Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data
Across Alaska’s Kenai Peninsula, disturbance events have removed large areas of forest over the last half century. Simultaneously, succession and landscape evolution have facilitated forest regrowth and expansion. Detecting forest loss within known pulse disturbance events is often straightforward g...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-10-01
|
Series: | Land |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-445X/9/10/382 |
id |
doaj-6d02cbeed2f04ffa9170d8acb6903f77 |
---|---|
record_format |
Article |
spelling |
doaj-6d02cbeed2f04ffa9170d8acb6903f772020-11-25T03:55:59ZengMDPI AGLand2073-445X2020-10-01938238210.3390/land9100382Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy DataCarson A. Baughman0Rachel A. Loehman1Dawn R. Magness2Lisa B. Saperstein3Rosemary L. Sherriff4U.S. Geological Survey—Alaska Science Center, 4210 University Dr., Anchorage, AK 99508, USAU.S. Geological Survey—Alaska Science Center, 4210 University Dr., Anchorage, AK 99508, USAKenai National Wildlife Refuge, U.S. Fish and Wildlife Service, 1 Ski Hill Rd., Soldotna, AK 99669, USAU.S. Fish and Wildlife Service Alaska Region, 1011 East Tudor Rd., Anchorage, AK 99503, USADepartment of Geography, Environment & Spatial Analysis, Humboldt State University, 1 Harpst St., Arcata, CA 95521, USAAcross Alaska’s Kenai Peninsula, disturbance events have removed large areas of forest over the last half century. Simultaneously, succession and landscape evolution have facilitated forest regrowth and expansion. Detecting forest loss within known pulse disturbance events is often straightforward given that reduction in tree cover is a readily detectable and measurable land-cover change. Land-cover change is more difficult to quantify when disturbance events are unknown, remote, or environmental response is slow in relation to human observation. While disturbance events and related land-cover change are relatively instant, assessing patterns of post-disturbance succession requires long term monitoring. Here, we describe a method for classifying land cover and quantifying land-cover change over time, using Landsat legacy imagery for three historical eras on the western Kenai Peninsula: 1973–2002, 2002–2017, and 1973–2017. Scenes from numerous Landsat sensors, including summer and winter seasons, were acquired between 1973 and 2017 and used to classify vegetation cover using a random forest classifier. Land-cover type was summarized by era and combined to produce a dataset capturing spatially explicit land-cover change at a moderate 30-m resolution. Our results document large-scale forest loss across the study area that can be attributed to known disturbance events including beetle kill and wildfire. Despite numerous and extensive disturbances resulting in forest loss, we estimate that the study area has experienced net forest gain over the duration of our study period due to reforestation within large fire events that predate this study. Transition between forest and graminoid non-forest land cover including wetlands and herbaceous uplands is the most common land-cover change—representing recruitment of a graminoid dominated understory following forest loss and the return of forest canopy given sufficient time post-disturbance.https://www.mdpi.com/2073-445X/9/10/382land coverLandsatremote sensingdisturbanceland changetime series |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carson A. Baughman Rachel A. Loehman Dawn R. Magness Lisa B. Saperstein Rosemary L. Sherriff |
spellingShingle |
Carson A. Baughman Rachel A. Loehman Dawn R. Magness Lisa B. Saperstein Rosemary L. Sherriff Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data Land land cover Landsat remote sensing disturbance land change time series |
author_facet |
Carson A. Baughman Rachel A. Loehman Dawn R. Magness Lisa B. Saperstein Rosemary L. Sherriff |
author_sort |
Carson A. Baughman |
title |
Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data |
title_short |
Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data |
title_full |
Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data |
title_fullStr |
Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data |
title_full_unstemmed |
Four Decades of Land-Cover Change on the Kenai Peninsula, Alaska: Detecting Disturbance-Influenced Vegetation Shifts Using Landsat Legacy Data |
title_sort |
four decades of land-cover change on the kenai peninsula, alaska: detecting disturbance-influenced vegetation shifts using landsat legacy data |
publisher |
MDPI AG |
series |
Land |
issn |
2073-445X |
publishDate |
2020-10-01 |
description |
Across Alaska’s Kenai Peninsula, disturbance events have removed large areas of forest over the last half century. Simultaneously, succession and landscape evolution have facilitated forest regrowth and expansion. Detecting forest loss within known pulse disturbance events is often straightforward given that reduction in tree cover is a readily detectable and measurable land-cover change. Land-cover change is more difficult to quantify when disturbance events are unknown, remote, or environmental response is slow in relation to human observation. While disturbance events and related land-cover change are relatively instant, assessing patterns of post-disturbance succession requires long term monitoring. Here, we describe a method for classifying land cover and quantifying land-cover change over time, using Landsat legacy imagery for three historical eras on the western Kenai Peninsula: 1973–2002, 2002–2017, and 1973–2017. Scenes from numerous Landsat sensors, including summer and winter seasons, were acquired between 1973 and 2017 and used to classify vegetation cover using a random forest classifier. Land-cover type was summarized by era and combined to produce a dataset capturing spatially explicit land-cover change at a moderate 30-m resolution. Our results document large-scale forest loss across the study area that can be attributed to known disturbance events including beetle kill and wildfire. Despite numerous and extensive disturbances resulting in forest loss, we estimate that the study area has experienced net forest gain over the duration of our study period due to reforestation within large fire events that predate this study. Transition between forest and graminoid non-forest land cover including wetlands and herbaceous uplands is the most common land-cover change—representing recruitment of a graminoid dominated understory following forest loss and the return of forest canopy given sufficient time post-disturbance. |
topic |
land cover Landsat remote sensing disturbance land change time series |
url |
https://www.mdpi.com/2073-445X/9/10/382 |
work_keys_str_mv |
AT carsonabaughman fourdecadesoflandcoverchangeonthekenaipeninsulaalaskadetectingdisturbanceinfluencedvegetationshiftsusinglandsatlegacydata AT rachelaloehman fourdecadesoflandcoverchangeonthekenaipeninsulaalaskadetectingdisturbanceinfluencedvegetationshiftsusinglandsatlegacydata AT dawnrmagness fourdecadesoflandcoverchangeonthekenaipeninsulaalaskadetectingdisturbanceinfluencedvegetationshiftsusinglandsatlegacydata AT lisabsaperstein fourdecadesoflandcoverchangeonthekenaipeninsulaalaskadetectingdisturbanceinfluencedvegetationshiftsusinglandsatlegacydata AT rosemarylsherriff fourdecadesoflandcoverchangeonthekenaipeninsulaalaskadetectingdisturbanceinfluencedvegetationshiftsusinglandsatlegacydata |
_version_ |
1724466990777630720 |