土壤背景对冠层NDVI的影响分析

归一化差值植被指数NDVI是植被遥感中应用最为广泛的指数之一, 但它受土壤背景等因素的干扰比较强烈。结合实测的土壤数据以及公式推导、PROSAIL 模型模拟等方法分析了这种影响。首先, 假定与土壤线性混合且叶片呈水平分布的植被冠层, 根据土壤与植被分别在红光、近红外波段处的反射率值、植被覆盖度等参数, 利用公式推导了土壤背景对不同覆盖度下冠层NDVI的影响。其次, 利用PROSAIL冠层光谱模拟模型, 模拟分析了土壤背景对不同LAI下冠层NDVI的影响。分析的结果表明:LAI 越小, 土壤背景的影响越大; 暗土壤背景下的冠层NDVI值大于亮土壤背景下冠层的NDVI值; 并且,暗土壤条件下,NDVI值对土壤亮度的变化更敏感,而亮土壤下,NDVI值则对LAI或覆盖度的变化更敏感。最后利用实测的不同土壤背景下的冬小麦冠层光谱数据, 验证了公式推导和模型模拟的结果。     

一种简单的估算植被覆盖度和恢复背景信息的方法

植被覆盖度是评估生态环境的一个重要参数，其对于全球环境变化和监测研究具有重要意义．如何从遥感资料估算植被覆盖度，并提高估算精度是建立全球或区域气候、生态模型的基础工作．该文从分析土壤、植被光谱信号的特点出发，根据植被覆盖度的定义，推导出计算植被覆盖度的方法，并进一步提出了计算植被覆盖度的三波段最大梯度差法．在此基础上，对部分植被覆盖下的土壤光谱实现重建．上述方法实现简单，适用范围广，并可有效分离植被、土壤的影响，因而有望替代常用的通过NDVI估算植被覆盖度的方法．     

利用“北京一号”小卫星数据估算植被覆盖度的可行性研究

利用"北京一号"小卫星数据,以密云水库流域为研究区域,采用归一化植被指数(NDVI)像元二分法,进行地面植被覆盖度估算研究,并对估算结果进行实地检验和分析,其估算值与实际值之间的相关性较高 (86%).结果表明,利用"北京一号"小卫星数据进行植被覆盖度估算及监测应用是可行的.     

基于MODIS植被指数估算青海湖流域植被覆盖度研究

将MODIS数据合成的4种植被指数作为输入参数,采用像元二分模型对研究区的植被覆盖度进行估算,利用2006年的TM数据解译结果和2011年8月的野外实测数据对反演结果进行验证。结果显示:采用ND-VI估算的植被覆盖度比较符合研究区实地状况,样点估算精度达到87.13%;其他3种植被指数估算的植被覆盖度值比实际值低,尤其是对该区域典型植被草原草甸的覆盖度估算结果明显偏低。研究表明:2011年8月青海湖流域植被覆盖度以中高覆盖度为主,占整个流域面积的57%以上;植被覆盖度在空间上呈中部高、西北低的分布特点。     

基于无人机影像的滩涂入侵种互花米草植被信息提取与覆盖度研究

以福建三沙湾为试验区,以地面光谱和低空无人机获取的可见光影像与ADC多光谱影像为数据源对入侵种互花米草植被信息和覆盖度进行研究。构建了基于可见光波段的改进型土壤调整植被指数V-MSAVI用于可见光影像植被信息提取,以NDVI指数模型对ADC多光谱影像进行了植被覆盖度估算。结果表明,V-MSAVI指数具有较好的适用性;在互花米草覆盖度方面以40%~60%和60%~80%中高等级分布为主。精度检验表明,基于V-MSAVI植被指数提取得到的互花米草总体精度为89%,Kappa系数为0.77;植被覆盖度的估算值与真实值之间的均方根误差(RMSE)为0.06,决定系数R~2为0.92。     

不同覆盖度下小麦农田土壤对NDVI影响模拟分析

生长于不同土壤类型背景条件下的相同长势小麦农田遥感像元尺度的归一化植被指数(NDVI)有很大差异,也一直困扰着利用NDVI进行小麦长势有效监测和精确评价。拟定小麦冠层光谱不变即小麦冠层NDVI为一常数条件下,选择反射率差异较大的我国9种典型土壤类型作为土壤背景,由小麦冠层和土壤背景的不同线性混合比模拟计算遥感像元尺度上的植被覆盖度,研究不同土壤类型背景对小麦农田NDVI信息的影响。研究结果表明:同一土壤类型背景条件下,随着植被覆盖度逐渐增加,小麦农田NDVI总体表现为增长的趋势,反之亦然;不同类型土壤背景对小麦农田NDVI造成很大差异,当植被覆盖度大于25%时,随着植被覆盖度的增加对小麦农田NDVI影响差异性逐渐减小;不同类型土壤背景也导致小麦农田NDVI对植被覆盖度的敏感性有明显差异,较低反射率土壤背景条件下的敏感性随着植被覆盖度增长呈现曲线下降的趋势,较高反射率土壤背景条件下敏感性随着植被覆盖度的增长而单调增加,为不同类型土壤背景的各小麦生长期遥感NDVI信息估算频次选择提供依据。     

基于Landsat８影像的额济纳荒漠绿洲植被覆盖度估算方法对比研究

荒漠绿洲是维持当地人类生存和社会发展的主要依托,但其地表植被稀疏,生态系统极其脆弱,而植被覆盖度是反映荒漠生态环境信息的重要指标之一.以黑河下游额济纳荒漠绿洲为例,基于Landsat８影像和野外实测植被覆盖度数据,对比和分析现有的适宜于干旱荒漠区的３类植被覆盖度提取方法(经验模型法、像元二分法和三波段梯度差法)在该区域的应用效果,并尝试利用基于转换型土壤调整植被指数(TSAVI)的像元二分模型法和修正的三波段梯度差法(MTGDVI)进行植被覆盖度估算,以期找到计算额济纳荒漠绿洲植被覆盖度的最佳模型. 研究结果表明:用TSAVI像元二分模型法的反演精度高而且能够较好地估算额济纳荒漠区域和绿洲区域的植被覆
盖度,适用于估算额济纳荒漠绿洲的植被覆盖度.     

基于Landsat 8影像的额济纳荒漠绿洲植被覆盖度估算方法对比研究

荒漠绿洲是维持当地人类生存和社会发展的主要依托,但其地表植被稀疏,生态系统极其脆弱,而植被覆盖度是反映荒漠生态环境信息的重要指标之一。以黑河下游额济纳荒漠绿洲为例,基于Landsat 8影像和野外实测植被覆盖度数据,对比和分析现有的适宜于干旱荒漠区的3类植被覆盖度提取方法(经验模型法、像元二分法和三波段梯度差法)在该区域的应用效果,并尝试利用基于转换型土壤调整植被指数(TSAVI)的像元二分模型法和修正的三波段梯度差法(MTGDVI)进行植被覆盖度估算,以期找到计算额济纳荒漠绿洲植被覆盖度的最佳模型。研究结果表明:用TSAVI像元二分模型法的反演精度高而且能够较好地估算额济纳荒漠区域和绿洲区域的植被覆盖度,适用于估算额济纳荒漠绿洲的植被覆盖度。     

基于多源信息融合的土壤含水量估算

遥感信息在大面积土壤水分监测中具有不可替代的优势。通过对试验区域的气象数据、土壤类型数据、土壤和水体的光谱特征曲线、多时相遥感影像数据等进行预处理,提取图像信息和属性数据,并对土地利用类型和植被覆盖度进行划分。基于土壤的光谱响应机制建立像元反射光谱信息分解模型,以此计算出该区域土壤容积含水率。结果表明该方法对于低植被区的监测精度较高(理论精度89.78%),可作为土壤水分监测预警的依据。     

基于MODIS影像的植被覆盖度提取研究

利用线性光谱混合模型对河南省三门峡地区MODIS1B影像进行植被覆盖度（fv）信息提取,将结果与反映植被覆盖度的NDVI比较,并提出在实测资料缺乏的情况下利用同期高分辨率ETM＋图像对选取样本区域进行定量验证的方法。结果表明,对于MODIS数据,线性光谱混合（LSMM）分解方法能有效提取大区域范围的植被覆盖度信息,比NDVI-fv经验统计方法更具有理论意义,为快速、准确、高效的植被监测提供了新思路。     

The impact of imperfect ground reference data on the accuracy of land cover change estimation

Error in the ground reference data set used in studies of land cover change can be a source of bias in the estimation of land cover change and of change detection accuracy. The magnitude of the bias introduced may be very large even if the ground reference data set is of a high accuracy. Sometimes the bias is of a predictable systematic nature and so may be reduced or even removed. The impacts of ground reference data error on the accuracy of estimates of the extent of change and on change detection accuracy were explored with simulated data. In one scenario illustrated, the producer's accuracy of change detection was estimated to be ～61% when in reality it was 80%, the substantial underestimation of accuracy arising through the use of a ground reference data set with an accuracy of 90%. In the same scenario, the extent of change was also substantially overestimated at 26%, when in reality a change of only 20% had occurred. Reducing the effect of error in ground reference data will enable more accurate estimation of land cover change and a more realistic appraisal of the quality of remote sensing as a source of data on land cover change.     

Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal forest cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize forest cover and gross forest loss over the study period. Forest cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official forest cover statistics of the Russian government. Gross forest cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross forest cover loss within European Russia 2000-2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 forest cover. At the regional scale, the highest proportional forest cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the forest cover depletion around large industrial cities as the hotspot of forest cover change in European Russia.     

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.     

Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover

Annual forest cover loss indicator maps for the humid tropics from 2000 to 2005 derived from time-series 500 m data from the MODerate Resolution Imaging Spectroradiometer (MODIS) were compared with annual deforestation data from the PRODES (Amazon Deforestation Monitoring Project) data set produced by the Brazilian National Institute for Space Research (INPE). The annual PRODES data were used to calibrate the MODIS annual change indicator data in estimating forest loss for Brazil. Results indicate that MODIS data may be useful in providing a first estimate of national forest cover change on an annual basis for Brazil. When directly compared with PRODES change at the MODIS grid scale for all years of the analysis, MODIS change indicator maps accounted for 75% of the PRODES change. This ratio was used to scale the MODIS change indicators to the PRODES area estimates. A sliding threshold of percent PRODES forest and 2000 to 2005 deforestation classes per MODIS grid cell was used to match the scaled MODIS to the official PRODES change estimates, and then to differentiate MODIS change within various sub-areas of the PRODES analysis. Results indicate significant change outside of the PRODES-defined intact forest class. Total scaled MODIS change area within the PRODES historical deforestation and forest area of study is 120% of the official PRODES estimate. Results emphasize the importance of synoptic monitoring of all forest change dynamics, including the cover dynamics of intact humid forest, regrowth, plantations, and cerrado tree cover assemblages. Results also indicate that operational MODIS-only forest cover loss algorithms may be useful in providing near-real time areal estimates of annual change within the Amazon Basin.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

一种用于检测转角、转速和转矩的新型多功能非接触式传感器

对一种基于纳米晶软磁合金的非接触式,用于测量转角、转速和转矩的多功能传感器进行了研究。介绍了传感器的结构和原理,推导出传感器的输出方程和灵敏度表达式。通过试验,分析了传感器激磁磁场强度对转矩测量精度的影响,并得出了最佳激磁磁场强度;在25℃～100℃范围研究了温度变化对传感器输出的影响,其零点温度漂移(25℃)为0.002%F.S/℃;在0～450 N.m范围进行转矩加载,得到了较高的测量灵敏度以及较低的线性度误差、重复度误差及迟滞静态误差;在500 r/min～3 523 r/min范围内,转速最大相对误差为0.60%。试验数据显示,该传感器的精度能够达到1.0%,对于一般工程应用是可行的。     

Evaluation of sediment yield by using remote sensing and GIS: A case study from the Phulang Vagu watershed,Nizamabad District (AP), India

The primary objective of this paper is to identify soil erosion zones and to suggest appropriate measures for control of soil erosion using remote sensing, GIS and conventional technique in the Phulang Vagu watershed in the Sriramsagar catchment area of Andhra Pradesh. The digital imagery data of the study area is obtained from the IRS-IC (LISS-III) satellite whereas the toposheets and rainfall data of the study area were obtained from the Survey of India. Satellite images were interpreted to prepare land use/land cover maps by using ERDAS image processing system. Out of 725.983 km 2 of the study area, about 301.435 km 2 is wasteland which is identified as susceptible for soil erosion. Toposheets of the study area were used to prepare drainage and slope maps. Drainage pattern is mainly dendritic with a density of 1.26 km -1 and the stream slope is 0.00614. The arithmetric average method is used to find average annual rainfall. The above parameters were used to calculate the amount of soil erosion from the catchment area. It was found that 882.389 m 3 km -2 year -1 of soil is being eroded from the catchment area which is more than the value adopted in the design of Sriramsagar reservoir. Therefore soil conservation measures such as vegetative cover in the waste land are needed and 12 check dam sites have been proposed by superimposing drainage map and slope map in conjunction with land use/land cover map. With these soil conservation measures, the soil erosion could be kept within the design value of Sriramsagar reservoir.     

Estimating maize ground cover using spectral data from Aqua-MODIS in Córdoba,Argentina

Ground cover by foliage is a biophysical property of vegetation linked both to the interception of photosynthetically active radiation and to the crop transpiration rate. The spectral information provided by the Moderate Resolution Imaging Spectroradiometer on board the Aqua (Aqua-MODIS) satellite, which has a spatial resolution of 250 m, is an observation and monitoring resource that may be appropriate for estimating the ground cover of maize when plots exceed 40 ha. In this research, 10 maize plots were monitored in the central region of the province of Córdoba, Argentina, during the 2005–2006 growing season, obtaining photographic records of ground cover and soil moisture data. The normalized difference vegetation index (NDVI) of the Aqua-MODIS images showed a significant linear relationship with maize ground cover which, when the complete cycle is taken into account, is sufficient to explain 87% of the variability of ground cover, with an RMSE of 9%, a level of accuracy that increases when the crop is in the vegetative stage and the moisture conditions of the soil are less limiting. Other vegetation indices and linear mixed models were assessed. In addition to using data from the red and near-infrared channels, they incorporate information about soil conditions, but they showed no predictive advantages compared to the NDVI, resulting in simple models that explained between 77% and 87% of the variability of ground cover, with RMSE values of between 9% and 14%.     

土壤可蚀性(K)值图编制方法的初步研究

介绍了利用土壤普查图件和剖面理化分析的成果资料，编制土壤可蚀性(K)值图的方法，沙及求取剖面点K值的查图表法、图斑分并与图斑K值的确定原则和编制程序等。应用上述方法，完成了我国第一张地区级K值图，效果良好。经分析，与公式算法比，查图表法的K值精度为85.45%;与径流小区实测值比，用K值图上相应K值监测的土攘年流失量的精度为86.0%.。该法既有更新水土流失调查方法的科学意义和指导流失治理的实
用价值，也值得进一步深入研究。     

Capability of SPOT XS data in producing detailed land cover maps at the urban-rural periphery

The capability of SPOT multi-spectral (XS) data in generating detailed land cover maps at the urban-rural fringe is critically evaluated using two images, one recorded in summer and another in winter. The factors affecting the mapping accuracy are also identified and assessed in this study. Covering an area of 90km2 in South Auckland, New Zealand, two subscenes of SPOT XS images were used to map 10 categories of land cover at level II of the Anderson scheme with an overall accuracy of 76.2 and 81.4 per cent from the winter and summer data, respectively. The higher accuracy achieved using the summer image is due to the higher distinctiveness of vegetative covers in summer. The main limiting factors are identified as the high heterogeneity of land-use patterns commonly found at the urban periphery, poor representativeness of training samples caused by the presence of the same land cover element across a diverse range of land cover classes, and varying conditions of vegetative covers.