Development of a modified bare soil and urban index for Landsat 8 satellite data

Mapping of urban area has always been a challenging task due to its similar spectral characteristics with bare soil. The spectral characteristics of urban and bare soil being similar, causes confusion and misclassification among themselves. A new modified normalized difference soil index (MNDSI) has been proposed using PAN and Band 7 of Landsat 8. PAN band of Landsat 8 provides increased contrast between vegetation and land areas without vegetation. Subsequently, MNDSI was used to develop a new normalized ratio urban index (NRUI) by enhancing the capability of biophysical composition index (BCI) in two stages. First, a ratio urban index (RUI) was developed which discriminates urban and soil better than BCI. Second, RUI was further enhanced, subsequently known as NRUI, which is able to discriminate urban area from soil even better than RUI. MNDSI and NRUI show a good discrimination between soil and urban and may be useful for such purposes.     

基于TM影像的城市建筑用地信息提取方法研究

本文选用金华市Landsat TM影像为研究的数据源,在归一化裸露指数基础上,利用归一化植被指数提取出非植被信息,通过图像二值化、叠加分析以及掩膜处理去除了低密度植被覆盖区域的噪音信息,自动提取了金华城市建筑用地信息。研究结果表明,归一化裸露指数和归一化植被指数相结合的方法弥补了单一利用归一化裸露指数来提取城市建筑用地信息的不足,提高了提取精度,而且结果客观可信,是一种不经人为干预的、快速有效的提取城市建筑用地方法。     

Developing soil indices based on brightness,darkness, and greenness to improve land surface mapping accuracy

Soil, as one of the three basic biophysical components, has been understudied using remote sensing techniques compared to vegetation and impervious surface areas (ISA). This study characterized land surfaces based on the brightness–darkness–greenness model. These three dimensions, brightness, darkness, and greenness, were represented by the first Tasseled Cap Transformation (TC1), Normalize Difference Snow Index (NDSI), and Normalized Difference Vegetation Index (NDVI), respectively. The Ratio Index for Bright Soil (RIBS) was developed based on TC1 and NDSI, and the Product Index for Dark Soil (PIDS) was established by TC1 and NDVI. Their applications to the Landsat 8 Operational Land Imager images and 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in China revealed the efficiency. The two soil indices proficiently highlighted soil covers with consistently the smallest values, due to larger TC1 and smaller NDSI values in bright soil, and smaller NDVI and TC1 values in dark soil. The RIBS is capable of distinguishing bright soil from ISA without masking vegetation and water body. The spectral separability bright soil and ISA were perfect, with a Jeffries–Matusita distance of 1.916. And the PIDS was the only soil index that could discriminate dark soil from other land covers including ISA. The soil areas in China were classified using a simple threshold method based on MODIS images. An overall accuracy of 94.00% was obtained, with the kappa index of 0.8789. This study provided valuable insights into developing indices for characterizing land surfaces from different perspectives.     

光谱指数趋势面的城市地表温度降尺度转换

针对城市及周边区域建造区和自然地表交织分布的特点,探讨了利用归一化植被指数(NDVI)和归一化建造指数(NDBI)构造趋势面的地表温度(LST)降尺度方法,以北京市市区及周边较平坦区域为例实现了LST自960 m向120 m的降尺度转换。分析了LST空间分布特征及NDVI、NDBI对地物的指示性特征;以北京市四至六环为界分析NDVI、NDBI趋势面对地表温度的拟合程度及各自的适用区域;在120 m、240 m、480 m和960 m 4个尺度上评价了NDVI、NDBI和NDVI+NDBI趋势面对LST的拟合程度和趋势面转换函数的尺度效应;对NDVI、NDBI和NDVI NDBI等3种方法的降尺度结果分覆盖类型、分区域对比评价。实验结果表明结合两种光谱指数的NDVI NDBI方法降尺度转换精度有所改善,改善程度取决于地表覆盖类型组合。     

Field based spectral reflectance studies to develop NDSI method for snow cover monitoring

Snow is highly reflective in the visible region of the electromagnetic spectrum making it possible to easily distinguish on a satellite image. However, cloud cover and mountain shadows pose a serious problem in the identification of snow in a mountainous region. Therefore, to identify snow in such an environment, a Normalized Difference Snow Index (NDSI) has been applied. The NDSI is based on the high reflectance of snow in the visible region and its low reflectance in the SWIR region, whereas, reflectance of cloud remains high compared to snow in the SWIR region. Efforts have been made to carry out field observations on reflectance of various land features near Manali in Himachal Pradesh (HP) to develop NDSI values for identifying snow. Field data have been collected using three field radiometers, viz., Multi-band Ground Truth Radiometer (GTR) operating in the 12 spectral bands ranging from visible to near-infrared wavelengths, Near-Infrared Ground Truth Radiometer (NIGTR) operating in the SWIR range, and Ratio-Radiometer (RR) operating in two spectral bands, one in the visible range, and another band in the SWIR range. All these three field radiometers have been designed and developed indigenously at the Space Applications Centre (ISRO), Ahmedabad. NDSI values for all types of snow, such as, fresh, clear, patchy and wet, have been found to be in the range 0.9 to 0.96. In addition, the NDSI value for snow under mountain shadow is found to be more than 0.9. This suggests the use of NDSI method for snow cover monitoring under mountain shadow. NDSI values for other land features such as soil, vegetation, and rock were substantially different than snow. However, water bodies have NDSI values close to snow and they need to be masked during snow cover delineation using NIR band.     

Modelling high arctic percent vegetation cover using field digital images and high resolution satellite data

In this study, digital images collected at a study site in the Canadian High Arctic were processed and classified to examine the spatial-temporal patterns of percent vegetation cover (PVC). To obtain the PVC of different plant functional groups (i.e., forbs, graminoids/sedges and mosses), field near infrared-green-blue (NGB) digital images were classified using an object-based image analysis (OBIA) approach. The PVC analyses comparing different vegetation types confirmed: (i) the polar semi-desert exhibited the lowest PVC with a large proportion of bare soil/rock cover; (ii) the mesic tundra cover consisted of approximately 60% mosses; and (iii) the wet sedge consisted almost exclusively of graminoids and sedges. As expected, the PVC and green normalized difference vegetation index (GNDVI; (R_NIR − R_Green)/(R_NIR + R_Green)), derived from field NGB digital images, increased during the summer growing season for each vegetation type: i.e., ∼5% (0.01) for polar semi-desert; ∼10% (0.04) for mesic tundra; and ∼12% (0.03) for wet sedge respectively. PVC derived from field images was found to be strongly correlated with WorldView-2 derived normalized difference spectral indices (NDSI; (R_x − R_y)/(R_x + R_y)), where R_x is the reflectance of the red edge (724.1 nm) or near infrared (832.9 nm and 949.3 nm) bands; R_y is the reflectance of the yellow (607.7 nm) or red (658.8 nm) bands with R²’s ranging from 0.74 to 0.81. NDSIs that incorporated the yellow band (607.7 nm) performed slightly better than the NDSIs without, indicating that this band may be more useful for investigating Arctic vegetation that often includes large proportions of senescent vegetation throughout the growing season.     

基于TM图像的“增强的指数型建筑用地指数”研究

以Landsat TM/ETM+图像为数据源,研究城镇和农村建筑用地信息的提取方法.首先利用TM7,4,2波段创建归一化差值裸地与建筑用地指数(normalized difference bareness and built- up index,NDBBI);然后根据裸地在裸土指数(bare doil index,BSI)图像上的亮度值最高、在改进型归一化差值水体指数(modified normalized difference water index,MNDWI)图像的亮度值最低的特征,提出了增强型裸土指数(enhanced baresoilindex,EBSI);最后选用NDBBI,EBSI,MNDWI和SAVI( soil adjustment vegetation index,SAVI)4个指数,构建一种新型的建筑用地指数,称为“增强的指数型建筑用地指数”( enhanced index - based built - up index,EIBI),可快速地提取建筑用地信息.实验结果表明,用EIBI提取的建筑用地信息客观,人为干预少,可信度高,提取精度可达90％以上,适合于同时提取城市和农村建筑用地信息.     

归一化光谱分解模型在城市化信息提取中的应用

以2001年7月11日LandsatETM7影像和2009年7月16日TM影像为数据源,基于V-I-S理论模型,采用归一化光谱分解模型提取了乌鲁木齐市区范围内2个时段的植被、土壤、不透水层3个连续地表参数分量。通过对不透水层不同阈值的划分,提取了2时段的乌鲁木齐市城市发展的空间信息,结果较为满意;通过空间叠加计算方式获取了8年来乌鲁木齐市城市化发展的空间信息和主要拓展方向。结果表明,乌鲁木齐城市化发展速度较快,特别是北扩趋势显著。     

北京城市不透水层覆盖度遥感估算

 应用线性光谱混合模型研究城市环境生物物理组成，端元的确定是其关键。城市地表同物异谱现象显著，光谱变异强烈，对于高反照率地物尤其突出。端元的光谱变异对线性光谱混合模型拟合结果产生重要影响。以同种纯净地物光谱曲线形状具有相似性为出发点，提出了一种端元优化选取方法，在此基础上计算了北京城市地表不透水层覆盖度。研究结果表明，该方法能够在一定程度上减小端元光谱变异性对线性光谱混合模型拟合结果的影响，进而提高城市不透水层覆盖度的估算精度。     

城市地表典型遥感参数与热岛效应时空分析研究——以桂林市为例

利用桂林市1991年和2013年的卫星遥感影像数据,分别提取两个时期的归一化建筑指数(NDBI)、归一化植被指数(NDVI)和改进的归一化差异水体指数(MNDWI)3个地表典型参数,并利用回归分析方法,分析了3个地表典型参数与利用Landsat卫星(热红外波段)反演的地表温度(LST)的相关关系。研究结果表明:桂林市中、高、极高温区3个温度类别的变化趋势与桂林市城市扩张趋势基本一致;NDBI与LST成显著的正相关关系,而NDVI、MNDWI则与LST成负相关关系,且各参数对地表温度响应的程度各有差异。该研究成果对于揭示桂林市热岛效应现状、缓解热岛效应及推进桂林市生态城市建设具有一定的参考意义。     

基于指数图像的城镇建设用地信息识别与提取——以岳阳市为例

以岳阳市Landsat ETM+影像为信息源,分析了典型地物的光谱特征及可分性,将地物种类归并为建设用地、植被、水体三大类,分别选用归一化裸露指数( NDBI)、重归一植被指数(RDVI)和改进的归一化水体指数(修改后的NDWI)作为三种地类的指示因子,通过阈值分割、掩膜处理去除了非建设用地区域的噪音信息,得到了比较准...     

Spectra and vegetation index variations in moss soil crust in different seasons,and in wet and dry conditions

Similar to vascular plants, non-vascular plant mosses have different periods of seasonal growth. There has been little research on the spectral variations of moss soil crust (MSC) over different growth periods. Few studies have paid attention to the difference in spectral characteristics between wet MSC that is photosynthesizing and dry MSC in suspended metabolism. The dissimilarity of MSC spectra in wet and dry conditions during different seasons needs further investigation. In this study, the spectral reflectance of wet MSC, dry MSC and the dominant vascular plant (Artemisia) were characterized in situ during the summer (July) and autumn (September). The variations in the normalized difference vegetation index (NDVI), biological soil crust index (BSCI) and CI (crust index) in different seasons and under different soil moisture conditions were also analyzed. It was found that (1) the spectral characteristics of both wet and dry MSCs varied seasonally; (2) the spectral features of wet MSC appear similar to those of the vascular plant, Artemisia, whether in summer or autumn; (3) both in summer and in autumn, much higher NDVI values were acquired for wet than for dry MSC (0.6 ∼ 0.7 vs. 0.3 ∼ 0.4 units), which may lead to misinterpretation of vegetation dynamics in the presence of MSC and with the variations in rainfall occurring in arid and semi-arid zones; and (4) the BSCI and CI values of wet MSC were close to that of Artemisia in both summer and autumn, indicating that BSCI and CI could barely differentiate between the wet MSC and Artemisia.     

On Urban Heat Island of Beijng Based on Landsat TM Data

IntroductionThe scientists have begun to retrieve land sur-face temperature (LST) fromsatellite data sincethe launch of TIROS-Ⅱin 60s of the 20th centu-ry . With the development of remote sensingtechnology and its application, more and moreLST retrieval …     

多尺度城市地表温度降尺度方法

针对目前星载热红外传感器的空间分辨率低,无法满足城市尺度的生态环境研究需求的现状,该文选择地表覆盖类型复杂的区域,根据研究区土地覆盖类型,选取归一化植被指数(NDVI)、城市不透水面指数(ISA)、改进的归一化差异水体指数(MNDWI)等因子加入DisTrad模型,采用移动窗口逐步回归统计地表温度和因子的线性关系,利用半方差曲线函数和均方根误差综合确定最优移动窗口的大小,以提高地表温度降尺度精度。研究结果表明:改进的DisTrad模型在地表覆盖类型复杂区域,具有良好的降尺度目视效果,且具有较高的降尺度精度,尤其在低植被覆盖的建筑区、水体区域具有更高的精度。     

On urban heat island of Beijing based on landsat TM data

Land surface temperature (LST) of Beijing area was retrieved from Landsat TM thermal band data utilizing a radiative transfer equation and the urban heat island (HUI) effects of Beijing and its relationship with land cover and normalized difference vegetation index (NDVI) were discussed. The result of LST showed that the urban LST was evidently higher than the suburban one. The average urban LST was found to 4. 5°C and 9°C higher than the suburban and outer suburban temperature, respectively, which demonstrated the prominent UHI effects in Beijing. Prominent negative correlation between LST and NDVI was found in the urban area, which suggested the low percent vegetation cover in the urban area was the main cause of the urban heat island.     

基于TM数据的植被覆盖度反演

本文首先对TM影像进行了几何纠正、辐射校正、大气校正;然后根据混合像元的结构特征,利用TM数据从植被指数(NDVI)中采用“等密度模型”和“非密度模型”提取了宜昌南部地区的植被覆盖度。在用“非密度模型”反演植被覆盖度的过程中,叶面积指数(LAI)是一个必要的参数,本文提出了一种改进的借助可见光波段和近红外波段反射值来提取叶面积指数(LAI)的方法。通过和MODIS数据反演结果比较表明:“非密度模型”的估算精度要高于“等密度模型”;利用“等密度模型”和“非密度模型”反演植被覆盖度是可行。     

Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery

Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km²). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r² = 0.64, r² = 0.71, respectively). Best results were achieved for LSU (r² = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r² = 0.39 for LSU, r² = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.     

An assessment of the impact of urbanization and land use changes in the fast-growing cities of Saudi Arabia

Understanding rates, patterns and types of land use and land cover (LULC) changes are essential for various decision-making processes. This study quantified LULC changes and the effect of urban expansion in three Saudi Arabian cities: Riyadh, Jeddah and Makkah using Landsat images of 1985, 2000 and 2014. Seasonal change of vegetation cover was conducted using normalised difference vegetation index, and object-based image analysis was used to classify the LULC changes. The overall accuracies of the classified maps ranged from 84 to 95%, which indicated sufficiently robust results. Urban area was the most changed land cover, and most of the converted land to urban was from bare soil. The seasonal analysis showed that the change of vegetation cover was not constant due to climatic conditions in these areas. The agricultural lands were significantly decreased between 1985 and 2014, and most of these lands were changed to bare soil due to dwindling groundwater resources.     

运用归一化光谱混合模型分析城市地表组成

运用归一化光谱混合分析(NSMA)方法,用ETM 数据调查广州市海珠区城市地表组成,采用亮度标准化方法减小亮度变化。通过标准化,使亮度差异在每个植被-非渗透性表面-土壤-水体(V-I-S-W)组成中减小或者消除,这样使得一个单一的端元能够代表一种地表组分。在此基础上,通过归一化影像,选择了植被、非渗透性表面、土壤和水体4种端元,运用一种约束光谱混合分析(SMA)模型,分解了不同种类的城市地表组成。通过与已有模型计算结果比较,认为本文所构建的模型较优,其对研究区非渗透性表面估计的均方根误差为12.6%。     

Built-up and vegetation extraction and density mapping using WorldView-II

This study demonstrates the use of high resolution WorldView-II satellite data in extraction of built-up land and vegetation using normalized index techniques. The PCA 1 and NIR 2 bands-based built-up index was proposed for extracting built-up land, which exhibit high accuracy. The normalized difference vegetation index based on Red Edge and NIR 2 bands of WorldView-II produced high accuracy inthe estimation of vegetation compared to the use of Red and NIR bands. The grid technique used in estimating built-up and vegetation density from precisely classified images provided better and accurate assessment of built-up and vegetation density in heterogeneous landscape of urban areas. This shows areas of very high to high built-up density are located in the central, western and southern parts, which are primarily devoid of vegetation. This study indicates possibilities of utilizing high resolution satellite data in urban landscape characterization using a grid-based technique.