ANFIS Based Land Cover/Land Use Mapping of LISS IV Imagery Using Optimized Wavelet Packet Features

Digital image classification is the process of sorting all the pixels in an image into a finite number of individual classes. But, it is difficult to classify satellite images since they include both pure pixels and boundary pixels. The boundary pixels are ‘mixed’ pixels, representing an area occupied by more than one ground cover. That is, class boundaries represented by pixels, are not sharp but fuzzy. This paper discuses the application of Adaptive Neuro-Fuzzy inference system (ANFIS) for classification of remotely sensed images that contains mixed pixels. Decision making was performed in two stages: feature extraction using the Wavelet Packet Transforms (WPT) and the ANFIS trained with the back propagation gradient descent method in combination with the least squares method for classification. Genetic Algorithms (GA) based approach is analysed for the selection of a subset from the combination of Wavelet Packet Statistical Features (WPSF) and Wavelet Packet Co-occurrence (WPC) textural feature set, which are used to classify the LISS IV images. GA has been employed to reduce the complexity and increase the accuracy of classification. Four indices—user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experiments show that the proposed approach produces better results compared to the results obtained when classical classifiers are used.     

Genetic Algorithm Based Feature Subset Selection for Land Cover/ Land Use Mapping Using Wavelet Packet Transform

A genetic algorithm based approach is used in this paper for the selection of a subset from the combination of Wavelet Packet Statistical and Wavelet Packet Co-occurrence textural feature sets to classify the LISS IV satellite images using neural networks. Generally, adding a new feature increases the complexity of training and classification. Hence there is a need to differentiate between those features that contribute ample information and others. Many current feature reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) involve linear transformations of the original pattern vectors to new vectors of lower dimensions. Hence a multi-objective Genetic Algorithm has been employed to reduce the complexity and increase the accuracy of classification. Four indices - user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experimental results show that the proposed Genetic Algorithm approach with lesser number of optimal features produces comparable results with that of our earlier approach using more features.     

Shallow resistivity measurement for cathodic protection of pipelines in the Niger Delta

The variation in the geo-electric resistivity of the superficial soils across the Niger Delta was investigated by running profiles spanning over 350 km and covering all the geomorphic zones of the Niger Delta, using a Schlumberger electrode configuration. The results indicate characteristic resistivity ranges. The spatial variation in resistivity was explained by differences in topographic elevation, depth to water table, soil type, and water quality. Based on the soil resistivities in the various geomorphic zones, the corrosivity of the soils were determined and methods of cathodic protection prescribed. A north–south traverse will generally experience a decreasing soil resistivity that is truncated at the beach ridges.     

Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration (n = 30) and test (n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m⁻²) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m⁻²), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m⁻²). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m⁻²) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m⁻², 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.     

LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements

The study shows that leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and LCC. We tested the utility of univariate techniques involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, including stepwise multiple linear regression and partial least squares regression. Among the various investigated models, CCC was estimated with the highest accuracy (, ). All methods failed to estimate LCC (), while LAI was estimated with intermediate accuracy ( values ranged from 0.49 to 0.69). Compared with narrow band indices and red edge inflection point, stepwise multiple linear regression generally improved the estimation of LAI. The estimations were further improved when partial least squares regression was used. When a subset of wavelengths was analyzed, it was found that partial least squares regression had reduced the error in the retrieved parameters. The results of the study highlight the significance of multivariate techniques, such as partial least squares regression, rather than univariate methods such as vegetation indices in estimating heterogeneous grass canopy characteristics.     

Low-frequency waves in the Earth’s magnetosheath: present status

The terrestrial magnetosheath contains a rich variety of low-frequency ( proton gyrofrequency) fluctuations. Kinetic and fluid-like processes at the bow shock, within the magnetosheath plasma, and at the magnetopause all provide sources of wave energy. The dominance of kinetic features such as temperature anisotropies, coupled with the high- conditions, complicates the wave dispersion and variety of instabilities to the point where mode identification is difficult. We review here the observed fluctuations and attempts to identify the dominant modes, along with the identification tools. Alfvén/ion-cyclotron and mirror modes are generated by T/T 1 temperature anisotropies and dominate when the plasma is low or high, respectively. Slow modes may also be present within a transition layer close to the subsolar magnetopause, although they are expected to suffer strong damping. All mode identifications are based on linearized theory in a homogeneous plasma and there are clear indications, in both the data and in numerical simulations, that nonlinearity and/or inhomogeneity modify even the most basic aspects of some modes. Additionally, the determination of the wave vector remains an outstanding observational issue which, perhaps, the Cluster mission will overcome.     

A note on the role of the lichen collema auriforma in solution basin development on a carboniferous limestone substrate

A study carried out on Carboniferous limestone in the north and west of Ireland supports the idea that rock substrate is removed by the direct mechanical action of lichens. An experiment in which the lichen Collema auriforma was subjected to a number of wetting-drying cycles, showed, using scanning electron microscopy, that contraction of the lichen thallus during the drying phase plucked rock fragments from the substrate surface. This process could contribute to the formation of karstic features including solution basins.     

Catchment-Wide Groundwater Budget for the Inkomati-Usuthu Water Management Area in South Africa

In South Africa, approximately 98% of the predicted total surface water resources are already being used up. Consequently, the National Water Resource Strategy considers groundwater to be important for the future planning and management of water resources. In this case, quantifying groundwater budgets is a prerequisite because they provide a means for evaluating the availability and sustainability of a water supply. This study estimated the regional groundwater budgets for the Inkomati-Usuthu Water Management Area (Usuthu, Komati, Sabie-Sand, and Crocodile) using the classical hydrological continuity equation. The equation was used to describe prevailing feedback loops between groundwater draft, recharge, baseflow, and storage change. The results were coarser scale estimates which, beforehand, were derived from the 2006 study. In the years to follow, groundwater reliance intensified and there was also the historic 2015/2016 drought. This inevitably led to an increased draft while the rest of the components of the groundwater budgets experienced decreases. Both Crocodile and Sabie-Sand experienced groundwater storage depletion which led to reduced baseflow and groundwater availability, while groundwater recharge contrarily increased due to capture. Conversely, the other two catchments experienced relatively lower drafts with correspondingly higher groundwater availability and recharge while storage change was positive. The results highlighted the need for adaptive water management whose effectiveness relies on predictive studies. Consequently, future models should be developed to capture the spatial and temporal dynamism of the natural groundwater budget due to climate change, water demands, and population growth predictions.     

Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI_(i,j) = (R_i − R_j)/(R_i + R_j), where R_i and R_j = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756–820 nm) and the water absorption feature centred at 1200 nm (1172–1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R² = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors.     

The High Resolution Imaging Science Experiment (HiRISE) during MRO’s Primary Science Phase (PSP)

The High Resolution Imaging Science Experiment (HiRISE) on the Mars Reconnaissance Orbiter (MRO) acquired 8 terapixels of data in 9137 images of Mars between October 2006 and December 2008, covering ∼0.55% of the surface. Images are typically 5-6 km wide with 3-color coverage over the central 20% of the swath, and their scales usually range from 25 to 60 cm/pixel. Nine hundred and sixty stereo pairs were acquired and more than 50 digital terrain models (DTMs) completed; these data have led to some of the most significant science results. New methods to measure and correct distortions due to pointing jitter facilitate topographic and change-detection studies at sub-meter scales. Recent results address Noachian bedrock stratigraphy, fluvially deposited fans in craters and in or near Valles Marineris, groundwater flow in fractures and porous media, quasi-periodic layering in polar and non-polar deposits, tectonic history of west Candor Chasma, geometry of clay-rich deposits near and within Mawrth Vallis, dynamics of flood lavas in the Cerberus Palus region, evidence for pyroclastic deposits, columnar jointing in lava flows, recent collapse pits, evidence for water in well-preserved impact craters, newly discovered large rayed craters, and glacial and periglacial processes. Of particular interest are ongoing processes such as those driven by the wind, impact cratering, avalanches of dust and/or frost, relatively bright deposits on steep gullied slopes, and the dynamic seasonal processes over polar regions. HiRISE has acquired hundreds of large images of past, present and potential future landing sites and has contributed to scientific and engineering studies of those sites. Warming the focal-plane electronics prior to imaging has mitigated an instrument anomaly that produces bad data under cold operating conditions.