Mapping and monitoring riparian vegetation distribution,structure and composition with regression tree models and post-classification change metrics

Riparian systems have become increasingly susceptible to both natural and human disturbances as cumulative pressures from changing land use and climate alter the hydrological regimes. This article introduces a landscape dynamics monitoring protocol that incorporates riparian structural classes into the land-cover classification scheme and examines riparian change within the context of surrounding land-cover change. We tested whether Landsat Thematic Mapper (TM) imagery could be used to document a riparian tree die-off through the classification of multi-date Landsat images using classification and regression tree (CART) models trained with physiognomic vegetation data. We developed a post-classification change map and used patch metrics to examine the magnitude and trajectories of riparian class change relative to mapped disturbance parameters. Results show that catchments where riparian change occurred can be identified from land-cover change maps; however, the main change resulting from the die-off disturbance was compositional rather than structural, making accurate post-classification change detection difficult.     

Sediment facies classification of a sandy shoreline by means of airborne imaging spectroscopy

Airborne imaging spectroscopy data (AISA Eagle and HyMap) were applied to classify the sediments of a sandy beach in seven sand type classes. On the AISA‐Eagle data, several classification strategies were tried out and compared with each other. The best classification results were obtained applying a linear discriminant classifier (LDC) in combination with feature selection based on sequential floating forward search (SFFS). The statistical LDC was used in a multiple binary approach. In the first step, the original bands were used in the classification, but transformation of the bands to wavelet coefficients enhanced the accuracy obtained. The combination of LDC with SFFS resulted in an overall accuracy of 82% (using three wavelet coefficients). Replacing the LDC with the non‐statistical SAM algorithm reduced the overall accuracy to 74% (using all bands or wavelet coefficients). When applying LDC, the optimal number of bands/wavelet coefficients to be used was defined: using more than two bands or three wavelet coefficients did not result in a higher classification accuracy. Finally, the HyMap data, featuring 126 bands in the VNIR‐SWIR range, were used to demonstrate that the VNIR range outperforms the SWIR range for this application.     

Technical note The effect of rate of decay of rain path profile on microwave communication

The attenuation of radio waves due to rain can be predicted with a good degree of accuracy, provided the rain rate characteristics over the entire path of propagation are known. The attenuation due to rain is usually deduced on the basis of point rain rate, which can lead to inaccuracy in the estimation of attenuation. We use the concept of rate of decay of rain path profile to estimate the attenuation due to rain. The attenuation has been deduced at 11GHz and 13.4 GHz for 56 elevation angle by using the theory of decay rate of rain path profile, its controlling factor, gamma, and rain rate distribution over Delhi, a tropical station in India. The theoretically-estimated attenuation is compared with observed values, as well as with values obtained using the International Radio Consultative Committee (CCIR) method. The CCIR model is found to overestimate the attenuation.     

An optimal image transform for threshold-based cloud detection using heteroscedastic discriminant analysis

We present a simple image transform that optimally combines four image channels into a greyscale image for threshold-based cloud detection. These image channels, namely blue, green, red and near infrared, are present on many low Earth-orbit resource satellites. Applying a single threshold to a greyscale image is a computationally efficient method suitable for onboard implementation. We used heteroscedastic discriminant analysis (HDA), which is a generalization of the popular dimension-reducing linear discriminant analysis, to transform the image. Comparative tests between HDA, existing transforms from the remote-sensing literature (the haze optimized and D transforms), as well as the single red and blue image channels were conducted. Although thin clouds remain challenging for global threshold-based techniques, the HDA transform consistently gave the best average segmentation errors across the test dataset. This dataset consisted of 32 1 megapixel Quickbird and Landsat images. HDA has not previously been applied to remote-sensing data.     

An integrated approach for groundwater potential zoning in shallow fracture zone aquifers

This study presents an integrated approach for the identification of groundwater occurrences in shallow fracture zone (SFZ) aquifers using remote-sensing, geological, and geophysical data. The Central Eastern Desert of Egypt was selected as a test site for the present study. The distribution of major faults and shear zones was extracted from a fusion image generated by injecting high-spatial resolution phased array L-band synthetic aperture radar (PALSAR) images into Landsat Enhanced Thematic Mapper images. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model was processed to extract the drainage systems, slope, and topographic wetness index (TWI). The multidate PALSAR imagery acquired during rainy and dry seasons was used to estimate the relative soil moisture content. The lithology, fractures, drainage density, slope, TWI, and soil moisture content were used as thematic layers for groundwater occurrence in the SFZ aquifers. A GIS model of groundwater potential was developed by selecting the most probable locations for groundwater in each layer. The results indicate that the spatial distribution of the existing water wells is in agreement with the model where all wells fall in the regions of high groundwater potential zones. A geophysical survey was conducted using ground penetrating radar (GPR), indicating that the high groundwater potential zones are promising for drilling shallow wells. The adopted approach can be used as a cost-effective tool for groundwater exploration in the SFZ aquifers in the study area and in areas of similar geologic and hydrogeologic settings elsewhere.     

Predicting Eucalyptus spp. stand volume in Zululand,South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets

Accurate, reliable, and up-to-date forest stand volume information is a prerequisite for a detailed evaluation of commercial forest resources and their sustainable management. Commercial forest responses to global climate change remain uncertain, and hence the mapping of stand volume as carbon sinks is fundamentally important in understanding the role of forests in stabilizing climate change effects. The aim of this study was to examine the utility of stochastic gradient boosting (SGB) and multi-source data to predict stand volume of a Eucalyptus plantation in South Africa. The SGB ensemble, random forest (RF), and stepwise multiple-linear regression (SMLR) were used to predict Eucalyptus stand volume and other related tree-structural attributes such as mean tree height and mean diameter at breast height (DBH). Multi-source data consisted of SPOT-5 raw spectral features (four bands), 14 spectral vegetation indices, rainfall data, and stand age. When all variables were used, the SGB algorithm showed that stand volume can be accurately estimated (R² = 0.78 and RMSE = 33.16 m³ ha^?1 (23.01% of the mean)). The competing RF ensemble produced an R² value of 0.76 and a RMSE value of 37.28 m³ ha^?1 (38.28% of the mean). SMLR on the other hand, produced an R² value of 0.65 and an RMSE value of 42.50 m³ ha^?1 (42.50% of the mean). Our study further showed that Eucalyptus mean tree height (R² = 0.83 and RMSE = 1.63 m (9.08% of the mean)) and mean diameter at breast height (R² = 0.74 and RMSE = 1.06 (7.89% of the mean)) can also be reasonably predicted using SGB and multi-source data. Furthermore, when the most important SGB model-selected variables were used for prediction, the predictive accuracies improved significantly for mean DBH (R² = 0.81 and RMSE = 1.21 cm (6.12% of the mean)), mean tree height (R² = 0.86 and RMSE = 1.39 m (7.02% of the mean)), and stand volume (R² = 0.83 and RMSE = 29.58 m³ ha^?1 (17.63% of the mean)). These results underscore the importance of integrating multi-source data with remotely sensed data for predicting Eucalyptus stand volume and related tree-structural attributes.     

Performance of atmospheric and topographic correction methods on Landsat imagery in mountain areas

An effective removal of atmospheric and topographic effects on remote-sensing imagery is an essential preprocessing step for mapping land cover accurately in mountain areas. Various techniques that remove these effects have been proposed and consist of specific combinations of an atmospheric and a topographic correction (TC) method. However, it is possible to generate a wide range of new combined correction methods by applying alternative combinations of atmospheric and TC methods. At present, a systematic overview of the statistical performance and data input requirement of preprocessing techniques is missing. In order to assess the individual and combined impacts of atmospheric and TC methods, 15 permutations of two atmospheric and/or four TC methods were evaluated statistically and compared to the uncorrected imagery. Furthermore, results of the integrated ATCOR3 method were included. This evaluation was performed in a study area in the Romanian Carpathian mountains. Results showed that the combination of a transmittance-based atmospheric correction (AC), which corrects the effects of Rayleigh scattering and water-vapour absorption, and a pixel-based C or Minnaert TC, which account for diffuse sky irradiance, reduced the image distortions most efficiently. Overall results indicated that TC had a larger impact than AC and there was a trade-off between the statistical performance of preprocessing techniques and their data requirement. However, the normalized difference vegetation index analysis indicated that atmospheric methods resulted in a larger impact on the spectral information in bands 3 and 4.     

Review article Multisensor image fusion in remote sensing: Concepts,methods and applications

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.     

Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area,Wuda, Inner Mongolia,China

Pixel‐based and object‐oriented classifications were tested for land‐cover mapping in a coal fire area. In pixel‐based classification a supervised Maximum Likelihood Classification (MLC) algorithm was utilized; in object‐oriented classification, a region‐growing multi‐resolution segmentation and a soft nearest neighbour classifier were used. The classification data was an ASTER image and the typical area extent of most land‐cover classes was greater than the image pixels (15 m). Classification results were compared in order to evaluate the suitability of the two classification techniques. The comparison was undertaken in a statistically rigorous way to provide an objective basis for comment and interpretation. Considering consistency, the same set of ground data was used for both classification results for accuracy assessment. Using the object‐oriented classification, the overall accuracy was higher than the accuracy obtained using the pixel‐based classification by 36.77%, and the user’s and producer’s accuracy of almost all the classes were also improved. In particular, the accuracy of (potential) surface coal fire areas mapping showed a marked increase. The potential surface coal fire areas were defined as areas covered by coal piles and coal wastes (dust), which are prone to be on fire, and in this context, indicated by the two land‐cover types ‘coal’ and ‘coal dust’. Taking into account the same test sites utilized, McNemar’s test was used to evaluate the statistical significance of the difference between the two methods. The differences in accuracy expressed in terms of proportions of correctly allocated pixels were statistically significant at the 0.1% level, which means that the thematic mapping result using object‐oriented image analysis approach gave a much higher accuracy than that obtained using the pixel‐based approach..     

ASTER‐derived emissivity and coal‐fire related surface temperature anomaly: a case study in Wuda,north China

Subsurface and surface coal fires form serious environmental, economic and safety problems in coal‐producing countries like China and India. Remote sensing offers the possibility of detecting and studying thermal anomalies due to coal fires. Emissivity plays an important role in determining the surface temperature of a body using remotely sensed data. In the present study an attempt is made to use satellite‐derived emissivity to estimate the surface temperature in Wuda, north China. With the use of multispectral thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (five bands in 8.125–11.65 µm region) in combination with a Temperature/Emissivity Separation (TES) algorithm, the anomalous pixels due to coal fires can be extracted from the background to achieve a qualitative study of coal fires. In the present study, during night‐time overpass of ASTER, satellite images have been recorded and simultaneous field measurements were collected. These field measurements were used to process the satellite thermal data and to validate the results obtained. Using the TES approach, satellite‐based temperature corresponded well with actual field measurements at selected locations.