生态恢复的卫星遥感监测——江西省兴国县为例(英文)

利用遥感手段研究了江西省兴国县造林及生态保护所带来的植被变化。使用数据为1985和2000年LandsatTM的2个时期图像以及土地利用的GIS数据。利用图像差值法计算近红外光(波段4)的辉度变化,以此分析植被动态。结果显示,兴国县植被在面积上变化不大,略有增加,但是植被状况有所改善,和当地多年造林的成效是一致的。研究结果还表明,利用卫星图像定量评价植被变化是一个有效而简洁的方法,包括面积、生产力、分布状况等

芒萁生物量分布特征

以花岗岩和紫色砂页岩典型丘陵区的芒萁(Dicranopteris dichotoma)为研究对象从生物学角度分析了其生物量分布的基本特征。结果表明:各样方间总生物量变化不大生境条件是影响芒萁地下与地上生物量分配的重要因素,自坡上到坡下,地上生物量升高地下生物量降低;芒萁叶生物量模型,以展幅的乘积(D1.D2)和株高(H1)为自变量建立的多元线性方程的相关性最好;芒萁根系集中分布在0~4cm的土层内,其中1cm附近最为集中,但砂土堆积导致其分布加深并成层分布;芒萁活根状茎长度与土壤深度呈负相关,与根系生物量呈线性正相关;芒萁是南方山区既经济又有效的水土保持植物,应大力加强保护和应用

冰冻雨雪灾害对江西林业影响的评估技术探讨

2008年初南方发生了持续冰冻雨雪灾害天气,造成了重大的经济损失。论文以江西林业在雪灾中的受损情况为研究实例,基于GIS、RS技术,在多数据源的支持下,阐释灾害评估的新技术研究方法与处理流程,快速地对江西林业受损情况做出相对精确的评估。根据MODIS提取出的雪灾前后的NDVI变化情况,同时利用多源土地利用数据进行融合处理获取江西植被分布数据,进一步结合研究区的DEM数据,利用GIS提供的叠合处理分析工具得出NDVI变化与树种林型、地形高程、坡度、坡向等各个因素的关系。文章最后根据上述分析结果,提出了灾后重建建议及对GIS、RS技术的结合应用提出展望

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies