Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map - with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 - using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities.     

The effect of seasonal variation on automated land cover mapping from multispectral airborne laser scanning data

Multispectral airborne laser scanning (MS-ALS) sensors are a new promising source of data for automated mapping methods. Finding an optimal time for data acquisition is important in all mapping applications based on remotely sensed datasets. In this study, three MS-ALS datasets acquired at different times of the growing season were compared for automated land cover mapping and road detection in a suburban area. In addition, changes in the intensity were studied. An object-based random forest classification was carried out using reference points. The overall accuracy of the land cover classification was 93.9% (May dataset), 96.4% (June) and 95.9% (August). The use of the May dataset acquired under leafless conditions resulted in more complete roads than the other datasets acquired when trees were in leaf. It was concluded that all datasets used in the study are applicable for suburban land cover mapping, however small differences in accuracies between land cover classes exist.     

Wheat yield estimates using multi-temporal NDVI satellite imagery

We examined seasonal growth profiles developed from AVHRR-NDVI for estimating wheat yield at regional and farm scales in Montana for the years 1989-1997. Both regions and farms showed strong relationships between wheat yields and integrated NDVI over the entire growing season, and with late-season NDVI parameters. The use of AVHRR-NDVI growth profiles at the regional level provided the strongest yield estimates. At the farm scale, the spatial resolution (1 km 2 ) limited the certainty for accurate portrayal of field locations. However, our models provide a basis for further examination of time-series satellite data.     

Visualization and unsupervised classification of changes in multispectral satellite imagery

The statistical techniques of multivariate alteration detection, minimum/maximum autocorrelation factors transformation, expectation maximization and probabilistic label relaxation are combined in a unified scheme to visualize and to classify changes in multispectral satellite data. The methods are demonstrated with an example involving bitemporal LANDSAT TM imagery.     

Comparison of hyperspectral imagery with aerial photography and multispectral imagery for mapping broom snakeweed

Broom snakeweed (Gutierrezia sarothrae (Pursh) Britt. & Rusby) is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne hyperspectral imagery and compare it with aerial colour-infrared (CIR) photography and multispectral digital imagery for mapping broom snakeweed infestations. Airborne hyperspectral imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The hyperspectral imagery was transformed using minimum noise fraction (MNF) and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM) classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps (snakeweed, mixed woody and mixed herbaceous) were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne hyperspectral imagery along with aerial photography and multispectral imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.     

Lithological discrimination in central Snowdonia using airborne multispectral scanner imagery†

Abstract

An evaluation has been carried out of the use of 11-channel airborne multispectral scanner data as an input to large-scale geological mapping in upland Britain. The optimum index factor (OIF) provides a ranking of possible three-band combinations in terms of their uncorrelated spectral data content, while principal components analysis may be used to compress multi-channel data, thereby minimizing information loss. An assessment of the geological information content of various imagery products concluded that both high-value OIF and principal component images were useful for lithological discrimination of solid and superficial deposits whereas aerial photographs were better for identifying structural features.     

Unsupervised building detection in complex urban environments from multispectral satellite imagery

A generic algorithm is presented for automatic extraction of buildings and roads from complex urban environments in high-resolution satellite images where the extraction of both object types at the same time enhances the performance. The proposed approach exploits spectral properties in conjunction with spatial properties, both of which actually provide complementary information to each other. First, a high-resolution pansharpened colour image is obtained by merging the high-resolution panchromatic (PAN) and the low-resolution multispectral images yielding a colour image at the resolution of the PAN band. Natural and man-made regions are classified and segmented by the Normalized Difference Vegetation Index (NDVI). Shadow regions are detected by the chromaticity to intensity ratio in the YIQ colour space. After the classification of the vegetation and the shadow areas, the rest of the image consists of man-made areas only. The man-made areas are partitioned by mean shift segmentation where some resulting segments are irrelevant to buildings in terms of shape. These artefacts are eliminated in two steps: First, each segment is thinned using morphological operations and its length is compared to a threshold which is determined according to the empirical length of the buildings. As a result, long segments which most probably represent roads are masked out. Second, the erroneous thin artefacts which are classified by principal component analysis (PCA) are removed. In parallel to PCA, small artefacts are wiped out based on morphological processes as well. The resultant man-made mask image is overlaid on the ground-truth image, where the buildings are previously labelled, for the accuracy assessment of the methodology. The method is applied to Quickbird images (2.4 m multispectral R, G, B, near-infrared (NIR) bands and 0.6 m PAN band) of eight different urban regions, each of which includes different properties of surface objects. The images are extending from simple to complex urban area. The simple image type includes a regular urban area with low density and regular building pattern. The complex image type involves almost all kinds of challenges such as small and large buildings, regions with bare soil, vegetation areas, shadows and so on. Although the performance of the algorithm slightly changes for various urban complexity levels, it performs well for all types of urban areas.     

Quantitative mapping of pasture biomass using satellite imagery

A knowledge of the amount of pasture biomass available in farm paddocks is crucial for improving utilization and productivity in the Australian grazing industry. A method to quantitatively map the biomass of annual pastures under grazing has been developed using the Normalized Difference Vegetation Index (NDVI) derived from high-resolution satellite imagery. Relationships between field-measured pasture biomass and the NDVI were examined for different transects in paddocks under different grazing regimes across three geographically dispersed farm sites. A significant linear relationship (R ² = 0.84) was observed when the NDVI was regressed against biomass. The slope of the relationship between the NDVI and biomass declined in a highly predictable (R ² = 0.82) exponential form as the growing season progressed and this pattern was consistent across four separate seasons. This knowledge was used to formulate a reliable model to predict paddock average pasture biomass using the NDVI. The model estimates were validated against observed biomass in the range 500–4000 kilograms of dry matter per hectare (kg DM ha^–1) with R ² = 0.85 and a standard error of 315 (kg DM ha^–1).     

Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery

An object-based approach to generating shrub cover change maps of potential use for monitoring shrubland habitat reserves was developed and tested. A high fidelity, bi-temporal airborne image data set was generated through frame-based image acquisition, precise image-to-image registration, radiometric normalization, and selection of near-anniversary image acquisition dates with similar precipitation conditions prior to both image acquisitions. Image segmentation and classification processes were applied to the bi-temporal layer stack of very high spatial resolution visible and near infrared (V/NIR) image data, such that shrub change objects were delineated and identified directly.Image segments derived from the bi-temporal V/NIR image data set having 1 m spatial resolution delineated most shrub change features in a qualitatively realistic manner. A Standard Nearest Neighbor classifier with segment mean and standard deviation measures of Red, NIR, and normalized difference vegetation index (NDVI) image features yielded the shrub change map that agreed more closely with reference data than the classifier based on fuzzy membership functions. The overall accuracy and kappa statistics for the optimal shrub change map were 0.83 and 0.64, respectively, with the predominant error being associated with “over-classification” of no-change objects as some type of shrub change. No statistical difference in accuracies of three- and five-class maps was found, suggesting that changes in true shrubs and sub-shrubs within coastal sage scrub vegetation communities can be differentiated reliably. A net 5% loss of shrub cover was determined for the 1998–2005 period from the shrub change map of the study area. The greatest decrease and net loss of shrub cover occurred within the urban edge zone and within flat-lying areas. Patterns of shrub loss appear to be more related to anthropogenic disturbance than effects of the severe seven-year drought that occurred between image acquisition dates.     

Trajectory-based warm season grassland mapping in Missouri prairies with multi-temporal ASTER imagery

Tallgrass prairie in North America has been largely converted to croplands and cool season grasslands. In Missouri, only 0.5% of the tallgrass prairie remains in a form of isolated prairie islands. This study sought to delineate prairie-native warm season grass (WSG) from cool season grass (CSG) using five ASTER images acquired on 03/11/2008, 05/12/2007, 07/12/2007, 08/16/2007 and 10/19/2007. Temporal trajectories of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were extracted to examine temporal variation of WSG and CSG grasslands in a growth cycle. It was found that the spring-summer period revealed maximal spectral differences between these two grass types. CSG reached peak NDVI in May while WSG tended to have peak NDMI in July. NDVI was more useful than NDMI in summer-fall. The NDVI trends in this period varied with both phenology and grassland treatments such as haying and grazing, which provided additional information in WSG/CSG delineation. A hierarchical decision tree was developed to delineate WSG and CSG grasslands in a 3-tier process. The WSG lands including those publically conserved prairies were identified in the lower Osage Plain in southwest Missouri. The accuracies were about 80% and could be improved with more frequent satellite observations. The trajectory-based decision tree in this study provides a robust approach of long-term WSG/CSG mapping.     

An assessment of validation techniques for estimating chlorophyll-a concentration from airborne multispectral imagery

Accurate estimates of chlorophyll-a levels in coastal waters are required for the assessment of waters potentially subject to eutrophication. Traditional laboratory analysis of water samples does not offer the required spatial or temporal density of sampling. Remote sensing methods have been suggested as a more appropriate representation of the variability in chlorophyll a concentration encountered in UK coastal waters. This paper examines the use of two established techniques for the calibration of airborne multispectral imagery to determine chlorophyll-a concentration: the blue/green ratio and Fluorescence Line Height methods. These methods have been developed for use in oceanic or Canadian coastal waters. The errors incurred in the use of these techniques for calibration of data of UK coastal waters have not previously been addressed. This paper describes a rigorous assessment of the errors incurred, allowing the limitations of the techniques to be established. This has resulted in recommendations for chlorophyll-a measurement in the coastal zone.     

Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion

This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation optimization and multi-features fusion. In the segmentation optimization, objects with optimized boundaries and proper sizes are generated by object intersection and merging (OIM) processes, which ensures the accurate information extraction from image objects. Within multi-features fusion and change analysis, the Dempster and Shafer (D-S) evidence theory and the Expectation-Maximization (EM) algorithm are implemented, which effectively utilize multidimensional features besides avoiding the selection of an appropriate change threshold. The main advantages of our proposed method lie in the improvement of object boundary and the fuzzy fusion of multi-features information. The proposed approach is evaluated using two different high-resolution remote-sensing data sets, and the qualitative and quantitative analyses of the results demonstrate the effectiveness of the proposed approach.     

Waterline mapping in flooded vegetation from airborne SAR imagery

Multifrequency, polarimetric airborne synthetic aperture radar (SAR) survey of a salt marsh on the east coast of the UK is used to investigate the radar backscattering properties of emergent salt marsh vegetation. Two characteristics of flooded vegetation are observed: backscatter enhanced by approximately 1.2 dB at C-band, and 180° HH-VV phase differences at L-band. Both are indicative of a double bounce backscattering mechanism between the horizontal water surface and upright emergent vegetation. The mapping of inundated vegetation is demonstrated for both these signatures, using a statistical active contour model for the C-band enhanced backscatter, and median filtering and thresholding for the L-band HH-VV phase difference. The two techniques are validated against the waterline derived from tidal elevation measured at the time of overpass intersected with an intertidal DEM derived from airborne laser altimetry. The inclusion of flooded vegetation is found to reduce errors in waterline location by a factor of approximately 2, equivalent to a reduction in waterline location error from 120 to 70 m. The DEM is also used to derive SAR waterline heights, which are observed to underpredict the tidal elevation due to the effects of vegetation. The underprediction can be corrected for vegetation effects using canopy height maps derived from the laser altimetry. This third technique is found to improve the systematic error in waterline heights from 20 to 4 cm, but little improvement in random error is evident, chiefly due to significant noise in the vegetation height map.     

Nontidal wetland mapping in South Carolina using airborne multispectral scanner data

High resolution multispectral scanner (MSS) imagery of Savannah River nontidal wetlands were analyzed to identify 1) useful spectral bands for discriminating among National Wetland Inventory classes, 2) where the classes cluster in n-dimensional feature space, and 3) what wetland classification accuracies can be expected. Spectral measurements in the green (0.55–0.60 μm), red (0.65–0.70 μm), and near-infrared wavelengths (0.70–0.79 μm and 0.92–1.10 μm) provided the most useful information. Emergent marsh (both persistent and nonpersistent), scrub-shrub, mixed deciduous swamp forest, and mixed deciduous upland forest were found to cluster in somewhat predictable regions of 2- and 3-dimensional feature space. The overall classification accuracy of the Steel Creek delta study area was 83% and was assessed by comparing the remote sensing derived thematic map with 1325 linear meters of transects sampled in situ. These results suggest that high resolution aircraft MSS data can provide detailed vegetation type information for mapping both thermally affected and rejuvenating nontidal wetland in the South Carolina Savannah River Swamp System.     

Mapping freshwater marsh species distributions using WorldView-2 high-resolution multispectral satellite imagery

Freshwater wetlands are highly diverse, spatially heterogeneous, and seasonally dynamic systems that present unique challenges to remote sensing. Maximum likelihood and support vector machine-supervised classification were compared to map wetland plant species distributions in a deltaic environment using high-resolution WorldView-2 satellite imagery. The benefits of the sensor’s new coastal blue, yellow, and red-edge bands were tested for mapping coastal vegetation and the eight-band results were compared to classifications performed using band combinations and spatial resolutions characteristic of other available high-resolution satellite sensors. Unlike previous studies, this study found that support vector machine classification did not provide significantly different results from maximum likelihood classification. The maximum likelihood classifier provided the highest overall classification accuracy, at 75%, with user’s and producer’s accuracies for individual species ranging from 0% to 100%. Overall, maximum likelihood classification of WorldView-2 imagery provided satisfactory results for species distribution mapping within this freshwater delta system and compared favourably to results of previous studies using hyperspectral imagery, but at much lower acquisition cost and greater ease of processing. The red-edge and coastal blue bands appear to contribute the most to improved vegetation mapping capability over high-resolution satellite sensors that employ only four spectral bands.     

Predicting the spatial pattern of trees by airborne laser scanning

The spatial pattern of trees can be defined as a property of their location in relation to each other. In this study, the spatial pattern was summarized into three categories, regular, random, and clustered, using Ripley's L-function. The study was carried out at 79 sample plots located in a managed forest in Finland. The goal was to study how well the spatial pattern of trees can be predicted by airborne laser scanning (ALS) data. ALS-derived predictions were based upon individual tree detection (ITD), semi-individual tree detection (semi-ITD), and plot-level metrics calculated from the canopy height model, AREA. The kappa value for ITD was almost zero, which indicates no agreement. The semi-ITD and AREA methods performed better, although kappa values were only 0.34 and 0.24, respectively. It appears difficult to detect a particularly clustered spatial pattern.     

Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties

The distribution of mangroves and other tropical and subtropical vegetation in the Greater Everglades Ecosystem is largely dependent on subtle variations in elevation, with mangroves occupying the lowest elevations. Combining a digital terrain model (DTM) derived from last-return light detection and ranging (LiDAR) data with IKONOS multispectral imagery in a maximum likelihood supervised classification resulted in a 7.1% increase in overall classification accuracy among seven classes (red mangrove, black mangrove, tropical hardwood hammock, coastal rock barren vegetation, mudflat, sand/rock and asphalt) compared with using the multispectral imagery alone, and the classification accuracy was improved for all four spectrally similar vegetation classes. A digital canopy model (DCM) was created by subtracting the digital terrain model from a digital surface model derived from LiDAR first returns. The DCM-recorded heights well correlated with mangrove canopy heights measured in the field but were systematically lower, by up to 2 m, for the tallest canopy. LiDAR has been documented to underestimate vegetation heights but the presence of water beneath some of the red mangrove canopy probably exacerbated this effect. The DCM and empirical allometric algorithms were used to estimate stem density and biomass for the classified red and black mangroves.     

Estimation of incident solar radiation on the ground from multispectral satellite sensor imagery

A simple and fast, physically based method for the estimation of global radiation is presented. It is applicable for clear‐sky multispectral satellite sensor imagery with channels at least in the VNIR region and works without the need for additional ground data. The atmospheric influence is taken into account using look‐up tables based on standard atmospheres from the MODTRAN code. The algorithm was tested with a time series of nine Landsat‐7 ETM+ scenes of a region in north‐eastern Germany. Remotely sensed global radiation is in close agreement with in situ measurements of the German Meteorological Service as indicated by RMS deviations of 20–24 W m^?2 depending on the bands and atmospheric parameterization employed. The image‐derived global radiation at this level of accuracy is a useful supplement for studies in landscape ecology and related fields, for example as input for regional modelling of evapotranspiration.     

Using very high spatial resolution multispectral satellite sensor imagery to monitor refugee camps

Detailed geographic information is a key factor in decision making processes during refugee relief operations. The forthcoming commercial very high spatial resolution (VHSR) satellite sensors will be capable of acquiring multispectral (MS) images at spatial resolutions of 1m (panchromatic) and 4m (multispectral) of refugee camps and their environment. This work demonstrates how refugee camp environment, area and population can be analysed using a VHSR MS satellite sensor image from the Russian KVR-1000 sensor. This image, with a spatial resolution of 3.3m, was used to study Thailand's Site 2 refugee camps, which were established to accommodate Khmer refugees on the Thai-Kampuchean border. At the time of image acquisition, the total population of Site 2's five refugee camps was close to 143000. The VHSR MS image was found to be suitable for mapping the refugee camp environment and area. A statistically significant linear relationship between camp area and population was determined. Accordingly, the study suggests that VHSR MS images in general may be useful for refugee camp planning and management and points toward the utilization of forthcoming commercial VHSR MS satellite sensor images in humanitarian relief operations.