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Classification of Wetland Vegetation Based on NDVI Time Series from the HLS Dataset
Natural wetlands are intrinsically heterogeneous and typically composed of a mosaic of ecosystem patches with different vegetation types. Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetl...
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Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2022-04, Vol.14 (9), p.2107 |
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description | Natural wetlands are intrinsically heterogeneous and typically composed of a mosaic of ecosystem patches with different vegetation types. Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. Our results reveal how changes in water elevation have changed the patch distribution in significant ways, leading to the local extinction of cattail by 2019 and a continuous increase in the area cover of water lily patches. |
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Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. Our results reveal how changes in water elevation have changed the patch distribution in significant ways, leading to the local extinction of cattail by 2019 and a continuous increase in the area cover of water lily patches.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs14092107</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Aquatic plants ; Carbon sequestration ; Classification ; Datasets ; Estuaries ; Floating plants ; Flow velocity ; Greenhouse gases ; High resolution ; HLS data ; Hydrology ; Image classification ; Image resolution ; Lake Erie ; Lakes ; Land cover ; Landsat satellites ; NDVI ; Nitrogen ; Nymphaea ; Patches (structures) ; Pixels ; Remote sensing ; Species extinction ; Time series ; Vegetation ; Vegetation changes ; vegetation classification ; Vegetation mapping ; Water quality ; Watersheds ; Wetlands</subject><ispartof>Remote sensing (Basel, Switzerland), 2022-04, Vol.14 (9), p.2107</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3037-f81cf5dc49472ce9d02d44a6bab1c91c58088d820334080c9eb3faa774350d6a3</citedby><cites>FETCH-LOGICAL-c3037-f81cf5dc49472ce9d02d44a6bab1c91c58088d820334080c9eb3faa774350d6a3</cites><orcidid>0000-0002-9209-9540 ; 0000000292099540</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2663126098/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2663126098?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,25752,27923,27924,37011,44589,74997</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/1865491$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Ju, Yang</creatorcontrib><creatorcontrib>Bohrer, Gil</creatorcontrib><title>Classification of Wetland Vegetation Based on NDVI Time Series from the HLS Dataset</title><title>Remote sensing (Basel, Switzerland)</title><description>Natural wetlands are intrinsically heterogeneous and typically composed of a mosaic of ecosystem patches with different vegetation types. Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. 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extinction</subject><subject>Time series</subject><subject>Vegetation</subject><subject>Vegetation changes</subject><subject>vegetation classification</subject><subject>Vegetation mapping</subject><subject>Water 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Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. Our results reveal how changes in water elevation have changed the patch distribution in significant ways, leading to the local extinction of cattail by 2019 and a continuous increase in the area cover of water lily patches.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs14092107</doi><orcidid>https://orcid.org/0000-0002-9209-9540</orcidid><orcidid>https://orcid.org/0000000292099540</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Aquatic plants Carbon sequestration Classification Datasets Estuaries Floating plants Flow velocity Greenhouse gases High resolution HLS data Hydrology Image classification Image resolution Lake Erie Lakes Land cover Landsat satellites NDVI Nitrogen Nymphaea Patches (structures) Pixels Remote sensing Species extinction Time series Vegetation Vegetation changes vegetation classification Vegetation mapping Water quality Watersheds Wetlands |
title | Classification of Wetland Vegetation Based on NDVI Time Series from the HLS Dataset |
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