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.     

Monitoring the regional and temporal variability of winter snowfall in the arid Andes using digital NOAA/AVHRR data

Abstract

This paper deals with the spatial distribution and the temporal variability of snowfall in the most arid part of the Andes (18°‐ 28°S) during southern hemisphere winter (May‐September). As the official precipitation data is of poor quality, analyses were carried out by means of digital image processing techniques, using NOAA/AVHRR satellite‐data. Through analysis of 24 different snowfall events from six winters, a previously unknown spatial and temporal precipitation pattern in this remote and unexplored area was revealed. Snowfall is most abundant in the southernmost part of the research area and on the western side of the Andes, indicating the Pacific origin of the snowfall.

Nevertheless, the typical snowfall pattern is modified during different periods of the winter. Three typical time periods could be defined and distinguished from one another. Each of these three periods is characterized by typical weather conditions (cold fronts and “cut‐offs “) leading to a distinct snowfall pattern.

As this study is part of a broader paleoclimatic project, the results will serve as a basis for paleoclimatic reconstruction of past climate. Only by knowing the modern circulation and precipitation patterns is it possible to interpret paleoclimatic signals and archives found in the study area (e.g. paleosol, moraines) correctly.     

High Resolution Köppen‐Geiger Classifications of Paleoclimate Simulations

The development and application of an algorithm to compute Köppen‐Geiger climate classifications from the Coupled Model Intercomparison Project (CMIP) and Paleo Model Intercomparison Project (PMIP) climate model simulation data is described in this study. The classification algorithm was applied to data from the PMIP III paleoclimate experiments for the Last Glacial Maximum, 21k years before present (yBP), Mid‐Holocene (6k yBP) and the Pre‐Industrial (0k yBP, control run) time slices. To infer detailed classification maps, the simulation datasets were interpolated to a higher resolution. The classification method presented is based on the application of Open Source Software, and the implementation is described with attention to detail. The source code and the exact input data sets as well as the resulting data sets are provided to enable the application of the presented approach.     

Contextual Spatiospectral Postreconstruction of Cloud-Contaminated Images

A general method has been proposed recently for the contextual reconstruction of cloud-contaminated areas in multitemporal multispectral images. It is based on the idea of making the prediction process learn from information available in the cloud-free neighborhood of contaminated areas. Though promising, this method does not fully exploit all available information, thus leaving room for further methodological enhancements. This letter presents a postreconstruction methodology for improving the contextual reconstruction process by opportunely capturing spatial and spectral correlations characterizing the considered image. In addition, we propose a solution to a problem that has not yet been addressed in the remote sensing literature, i.e., the generation of an error map beside the reconstructed images to provide end-users with helpful indications about reconstruction reliability. Thorough experiments conducted on a multitemporal sequence of Landsat-7 ETM+ images are reported and discussed.     

Modeling errors in upward continuation for INS gravity compensation

Accurate upward continuation of gravity anomalies supports future precision, free-inertial navigation systems, since the latter cannot by themselves sense the gravitational field and thus require appropriate gravity compensation. This compensation is in the form of horizontal gravity components. An analysis of the model errors in upward continuation using derivatives of the standard Pizzetti integral solution (spherical approximation) shows that discretization of the data and truncation of the integral are the major sources of error in the predicted horizontal components of the gravity disturbance. The irregular shape of the data boundary, even the relatively rough topography of a simulated mountainous region, has only secondary effect, except when the data resolution is very high (small discretization error). Other errors due to spherical approximation are even less important. The analysis excluded all measurement errors in the gravity anomaly data in order to quantify just the model errors. Based on a consistent gravity field/topographic surface simulation, upward continuation errors in the derivatives of the Pizzetti integral to mean altitudes of about 3,000 and 1,500 m above the mean surface ranged from less than 1 mGal (standard deviation) to less than 2 mGal (standard deviation), respectively, in the case of 2 arcmin data resolution. Least-squares collocation performs better than this, but may require significantly greater computational resources.     

Transverse Mercator with an accuracy of a few nanometers

Implementations of two algorithms for the transverse Mercator projection are described; these achieve accuracies close to machine precision. One is based on the exact equations of Thompson and Lee and the other uses an extension of Krüger’s series for the mapping to higher order. The exact method provides an accuracy of 9 nm over the entire ellipsoid, while the errors in the series method are less than 5 nm within 3900 km of the central meridian. In each case, the meridian convergence and scale are also computed with similar accuracy. The speed of the series method is competitive with other less accurate algorithms and the exact method is about five times slower.     

Supervised SAR Image MPM Segmentation Based on Region-Based Hierarchical Model

This letter presents a novel method of supervised multiresolution segmentation for synthetic aperture radar images. The method uses a region-based half-tree hierarchical Markov random field model for multiresolution segmentation. To form the region-based multilayer model, the watershed algorithm is employed at each resolution level independently. The nodes of a quadtree in the proposed model are defined as regions instead of pixels. The relationship over scale is studied, and the region-based upward and downward maximization of posterior marginal estimations are deduced. The experimental results for the segmentation of homogeneous areas prove the region-based model much better in terms of robustness to speckle and preservation of edges compared to the pixel-based hierarchical model and the Gibbs sampler with the single-resolution model     

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.     

A community-based urban forest inventory using online mapping services and consumer-grade digital images

Community involvement in gathering and submitting spatially referenced data via web mapping applications has recently been gaining momentum. Urban forest inventory data analyzed by programs such as the i-Tree ECO inventory method is a good candidate for such an approach. In this research, we tested the feasibility of using spatially referenced data gathered and submitted by non-professional individuals through a web application to augment urban forest inventory data. We examined the use of close range photogrammetry solutions of images taken by consumer-grade cameras to extract quantitative metric information such as crown diameter, tree heights and trunk diameters. Several tests were performed to evaluate the accuracy of the photogrammetric solutions and to examine their use in addition to existing aerial image data to supplement or partially substitute for standard i-Tree ECO field measurements.