Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

Wetlands Ecology and Management, Journal Year: 2009, Volume and Issue: 18(3), P. 281 - 296

Published: Dec. 10, 2009

Language: Английский

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Deep learning based multi-temporal crop classification

Remote Sensing of Environment, Journal Year: 2018, Volume and Issue: 221, P. 430 - 443

Published: Dec. 3, 2018

Language: Английский

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Remote Sensing Technologies for Enhancing Forest Inventories: A Review

Canadian Journal of Remote Sensing, Journal Year: 2016, Volume and Issue: 42(5), P. 619 - 641

Published: July 27, 2016

Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set economic, environmental, social policy objectives. Advanced remote sensing technologies provide data to assist addressing these escalating information needs support subsequent development parameterization models for even broader range needs. This special issue contains papers that use a variety derive forest or inventory-related information. Herein, we review potential 4 advanced technologies, which posit as having greatest influence inventories designed characterize resource strategic, tactical, operational planning: airborne laser scanning (ALS), terrestrial (TLS), digital aerial photogrammetry (DAP), high spatial resolution (HSR)/very (VHSR) satellite optical imagery. ALS, particular, has proven be transformative technology, offering required detail accuracy across large areas diverse types. The coupling DAP with ALS will likely have impact on practices next decade, providing capacity suite attributes, well monitoring growth over time.

Language: Английский

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Lidar sampling for large-area forest characterization: A review

Remote Sensing of Environment, Journal Year: 2012, Volume and Issue: 121, P. 196 - 209

Published: March 5, 2012

The ability to use digital remotely sensed data for forest inventory is often limited by the nature of measures, which, with exception multi-angular or stereo observations, are largely insensitive vertically distributed attributes. As a result, empirical estimates typically made characterize attributes such as height, volume, biomass, known asymptotic relationships signal saturation occurs. Lidar (light detection and ranging) has emerged robust means collect subsequently been established an appropriate source purposes; however, large area monitoring mapping activities lidar remain challenging due logistics, costs, volumes involved. sampling tool large-area estimation may mitigate some all these problems. A number factors drive, common to, airborne profiling, scanning, spaceborne systems tools measuring resources across areas that range in size from tens thousands millions square kilometers. In this communication, we present case enable timely characterizations. We briefly outline different data, followed theoretical statistical underpinnings sampling. Current applications presented future potential using integrated framework ecosystem characterization presented. also include recommendations regarding statistics, schemes, (including integration stratification), subsequent information generation.

Language: Английский

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Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2018, Volume and Issue: 67, P. 30 - 42

Published: Jan. 3, 2018

Language: Английский

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The View from Above: Applications of Satellite Data in Economics

The Journal of Economic Perspectives, Journal Year: 2016, Volume and Issue: 30(4), P. 171 - 198

Published: Oct. 28, 2016

The past decade or so has seen a dramatic change in the way that economists can learn by watching our planet from above. A revolution taken place remote sensing and allied fields such as computer science, engineering, geography. Petabytes of satellite imagery have become publicly accessible at increasing resolution, many algorithms for extracting meaningful social science information these images are now routine, modern cloud-based processing power allows to be run global scale. This paper seeks introduce remotely sensed data, give flavor how this new source data been used far what might done future.

Language: Английский

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Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data

Remote Sensing of Environment, Journal Year: 2012, Volume and Issue: 123, P. 258 - 270

Published: April 25, 2012

Language: Английский

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Review of optical-based remote sensing for plant trait mapping

Ecological Complexity, Journal Year: 2013, Volume and Issue: 15, P. 1 - 16

Published: July 27, 2013

Language: Английский

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Satellite remote sensing of grasslands: from observation to management

Journal of Plant Ecology, Journal Year: 2016, Volume and Issue: 9(6), P. 649 - 671

Published: Feb. 2, 2016

Grasslands are the world's most extensive terrestrial ecosystem, and a major feed source for livestock. Meeting increasing demand meat other dairy products in sustainable manner is big challenge. At field scale, Global Positioning System ground-based sensor technologies provide promising tools grassland herd management with high precision. With growth availability of spaceborne remote sensing data, it therefore important to revisit relevant methods applications that can exploit this imagery. In article, we have reviewed (i) current status monitoring/observation based on satellite (ii) technological methodological developments retrieve different biophysical parameters characteristics (i.e. degradation, grazing intensity) (iii) identified key remaining challenges some new upcoming trends future development. The retrieval evolved recent years from classical regression analysis more complex, efficient robust modeling approaches, driven by likely continue be method deriving information, however these require quality calibration validation data. We found hypertemporal data widely used time series generation, particularly overcome cloud contamination issues, but low spatial resolution instruments precludes their use field-scale application many countries. This trend may change rise launch constellations, such as RapidEye, Sentinel-2 even microsatellites those operated Skybox Imaging. Microwave imagery has not been applications, better understanding backscatter behaviour phenological stages needed reliable cloudy regions. development hyperspectral instrumentation analytical will help detailed discrimination habitat types, greater end-user operation.

Language: Английский

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The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

Remote Sensing, Journal Year: 2018, Volume and Issue: 11(1), P. 43 - 43

Published: Dec. 28, 2018

Wetlands are one of the most important ecosystems that provide a desirable habitat for great variety flora and fauna. Wetland mapping modeling using Earth Observation (EO) data essential natural resource management at both regional national levels. However, accurate wetland is challenging, especially on large scale, given their heterogeneous fragmented landscape, as well spectral similarity differing classes. Currently, precise, consistent, comprehensive inventories national- or provincial-scale lacking globally, with studies focused generation local-scale maps from limited remote sensing data. Leveraging Google Engine (GEE) computational power availability high spatial resolution collected by Copernicus Sentinels, this study introduces first detailed, inventory map richest Canadian provinces in terms extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 optical Sentinel-2 composites were used to identify distribution five three non-wetland classes Island Newfoundland, covering an approximate area 106,000 km2. The classification results evaluated pixel-based object-based random forest (RF) classifications implemented GEE platform. revealed superiority approach relative mapping. Although was more compared SAR, inclusion types significantly improved accuracies overall accuracy 88.37% Kappa coefficient 0.85 achieved SAR/optical composite RF classification, wherein all correctly identified beyond 70% 90%, respectively. suggest paradigm-shift standard static products approaches toward generating dynamic, on-demand, large-scale coverage through advanced cloud computing resources simplify access processing “Geo Big Data.” addition, resulting ever-demanding Newfoundland interest can be many stakeholders, including federal provincial governments, municipalities, NGOs, environmental consultants name few.

Language: Английский

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