Next Article in Journal
Mice and Habitat Complexity Attract Carnivorans to Recently Burnt Forests
Previous Article in Journal
A Limited Rapid Assessment of Forest Regeneration in 24 Cypress and Tupelo Bottomland Swamps Following Clearcutting and Shovel Logging in the Coastal Plain of North Carolina
Previous Article in Special Issue
US National Maps Attributing Forest Change: 1986–2010
Article

Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data?

1
USDA Forest Service, Rocky Mountain Research Station, Forest Inventory and Analysis, 507 25th Street, Ogden, UT 84401, USA
2
Department of Mathematics, Reed College, 3203 SE Woodstock Blvd, Portland, OR 97202, USA
3
USDA Forest Service, Southern Research Station, Forest Inventory and Analysis, 4700 Old Kingston Pike, Knoxville, TN 37919, USA
4
RedCastle Resources, Inc., Geospatial Technology and Applications Center, 125 South State Street, Suite 7105, Salt Lake City, UT 84138, USA
*
Author to whom correspondence should be addressed.
Forests 2020, 11(8), 856; https://doi.org/10.3390/f11080856
Received: 4 June 2020 / Revised: 30 July 2020 / Accepted: 31 July 2020 / Published: 6 August 2020
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. View Full-Text
Keywords: forest trends; model-assisted estimation; post-stratification; image-based change estimation (ICE); TimeSync forest trends; model-assisted estimation; post-stratification; image-based change estimation (ICE); TimeSync
Show Figures

Figure 1

MDPI and ACS Style

Moisen, G.G.; McConville, K.S.; Schroeder, T.A.; Healey, S.P.; Finco, M.V.; Frescino, T.S. Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? Forests 2020, 11, 856. https://doi.org/10.3390/f11080856

AMA Style

Moisen GG, McConville KS, Schroeder TA, Healey SP, Finco MV, Frescino TS. Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data? Forests. 2020; 11(8):856. https://doi.org/10.3390/f11080856

Chicago/Turabian Style

Moisen, Gretchen G., Kelly S. McConville, Todd A. Schroeder, Sean P. Healey, Mark V. Finco, and Tracey S. Frescino 2020. "Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data?" Forests 11, no. 8: 856. https://doi.org/10.3390/f11080856

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop