Integrating remote sensing and local ecological knowledge to monitor rangeland dynamics
Integrating remote sensing and local ecological knowledge to monitor rangeland dynamics
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DOI：
10.1016/j.ecolind.2017.06.033
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发表时间：
2017-11-01
影响因子：
6.9
通讯作者：
Shibkove, Evgenii
中科院分区：
文献类型：
--
作者：
Eddy, Ian M. S.;Gergel, Sarah E.;Shibkove, Evgenii
牧场是地球上最广泛的人为景观之一，支持近5亿人。在牧场降级的程度和严重程度上的分歧会影响牧民生计，尤其是在混淆干旱和过度放牧的影响时。尽管植被指数（例如NDVI或归一化差异指数）通常用于监视牧场，但它们与当地生态知识（LEK）的战略整合（LEK）被低估了。在这里，我们在吉尔吉斯斯坦富裕的纳林省探索了这些补充方法，在那里，关于牧场退化的分歧可以从其他信息中受益匪浅。我们研究了MODIS卫星图像的时间序列（2000-2015），以表征植被的褐变趋势，并区分气候和放牧引起的趋势。我们还比较和对比度测量的趋势与LEK对牧场降解的看法。为此，我们首先检查了NDVI的统计趋势以及在使用气象数据降低趋势后NDVI残留物中的统计趋势。其次，我们使用参与式映射来确定地方牧场经理认为过度放牧的领域，这是一种特别有用的方法，代替了该地区牲畜的可靠历史存放率。最后，我们比较了LEK的优势和缺点和景观监测的遥感。褐变趋势广泛存在，因为NDVI（和NDVI残留物）的趋势下降分别覆盖了24％（和9％）景观。与气候控制的NDVI残差相比，当地经理对牧场降解的看法更好地反映了NDVI中的趋势，这表明后者的模式对经理而言较不明显。我们的方法证明了两种廉价且有效的牧场监测方法非常适合该国需求的潜力。尽管由于地形造成的局限性，但我们的方法在半干旱的草原中最成功，因为牧场降解被认为最严重。在世界许多地方，长期空间广泛的数据的来源很少见，甚至不存在。因此，配对的LEK和遥感可以有助于对土地退化的全面评估，尤其是在有争议的管理问题与稀疏数据可用性相交的情况下。 Lek是遥感信息的宝贵信息来源，应更常规和正式地集成到景观监控中。为了帮助这项努力，我们合成了将LEK和遥感界面跨不同景观情况链接的建议。
Rangelands are among the most extensive anthropogenic landscapes on earth, supporting nearly 500 million people. Disagreements over the extent and severity of rangeland degradation affect pastoralist livelihoods, especially when impacts of drought and over-grazing are confounded. While vegetation indices (such as NDVI, or Normalized Difference Vegetation Index) derived from remotely sensed imagery are often used to monitor rangelands, their strategic integration with local ecological knowledge (LEK) is under-appreciated. Here, we explore these complementary approaches in Kyrgyzstan's pasture-rich province of Naryn, where disagreements regarding pasture degradation could greatly benefit from additional information. We examine a time series of MODIS satellite imagery (2000-2015) to characterize browning trends in vegetation as well as to distinguish between climate- and grazing-induced trends. We also compare and contrast measured trends with LEK perceptions of pasture degradation. To do so, we first examine statistical trends in NDVI as well as in NDVI residuals after de-trending with meteorological data. Second, we use participatory mapping to identify areas local pasture managers believe are overgrazed, a particularly useful approach in lieu of reliable historical stocking rates for livestock in this region. Lastly, we compare the strengths and weaknesses of LEK and remote sensing for landscape monitoring.Browning trends were widespread as declining trends in NDVI (and NDVI residuals) covered 24% (and 9%) of the landscape, respectively. Local managers' perceptions of pasture degradation better reflected trends seen in NDVI than in climate-controlled NDVI residuals, suggesting patterns in the latter are less apparent to managers. Our approach demonstrated great potential for the integration of two inexpensive and effective methods of rangeland monitoring well-suited to the country's needs. Despite limitations due to terrain, our approach was most successful within the semi-arid steppe where pasture degradation is believed to be most severe. In many parts of the world, sources of long-term spatially extensive data are rare or even non-existent. Thus, paired LEK and remote sensing can contribute to comprehensive and informative assessments of land degradation, especially where contentious management issues intersect with sparse data availability. LEK is a valuable source of complementary information to remote sensing and should be integrated more routinely and formally into landscape monitoring. To aid this endeavor, we synthesize advice for linking LEK and remote sensing across diverse landscape situations.