Field Measurements of Plant Emissivity Spectra: An Experimental Study on Remote Sensing of Vegetation in the Thermal Infrared Region

Spectral features of plant species in the visible to SWIR (0.4–2.5 μm) region have been studied extensively, but scanty attention has been given to plant thermal infrared (TIR: 4–14 μm) properties. This paper presents preliminary results of a study that was conducted first time in India to measure radiance and emissivity properties of eight plant species in TIR spectral region in the field conditions using a FTIR (Fourier Transform Infrared) field spectroradiometer working in 4–14 μm at an agriculture experimental farm. Several spectral features in the emissivity spectra of plant species were observed that are probably related to the leaf chemical constituents, such as cellulose and xylan (hemicellulose) and structural aspects of leaf surface like abundance of trichomes and texture. Observations and results from the field measurements were supported by the laboratory measurements like biochemical analysis. These preliminary field emissivity measurements of leaves in TIR show that there is useful spectral information that may be detectable by field-based instrument. More detailed field and laboratory measurements are underway to explore this research theme.     

Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis

Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively.     

Spheroidal equal angular DEMs: The specificity of morphometric treatment

Digital elevation models (DEMs) are commonly constructed using two main types of regular grids: plane square grids and spheroidal equal angular grids. Methods and algorithms intended for plane square‐gridded DEMs should not be directly applied to spheroidal equal angular DEMs. This is because these grids have fundamentally different geometry. However, some researchers continue to apply square‐grid algorithms to spheroidal equal angular DEMs. It seems appropriate to consider once again the specifity of morphometric treatment of spheroidal equal angular DEMs. This article, first, demonstrates possibilities of direct calculation of local, nonlocal, and combined morphometric variables from spheroidal equal angular DEMs exemplified by slope gradient, catchment area, and topographic index. Second, the article shows computational errors when algorithms for plane square‐gridded DEMs are unreasonably applied to spheroidal equal angular DEMs. The study is exemplified by two DEMs. A medium‐resolution DEM of a relatively small, high‐mountainous area (Mount Elbrus) was extracted from the SRTM1 DEM. A low‐resolution DEM of a vast region with the diverse topography (the central and western regions of Kenya) was extracted from the SRTM30_PLUS DEM. The results show that application of square‐grid methods to spheroidal equal angular DEMs leads to substantial computational errors in models of morphometric variables.     

Microwave response of seasonal snow-cover measured by using a ground-based radiometer at 6.93 and 18.7 ghz frequencies and at dual polarization

Snow cover is an important variable for climatic and hydrologic models due to its effects on surface albedo, energy, and moisture budgets. Passive microwave sensors can be used to monitor temporal and spatial variations in large-scale snow cover parameters, avoiding problems of cloud cover and polar nights. In the present study, brightness temperature values were estimated (using calibration curves) for moist snow on natural and blackbody/metal surface. T_B response on snow depth, density, SWE and angular variation from nadir were measured and found that T_B decreases with increase of snow depth and with increase of angle from nadir. Empirical relations were used to estimate emissivity, dielectric constant and dielectric loss factor. It was observed that emissivity decreases with the increase of dielectric constant. The dielectric constant and dielectric loss factor both increases with the increase of density. Experiments were performed during winter of year 2005 at Dhundi and Solang (H.P.), India, using ground based passive microwave radiometer having 6.9 and 18.7 GHz antenna frequencies at dual polarization.     

Feature Extraction in Remote Sensing High-Dimensional Image Data

High-dimensional image data open new possibilities in remote sensing digital image classification, particularly when dealing with classes that are spectrally very similar. The main problem refers to the estimation of a large number of classifier's parameters. One possible solution to this problem consists in reducing the dimensionality of the original data without a significant loss of information. In this letter, a new approach to reduce data dimensionality is proposed. In the proposed methodology, each pixel's curve of spectral response is initially segmented, and the digital numbers (DNs) at each segment are replaced by a smaller number of statistics. In this letter, the proposed statistics are the mean and variance of the segment's DNs, which are supposed to carry information about the segment's position and shape, respectively. Tests were performed by using Airborne Visible/Infrared Imaging Spectrometer hyperspectral image data. The experiments have shown that this methodology is capable of providing very acceptable results, in addition of being computationally efficient     

Testing satellite and ground thermal imaging of low-temperature fumarolic fields: The dormant Nisyros Volcano (Greece)

The Nisyros Volcano (Greece) was monitored by satellite and ground thermal imaging during the period 2000–2002. Three night-scheduled Landsat-7 ETM⁺ thermal (band 6) images of Nisyros Island were processed to obtain land surface temperature. Ground temperature data were also collected during one of the satellite overpasses. Processed results involving orthorectification and 3-D atmospheric correction clearly show the existence of a thermal anomaly inside the Nisyros Caldera. This anomaly is associated mainly with the largest hydrothermal craters and has land surface temperatures 5–10 °C warmer than its surroundings. The ground temperature generally increased by about 4 °C inside the main crater over the period 2000–2002. Ground thermal images of the hydrothermal Stephanos Crater were also collected in 2002 using a portable thermal infrared camera. These images were calibrated to ground temperature data and orthorectified. A difference of about 0–2 °C was observed between the ground thermal images and the ground temperature data. The overall study demonstrates that satellite remote sensing of low-temperature fumarolic fields within calderas can provide a reliable long-term monitoring tool of dormant volcanoes that have the potential to reactivate. Similarly, a portable thermo-imager can easily be deployed for real-time monitoring using telemetric data transfer. The operational costs for both systems are relatively low for an early warning system.     

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.     

Software for validating parameters retrieved from satellite

A PC-based interactive software has been developed and presented here for validating geophysical data retrieved from satellite mounted sensors operating in visible, infrared and microwave frequencies. The program, coded in Visual Basic, is user interactive and runs on Windows-98 or higher platforms. The system prepares the database on a pre-selected Microsoft platform to enhance processing efficiency. Sub-setting option is also provided to reduce the processing time. Data retrieved from ‘Multi-channel Scanning Microwave Radiometer (MSMR) onboard the Indian satellites Oceansat-1 during 1999–2001 were validated using this software as a case study. The program has several added advantages over the conventional method of validation that involves strenuous efforts to incorporate subroutines to meet every minute requirement. Satellite-sea truth relationships on various space-time window combinations are determined and exhibited in matrix form to visualize the nature of correlation. User has the option to visualize the satellite-sea truth relationship through graphical representations before selecting optimum relationship for prediction.