Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets
Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detec...
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ftmdpi:oai:mdpi.com:/2072-4292/8/9/733/ 2023-08-20T04:04:03+02:00 Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets Zachary Langford Jitendra Kumar Forrest Hoffman Richard Norby Stan Wullschleger Victoria Sloan Colleen Iversen agris 2016-09-06 application/pdf https://doi.org/10.3390/rs8090733 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs8090733 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 8; Issue 9; Pages: 733 Arctic WorldView-2 plant functional type interpolation clustering Text 2016 ftmdpi https://doi.org/10.3390/rs8090733 2023-07-31T20:56:47Z Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June–August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by ... Text Arctic Tundra MDPI Open Access Publishing Arctic Remote Sensing 8 9 733 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Arctic WorldView-2 plant functional type interpolation clustering |
spellingShingle |
Arctic WorldView-2 plant functional type interpolation clustering Zachary Langford Jitendra Kumar Forrest Hoffman Richard Norby Stan Wullschleger Victoria Sloan Colleen Iversen Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets |
topic_facet |
Arctic WorldView-2 plant functional type interpolation clustering |
description |
Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June–August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by ... |
format |
Text |
author |
Zachary Langford Jitendra Kumar Forrest Hoffman Richard Norby Stan Wullschleger Victoria Sloan Colleen Iversen |
author_facet |
Zachary Langford Jitendra Kumar Forrest Hoffman Richard Norby Stan Wullschleger Victoria Sloan Colleen Iversen |
author_sort |
Zachary Langford |
title |
Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets |
title_short |
Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets |
title_full |
Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets |
title_fullStr |
Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets |
title_full_unstemmed |
Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets |
title_sort |
mapping arctic plant functional type distributions in the barrow environmental observatory using worldview-2 and lidar datasets |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2016 |
url |
https://doi.org/10.3390/rs8090733 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Tundra |
genre_facet |
Arctic Tundra |
op_source |
Remote Sensing; Volume 8; Issue 9; Pages: 733 |
op_relation |
https://dx.doi.org/10.3390/rs8090733 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs8090733 |
container_title |
Remote Sensing |
container_volume |
8 |
container_issue |
9 |
container_start_page |
733 |
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1774714482151391232 |