Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data?
Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/11/8/856 |
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| author | Gretchen G. Moisen Kelly S. McConville Todd A. Schroeder Sean P. Healey Mark V. Finco Tracey S. Frescino |
| author_facet | Gretchen G. Moisen Kelly S. McConville Todd A. Schroeder Sean P. Healey Mark V. Finco Tracey S. Frescino |
| author_sort | Gretchen G. Moisen |
| collection | DOAJ |
| description | Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes. |
| first_indexed | 2024-03-10T17:52:02Z |
| format | Article |
| id | doaj.art-a245cfaddd6948ec8ebc3c8ff97aff1c |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T17:52:02Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-a245cfaddd6948ec8ebc3c8ff97aff1c2023-11-20T09:18:58ZengMDPI AGForests1999-49072020-08-0111885610.3390/f11080856Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data?Gretchen G. Moisen0Kelly S. McConville1Todd A. Schroeder2Sean P. Healey3Mark V. Finco4Tracey S. Frescino5USDA Forest Service, Rocky Mountain Research Station, Forest Inventory and Analysis, 507 25th Street, Ogden, UT 84401, USADepartment of Mathematics, Reed College, 3203 SE Woodstock Blvd, Portland, OR 97202, USAUSDA Forest Service, Southern Research Station, Forest Inventory and Analysis, 4700 Old Kingston Pike, Knoxville, TN 37919, USAUSDA Forest Service, Rocky Mountain Research Station, Forest Inventory and Analysis, 507 25th Street, Ogden, UT 84401, USARedCastle Resources, Inc., Geospatial Technology and Applications Center, 125 South State Street, Suite 7105, Salt Lake City, UT 84138, USAUSDA Forest Service, Rocky Mountain Research Station, Forest Inventory and Analysis, 507 25th Street, Ogden, UT 84401, USAThroughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes.https://www.mdpi.com/1999-4907/11/8/856forest trendsmodel-assisted estimationpost-stratificationimage-based change estimation (ICE)TimeSync |
| spellingShingle | Gretchen G. Moisen Kelly S. McConville Todd A. Schroeder Sean P. Healey Mark V. Finco Tracey S. Frescino Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? Forests forest trends model-assisted estimation post-stratification image-based change estimation (ICE) TimeSync |
| title | Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? |
| title_full | Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? |
| title_fullStr | Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? |
| title_full_unstemmed | Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? |
| title_short | Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? |
| title_sort | estimating land use and land cover change in north central georgia can remote sensing observations augment traditional forest inventory data |
| topic | forest trends model-assisted estimation post-stratification image-based change estimation (ICE) TimeSync |
| url | https://www.mdpi.com/1999-4907/11/8/856 |
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