Evaluating environmental influences of zoning in urban ecosystems with remote sensing

The influence of zoning on Normalized Difference Vegetation Index (NDVI) and radiant surface temperature (T_s) measurements is investigated in the City of Indianapolis, IN, USA using data collected by the Enhanced Thematic Mapper Plus (ETM+) remote sensing system. Analysis of variance indicates statistically significant differences in mean T_s and NDVI values associated with different types of zoning. Multiple comparisons of mean T_s and NDVI values associated with specific pairings of individual zoning categories are also shown to be significantly different. An inverse relationship between T_s and NDVI was observed across the city as a whole and within all but one zoning category. A range of environmental influences on sensible heat flux and urban vegetation was detected both within and between individual zoning categories. Examples for implementing these findings in urban planning applications to find examples of high and low impact development are demonstrated.     

Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels

The objective of this study is to determine spatio-temporal variations of water volume over inundated areas located in large river basins using combined observations from the Synthetic Aperture Radar (SAR) onboard the Japanese Earth Resources Satellite (JERS-1), the Topex/Poseidon (T/P) altimetry satellite, and in-situ hydrographic stations. Ultimately, the goal is to quantify the role of floodplains for partitioning water and sediment fluxes over the great fluvial basins of the world. SAR images are used to identify the type of surface (open water, inundated areas, forest) and, hence, the areas covered with water. Both radar altimetry data and in-situ hydrographic measurements yield water level time series. The basin of the Negro River, the tributary which carries the largest discharge to the Amazon River, was selected as a test site. By combining area estimates derived from radar images classification with changes in water level, variations of water volume (focusing on a seasonal cycle) have been obtained. The absence of relationship between water volume and inundated area, reflecting the diverse and widely dispersed floodplains of the basin, is one of the main result of this study.     

Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing

Coral reef benthic communities are mosaics of individual bottom-types that are distinguished by their taxonomic composition and functional roles in the ecosystem. Knowledge of community structure is essential to understanding many reef processes. To develop techniques for identification and mapping of reef bottom-types using remote sensing, we measured 13,100 in situ optical reflectance spectra (400-700 nm, 1-nm intervals) of 12 basic reef bottom-types in the Atlantic, Pacific, and Indian Oceans: fleshy (1) brown, (2) green, and (3) red algae; non-fleshy (4) encrusting calcareous and (5) turf algae; (6) bleached, (7) blue, and (8) brown hermatypic coral; (9) soft/gorgonian coral; (10) seagrass; (11) terrigenous mud; and (12) carbonate sand. Each bottom-type exhibits characteristic spectral reflectance features that are conservative across biogeographic regions. Most notable are the brightness of carbonate sand and local extrema near 570 nm in blue (minimum) and brown (maximum) corals. Classification function analyses for the 12 bottom-types achieve mean accuracies of 83%, 76%, and 71% for full-spectrum data (301-wavelength), 52-wavelength, and 14-wavelength subsets, respectively. The distinguishing spectral features for the 12 bottom-types exist in well-defined, narrow (10-20 nm) wavelength ranges and are ubiquitous throughout the world. We reason that spectral reflectance features arise primarily as a result of spectral absorption processes. Radiative transfer modeling shows that in typically clear coral reef waters, dark substrates such as corals have a depth-of-detection limit on the order of 10-20 m. Our results provide the foundation for design of a sensor with the purpose of assessing the global status of coral reefs.     

Subsurface microwave remote sensing of soil-water content: Field studies in the Negev Desert and optical modelling

The population increase in the Middle East and the respective decrease of water resources necessitate innovative methods for utilization and monitoring of water resources. Development of remote sensing tools can pave the way for remote, rapid mapping of soil-water content, control of excessive irrigation, and prevention of water waste. This paper describes a series of experiments conducted in the Negev Desert that were aimed at developing such tools for monitoring soil-water content. The use of visible near infrared and microwave techniques seems suitable. All provide good correlation with soil-water content measured on the ground. However, the microwave techniques presented here using a P-band scatterometer and ERS-2 SAR seem the most promising. Finally the possibility of optical simulation of the microwave processes is presented in an effort to improve the physical basis for empirical studies. A method of fabrication of optical samples that model soils with different water content and different surface roughness is developed, and a system for measuring backscattered signals is designed. It is shown that the reflectivity of a layered medium is a non-monotonic function of the water content. The effect of the surface roughness on the reflection from a strong buried reflector is being studied.     

浅谈遥感新技术及其发展动态

遥感技术已经在国内外的各个领域得到了广泛的应用,在各国的经济建设中起到了重要作用。本文主要介绍了有关国内外遥感技术的最新进展情况和今后的发展趋势问题。     

Hidden Markov Models for crop recognition in remote sensing image sequences

This work proposes a Hidden Markov Model (HMM) based technique to classify agricultural crops. The method uses HMM to relate the varying spectral response along the crop cycle with plant phenology, for different crop classes, and recognizes different agricultural crops by analyzing their spectral profiles over a sequence of images. The method assigns each image segment to the crop class whose corresponding HMM delivers the highest probability of emitting the observed sequence of spectral values. Experimental analysis was conducted upon a set of 12 co-registered and radiometrically corrected LANDSAT images of region in southeast Brazil, of approximately 124.100 ha, acquired between 2002 and 2004. Reference data was provided by visual classification, validated through extensive field work. The HMM-based method achieved 93% average class accuracy in the identification of the correct crop, being, respectively, 10% and 26% superior to multi-date and single-date alternative approaches applied to the same data set.     

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.     

Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction

Time series of snow covered area (SCA) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper (ETM+) were merged with a spatially explicit snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km²). A linear optimization scheme was used to derive SCA estimates that preserve the statistical moments of the higher spatial resolution (i.e. 30 m) ETM+ data and resolve the superior temporal signal (i.e. ∼ daily) of the MODIS data. It was found that merging the two SCA products led to an 8% decrease and an 18% increase in the basinwide SWE in 2001 and 2002, respectively, compared to the SWE estimated from ETM+ only. Relative to SWE simulations using only ETM+ data, the hybrid SCA estimates reduced the mean absolute SWE error by 17 and 84% in 2001 and 2002, respectively; errors were determined using intensive snow survey data and two separate methods of scaling snow survey field measurements of SWE to the 1-km model pixel resolution. SWE bias for both years was reduced by 49% and skewness was reduced from − 0.78 to 0.49. These results indicate that the hybrid SWE was closer to being an unbiased estimate of the measured SWE and errors were distributed more normally. The accuracy of the SCA estimates is likely dependent on the vegetation fraction.     

Assessments of urban growth in the Tampa Bay watershed using remote sensing data

Urban development has expanded rapidly in the Tampa Bay area of west-central Florida over the past century. A major effect associated with this population trend is transformation of the landscape from natural cover types to increasingly impervious urban land. This research utilizes an innovative approach for mapping urban extent and its changes through determining impervious surfaces from Landsat satellite remote sensing data. By 2002, areas with subpixel impervious surface greater than 10% accounted for approximately 1800 km², or 27 percent of the total watershed area. The impervious surface area increases approximately three-fold from 1991 to 2002. The resulting imperviousness data are used with a defined suite of geospatial data sets to simulate historical urban development and predict future urban and suburban extent, density, and growth patterns using SLEUTH model. Also examined is the increasingly important influence that urbanization and its associated imperviousness extent have on the individual drainage basins of the Tampa Bay watershed.     

Selecting and conserving lands for biodiversity: The role of remote sensing

A major focus of conservation is on protecting areas to ensure the persistence of biological diversity. Because such areas may be large, not easily accessible, subject to change, and sensitive to the surrounding landscape, remote sensing can be a valuable tool in establishing and managing protected areas. We describe three case studies to illustrate how remote sensing can contribute to setting priorities for conservation actions, monitoring the status of conservation targets, and evaluating the effectiveness of conservation strategies. In the Connecticut River watershed, remote sensing has been used to assess flood regimes and identify key areas of floodplain forests and their context for conservation planning. At Eglin Air Force Base in Florida, remote sensing has provided information to assess the effectiveness of management strategies to restore fire to the longleaf pine sandhills ecosystem, control invasive species, and prioritize annual prescribed burns. In eastern US forests, remote sensing is being used to evaluate the ecological condition and changes at properties where direct access would be difficult.As the resolution and capacities of remote-sensing technology continue to develop, however, several issues are becoming increasingly important. It is essential that the spatial and temporal resolution of remote-sensing data be matched to the relevant scales of biodiversity, major threats, and management actions. Data layers must be compatible, both in scale and in measurement properties, and key patterns must be distinguished from irrelevant detail, especially at the finer scales of application in local management. Combining remote sensing with ground surveys can expand the array of information used in management and contribute to the ecological interpretation of remote-sensing data. Because conservation funds are always limited, remote sensing also must be cost effective. This requires balancing the wealth of detail afforded by ever-finer resolution of remote-sensing data with what is actually needed to implement sound conservation and management. Remote sensing is a valuable tool, but it is not a panacea for all of the challenges of conservation monitoring and management.     

A forward image model for passive optical remote sensing of river bathymetry

By facilitating measurement of river channel morphology, remote sensing techniques could enable significant advances in our understanding of fluvial systems. To realize this potential, researchers must first gain confidence in image-derived river information, as well as an appreciation of its inherent limitations. This paper describes a forward image model (FIM) for examining the capabilities and constraints associated with passive optical remote sensing of river bathymetry. Image data are simulated “from the streambed up” by first using information on depth and bottom reflectance to parameterize models of radiative transfer within the water column and atmosphere and then incorporating sensor technical specifications. This physics-based framework provides a means of assessing the potential for spectrally-based depth retrieval from a particular river of interest, given a sensor configuration. Forward image modeling of both a hypothetical meander bend and an actual gravel-bed river indicated that bathymetric accuracy and precision vary spatially as a function of channel morphology, with less reliable depth estimates in pools. A simpler, more computationally efficient analytical model highlighted additional controls on bathymetric uncertainty: optical depth and the ratio of the smallest detectable change in radiance to the bottom-reflected radiance. Application of the FIM to a complex, natural channel illustrated how the model can be used to quantify the effects of various sensor characteristics. Bathymetric accuracy was determined primarily by spatial resolution, due to mixed pixels along the banks and sub-pixel scale variations in depth, whereas depth retrieval precision depended on the sensor's ability to resolve subtle changes in radiance. This flexible forward modeling approach thus allows the utility of image-derived river information to be evaluated in the context of specific investigations, leading to more efficient, more informed use of remote sensing methods across a range of fluvial environments.     

Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa

With increased availability of satellite data products used in mapping surface energy balance and evapotranspiration (ET), routine ET monitoring at large scales is becoming more feasible. Daily satellite coverage is available, but an essential model input, surface temperature, is at 1 km or greater pixel resolution. At such coarse spatial resolutions, the capability to monitor the impact of land cover change and disturbances on ET or to evaluate ET from different crop covers is severely hampered. The effect of sensor resolution on model output for an agricultural region in central Iowa is examined using Landsat data collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This study was conducted in concert with the Soil Moisture Experiment 2002 (SMEX02). Two images collected during a rapid growth period in soybean and corn crops are used with a two-source (soil+vegetation) energy balance model, which explicitly evaluates soil and vegetation contributions to the radiative temperature and to the net turbulent exchange/surface energy balance. The pixel resolution of the remote sensing inputs are varied from 60 m to 120, 240, and 960 m. Model output at high resolution are first validated with tower and aircraft-based flux measurements to assure reliability of model computations. Histograms of the flux distributions and resulting statistics at the different pixel resolutions are compared and contrasted. Results indicate that when the input resolution is on the order of 1000 m, variation in fluxes, particularly ET, between corn and soybean fields is not feasible. However, results also suggest that thermal sharpening techniques for estimating surface temperature at higher resolutions (∼250 m) using the visible/near infrared waveband resolutions could provide enough spatial detail for discriminating ET from individual corn and soybean fields. Additional support for this nominal resolution requirement is deduced from a geostatistical analysis of the vegetation index and surface temperature images.     

Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery

Drought is one of the most frequent climate-related disasters occurring across large portions of the African continent, often with devastating consequences for the food security of agricultural households. This study proposes a novel method for calculating the empirical probability of having a significant proportion of the total agricultural area affected by drought at sub-national level. First, we used the per-pixel Vegetation Health Index (VHI) from the Advanced Very High Resolution Radiometer (AVHRR) averaged over the crop season as main drought indicator. A phenological model based on NDVI was employed for defining the start of season (SOS) and end of the grain filling stage (GFS) dates. Second, the per-pixel average VHI was aggregated for agricultural areas at sub-national level in order to obtain a drought intensity indicator. Seasonal VHI averaging according to the phenological model proved to be a valid drought indicator for the African continent, and is highly correlated with the drought events recorded during the period (1981-2009). The final results express the empirical probability of drought occurrence over both the temporal and the spatial domain, representing a promising tool for future drought monitoring.     

A multi-spectral non-local method for retrieval of boundary layer cloud properties from optical remote sensing data

A multi-spectral non-local (MSN) method is developed for advanced retrieval of boundary layer cloud properties from remote sensing data, as an alternative to the independent pixel approximation (IPA) method. The non-local method uses data at both the target pixel and neighboring pixels to retrieve cloud properties such as pixel-averaged cloud optical thickness and effective droplet radius. Radiance data to be observed from space were simulated by a three-dimensional (3D) radiation model and a stochastic boundary layer cloud model with two-dimensional (horizontal and vertical) variability in cloud liquid water and effective radius. An adiabatic assumption is used for each cloud column to model the geometrical thickness and vertical profiles of cloud liquid water content and effective droplet radius, neglecting drizzle and cloud brokenness for simplicity. The dependence of radiative smoothing and roughening on horizontal scale, optical thickness and single scattering albedo are investigated. Then, retrieval methods using 250-m horizontal resolution data onboard new generation satellites are discussed. The regression model for the MSN method was trained based on datasets from numerical simulations. The training was performed with respect to various domain averages of optical thickness and effective radius, because smoothing and roughening effects are strongly dependent on the two variables. Retrieval accuracy is discussed here with datasets independent of those used in the training, towards assessing the generality of the technique. It is demonstrated that retrieval accuracy of cloud optical thickness, which is often retrieved from single-spectral visible-wavelength data, is improved the most using neighboring pixel data and secondly using multi-spectral data, and ideally with both. When the IPA retrieval method is applied to optical thickness and effective radius, the root-mean-square relative errors can be 15-90%, depending on solar and view directions. In contrast, the MSN method has errors of 4-10%, which is smaller than IPA by a factor of 2-10. It is also suggested that the accuracy of the MSN method is insensitive to some assumptions in the inhomogeneous cloud input data used to train the regression model.     

Role of sensor noise in hyperspectral remote sensing of natural waters: Application to retrieval of phytoplankton pigments

An algorithm is derived to retrieve the concentration of optically active materials, e.g., phytoplankton pigments, etc., from remotely measured spectra of up welled oceanic light. The algorithm takes into account sensor noise in deriving equations for the best linear estimate of concentration mean and residual variance. The algorithm is applied to the problem of phytoplankton concentration retrieval using a modeled hyperspectral sensor based roughly on the LASH imager. The algorithm requires knowing the joint distribution of radiance spectra and concentration. This joint distribution is obtained by simulation using ocean radiance models. It is shown that sensor noise (both shot and dark current) markedly decreases the accuracy of concentration retrieval. However, accuracy is greatly improved if a priori information about observation conditions is known and included in the algorithm. Thus accounting for sensor noise improves retrieval accuracy and affects the choice of observation method.     

Spectroscopic characterization of oils yielded from Brazilian offshore basins: Potential applications of remote sensing

Spectroscopy is the basis to detect and characterize offshore hydrocarbon (HC) seeps through optical remote sensing. Diagnostic spectral features of HCs are linked to their chemical composition and fundamental molecular vibrations (SWIR-TIR features), as well as overtones and combinations of these vibrations (VNIR-SWIR). These features allow for the characterization of oil, oil on water and emulsified oil. This work shows the results of lab and field spectral measurements of 17 petroleum samples yielded from key, oil-rich sedimentary basins in Brazil. Measurements comprised reflectance data (VNIR- SWIR), Attenuated Total Reflectance (ATR), Directional Hemispherical Reflectance (DHR), and emissivity data (TIR). These spectra were analyzed by multivariate techniques, such as Principal Components Analysis (PCA) and Partial Least-Square analysis (PLS). The experimental results indicate that for the VNIR-SWIR range: (i) spectral features can be recognized for crude oil, emulsified oil and oil on ocean water; (ii) different oil types can be qualitatively distinguished based on these features (i.e. light or heavy), even considering oil on water; (iii) the same applies for oil measurements simulated at the spectral resolution of hyperspectral (357-bands/ProSpecTIR) and multispectral (9-bands/ASTER) sensors. Within TIR wavelengths (3-14 μm), typical HC spectral features can also be resolved and oil types qualitatively discriminated using PCA/PLS, including both full-resolution spectra and spectra resampled to hyperspectral sensor (128-bands/SEBASS). However, despite the fact that oil emissivity is always lower than that of water, such separation seems unfeasible using 8-12 μm TIR features only; emissivity spectra are essentially flat for all samples in this interval. This research demonstrated that oil can be qualitatively distinguished based on both VNIR-SWIR and TIR spectroscopy data, with important implications for remote off-shore oil exploration and classification of oil leakages.     

Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Detecting regions or clusters of such widely varying sizes presents a challenging task. A modified differential evolution based fuzzy clustering technique, is proposed in this article. Real-coded encoding of the cluster centres is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for several synthetic and real life data sets as well as for some benchmark functions. Different landcover regions in remote sensing imagery have also been classified using the proposed technique to establish its efficiency. Statistical significance tests have been performed to establish the superiority of the proposed algorithm.     

Satellite remote sensing of surface oceanic fronts in coastal waters off west-central Florida

Two algorithms designed to detect deepwater oceanic features and arbitrary edge profiles were tuned to automatically delineate fronts in coastal waters off west-central Florida using satellite-derived sea surface temperature (SST), chlorophyll-a concentration (Chl), normalized water-leaving radiance (nL_w), and fluorescence line height (FLH) images during select periods in the spring and fall of 2004 and 2005. The dates correspond to recreational king mackerel, Scomberomorus cavalla, tournaments. A histogram-based algorithm was useful to detect coastal surface SST, nL_w, and FLH fronts, specifically. A gradient-based algorithm, with a smaller kernel box of 3 × 3 pixels, best identified nearshore (< 10 m depth) features in Chl images at the mouth of Tampa Bay, but was less effective for fronts farther offshore where gradients were weaker. Local winds and tide levels estimated from a coastal observing buoy, and bathymetric gradients were examined to help understand the factors that influenced front formation and stability. Periods of strong and variable winds led to front movement of up to 10 km per day or dissipation within 2-3 days in over 80% of the fronts detected in SST, Chl, nL_w, and FLH imagery. Short episodes of less variable wind velocities typically led to more stable and stationary fronts, within 3-5 km, for up to four days. The occurrence of fronts closely associated with the coastal bathymetry, namely at the 20 m and 30 m isobaths, was significantly higher in the fall SST imagery and in the spring Chl imagery. Fall SST fronts related to bathymetric gradients likely resulted from progressive cooling of the water with depth. Stronger Chl and nL_w443 gradients at the mouths of estuaries in the fall compared to the spring were attributed to increased precipitation and periods of stronger winds or tides. The FLH imagery was most useful in delineating coastal algal blooms. The automatic front detection techniques applied here can be an important tool for resource managers to track coastal oceanographic features daily, over synoptic spatial scales.     

Convex regularization-based hardware/software co-design for real-time enhancement of remote sensing imagery

In this paper, we address a new approach for high-resolution reconstruction and enhancement of remote sensing (RS) imagery in near-real computational time based on the aggregated hardware/software (HW/SW) co-design paradigm. The software design is aimed at the algorithmic-level decrease of the computational load of the large-scale RS image enhancement tasks via incorporating into the fixed-point iterative reconstruction/enhancement procedures the convex convergence enforcement regularization by constructing the proper projectors onto convex sets (POCS) in the solution domain. The established POCS-regularized iterative techniques are performed separately along the range and azimuth directions over the RS scene frame making an optimal use of the sparseness properties of the employed sensor system modulation format. The hardware design is oriented on employing the Xilinx Field Programmable Gate Array XC4VSX35-10ff668 and performing the image enhancement/reconstruction tasks in a computationally efficient parallel fashion that meets the near-real time imaging system requirements. Finally, we report some simulation results and discuss the implementation performance issues related to enhancement of the real-world RS imagery indicative of the significantly increased performance efficiency gained with the developed approach.