUTOLISP 语言 编 制 一 些 适合 绘 制 矿 图通 用符 号 ,提 高绘 图的 精 度和 速度 。 随着 CAD 绘 图技 术的 发 展,其在 煤矿 生 产中 将会 发挥 更 大的 作用 。     

Possible stellar asterisms carved on a protohistoric stone

Chisel marks on a stone uncovered in Rupinpiccolo protohistoric hill fort from north-eastern Italy were suggested to be a representation of the night sky (Bernardini et al. 2022 Documenta Praehistorica XLIX). The patterns of the 29 marks are analyzed here to establish if they reproduce popular stellar asterisms. Nine marks are found to match the Tail of Scorpius and five the Orion's Belt, together with Rigel and Betelgeuse. Nine marks are found in the approximate position of the Pleiades showing some match with the cluster members. On the back side, 5 marks possibly reproduce Cassiopeia. One mark slightly North of Orion cannot be identified. The 28 marks show a Pearson correlation coefficient $$(28) with stellar positions higher than 0.99 with a probability $$ of a wrong correlation lower than 0.001. Departures are about one degree, or about 7 mm, as the mean diameter of the marks, which suggests a manufacturing limitation in the charting. The fort dates $$1800–400 BCE when Scorpius and Orion showed about the same orientation at the heliacal rising. The unidentified mark challenges the whole picture. We suggest it could have been the progenitor of a failed supernova, thus offering also the possibility of a verification.     

Remote sensing classification approach to large-scale crop cultivation identification: A case study of the Aral Sea Basin

Since the collapse of the Soviet Union, the crop cultivation structure in the Aral Sea Basin has changed dramatically, and these changes are worth studying. However, historical crop remote sensing mapping at the watershed scale remains challenging, especially crop misclassification at the cropland edge due to mixed pixels. Therefore, we proposed a field segmentation approach to constrain field edges based on time-series Sentinel-2 remote sensing images and the Google Earth Engine platform and then employed the random forest algorithm to perform crop classification based on time series Landsat/Sentinel-2 images and crop phenology information to produce historical crop maps in the Aral Sea Basin from the 1990s onward. The results showed that the intersection over union between the extracted field edges and in situ-measured field size data was 0.65. The overall accuracy of crop mapping was 95.2% in 2019. Then, we extended our method to historical mapping over the 1991–2015 period with accuracies ranging from 82.8% to 91.3%. Moreover, our method applied to historical mapping works well in terms of accuracy and policy matching. These findings indicate that our method can accurately distinguish cropland edges to reduce classification errors due to mixed pixels. This method is promising for solving the cropland edge problem for historical crop mapping in the Aral Sea Basin and can potentially provide a reference for historical crop classification in other watersheds of the world.     

长时序Landsat的北极Lena河DOC浓度变化及驱动力分析

北极Lena河是世界第十长河,也是北极地区第二大河,其年径流量约占北冰洋淡水总量的20％,同时将陆地生态系统上的大量有机物携带进海洋,并且在全球碳循环中起到非常重要的作用.卫星遥感数据是监测河流有机碳循环的重要数据源,特别是在无冰期的高纬度地区.本研究的目的是:(1)建立一种高精度反演算法估算北极Lena河的溶解有机碳...     

Google Earth Engine云平台对遥感发展的改变

近年来,随着遥感技术的快速发展,积累了海量的对地观测遥感数据.传统桌面端遥感处理平台(例如ERDAS和ENVI等)无法满足当前遥感大数据的应用需求.作为领先的遥感云计算平台,GEE (Google Earth Engine)的出现改变了传统遥感数据处理和分析模式,为海量数据快速处理与信息挖掘带来了新的契机.截止目前,科...