泉州市城市扩展的遥感监测及其城市化核分析

以泉州市为研究区,采用仿归一化植被指数(NDBI)和监督分类法对1989年和2000年的TM/ETM⁺图像进行城市空间信息提取,分析了城市空间扩展的趋势,探索出泉州市是以乡镇企业为城市化核来进行城市化过程的     

西藏自治区盐湖矿产资源遥感信息提取方法

西藏自治区地域广,湖泊多,盐湖矿产资源十分丰富,但调查研究程度较低。采用常规调查方法,周期长、费用高、难度大。以TM(ETM⁺)数据为主要信息源,采用波段合成、密度分割、比值处理等方法进行信息提取,取得了良好效果。圈划出的成矿远景区准确、可靠,经验证新发现了一批盐类矿产地。     

西藏色林错湖面增长遥感信息动态分析

根据1972年陆地卫星MSS,1992年TM与1999年ETM⁺3个时相的遥感数据信息,对西藏色林错湖面增长进行动态分析后,发现近30a来湖面逐渐扩大,从1707km²增大到了1823km².本文分别从气象学、构造地质学及高原隆升等方面分析了湖面扩大的原因.认为很有可能是由于温室效应,使得气温上升,冰雪、冰川融化和冻土软化释放水注入色林错,进而引起湖面增长.并就其所揭示的环境信息进行了初步探讨.     

基于DEM的ETM+图像辐射校正及汉江流域反照率的计算

对Gilabert[1]大气校正方法进行改进,提出了一种简单易行的辐射校正方法。该方法只需从ETM⁺图像上获取两个不同海拔高程的暗像元,再依据DEM进行大气校正,反演出各个高程上的大气及辐射参数,并在此基础上进行地形校正,进而获取LandsatETM⁺1～5和7波段的地表反射率,通过对各窄波段地表反射率的线性组合获取反照率。结果表明,该方法可行,精度较高。     

黑河流域叶面积指数的遥感估算

研究利用Landsat7ETM⁺遥感数据获取黑河流域植被叶面积指数(LAI)空间分布的可行性。该研究是基于黑河流域分布式水文模型的一个重要输入项———LAI空间分布数据的需要而产生的。文章在详尽的野外观测数据基础上,分别探究实测LAI与同时相ETM⁺3、4、5、7波段反射率及相关植被指数(SR、NDVI、ARVI、RSR、SAV I、PVI、GESAVI)的相关关系,率定最佳的LAI遥感反演及其空间分布方案。研究发现,针对特定的自然条件,将研究区分为植被覆盖度小的稀疏立地和覆盖度大的密集立地,分别采用土壤调节植被指数(SAVI)和大气阻抗植被指数(ARVI)进行2种林地的LAI估算最为可靠,在此基础上,提出黑河地区LAI估算及其空间分布的遥感制图方案。     

基于遥感的龙海市水田专题信息提取方法研究

以Landsat-7ETM⁺图像为基本资料,采用人机交互式非监督分类法、最大似然法和谱间关系阈值法分别提取龙海市水田信息。研究表明,3种方法各有优缺点,后2种方法提取精度较高,其中,谱间关系阈值法在分析不同类型水体与其背景地物光谱特征差异的基础上,挖掘谱间结构,总结各地物相分离的规律,因此提取效果最好。     

海水背景下不同浓度的甲烷含量高光谱定量反演

在油气资源遥感探测中,通过烃渗漏引起的海表面甲烷气浓度异常来探测海底气藏是最直接的方法之一。为了更好地识别海表甲烷异常,提高遥感反演精度,对海表甲烷气含量进行定量光谱分析研究。设计室内甲烷波谱测试平台,获取海水背景下不同含量甲烷高光谱数据为数据,对光谱数据预处理及进行比值导数光谱法,并提取光谱吸收特征参数,对甲烷含量与光谱参数之间进行相关性分析,构建甲烷含量的反演模型。比值导数光谱法确实抑制了海水背景信息,突出了甲烷特征。1650—1664 nm和2180—2210 nm波段范围的光谱参数与甲烷含量相关性显著;其中,波谷、波深、面积、斜率与甲烷含量显著相关。基于2180—2210 nm波段范围建立的波谷、波深、面积、斜率四元回归方程y=-14.356 - 5931.796x₁ - 4325.081x₂+241.481x₃+7531.973x₄拟合效果最好,R²为0.9817;且在此波段范围内基于波深建立的单变量甲烷反演模型y = 2047.571x - 9.758,R²为0.9741,比基于其他变量所建立的反演模型效果要好。成功获取了和海水背景下甲烷含量线性相关显著的对应波段和吸收特征,可为利用多/高光谱遥感预测勘探海表面甲烷气浓度提供一定的理论和技术依据。     

逐步回归分析在岩石波谱及化学成分分析中的应用

该文主要介绍应用回归分析方法探讨岩石波谱与岩石化学成分之间的相关性, 据其进一步查明控制沉积岩、岩浆岩、喷出岩和变质岩岩石波谱特征的主要化学成分的研究成果。研究发现H₂0⁺和H₂O^-的波谱特性是不同的。该研究成果对评价和应用岩石波谱特征提供了一种有前景的新方法。     

联合星载高光谱影像和堆栈集成学习回归算法的红树林冠层叶绿素含量遥感反演

红树林是世界上生产力最高、价值最高的湿地生态系统之一。冠层叶绿素含量CCC（Canopy Chlorophyll Content）作为红树林重要的生物物理参量,是估算其生产力和评价其健康状况的重要指标。本文利用珠海一号高光谱卫星（OHS）影像与Sentinel-2A多光谱数据计算传统植被指数与组合植被指数并构建了高维数据集,综合利用正态分布检验、最大相关系数法与变量重要性评价进行数据降维和变量优选;分别基于单一线性回归算法、机器学习回归算法和堆栈集成学习回归算法构建了红树林CCC遥感反演模型,探明北部湾红树林CCC的最佳遥感反演模型,验证OHS高光谱影像与Sentinel-2A数据反演红树林CCC的精度差异,评估SNAP-SL2P算法反演红树林CCC的适用性。研究结果表明：（1）通过数据降维和变量选择处理,从高维度OHS数据集选取了8个特征变量,其中RSI_（12,17）、DSI_（12,18）和NDSI_（6,12）组合植被指数对红树林CCC反演精度的贡献率较高;（2）联合OHS数据和最优堆栈GBRT集成学习回归模型（Score=0.999,RMSE=0.963 μg/cm²）的训练精度优于最优RF机器学习回归模型（RMSE降低了7.531 μg/cm²）,明显优于最优Lasso线性回归模型（RMSE降低了19.383 μg/cm²）;（3）在最优堆栈集成学习回归模型下,OHS数据反演红树林CCC的精度（R²=0.761,RMSE=16.738 μg/cm²）高于Sentinel-2A影像（R²=0.615,RMSE=20.701 μg/cm²）;（4）联合OHS和Sentinel-2A数据的最优堆栈集成学习回归模型反演红树林CCC的精度都明显优于SNAP-SL2P算法（R²=0.356,RMSE=49.419 μg/cm²）。研究结果论证了正态分布检验、最大相关系数法和基于XGBoost的特征选择方法有效降低了高维数据集的维度,并得到了最优特征变量;OHS数据的最优堆栈GBRT集成学习回归模型训练精度最高,是估算红树林CCC的最优反演模型;OHS和Sentinel-2A数据都能有效反演红树林CCC（R²均大于0.61）,而OHS数据的估算精度更高（R²大于0.75）;SNAP-SL2P算法不能有效反演红树林CCC（R²小于0.4）,且对红树林CCC数值存在系统性低估。     

辽西肖家营子地区金矿遥感信息提取及成矿预测

对TM图像作比值及主成分分析,选择能突出金矿蚀变的高特征向量值所在的主分量进行彩色合成,通过空间滤波及非监督分类处理,图像上显示出似晕圈状的彩色异常区。经野外验证,部分彩色异常区与化探异常分布范围相叠合,新发现的3处金矿点金品位分别达到2.74×10^-6、9.61×10^-6和9.6×10^-6。结合图像上的彩色异常信息和取样分析结果,在进行成矿条件分析后,提出了三处金(银铜)遥感综合成矿预测区。     

Investigating relationships between Landsat-7 ETM data and spatial segregation of LULC types under shifting agriculture in southern Cameroon

This paper investigates statistical relationships between land use/land cover (LULC), Landsat-7 ETM⁺ imagery and landscape mosaic structure in southern Cameroon where the conversion of tropical rain forest to shifting cultivation leads to dynamic processes, acting on the spatial aggregation of various LULC types. A Global Positioning System (GPS) was used in the field to identify a total of 171 shifting cultivation patches representing eight LULC types in two sub-areas. Because of the lack of a cloud-free image for the date of field sampling, the ETM⁺ imagery was acquired 2 months after field survey, during which it was assumed that no significant changes in LULC occurred (all dry season). Per pixel correlations were developed between spectral reflectance data, vegetation indices and LULC. As an exploratory study, several statistical methods (analysis of variance, means separations (Tukey HSD), principal component analysis (PCA), geo-statistical analysis, image classification and landscape metrics) were applied on point data and sensor images for evaluating the spatial variability within the landscape. Most variables explained 30–72% of LULC variation in the whole dataset. Those variables with high information content of LULC (infrared bands 4, 5, 7 and derived indices and PC1) also showed long ranges (6 km) spatial dependence as compared to those varying only within 1 km range. The results of these statistical analyses suggested the need to group some LULC types and the application of the Maximum Likelihood Classifier (MLC) for supervised classification provided a LULC map with the highest accuracy (81%) after consolidation of perennial LULC types, such as bush fallow, forest fallow and cocoa plantations. Landscape metrics computed from this map showed a high level of patch diversity and connectivity within the landscape and provided input data that can further be used to simulate predictive maps as substitute to cloud-covered sensor imageries. Landsat-7 ETM⁺ imagery proved to be useful in discriminating (with about 80% accuracy) the most dynamic LULC types such cropped plots and young fallow patches (shifting every season) and the extension front of the agricultural landscape.     

The Utility of Very High Spatial Resolution Images to Identify Urban Objects

Abstract

Landsat MSS, TM and SPOT XS imageries were used in conjunction with unsupervised, supervised and hybrid classilication techniques to classify land cover types in semi‐arid savannas of Mathison Pastoral Station in the Katherine region of northern Australia. Accuracy assessment was based on field data from 246 ground survey sites over a 745‐km² study area. Of 14 land cover classes identified by traditional mapping means, all combinations of imageries and classification techniques differentiated at least seven land cover types. The overall accuracy for these classifications ranged between 43% and 67%. SPOT XS image delivered the best accuracy followed by TM and MSS; unsupervised classification performed better than supervised and hybrid methods. User's and producer's accuracy of individual land units ranged from 0% to 100%. Riparian woodlands, woodland on limestone slopes, shrubland on clay plains, woodland on limestone plains and shadows were the best‐mapped classes. The land units that were associated with undulating hills were not mapped accurately. However, incorporation of a digital elevation model (DEM) in a GIS improved the overall accuracy. The user's and producer's accuracy of dominant land cover types were also enhanced. The classification results and the efficacy of the techniques at Mathison were similar to those found for a nearby semi‐arid area (Kidman Springs) about 200 km from Mathison. However, the overall accuracy was lower at Mathison than at Kidman Springs. Spectral classification masks were developed from the SPOT XS and TM imageries at Kidman Springs, and were applied to classify SPOT XS and TM imageries at Mathison. Initial results showed that the classification mask could be successfully extrapolated to map dominant land cover types but only with moderate accuracy (50%).     

Estimation of Trophic State Index of Sukhna Lake Using Remote Sensing and GIS

The present study is focussing on that how Landsat 7 ETM⁺ can be effectively used for estimation of Trophic State Index for Sukhna Lake. Sukhna Lake in Chandigarh has undergone lot of changes in last few decades. The depth and area both reduced tremendously. The shrinkage of the lake is due to the siltation and inadequate water volumes flowing to it. The Trophic State Index has been estimated by using secchi disk transparency and Landsat 7 ETM⁺ data. The in situ observations for parameters like pH, DO were measured by using multiparameter water quality instrument TROLL 9500. The best tested interpolation technique has been used to generate in situ images. The results have shown that the lake is in Hypereutrophic condition since 2000.     

Comparison of ETM+ and MODIS Data for Tropical Forest Degradation Monitoring in the Peninsular Malaysia

This study was undertaken the use of course and moderate spatial resolution remote sensing data to assess the forest degradation in the Peninsular Malaysia. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used as coarse spatial resolution data, while Landsat Enhanced Thematic Mapper⁺ (ETM⁺) imagery was used as moderate spatial resolution to compare the accuracy. Geometric and radiometric correction and re-sampling were performed in pre-processing to enhance the analysis and results. Canopy fractional cover was used as an approach to assess the forest degradation in this study. Then, an optimum vegetation index was selected to apply on canopy fractional cover to enhance the detection of forest canopy damage. At the same time, accuracy assessment for the approach was referred to the location of Neobalanocarpus Heimii and correlate with global evapotranspiration rate. The forest degradation analysis was also applied and compared for all of the states in the Peninsular Malaysia. In conclusion, Landsat ETM⁺ imagery obtained higher accuracy compare to MODIS using canopy fractional cover approach for forest degradation assessment, and can be more broadly applicable to use for forest degradation investigation.     

Detection of Changes in Glacier Mass Balance Using Satellite and Meteorological Data in Tirungkhad Basin Located in Western Himalaya

Himalayan glaciers and their mass balance are poorly sampled through direct mass balance measurements. Thus, based on Landsat datasets of ETM⁺ (2000), ETM⁺ (2006) and TM (2011), mass balance studies of 32 glaciers was carried out using accumulation area ratio (AAR) method in the Tirungkhad river basin, a tributary of Satluj River, located in western Himalayan region. The overall specific mass balance was negative varying from ?27 cm (2000) to ?41 cm (2011). Out of 32 glaciers, 27 glaciers (81.2 %) showed negative mean mass balance and 5 glaciers (18.7 %) showed positive mean mass balance. Mean of specific mass balance for the year 2000, 2006 and 2011 was found to be ?48 cm, ?55 cm and ?0.61 cm respectively, in case of glaciers with negative mass balance while in case of glaciers with positive mass balance, it was 0.67 cm (2000), 0.56 cm (2006) and 0.47 cm (2011). The investigations suggested a loss of ?0.034 km³ of glacial ice for 2000, 0.036 km³ for 2006 and 0.038 km³ for 2011 respectively. The negative mass balance of the glaciers since 2000 correlates well with the increasing trend of annual mean temperature and decreasing trend of precipitation (snow water equivalent (SWE) and rainfall). Based on Mann Kendall test the temperature and SWE trends were significant at 95 % confidence level, however, the rainfall trend was insignificant.     

Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain,UAE, 1992-2030

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012. Land Use and Land Cover maps for the study area were derived from 1992, 2006, and 2012 Landsat satellite images (TM, ETM⁺). The models achieved an overall accuracy of approximately 80 %. A Markov model was applied for 2006 and 2012 to predict land cover in 2030. The results conformed to the general trend of the Al Ain Master Plan 2030. This study demonstrates that remote sensing, with the availability of free Landsat data, is a viable technology that could be used to help in the prediction process especially in developing countries, where data availability is a problem.     

Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

Comparison of ERS-SAR and spot data for sugar beet crop cover assessment

The present work was aimed to compare the abilities of radar and optical satellite data to estimate crop canopy cover, which is a key component of productivity estimates. Three ERS-1 SAR images were obtained of East Anglia (UK) in 1995 and one ERS-2 SAR image in 1996. The images covered a study area around the IACR Brooms Barn Sugar Beet Research Institute. Field data comprising radiometric and biophysical measurements of the crop canopy were collected in two fields from June 22 to August 3, 1995 to coincide with ERS-1 SAR overpass dates. In 1996, field data were collected in two fields from June 11 to July 29 on a weekly basis. A previously calibrated version of the water cloud model was inverted to estimate Leaf Area Index (LAI) from ERS-1 and ERS-2 SAR backscatter and soil moisture samples. Canopy cover was estimated from the radar-estimated LAI using a standard exponential relationship that has a well-established coefficient for sugar beet. Radio-metrically and atmospherically corrected data from three SPOT images in 1995 and one SPOT image in 1996 were used to calculate the Optimised Soil Adjusted Vegetation Index (OSAVI), from which crop canopy cover was estimated using a relationship determined previously by canopy modelling. The crop cover values estimated by satellite were in good agreement with those measured on ground with the Parkinson radiometer. Radar data may be able to provide useful estimates of canopy cover for crop production modelling, especially in the case of loss of optical data due to cloud.     

Remote classification of Cerrado (Savanna) and agricultural land covers in northeastern Brazil

Remote classification of land-use/land-cover (LULC) types in Brazil's Cerrado ecoregion is necessary because knowledge of Cerrado LULC is incomplete, sources of inaccuracy are unknown, and high-resolution data are required for the validation of moderate-resolution LULC maps. The aim of this research is to discriminate between Cerrado and agriculture using high-resolution Landsat 7 ETM⁺ imagery for the western region of Bahia state in northeastern Brazil. The Maximum Likelihood Classification (MLC) and Spectral Angle Mapper (SAM) algorithms were applied to a ～3000 km² subset, yielding comparable classification accuracies. The panchromatic band was reserved for validation. User's and producer's accuracies were highest for non-irrigated agriculture (～94%) but lower for Cerrado Lato Sensu (89%). Classification errors likely resulted from spatial and spectral characteristics of particular classes (e.g. riparian forest and burned) and overestimation of other classes (e.g. Eucalyptus and water). Manual misinterpretation of validation data may have also led to lower reported classification accuracies.     

SPOT5与ERS-2卫星影像的联合立体定位

本文将SPOT5与ERS-2卫星影像联立构成一组立体像对,并通过立体定位的方式获取了地面点的三维坐标。根据本文立体像对前方交会的特殊几何图形,推导了立体定位理论精度的计算公式,并利用实验验证了公式的正确性,同时分析了影响立体定位精度的主要因素。实验结果表明,联合具有一定重叠度的SPOT5和ERS-2影像进行立体定位具有很强的可行性,其精度已可以满足部分测绘任务的要求。