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.     

Inter sensor comparison between RESOURCESAT LISS III, LISS IV and AWiFS with reference to coastal landuse/landcover studies

Coastal zone assumes importance due to high productivity of ecosystems, man-made developmental activities, natural hazards and dynamic nature of the coast. As costal ecosystems are unique and fragile, understanding the impact of developmental activities on the sustainability of the coastal zone is very important. Remote sensing, because of repetitive and synoptic nature is an ideal tool for studying this. Time series data analyses for monitoring coastal zone require different type of sensors. Present study deals with atmospheric correction of satellite data, reflectance, selection of coastal features like, mudflat, mangroves, vegetated dune, coastal water, etc. and their inter-comparison using different sensor data of RESOURCESAT sensors. Reflectance values give better separateability for various coastal features in comparison to DN values. LISS IV can be used in place of LISS III or merged (LISS III + PAN) for long-term coastal zone studies.     

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.     

Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

This paper presents a method for using the intensity of returns from a scanning light detection and ranging (lidar) system from a single viewing point to identify the location and measure the diameter of tree stems within a forest. Such instruments are being used for rapid forest inventory and to provide consistent supporting information for airborne lidars. The intensity transect across a tree stem is found to be consistent with a simple model parameterised by the range and diameter of the trunk. The stem diameter is calculated by fitting the model to transect data. The angular span of the stem can also be estimated by using a simple threshold where intensity values are tested against the expected intensity for a stem of given diameter. This is useful when data are insufficient to fit the model or the stem is partially obscured. The process of identifying tree positions and trunk diameters is fully automated and is shown to be successful in identifying a high proportion of trees, including some that are partially obscured from view. The range and bearing to trees are in excellent agreement with field data. Trunk angular span and diameter estimations are well correlated with field measurements at the plot scale. The accuracy of diameter estimation is found to decrease with range from the scanning position and is also reduced for stems subtending small angles (less than twice the scanning resolution) to the instrument. A method for adjusting survey results to compensate for trees missed due to obscuration from the scanning point and the use of angle count methods is found to improve basal area estimates and achieve agreement within 4% of field measurements.     

Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data

The Phase 1 Survey is the most comprehensive and widely used national level map of semi-natural habitats in Wales. However, the survey was based largely on field survey and was conducted over several decades, before being completed in 1997. Given that resources for a repeat survey were limited, this study has used an object-orientated rule-based classification implemented within eCognition of multi-temporal satellite sensor data acquired between 2003 and 2006 to map semi-natural habitats and agricultural land across Wales, thereby allowing a progressive update of the Phase 1 Survey. The classification of objects to Phase 1 habitat classes was undertaken in two steps; firstly the landscape of Wales was divided into objects using orthorectified SPOT-5 High Resolution Geometric (HRG) reflectance data (10 m spatial resolution) and Land Parcel Information System (LPIS) boundaries. A rule-base was then developed to progressively discriminate and map the distribution of 105 sub-habitats across Wales based on time-series of SPOT HRG, Terra-1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS) LISS-3 data, derived datasets (e.g., vegetation indices, fractional images) and ancillary information (e.g., topography). The rules coupled knowledge of ecology and the information content of these remote sensing data using a combination of thresholds, Boolean operations and fuzzy membership functions. A second rule-base was then developed to translate the more detailed sub-habitat classification to Phase 1 habitat classes. Indicative accuracies of the revised Phase 1 mapping, based on comparisons with the later Phase 2 survey (for selected habitats), were >80% overall and typically between 70% and 90% for many classes. Through this exercise, Wales has become the first country in Europe to produce a national map of habitats (as opposed to land cover) through object-orientated classification of satellite sensor data. Furthermore, the approach can be adapted to allow continual monitoring of the extent and condition of habitats and agricultural land.     

Constructing a Near Real‐time Space‐time Cube to Depict Urban Ambient Air Pollution Scenario

This study adopts a near real‐time space‐time cube approach to portray a dynamic urban air pollution scenario across space and time. Originating from time geography, space‐time cubes provide an approach to integrate spatial and temporal air pollution information into a 3D space. The base of the cube represents the variation of air pollution in a 2D geographical space while the height represents time. This way, the changes of pollution over time can be described by the different component layers of the cube from the base up. The diurnal ambient ozone (O₃) pollution in Houston, Texas is modeled in this study using the space‐time air pollution cube. Two methods, land use regression (LUR) modeling and spatial interpolation, were applied to build the hourly component layers for the air pollution cube. It was found that the LUR modeling performed better than the spatial interpolation in predicting air pollution level. With the availability of real‐time air pollution data, this approach can be extended to produce real‐time air pollution cube is for more accurate air pollution measurement across space and time, which can provide important support to studies in epidemiology, health geography, and environmental regulation.     

Reviewers of the Journal of Geodesy for Volume 79

Acknowledgement for Referees

Reviewers of the Journal of Geodesy for Volume 79     

Effect of signal contamination in matched-filter detection of the signal on a cluttered background

To derive a matched filter for detecting a weak target signal in a hyperspectral image, an estimate of the band-to-band covariance of the target-free background scene is required. We investigate the effects of including some of the target signal in the background scene. Although the covariance is contaminated by the presence of a target signal (there is increased variance in the direction of the target signature), we find that the matched filter is not necessarily affected. In fact, if the variation in plume strength is strictly uncorrelated with the variation in background spectra, the matched filter and its signal-to-clutter ratio (SCR) performance will not be impaired. While there is little a priori reason to expect significant correlation between the plume and the background, there usually is some residual correlation, and this correlation leads to a suppressing effect that limits the SCR obtainable even for strong plumes. These effects are described and quantified analytically, and the crucial role of this correlation is illustrated with some numerical examples using simulated plumes superimposed on real hyperspectral imagery. In one example, we observe an order-of-magnitude loss in SCR for a matched filter based on the contaminated covariance.     

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.     

Geomasking sensitive health data and privacy protection: an evaluation using an E911 database

Geomasking is used to provide privacy protection for individual address information while maintaining spatial resolution for mapping purposes. Donut geomasking and other random perturbation geomasking algorithms rely on the assumption of a homogeneously distributed population to calculate displacement distances, leading to possible under-protection of individuals when this condition is not met. Using household data from 2007, we evaluated the performance of donut geomasking in Orange County, North Carolina. We calculated the estimated k-anonymity for every household based on the assumption of uniform household distribution. We then determined the actual k-anonymity by revealing household locations contained in the county E911 database. Census block groups in mixed-use areas with high population distribution heterogeneity were the most likely to have privacy protection below selected criteria. For heterogeneous populations, we suggest tripling the minimum displacement area in the donut to protect privacy with a less than 1% error rate.