622 research outputs found

    Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (Epics) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors

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    An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to help the understanding of the Earth’s complex systems and to monitor significant changes to them. The quality of data recorded by these sensors is a primary concern, as it critically depends on accurate radiometric calibration for each sensor. Pseudo Invariant Calibration Sites (PICS) have been extensively used for radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. In addition, this work proposes a technique to generate a representative hyperspectral profile for these clusters, as the hyperspectral profile of these identified clusters are mandatory in order to utilize them for performing cross-calibration of optical satellite sensors. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. Furthermore, this work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor crosscalibration. Eventually, North Africa can be used a continental scale PICS

    Remote sensing of irrigated crop types and its application to regional water balance estimation

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    The strengths of moderate to coarse resolution satellite remote sensing in both identifying crop types and estimating crop area has resulted in the widespread use of this technology for agricultural monitoring. Although the spectral information and cost of these remote sensing data are attractive, their spatial resolutions are often perceived as being inadequate for agricultural management at both the individual holding and the paddock level in the rice areas of New South Wales (NSW). Conversely, fine resolution remote sensing (e.g., aerial photography) very often contain spatial detail that will allow management decisions to be made at the paddock level, but these data can be expensive to acquire and subsequent manual digitisation of crop areas is labour intensive when performed each year. This raises at least two associated research questions for the rice industry in southern NSW: (1) ‘how is the rice area best mapped when considering cost, accuracy, timing, and complexity while reconciling the above issues? ‘; and (2) ‘how can spatial accuracy (concerning both areas and positions) be measured and related to relevant management practices in order to influence decisions?’. Additionally, many operational users of remote sensing data perceive it as being an overwhelming data source as it often requires time consuming training and expensive computer software. This results in a further series of issues: (3) ‘can remote sensing be used operationally within the NSW rice industry so that simple methods can be applied using inexpensive software with minimal training in order to achieve similar or increased accuracies?’. Furthermore, use of spatially accurate GIS paddock boundaries has been shown to increase crop classification accuracy. However, this raises further questions: (4) ‘what is the influence of spatial error on management decisions?’; (5) ‘how can the accuracy of GIS data be measured?’; and (6) ‘how are these issues altered when considering the other major summer crops in the region?’. As satellite hyperspectral data (e.g., >100 spectral bands per image) are now available this again raises some questions, such as: (7) ‘does this extra spectral information content translate into additional or more accurate agricultural metrics’; and (8) ‘what is the current capacity in the rice industry of NSW to process this sort of information quickly as to impact management decisions?’. These and other related issues have made up the vast majority of the research from project 1105. Recommendations have been made wherever possible regarding the improvement of spatial analysis or mapping efficiencies. Importantly, the research from project 1105 has been adopted by the local industry – this is proof of ‘impact’ as opposed to only producing ‘outcomes’. The work reported here has concentrated on practical issues with an emphasis on transferring the knowledge gained to industry partners. Prior to addressing these issues, a comprehensive literature review concerning the utility of remote sensing in rice base irrigation systems was performed to ensure that past, present and current opportunities (and constraints) concerning the use of time series remote sensing in the local, national and international context were known and understood. Due to wanting to optimise research results by acquiring as many images as possible with our operating budget for image acquisition all new research (as opposed to the literature review) was conducted on the smallest irrigation areas in southern NSW: Coleambally Irrigation Area (CIA). Before methods can be transferred to the other irrigation areas (i.e., Murrumbidgee and Murray Valley Irrigation areas) some assessment of the similarities of the irrigation systems in terms of non-rice crops and their phenology needs to be performed

    Remote sensing of irrigated crop types and its application to regional water balance estimation

    Get PDF
    The strengths of moderate to coarse resolution satellite remote sensing in both identifying crop types and estimating crop area has resulted in the widespread use of this technology for agricultural monitoring. Although the spectral information and cost of these remote sensing data are attractive, their spatial resolutions are often perceived as being inadequate for agricultural management at both the individual holding and the paddock level in the rice areas of New South Wales (NSW). Conversely, fine resolution remote sensing (e.g., aerial photography) very often contain spatial detail that will allow management decisions to be made at the paddock level, but these data can be expensive to acquire and subsequent manual digitisation of crop areas is labour intensive when performed each year. This raises at least two associated research questions for the rice industry in southern NSW: (1) ‘how is the rice area best mapped when considering cost, accuracy, timing, and complexity while reconciling the above issues? ‘; and (2) ‘how can spatial accuracy (concerning both areas and positions) be measured and related to relevant management practices in order to influence decisions?’. Additionally, many operational users of remote sensing data perceive it as being an overwhelming data source as it often requires time consuming training and expensive computer software. This results in a further series of issues: (3) ‘can remote sensing be used operationally within the NSW rice industry so that simple methods can be applied using inexpensive software with minimal training in order to achieve similar or increased accuracies?’. Furthermore, use of spatially accurate GIS paddock boundaries has been shown to increase crop classification accuracy. However, this raises further questions: (4) ‘what is the influence of spatial error on management decisions?’; (5) ‘how can the accuracy of GIS data be measured?’; and (6) ‘how are these issues altered when considering the other major summer crops in the region?’. As satellite hyperspectral data (e.g., >100 spectral bands per image) are now available this again raises some questions, such as: (7) ‘does this extra spectral information content translate into additional or more accurate agricultural metrics’; and (8) ‘what is the current capacity in the rice industry of NSW to process this sort of information quickly as to impact management decisions?’. These and other related issues have made up the vast majority of the research from project 1105. Recommendations have been made wherever possible regarding the improvement of spatial analysis or mapping efficiencies. Importantly, the research from project 1105 has been adopted by the local industry – this is proof of ‘impact’ as opposed to only producing ‘outcomes’. The work reported here has concentrated on practical issues with an emphasis on transferring the knowledge gained to industry partners. Prior to addressing these issues, a comprehensive literature review concerning the utility of remote sensing in rice base irrigation systems was performed to ensure that past, present and current opportunities (and constraints) concerning the use of time series remote sensing in the local, national and international context were known and understood. Due to wanting to optimise research results by acquiring as many images as possible with our operating budget for image acquisition all new research (as opposed to the literature review) was conducted on the smallest irrigation areas in southern NSW: Coleambally Irrigation Area (CIA). Before methods can be transferred to the other irrigation areas (i.e., Murrumbidgee and Murray Valley Irrigation areas) some assessment of the similarities of the irrigation systems in terms of non-rice crops and their phenology needs to be performed

    EO-1 Data Quality and Sensor Stability with Changing Orbital Precession at the End of a 16 Year Mission

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    The Earth Observing One (EO-1) satellite has completed 16 years of Earth observations in early 2017. What started as a technology mission to test various new advancements turned into a science and application mission that extended many years beyond the satellites planned life expectancy. EO-1s primary instruments are spectral imagers: Hyperion, the only civilian full spectrum spectrometer (430-2400 nm) in orbit; and the Advanced Land Imager (ALI), the prototype for Landsat-8s pushbroom imaging technology. Both Hyperion and ALI instruments have continued to perform well, but in February 2011 the satellite ran out of the fuel necessary to maintain orbit, which initiated a change in precession rate that led to increasingly earlier equatorial crossing times during its last five years. The change from EO-1s original orbit, when it was formation flying with Landsat-7 at a 10:01am equatorial overpass time, to earlier overpass times results in image acquisitions with increasing solar zenith angles (SZAs). In this study, we take several approaches to characterize data quality as SZAs increased. Our results show that for both EO-1 sensors, atmospherically corrected reflectance products are within 5 to 10 of mean pre-drift products. No marked trend in decreasing quality in ALI or Hyperion is apparent through 2016, and these data remain a high quality resource through the end of the mission

    The earth observing one (EO-1) Hyperion and Advanced land imager sensors for use in tundra classification studies within the Upper Kuparuk river basin, Alaska

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    The heterogeneity of Arctic vegetation can make land cover classification very difficult when using medium to small resolution imagery (Schneider et al., 2009; Muller et al., 1999). Using high radiometric and spatial resolution imagery, such as the SPOT 5 and IKONOS satellites, have helped arctic land cover classification accuracies rise into the 80 and 90 percentiles (Allard, 2003; Stine et al., 2010; Muller et al., 1999). However, those increases usually come at a high price. High resolution imagery is very expensive and can often add tens of thousands of dollars onto the cost of the research. The EO-1 satellite launched in 2002 carries two sensors that have high spectral and/or high spatial resolutions and can be an acceptable compromise between the resolution versus cost issues. The Hyperion is a hyperspectral sensor with the capability of collecting 242 spectral bands of information. The Advanced Land Imager (ALI) is an advanced multispectral sensor whose spatial resolution can be sharpened to 10 meters. This dissertation compares the accuracies of arctic land cover classifications produced by the Hyperion and ALI sensors to the classification accuracies produced by the Systeme Pour l' Observation de le Terre (SPOT), the Landsat Thematic Mapper (TM) and the Landsat Enhanced Thematic Mapper Plus (ETM+) sensors. Hyperion and ALI images from August 2004 were collected over the Upper Kuparuk River Basin, Alaska. Image processing included the stepwise discriminant analysis of pixels that were positively classified from coinciding ground control points, geometric and radiometric correction, and principle component analysis. Finally, stratified random sampling was used to perform accuracy assessments on satellite derived land cover classifications. Accuracy was estimated from an error matrix (confusion matrix) that provided the overall, producer's and user's accuracies. This research found that while the Hyperion sensor produced classification accuracies that were equivalent to the TM and ETM+ sensor (approximately 78%), the Hyperion could not obtain the accuracy of the SPOT 5 HRV sensor. However, the land cover classifications derived from the ALI sensor exceeded most classification accuracies derived from the TM and ETM+ sensors and were even comparable to most SPOT 5 HRV classifications (87%). With the deactivation of the Landsat series satellites, the monitoring of remote locations such as in the Arctic on an uninterrupted basis throughout the world is in jeopardy. The utilization of the Hyperion and ALI sensors are a way to keep that endeavor operational. By keeping the ALI sensor active at all times, uninterrupted observation of the entire Earth can be accomplished. Keeping the Hyperion sensor as a "tasked" sensor can provide scientists with additional imagery and options for their studies without overburdening storage issues

    Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

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    Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS)

    The Development of Dark Hyperspectral Absolute Calibration Model Using Extended Pseudo Invariant Calibration Sites at a Global Scale: Dark EPICS-Global

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    This research aimed to develop a novel dark hyperspectral absolute calibration (DAHAC) model using stable dark targets of Global Cluster - 36 (GC-36), one of the clusters from 300 Class Global Classification. The stable dark sites were identified from GC-36 called Dark EPICS-Global covering the surface types viz; dark rock, volcanic area, and dark sand. The Dark EPICS-Global shows a temporal variation of 0.02 unit reflectance. This work uses the Landsat-8 (L8) Operational Land Imager (OLI) , Sentinel-2A (S2A) Multispectral Instrument (MSI) , and Earth Observing One (EO-1) Hyperion data for the DAHAC model development, where well-calibrated L8 and S2A are used as the reference sensors while EO-1 Hyperion with 10 nm spectral resolution is used as a hyperspectral library. The dark hyperspectral dataset (DaHD) is generated by combining the normalized hyperspectral profile of L8 and S2A for the DAHAC model development. The DAHAC model developed in this study takes into account the solar zenith and azimuth angles as well as the view zenith and azimuth angles in Cartesian coordinates form. This model is capable of predicting TOA reflectance in all existing spectral bands of any sensor. The DAHAC model was then validated with Landsat-7 (L7) , Landsat-9 (L9) , and Sentinel-2B (S2B) satellites from their launch dates to March 2022. These satellite sensors vary in terms of their spectral resolution, equatorial crossing time, spatial resolution, etc. The comparison between the DAHAC model and satellite measurements shows accuracy within 0.01 unit reflectance across overall spectral bands. The proposed DAHAC model uncertainty level is determined using Monte Carlo Simulation and found to be 0.04 and 0.05 unit reflectance for VNIR and SWIR channels, respectively. The DAHAC model double ratio is used as a tool to perform the inter-comparison between two satellites. The sensor inter-comparison results for L8 and L9 shows a 2% difference and 1% for S2A and S2B across all spectral bands

    The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharum spp. hybrid)

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    Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010
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