Analyzing the impacts of land use policies on selected ecosystem services in the upper Chattahoochee Watershed, Georgia, United States

Abstract

The rapid rate of urbanization within the Upper Chattahoochee Watershed (UCW) is threatening the provision of ecosystem services (ESs) for six million residents of the Atlanta Metropolitan Area. This study uses the land cover change model TerrSet to project future land cover from 2016 to 2040. The modular toolset InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) is used to assess the efficacy of four land use policies in maintaining the provision of three ESs (carbon storage, wildlife habitat, and water quality) within the UCW. The Baseline scenario represents past urbanization trends, whereas the Urbanization scenario accounts for a higher urban growth rate. The Plan 2040 scenario includes existing policy guidelines, and the Conservation scenario adds forested riparian buffer areas. Two integrated indexes and an economic valuation of ESs were used to combine all ESs and analyze the overall performance of each policy. The first index uses unequal weights for ESs based on the Analytical Hierarchical Process, whereas the second index uses equal weights. The values of both integrated indexes and economic values were highest in the Conservation scenario and lowest in the Urbanization scenario. No significant differences in the provision of ESs were found between the Baseline and the Plan 2040 scenarios. However, the integrated indexes and economic values for both land use policies declined over time. Our study will feed into the ongoing movement of sustainable watershed management for ensuring the provision of ESs, especially for rapidly urbanizing cities worldwide, in general, and in the United States, in particular.

Incorporating Social and Policy Drivers into Land-Use and Land-Cover Projection

“…Future LULC projection models that lack landowner decision-making processes and policy incentives and disincentives cannot be expected to simulate future LULCs realistically [42]. Hence, some scientists have worked to develop more reliable models to project future LULC patterns by including econometric factors (e.g., Gross Domestic Production, income, poverty rate) [16,[43][44][45] or by combining biophysical features and voluntary and regulatory policies [3,10,27]. The current study builds upon previous attempts to develop a realistic model to project future LULCs by incorporating prominent socio-economic factors, mandatory and voluntary policies, societal preferences, and landscape biophysical features within a CA-MC approach.…”

“…The module Land Change Modeler (LCM), developed as an empirically parameterized land change projection tool embedded in the software TerrSet 19.0.7, was utilized to project a LULC map for the UFW for the year 2040. The application of LCM across many disciplines in varied geographic areas since its introduction in 2006 is well documented [10,27,56]. LCM uses two LULC maps from distinct periods as input data to analyze transitions among land-cover classes.…”

“…Advancements in remote sensing, GIS capabilities, and machine learning techniques [5] have allowed for the development of varied projection models that are diverse in their conceptual foundations, thematic foci, spatial characteristics, and modeling methodologies. Cellular automation (CA) in conjunction with Markov chain (MC) analysis has historically been one of the most popular approaches [6] and has been used in diverse regions to analyze past, present, and future land-use changes [7][8][9][10]. This approach incorporates both spatial and temporal components of land-cover dynamics in detecting past and simulating future land-use change patterns [11].…”

“…Moreover, projections from such models have been applied to model changes in various ecosystem services [3,[12][13][14][15][16]. However, general models, including many that employ CA-MC processes, fail to explain transitions as a function of human decision-making, despite the fact that LULCs are continually affected by a variety of human-driven forces [10,17].…”

Scenario Simulation of the Relationship between Land-Use Changes and Ecosystem Carbon Storage: A Case Study in Dongting Lake Basin, China

“…In its study of the Jiroft Plain in Iran, moreover, by comparing multiple planning scenarios, only environmentally friendly land planning policies would reverse the declining trend of CS and result in an average annual increase of 60.6 × 10 4 Mg [ 16 ]. However, existing studies tend to focus more on the carbon sink effects of individual ecosystems or ecological protection policies and lack consideration of the complete terrestrial ecosystem and sustainable policies [ 17 ]. Furthermore, land-use changes are a multifactorial process, and the main challenge in balancing the terrestrial carbon cycle is to identify the best match between the many land planning options.…”