Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans

In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels.     

运用遥感指数的崩滑检测方法

以汶川地震时出现的崩滑为研究对象,以该地区震前/后TM影像为研究数据,利用变化检测的方法,比较和分析了归一化植被指数(NDVI)、归一化建筑指数(NDBI)和归一化燃烧率(NBR)应用在崩滑检测中的可行性和优劣性。结果表明,3种遥感指数均可以用于崩滑检测,但效果不同:NDVI对崩滑检测最全面但可信度最低,NBR对崩滑检测最可信但遗漏最多,NDBI处于中间水平。     

Cotton Crop Discrimination Using Fuzzy Classification Approach

Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single pixel) problem had been handled using soft computing techniques. Possibilistic fuzzy classification approach is used to handle mixed pixels for extracting single class of interest. The classification results with respect to various indices were compared in terms of image to image fuzzy overall classification accuracy. It was observed that temporal SAVI indices database with data set-2 outperformed other temporal indices database for cotton crop discrimination. Temporal SAVI indices database gave highest fuzzy overall accuracy of 93.12% with data set-2 in comparison to others.     

K-L变换与NDBI指数法提取ASTER影像城市用地信息的比较

运用K-L变换和NDBI(Normalized Difference Barren Index)指数法,对试验区--沧州市及其周边地区的ASTER遥感影像进行处理,然后分别对两种方法处理后的图像采用最小距离法监督分类,提取城市用地信息,并对分类后的图像进行对比,结果表明:NDBI指数法对城市用地信息提取的效果较好.     

Semi-automatic mapping of anthropogenic impervious surfaces in an urban/suburban area using Landsat 8 satellite data

Impervious surfaces have a significant impact on urban runoff, groundwater, base flow, water quality, and climate. Increase in Anthropogenic Impervious Surfaces (AIS) for a region is a true representation of urban expansion. Monitoring of AIS in an urban region is helpful for better urban planning and resource management. Cost effective and efficient maps of AIS can be obtained for larger areas using remote sensing techniques. In the present study, extraction of AIS has been carried out using Double window Flexible Pace Search (DFPS) from a new index named as Normalized Difference Impervious Surface Index (NDAISI). NDAISI is developed by enhancing Biophysical Composition Index (BCI) in two stages using a new Modified Normalized Difference Soil Index (MNDSI). MNDSI has been developed from Band 7 and Band 8 (PAN) of Landsat 8 data. In comparison to existing impervious surface extraction methods, the new NDAISI approach is able to improve Spectral Discrimination Index (SDI) for bare soil and AIS significantly. Overall accuracy of mapping of AIS, using NDAISI approach has been found to be increased by nearly 23% when compared with existing impervious surface extraction methods.     

改进型归一化差值不透水表面指数探讨——以兰州市建成区为例

深入分析研究区不透水表面和其他城市基本组分土地覆盖类型的特征后,通过研究已有归一化不透水表面指数(NDISI)提取方案的适用性和研究区域特点,创建出一种改进型归一化差值不透水表面指数(MNDISI),并结合实地调查分析,在中等分辨率影像上运用基于对象的方法,提出新的指数增强提取方案,实现不透水表面信息的自动准确提取,并将其作为一个范例供其他西北内陆城市借鉴。     

Drought mapping using Geoinformation technology for some sites in the Iraqi Kurdistan region

Abstract

Iraq has suffered severely from drought in recent years and the year 2008 was the driest, particularly in the Iraqi Kurdistan region. This study incorporated Geoinformation technology into mapping the drought that severely affected the Kurdistan region in the years 2007–2008. Geoinformation technology provides support in the theories, methods and techniques for building, and development of Digital Earth aspect. Five vegetation, soil, water, and land surface temperature (LST) indices were applied to two Landsat 7 ETM+ imageries of June 2007 and June 2008, to assess the drought impacts in Erbil governorate Kurdistan during the study period. The indices that were employed in this study were Normalized Difference Vegetation Index, Bare Soil Index, Normalized Differential Water Index, Tasseled Cap Transformation Wetness, and LST. The results revealed a significant decrease in the vegetative cover (56.7%) and a decline in soil/vegetation wetness (29.9%) of the total study area. Likewise, there was a significant reduction in the water bodies surface area in the region such as Dokan Lake, which lost 32.5% of its surface area in comparison with the previous year, 2007. The study results showed that the soil moisture content was the most effective actor on the vegetative cover, LST, and drought status in the study area.     

基于谱间特征与多种指数分析的居民地信息提取方法

本文在分析现有居民地提取方法的基础上,提出将归一化建筑指数(NDBI)、改进归一化差异水体指数(MNDWI)、土壤调节植被指数(SAVI)、比值居民地指数(RRI)相结合进行居民地信息提取的方法。以浙江省宁波市为例,通过光谱采样及各类地物在4种指数上的取值分析,建立模型进行居民地信息提取及精度验证,结果表明:利用该模型可以实现居民地信息的自动提取,能提高居民地与裸地的可分性,减少背景地物的影响,总体精度为91.08%。     

阜新地区植被覆盖度变化提取及分析

植被覆盖度是反应地区生态环境的重要指标,利用1995,2007年的两期TM遥感数据,以归一化植被指数(NDVI)像元二分法为植被覆盖度估算模型,计算阜新地区不同时期的植被覆盖度并得出阜新地区植被覆盖度等级图以及阜新地区植被覆盖度变化等级图。得出如下结论:1995年到2007年阜新地区植被覆盖度退化面积为64.817%,好转面积为6.547%,基本无变化区域为28.636%,阜新地区植被覆盖度退化严重。     

基于综合指数分析的城市建筑用地信息提取研究

以西安市长安区TM影像为例,研究关于城市建筑用地信息快速、准确提取的方法。通过对归一化差异型指数构成原理的分析,选取土壤调节植被指数（ SAVI ）、归一化水体指数（ NDWI ）和归一化差异型建筑指数（ NDBI ）来提取植被、水体和城市建筑用地专题影像,并将其构建为一幅新影像,分析新影像谱间特征,运用逻辑运算将城市建筑用地信息提取出来。本文方法总体提取效果十分有效,尤其是对于面积较大的城市建筑用地,总精度高达85．3％。综合指数法弥补了单靠某一指数提取城市建筑用地信息的不足,提取结果客观可信,是一种不经人为干预、快速有效的提取城市建筑用地的方法。     

基于NDVI城镇土地利用变化检测探讨

通过归一化植被指数（NDVI）结合影像差值,对经过辐射校正后的1993年和2005年武汉地区TM影像进行了土地利用变化检测,与监督法分类进行比较,得出NDVI更宜于实现变化信息探测和提取。     

Predicting Wheat Grain and Biomass Yield Using Canopy Reflectance of Booting Stage

Field experiment was conducted in a sandy loam soil of Indian Agricultural Research Institute, New Delhi during the year 2011–13 to see the effect of irrigation, mulch and nitrogen on canopy spectral reflectance indices and their use in predicting the grain and biomass yield of wheat. The canopy reflectances were measured using a hand held ASD FieldSpec Spectroradiometer at booting stage of wheat. Four spectral reflectance indices (SRIs) viz. RNDVI (Red Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio) and WI (Water Index) were computed using the spectral reflectance data. Out of these four indices, RNDVI, GNDVI and SR were significantly and positively related with the grain and biomass yield of wheat whereas WI was significantly and negatively related with the grain and biomass yield of wheat. Calibration with the second year data showed that among the SRIs, WI could account for respectively, 85 % and 86 % variation in grain and biomass yield of wheat with least RMSE (395 kg ha^?1 (15 %) for grain yield and 1609 kg ha^?1 (20 %) for biomass yield) and highest d index (0.95 for grain yield and 0.91 for biomass yield). Therefore it can be concluded that WI measured at booting stage can be successfully used for prediction of grain and biomass yield of wheat.     

植被指数和纹理特征在影像解译中的应用

以QuickBird影像为例,分别分析了植被指数和纹理特征两大解译标志,植被指数主要介绍了比值植被指数、归一化植被指数、修正型土壤调整植被指数、差值植被指数四种,纹理特征主要基于灰度共生矩阵进行分析,并通过对比分类实验验证了这两大解译标志在遥感影像分类中的作用。     

基于决策树模型的武汉市地表比辐射率反演研究

地表比辐射率值是反演地表温度中的关键参数,它通过高效的遥感影像分类方法来获取。利用NDVI指数,MNDWI指数及NDBI指数,探讨基于决策树模型的比辐射率估算方法,并通过与传统方法的对比研究,认为针对武汉市的特点,采用决策树的比辐射率估算方法精度高于传统模型,较适合研究区的地表组成特征。     

CBERS02B卫星CCD传感器数据反演陆地气溶胶

研究利用CBERS02B卫星的CCD传感器数据反演陆地气溶胶的方法。采用的方法是暗像元法。具体步骤为: 根据地面采集的植被光谱数据, 结合CCD传感器特点, 建立浓密植被(暗像元)红蓝波段(CCD传感器的第三和第一波段)反射率与地表反射率之间的关系, 确定了暗像元识别的阈值, 讨论气溶胶光学厚度对暗像元识别的影响以及消除这种影响的方法; 利用6S进行辐射传输运算, 构建查找表; 根据CBERS02B卫星的CCD传感器数据, 从查找表插值得到气溶胶光学厚度, 并进行了算法的误差分析。用广西南宁市及北京地区附近的两景数据进行了实际的反演试验, 使用MODIS的气溶胶产品与反演结果进行比对。结果显示, CBERS02B卫星的CCD传感器数据能够较好的反演陆地气溶胶。     

An Improved Optimal Segmentation Threshold Algorithm and Its Application in the Built-up Quick Mapping

For purpose of improving the accuracy of the built-up quick mapping, this paper proposed an improved optimal segmentation threshold algorithm, namely the improved double-window flexible pace search (IDFPS) approach, by redesigning the valuation criteria and the sampling method based on the double-window flexible pace search (DFPS) approach. Moreover, the Normalized Difference Built-up Index (NDBI), the Index-based Built-up Index (IBI), the Enhanced Built-up and Bareness Index (EBBI) and the Urban Index (UI) inversed from Landsat 5 TM images were used for quick mapping by the IDFPS approach and the DFPS approach in different geographical areas. Results from the experiments exemplified by Chongqing (a mountain city) and Chengdu (a plain city) showed that the IDFPS approach was comprehensively superior to the DFPS approach. The IDFPS approach had more than 4.30% higher overall accuracy and 0.12 higher Kappa coefficients than the DFPS approach when both were implemented simultaneously at both the above-mentioned study areas. Besides, a new discovery in this paper was found that the UI had a better performance with higher overall accuracy and Kappa coefficient, lower omission error and commission error than the NDBI, IBI and EBBI because of the strong relationship between the UI and the density of built-up land. This new method has an important reference value for built-up quick mapping and some other applied researches.     

Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.     

Assessing optical earth observation systems for mapping and monitoring temporary ponds in arid areas

Remote sensing methods for locating and monitoring temporary ponds over large areas in arid lands were tested on a study site in Northern Senegal. Three main results are presented, validated with field data and intended to highlight different spectral, spatial and temporal characteristics of the methods: (1) Among several water indices tested, two Middle Infrared-based indices (MNDWI—Modified Normalized Difference Water Index and NDWI₁—Normalized Difference Water Index) are found to be most efficient; (2) an objective method is given prescribing the necessary sensor spatial resolution in terms of minimal detected pond area; and (3) the potential of multi-temporal MODIS imagery for tracking the filling phases of small ponds is illustrated. These results should assist in epidemiological studies of vector-borne diseases that develop around these ponds, but also more generally for land and water management and preservation of threatened ecosystems in arid areas.     

Analysis of spectral characteristics among different sensors by use of simulated RS images

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基于植被指数模型的淡水湖泊湿地景观信息提取

高邮湖湿地是江苏省重要湿地之一,对生态、环境控制、调节气候和保护生物多样性具有重要意义。采用2007年的LandsatTM影像作为遥感信息源,选择影像的光谱特征和比值植被指数(RVI)、差值植被指数(DVI)、归一化植被指数(NDVI)、归一化差异绿度指数(NDGI)、土壤调节植被指数(SAVI)和最佳土壤调节植被指数(OSAVI)6种植被指数做了光谱特征分析,从而确定出最佳指数模型,并基于决策树方法,实现研究区景观信息的遥感分类。研究结果表明,决策树分类法易于综合多种特征进行遥感影像分类,植被指数参与到决策树分类中能够提高分类的总体精度,其总体精度达到79.58%,Kappa系数为0.721 0,分类结果理想且人工参与灵活。