A segmentation and hierarchical classification approach applied to QuickBird multispectral satellite data was implemented, with the goal of delineating residential land use polygons and identifying low and high socio-economic status of neighbourhoods within Accra, Ghana. Two types of object-based classification strategies were tested, one based on spatial frequency characteristics of multispectral data, and the other based on proportions of Vegetation-Impervious-Soil sub-objects. Both approaches yielded residential land-use maps with similar overall percentage accuracy (75%) and kappa index of agreement (0.62) values, based on test objects from visual interpretation of QuickBird panchromatic imagery.
The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R(2) = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change.
Satellite remote sensing technology has shown promising results in characterizing the environment in which plants and animals thrive. Remote sensing scientists, biologists, and epidemiologists are adopting remotely sensed imagery to compensate for the paucity of weather information measured by weather stations. With measured humidity from three stations as baselines, our study reveals that Normalised Difference Vegetation Index (NDVI) and atmosphere saturation deficits at the 780 hPa pressure level (D MODIS), both of which were derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor, were significantly correlated with station saturation deficits (D stn)(ȣrȣ = 0.42-0.63, p < 0.001). These metrics have the potential to estimate saturation deficits over east Africa. Four to nine days of lags were found in the NDVI responding to D stn. For the daily estimations of D stn, D MODIS had a better performance than the NDVI. However, both of them poorly explained the variances in daily D stn using simple regression models (adj. R (2) = 0.17-0.39). When the estimation temporal scale was changed to 16-day, their performances were similar, and both were better than daily estimations. For D stn estimations at coarser geographic scales, given that many factors such as soil, vegetation, slope, aspect, and wind speed might complicate the NDVI response lags and model construction, D MODIS is more favourable as a proxy of the saturation deficit over ground due to its simple relationship with D stn.
The aim of this study was to determine whether remotely sensed data could be used to identify rice-related malaria vector breeding habitats in an irrigated rice growing area near Niono, Mali. Early stages of rice growth show peak larval production, but Landsat sensor data are often obstructed by clouds during the early part of the cropping cycle (rainy season). In this study, we examined whether a classification based on two Landsat Enhanced Thematic Mapper (ETM)+ scenes acquired in the middle of the season and at harvesting times could be used to map different land uses and rice planted at different times (cohorts), and to infer which rice growth stages were present earlier in the season. We performed a maximum likelihood supervised classification and evaluated the robustness of the classifications with the transformed divergence separability index, the kappa coefficient and confusion matrices. Rice was distinguished from other land uses with 98% accuracy and rice cohorts were discriminated with 84% accuracy (three classes) or 94% (two classes). Our study showed that optical remote sensing can reliably identify potential malaria mosquito breeding habitats from space. In the future, these 'crop landscape maps' could be used to investigate the relationship between cultivation practices and malaria transmission.
This paper provides a comparative analysis of land use and land cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired in the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes - forest, savanna, other-vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water, was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition and rates among the three study areas and indicates the importance of analyzing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g., urban expansion, roads, and land use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.
Recent advances in atmospheric remote sensing offer a unique opportunity to compute indirect estimates of air quality, particularly for developing countries that lack adequate spatial-temporal coverage of air pollution monitoring. The present research establishes an empirical relationship between satellite-based aerosol optical depth (AOD) and ambient particulate matter (PM) in Delhi and its environs. The PM data come from two different sources. Firstly, a field campaign was conducted to monitor airborne particles ≤ 2.5 μm and ≤10 μm in aerodynamic diameter (PM(2.5) and PM(10) respectively) at 113 spatially dispersed sites from July to December 2003 using photometric samplers. Secondly, data on eight hourly PM(10) and total suspended particulate (TSP) matter, collected using gravimetric samplers, from 2000 to 2005 were acquired from the Central Pollution Control Board (CPCB). The aerosol optical depths were estimated from MODIS data, acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center from 2000 to 2005. Both the PM and AOD data were collocated by time and space: PM mass ± 150 min of AOD time, and ± 2.5 and 5 km radius (separately) of the centroid of the AOD pixel for the 5 and 10 km AOD, respectively. The analysis here shows that PM correlates positively with the 5 km AOD; a 1% change in the AOD explains 0.52% ± 0.20% and 0.39% ± 0.15% changes in PM(2.5) within 45 and 150 min intervals (of AOD data) respectively. At a coarser spatial resolution, however, the relationship between AOD and PM is relatively weak. But, the relationship turns significantly stronger when monthly estimates are analysed over a span of six years (2000 to 2005), especially for the winter months, which have relatively stable meteorological conditions.
We explored the use of the European Remote Sensing Satellite 2 Synthetic Aperture Radar (ERS-2 SAR) to trace the development of rice plants in an irrigated area near Niono, Mali and relate that to the density of anopheline mosquitoes, especially An. gambiae. This is important because such mosquitoes are the major vectors of malaria in sub-Saharan Africa, and their development is often coupled to the cycle of rice development. We collected larval samples, mapped rice fields using GPS and recorded rice growth stages simultaneously with eight ERS-2 SAR acquisitions. We were able to discriminate among rice growth stages using ERS-2 SAR backscatter data, especially among the early stages of rice growth, which produce the largest numbers of larvae. We could also distinguish between basins that produced high and low numbers of anophelines within the stage of peak production. After the peak, larval numbers dropped as rice plants grew taller and thicker, reducing the amount of light reaching the water surface. ERS-2 SAR backscatter increased concomitantly. Our data support the belief that ERS-2 SAR data may be helpful for mapping the spatial patterns of rice growth, distinguishing different agricultural practices, and monitoring the abundance of vectors in nearby villages.
This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.
This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA-AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift-corrected NOAA-AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA-AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data.
The value of Shuttle Imaging Radar images for the estimation of population is considered using a 1981 example for Tunisia. The results are compared with 1975 census data. The results show that the relationship of image areas to population is reasonably strong in areas where settlements are relatively small and have a uniform and low building density.
This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863-881 nm) and the H18 (745-751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.
This research selects two study areas with different urban developments, sizes, and spatial patterns to explore the suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification, and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of "salt-and-pepper" pixels, and segmentation based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. In order to accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance.
An atmospheric correction methodology was developed for correcting
AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) images that
were acquired during the 1991 NASA/JPL campaign over “The
Landes” (S.W. France). It is based on the inversion of the 5S
atmospheric model, through an iterative approach that uses the Gauss
Seidel principle. Adjacency effects are fully taken into account with
the use of circular neighbourhoods, for each pixel, the radii of which
vary with wavelength. Atmospheric optical parameters were estimated with
in-situ atmospheric profile and visibility measurement combined with the
5S model. Due to the poor accuracy of the visibility parameter a
procedure was developed for retrieving the aerosol optical depth
directly from the remotely acquired image alone. At different
wavelengths, the aerosol optical depth must be such that the reflectance
of dark objects, which is supposed to lie in an a-priori defined
interval, lead to the right apparent reflectances. For each pixel, the
target reflectance is computed as the most probable. This approach led
to the determination of an aerosol optical depth map, after some spatial
interpolation. This map is used as an input parameter in the atmospheric
correction algorithm. For all AVIRIS bands, convergence was attained
after less than 5 iterations. Accuracy better that 0.54% was obtained
with radii of neighbourhoods at least equal to 30 pixels in the visible
region and 5 pixels in the near-infrared region. The use of these radii
values resulted in reflectance variations as large as 60%, 40%, and 6%
in the VIS, PIR and MIR regions, respectively
Three possible methods of combining soft classification outputs to increase soft classification accuracy were assessed. These methods were (i) an approach that selects the most accurate predictions on a class-specific basis, (ii) Dempster-Shafer theory of evidence and (iii) an approach which degrades the soft classification output into a set of ordered classes and then combines these through the use of a conventional ensemble approach. The potential of these approaches was assessed using coarse spatial resolution NOAA AVHRR imagery of Australia. The data were classified using two neural networks (a multi-layer perceptron and a radial basis function network) as well as a probabilistic classifier. All three approaches to combine the classifications were applied to combine the soft classification outputs and had been shown to increase classification accuracy. Relative to the most accurate individual classification, the increases in overall accuracy derived ranged from 2.73 to 4.45% and large increases in individual class accuracy were also observed. The results highlight that ensemble based approaches may be used to increase soft classification accuracy.
The potential of synthetic aperture radar (SAR) in monitoring soil
and vegetation parameters is being evaluated worldwide through extensive
investigations. A significant experiment performed on this subject was
the Multi-sensor Airborne Campaign (MAC 91), carried out during the
northern summer of 1991 on several sites in Europe, based on the
NASA/JPL polarimetric synthetic aperture radar (AIRSAR). The site of
Montespertoli (Italy) was imaged three times during this campaign.
Calibrated polarimetric data collected over the agricultural area of
this site have been analysed and critical analysis of the information
contained in linear and circular co-polar and cross-polar data has also
been carried out
The 100 meter JERS-1 Amazon mosaic image was used in a new
classifier to generate a 1 km resolution land cover map. The inputs to
the classifier were 1 km resolution mean backscatter and seven first
order texture measures derived from the 100 m data by using a
10×10 independent sampling window. The classification approach
included two interdependent stages: 1) a supervised maximum a posteriori
Baysian approach to classify the mean backscatter image into 5 general
land cover categories of forest, savanna, inundated, white sand, and
anthropogenic vegetation classes, and 2) a texture measure decision rule
approach to further discriminate subcategory classes based on taxonomic
information and biomass levels. Fourteen classes were successfully
separated at 1 km scale. The results were verified by examining the
accuracy of the approach by comparison with the IBGE and the AVHRR 1 km
resolution land cover maps
Full-bandwidth C-band synthetic aperture radar (SAR) data are compared with 7-look and 3-look data. The peak-to-background ratio of the image intensity power spectrum describing the wave detectability is found to be on average 8-9dB higher for the 7-look data and 2-5dB higher for the 3-look data than the single-look data. This is mainly due to the decrease in the speckle noise level when going from single-look to multi-look processing. In addition, look-sum processing is evaluated against spectral-sum processing for various temporal look separations. A significant improvement in image spectral peak contrast is observed for the spectral-sum data versus the look-sum data, with increasing temporal separations between the looks. No such improvement is observed in the corresponding image spectral noise contrast parameter. These observations are in agreement with the spatial misregistration inherent in look-sum data. Finally, the acceleration contribution to the observed aximuth smearing in the spectra is found to be negligible compared with the velocity smearing contribution.
The application of an artificial neural network to the development of an inversion algorithm for radar scattering from vegetation canopies is considered. The Michigan Microwave Canopy Scattering (MIMICS) model, which has shown remarkable success in predicting the radar response to vegetation canopies, was used, as were measured polarimetric backscatter values. Hence, the radiative transfer simulation code, MIMICS, was used to produce some of the training data. The inputs to the neural network were the expected polarimetric backscatter values from specific canopies, while the outputs were the desired parameters, such as tree heights, crown thickness, leaf density, etc. Two special cases were examined: 1) inversion of MIMICS given modelled aspen stands of different ages; 2) inversion of measured data from the Duke forest loblolly pine stands. -from Authors
During the August 2002 Elbe river flood, different satellite data were acquired, and especially ASAR data from ENVISAT. The advanced synthetic aperture radar instrument was activated in different image modes. Thus, the comparison with quasi-simultaneous ERS-2 data enables to evaluate the contribution of polarisation configurations on flood surface detection. This study highlights the increased capabilities of ASAR for flood mapping, especially benefit of a common use of like- and cross-polarisations.
Studies the capability of airborne (AVIRIS) and laboratory high
spectral resolution information for assessing the chemical composition
(lignin, nitrogen, cellulose, ...) of a pine forest (Les Landes, SW
France). Simultaneously with AVIRIS acquisition, an atmospheric profile
and a forest vegetation sampling for chemical and laboratory spectral
analyses, were collected. Predictive relationships between
concentrations of nitrogen (r=97%), ligin (r=89%), cellulose (r=83%),
and reflectances of pre-treated pine needles were determined through
stepwise regression analyses. A methodology was designed to assess their
extrapolation to remotely acquired spectrometric data: (1) geometric and
atmospheric corrections, (2) registration within a biophysical data base
(LAI, biomass, ...), and (3) comparative statistical analysis of
laboratory and airborne spectrometric information. Nitrogen and
cellulose concentrations predicted with canopy reflectances were
relatively correlated with actual concentrations (74% and 79%
respectively); poorer results were obtained for lignin (55%).
Atmospheric corrections did not improve correlations. It was attempted
to improve these results while taking into account the influence of the
canopy structure and total quantity of chemical compounds. Predictive
equations base on laboratory measurements were applied to reflectances
of pine needles that were computed through the inversion of two
reflectance models. This approach only improved correlations for lignin
(74%). Finally, chemical concentrations in the studied area were mapped
in order to provide spatial information suitable for ecosystem models
Landsat Thematic Mapper (TM) digital data were used to map the distributions and concentrations of selected water quality indicators in and around Augusta Bay, Sicily. The general approach involved near-simultaneous aquisition of TM data and water quality samples from 42 sites, laboratory analysis of samples, extraction of sample site digital numbers from the TM data, development and validation of regression models based on sample data, application of models to the entire study area, and generation of colour-coded output maps. Results were good for modelling temperature, turbidity, Secchi disk depth and chlorophyll-a, and indicate that remotely-sensed data may be applicable to monitoring water quality in this geographic area.
Conventional motion compensation schemes correct for unwanted synthetic aperture radar (SAR) platform motions using information from an inertial measurement unit (IMU). Autofocus techniques, which focus SAR images, produce an ‘autofocus parameter’ which is related to the platform motion. In this paper, strong evidence is presented to support the assumption that the contrast optimization autofocus algorithm behaves as a least-squares quadratic fitting to the SAR platform trajectory. Using this assumption, the relationship between the autofocus parameter and across-track accelerations of the SAR platform is derived. This allows the SAR platform motion to be estimated from the autofocus parameter measurements and incorporated in a motion compensation instead of IMU measurements. Three implementations of motion compensation using autofocus are compared and the achievable image quality is quantified.
Radar backscattering from soils under different agricultural crops and moisture conditions has been measured during the LOTREX campaign (Land-Surface Transverse Experiment in north western Germany. Measurements were made with a four-band coherent Doppler scatterometer in L-, C-, X- and Ku-band at cross- and like-polarizations. The backscattered radar signals were compared with in situ soil moisture data. It is found that the scatterometer data can clearly be related to soil moisture only at L-band frequencies. Here, a significant correlation with soil moisture exists irrespective of the crop type. The results do not show any distinctive dependency on the polarization used. -Authors
Passive microwave signatures of different Baltic Sea ice types and
open water leads were measured in the spring of 1995 and in March 1997
with airborne non-imaging microwave radiometers (MWR) operating in the
frequency range from 6.8 to 36.5 GHz. The Baltic Sea is a semi-enclosed
brackish sea water (salinity <6 ppt) basin in northern Europe. The
ice cover in the Baltic Sea begins to form in November. It reaches its
maximum extent usually in late February or early March, and usually all
ice melts by the end of May. The maximum annual ice cover ranges from
12% to 100% of the whole Baltic Sea area. On the average, the maximum is
around 50%. The ice in the Baltic Sea occurs as fast ice and drift ice.
Fast ice is found in the coastal and archipelago areas. Sea ice in the
open sea occurs as drift ice which can be level, rafted or ridged with a
0-100% coverage. The MWR data sets are used to study: 1) number of main
dimensions of data sets, 2) discrimination of open water from sea ice
and classification of various ice types using different MWR channel
combinations, and 3) suitability of the SSM/I ice concentration
algorithms for mapping the Baltic Sea ice. The effect of snow cover
wetness is also studied
The sensitivity of bistatic scattering coefficient σ° to soil moisture content (SMC) and surface roughness was investigated by means of model simulations of the incoherent scattered fields performed with the advanced integral equation model (AIEM) and the second order small perturbation model (SPM). The study was performed by simulating scattering on the whole upper half space, for different values of incident angles. The achieved results, represented as maps of σ° as a function of azimuth and zenith angles, were evaluated by means of a quality index which takes into consideration the effect of roughness on SMC measurement. The sensitivity analysis has pointed out that for measuring SMC a bistatic observation, by itself or combined with the monostatic one, can make appreciable improvements with respect to classical monostatic radar. Appendix A contains the AIEM formulas corrected for several typographical errors present in the specific literature.
Using two hybrid radiative transfer models to represent conifer
canopies and stands the authors develop and evaluate algorithms to infer
several important structural parameters of stands of black spruce (Picea
mariana), the most common boreal forest dominant. Using spectral mixture
analysis and multispectral reflectance data for 31 black spruce stands
of varying density and structure, the authors infer the values for the
areal proportions of sunlit canopy, sunlit background and shadow
fraction-they call radiometric elements-then show that the areal
proportions of these radiometric elements are strongly related to leaf
area index, biomass density and annual above ground net primary
production. The best overall correlations between the radiometric
elements and biophysical variables was shadow fraction obtained with the
cone-based variable canopy reflectance model
In this paper, we propose a method for urban building damage detection from multitemporal high resolution images using spectral and spatial information combined. Given the spectral similarity between damaged and undamaged areas in the images, two spatial features are used in the damage detection, i.e. invariant moments and LISA (local indicator of spatial association) index. These two spatial features were computed for each image object, which is produced by image segmentation. The One-Class Support Vector Machine (OCSVM), a recently developed one-class classifier was used to classify the multitemporal data to obtain building damage information. The uses of spectral data alone and plus obtained spatial features for building damage detection were separately evaluated using bitemporal Quickbird images acquired in Dujiangyan area of China, which was heavily hit by the Wenchuan earthquake. The results show that the combined use of spectral and spatial features significantly improved the damage detection accuracy, compared to that of using spectral information alone.
The long wavelength edge of the major chlorophyll absorption feature in the spectrum of a vegetation canopy moves to longer wavelengths with an increase in chlorophyll content. The position of this red-edge has been used successfully to estimate, by remote sensing, the chlorophyll content of vegetation canopies. Techniques used to estimate this red-edge position (REP) have been designed for use on small volumes of continuous spectral data rather than the large volumes of discontinuous spectral data recorded by contemporary satellite spectrometers. Also, each technique produces a different value of REP from the same spectral data and REP values are relatively insensitive to chlorophyll content at high values of chlorophyll content. This paper reports on the design and indirect evaluation of a surrogate REP index for use with spectral data recorded at the standard band settings of the Medium Resolution Imaging Spectrometer (MERIS). This index, termed the MERIS terrestrial chlorophyll index (MTCI), was evaluated using model spectra, field spectra and MERIS data. It was easy to calculate (and so can be automated), was correlated strongly with REP but unlike REP was sensitive to high values of chlorophyll content. As a result this index became an official MERIS level-2 product of the European Space Agency in March 2004. Further direct evaluation of the MTCI is proposed, using both greenhouse and field data.
We propose a new algorithm for remotely sensed image texture classification and segmentation in this paper. We observe that the traditional method LSE is unstable in practical applications. This motivates us to develop more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation, abbreviated TV, for reducing instability in texture analysis, and apply which to remotely sensed image texture classification and segmentation. Experiment results on remotely sensed images demonstrate our new algorithm is superior to LSE and seems promising in applications.
This paper concentrates on a linear feature extraction method for
neural network classifiers. The considered feature extraction method is
based on discrete wavelet transformations (DWTs) and a cluster-based
procedure, i.e., cluster-based feature extraction of the wavelet
coefficients of remote sensing and geographic data is considered. The
cluster-based feature extraction is a preprocessing routine that
computes feature-vectors to group the wavelet coefficients in an
unsupervised way. These feature-vectors are then used as a mask or a
filter for the selection of representative wavelet coefficients that are
used to train the neural network classifiers. In experiments, the
proposed feature extraction methods performed well in neural networks
classifications of multisource remote sensing and geographic data
In this paper, we describe a method for geometric correction of
optical satellite images. The method is based on modeling the imaging
process, including satellite orbital modeling, and uses a set of ground
control points. We also present an algorithm for automatic detection of
ground control points based on a matching technique. The performance of
the correction procedure was investigated on three kinds of data: (i)
NOAA/AVHRR images with pixel size 1000×1000 m<sup>2</sup> (ii)
system-corrected Landsat TM image with pixel size 30×30 m<sup>2
</sup>; (iii) an image with pixel size 240×240 m<sup>2</sup>
(similar to EOS MODIS) derived from the system-corrected Landsat TM
image by resampling. For the NOAA/AVHRR images and the image similar to
EOS MODIS, we obtained satisfactory results with a root mean square
error of about one pixel unit. The model has to be further improved for
high-resolution data, such as Landsat TM
A highly dynamic learning (DL) neural network is developed and
applied to perform the inversion of rough surface parameters: dielectric
constant, surface rms height, and correlation length. The network
training scheme is based on the Kalman filter technique which lends
itself to a highly dynamic and adaptive merit during the learning stage.
The training data sets utilized were obtained from the integral equation
model (IEM) which has a wide range of frequency. The training speed of
the network is found to be much faster than the backpropagation (BP)
trained multi-layer perceptron (MLP) with the same degree of accuracy.
When applied to invert the surface parameters, the DL network shows a
very satisfactory result in terms of learning time and process accuracy,
thus enhances its potential applications to remote sensing of rough
Two separate SAR image change detection schemes are described. In the absence of ground truth one must judge these schemes against the performance that may be achieved by eye. On this basis, both schemes are found to work well. The neutral network approach appears to perform slightly better than the model-based scheme in terms of both time and detection rates. Given the fact that the author has the option of training the networks on a more realistic object set than that currently used it would appear that the network approach is to be preferred. It is estimated that both schemes will run in real-time on a new transputer-based vector processor
A prerequisite for the design of any target detection scheme is an
understanding of the clutter environment against which targets are to be
detected. For polarimetric synthetic aperture radar (SAR) imagery,
statistical models for homogeneous clutter based on both multi-variate
Gaussian distributions and multi-variate K distributions have been
extensively investigated. The first aim of this paper is to investigate
the extent to which such models can be used to represent genuine
polarimetric SAR clutter. To this end, the models have been tested on
imagery obtained using a C-band airborne polarimetric SAR system, which
is described. The validity of the Gaussian model as a description of the
clutter statistics of a particular extended region is discussed.
However, not all clutter regions can be represented by the simple
Gaussian speckle model as is demonstrated in the case where the validity
of using low order K distributions to describe woodland clutter is
assessed. A point of particular interest is whether, when a low order K
distribution description is most appropriate, the order parameters are
significantly different between polarimetric channels. Suitable
statistical tests have been used to address this question as described.
The second aim is to investigate the spatial statistics of polarimetric
SAR imagery using data provided by the C-band system. The measurement of
correlation shapes and lengths and their use to determine whether there
is significant variation in correlation properties is also discussed
A new method for reducing the effects of space-varying, wavelength-dependent scattering in multispectral imagery caused by smoke and haze is described. It is intended for use in situations where atmospheric scattering affects the shorter wavelengths and varies in space. The method converts an image in which space-varying scattering is present into an image where the scattering has been equalized over the entire image, so that previously developed technique for removing constant scattering effects can be used. The spectral measurement space is viewed as consisting of two subspaces: one spanned by the bands that are affected by scattering, the other by the bands that are not. A correspondence between the two subspaces is established and used to predict the values of the former bands (i.e., what their values would be without scattering) from the latter. Our haze equalization algorithm is compared to an earlier de-hazing algorithm developed by Lavreau that subtracts a portion of the fourth tasselled cap feature, used as an estimate of the atmospheric component, from the visible bands. While both are shown to be effective in removing space-varying smoke and haze, the de-hazing algorithm tends to remove subtle detail and increases the spectral correlation between the visible bands, while the haze-equalization algorithm preserves subtle detail and maintains the spectral balance between bands.
In recognition of the limited application of the satellite-based
`nadir' viewpoint for monitoring urban microclimate, a study was carried
out to investigate the three-dimensional temperature relationships in
high rise housing estates in Singapore. The data derived from this study
are combined with LANDSAT TM thermal data to construct an automated
3-dimensional model of the high rise urban environment which can be
varied according to Sun angle and azimuth at the time of imaging and the
viewing angle required by the user
The turbulent wake of the USNS Hayes, a twin hulled ship, was imaged simultaneously by a thermal infrared scanner, an X-band microwave radar and a 35 mm strip camera mounted in an NRL RP-3A aircraft. Thermal surface effects and centimeter-scale surface roughness characteristics were determined for both natural ship wakes and those treated with oleyl alcohol, an organic material which produced a monomolecular film on the surface of the turbulent wake. The turbulent motions and the presence of the monomolecular film at the wake surface strongly influenced the centimeter-scale surface roughness. This influence appeared as a significant reduction in the power of the reflected microwave signal from the wake surface compared to the surrounding ambient surface. The persistence of this reduction appeared to increase when the film was present. Various computer codes were employed to analyze the digitized IR video data. They generated temperature contour plots and temperature profiles across the wake at various locations behind the ship. These computer plots along with the original 70 mm photographic representation of the data and the corresponding water temperature data indicated that all the wakes were significantly cooler than the surrounding ambient surface water and slightly cooler than the water at keel depth. The thermal signatures of the wakes treated with the surface film were more persistent than the natural wakes, and the cool surface was maintained over a broader cross section of the treated wakes. These observations can be explained on the basis of changes in emissivity and related surface properties, the ship wake hydrodynamics, wind stress considerations and surface film physics.
This study evaluates the potential of measuring/mapping forest decline in spruce-fir forests using airborne NS-001 TMS data. Using field instruments, it was found that ratios of 1.65/1.23 and 1.65/0.83-micron reflectance discriminated between spruce samples of low and high-damage sites. Using TMS data, band ratios were found to be strongly correlated with ground-based measurements of forest damage. Ratio colo-density slice images using these band ratios, and images using 0.56 and 1.65-micron bands with either of these band ratios in a false-color composite, provide accurate means of detecting, quantifying and mapping levels of forest decline.
Imagery covering the Socompa volcano and debris avalanche deposit in northern Chile was acquired by MOMS-01 when the sun was low in the western sky. Illumination from the west shows many important topographic features to advantage. These are inconspicuous or indistinguishable on Landsat TM images acquired at higher solar elevation. The effective spatial resolution of MOMS-01 is similar to that of the TM and its capacity for spectral discrimination is less. A technique has been developed to combine the multispectral information offered by TM with the topographic detail visible on MOMS-01 imagery recorded at a time of low solar elevation.
MOMS-02 is a push-broom scanner with four spectral bands in the 450-810 nm region (each with a 15 m ground resolution element at a 310 km orbit) and a panchromatic (520-760 nm) stereo mode with on-track stereoscopic capability. The stereo mode employs three look angles, including nadir (with a 5 m ground resolution element), 24-deg forward and backward (each with a 10 m ground resolution element). The sensor which is funded by the German Ministry for Research and Technology, is scheduled for launch on a Space Shuttle mission at the end of 1991. The selection and radiometric performance of the panchromatic and the multispectral bands are discussed.
The Arkansas tornadoes on 11 April 1976 were investigated using regular 30 min interval GOES digital infrared data, rawinsonde observations, Doppler sounder records and radar summaries covering the 3 hour time period immediately preceding the touchdown of the tornadoes. Clouds associated with the tornado were compared to other clouds that were not associated with tornadoes. It appears as if the altitude to which the overshooting cloud top penetrates above the tropopause is the major factor that controls tornado formation. Gravity waves were first observed at ionospheric heights when the overshooting cloud top began to penetrate the tropopause. Rawinsonde and radar observations were used to establish the background meterological conditions.
The post-launch degradation of the visible (channel 1: 0.58- 068 microns) and near-infrared (channel 2: approx. 0.72 - l.l microns) channels of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-7, -9, and -11 Polar-orbiting Operational Environmental Satellites (POES) was estimated using the south-eastern part of the Libyan Desert as a radiometrically stable calibration target. The relative annual degradation rates, in per cent, for the two channels are, respectively: 3.6 and 4.3 (NOAA-7); 5.9 and 3.5 (NOAA-9); and 1.2 and 2.0 (NOAA-11). Using the relative degradation rates thus determined, in conjunction with absolute calibrations based on congruent path aircraft/satellite radiance measurements over White Sands, New Mexico (USA), the variation in time of the absolute gain or slope of the AVHRR on NOAA-9 was evaluated. Inter-satellite calibration linkages were established, using the AVHRR on NOAA-9 as a normalization standard. Formulae for the calculation of calibrated radiances and albedos (AVHRR usage), based on these interlinkages, are given for the three AVHRRs.
Based on ground temperature measurement, thermal anomalies
induced by underground coal fires were extracted from airborne night-time
8–12.5 µm, daytime 8–12.5 µm, and daytime 3–5 µm images by density slicing.
The thermal anomalies extracted from these images had different sizes and were
partially overlapped. Integration of the three processed images revealed detailed
structures of the surface thermal anomalies. The temperature difference (DT)
between a threshold temperature and the background temperature can be taken
as a criterion for the evaluation of detection capabilities of different thermal
data. The DTs for night-time, daytime 8–12.5 µm data and 3–5 µm data are 2.7,
4.4 and 7.1°C, respectively. The detection capabilities for sub-pixel sized coal
fires from different thermal data were evaluated by assuming these fires cause the
equivalent spectral radiance as a full pixel thermal anomaly does. The 3–5 µm
data are more sensitive to sub-pixel-sized high-temperature (i.e. w165°C)
An empirical relationship has been determined between the difference of vertically and horizontally polarized brightness temperatures noted at the 37 GHz frequency of the Nimbus-7 SMMR and primary productivity over hot arid and semiarid regions of Africa and Australia. This empirical relationship is applied to estimate the primary productivity over the Thar Desert between 1979 and 1985, giving an average value of 0.271 kg/sq m per yr. The spatial variability of the productivity values is found to be quite significant, with a standard deviation about the mean of 0.08 kg/sq m per yr.
National Oceanic and Atmospheric Administration (NOAA) satellite data from the Advanced Very High Resolution Radiometer (AVHRR) sensor were analysed to document the vegetation biomass dynamics associated with the regional desert-locust upsurge in West Africa during 1980/81, which affected an area of some 600 000 km in Mali, Niger and Algeria. Comparisons were made among locust population survey reports, rainfall records from eighteen stations in the same area, and the satellite data in vegetation index format. The satellite-recorded temporal and spatial distributions of desert vegetation biomass were closely correlated with both the locust population surveys and the available rainfall data. An attempt was made to develop a quantitative relationship between a satellite-derived potential breeding activity factor (PBAF) and the observed desert locust populations. Analysis of the multitemporal satellite data set indicates that, had the NOAA/AVHRR vegetation index data been operationally available in June 1980, effective preventive control measures would have only been necessary for an area of 600 km.
Images are presented that show the mean and coefficient of variation of nine years (1981-1989) of NOAA AVHRR normalized difference vegetation index (NDVI) data for the growing season (July-October) in Africa, north of the equator. The variation in the growing season NDVI is represented by the coefficient of variation image that shows the large variation in the Sahelian growing season between years. It is concluded that these images illustrate some aspects of the perspective being brought to regional and continental scale processes by coarse resolution satellite sensors and the potential of these sensors to provide consistent, long-term datasets.
Normalized difference vegetation index data derived from the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite for the 1983 growing season for the Sahelian Zone of Niger are compared with biomass estimates derived from an empirical grassland productivity model. The model used daily rainfall data to estimate the potential biomass production for fourteen meteorological stations through the growing season. A good general correspondence (r = 0·75) was seen between the productivity model and the satellite-derived integrated NDV1, although specific differences were apparent between actual and potential biomass. The study shows the utility of high-temporal-resolution satellite data for monitoring grassland conditions at a local and regional scale and emphasizes the importance of a maximum value compositing approach to the analysis. The study also shows the potential of the satellite data for quantifying phenological characteristics of vegetation
Normalized difference vegetation index data obtained from polar-orbiting meteorological satellites were used to compare the growing or rainy seasons of 1984 and 1985 for the Sahelian zone of Africa. A substantial difference was found between these two years, with 1985 generally having higher normalized difference vegetation index values indicating higher levels of primary production in 1985 than in 1984. 1 km data were compared for Senegal, Mali, Niger and Sudan, and 7 km data were compared for sub-Saharan Africa. The qualitative comparison of these data suggests the use of similar data to assist in centralized monitoring of rangeland conditions, to identify areas of deficiencies in primary production and provide synoptic information in support of regional drought monitoring.