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Unmanned aerial vehicles (UAVs) offer a viable alternative to conventional platforms for acquiring high-resolution remote-sensing data at lower cost and increased operational flexibility. UAVs include various configurations of unm...
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Unmanned aerial vehicles (UAVs) offer a viable alternative to conventional platforms for acquiring high-resolution remote-sensing data at lower cost and increased operational flexibility. UAVs include various configurations of unmanned aircraft, multirotor helicopters (e.g., quadcopters), and balloons/blimps of different sizes and shapes. Quadcopters and balloons fill a gap between satellites and aircraft when a stationary monitoring platform is needed for relatively long-term observation of an area. UAVs have advanced designs to carry small payloads and integrated flight control systems, giving them semiautonomous or fully autonomous flight capabilities. Miniaturized sensors are being developed/adapted for UAV payloads, including hyperspectral imagers, LIDAR, synthetic aperture radar, and thermal infrared sensors. UAVs are now used for a wide range of environmental applications, such as coastal wetland mapping, LIDAR bathymetry, flood and wildfire surveillance, tracking oil spills, urban studies, and Arctic ice investigations.
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This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hype...
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This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hyperspectral imaging systems ranging from ground to airborne and satellite platforms have been designed, built, and operated. These include the field imaging spectrometer system (FISS), the Modular Airborne Imaging Spectrometer (MAIS), and the Chang'E-I Interferometer Spectrometer (IIM). In addition to developing sensors, Chinese scientists have proposed various novel image processing techniques. Applications of hyperspectral imaging in China have been also performed including mineral exploration in the Qilian Mountains and oil exploration in Xinjiang province. To promote the development of HRS, many generic and professional software tools have been developed. These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained in HRS data, allowing them to meet the demands of both common users and researchers in the scientific community.
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Abundant spectral information endows unique advantages of hyperspectral remote sensing images in target location and recognition. Target detection techniques locate materials or objects of interest from hyperspectral images with g...
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Abundant spectral information endows unique advantages of hyperspectral remote sensing images in target location and recognition. Target detection techniques locate materials or objects of interest from hyperspectral images with given prior target spectra, and have been widely used in military, mineral exploration, ecological protection, etc. However, hyperspectral target detection is a challenging task due to high-dimension data, spectral changes, spectral mixing, and so on. To this end, many methods based on optimization and machine learning have been proposed in the past decades. In this paper, we review the representatives of hyperspectral image target detection methods and group them into seven categories: hypothesis testing-based methods, spectral angle-based methods, signal decomposition-based methods, constrained energy minimization (CEM)-based methods, kernel-based methods, sparse representation-based methods, and deep learning-based methods. We then comprehensively summarize their basic principles, classical algorithms, advantages, limitations, and connections. Meanwhile, we give critical comparisons of the methods on the summarized datasets and evaluation metrics. Furthermore, the future challenges and directions in the area are analyzed.
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A global operational land imager (GOLI) Landsat-8 daytime active fire detection algorithm is presented. It utilizes established contextual active fire detection approaches but takes advantage of the significant increase in fire re...
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A global operational land imager (GOLI) Landsat-8 daytime active fire detection algorithm is presented. It utilizes established contextual active fire detection approaches but takes advantage of the significant increase in fire reflectance in Landsat-8 band 7 (2.20 mu m) relative to band 4 (0.66 mu m). The detection thresholds are fixed and based on a statistical examination of 39 million non-burning Landsat-8 pixels. Multi-temporal tests based on band 7 reflectance and relative changes in normalized difference vegetation index in the previous six months are used to reduce commissions errors. The probabilities of active fire detection for the GOLI and two recent Landsat-8 active fire detection algorithms are simulated to provide insights into their performance with respect to the fire size and temperature. The algorithms are applied to 11 Landsat-8 images that encompass a range of burning conditions and environments. Commission and omission errors are assessed by visual interpretation of detected active fire locations and by examination of the Landsat-8 images and higher spatial resolution Google Earth imagery. The GOLI algorithm has lower omission and comparable commission errors than the recent Landsat-8 active fire detection algorithms. The GOLI algorithm has demonstrable potential for global application and is suitable for implementation with other Landsat-like reflective wavelength sensors.
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Recent advances in global biogeochemical research demonstrate a critical need for long-term ocean color satellite data records of consistent high quality. To achieve that quality, spaceborne instruments require on-orbit vicarious ...
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Recent advances in global biogeochemical research demonstrate a critical need for long-term ocean color satellite data records of consistent high quality. To achieve that quality, spaceborne instruments require on-orbit vicarious calibration, where the integrated instrument and atmospheric correction system is adjusted using in situ normalized water-leaving radiances, such as those collected by the marine optical buoy (MOBY). Unfortunately, well-characterized time-series of in situ data are scarce for many historical satellite missions, in particular, the NASA coastal zone color scanner (CZCS) and the ocean color and temperature scanner (OCTS). Ocean surface reflectance models (ORMs) accurately reproduce spectra observed in clear marine waters, using only chlorophyllα (C_α) as input, a measurement for which long-term in situ time series exist. Before recalibrating CZCS and OCTS using modeled radiances, however, we evaluate the approach with the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS). Using annual Ca climatologies as input into an ORM, we derive SeaWiFS vicarious gains that differ from the operational MOBY gains by less than ±0.9% spectrally. In the context of generating decadal C_α climate data records, we quantify the downstream effects of using these modeled gains by generating satellite-to-in situ data product validation statistics for comparison with the operational SeaWiFS results. Finally, we apply these methods to the CZCS and OCTS ocean color time series.
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Small spectral differences from the mean remote sensing reflectance (Rrs) of the ocean - anomalies - can provide unique environmental information from ocean color satellite data. First, we describe the average relationship between...
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Small spectral differences from the mean remote sensing reflectance (Rrs) of the ocean - anomalies - can provide unique environmental information from ocean color satellite data. First, we describe the average relationship between three input spectral bands and an output band by developing a look-up table (WT) based on the fully normalized Rrs from the MODIS AQUA sensor. By dividing the Rrs measured at the output wavelength by the prediction from the LUT, we obtain several anomalies depending on the combination of input and output bands. None of these anomalies are correlated with chlorophyll concentration on the global scale. Some anomalies are strongly correlated with previously described data products (e.g., CDOM index, backscattering coefficients from semi-analytical inversion models), but others are not correlated with any product currently distributed by NASA. In the latter case, new information about oceanic optical properties is extracted from the ocean color spectra, which allows identification of water masses that was otherwise impossible with standard ocean color products. It was not possible, in some cases, to identify the optical source of this information, which may be spatially and temporally variable. We also show that by removing the main source of variability, the anomalies show interesting potential to identify subtle shifts in sensor response in satellite time series. (C) 2016 Elsevier Inc. All rights reserved.
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An imaging lidar instrument with the capability of measuring the frequency response of a backscattered return signal up to 3.6 kHz is demonstrated. The instrument uses a commercial microchip frequency-doubled pulsed Nd:YAG laser w...
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An imaging lidar instrument with the capability of measuring the frequency response of a backscattered return signal up to 3.6 kHz is demonstrated. The instrument uses a commercial microchip frequency-doubled pulsed Nd:YAG laser with a 7.2 kHz pulse repetition rate, a pulse duration of less than 1 ns, and a pulse energy of greater than 10 μJ. A 15.2 cm commercial telescope is used to collect the backscattered signal, and a photomultiplier tube is used to monitor the scattered light. This instrument is designed for range- and angle-resolved optical detection of honeybees for explosives and land-mine detection. The instrument is capable of distinguishing between the scattered light from honeybees and other sources through the frequency content of the return signal caused by the wing-beat modulation of the backscattered light. Detection of honeybees near a bee hive and spatial mapping of honeybee densities near feeders are demonstrated.
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The fields of tropical biology and conservation face significant transformations due to rapid technological developments in remote sensing. Other fields (e.g. Archeology) are experiencing this momentous change even more rapidly. I...
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The fields of tropical biology and conservation face significant transformations due to rapid technological developments in remote sensing. Other fields (e.g. Archeology) are experiencing this momentous change even more rapidly. In this article, we review some of the challenges that the fields of tropical biology and conservation face during the first quarter of the twenty-first century from the perspective of various remote sensing technologies, and discuss the transformations that they may bring to these disciplines. In addition, we review two emerging technologies driving paradigm changes in the nexus of ecology, remote sensing, and analytics: near-surface remote sensing and Wireless Sensor Networks. These two technologies, arising from the eScience paradigm, offer unique opportunities to integrate field observations at hyper-temporal and spatial resolutions that were not possible as recently as 5years ago.
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In many land-surface models using bulk transfer (one-source) approaches, the application of radiometric surface temperature observations in energy flux computations has given mixed results. This is due in part to the non-unique re...
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In many land-surface models using bulk transfer (one-source) approaches, the application of radiometric surface temperature observations in energy flux computations has given mixed results. This is due in part to the non-unique relationship between the so-called aerodynamic temperature, which relates to the efficiency of heat exchange between the land surface and overlying atmosphere, and a surface temperature measurement from a thermal-infrared radiometer, which largely corresponds to a weighted soil and canopy temperature as a function of radiometer viewing angle. A number of studies over the past several years using multi-source canopy models and/or experimental data have developed simplified methods to accommodate radiometric-aerodynamic temperature differences in one-source approaches. A recent investigation related the variability in the radiometric-aerodynamic relation to solar radiation using experimental data from a variety of landscapes, while another used a multi-source canopy model combined with measurements over a wide range in vegetation density to derive a relationship based on leaf area index. In this study, simulations by a detailed multi-source soil-plant-environment model, Cupid, which considers both radiative and turbulent exchanges across the soil-canopy-air interface, are used to explore the radiometric-aerodynamic temperature relations for a semi-arid shrubland ecosystem under a range of leaf area/canopy cover, soil moisture and meteorological conditions. The simulated radiometric-aerodynamic temperatures indicate that, while solar radiation and leaf area both strongly affect the magnitude of this temperature difference, the relationships are non-unique, having significant variability depending on local conditions. These simulations also show that soil-canopy temperature differences are highly correlated with variations in the radiometric-aerodynamic temperature differences, with the slope being primarily a function of leaf area. This result suggests that two-source schemes with reliable estimates of component soil and canopy temperatures and associated resistances may be better able to accommodate variability in the radiometric-aerodynamic relation for a wider range in vegetated canopy cover conditions than is possible with one-source schemes. However, comparisons of sensible heat flux estimates with Cupid using a simplified two-source model and a one-source model accommodating variability in the radiometric-aerodynamic relation based on vegetation density gave similar scatter. On the other hand, with experimental data from the shrubland site, the two-source model generally outperformed the one-source scheme. Clearly, vegetation density/leaf area has a major effect on the radiometric-aerodynamic temperature relation and must be considered in either one-source or two-source formulations. Hence these adjusted one-source models require similar inputs as in two-source approaches, but provide as output only bulk heat fluxes; this is not as useful for monitoring vegetation conditions.
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In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study co...
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In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech nadir-view sensor data and QuickBird satellite multi-spectral data to evaluate several green waveband vegetation indices to assess the N status of irrigated maize. It also sought to determine if QuickBird multi-spectral imagery could be used to develop plant N status maps as accurately as those produced by ground-based sensor systems. The green normalized difference vegetation index normalized to a reference area (NGNDVI) clustered the data for three clear-day data acquisitions between QuickBird and Exotech data producing slopes and intercepts statistically not different from 1 and 0, respectively, for the individual days as well as for the combined data. Comparisons of NGNDVI and the N Sufficiency Index produced good correlation coefficients that ranged from 0.91 to 0.95 for the V12 and V15 maize growth stages and their combined data. Nitrogen sufficiency maps based on the NGNDVI to indicate N sufficient (E0.96) or N deficient (<0.96) maize were similar for the two sensor systems. A quantitative assessment of these N sufficiency maps for the V10-V15 crop growth stages ranged from 79 to 83% similarity based on areal agreement and moderate to substantial agreement based on the kappa statistics. Results from our study indicate that QuickBird satellite multi-spectral data can be used to assess irrigated maize N status at the V12 and later growth stages and its variability within a field for in-season N management. The NGNDVI compensated for large off-nadir and changing target azimuth view angles associated with frequent QuickBird acquisitions.
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