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In the southeastern US, Atlanta is always the focus of attention, despite the rapid expansion of small and medium-sized cities (SMSCs) in the region. Clearly, larger cities have more people, resulting in more loss during disasters. However, SMSCs also face natural calamities and must be made robust and sustainable. Keeping this in mind, this study chooses to focus on ten SMSCs in Alabama (Population > 40,000) which have encountered at least a 6% increase in population size between 1990 and 2020, out of which two large cities (Population > 180,000) which experienced loss during the same time. This paper examines the change in urban built-up area between 1990 and 2020 using the random forest algorithm in Google Earth Engine (GEE) and estimates future 2050 urban expansion scenarios using the Cellular Automata (CA) Markov model in TerrSet's Land Change Modeler (LCM). The results revealed urban built-up areas grew rapidly from 1990 to 2020, with some cities doubling or tripling in size due to population growth. The future growth model predicted growth for most cities and urban expansion along transportation networks. The outcome of this research showcases the importance of proper planning and building sustainably in SMSCs for future natural disaster events. [ABSTRACT FROM AUTHOR]

Due to recent Supreme Court rulings, there has been an increased interest in the isolated wetlands of the United States. These types of wetlands serve vital ecological roles such as water quality regulation and as a habitat of biological diversity. This study focuses specifically on mapping of geographically isolated wetlands, or those that are separated from traditional wetlands by a given spatial extent, using Geographic Object-Based Image Analysis (GeOBIA). GeOBIA is a type of remote sensing analysis that identifies objects and features in data-sets via automated methodologies. This type of analysis offers the opportunity to increase the efficiency of what has traditionally been a very labour intensive process of manual photo-interpretation. This analysis resulted in the delineation of 26,424 areas as geographically isolated wetlands. These results were assessed for accuracy through both manual inspection of aerial imagery and field verification which yielded accuracies of 83.7 and 87.7%, respectively. [ABSTRACT FROM AUTHOR]

In this study, we used public participation geographic information systems methods to collect spatial data identifying places that stakeholders in Mobile Bay, Alabama think are important providers of watershed services. These methods allowed us to spatially analyze participatory data from general public respondents and directly compare them with other scientific data in a geographic information systems database. This study identified which places in the region participants believe are important providers of specific watershed services, including fish nurseries, storm protection, flood protection, and water quality protection, which would likely have public support for conservation. Additionally, we assessed the accuracy of participant watershed service identification using land cover data to identify inconsistencies and participant knowledge gaps. This information can be used to target outreach education efforts. We found that the accuracy with which participants correctly identified places with the necessary land cover to provide each service varied considerably. We believe this to be a useful tool for managers to elicit stakeholder input and to identify knowledge gaps regarding the provisioning of watershed services. [ABSTRACT FROM AUTHOR]

Persistent cloud-cover in the humid southeastern USA and the low temporal resolution of Landsat sensors limit the derivation of seasonal evapotranspiration (ET) maps at moderate spatial resolution. This article introduces a Landsat Moderate Resolution Imaging Spectroradiometer (Landsat–MODIS) ET fusion model that uses simple linear regression to integrate Landsat-derived reference ET fraction (ETrF) from mapping ET at high resolution with internalized calibration (METRIC) model and the vegetation temperature condition index (VTCI) derived from MODIS images. For a study site in Florida, model-estimated ET and ET estimated using energy budget eddy covariance at a US Geological Survey (USGS) station in Ferris Farm, Florida, were found to be in a good agreement with a root mean squared error of 0.44 mm day–1, coefficient of determination (R2) of 0.80, Nash–Sutcliffe efficiency of 0.79 for daily ET (ETd), and 2% relative error for cumulative seasonal ET during the growing season of 2001. At another study site in Alabama, the model underestimated 2008 annual water balance ET for the Fish River Watershed by 39 mm or 4%. Comparisons of model-estimated ET with that obtained using a non-fusion Landsat-only approach at both sites indicated that the fusion of Landsat and MODIS ET values reduces potential errors in ET estimation that would otherwise arise due to insufficient availability of cloud-free Landsat images for METRIC processing. Validation results and application of the model in deriving seasonal/annual ET for different land-cover classes in the Fish River Watershed suggested that the fusion model has the potential to be used in continuously monitoring ET for field- to watershed-level agricultural and hydrological applications in the southeastern USA. [ABSTRACT FROM PUBLISHER]