Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

A Dutch multi-date land use database: Identification of real and methodological changes

Land cover and land use are important information sources for environmental issues. One of the most important changes at the Earth's surface concerns land cover and land use. Knowledge about the location and type of these changes is essential for environmental modeling and management. Remote sensing data in combination with additional spatial data are recognized as an important source of information to detect these land cover and land use changes.     

Hyperspectral anomaly detection within the signal subspace

This letter describes the extension of signal subspace processing (SSP) to the arena of anomaly detection. In particular, we develop an SSP-based, local anomaly detector that exploits the rich information available in the multiple bands of a hyperspectral (HS) image. This SSP approach is based on signal processing considerations, and its entire formulation reduces to a straightforward (and intuitively pleasing) geometric and algebraic development. We extend the basic SSP concepts to the HS anomaly detection problem, develop an SSP HS anomaly detector, and evaluate this algorithm using multiple HS data files.     

Remote sensing study of granulitic terrain in parts of Gujarat and Rajasthan

The geology of northwestern part of Indian peninsula is considered to be important due to complete preservation of rocks from Archaean to Upper Proterozoic. Further, these rocks have served as ideal host of varieties of economic minerals. The present work is an attempt to study the structurally deformed granulitic terrain in parts of Gujarat and Rajasthan in light of remote sensing. The study area falls under Sirohi, Banas Kantha and Sabar Kantha districts of Rajasthan and Gujarat. Remote sensing technique is utilized for the understanding of structural geology and deciphering the shear pattern. The methods adopted in this study include generation of False Color Composite (FCC) of satellite data, interpretation of lineaments from FCC and study the drainage pattern, structural basin delineation, profiling, and field mapping. It is observed that the area has undergone extensive deformation. There are two major sets of lineaments interpreted in the granulitic terrain such as WNW-ESE and NE-SW directions. Majority of the WNW-ESE lineaments are brittle in nature and N-S, NE-SW trending lineaments are ductile in nature. Overall the study area bifurcated into seven structural basins comprises of basic granulites, calc granulites and pelitic granulites.     

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.     

Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India

Estimation and monitoring of crop evapotranspiration (ET_c) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ET_c by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ET_c was estimated 2.72 and 2.47 in mm day^?1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ET_c.     

Estimating surface ice velocity on Chhota Shigri glacier from satellite data using Particle Image Velocimetry (PIV) technique

Information about the surface ice velocity is one of the important parameters for Mass balance and Glacier dynamics. This study estimates the surface ice velocity of Chhota Shigri glacier using Landsat (TM/ETM+) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) temporal data-sets from a period of 2009 to 2016 and 2006 to 2007, respectively. A correlation based Particle Image Velocimetry (PIV) technique has been used for the estimation of surface ice velocity. This technique uses multiple window sizes in the same data-set. Four window sizes (low, medium, high, very high) are used for each image pair. Estimated results have been compared with the published data. The outcomes attained from the medium window size closely matches with the published results. The estimated mean surface ice velocities of medium window size are 24 and 28.5 myr^?1 for 2009/2010 and 2006/2007 images pair. Highest velocity is observed in middle part of the glacier while lowest in the accumulation zone of the glacier.     

Snow cover area change and its relations with climatic variability in Kashmir Himalayas,India

Snowmelt makes an essential component of the hydrological system of Kashmir Himalayas. The present study was carried out to examine the status of Snow Cover Area (SCA) using Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day Snow Cover Product between 2000 and 2016. The intra- and inter-annual variability in SCA and in meteorological parameters was observed and various statistical tests were used to study the interrelationship. Results of statistical analysis indicate decrease in maximum temperature (?0.05 °C/year) and minimum temperatures (?0.02 °C/year) while rise in precipitation (19.13 mm/year). It also showed an increase in annual mean SCA (43.5 sq km) during the study period. The analysis was also carried out on a seasonal basis. The results revealed that in Kashmir Himalayas, climate plays a dominating role in controlling the SCA. The results depict the short-term fluctuations in SCA and show the magnitude of change between two successive values being very large in SCA.     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.