植被指数对旱灾的响应研究—以中国西南地区2009年—2010年特大干旱为例

基于中国西南地区5个省(市)2001年—2010年期间由中分辨率成像光谱仪MODIS影像资料反演得到的归一化植被指数NDVI产品数据和区内气象站点的连续观测资料,提取了研究区内各气象站点印迹区的NDVI值,计算了降水距平百分率Pa和D指数(降水量与潜在蒸散量之差)这两种气象干旱指数。依据全国植被类型图(2000年版),对研究区内的主要植被类型在季节时间尺度上开展了这两种气象干旱指数与距平NDVI的相关性分析。研究结果表明:距平NDVI对D指数的最大响应滞后约一个月,在此尺度上表现出明显的线性相关性,所选取的6个季度的相关系数均接近或大于0.7,显著性水平小于0.01;对干旱敏感的植被类型如旱地和草地等,表现出更显著的相关性,其相关系数分别达到了0.83和0.71(平均);在干旱季节,D指数与距平NDVI表现出较为一致的空间分异规律,而Pa指数仅在旱情比较严重的情况下或对干旱比较敏感的植被类型区与距平NDVI表现出一致性分布。     

Monitoring vegetation dynamics in East Rennell Island World Heritage Site using multi-sensor and multi-temporal remote sensing data

ABSTRACT

East Rennell of Solomon Island is the first natural site under customary law to be inscribed on UNESCO’s World Heritage List. Potential threats due to logging, mining and agriculture led to the site being declared a World Heritage in Danger in 2013. For East Rennell World Heritage Site (ERWHS) to ‘shed’ its ‘Danger’ status the management must monitor forest cover both within and outside of ERWHS. We used satellite data from multiple sources to track forest cover changes for the entire East Rennell island since 1998. 95% of the island is still covered by undisturbed forests; annual average normalized difference vegetation index (NDVI) for the whole island was above 0.91 in 2015. However, vegetation cover in the island has been slowly decreasing, at a rate of –0.0011 NDVI per year between 2000 and 2015. This decrease less pronounced inside ERWHS compared to areas outside. While potential threats due to forest clearing outside ERWHS remain the forest cover change from 2000 to 2015 has been below 15%. We suggest ways in which the Government of Solomon Islands could use our data as well as unmanned air vehicles and field surveys to monitor forest cover change and ensure the future conservation of ERWHS.     

结合深度学习的高光谱与多源遥感数据融合分类

高光谱数据具有丰富的光谱特征,但是其空间分辨率相对较低.一些遥感数据具有与高光谱数据互补的优势,例如提供更精细的空间信息的高空间分辨率数据和具有高度信息的激光雷达LiDAR(Light Detection and Ranging)数据.通过将高光谱数据与多源遥感数据进行融合,可以弥补高光谱数据空间分辨率相对较低,空间特...     

基于RS和GIS的农作物估产方法研究进展

实时获取农作物长势及产量等信息对于现代农业的发展具有重要意义。近年来,随着遥感技术(remote sensing,RS)和地理信息系统(geographic information system,GIS)广泛应用于农作物估产领域,相继出现了一些较为实用的估产方法,主要有结合辅助数据的估产方法、基于植被指数的估产方法、基于特定模型的估产方法和基于农作物估产平台(软件)的开发等。其中,基于植被指数的估产方法又分为单一和多植被指数估产2类方法。在对近年来该领域大量文献深入研究的基础上,着重就几类热点方法展开论述,并对每类方法的优势和缺陷进行了评述,最后对该领域需要进一步研究的方向进行了探讨和展望,以期为后续研究提供参考。