Remote classification of Cerrado (Savanna) and agricultural land covers in northeastern Brazil

Remote classification of land-use/land-cover (LULC) types in Brazil's Cerrado ecoregion is necessary because knowledge of Cerrado LULC is incomplete, sources of inaccuracy are unknown, and high-resolution data are required for the validation of moderate-resolution LULC maps. The aim of this research is to discriminate between Cerrado and agriculture using high-resolution Landsat 7 ETM⁺ imagery for the western region of Bahia state in northeastern Brazil. The Maximum Likelihood Classification (MLC) and Spectral Angle Mapper (SAM) algorithms were applied to a ～3000 km² subset, yielding comparable classification accuracies. The panchromatic band was reserved for validation. User's and producer's accuracies were highest for non-irrigated agriculture (～94%) but lower for Cerrado Lato Sensu (89%). Classification errors likely resulted from spatial and spectral characteristics of particular classes (e.g. riparian forest and burned) and overestimation of other classes (e.g. Eucalyptus and water). Manual misinterpretation of validation data may have also led to lower reported classification accuracies.     

A global inventory of coral reef stressors based on satellite observed nighttime lights

In this article, we present a satellite-based approach to gather information about the threat to coral reefs worldwide. Three chosen reef stressors – development, gas flaring and heavily lit fishing boat activity – are analysed using nighttime lights data derived from the Defense Meteorological Satellite Program (DMSP) produced at the National Oceanic & Atmospheric Administration, National Geophysical Data Center (NOAA/NGDC). Nighttime lights represent a direct threat to coral reef ecosystems and are an excellent proxy measure for associated human-caused stressors. A lights proximity index (LPI) is calculated, measuring the distance of coral reef sites to each of the stressors and incorporating the stressor's intensity. Colourized maps visualize the results on a global scale. Area rankings clarify the effects of artificial night lighting on coral reefs on a regional scale. The results should be very useful for reef managers and for state administrations to implement coral reef conservation projects and for the scientific world to conduct further research.     

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.     

Multi‐sensor Image Maps from SPOT,ERS and JERS

Abstract

Image mapping using data from visible and infrared sensors has, as a major drawback, the frequent cloud cover experienced in many countries. This is one of the main reasons why topographic maps at 1:100,000 scale and larger are often outdated. The results of a study which investigated the possibilities of fusing up‐to‐date spaceborne microwave data with existing images from optical sensors for topographic map updating at a scale of 1:100, 000 are presented in this paper. A key issue researched was the influence of geometric distortions and corrections of remote sensing data on the results of pixel based digital image fusion. After having terrain‐geocoded and radiometrically enhanced imagery from the Landsat, SPOT, ERS‐1 and JERS‐1 satellites, the data were fused applying a variety of colour transformation techniques as well as statistical or arithmetic methods. Initially, the image fusion was implemented using images covering a test site in the north of The Netherlands in order to calibrate specified combinations and techniques in a rather flat area. With the experience gained, the remote sensing data acquired over the research site were processed. The research test site is located in a typical Developing Country in the humid Tropics, on the mountainous south‐west coast of Sumatra in Indonesia. The results of the various applied techniques and image combinations were evaluated with reference to their capability to overcome the cloud cover problem. New combinations of techniques and images were developed as result of an optimisation process. The research produced two prototypes of annotated 1:100,000 scale image maps containing fused, cloud‐free optical/microwave imagery.     

BUSINESS AND PLEASURE

Abstract

Mr. Surveyor A walked into the Club, his first visit after a four-months' field season. He entered the lounge, sank into one of the deep luxurious leather arm-chairs and called for his“sundowner“. In a little while he was joined by several other Government officers, glad to see him after his long absence and eager to hear of his bush experiences and to cap them with stories of their own.     

A New Combined Assessment of Mixed Uncertainty in Spatial Models: Conceptualization and Implementation

Uncertainty quantification is not often performed in spatial modeling applications, especially when there is a mixture of probabilistic and non‐probabilistic uncertainties. Furthermore, the effect of positional uncertainty is often not assessed, despite its relevance to geographical applications. Although there has been much work in investigating the aforementioned types of uncertainty in isolation, combined approaches have not been much researched. This has resulted in a lack of tools for conducting mixed uncertainty analyses that include positional uncertainty. This research addresses the issue by first presenting a new, flexible, simulation‐oriented conceptualization of positional uncertainty in geographic objects called F‐Objects. F‐Objects accommodates various representations of uncertainty, while remaining conceptually simple. Second, a new Python‐based framework is introduced, termed Wiggly and capable of conducting mixed uncertainty propagation using fuzzy Monte Carlo simulation (FMCS). FMCS combines both traditional Monte Carlo with fuzzy analysis in a so‐called hybrid approach. F‐Objects is implemented within the Wiggly framework, resulting in a tool capable of considering any combination of: (1) probabilistic variables; (2) fuzzy variables; and (3) positional uncertainty of objects (probabilistic/fuzzy). Finally, a realistic GIS‐based groundwater contamination problem demonstrates how F‐Objects and Wiggly can be used to assess the effect of positional uncertainty.     

A Novel Approach of Despeckling SAR Images Using Nonlocal Means Filtering

Synthetic aperture radar (SAR) is a day and night, all weather satellite imaging technology. Inherent property of SAR image is speckle noise which produces granular patterns in the image. Speckle noise occurs due to the interference of backscattered echo from earth’s rough surface. There are various speckle reduction techniques in spatial domain and transform domain. Non local means filtering (NLMF) is the technique used for denoising which uses Gaussian weights. In NLMF algorithm, the filtering is performed by taking the weighted mean of all the pixels in a selected search area. The weight given to the pixel is based on the similarity measure calculated as the weighted Euclidean distance over the two windows. Non local means filtering smoothes out homogeneous areas but edges are not preserved. So a discontinuity adaptive weight is used in order to preserve heterogeneous areas like edges. This technique is called as discontinuity adaptive non local means filtering and is well-adapted and robust in the case of Additive White Gaussian Noise (AWGN) model. But speckle is a multiplicative random noise and hence Euclidean distance is not a good choice. This paper presents evaluation results of using different distance measures for improving the accuracy of the Non local means filtering technique. The results are verified using real and synthetic images and from the results it can be concluded that the usage of Manhattan distance improves the accuracy of NLMF technique. Non local approach is used as a preprocessing or post processing technique for many denoising algorithms. So improving NLMF technique would help improving many of the existing denoising techniques.     

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.     

Unsupervised Change Detection From Multichannel SAR Images

Multichannel synthetic aperture radar (SAR) data present a good potential for environmental monitoring and disaster management, owing both to their insensitivity to atmospheric and sun-illumination conditions, and to the improved discrimination capability they may provide as compared to single-channel SAR. However, this requires accurate and possibly automatic techniques to generate change maps from multichannel SAR images acquired from the same geographic area at different times. In this letter, an automatic unsupervised contextual change-detection method is proposed for two-date multichannel SAR images, by integrating a SAR-specific extension of the Fisher transform with a variant of the expectation-maximization algorithm and with Markov random fields. The method is validated by experiments on SIR-C/XSAR data