Showing papers in "Journal of remote sensing in 2007"
TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.
2,741 citations
TL;DR: In this article, an extensive evaluation of 10 different satellite rainfall products was performed using station network over a complex topography, where elevation varies from below sea level to 4620 m. Evaluation was for two groups of products: low spatial (2.5°) and temporal (monthly) resolution, and the Tropical Rainfall Measuring Mission (TRMM-3B43).
Abstract: An extensive evaluation of 10 different satellite rainfall products was performed using station network over a complex topography, where elevation varies from below sea level to 4620 m. Evaluation was for two groups of products. The first group had low spatial (2.5°) and temporal (monthly) resolution and included the Global Precipitation Climatology Project (GPCP), the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) merged analysis (CMAP), and the Tropical Rainfall Measuring Mission (TRMM-3B43). The second group comprised products with relatively high spatial (0.1° to 1°) and temporal (3-hourly to 10-daily) resolution. These included the NOAA-CPC African rainfall estimation algorithm, GPCP one-degree-daily (1DD), TRMM-3B42, Tropical Applications of Meteorology using SATellite and other data (TAMSAT) estimates, and the CPC morphing technique (CMORPH). These products were aggregated to a 10-day total and remapped to spatial resolutions of 1°, 0.5° and 0.25°. TRMM-3B43 and CMAP from the first group and CMORPH, TAMSAT and TRMM-3B42 from the second group performed reasonably well.
579 citations
TL;DR: The primary findings of this study are that Nightsat should collect data from a near‐synchronous orbit in the early evening with 50 to 100 m spatial resolution and have detection limits of 2.5E−8 Watts cm−2sr−1µm−1 or better.
Abstract: Nightsat is a concept for a satellite system capable of global observation of the location, extent and brightness of night-time lights at a spatial resolution suitable for the delineation of primary features within human settlements. Based on requirements from several fields of scientific inquiry, Nightsat should be capable of producing a complete cloud-free global map of lights on an annual basis. We have used a combination of high-resolution field spectra of outdoor lighting, moderate resolution colour photography of cities at night from the International Space Station, and high-resolution airborne camera imagery acquired at night to define a range of spatial, spectral, and detection limit options for a future Nightsat mission. The primary findings of our study are that Nightsat should collect data from a near-synchronous orbit in the early evening with 50 to 100 m spatial resolution and have detection limits of 2.5E-8 Watts cm-2sr-1µm-1 or better. Although panchromatic low-light imaging data would be useful, multispectral low-light imaging data would provide valuable information on the type or character of lighting; potentially stronger predictors of variables such as ambient population density and economic activity; and valuable information to predict response of other species to artificial night lighting. The Nightsat mission concept is unique in its focus on observing a human activity, in contrast to traditional Earth observing systems that focus on natural systems.
264 citations
TL;DR: In this paper, a method for automatic calculation of a landslide displacement field is presented based on a piecewise application of the Iterative Closest Point (ICP) algorithm and is made possible by the robustness of this algorithm against noise and small morphological modifications.
Abstract: A terrestrial laser scanner (TLS) allows the generation of a detailed model of a landslide surface. In this way, when two or more georeferenced models obtained by multi-temporal scans are available, the landslide displacement field can be computed. Nevertheless, such a computation is a relatively complex task because the recognition of correspondences among the multi-temporal models is required. The Iterative Closest Point (ICP) algorithm allows the alignment of two 3D objects having a common part by iterative shape matching. A new method for the automatic calculation of a landslide displacement field is presented here. It is based on a piecewise application of the ICP algorithm and is made possible by the robustness of this algorithm against noise and small morphological modifications. After a series of numerical experimentations, this method was successfully applied to two test sites located in the North-Eastern Italian Alps affected by high-risk landslides of the slump type (Perarolo di Cadore and Lamosano) with very different observational conditions.
244 citations
TL;DR: The relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) associated with urban land use type and land-use pattern was discussed in the City of Shanghai, China using data collected by the Enhanced Thematic Mapper Plus (ETM+) and aerial photographic remote sensing system as discussed by the authors.
Abstract: The relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) associated with urban land-use type and land-use pattern is discussed in the City of Shanghai, China using data collected by the Enhanced Thematic Mapper Plus (ETM+) and aerial photographic remote sensing system. There is an apparent correlation between LST and NDVI from the visual interpretation of LST and NDVI contrasts. Mean LST and NDVI values associated with different land-use types are significantly different. Multiple comparisons of mean LST and NDVI values associated with pairings of each land-use type are also shown to be significantly different. The result of a regressive analysis shows an inverse correlation relationship between LST and NDVI within all land-use polygons, the same to each land-use type, but correlation coefficients associated with land-use types are different. An analysis on the relationship between LST, NDVI and Shannon Diversity Index (SHDI) shows a positive correlation between LST and SHDI and a negative correlation between NDVI and SHDI. According to the above results, LST, SHDI and NDVI can be considered to be three basic indices to study the urban ecological environment and to contribute to further validation of the applicability of relatively low cost, moderate spatial resolution satellite imagery in evaluating environmental impacts of urban land function zoning, then to examine the impact of urban land-use on the urban environment in Shanghai City. This provides an effective tool in evaluating the environmental influences of zoning in urban ecosystems with remote sensing and geographical information systems.
239 citations
TL;DR: The case study shows that the ordinary kriging techniques may provide a powerful tool for interpolating the missing pixels in the SLC‐off ETM+ imagery, and demonstrates that the standardized ordinary cokriging provides little improvement in interpolation of the data gap.
Abstract: Using appropriate techniques to fill the data gaps in SLC-off ETM+ imagery may enable more scientific use of the data. The local linear histogram-matching technique chosen by USGS has limitations if the scenes being combined exhibit high temporal variability and radical differences in target radiance due, for example, to the presence of clouds. This study proposes using an alternative interpolation method, the kriging geostatistical technique, for filling the data gaps. The case study shows that the ordinary kriging techniques may provide a powerful tool for interpolating the missing pixels in the SLC-off ETM+ imagery. While the standardized ordinary cokriging has been shown to be particularly useful when samples of the variable to be predicted are sparse and samples of a second, related variable are plentiful, the case study demonstrates that it provides little improvement in interpolating the data gap in the SLC-off imagery.
208 citations
TL;DR: In this article, the authors developed a methodology for integration of remote sensing and census data within a GIS framework to assess the quality of life in Indianapolis, Indiana, United States.
Abstract: This paper develops a methodology for integration of remote sensing and census data within a GIS framework to assess the quality of life in Indianapolis, Indiana, United States. Environmental variables, i.e. greenness, impervious surface and temperature, were derived from a Landsat ETM+ image. Socio-economic variables, including population density, income, poverty, employment rate, education level and house characteristics from US census 2000, were integrated with the environmental variables at the block group level to derive indicators of quality of life. Pearson's correlation was computed to analyse the relationships among the variables. Further, factor analysis was conducted to extract unique information from the combined dataset. Three factors were identified and interpreted as material welfare, environmental conditions and crowdedness respectively. Each factor was viewed as a unique aspect of the quality of life. A synthetic index of the urban quality of life was created and mapped based on weighted factor scores of the three factors. Finally, regression models were built to estimate the quality of life in the city of Indianapolis based on selected environmental and socioeconomic variables.
182 citations
TL;DR: In this article, a direct inversion of spectral top-of-atmosphere (TOA) radiances into spectral remote sensing reflectances at the bottom of the BOA (BOA), with additional output of the aerosol optical thickness (AOT) at four wavelengths for validation purposes is described.
Abstract: The development and validation of an atmospheric correction algorithm designed for the Medium Resolution Imaging Spectrometer (MERIS) with special emphasis on case-2 waters is described. The algorithm is based on inverse modelling of radiative transfer (RT) calculations using artificial neural network (ANN) techniques. The presented correction scheme is implemented as a direct inversion of spectral top-of-atmosphere (TOA) radiances into spectral remote sensing reflectances at the bottom-of-atmosphere (BOA), with additional output of the aerosol optical thickness (AOT) at four wavelengths for validation purposes. The inversion algorithm was applied to 13 MERIS Level1b data tracks of 2002-2003, covering the optically complex waters of the North and Baltic Sea region. A validation of the retrieved AOTs was performed with coincident in situ automatic sun-sky scanning radiometer measurements of the Aerosol Robotic Network (AERONET) from Helgoland Island located in the German Bight. The accuracy of the derived reflectances was validated with concurrent ship-borne reflectance measurements of the SIMBADA hand-held field radiometer. Compared to the MERIS Level2 standard reflectance product generated by the processor versions 3.55, 4.06 and 6.3, the results of the proposed algorithm show a significant improvement in accuracy, especially in the blue part of the spectrum, where the MERIS Level2 reflectances result in errors up to 122% compared to only 19% with the proposed algorithm. The overall mean errors within the spectral range of 412.5-708.75 nm are calculated to be 46.2% and 18.9% for the MERIS Level2 product and the presented algorithm, respectively.
173 citations
TL;DR: In this paper, a computer code (acronym 5S) is developed that allows estimation of the solar radiation backscattered by the Earth-surface-atmosphere system, as it is observed by a satellite sensor.
Abstract: A computer code (acronym 5S) has been developed that allows estimation of the solar radiation backscattered by the Earth-surface-atmosphere system, as it is observed by a satellite sensor. Given the Lambertian ground reflectance, the apparent reflectance of the observed pixel is estimated by taking into account the effects of gaseous absorption, scattering by molecules and aerosols and, to some extent, inhomogeneity in the ground reflectance. The input parameters (observation geometry, atmosphere model, ground reflectance and spectral band) can be either selected from some proposed standard conditions (e.g. spectral bands of a satellite sensor) or user-defined. Besides the pixel apparent reflectance, the code provides the gaseous transmittance, the irradiance at the surface and the different contributions to the satellite signal according to the origin of the measured radiance. Some complementary results are also available; among others, benchmark calculations permit assessment of the code accuracy.
172 citations
TL;DR: In this article, an integrated hydrogeological investigation has been made to delineate the groundwater-potential zones of the Muvattupuzha river basin, Kerala, along the southwest coast of India.
Abstract: An integrated hydrogeological investigation has been made to delineate the groundwater-potential zones of the Muvattupuzha river basin, Kerala, along the southwest coast of India. The basin is characterized by charnockites and gneisses of Archean age covering more than 80% of the area and the remaining by Pleistocene laterites and Miocene formation. The basin receives high rainfall, measuring 3100 mm/year. However, acute water shortage occurs during the premonsoon season and hence, a number of dug wells are made to tap the groundwater. Seasonal rainfall during NE and SW monsoons is the major source of groundwater recharge. Further, hydrogeomorphology, geology, fracture systems and the slope of the terrain also play a significant role on the movement and behaviour of the groundwater of this basin. The integration of conventional and remote sensing data has been made through geographic information system (GIS) and it is found that about 50% of the area can be identified as very good or good potential zones, whereas the remaining area falls under moderate and poor categories. Most of the Muvattupuzha sub-basin and the western part of the Kothamangalam and Kaliyar sub-basins are classified as good groundwater-potential zones, although the eastern upstream part of the basin has poor groundwater potential.
170 citations
TL;DR: The results indicate this product will be useful for a wide variety of applications, including regional‐scale studies, general land cover mapping, crop‐specific mapping and monitoring, and visual assessments.
Abstract: On 31 May 2003, the Landsat Enhanced Thematic Plus (ETM+) Scan Line Corrector (SLC) failed, causing the scanning pattern to exhibit wedge-shaped scan-to-scan gaps. We developed a method that uses coincident spectral data to fill the image gaps. This method uses a multi-scale segment model, derived from a previous Landsat SLC-on image (image acquired prior to the SLC failure), to guide the spectral interpolation across the gaps in SLC-off images (images acquired after the SLC failure). This paper describes the process used to generate the segment model, provides details of the gap-fill algorithm used in deriving the segment-based gap-fill product, and presents the results of the gap-fill process applied to grassland, cropland, and forest landscapes. Our results indicate this product will be useful for a wide variety of applications, including regional-scale studies, general land cover mapping (e.g. forest, urban, and grass), crop-specific mapping and monitoring, and visual assessments. Applications that need to be cautious when using pixels in the gap areas include any applications that require per-pixel accuracy, such as urban characterization or impervious surface mapping, applications that use texture to characterize landscape features, and applications that require accurate measurements of small or narrow landscape features such as roads, farmsteads, and riparian areas.
TL;DR: In this paper, a new combined index derived from the Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) in ratio obtained the best regression relationships with chlorophyll meter values (Minolta Soil Plant Analysis Development (SPAD) and flag leaf N).
Abstract: This study assessed whether vegetation indices derived from broadband RapidEye™ data containing the red edge region (690-730 nm) equal those computed from narrow band data in predicting nitrogen (N) status of spring wheat (Triticum aestivum L.). Various single and combined indices were computed from in-situ spectroradiometer data and simulated RapidEye™ data. A new, combined index derived from the Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) in ratio obtained the best regression relationships with chlorophyll meter values (Minolta Soil Plant Analysis Development (SPAD) 502 chlorophyll meter) and flag leaf N. For SPAD, r 2 values ranged from 0.45 to 0.69 (p<0.01) for narrow bands and from 0.35 and 0.77 (p<0.01) for broad bands. For leaf N, r 2 values ranged from 0.41 to 0.68 (p<0.01) for narrow bands and 0.37 to 0.56 (p<0.01) for broad bands. These results are sufficiently promising to suggest that MCARI/MTVI2 employing broadband RapidEye™ data is useful for predicting wheat N status.
TL;DR: In this article, the authors used the mid-infrared bispectral index (MIRBI) with a fixed threshold of > 1.75 to map burned and unburned surfaces.
Abstract: Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution ( 65% at the MODIS scale, presumably because of the decrease in signal-to-noise ratio as compared to the Landsat scale. At the MODIS scale the Mid-Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r 2 = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km2 during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.
TL;DR: In this article, the results of the above fusion methods were compared and comments on the fusion methods and the potential of evaluation indicators were made, including two-dimensional correlation, relative difference of means, relative variation, deviation index, entropy difference, peak signal-to-noise ratio index and universal image quality index, as well as photo-interpretation methods and techniques.
Abstract: Various fusion methods have been developed for improving data spatial resolution. The methods most encountered in the literature are the intensity-hue-saturation (IHS) transform, the Brovey transform, the principal components algorithm (PCA) fusion method, the Gram-Schmidt fusion method, the local mean matching method, the local mean and variance matching method, the least square fusion method, the discrete wavelet fusion method including Daubechies, Symlet, Coiflet, biorthogonal spline, reverse biorthogonal spline, and Meyer wavelets, the wavelet-PCA fusion method, and the crossbred IHS and wavelet fusion method. Using various evaluation indicators such as two-dimensional correlation, relative difference of means, relative variation, deviation index, entropy difference, peak signal-to-noise ratio index and universal image quality index, as well as photo-interpretation methods and techniques, results of the above fusion methods were compared and comments on the fusion methods and potential of evaluation indicators were made. Among data fusion methods and indicators the local mean and variance matching methods proved the most efficient and the peak signal-to-noise ratio indicator proved the most appropriate for the evaluation of data fusion results.
TL;DR: In this article, a framework starting with the definition of meaningful objects is proposed to find optimal segmentations for a given feature type, which is tested to segment three exemplary artificial feature types (sports fields, roads, and residential buildings) in IKONOS multi-spectral images, based on a sampling scheme of all the parameters required by the algorithm.
Abstract: Multi-resolution segmentation, as one of the most popular approaches in object-oriented image segmentation, has been greatly enabled by the advent of the commercial software, eCognition. However, the application of multi-resolution segmentation still poses problems, especially in its operational aspects. This paper addresses the issue of optimization of the algorithm-associated parameters in multi-resolution segmentation. A framework starting with the definition of meaningful objects is proposed to find optimal segmentations for a given feature type. The proposed framework was tested to segment three exemplary artificial feature types (sports fields, roads, and residential buildings) in IKONOS multi-spectral images, based on a sampling scheme of all the parameters required by the algorithm. Results show that the feature-type-oriented segmentation evaluation provides an insight to the decision-making process in choosing appropriate parameters towards a high-quality segmentation. By adopting these feature-type-based optimal parameters, multi-resolution segmentation is able to produce objects of desired form to represent artificial features.
TL;DR: In this article, the tasselled cap concept is extended to MODIS Nadir BRDF-Adjusted Reflectance (NBAR, MOD43) data and the transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16-day composites.
Abstract: The tasselled cap concept is extended to Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-Adjusted Reflectance (NBAR, MOD43) data. The transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16-day composites. To provide a standard for MODIS tasselled cap axes, we recommend an orientation in MODIS spectral band space as similar as possible to the orientation of the Landsat Thematic Mapper (TM) tasselled cap axes. To achieve this we first transformed our global sample of MODIS NBAR reflectance values to TM tasselled cap values using the existing TM transformation, then used an existing algorithm (Procrustes) to compute the transformation that minimizes the mean square difference between the TM transformed NBAR values and NBAR PCA values. This transformation can then be used as a standard to rotate the MODIS NBAR PCA axes into a new MODIS Kauth-Thomas (KT) orientation. Global land cover patterns in tasselled cap space are demonstrated graphically by linking the global sample with several other products, including the MODIS Land Cover product (MOD12) and the MODIS Vegetation Continuous Fields product (MOD44). Patterns seen at this global scale agree with previous explorations of TM tasselled cap space, but are shown here in greater detail with a globally representative sample. Temporal trends of eight smaller-scale BigFoot Project (www.fsl.orst.edu/larse/bigfoot) sites were also examined, confirming the spectral shifts in tasselled cap space related to phenology.
TL;DR: In this article, the authors examined the extent to which cloudiness can restrict the monitoring of the Brazilian Cerrado from Landsat-like sensors, and estimated the percent cloud cover from more than 35 500 Landsat quick-looks by the K-means unsupervised classification technique.
Abstract: Remotely sensed data are the best and perhaps the only possible way for monitoring large-scale, human-induced land occupation and biosphere-atmosphere processes in regions such as the Brazilian tropical savanna (Cerrado). Landsat imagery has been intensively employed for these studies because of their long-term data coverage (>30 years), suitable spatial and temporal resolutions, and ability to discriminate different land-use and land-cover classes. However, cloud cover is the most obvious constraint for obtaining optical remote sensing data in tropical regions, and cloud cover analysis of remotely sensed data is a requisite step needed for any optical remote sensing studies. This study addresses the extent to which cloudiness can restrict the monitoring of the Brazilian Cerrado from Landsat-like sensors. Percent cloud cover from more than 35 500 Landsat quick-looks were estimated by the K-means unsupervised classification technique. The data were examined by month, season, and El Nino Southern Oscillation event. Monthly observations of any part of the biome are highly unlikely during the wet season (October-March), but very possible during the dry season, especially in July and August. Research involving seasonality is feasible in some parts of the Cerrado at the temporal satellite sampling frequency of Landsat sensors. There are several limitations at the northern limit of the Cerrado, especially in the transitional area with the Amazon. During the 1997 El Nino event, the cloudiness over the Cerrado decreased to a measurable but small degree (5% less, on average). These results set the framework and limitations of future studies of land use/land cover and ecological dynamics using Landsat-like satellite sensors.
TL;DR: In this paper, the authors compared the ability of Landsat ETM+, Quickbird and three image classification methods for discriminating amongst coral reefs and associated habitats in Pacific Panama, and the integration of object-oriented classification with non-spectral information in eCognition produced the most accurate results.
Abstract: This research compared the ability of Landsat ETM+, Quickbird and three image classification methods for discriminating amongst coral reefs and associated habitats in Pacific Panama. Landsat ETM+ and Quickbird were able to discriminate coarse and intermediate habitat classes, but this was sensitive to classification method. Quickbird was significantly more accurate than Landsat (14% to 17%). Contextual editing was found to improve the user's accuracy of important habitats. The integration of object-oriented classification with non-spectral information in eCognition produced the most accurate results. This method allowed sufficiently accurate maps to be produced from Landsat, which was not possible using the maximum likelihood classifier. Object-oriented classification was up to 24% more accurate than the maximum likelihood classifier for Landsat and up to 17% more accurate for Quickbird. The research indicates that classification methodology should be an important consideration in coral reef remote sensing. An object-oriented approach to image classification shows potential for improving coral reef resource inventory.
TL;DR: In this paper, several methods for land cover/change detection using Landsat TM/ETM+ imagery were employed for Lake Baringo catchment in Kenya, East Africa.
Abstract: Many parts of East Africa are experiencing dramatic changes in land-cover/use at a variety of spatial and temporal scales, due to both climatic variability and human activities. Information about such changes is often required for planning, management, and conservation of natural resources. Several methods for land cover/change detection using Landsat TM/ETM+ imagery were employed for Lake Baringo catchment in Kenya, East Africa. The Lake Baringo catchment presents a good example of environments experiencing remarkable land cover change due to multiple causes. Both the NDVI differencing and post-classification comparison effectively depicted the hotspots of land degradation and land cover/use change in the Lake Baringo catchment. Change-detection analysis showed that the forest cover was the most affected, in some sections recording reductions of over 40% in a 14-year period. Deforestation and subsequent land degradation have increased the sediment yield in the lake resulting in reduction in lake surface area by over 10% and increased turbidity confirmed by the statistically significant increase (t = -84.699, p<0.001) in the albedo between 1986 and 2000. Although climatic variations may account for some of the changes in the lake catchment, most of the changes in land cover are inherently linked to mounting human and livestock population in the Lake Baringo catchment.
TL;DR: A spectrally segmented PCA based on the spectral characteristics of vegetation over different wavelength regions is developed that performs better than correlation‐based segmented principal component transformation (SPCT) and conventional PCA in detecting the target plant species, as well as mapping other vegetation covers.
Abstract: Principal component analysis (PCA) is one of the most commonly adopted feature reduction techniques in remote sensing image analysis. However, it may overlook subtle but useful information if applied directly to the analysis of hyperspectral data, especially for discriminating between different vegetation types. In order to accurately map an invasive plant species (horse tamarind, Leucaena leucocephala) in southern Taiwan using Hyperion hyperspectral imagery, this study developed a spectrally segmented PCA based on the spectral characteristics of vegetation over different wavelength regions. The developed algorithm can not only reduce the dimensionality of hyperspectral imagery but also extracts helpful information for differentiating more effectively the target plant species from other vegetation types. Experiments conducted in this study demonstrated that the developed algorithm performs better than correlation-based segmented principal component transformation (SPCT) and conventional PCA (overall accuracy: 86%, 76%, 66%; kappa value: 0.81, 0.69, 0.57) in detecting the target plant species, as well as mapping other vegetation covers.
TL;DR: In this paper, a new approach for quantifying vegetation pigment concentrations through wavelet decomposition of hyperspectral remotely sensed data was reported. But, the results of the wavelet-based technique were examined using reflectance spectra and pigment data collected for a range of plant species at leaf and canopy scales.
Abstract: This paper reports a new approach for quantifying vegetation pigment concentrations through wavelet decomposition of hyperspectral remotely sensed data. Wavelets are a group of functions that vary in complexity and mathematical properties, that are used to dissect data into different frequency components and then characterize each component with a resolution appropriate to its scale. Wavelet analysis of a reflectance spectrum is performed by scaling and shifting the wavelet function to produce wavelet coefficients that are assigned to different frequency components. By selecting appropriate wavelet coefficients, a spectral model can be established between the coefficients and biochemical concentrations. Hence, wavelet analysis has the potential to capture much more of the information contained within high-resolution spectra than previous approaches and offers the prospect of developing robust, generic methods for pigment determinations. The capabilities of the wavelet-based technique were examined using reflectance spectra and pigment data collected for a range of plant species at leaf and canopy scales. For the combined data set and all of the individual vegetation types, methods based on wavelet decomposition appreciably outperformed narrowband spectral indices and stepwise selection of narrowband reflectance. However, there was variation between vegetation types in the relative performance of the three different feature extraction techniques employed for selecting the wavelet coefficients for use in predictive models. There was also considerable variability in the performance of predictive models according to the wavelet function used for spectral decomposition and the optimum wavelet functions differed between vegetation types and between individual pigments within the same vegetation type. The research indicates that wavelet analysis holds promise for the accurate determination of chlorophyll a and b and the carotenoids, but further work is needed to refine the approach.
TL;DR: In this paper, two automated methods, supervised and unsupervised classification of 10m multi-spectral SPOT-5 imagery, were tested for their ability to identify and map landslide areas before and after the two storm events.
Abstract: Two large tropical cyclones struck Taiwan in the summer of 2004 and landslides triggered by these events caused not only casualties and housing damage but also produced large volumes of sediment that entered rivers and reservoirs. For reservoir and watershed management it is important to quickly identify the location and areal extent of new landslides for coordinating mitigation efforts. In this study, two automated methods, supervised and unsupervised classification of 10 m multi-spectral SPOT-5 imagery, were tested for their ability to identify and map landslide areas before and after the two storm events. A slope map was applied to mask roads, riverbeds and agricultural fields erroneously commissioned as landslides. The automated classification results were compared with manually delineated landslides using SPOT-5 supermode satellite imagery with a resolution of 2.5 m. Statistical testing and spatial analysis of the mapping results were performed. Finally, the results from all three methods were validated by using 0.35 m orthophotographs. This paper reports the results and discusses the salient differences between the automated and manual methods.
TL;DR: In this paper, a freely available data processor for the B asic E RS & ENVISAT ( A )ATSR and M ERIS Toolbox (BEAM) was developed to retrieve atmospheric and oceanic properties above and of Case-2 waters from Medium Resolution Imaging Spectrometer (MERIS) Level 1b data.
Abstract: A freely available data processor for the B asic E RS & ENVISAT ( A )ATSR and M ERIS Toolbox (BEAM) was developed to retrieve atmospheric and oceanic properties above and of Case-2 waters from Medium Resolution Imaging Spectrometer (MERIS) Level1b data. The processor was especially designed for European coastal waters and uses MERIS Level1b Top-Of-Atmosphere (TOA) radiances to retrieve atmospherically corrected remote sensing reflectances at the Bottom-Of-Atmosphere (BOA), spectral aerosol optical thicknesses (AOT) and the concentration of three water constituents, namely chlorophyll-a (CHL), total suspended matter (TSM) and the absorption of yellow substance at 443 nm (YEL). The retrieval is based on four separate artificial neural networks which were trained on the basis of the results of extensive radiative transfer (RT) simulations by taking various atmospheric and oceanic conditions into account. The accuracy of the retrievals was acquired by comparisons with concurrent in situ ground measurements and was published in full detail elsewhere. For the remote sensing reflectance product a mean absolute percentage error (MAPE) of 18% was derived within the spectral range 412.5-708.75 nm while the accuracy of the AOT at 550 nm in terms of MAPE was calculated to be 40%. The accuracies for CHL, TSM and YEL were derived from match-up analysis with MAPEs of 50%, 60% and 71%, respectively.
TL;DR: Wang et al. as discussed by the authors used spectral reflectances in the red and near infrared bands of VGT sensor to identify the water body of Ebinur Lake, achieving an overall accuracy of 91.4%.
Abstract: Ebinur Lake is located in a typical arid region in the north-west of China. It is an area with the lowest elevation in the Junggar Basin in the Province of Xinjiang. Recent monitoring indicates that the lake surface area has increased. To obtain a continuous record of the change in lake area, a radiometric analysis of SPOT/VEGETATION (VGT) imagery was carried out based on methodology developed for regional lake area mapping. Two indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were selected to identify the water body of Ebinur Lake. The indices are calculated based on the spectral reflectances in the red and near infrared bands of VGT sensor. If the NDVI is less than a critical value (0) and if the NDWI is larger than a critical value (0), the pixel is flagged as a water body. Validation indicates that the methodology to identify water bodies based on multi-spectral VGT data is applicable in our study area achieving an overall accuracy of 91.4%. Independent monitoring results elicit that the lake surface area was at its lowest in 1998. The yearly average surface area is about 503 km2. The lake area increased to 603 km2 during 1999. In the period 1999-2001 the area changes are marginal. A large area increase occurred from 2001 to 2002 till the lake area reached a surface area of 791 km2. The lake area peaks to 903 km2 in 2003 and subsequently decreased to areas of 847 km2 in 2004 and 746 km2 in 2005. Similar area change dynamics are observed when applying the remote sensing based technique. Seasonally, the typical dynamics elicit a larger surface area in spring and winter and a smaller one during summer.
TL;DR: In this article, an algorithm using the Vegetation Health (VH) Indices (Vegetation Condition Index and Temperature Condition Index (TCI)) computed for each week over a period of 23 years (1982-2004) from Advance Very High Resolution Radiometer (AVHRR) data was used for estimating winter wheat yield in Kansas.
Abstract: This paper shows the application of remote sensing data for estimating winter wheat yield in Kansas. An algorithm uses the Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed for each week over a period of 23 years (1982-2004) from Advance Very High Resolution Radiometer (AVHRR) data. The weekly indices were correlated with the end of the season winter wheat (WW) yield. A strong correlation was found between winter wheat yield and VCI (characterizing moisture conditions) during the critical period of winter wheat development and productivity that occurs during April to May (weeks 16 to 23). Following the results of correlation analysis, the principal components regression (PCR) method was used to construct a model to predict yield as a function of the VCI computed for this period. The simulated results were compared with official agricultural statistics showing that the errors of the estimates of winter wheat yield are less than 8%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.
TL;DR: 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.
Abstract: 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.
TL;DR: In this paper, an NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005.
Abstract: An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005. To create the dataset, (1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel-specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and (2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor-quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km×5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates (root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula.
TL;DR: In this paper, three southern USA forestry species, loblolly pine, Virginia pine and shortleaf pine, were shown to be spectrally separable using data from a full-range spectroradiometer (400-2500nm) acquired above tree canopies.
Abstract: Three southern USA forestry species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), and shortleaf pine (Pinus echinata), were previously shown to be spectrally separable (83% accuracy) using data from a full-range spectroradiometer (400-2500 nm) acquired above tree canopies. This study focused on whether these same species are also separable using hyperspectral data acquired using the airborne visible/infrared imaging spectrometer (AVIRIS). Stepwise discriminant techniques were used to reduce data dimensionality to a maximum of 10 spectral bands, followed by discriminant techniques to measure separability. Discriminatory variables were largely located in the visible and near-infrared regions of the spectrum. Cross-validation accuracies ranged from 65% (1 pixel radiance data) to as high as 85% (3×3 pixel radiance data), indicating that these species have strong potential to be classified accurately using hyperspectral data from air-or space-borne sensors.
TL;DR: In this paper, the authors used a combination of spectral information and LiDAR data to classify the vegetation in a semi-natural floodplain along the river Waal in the Netherlands.
Abstract: To safeguard the goals of flood protection and nature development, a river manager requires detailed and up-to-date information on vegetation structures in floodplains. In this study, remote-sensing data on the vegetation of a semi-natural floodplain along the river Waal in the Netherlands were gathered by means of a Compact Airborne Spectrographic Imager (CASI; spectral information) and LiDAR (structural information). These data were used to classify the floodplain vegetation into eight and five different vegetation classes, respectively. The main objective was to fuse the CASI and LiDAR-derived datasets on a pixel level and to compare the classification results of the fused dataset with those of the non-fused datasets. The performance of the classification results was evaluated against vegetation data recorded in the field. The LiDAR data alone provided insufficient information for accurate classification. The overall accuracy amounted to 41% in the five-class set. Using CASI data only, the overall accuracy was 74% (five-class set). The combination produced the best results, raising the overall accuracy to 81% (five-class set). It is concluded that fusion of CASI and LiDAR data can improve the classification of floodplain vegetation, especially for those vegetation classes which are important to predict hydraulic roughness, i.e. bush and forest. A novel measure, the balance index, is introduced to assess the accuracy of error matrices describing an ordered sequence of classes such as vegetation structure classes that range from bare soil to forest.
TL;DR: In this article, the authors describe the study of precisely detected shadow in satellite images and recovering information from the surface covered in shadow from very high resolution (VHR) satellite imagery.
Abstract: It is usually quite difficult to extract and recover shadow information in the urban environment from remote sensing imagery. This paper describes the study of precisely detected shadow in satellite images and recovering information from the surface covered in shadow from very high resolution (VHR) satellite imagery.