耕地复种指数遥感监测研究进展

复种指数是进行粮食估产、耕地集约利用评价、农业生态系统模拟等的关键参数,及时、准确地提取复种指数对于粮食安全、土地管理和生态环境安全具有重要意义。在传统的研究中,复种指数主要来源于地面统计数据。使用统计数据来计算复种指数虽然过程简单,但是计算结果存在信息滞后、无法体现统计单元内部的空间异质性、精度低等不足。遥感技术因具有大范围、高时效、低成本等优点而被用于耕地复种指数监测,已有学者对耕地复种指数的遥感监测开展了大量工作。本文以复种指数遥感提取的关键环节为主线,对1997—2020年国内外相关研究进行综述：首先,梳理了已有研究中的监测方法、高质量时间序列遥感数据获取方法及提取结果精度验证方法,并对不同方法的优缺点进行了总结;其次,对已有研究中存在的不足进行了探讨,并提出未来研究的侧重点：① 开展已有监测方法的对比和分析;② 加强地形复杂地区、小农尺度的监测力度;③ 提高遥感数据时空分辨率及处理效率;④ 对提取结果进行多尺度验证。     

农作物遥感估产的方法综述

概述了遥感在农作物长势预测、种植面积测算和产量估算方面的原理、技术路线、研究进展,并分析了遥感估产在现实利用上存在的问题。     

遥感图像信息容量与植被指数的耦合关系研究

遥感信息容量能表征地表复杂度,为深入探究信息容量与植被之间关系,研究对植被盖度、NDVI、RVI等5种植被指数的均值、均方差与信息容量做了多种回归分析。结果表明:植被覆盖度均值、植被指数均方差与信息容量有明显相关性,其中植被盖度均方差与信息容量相关性最大,确定系数R~2为0.851 8;相关系数最大R~2为0.8642,说明遥感信息容量与植被指数、植被盖度之间有明显的相关性,证明了信息容量能有效地表征地表覆盖的变化。     

Response to climate change of different tree species and NDVI variation since 1923 in the middle arid region of Ningxia, China

The normalized difference vegetation index （NDVI） is used extensively to describe vegetation cover and ecological environ- ment change. The purpose of this study was to contrast the response of different tree species growing in the same habitat to climate change and retrieve past NDVI using tree-ring width data from tree cores collected from the transitional zone of Pinus tabulaeformis and Picea crassifolia in the Luoshan Mountains in the middle arid region of Ningxia. Correlation analysis indi- cated that radial growth ofP tabulaeJbrmis is more sensitive to precipitation and temperature change than that ofP crassifolia. Natural factors such as water availability and heat at this elevation are more suited to the growth ofP crassifolia, and are more advantageous to its renewal and succession. P. crassifolia is probably the better of the two species for protecting the forest ecosystem and conserving water in the Luoshan desertification area. Ring width of P. crassifolia correlates significantly with average NDVI for April-May （r =0.641, p 〈0.01）, and both of them are influenced positively by precipitation in April-May. The reconstructed NDVI for 1923-2007 shows the relatively low vegetation cover occurred in the 1920s-1930s, the 1960s-1970s, and the early 21 st century. The reconstructed NDVI better reflected the drought climate in the study area.     

Characterizing landscape spatial heterogeneity in multisensor images with variogram models

Most evaluation of the consistency of multisensor images have focused on Normalized Difference Vegetation Index （NDVI） products for natural landscapes, often neglecting less vegetated urban landscapes. This gap has been filled through quantifying and evaluating spatial heterogeneity of urban and natural landscapes from QuickBird, Satellite pour l＇observation de la Terre （SPOT）, Ad- vanced Spacebome Thermal Emission and Reflection Radiometer （ASTER） and Landsat Thematic Mapper （TM） images with variogram analysis. Instead of a logarithmic relationship with pixel size observed in the corresponding aggregated images, the spatial variability decayed and the spatial structures decomposed more slowly and complexly with spatial resolution for real multisensor im- ages. As the spatial resolution increased, the proportion of spatial variability of the smaller spatial structure decreased quickly and only a larger spatial structure was observed at very coarse scales. Compared with visible band, greater spatial variability was observed in near infrared band for both densely and less densely vegetated landscapes. The influence of image size on spatial heterogeneity was highly dependent on whether the empirical sernivariogram reached its sill within the original image size. When the empirical semivariogram did not reach its sill at the original observation scale, spatial variability and mean characteristic length scale would increase with image size; otherwise they might decrease. This study could provide new insights into the knowledge of spatial heterogeneity in real multisen- sor images with consideration of their nominal spatial resolution, image size and spectral bands.     

大伙房库区近30年来植被覆盖变化分析

根据像元二分模型原理,利用1983年、2000年、2011年大伙房库区遥感影像数据提取植被覆盖遥感信息,结合植被指数计算植被覆盖度,分析比较大伙房库区不同时期植被覆盖的变化,研究大伙房库区这三个时期植被覆盖动态变化规律,为大伙房库区的生态建设和可持续发展提供决策支持。结果显示:1近30年以来,大伙房库区植被覆盖等级分布不均衡,高植被覆盖区过分集中在北部地带,西部则多为低植被覆盖区;2大伙房库区高植被覆盖面积在减少、低植被覆盖面积在增加;3大伙房库区植被覆盖总面积在下降,库区的生态环境已经遭到了破坏;4政策建议:加强监测与监管力度,合理开发利用矿产资源,积极响应国家"退耕还林、退耕还草"号召。     

一种复合型校正植被信息的方法

遥感技术能快速获取大范围的植被信息,但却存在植被信息混分等问题。基于地物光谱差异特征,本文采用复合植被指数的形式提出了一种校正植被信息的方法。结果表明：在增强植被信息方面,该方法弥补了NDVI_Rad过饱和的缺点,使得高覆盖植被的信息值在0.7以上;在抑制非植被方面,该方法将植被和非植被进行了分离,使得大部分非植被的信息值在0以下;此外,在“抗地形”方面,该方法引入了“抗地形”因子（δ/Red）,一定程度上削减了地形对植被提取带来的影响。     

基于经验模型的Hyperion数据植被叶绿素含量反演

对于反演植被叶绿素含量而言,基于Hyperion等高光谱传感器数据、利用经验方法建模是一种快速准确的方法。利用多种植被的实测数据以及Hyperion模拟数据,分析植被反射率及其变化形式与叶绿素含量的相关性,并进一步针对红边参数、植被指数等分析植被反射率与叶绿素含量的关系,选取最准确的经验建模方法。经过对比,改进的简单比值指数(modified simple ratio,MSR)与叶绿素含量相关性最高,其回归模型能比较准确地反演出叶绿素含量。通过Hyperion图像、利用MSR指数与实测叶绿素含量得到回归模型,建立区域叶绿素含量分布图;并对张掖地区植被叶绿素含量进行了反演,反演结果具有较高精度,相对误差低于5%。     

采用6S模型的多时相MODIS植被指数NDVI归一化方法

基于6S辐射传输模型,对连续6个时相的MODIS影像进行逐像元大气校正获取地表反射率,以此实现MODIS NDVI的时相归一化。归一化后,地物NDVI的时相趋势与校正前相比差异显著,能更加准确地描述地物随时间变化的规律,在浓密植被区域表现更为明显。开展对NDVI时相归一化的研究,对全球变化、作物物候监测等遥感时序分析相关应用具有着重要的意义和实用价值。