基于时间序列统计特性的森林变化监测

森林动态变化分析对揭示生态系统环境变化及植被恢复和布局重建等具有重要意义,时间序列的遥感数据为森林监测提供了基础数据。本文根据森林植被的统计学特性,在暗目标法的基础上,利用归一化植被指数NDVI实现森林样本自动选择;并融合NDVI构建了新的综合森林特征指数(Integrated Forest Z-Score,IFZ);以时间序列的IFZ分析森林动态信息,实现森林变化动态监测。以三峡大坝及周边区域森林为研究区,利用2001年至2012年每年生长季节(5月—10月)的Landsat TM影像检验本文算法。基于2002年、2006年和2010年三期7月—9月的Quick Bird影像的精度分析结果发现:研究区森林变化检测的总体精度可达96.53%,Kappa系数为0.9512。在添加NDVI指数后构建的IFZ提高了总体监测精度。其中,毁林类别的检测精度提高显著,漏检率和误检率分别为2.74%和3.64%;干扰后重建的森林类别的检测精度有一定提高,其漏检率和误检率分别为10.79%和10.51%。研究结果表明,改进暗目标法能提高森林样本的选样效率,添加NDVI的IFZ能提高森林动态变化的识别度。此外,本算法不仅能定性识别森林变化,而且能定量提供森林干扰发生时间和干扰强度。     

NOAA-AVHRR数据在吉林省东部林火信息提取中的应用

概述了利用NOAA-AVHRR数据进行林火监测的原理和4种方法。针对吉林省东部的森林火灾,运用4种方法进行了火点信息提取与分析,最后对阈值法进行了改进,提取精度达到了89.2%。分析了NOAA-AVHRR应用于林火遥感监测的可行性和不足。     

基于Sentinel-1多时相InSAR影像的云南松切梢小蠹危害程度监测

森林病虫害是森林健康生长的重要威胁之一,开展其危害程度监测对森林保护具有重要意义。基于多时相Sentinel-1C波段雷达数据、云南松物候和地面高度2 m处的相对湿度资料,对SAR相干系数和后向散射系数的时变特征及与相对湿度的相关性进行了分析,提出一种利用合成孔径雷达干涉(interferometric synthetic apertrue Radar,InSAR)影像进行森林病虫害危害程度监测的方法;并以云南省祥云县为研究区,进行了云南松健康林与不同程度受害林的分类研究。结果表明:(1)后向散射系数和相干系数的时序变化均与云南松物候期相关;(2)相干系数与相对湿度的相关性很小,后向散射系数与相对湿度有一定的相关性,其中轻度受害林的相关性达到0. 78;(3)通过实测数据验证,用多时相相干系数进行分类,精度高于后向散射系数分类,其中降轨数据的精度最高,可达到83. 15%,表明多时相C波段SAR相干数据可有效识别健康林与不同程度的受害林;(4)该方法对多云雨地区的森林病虫害监测与分类有一定的优势,可以进一步提升遥感监测病虫害的能力。     

松毛虫灾害的TM影像监测技术

中国森林病虫害日趋严重 ,每年都造成巨大损失 ,其主要原因之一就是不能实现森林病虫害的及时准确监测与中长期预测预报 ,以便把灾害控制在萌芽状态 ,虽然目前科学技术和研究水平还不能准确预测森林病虫害的发生发展 ,但可以及时监测早期灾害点 ,尽力把损失降到最低限度。该文主要介绍了利用陆地卫星TM数据开展早期灾害点 (或虫源地 )监测的方法和利用航天遥感数据对“虫源地”实施的有效监测 ,为航天遥感技术用于重大森林病虫害的宏观监测和预警提供了实例     

GF-6WFV黄边波段在森林扰动监测中的应用潜力研究

为了探讨国产高分六号卫星多光谱宽视场成像仪(WFV)八波段数据在森林扰动研究中的应用潜力,本文基于北京市西山林场2018年10月-12月期间的4期高分六号卫星WFV数据,经数据标准化处理后,比较4波段和7波段黄边归一化植被指数(YNDVI)与标准NDVI在森林扰动信息提取方面的差异。研究结果表明在初秋至初冬的时间序列内,植被的季节性变化明显,GF-6 WFV的近红外波段对于森林扰动监测作用最大;基于近红外和黄边波段建立的YNDVI比近红外和红波段的NDVI更能有效反映森林扰动信息。因此,GF-6WFV传感器的新增波段在森林资源监测、病虫害监测、森林火灾预警等多个方面具有巨大应用潜力。     

利用NOAA-AVHRR资料对浙江省植被生态环境进行动态监测研究

本文讨论了应用NOAA-AVHRR资料进行植被生态变化的监测方法,提出了制作植被指数分布图的方法和步骤,并利用植被指数分布图对全省植被手相变化进行了监测。以绍兴县为例,通过几个典型季节的植被指数图,进行了定量和定性的分析,获得了有实际意义的结果。     

3S技术在森林病虫害监测预警中的应用与展望

概述了我国森林病虫害的危害现状,阐明了3S技术在森林病虫害监测预警中的重要性,总结了近年来国内外运用"3S"技术在森林病虫害监测预警中的应用现状,并指出森林病虫害"3S"监测预警存在的问题及未来的发展趋势。     

利用NOAA－AVHRR资料提取水体信息的初步研究

本文讨论了应用NOAA-AVHRR资料来提取水体信息的方法。采用AVHRR一、二通道(下面简写为CH1、CH2)获得的地面反射率数据构成的归一化植被指数(NDVI)来识别水体,并初步提出了应用模糊数学的方法提取混合像元中的水体面积信息的技术思路。     

1983—1992年中国陆地植被NDVI演变特征的变化矢量分析

以NDVI时序资料为基本数据源，综合应用变化矢量分析和主成分分析方法对1983年至1992年中国陆地植被NDVI的变化强度、变化类型及空间结构变化特征进行了分析。研究结果表明在此期间中国陆地植被NDVI变化有以下特点：（1）十年间NDVI变化东西分异明显，东部变化幅度远大于西部。NDVI变化整体表现为稳中略增，增加区主要分布在台湾、福建、四川、河南等地；减少区主要分布在云南省和新疆北部等地。（2）空间结构信息表现了景观异质性，其变化主要发生在南方，反映了植被的生长和衰老过程及地形（山脉走向）变化。     

蝗虫遥感监测预警研究现状与展望

气候变化背景下全球蝗灾日益肆虐，为支撑虫害及时精准防控，迫切需要开展大面积蝗虫动态监测预警研究。本文从蝗虫生境遥感监测、蝗虫发生动态遥感预警，以及蝗灾遥感损失评估3个方面介绍了当前研究现状，并指出当前存在的问题主要包括3个方面：蝗虫监测预警的时空分辨率较粗，无法精准定位虫害热点发生区和重点危害区；遥感虫害响应机制与虫害生物学扩散模型耦合度较低，导致模型时空普适性较差；缺乏高时空精度的虫害监测预警空间信息服务产品。因此，当前急需发展面向全球、洲际、全国、区域的多尺度、长时序、高精度虫害精准监测预警平台。通过建立时空精细尺度的虫害监测预警指标体系，研制遥感机制与虫害生物学机理深度耦合的高精度预测预报模型，发布多尺度高时频的虫害监测预警空间信息产品和服务，以实现海量数据的自动入库和智能存储、多层次模型的快速调用和高性能计算、虫害测报产品的在线生产和可视化服务。建立从数据到模型到产品服务的全链路，从而切实提升全球应对重大迁飞性虫害的智能化水平，为保障粮食安全、维护区域稳定和可持续发展提供科技支撑。     

Using cumulative NOAA-AVHRR spectral indices for estimating fire danger codes in northern boreal forests

In Canada, fire danger is rated by the Canadian forest fire danger rating system (CFFDRS). One of its components is the fire weather index (FWI) system, which has among others the drought code (DC). DC is used here as a surrogate of dead forest fuel moisture. DC values were computed from weather data acquired between 1993 and 1999 and compared to 10-day composite NOAA-AVHRR images acquired over Canadian northern boreal forests. They were yearly correlated with single compositing period and cumulative NDVI and surface temperature (ST) NOAA-AVHRR data. Correlations with cumulative spectral variables were stronger than with single compositing period variables and the best correlations occurred for the spring compositing periods (R between 0.57 and 0.80). Spring DC models using both single compositing period and cumulative spectral variables were established. Surface temperature-based indices were more often used in the models than NDVI-based indices. The models were stronger for dry or normal years than for wet years. Limitations and possible improvements of the models are discussed.     

Generation of fraction images from AVHRR data using linear mixing model

The coarse resolution satellite data have been widely used for regional and global studies as they provide high temporal frequency. The information contained in the coarse resolution pixels are mostly mixture of several components. The extraction of information contained in a pixel find its role in Geosphere-Biosphere context. The present study address the utility of constrained least square model applied to coarse spatial resolution data from NOAA-AVHRR for generating fraction images of vegetation, soil and water/shade. The red and near-infrared channels have been used to run the constrained least square model to generate fraction images. The derived fraction images are related to normalised difference vegetation index (NDVI) for model validation. The results suggest that vegetation fraction components are strongly correlated with NDVI values (r²=0.98). The soil fractions (r²=?0.84) and water/shade fractions (4²=?0.97) are negatively correlated with NDVI. The relationship between the fraction images and NDVI show the potential of the model in deriving sub-pixel component information using coarse resolution data.     

应用MSS数字图像处理进行森林资源动态监测的探讨

本文探讨了应用多时相Landsat Mss图像进行森林动态监测的方法。研究了差值图像法、比值植被指数差值法、归一化植被指数差值法、多时相主分量分析法(垂直植被指数法)和分类比较法对提取森林动态变化信息的作用。结果表明,3种植被指数法都能较好地提取植被动态信息,可用于森林面积消长的动态监测。     

A Comparative Analysis of Phenological Curves for Major Crops in Kansas

The goal of this research was to conduct an initial investigation into whether a time-series NDVI reference curve library for crops over a growing season for one year could be used to map crops for a different year. Time-series NDVI libraries of curves for 2001 and 2005 were investigated to ascertain whether or not the 2001 dataset could be used to map crops for 2005. The 2005 16-day composite MODIS 250 m NDVI data were used to extract NDVI values from 1,615 field sites representing alfalfa, corn, sorghum, soybeans, and winter wheat. A k-means cluster analysis of NDVI values from the field sites was performed to identify validation sites with time-series NDVI spectral profiles characteristic of the major crop types grown in Kansas. After completing the field site refinement process, there were 1,254 field sites retained for further analysis, referred to as "final" field sites. The methods employed to evaluate whether the MODIS-based NDVI profiles for major crops in Kansas are stable from year-to-year involved both graphical and statistical analyses. First, the time-series NDVI values for 2005 from the final field sites were aggregated by crop type and the crop NDVI profiles were then visually assessed and compared to the profiles of 2001 to ascertain if each crop's unique phenological pattern was consistent between the two years. Second, separability within each crop class in the time-series NDVI data between 2001 and 2005 was investigated numerically using the Jeffries-Matusita (JM) distance statistic. The results seem to suggest that time-series NDVI response curves for crops over a growing period for one year of valid ground reference data may be useful for mapping crops for a different year when minor temporal shifts in the NDVI values (resulting from inter-annual climate variations or changes in agricultural management practices) are taken into account.     

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.     

Detecting the changes in rural communities in Taiwan by applying multiphase segmentation on FORMOSA-2 satellite imagery

Agricultural activities mainly occur in rural areas; recently, ecological conservation and biological diversity are being emphasized in rural communities to promote sustainable development for rural communities, especially for rural communities in Taiwan. Therefore, since 2005, many rural communities in Taiwan have compiled their own development strategies in order to create their own unique characteristics to attract people to visit and stay in rural communities. By implementing these strategies, young people can stay in their own rural communities and the rural communities are rejuvenated. However, some rural communities introduce artificial construction into the community such that the ecological and biological environments are significantly degraded. The strategies need to be efficiently monitored because up to 67 rural communities have proposed rejuvenation projects. In 2015, up to 440 rural communities were estimated to be involved in rural community rejuvenations. How to monitor the changes occurring in those rural communities participating in rural community rejuvenation such that ecological conservation and ecological diversity can be satisfied is an important issue in rural community management. Remote sensing provides an efficient and rapid method to achieve this issue. Segmentation plays a fundamental role in human perception. In this respect, segmentation can be used as the process of transforming the collection of pixels of an image into a group of regions or objects with meaning. This paper proposed an algorithm based on the multiphase approach to segment the normalized difference vegetation index, NDVI, of the rural communities into several sub-regions, and to have the NDVI distribution in each sub-region be homogeneous. Those regions whose values of NDVI are close will be merged into the same class. In doing so, a complex NDVI map can be simplified into two groups: the high and low values of NDVI. The class with low NDVI values corresponds to those regions containing roads, buildings, and other manmade construction works and the class with high values of NDVI indicates that those regions contain vegetation in good health. In order to verify the processed results, the regional boundaries were extracted and laid down on the given images to check whether the extracted boundaries were laid down on buildings, roads, or other artificial constructions. In addition to the proposed approach, another approach called statistical region merging was employed by grouping sets of pixels with homogeneous properties such that those sets are iteratively grown by combining smaller regions or pixels. In doing so, the segmented NDVI map can be generated. By comparing the areas of the merged classes in different years, the changes occurring in the rural communities of Taiwan can be detected. The satellite imagery of FORMOSA-2 with 2-m ground resolution is employed to evaluate the performance of the proposed approach. The satellite imagery of two rural communities (Jhumen and Taomi communities) is chosen to evaluate environmental changes between 2005 and 2010. The change maps of 2005–2010 show that a high density of green on a patch of land is increased by 19.62 ha in Jhumen community and conversely a similar patch of land is significantly decreased by 236.59 ha in Taomi community. Furthermore, the change maps created by another image segmentation method called statistical region merging generate similar processed results to multiphase segmentation.     

融合高时空分辨率数据估算植被净初级生产力

植被净初级生产力NPP(Net Primary Production)遥感估算与分析,有赖于高时空分辨率的遥感数据,但目前中高分辨率的遥感数据受卫星回访周期及天气的影响,在中国南方地区难以获取连续时间序列的数据,从而影响了高精度的区域植被净初级生产力的遥感估算。为此,提出一种基于多源遥感数据时空融合技术与CASA模型估算高时空分辨率NPP的方法。首先,利用多源遥感数据,即Landsat8 OLI数据与MODIS13Q1数据,采用遥感数据时空融合方法,获得了时间序列的Landsat8 OLI融合数据;然后,基于Landsat8 OLI时空融合数据,并采用CASA模型,以长株潭城市群核心区为例,进行区域植被NPP的遥感估算。研究结果表明,基于时间序列Landsat融合数据估算的30m分辨率的NPP具有良好的空间细节信息,且估算值与实测值的相关系数达0.825,与实测NPP数据保持了较好的一致性。     

基于HJ-1A-B卫星影像的污水灌区植被指数研究

污水灌区植被指数的研究对污水排放、生态保护等方面具有指导意义。利用山东省龙口市2008-2010年21个时相的中国环境与灾害监测预报小卫星(HJ-1A-B)CCD影像数据,选取易于比较的典型污水灌区和清水灌区,提取NDVI平均值,得到3年同期的数据,进行横向和纵向的量化比较。结果表明,NDVI能够动态监测植被变化,污水灌区与清水灌区内的植被指数随时间的变化趋势基本一致,但污水灌区内的植被指数呈现逐年递减的变化趋势;生活污水灌区内的植被表现出早熟现象,工业废水灌区内的植被指数是所有研究区内最低的。     

THE DEPARTMENT OF PHOTOGRAMMETRY AND REMOTE SENSING AT WUHAN TECHNICAL UNIVERSITY,PEOPLE'S REPUBLIC OF CHINA

Many remote sensing applications are predicated on the fact that there is a known relationship between climate and vegetation dynamics as monitored from space. However, few studies investigate vegetation index variation on individual homogeneous land cover units as they relate to specific climate and environmental influences at the local scale. This study focuses on the relationship between the Palmer Drought Severity Index (PDSI) and different vegetation types through the derivation of vegetation indices from Landsat 7 ETM+ data (NDVI, Tasseled Cap, and SAVI). A series of closely spaced through time images from 1999 to 2002 were selected, classified, and analyzed for an area in northeastern Ohio. Supervised classification of the images allowed us to monitor the response in individual land cover classes to changing climate conditions, and compare these individual changes to those over the entire larger areas. Specifically, the images were compared using linear regression techniques at various time lags to PDSI values for these areas collected by NOAA. Although NDVI is a robust indicator of vegetation greenness and vigor, it may not be the best index to use, depending on the type of vegetation studied and the scale of analysis used. A combination of NDVI and other prominent vegetation indices can be used to detect subtle drought conditions by specifically identifying various time lags between climate condition and vegetation response.     

Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies

The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and while it increased for the mature and old age classes with time. We concluded that the method could be useful for the identification of anomalous patches using Landsat scale time series data to monitor large coffee plantations and provide an indication of areas requiring particular field attention.