Hutchinson J M S, Jacquin A, Hutchinson S L, Verbesselt J
Department of Geography, Kansas State University, 118 Seaton Hall, Manhattan, KS 66506-2904, USA.
Université de Toulouse, INPT, Ecole d'Ingénieurs de Purpan, UMR 1201 DYNAFOR, 75, voie du TOEC, BP 57611, F-31076 Toulouse Cedex 03, France.
J Environ Manage. 2015 Mar 1;150:355-366. doi: 10.1016/j.jenvman.2014.08.002. Epub 2014 Nov 28.
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.
鉴于美国国防部拥有大量土地，且这些土地对于支持各种具有内在破坏性的活动至关重要，因此应用合理的自然资源保护原则和积极的监测实践对于以可持续方式管理军事训练用地是必要的。本研究探索了一种方法及其效用，即分析植被状况和趋势，将其作为可持续性指标，供国家和地方层面的军事指挥官和土地管理者用于确定何时何地可能存在与植被相关的环境影响。BFAST时间序列分解方法应用于堪萨斯州东北部莱利堡军事设施和孔扎草原生物站（KPBS）的十年MODIS NDVI时间序列数据集。为时间序列分析所选的影像为16天的MODIS NDVI（MOD13Q1 Collection 5）合成影像，能够表征人类活动和气候变率引起的植被变化。从BFAST分解产生的趋势分量中计算出与每个像素的逐年渐变或年内突变植被变化相关的三个指标。对逐年渐变的NDVI趋势评估表明，莱利堡的大部分地区在2001年至2010年间出现了植被变褐。使用高空间分辨率影像进行的验证支持了这一结果。在莱利堡和KPBS检测到的线性趋势的观测频率与预期频率显著不同，这表明军事训练活动和/或土地管理实践之间存在因果联系。虽然两个地点在总体干扰频率以及单调或间断趋势的相对空间范围方面相似，但干扰后的植被轨迹却显著不同。这表明每个地点特有的干扰类型和强度导致了不同的干扰后植被响应。使用带有BFAST的遥感植被指数时间序列和本文概述的指标，可以对军事训练用地进行一致且相对快速的评估，并且适用于草原生物群落之外的地区。表征植被的总体趋势和干扰响应可以促进军事用地的可持续利用，并帮助土地管理者确定各种恢复活动的特定目标区域。
