Prospective HyspIRI global observations of tidal wetlands

Turpie, Kevin R.; Klemas, V.; Byrd, Kristin B.; Kelly, Maggi; Jo, Young‐Heon

doi:10.1016/j.rse.2015.05.008

Cited by 43 publications

(42 citation statements)

References 111 publications

(149 reference statements)

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“…Thus, it is crucial for the wetland management department to develop an effective and robust monitoring method, to understand the land use/cover changes of wetlands and their surrounding areas in a timely manner. Research has proved that remote sensing can offer valuable information for monitoring the wetland changes [3][4][5]. However, the land cover classification of remotely sensed data is relatively difficult for arid areas due to the strong soil background interference, and the challenge and cost for in situ data collection [6].…”

Section: Introductionmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

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Abstract:The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%-10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas.

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Section: Introductionmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

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“…This capability has been successfully demonstrated using airborne spectrometers for many traits at regional scales across multiple biomes 34,35 . Similar techniques exist at various stages of development for characterizing freshwater 36 and tidal ecosystems 37 , marine phytoplankton 38,39 and coral reefs 40 . Satellite technology is now poised to provide global coverage at spatial resolutions sufficiently fine (30 m to 60 m pixel size) to support biodiversity inference and applications.…”

Section: The World's Ecosystems Are Losing Biodiversity Fast a Satelmentioning

confidence: 99%

Erratum: Monitoring plant functional diversity from space

Jetz¹,

Cavender‐Bares²,

Pavlick³

et al. 2016

Nature Plants

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The world's ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change and how to manage it.The ability to view Earths' vegetation from space is a hallmark of the space age. Yet decades of satellite measurements have provided relatively little insight into the immense diversity of form and function in the plant kingdom in space and time. Humans are rapidly impacting biodiversity around the globe 1,2 , leading to the loss of ecosystem function 3 , and the goods and services they provide 4,5 . Recognizing the gravity of this threat, the international community has committed to urgent action to halt biodiversity loss [6][7][8][9] .Ecosystem processes [10][11][12] are often directly linked to the functional biodiversity of plants, that is, to a wide range of plant chemical, physiological and structural properties, connected to the uptake, use and allocation of resources. The functional biodiversity of plants varies in space and time and across scales of biological organization. Capturing and understanding this variation is vitally important for tracking the status and resilience of Earth's ecosystems, and for predicting how our ecological life support systems will function in the future.We currently lack consistent, repeated, high-resolution global-scale data on the functional biodiversity of the Earth's vegetation 2,10-12 . However, the technological tools, informatics infrastructure, theoretical basis, and analytical capability now exist to produce this essential data. Here we suggest that this capability is utilized in a satellite mission supporting a Global Biodiversity Observatory, that tracks temporal changes in plant functional traits across the globe to fill critical knowledge gaps, aid in the assessment of global environmental change, and improve predictions of future change. The continuous, global coverage in space and time such a mission would provide has the potential to transform basic and applied science on diversity and function, and to pave the way to a more mechanistically detailed representation of the terrestrial biosphere in Earth system models.The data and knowledge gap Plant functional biodiversity encompasses the wide-ranging variation in the chemical physiological and morphological properties of plants, such as the concentration of metabolites and nonstructural carbohydrates in leaves and the ratio of leaf mass to leaf area. These attributes are related functionally to the uptake, allocation and use of resources, such as carbon and nutrients, within the plant, and to defense against pests and environmental stresses.Functional properties vary within and among individuals (for instance as determined by the position of a leaf on a plant, or a tree in a forest), populations, species and communities, and may be measured at any of these levels of biological organization. With increasing spatial scale (and thus decreasing spatial resolution of measurem...

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“…In the period 2013-2015, the HyspIRI Airborne Campaign collected data from five flights over California using AVIRIS and the MODIS/ASTER Airborne Simulator (MASTER) from a high altitude platform (NASA ER-2) to simulate the data that would be acquired by HyspIRI and to determine its appropriate sensor resolutions [138,140]. Various teams have explored the potential of HyspIRI to monitor inland waters and wetlands [26,136,141], coastal waters [139,142,143] and the open ocean [138,144]. A study was also conducted to assess whether HyspIRI data could enable improvements to the Atmospheric Removal Program (ATREM) atmospheric correction method [145].…”

Section: Sensor Resolutionsmentioning

confidence: 99%

“…This spatial resolution enables the characterisation of 90% of freshwater ecosystems in Europe, but less than 20% in Australia [26]. The initial sensor design has been updated, so a spatial resolution of 30 m is now expected [141]. However, even a 30-m resolution is inadequate when waters contain certain buoyancy regulating phytoplankton (e.g., cyanobacteria), as their distribution leads to underestimation of chlorophyll levels [146].…”

Section: Sensor Resolutionsmentioning

confidence: 99%

“…However, studies as part of the HyspIRI Airborne campaign found that a spatial resolution of 60 m would be unsuitable for monitoring certain areas that contain diverse mixtures of phytoplankton, in particular in inland waters [26]. A spatial resolution of 30 m is now expected for HyspIRI [141], and this will enable the identification of small-scale turbidity features [99]. However, a 30-m resolution is still inadequate for inland and coastal waters that contain certain buoyancy regulating phytoplankton (e.g., cyanobacteria) [146].…”

Section: Hyperspectral Approaches For Atmospheric Correctionmentioning

confidence: 99%

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Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

et al. 2015

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Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

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Prospective HyspIRI global observations of tidal wetlands

Cited by 43 publications

References 111 publications

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Erratum: Monitoring plant functional diversity from space

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

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