Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

Abstract

15The U.S. Geological Survey's Land Change Monitoring, Assessment, and Projection (LCMAP) initiative 16 is a new end-to-end capability to continuously track and characterize changes in land cover, use, and 17 condition to better support research and applications relevant to resource management and environmental 18 change. Among the LCMAP product suite, there are annual land cover maps that will be available to the 19 public. This paper describes an approach to optimize the selection of training and auxiliary data for 20 deriving the thematic land cover maps based on all available clear observations from Landsats 4-8. land cover classification, Landsat 40 community, data needs are changing and innovative products and mapping approaches to characterize 47 land cover are required to meet the community's appetite for science-quality, geospatial land cover data. 48The emergence of land change science has had a particularly significant influence on land cover mapping 49 and monitoring requirements. Driven by concerns about climate change and resource sustainability, 50 Rindfuss et al. (2004) and Turner et al. (2007) defined land change science as a framework to understand 51the interactions between people and nature that lead to changes in the type, magnitude, condition, and 52 location of land use and cover. Implicit is the need to use remote sensing, modeling, and synthesis to 53 understand and explain land change rates, causes, and consequences. Meeting the diverse needs of land 54 cover data users means that the data producers must transform traditional approaches into new strategies 55 that generate higher quality products with additional land cover variables, more detailed legends, and 56 more frequent geospatial information and statistics on change. 57The USGS response to the growing requirements for land use, cover, and condition data, information, and 58 knowledge is the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. LCMAP is 59 an end-to-end capability to continuously track and characterize changes in land cover, use, and condition 60 and translate such information into assessments of current and historical processes of cover and change 61 that can serve as the science foundation for research and applications relevant to resource management 62 and environmental change. As implied in the name, key elements include mapping and monitoring 63 change, analyzing and assessing the rates, causes, and consequences of change, and providing scenario-64 based projections of future land change. LCMAP continues the long-standing USGS commitment to land 65 cover research and development and represents the foundation for a Landsat-based Federal land 66 monitoring system. Furthermore, LCMAP will stimulate geographic research on understanding the 67 connections between human activity and natural systems, and ultimately represents an essential step 68 towards better understanding the combined impacts of climate and land use and land cover change. 69

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

“…CCDC models were fit to time series of Tasseled Cap Greenness observations from the 2005-2015 base period using a Fourier-style regression model. We chose to use harmonic regression over other non-parametric models and data filters because harmonic models (1) characterize general seasonal patterns across years, (2) produce a fitted equation for each pixel that can be used for predicting images at any given day of year, and (3) are becoming more widely used for operational monitoring of land cover, condition, and change [27,28,34,35]. The models used in this study included 12-month and 4-month harmonics based on the following functional form:…”

“…In Southern New England, gypsy moth has persisted at relatively low population densities since the 1980s, therefore, we assume an 11-year stable base period leading up to our 2016 monitoring period. In other parts of the country such as the central Appalachian Mountains, defoliation events may have occurred more frequently in previous decades [35,39]. In these places, harmonic models could be fit to the subset of years with lower defoliation.…”

“…In fact, the FHTET product was generated using Earth Engine [36], and it may be feasible to extend some lessons learned to scaling the synthetic image approach. Additionally, the USGS's Land Cover Change Monitoring, Assessment and Projection (LCMAP) initiative [35] aims to run a version of the CCDC algorithm used to generate synthetic images for all of the contiguous United States, fitting models to time series of all clear Landsat observations from 1985 to 2015. It may be possible to use these results to aid in defoliation monitoring and other synthetic-image-based analyses.…”

Evaluation of the Initial Thematic Output from a Continuous Change-Detection Algorithm for Use in Automated Operational Land-Change Mapping by the U.S. Geological Survey

“…For training, a minimum of 600 pixels for any class was found to provide the best classification result [30]. For several of the 14 path/rows the limited number of LC Trends blocks available did not provide class populations of 600 pixels for one or more classes (for example, see Table 10a for Montana and Arizona).…”

“…We extracted training data from LC Trends blocks based on criteria developed from an analysis of best practices [30]. That analysis found that a total of 20,000 pixels distributed across classes in proportion to the LC Trends class distribution was optimal, with a minimum of 600 pixels and a maximum of 8000 pixels required for each class (note, if the total number of pixels for a given class was less than 600, we extracted all available pixels).…”