title: Monitoring vegetation change and dynamics on U.S. Army training lands using satellite image time series analysis.
abstract: Given the significant land holdings of the U.S. Department of Defense, and the importance of those lands to support a variety of inherently damaging activities, application of sound natural resource conservation principles and proactive monitoring practices are necessary to manage military training lands in a sustainable manner. This study explores a method for, and the utility of, analyzing vegetation condition and trends as sustainability indicators for use by military commanders and land managers, at both the national and local levels, in identifying when and where vegetation-related environmental impacts might exist. The BFAST time series decomposition method was applied to a ten-year MODIS NDVI time series dataset for the Fort Riley military installation and Konza Prairie Biological Station (KPBS) in northeastern Kansas. Imagery selected for time-series analysis were 16-day MODIS NDVI (MOD13Q1 Collection 5) composites capable of characterizing vegetation change induced by human activities and climate variability. Three indicators related to gradual interannual or abrupt intraannual vegetation change for each pixel were calculated from the trend component resulting from the BFAST decomposition. Assessment of gradual interannual NDVI trends showed the majority of Fort Riley experienced browning between 2001 and 2010. This result is supported by validation using high spatial resolution imagery. The observed versus expected frequency of linear trends detected at Fort Riley and KPBS were significantly different and suggest a causal link between military training activities and/or land management practices. While both sites were similar with regards to overall disturbance frequency and the relative spatial extents of monotonic or interrupted trends, vegetation trajectories after disturbance were significantly different. This suggests that the type and magnitude of disturbances characteristic of each location result in distinct post-disturbance vegetation responses. Using a remotely-sensed vegetation index time series with BFAST and the indicators outlined here provides a consistent and relatively rapid assessment of military training lands with applicability outside of grassland biomes. Characterizing overall trends and disturbance responses of vegetation can promote sustainable use of military lands and assist land managers in targeting specific areas for various rehabilitation activities.
authors.affiliation: Department of Geography, Kansas State University, 118 Seaton Hall, Manhattan, KS 66506-2904, USA. Electronic address: shutch@ksu.edu.
authors.forename: J M S
authors.initials: JMS
authors.lastname: Hutchinson
authors.affiliation: Université de Toulouse, INPT, Ecole d'Ingénieurs de Purpan, UMR 1201 DYNAFOR, 75, voie du TOEC, BP 57611, F-31076 Toulouse Cedex 03, France.
authors.forename: A
authors.initials: A
authors.lastname: Jacquin
authors.affiliation: Department of Biological and Agricultural Engineering, Kansas State University, 129 Seaton Hall, Manhattan, KS 66506-2904, USA.
authors.forename: S L
authors.initials: SL
authors.lastname: Hutchinson
authors.affiliation: Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands.
authors.forename: J
authors.initials: J
authors.lastname: Verbesselt
mesh_terms: D044822:Biodiversity; D003247:Conservation of Natural Resources; D004784:Environmental Monitoring / Q000379:methods; D040362:Geographic Information Systems; D006801:Humans; D007615:Kansas; D058749:Military Facilities; D058998:Remote Sensing Technology; D014481:United States
keywords: BFAST; Disturbance; Military training activities; Remote sensing; Trend; Vegetation dynamics