SPOT multispectral analysis for producing supraglacial debris‐load estimates for Batura glacier,Pakistan

Abstract

SPOT multispectral and panchromatic data were evaluated to determine their utility to detect debris‐load characteristics of the Batura Glacier located in the Karakoram Himalaya. Debris‐depth measurements, surface samples, and ground photography were obtained and used with satellite‐derived information to produce supraglacial debris‐load and discharge estimates. Visual analysis of panchromatic data indicated that structural characteristics of the glacier exhibited unique textures associated with surface structure characteristics. Multispectral analysis revealed that stratified unsupervised classification of principal components can be used to produce classifications depicting supraglacial lithology and shallow debris‐load variability. Debris‐load discharge estimates ranged from 48–97 x 103 m3 yr1. These results indicate that SPOT multispectral data may be used to produce reasonable quantitative estimates of debris‐load characteristics for glacier mass balance and regional denudation studies.     

Rule‐based Integration of Remotely‐sensed Data and GIS for Land Cover Mapping in NE Costa Rica

Abstract

A classification method was developed for mapping land cover in NE Costa Rica at a regional scale for spatial input to a biogeochemical model (CENTURY). To distinguish heterogeneous cover types, unsupervised classifications of Landsat Thematic Mapper data were combined with ancillary and derived data in an iterative process. Spectral classes corresponding to ground control types were segregated into a storage raster while ambiguous pixels were passed through a set of rules to the next stage of processing. Feature sets were used at each step to help sort spectral classes into land cover classes. The process enabled different feature sets to be used for different types while recognizing that spectral classification alone was not sufficient for separating cover types that were defined by heterogeneity. Spectral data included the TM reflective bands, principal components and the NDVI. Ancillary data included GIS coverages of swamp extents, banana plantation boundaries and river courses. Derived data included neighborhood variety and majority measures that captured texture. The final map depicts 18 land cover types and captures the general patterns found in the region. Some confusion still exists between closely related types such as pasture with different amounts of tree cover.     

机载多光谱LiDAR数据的地物分类方法

机载多光谱LiDAR系统能够快速地获取大范围地表面上地物光谱和几何数据,并能够保证所获取的光谱与空间几何数据在空间和时间上相对完整和一致性。支持向量机（SVM）是一种基于小样本的学习方法,它避开了从归纳到演绎的传统分类过程。因此,本文提出了基于SVM多光谱LiDAR数据的地物目标分类方法。该方法首先将多个独立波段的LiDAR数据融合为单一的、包含多个波段信息的点云数据,然后将融合后的点云内插为距离影像和多光谱影像,最后利用SVM进行多光谱LiDAR数据的地物覆盖分类。通过对加拿大Optech公司的Titan机载多光谱LiDAR数据的试验证明：相对于传统的单波段LiDAR数据,多光谱LiDAR数据可以获得较好的地物分类精度;比较试验发现SVM分类方法适用于多光谱LiDAR数据的地物分类。     

Evaluating the Relationships between Fire Induced Canopy Mortality and Pre‐fire Multispectral Patterns

Abstract

Statistical tools were used to evaluate the relationships between observed fire effects and characteristics identifiable in pre‐fire multispectral and terrain data. Random points were placed within field delimited polygons representing areas of high and low canopy mortality. Each point was then used to extract Landsat TM based pre‐fire spectral characteristics and DEM derived terrain characteristics. The values for these random points were subjected to a multivariate discriminant analysis to ascertain whether specific spectral bands, indices, terrain characteristics, or specific combinations of these, could be effectively associated with the observed fire effects. Data values for high and low mortality points were found to be significantly different for all the pre‐fire data sets. The normalized difference vegetation index (NDVI) and tasseled cap greenness values provided the highest magnitude of direct differentiation between high and low mortality points. Discriminant analysis revealed that NDVI had the highest correspondence to degree of future canopy mortality, while the combined effect of the pre‐fire spectral response provided a prediction of observed fire effects with 87% accuracy, and the addition of terrain data improved accuracy to 90%.     

Evaluation of MIVIS Hyperspectral Data for Mapping Soil Degradation in an Upland Ecosystem of Southern Italy

Abstract

This paper describes the first stage of an experiment aiming to evaluate the potential and limitations of MIVIS data for mapping the degradational state of soils in a sub‐scene of a southern Apennines study area (Italy). After radiometric rectification of the image data and the collection of a field/laboratory spectral library, linear spectral mixture modelling (SMA) was used to decompose image spectra into fractions of spectrally distinct mixing components. Spectral endmember selection was based upon a principal component analysis (PCA) applied to a set of soil spectra, collected from the spectral library. The resulting abundance estimates (fractions) trough SMA were then analysed to identify soil conditions and to obtain an improved measure of dry and green vegetation cover. A map of soil conditions and dry‐green vegetation abundance, based upon MIVIS data was then derived from normalised fractions of soil‐vegetation endmembers obtained from SMA.     

Mapping Soil Resources in Part of Northern India Using Spaceborne Multispectral Data

Abstract

Spaceborne multispectral measurements have been found very useful tool in delineating soilscape boundaries. The Indian Remote Sensing Satellite (IRS 1B) Linear Imaging Self‐scanning Sensor (LISS‐II) data in the form of false colour composite (FCC) prints at 1:50,000 scale covering part of a complex terrain ‐ hard rock intermixed with the alluvium, were interpreted visually for mapping soil resources. The physiography and lithology of the terrain have been found to have a direct bearing on the occurrence of soils. The image elements which are the reflection of surface drainage, land use/land cover, wetness, etc have been helpful in segregating the broad physiographic units into their components. These sub‐divisions were ultimately found to be associated with the characteristic soils. The methodology and results are discussed in detail.     

Use of principal components of UAV-acquired narrow-band multispectral imagery to map the diverse low stature vegetation fAPAR

The fraction of absorbed photosynthetically active radiation (fAPAR) is an important plant physiological index that is used to assess the ability of vegetation to absorb PAR, which is utilized to sequester carbon in the atmosphere. This index is also important for monitoring plant health and productivity, which has been widely used to monitor low stature crops and is a crucial metric for food security assessment. The fAPAR has been commonly correlated with a greenness index derived from spaceborne optical imagery, but the relatively coarse spatial or temporal resolution may prohibit its application on complex land surfaces. In addition, the relationships between fAPAR and remotely sensed greenness data may be influenced by the heterogeneity of canopies. Multispectral and hyperspectral unmanned aerial vehicle (UAV) imaging systems, conversely, can provide several spectral bands at sub-meter resolutions, permitting precise estimation of fAPAR using chemometrics. However, the data pre-processing procedures are cumbersome, which makes large-scale mapping challenging. In this study, we applied a set of well-verified image processing protocols and a chemometric model to a lightweight, frame-based and narrow-band (10 nm) UAV imaging system to estimate the fAPAR over a relatively large cultivated land area with a variety of low stature vegetation of tropical crops along with native and non-native grasses. A principal component regression was applied to 12 bands of spectral reflectance data to minimize the collinearity issue and compress the data variation. Stepwise regression was employed to reduce the data dimensionality, and the first, third and fifth components were selected to estimate the fAPAR. Our results indicate that 77% of the fAPAR variation was explained by the model. All bands that are sensitive to foliar pigment concentrations, canopy structure and/or leaf water content may contribute to the estimation, especially those located close to (720 nm) or within (750 nm and 780 nm) the near-infrared spectral region. This study demonstrates that this narrow-band frame-based UAV system would be useful for vegetation monitoring. With proper pre-flight planning and hardware improvement, the mapping of a narrow-band multispectral UAV system could be comparable to that of a manned aircraft system.     

Interpretation and analysis of multispectral data for aricultural crop cover types-role of crop spectral response and crop sectral signature

Airborne multispectral data obtained over mono and multiple cropping systems of small farming agriculture was studied for two cropping seasons for a possible development of crop spectral signatures and to utilize such signatures for interpretation of multispectral data and for assessing agricultural potentials of a region. In multiple cropping system, the unique crop spectral response exhibited by crop species at specific growth stages facilitated interpretation and analysis of multispectral data with the knowledge of crop phenology. For resolving spectral confusion between crop species due to growtn stages of different crop species, temporal data were observed to be useful. Development and use of crop spectral sigrature for interpretation and analysis multispectral data related to mono cropping system were found to be relatively less complex and offer great promise because of minimum spectral confusion.     

主成分变换和彩色变换在TM图像信息提取中的应用——以苏州市为例

本文讨论了IHS彩色变换和主成分变换在苏州市TM多波段图像信息提取研究中的应用效果。研究表明,地物在多波段数据的彩色合成图像上具有较为稳定、特征的色调信息。利用苏州市水体、植被、城镇三大类地物的色调特征对TM_3(红)、TM_4(绿)、TM_5(蓝)彩色合成图像的色调成分H作了伪彩色密度分割,准确地反映了它们的分布状况。在主成分图像的IHS反变换彩色合成图像上,主成分的信息内容得到了定量、形象直观的反映,苏州市新旧城区的光谱差异、该市现状以及现代城市规划特点得到了清晰的显现。水稻、菜地、山地之间的光谱差异也得到明显的增强。主成分变换和彩色变换相结合,为多波段数据信息提取提供了一种新的方法。     

Multiple band digital orthophoto quadrangle data: A source for generating land use maps

Abstract

The purpose of this study was to investigate the use of color infrared‐digital orthophoto quadrangle (CIR‐DOQ) data to generate land use/land cover (LULC) maps and to incorporate them as data layers in geographic information systems (GIS) involving various resource management scenarios. The Danville 7.5‐minute quadrangle located in the southern part of Limestone and Morgan counties, Alabama, was used as the study site. Data for the special CIR‐DOQ were generated by scanning four 9x9 inch CIR aerial photographs at a uniform pixel sample grid of 25 microns resulting in 2 meters ground sample resolution. One‐half of the quadrangle was used to identify training sites for performing a supervised classification of the data and the other half to verify the accuracy of the classification. The CIR‐DOQ data were found to be adequate for using a supervised classification algorithm to differentiate major LULC classes, resulting in a classification accuracy of 93 percent. The superior spatial quality of the data over commençai satellite data affords resource managers an opportunity to more effectively study land cover and surface hydrological properties of an area, soil moisture and surface soil textures, as well as differentiate among vegetation species, using remote sensing techniques. However, caution must be exercised when using multispectral classification techniques to classify mosaicked CIRDOQ data because of the image enhancements used to generate the final product. In its present form, there are some limitations to the use of the data for performing spectral classifications. Hozvever, the high spatial resolution of the data enables even the novice resource planner to effectively use the data in visual interpretations of major LULC classes.     

Multiband Versus Multispectral Supervised Classification of Architectural Images

For the conservation of historic monuments, there may be considerable value in automating the methods of detection and analysis of surface condition and deterioration. This paper describes tests using a range of multiband and multispectral images for the assessment of architectural façade cover by means of supervised image classifications. From the spectral training sets, both pairwise distances (the Euclidean distance and the Jeffries-Matusita (J-M) distance) are calculated and are used to predict the a posteriori accuracy of image classification. Furthermore, the effects of increasing the number of spectral bands (blue, green, red and near-infrared) in the supervised maximum-likelihood classification procedures are also analysed, as are the benefits of applying principal components. The resultant multiband datasets increased both the J-M distance and the classification accuracy of the architectural façade, and thus enabled better identification and recognition of the different kinds of façade-cover features.     

Fusion of TerraSAR-x and Landsat ETM+ data for protected area mapping in Uganda

TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping. A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99%, 83.12% and 85.38% and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda.     

Landcover Classification in the Taieri River Catchment,New Zealand: A Focus on the Riparian Zone

Abstract

Riparian vegetation has a fundamental influence on the biological, chemical and physical nature of rivers. The quantification of riparian landcover is now recognised as being essential to the holistic study of the ecosystem characteristics of rivers. Medium resolution satellite imagery is now commonly used as an efficient and cost effective method for mapping vegetation cover; however such data often lack the resolution to provide accurate information about vegetation cover within riparian corridors. To assess this, we measure the accuracy of SPOT multispectral satellite imagery for classification of riparian vegetation along the Taieri River in New Zealand. In this paper, we discuss different sampling strategies for the classification of riparian zones. We conclude that SPOT multispectral imagery requires considerable interpretative analysis before being adequate to produce sufficiently detailed maps of riparian vegetation required for use in stream ecological research.     

Monitoring Detailed Land‐Cover Changes in Shrubland Habitat Reserves Using Multi‐temporal IKONOS Data

Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.     

Mapping the Land Cover of Mexico Using AVHRR Time‐Series Data Sets

Abstract

An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the country's land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine‐band time‐series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi‐weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computer's screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine‐oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60‐80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm.     

Discrimination of tropical vegetation types using SPOT multispectral data

Vegetation types were discriminated using SPOT multispectral data on Miti'aro, a tropical oceanic island in the Cook Islands, Polynesia. Vegetation categories included undisturbed and disturbed forest on limestone, scrub, marsh, and other forest vegetation (including secondary upland forest and agroforestry). Most category pairs had high separability as measured by Jeffries‐Matusita distance and Euclidean distance for training site data. However, there was some class overlap as illustrated by unsuperaised clustering and assigning spectral clusters to vegetation classes using a reference map. Cloud cover was a problem encountered in optical imaging of this maritime tropical study area.     

Knowledge‐based digital mapping of vegetation types in Big Bend National park,Texas

A knowledge‐based strategy is utilized to develop a model for performing automated mapping of twenty vegetation cover types occurring within Big Bend National P ark, Texas. Many of the cover types found within this desert region cannot be reliably identified solely on a spectral basis, even on large‐scale, aircraft‐borne color imagery. Positive identification may be improved, however, by incorporating additional spatial information that may distinguish given cover types on a non‐spectral basis. In this study, digital soils and digital terrain data are utilized with spectral imagery from Landsat Thematic Mapper.

This knowledge‐based strategy is comprised of three primary elements: knowledge acquisition, rules development, and model structuring. Knowledge acquisition identifies the vegetation composition and non‐vegetative site characteristics associated with the occurrence of each cover type. Rules development compares and contrasts these characteristics among pairs of cover types and their subsets Model structuring places the presumed digital analogs of these characteristics within a multi‐layered classification.

After implementing the automated mapping model, its quality was evaluated with an accuracy assessment. Based upon the cover types field‐truthed at 142 sites within the park, the model performed at an 72% level of accuracy. For comparative purposes, a traditional supervised (spectral, statistical) classification yielded a 42% accuracy. The superiority of the model is attributed to its incorporation of knowledge‐based information; in essence, identification by considering only those cover types likely to occur over given spectral and physiographic conditions.     

西藏扎布耶盐湖氧化硼含量空间分布遥感研究

利用Landsat-7多波段数据和氧化硼含量实测数据，在对数据进行预处理、波谱分析及相关分析等基础上，采用比值增强、主成分分析及彩色密度分割等处理方法，对浓度高、成分复杂的扎布耶盐湖氧化硼含量的空间分布进行了研究，揭示了扎布耶盐湖氧化硼含量的空间分布规律。     

Multi-scale assessment of invasive plant species diversity using Pléiades 1A,RapidEye and Landsat-8 data

We used a full remote sensing-based approach to assess plant species diversity in large and inaccessible areas affected by Lantana camara L., a common invasive species within the deciduous forests of Western Himalayan region of India, using spectral heterogeneity information extracted from optical data. The spread of L. camara was precisely mapped by Pléiades 1A data, followed by comparing Pléiades 1A, RapidEye and Landsat-8 OLI – assessed plant species diversities in invaded areas. The single plant species analysis was improved by Pléiades 1A-based diversity analysis, and higher species diversity values were observed for mixed vegetation cover. Furthermore, lower Coefficient of Variation and Renyi diversity values were observed where L. camara was the only species, while higher variations were observed in areas with a mixed spectral reflectance. This study was concluded to add a crucial baseline to the previous studies on remote sensing-based solutions for rapid estimation of biodiversity attributes.     

Land cover monitoring in Asia by NOAA GVI data

NOAA/AVHRR Global Vegetation Index (GVI) data of Asia in 1983 and 1987 were used to evaluate their usefulness for global land cover monitoring. Color composite images of monthly GVI data and color composite images of principal components from 12 successive monthly GVI data were found to be useful for visual interpretation of seasonal vegetation dynamics. The results of cluster analysis applied to monthly GVI data for a one‐year period, indicate that unsupervised classification method is useful for global or continental land cover classification without ground truth. In order to detect land cover changes, the difference between the 1983 and 1987 12‐month GVI data was calculated. The results show that it is difficult to detect land cover changes due to cloud contamination in monthly GVI data and poor registration of GVI products.