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Drought index is a useful tool to assess and respond to drought. However, current drought indices could not fully reveal land use effects and they have limitations in applications. Besides, El Niño Southern Oscillation (ENSO), strongly influences the climate of Florida. Hence, understanding ENSO patterns on a regional scale and developing a new land use drought index suitable for Florida are critical in agriculture and water resources planning and management. This paper presents a 32 km high resolution land use adapted drought index, which relies on five types of land uses (lake, urban, forest, wetland, and agriculture) in Florida. The land uses were obtained from National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data from 1979 to 2002. The results showed that Bowen ratio responded to land use and could be used as an indicator to monitor drought events. Then, an innovative regional land use drought index was developed from the normalized Bowen ratio, which could reflect not only the level of severity during drought events resulting from land use effects, but also La Niña driven drought impacts. The proposed new index may help scientists answer the critical questions about drought effect on various land uses and potential feedbacks of changes in land use and land cover to climate. [ABSTRACT FROM AUTHOR]

The state of Florida is renowned for its globally recognized biodiversity richness, but it currently suffers from an ongoing population boom and corresponding urban sprawl resulting in the emergence of severe conservation conflicts, especially in southern parts of Florida. To mitigate the intense competing land use situation and comprehend the dynamic complex relationship between conservation and development, this study argues that both ecological and social dimensions should be taken into account for spatial analysis and underpin zoning decisions empirically in the phase of landscape planning. Choosing South Florida as the study site, we implemented focal-species-based spatial conservation prioritization analysis using Zonation software to identify the highest priority areas and accordingly evaluate two varying land use scenarios provided by the Florida 2070 Project. From a novel perspective of impact avoidance, the inverse prioritization method was applied in this study, intended to minimize negative human impacts and examine the effectiveness and suitability of Florida's future land use projections. After comparing and integrating social-ecological data through mapping, the study uncovered a holistic view of conservation conflicts in Florida and articulated trade-offs for all parties of the local ecosystem striving to reconcile human–wildlife conflicts in Florida and imply a sustainable win-win strategy in the stage of regional landscape planning. [ABSTRACT FROM AUTHOR]

Land-use and land-cover (LULC) change is a primary driver of terrestrial carbon release, often through the conversion of forest into agriculture or expansion of urban areas. Classification schemes are a key component of landscape analyses. This study creates a novel LULC classification scheme by incorporating ecological data to redefine classes of an existing LULC classification based on variation in above-ground tree carbon. A tree inventory was conducted for 531 plots within a subbasin of the Tampa Bay Watershed, Florida, USA. Above-ground tree carbon was estimated using the i-Tree model. Plots were classified using the Florida Land Use Cover Classification System. Mean quantities of above-ground tree carbon, by class, were tested for statistical differences. A reclassification was conducted based on these differences. Sub-classes within a given "land cover" class were similar for six of the seven classes. Significant differences were found within the "Wetlands" class based on vegetation cover, forming two distinct groups: "Forested Wetlands" and "Non-forested and Mangrove Wetlands". The urban "land use" class showed differences between "Residential" and "Non-residential" sub-classes, forming two new classes. LULC classifications can sometimes aggregate areas perceived as similar that are in fact distinct regarding ecological variables. These aggregations can obscure the true variation in a parameter at the landscape scale. Therefore, a study's classification system should be designed to reflect landscape variation in the parameter(s) of interest. [ABSTRACT FROM AUTHOR]