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

Gretchen G Moisen,Todd A Schroeder,Sean P Healey,Mark V Finco,Tracey S Frescino,Kelly S Mcconville

doi:10.3390/f11080856

Abstract

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.

Highlights

Considering the numerous unintended consequences of land-use change, including the loss of biodiversity, climate feedbacks, and altered hydrologic processes [1,2], there is a growing need for land use and land cover (LULC) change information to help improve the sustainable management of Earth’s remaining resources [3]
Land use information was historically constrained to simple forest/non-forest classifications [5], while land cover focused mainly on resolving tree canopy cover
Land cover information was available for only a few years for both Forest Inventory and Analysis (FIA) and image-based change estimation (ICE), so the comparisons span fewer years

Summary

Introduction

Considering the numerous unintended consequences of land-use change, including the loss of biodiversity, climate feedbacks, and altered hydrologic processes [1,2], there is a growing need for land use and land cover (LULC) change information to help improve the sustainable management of Earth’s remaining resources [3]. In the United States, the US Department of Agriculture’s Forest Inventory and Analysis (FIA) Program collects information on statuses and trends in forested ecosystems across the country [4]. Land use information was historically constrained to simple forest/non-forest classifications [5], while land cover focused mainly on resolving tree canopy cover ( as it related to forest land use definitions). Motivated by events such as its transition to a nationally consistent annual inventory [6] and the refinement of forest land definitional changes, FIA has continued to expand the thematic detail in its LULC classifications [7].

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Discussion

Conclusion