基于NDVI与LAI的水稻生长状况研究

在水稻反射光谱特性与水稻生物参数关系的支持下 ,以吉林省德惠市夏家点镇为研究区 ,探讨了一条基于TM遥感影像反演得到的归一化植被指数 (NDVI)与地面观测数据叶面积指数 (LAI)的水稻生长状况的研究途径 ,并利用NDVI以及LAI对该区 2 0 0 0年和 2 0 0 1年的水稻生长状况进行了分析研究。     

利用NOAA NDVI数据集监测冬小麦生育期的研究

探索了利用NDVI研究作物生育期的方法，对黄淮海冬麦区的返青期、抽穗期、成熟期进行了估测，并利用地面实际观测资料进行了验证。结果表明，NDVI数据对大范围农作物生育期监测是可行的。冬小麦遥感反青期由南到北依次推迟，符合春季绿波由南到北推移规律。对冬小麦遥感生育期年际变化分析表明，黄淮海平原返青期变化相对较大，而抽穗期和成熟期变化较小。根据历年月平均温度与返青期分析，冬小麦返青日期与2月份平均温度密切相关。对于局部地区，利用5d合成1km分辨率数据，且按农业生态分区分别制定生育期判别标准，估测效果将更好。     

Detecting Drought-Prone Areas of Rice Agriculture Using a MODIS-Derived Soil Moisture Index

This study examined the use of remote sensing in detecting and assessing drought in Iloilo Province, Philippines. A remote sensing-based soil moisture index (SMI), rainfall anomaly data from the Tropical Rainfall Measuring Mission (TRMM), and rice production departure (P_d ) data were used for drought detection and validation. The study was conducted using two drought years (2001, 2005) and one non-drought year (2002). According to SMI data, the drought distribution was classified into four major groups. SMI values > 0.3 were considered not to be drought and SMI values ≤ 0.3 were classified as slight, moderate, and severe drought. Results based on SMI revealed that the study area experienced drought in 2001 and 2005, while 2002 exhibited no drought. On the other hand, TRMM-based rainfall anomaly data revealed negative values in 2001 and 2005 and positive values in 2002. Below-normal P_d values were observed in 2005 and above-normal values in 2002, whereas nearly normal values prevailed in 2001. Yield indicator data were crucial for the assessment of drought impacts on rice production. In most cases, the pattern of rice production and productivity revealed that the decline in the production or productivity of rice for a particular year coincided with lower SMI values and greater rainfall departure or negative anomaly.     

Directional effects on NDVI and LAI retrievals from MODIS: A case study in Brazil with soybean

The Moderate Resolution Imaging Spectroradiometer (MODIS) is largely used to estimate Leaf Area Index (LAI) using radiative transfer modeling (the “main” algorithm). When this algorithm fails for a pixel, which frequently occurs over Brazilian soybean areas, an empirical model (the “backup” algorithm) based on the relationship between the Normalized Difference Vegetation Index (NDVI) and LAI is utilized. The objective of this study is to evaluate directional effects on NDVI and subsequent LAI estimates using global (biome 3) and local empirical models, as a function of the soybean development in two growing seasons (2004–2005 and 2005–2006). The local model was derived from the pixels that had LAI values retrieved from the main algorithm. In order to keep the reproductive stage for a given cultivar as a constant factor while varying the viewing geometry, pairs of MODIS images acquired in close dates from opposite directions (backscattering and forward scattering) were selected. Linear regression relationships between the NDVI values calculated from these two directions were evaluated for different view angles (0–25°; 25–45°; 45–60°) and development stages (<45; 45–90; >90 days after planting). Impacts on LAI retrievals were analyzed. Results showed higher reflectance values in backscattering direction due to the predominance of sunlit soybean canopy components towards the sensor and higher NDVI values in forward scattering direction due to stronger shadow effects in the red waveband. NDVI differences between the two directions were statistically significant for view angles larger than 25°. The main algorithm for LAI estimation failed in the two growing seasons with gradual crop development. As a result, up to 94% of the pixels had LAI values calculated from the backup algorithm at the peak of canopy closure. Most of the pixels selected to compose the 8-day MODIS LAI product came from the forward scattering view because it displayed larger LAI values than the backscattering. Directional effects on the subsequent LAI retrievals were stronger at the peak of the soybean development (NDVI values between 0.70 and 0.85). When the global empirical model was used, LAI differences up to 3.2 for consecutive days and opposite viewing directions were observed. Such differences were reduced to values up to 1.5 with the local model. Because of the predominance of LAI retrievals from the MODIS backup algorithm during the Brazilian soybean development, care is necessary if one considers using these data in agronomic growing/yield models.     

基于MODIS植被指数的塔河流域植被时空变化特征研究

基于获取的塔河流域2000~2014年历年4~10月间逐月MODIS植被指数产品,采用时间序列谐波分析法(HANTS)对最大值合成的逐月NDVI时间序列数据进行了重建,用趋势线分析法对塔河流域近15年生长季(4~10月)MODIS NDVI的时间变化进行计算,用一元线性回归趋势法计算得到了塔河流域近15年生长季(4~10月)NDVI变化趋势的空间分布。结合植被类型分布图对计算得到的实验结果进行了研究分析,总结了塔河流域多年植被覆盖的时空分布及其变化规律,成果可为塔河流域综合治理及生态环境评价提供依据。     

Fuzzy Modelling of African Ecoregions and Ecotones Using AVHRR NDVI Temporal Imagery

Abstract

Conventional methods of deriving global or continental vegetation maps from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) time series data are based on two‐value Boolean logic, which cannot properly model the so‐called ecotone, the transition zone between adjacent ecosystems. New methods and data models that have been developed on the basis of fuzzy logic to address the “mixed pixel” issue in multi‐spectral imagery can also be used with multi‐temporal imagery to handle the mixture of vegetation types within an ecotone. This study introduces the concept of semantic space and its transformation from spectral feature space, which utilizes a fuzzy logic approach to characterize the continuum of vegetation communities in the African continent from AVHRR multi‐temporal (12 months for three years from 1986 to 1988) NDVI data. The fuzzy procedure was based on the Fuzzy c‐Means (FCM) algorithm with significant modifications to improve processing speed for handling large volumes of data. A second‐order mapping approach was also devised to explicitly represent subdominant vegetative coverage in ecotones and other heterogeneous regions. Comparisons between a Sub‐Saharan African Vegetation Map compiled by the International Union for Conservation of Nature (IUCN) in 1986 and the maps derived from this study demonstrated that fuzzy modeling and classification might provide a better and more realistic representation of the vegetative characteristics of the region.     

Using NDVI Differencing and Temporal Logic to Enhance ISODATA Classification in Urban Environments

This paper presents a new approach to improving land use/cover mapping accuracy in an urban environment. Bi-temporal Landsat TM images (1987 and 1997) were initially classified using the ISODATA method. An NDVI difference image was derived and classified, with each class indicating certain land use/cover changes. Temporal logical reasoning was then performed on the classified NDVI difference map and the initial land use/cover maps. The procedure successfully resolved the confusion between forest clear-cuts/fallow cropland and urban, as well as between forest clear-cuts and cropland. The kappa analysis test led to a Z value of 1.837 with the p-value of 0.026 for the year 1987, and a Z value of 1.924 with the p-value of 0.014 for 1997, indicating significant enhancement at the 95% confidence level.     

Deriving Crop Phenology Metrics and Their Trends Using Times Series NOAA-AVHRR NDVI Data

In this study, an attempt has been made to derive the spatial patterns of temporal trends in phenology metrics and productivity of crops grown, at disaggregated level in Indo-Gangetic Plains of India (IGP), which are helpful in understanding the impact of climatic, ecological and socio-economic drivers. The NOAA-AVHRR NDVI PAL dataset from 1981 to 2001 was stacked as per the crop year and subjected to Savitzky-Golay filtering. For crop pixels, maximum and minimum values of normalized difference vegetation index (NDVI), their time of occurrence and total duration of kharif (June-October) and rabi (November–April) crop seasons were derived for each crop year and later subjected to pixel-wise regression with time to derive the rate and direction of change. The maximum NDVI value showed increasing trends across IGP during both kharif and rabi seasons indicating a general increase in productivity of crops. The trends in time of occurrence of peak NDVI during kharif dominated with rice showed that the maximum vegetative growth stage was happening early with time during study period across most of Punjab, North Haryana, Parts of Central and East Uttar Pradesh and some parts of Bihar and West Bengal. Only central parts of Haryana showed a delay in occurrence of maximum vegetative stage with time. During rabi, no significant trends in occurrence of peak NDVI were observed in most of Punjab and Haryana except in South Punjab and North Haryana where early occurrence of peak NDVI with time was observed. Most parts of Central and Eastern Uttar Pradesh, North Bihar and West Bengal showed a delay in occurrence of peak NDVI with time. In general, the rice dominating system was showing an increase in duration with time in Punjab, Haryana, Western Uttar Pradesh, Central Uttar Pradesh and South Bihar whereas in some parts of North Bihar and West Bengal a decrease in the duration with time was also observed. During rabi season, except Punjab, the wheat dominating system was showing a decreasing trend in crop duration with time.     

基于Landsat数据及光谱混合分析的城市扩展及物候变化提取与分析

城市化是目前全球关注的热点问题之一。城市化导致城市范围的显著扩展,并引起局地气候的变化,从而进一步导致城市区域植被物候的变化。因此,利用多年的时间序列遥感影像提取城市扩展与植被物候的变化,可为分析城市化及其影响提供依据。Landsat系列卫星数据的免费开放,为分析城市的时空变化及其环境影响提供了丰富数据。本文利用由Landsat时间序列数据及标准化光谱混合模型得到的光谱端元组分时间序列,探索了城市及周边植被物候变化提取以及城市扩展提取的新方法,并以北京市为例,提取了1984～2015年32年间的城市扩展以及3个年段的植被物候变化,分析了城市扩展的时空演化以及植被物候的时空变化,并定性分析了二者的关联。论文的主要创新点包括三个方面:(1)提出了基于Landsat时间序列数据和标准化光谱混合模型的城市及周边区域植被物候及变化的提取方法。首先,该方法针对城市区域光谱混合问题及现有植被指数的饱和等问题,采用基于标准化光谱混合模型的全球端元组分的植被丰度时间序列进行植被物候及变化的提取,避免了光谱混合分析中端元选择的问题,同时植被丰度结果在不同地区不同时间上具有可比性;其次,该方法考虑城市及周边区域植被类型多样及变化复杂的特点,适用于多种物候模型的植被物候提取,能同时提取出不同植被类型的物候结果,同时,植被物候变化结果中剔除了不同地物类型及不同植被类型的变化区域,能更好进行物候变化分析。(2)提出了基于标准化光谱混合模型及全球端元组分的城市指数(SVDUI)。该指数基于物理统计的光谱混合分析进行构建,与现有基于像元构建的指数相比更适于光谱混合严重的城市区域的研究。该指数能更好地表达城市与其它地物类别的差异,突出城市特征,并更好地提取出城市范围,因此,SVDUI指数可为城市范围提取提供一种新的途径。此外,该指数能保持时间上的一致性与可比性,具有广泛适用性。(3)提出了一种基于SVDUI城市指数的时间序列变化检测方法,该方法可以快速提取城市扩展的时间、空间及强度信息。该方法能快速、有效地提取出长时间序列的城市扩展结果,同时能充分利用年内及年际变化的时间信息,有效去除单时相结果上的噪声影响,不依赖于单时相影像的结果好坏。     

Assessment of Late Blight Induced Diseased Potato Crops: A Case Study for West Bengal District Using Temporal AWiFS and MODIS Data

Remote sensing technology becomes an effective and inexpensive technique for detecting disease in vegetation. In this study, an attempt has been done to discriminate healthy and late blight affected crop using remote sensing based indices such as NDVI and LSWI. NDVI and LSWI spectral profiles between healthy and late blight affected crop shows large difference. Mean difference in reflectance between two acquired dates Jan. 10 and 29, 2009 crop clusters varied from 31.28 % in red band, 7.7 % in NIR band and 6.23 % in SWIR bands in healthy crops while in late blight affected crops it is ?15.5 % in red, 44.4 % in NIR and ?14.61 % in SWIR bands. Negative percentage differences in reflectance indicate reflectance increases from Jan. 10, 2009 to Jan. 29, 2009, while positive difference indicate decrease in reflectance between the two dates. Since potato is an irrigated crop, these differences in reflectance are attributed to prevalent disease at that time. It is found that severely affected areas are Bardhman, Arambag, Bishnupur, Ghatal and Hugli taluka with crop damage areas are 4036.66, 1138.68, 2025.23, 469.15, and 380.08 ha, respectively.     

Evaluating the Performance of Multi-Class and Single-Class Classification Approaches for Mountain Agriculture Extraction Using Time-Series NDVI

Supervised multi-class classification (MCC) approach is widely being used for regional-level land use–land cover (LULC) mapping and monitoring. However, it becomes inefficient if the end user wants to map only one particular class. Therefore, an improved single-class classification (SCC) approach is required for quick and reliable map production purpose. In this regard, the current study attempts to evaluate the performance of MCC and SCC approaches for extracting mountain agriculture area using time-series normalized differential vegetation index (NDVI). At first, samples of eight LULC classes were acquired using Google Earth image, and corresponding temporal signatures (TS) were extracted from time-series NDVI to perform classification using minimum distance to mean (MDM) and spectral angle mapper (i.e., multi-class SAM—MCSAM) under MCC approach. Secondly, under SCC approach, the TS of three agriculture classes (i.e., agriculture, mixed agriculture and plantation) were utilized as a reference to extract agriculture extent using Euclidean distance (ED) and SAM (i.e., single-class SAM—SCSAM) algorithms. The area of all four maps (i.e., MDM—19.77% of total geographical area (TGA), MCSAM—21.07% of TGA, ED—15.23% of TGA, SCSAM—13.85% of TGA) was compared with reference agriculture area (14.54% of TGA) of global land cover product, and SCC-based maps were found to have close agreement. Also, the class-wise detection accuracy was evaluated using random sample point-based error matrix which reveals the better performance of ED-based map than rest three maps in terms of overall accuracy and kappa coefficient.     

土地信息特征与土地信息学

土地信息与地理信息是两个互有联系但又有实质性区别的概念。土地信息学是研究土地信息的组成及其相互联系的一般规律，以及土地信息的获取、处理、表达和分析应用的综合性学科。本文主要论述土地信息的基本特征和土地信息学的主要研究方向。     

利用Landsat时序NDVI数据进行新疆石河子垦区灌溉作物分类

精确的农作物分类信息对于农业环境评估、水资源利用规划非常重要,尤其是在干旱、半干旱地区。本文利用30 m分辨率的Landsat NDVI时间序列数据进行了新疆石河子垦区混合农作物精确区分的潜力研究。首先利用S-G滤波重构了Landsat NDVI时间序列,然后基于SVM模型对研究区域农业类型进行了精确分类。在SVM分类模型作用下,S-G重构后的时间序列有效地将该地区棉花、玉米、小麦等主要作物区分开来,精度高于0.86,Kappa系数大于0.82。结果表明,S-G滤波能够有效提高NDVI时间序列数据质量;TM影像时间序列在监测干旱、半干旱地区的作物类型和种植方式随时间的变化方面存在巨大潜力。     

基于NDVI和LSMM的干旱区植被信息提取研究——以新疆吐鲁番市为例

植被是干旱区生态建设重要的组成部分,而植被覆盖度是生态环境变化的重要指示,是评价生态系统健康的前提条件。本文在遥感等技术的支持下,以landsatTM影像为数据源,选用归一化植被指数(NDVI)和线性光谱混合分析模型(LSMM)两种方法进行分析比较,提取吐鲁番市近20年植被覆盖度,并对该地区植被覆盖度的演变特征进行分析。结果表明:①LSMM方法能较好地提取干旱区植被信息,指标简单且分类精度较高。②NDVI方法提取植被时,受到很多限制,在干旱区不宜采用。     

作物LAI的遥感尺度效应与误差分析

以黑河中游盈科绿洲为研究区, 利用Hyperion高光谱数据, 采用双层冠层反射率模型(ACRM)迭代运算反演LAI; 通过LAI的均值化(LAImean)以及Hyperion数据反射率线性累加反演LAI(LAIp), 定量分析LAI反演的尺度效应; 从模型的非线性和地表景观结构的空间异质性2个方面分析引起反演误差的原因, 并在LAI-NDVI回归方程的基础上利用泰勒展开的方法对低分辨率数据反演结果进行了误差纠正。结果表明, 地表景观结构的空间异质性是造成多尺度LAI反演误差的关键因素, 通过泰勒展开式能很好地实现大尺度数据LAI反演结果的误差纠正。     

Estimating Interannual Variations in Vegetated Areas of Sardinia Island Using SPOT/VEGETATION NDVI Temporal Series

Principal component analysis (PCA) has been applied to a temporal series 1999-2002 of a yearly maximum value composite of the SPOT/VEGETATION normalized difference vegetation index for the Sardinia Island for extracting interannual variations affecting vegetation covers. Both naturally vegetated areas (forest, shrub-land, and herbaceous cover) and agricultural lands have been investigated in order to obtain information on the most prominent natural and/or man-induced alterations affecting vegetation behavior. Although a correct interpretation of PCA results generally requires additional information, such as geographical knowledge, climatological data, and field surveys, the main finding of the current investigation suggests that PCA can be a feasible tool to separately map areas showing different degrees of interannual variability, providing valuable information for discriminating unidirectional changes     

2003—2019年邯郸市叶面积指数时空变化特征

研究和掌握叶面积指数(LAI)时空变化特征,对区域植被保护、植树造林和环境保护具有重要的参考意义.本文以邯郸市为例,基于MCD15A3H遥感影像数据、DEM数据和土地利用等多元数据,引入Sen趋势与Mann-Kendall趋势检验分析方法,系统分析了邯郸市像素尺度上的LAI数值的变化特征,并基于不同的土地利用类型、不同...     

Vegetation图像植被指数与实测水稻叶面积指数的关系

水稻的叶面积指数 (LAI)是水稻生长的一项重要参数 ,与水稻的生物量与产量直接相关。利用 1999年在江苏省江宁县实测的水稻叶面积指数与同期Vegetation/SPOT的植被指数作了对比分析 ,结果发现同期的LAI与植被指数表现相近的变化特征 ,两者具有良好的相关关系。     

中国地表植被覆盖变化及其与气候因子关系

本文利用１９８５－１９９０年连续６９个月的ＮＯＡＡ时间序列数据,进行中国植被覆盖变化的空间,以及时间序列分析,并且结合同期的平均气温,降水数据,进行植被覆盖变化与气候因子相关性分析,该文证实了在中国植被覆盖随时间的推移规律,空间分布规律,以及植被覆盖变化与气温,降水的定量关系。