Selecting the spatial resolution of airborne MSS imagery for small-scale agricultural mapping

The spatial resolution determines the number of data and amount of information in a remotely sensed image of a given scene. The 'optimal' spatial resolution may be defined as that which maximizes the information per pixel, and this maximum is realized when the semivariance at a lag of one pixel (the average squared difference between neighbouring pixels) is maximized. For mapping, a spatial resolution should be chosen that is much finer than the 'optimal' spatial resolution as defined above. Airborne MSS images in both red and near-infrared wavelengths for three different dates and two sites were investigated to determine a spatial resolution suitable for mapping spatial variation in agricultural fields in the U.K. The spatial resolution most appropriate for mapping the spatial variation in the images was between 0.5 m and 3 m.     

Historical tropical successional forest cover mapped with Landsat MSS imagery

Maps of tropical successional forest cover of the 1970s and 1980s are needed for long-term modelling of tropical forest-cover change, carbon flux and habitat change. Landsat Multispectral Scanner System (MSS) imagery may provide a basis for such maps, but its capability in this respect is poorly unexplored if not discounted. This article examines how reliably single-date MSS imagery may distinguish tropical successional forest. Statistical and graphical analyses of 2043 MSS pixels of successional forest cover, pasture and mature forest cover of Central Panama indicate that successional forest may be accurately mapped, with a maximum-likelihood classification accuracy of 86–90%. Detectable successional cover is unlikely to be older than 10 years approximately. These findings indicate that MSS imagery may provide a new baseline for historical mapping and long-term modelling of tropical forest-cover change that, unlike that of Advanced Very High Resolution Radiometer (AVHRR) imagery used for this purpose, is amenable to fine-scale spatial analysis and reliable accuracy assessment.     

Processing airborne MSS data for vegetation studies

Abstract

This letter discusses the need for accurate processing of airborne multispectral data for vegetation studies. The processing includes radiometric and atmospheric corrections and allowances for environmental influences on the data. A methodology for such corrections using easily available collateral data is described and successfully applied to airborne multispectral data recorded in the Bawtry area of South Yorkshire, England.     

Assessment of grassland phytomass with airborne video imagery

Airborne video imagery was evaluated for assessing phytomass production within grass plots fertilized with five rates of nitrogen. Video imagery was taken with two black-and-white video cameras—one visible light and the other visible/near-infrared light sensitive. Red (0.644–0.656-μm) and infrared (0.815–0.827-μm) narrowband filters were used with the visible and visible/near-infrared light sensitive cameras, respectively. Hand-held red and infrared radiometric reflectance and phytomass measurements were made on the day that imagery was obtained. Red and infrared digital video data were obtained from the plots using an image processor. The plots were studied on two dates: 15 April and 22 May 1985. On 15 April, three phytomass levels could be distinguished within the infrared video image. Moreover, a infrared/red ratio video composite produced on an image processor enhanced differences among nitrogen treatments to the extent that generally four levels of phytomass could be identified. Coefficients of determination (r²), obtained by regressing red, infrared, and infrared/red reflectance, and red, infrared, and infrared/red digital video data on phytomass measurements were significant statistically. Imagery acquired on 22 May, however, showed few differences among treatments. This may have been caused by plant phenological and canopy architectural differences among the grass species. Nevertheless, video imagery should be a useful tool to assess phytomass production on rangelands when grasses are actively growing.     

Multitemporal land-cover classification of Mexico using Landsat MSS imagery

A complete land-cover classification of Mexico was performed using Landsat Multi-Spectral Scanner (MSS) imagery corresponding to years 1974, 1986 and 1990 ( - 1 y). The categorization of the approximately 2 M km 2 geographical region included the classification of approximately 300 equivalent scene images. Vegetation experts throughout the country provided an initial 250-class inventory of major vegetation associations by applying an unsupervised classification approach. A final regrouping was performed to produce a generalized thematic product containing 12 classes to provide a consistent national scale product. Classification accuracies were evaluated for each scene by means of cartographic comparison using two independently developed reference datasets corresponding to the 1970s and 1990s. An automated evaluation procedure was developed that incorporated decision rules to duplicate the results obtained using a manual accuracy assessment procedure. Overlaying both the image and the digital cartographic information allowed for the comparison of randomly selected pixels within each image scene. An overall accuracy for the three epochs of 62% was obtained for the 300 image scenes. Study results have provided a historical baseline documenting vegetation extent and distribution across Mexico over the two-decade period. This study serves as a possible model for subsequent North American land-cover characterization efforts.     

An analysis of geologic lineaments seen on LANDSAT MSS imagery

Lineament maps drawn from several LANDSAT images of a part of north Wales and western England display considerable variation in the number of lineaments identified. Analysis of the maps shows that it is not the case that maps with fewer lineaments are simply subsets of those with many lineaments. Rather, each map contains a high proportion of lineaments that are unique to it. Despite these differences, the same preferred lineament orientation is identified from almost all maps. These results imply that all available LANDSAT imagery may usefully contribute to a lineament analysis, little value may be placed on the density of lineaments seen on any one image and preferred lineament orientation is relatively easy to identify. It is concluded that guarded use may be made of lineament analysis in geology. Lineament maps may be employed to suggest hypotheses rather than to test them.     

Land-use classification in central Spain using SIR-A and MSS imagery

Abstract

Methods for making more efficient use of synthetic aperture radar (SAR) imagery are considered. Local standard deviation and autocorrelation texture measures are used to provide information on the spatial variability in the scattering cross-section. Use of these statistics in a window of 180×180m improved classification success rates from 39 to 66 per cent with digitized shuttle imaging radar (S1R-A) data. Multispectral scanner (MSS) achieves 70 per cent success with the same window size and, by combining this with SAR, a 78 per cent success rate is reached.     

Procedures for correcting high resolution airborne video imagery

Airborne videography is a useful way of producing very high resolution remotely-sensed data. Before video data can be used for digital analysis, individual frames must be correcled for geometric distortion and brightness variation. The most obvious geometric distortion occurs because the odd and even fields in a video frame are collected sequentially and can be displaced relative to each other by aircraft roll and forward motion. We describe a procedure for correcting this distortion based on the cross-correla lion between individual fields at different spatial lags. Brightness variation across frames occurs because of dilTerences in viewing geometry, bi-directional reflectance variation and atmospheric scattering. We describe a method for removing much of this variation by calculating scattering angle across the image with an optional adjustment for sensor plane tilt. Relations between scattering angle and scene brightness statistics may then be calculated from sequences of images collected along a given transect. These relations can be used 10 normalise brightness. Tests of normalisation procedures based on mean, standard deviation, and median brightness show that the median gives the best results. This approach also produces better results than commonly-used band ratioing procedures.     

Parametric geometric correction of airborne thematic mapper imagery

The geometric correction of airborne scanner imagery has conventionally been performed in a non-parametric manner using ground control. Parametric geometric correction methods are less reliant on ground control and have potential for automation.Recently the possibility of correcting parametrically airborne scanner imagery has become viable because of advances in the availability and accuracy of devices that may be used to measure the exterior orientation of an airborne platform. This paper describes a study initiated by the U.K. Natural Environment Research Council (NERC) to develop and investigate a provisional parametric correction system. Flight trials were flown at an altitude of 1000 m and exterior orientation parameters measured by an inertial navigation system were used to produce parametrically corrected images. The results of this study demonstrate the feasibility of the parametric correction approach.     

Vineyard identification in an oak woodland landscape with airborne digital camera imagery

Using airborne multispectral digital camera imagery, we compared a number of feature combination techniques in image classification to distinguish vineyard from non-vineyard land-cover types in northern California. Image processing techniques were applied to raw images to generate feature images including grey level co-occurrence based texture measures, low pass and Laplacian filtering results, Gram-Schmidt orthogonalization, principal components, and normalized difference vegetation index (NDVI). We used the maximum likelihood classifier for image classification. Accuracy assessment is performed using digitized boundaries of the vineyard blocks. The most successful classification as determined by t-tests of the Kappa coefficients was achieved based on the use of a texture image of homogeneity obtained from the near infrared image band, NDVI and brightness generated through orthogonalization analysis. This method averaged an overall accuracy of 81 per cent for six frames of images tested. With post-classification morphological processing (clumping and sieving) the overall accuracy was significantly increased to 87 per cent (with a confidence level of 0.99).     

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.     

Interpretation of irrigation methods using airborne thermal infrared imagery

In areas of limited water resources for agriculture, management decisions could be enhanced by the availability of additional data about the amount and type of irrigation systems employed. Imagery produced using an HRB Singer AN/A AS 14 optical-electronic thermal infrared scanning system was found to be of great use for the interpretation of irrigation methods. Comparison of the interpreted irrigation method with ground information show agreement within 18 per cent.     

De-shadowing of airborne imagery using at-sensor downwelling irradiance data

Cast shadows caused by sparse clouds usually degrade spaceborne and airborne imagery. They result from the decrease of the direct solar beam due to the presence of a non-transparent cloud. The reduction of the downwelling solar flux density can be quantified during an air campaign, if the aircraft flies beneath the cloud and is equipped with an add-on instrument that measures the total downwelling solar irradiance. The objective of this work is to exploit such data for the de-shadowing of airborne hyperspectral imagery. Initially, the specific illumination and viewing conditions during the image acquisition, which allow the use of at-sensor downwelling irradiance data for the de-shadowing of airborne hyperspectral imagery, are outlined. Then a methodology is proposed that estimates the radiometric enhancement coefficients from the at-sensor irradiance data and correlates them with the image data using a shadow map. Improvements of the quality of the shadow maps are suggested. Performance assessment showed that at-sensor irradiance data could be satisfactorily utilized for compensating the cast shadow effects on remotely sensed imagery. It also highlighted the importance of generating and using an accurate shadow map and the particular difficulties for the air campaign planning raised by the requirement of exploitable at-sensor irradiance data.     

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.     

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.     

Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery

Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25-4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical-optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%-1.07% and 0.36%-1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R² = 0.89 to R² = 0.97 and discrepancies of 0.02%-3.63% and 0.24%-7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R² = 0.47, RMSE = 4.34 μg/cm², and jackknifed RMSE of 5.69 μg/cm² for needle chlorophyll content ranging from 24.9 μg/cm² to 37.6 μg/cm². The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R² = 0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index.     

Geometric correction of airborne scanner imagery by matching Delaunay triangles

Abstract

A major factor limiting the usefulness of airborne scanner imagery such as ATM has been the inability of conventional geometric correction procedures to remove high frequency distortion induced by platform and relief effects. An algorithm is described which uses matching Delaunay triangles to achieve a form of geometric correction that can cope with high frequency distortion. Early experimental results show that the procedure has the capacity to correct images which exhibit very complex patterns of distortion.     

Large-scale leaf area index inversion algorithms from high-resolution airborne imagery

Large-scale leaf area index (LAI) inversion algorithms were developed to determine the LAI of a forest located in Gatineau Park, Canada, using high-resolution colour and colour infrared (CIR) digital airborne imagery. The algorithms are parameter-independent and developed based on the principles of optical field instruments for gap fraction measurements. Cloud-free colour and CIR images were acquired on 21 August 2007 with 35 and 60 cm nominal ground pixel size, respectively. Normalized Difference Vegetation Index (NDVI), maximum likelihood and object-oriented classifications, and principal component analysis (PCA) methods were applied to calculate the mono-directional gap fraction. Subsequently, LAI was derived from inversion and compared with ground measurements made in 54 plots of 20 by 20 m using hemispherical photography between 10 and 20 August 2007. There was high inter-correlation (the Pearson correlation coefficient, R > 0.5, p < 0.01) among LAI values inverted using the classifications and PCA methods, but neither were highly correlated with LAI inverted from the NDVI method. LAI inverted from the NDVI-based gap fraction significantly correlated with ground-measured LAI (R?=?0.63, root mean square error (RMSE) = 0.52), while LAI inverted from the classification and PCA-derived gap fraction showed poor correlation with ground-measured LAI. Consequently, the NDVI method was used to invert LAI for the whole study area and produce a 20‐m resolution LAI map.     

Assessment of deforestation in the Lower Amazon floodplain using historical Landsat MSS/TM imagery

The floodplain forests bordering the Amazon River have outstanding ecological, economic, and social importance for the region. However, the original distribution of these forests is not well known, since they have suffered severe degradation since the 16th century. The previously published vegetation map of the Amazon River floodplain (Hess et al., 2003), based on data acquired in 1996, shows enormous difference in vegetation cover classes between the regions upstream and downstream of the city of Manaus. The upper floodplain is mostly covered by forests, while the lower floodplain is predominantly occupied by grasses and shrubs.This study assesses deforestation in the Lower Amazon floodplain over a ~ 30 year period by producing and comparing a historical vegetation map based on MSS/Landsat images acquired in the late 1970s with a recent vegetation map produced from TM/Landsat images obtained in 2008. The maps were generated through the following steps: 1) normalization and mosaicking of images for each decade; 2) application of a linear mixing model transformation to produce vegetation, soil and shade fraction-images; and 3) object-oriented image analysis and classification. For both maps, the following classes were mapped: floodplain forest, non-forest floodplain vegetation, bare soil and open water. The two maps were combined using object-level Boolean operations to identify time transitions among the mapped classes, resulting in a map of the land cover change occurred over ~ 30 years. Ground information collected at 168 ground points was used to build confusion matrices and calculate Kappa indices of agreement. A survey strategy combining field observations and interviews allowed the collection of information about both recent and historical land cover for validation purposes. Kappa values (0.77, 0.75 and 0.75) indicated the good quality of the maps, and the error estimates were used to adjust the estimated deforested area to a value of 3457 km² ± 1062 km² (95% CI) of floodplain deforestation over the ~ 30 years.