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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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Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can m...
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Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active remote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.
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Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can m...
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Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active remote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.
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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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