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The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4–8. Training data were selected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening “Function of mask” (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy). [ABSTRACT FROM AUTHOR]

Pollination is a critical ecosystem service affected by various drivers of land-use change, such as policies and programs aimed at land resources, market values for crop commodities, local land-management decisions, and shifts in climate. The United States is the world's most active market for pollination services by honey bees, and the Northern Great Plains provide the majority of bee colonies used to meet the Nation's annual pollination needs. Legislation requiring increased production of biofuel crops, increasing commodity prices for crops of little nutritional value for bees in the Northern Great Plains, and reductions in government programs aimed at promoting land conservation are converging to alter the regional landscape in ways that challenge beekeepers to provide adequate numbers of hives for national pollination services. We developed a spatially explicit model that identifies sites with the potential to support large apiaries based on local-scale land-cover requirements for honey bees. We produced maps of potential apiary locations for North Dakota, a leading producer of honey, based on land-cover maps representing (1) an annual time series compiled from existing operational products and (2) a realistic scenario of land change. We found that existing land-cover products lack sufficient local accuracy to monitor actual changes in landscape suitability for honey bees, but our model proved informative for evaluating effects on suitability under scenarios of land change. The scenario we implemented was aligned with current drivers of land-use change in the Northern Great Plains and highlighted the importance of conservation lands in landscapes intensively and extensively managed for crops. [ABSTRACT FROM AUTHOR]

A large number of factors are known to affect amphibian population viability, but most authors agree that the principal causes of amphibian declines are habitat loss, alteration, and fragmentation. We provide a global assessment of land use dynamics in the context of amphibian distributions. We accomplished this by compiling global maps of amphibian species richness and recent rates of change in land cover, land use, and human population growth. The amphibian map was developed using a combination of published literature and digital databases. We used an ecoregion framework to help interpret species distributions across environmental, rather than political, boundaries. We mapped rates of land cover and use change with statistics from the World Resources Institute, refined with a global digital dataset on land cover derived from satellite data. Temporal maps of human population were developed from the World Resources Institute database and other published sources. Our resultant map of amphibian species richness illustrates that amphibians are distributed in an uneven pattern around the globe, preferring terrestrial and freshwater habitats in ecoregions that are warm and moist. Spatiotemporal patterns of human population show that, prior to the 20th century, population growth and spread was slower, most extensive in the temperate ecoregions, and largely exclusive of major regions of high amphibian richness. Since the beginning of the 20th century, human population growth has been exponential and has occurred largely in the subtropical and tropical ecoregions favored by amphibians. Population growth has been accompanied by broad-scale changes in land cover and land use, typically in support of agriculture. We merged information on land cover, land use, and human population growth to generate a composite map showing the rates at which humans have been changing the world. When compared with the map of amphibian species richness, we found that many of the regions of the earth supporting the richest assemblages of amphibians are currently undergoing the highest rates of landscape modification. [ABSTRACT FROM AUTHOR]