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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis
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Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis

机译：用于监测半干旱林地长期树木覆盖动态的Landsat遥感方法：植被指数比较和光谱混合分析

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Tree canopy cover is a major biophysical attribute of dryland ecosystems. Monitoring its long-term changes over large spatial extents is critical for understanding woody vegetation response to climate variability and global change. However, quantifying tree canopy cover with remotely sensed data remains a challenge for dryland ecosystems where vegetation is sparse and trees, shrubs, and grasses often co-exist at fine spatial scales. In this study, we developed a full SMA (spectral mixture analysis) method that regressed photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and shade components of the SMA with dryland tree cover to monitor tree cover dynamics on a pinyon-juniper woodland landscape in Nevada, USA using Landsat TM data. We assessed 1) how well this method could estimate tree cover in both disturbed (chained and burned) and non-disturbed woodland patches and 2) how sensitive this method was to the confounding effects of climatic variations. The assessment was conducted in comparison with two other more commonly used methods that regressed NDVI or PV with tree cover. Our results showed that although PV performed better than NDVI, both methods overestimated tree canopy cover within recently disturbed woodland patches where the confounding effects of shrubs on greenness index were higher than in non-disturbed patches. The full SMA efficiently quantified variations within post-chaining patches in addition to non-disturbed patches, but overestimated tree cover within burned patches. Of the three methods tested, only full SMA showed promising capability for mitigating the confounding effects of interannual climatic variations on monitoring the woodland recovery process. Our results are generalizable to other semi-arid landscapes comprising a mosaic of small-statured trees intermixed with shrub steppe vegetation.
机译：树木冠层覆盖是旱地生态系统的主要生物物理属性。监测其在较大空间范围内的长期变化对于了解木质植被对气候变化和全球变化的响应至关重要。然而，对于植被稀疏且树木，灌木和草经常在精细空间尺度上共存的旱地生态系统而言，用遥感数据量化树冠覆盖度仍然是一个挑战。在这项研究中，我们开发了一种完整的SMA（光谱混合分析）方法，该方法可以对具有旱地树木覆盖率的SMA的光合植被（PV），非光合植被（NPV）和阴影成分进行回归，以监控松树上的树木覆盖动态。使用Landsat TM数据在美国内华达州的杜松林地景观。我们评估了1）此方法在扰动（连锁和烧毁）和未受干扰的林地中估计树木覆盖的能力，以及2）此方法对气候变化的混杂影响有多敏感。评估是与其他两种较常用的方法进行比较的，该方法使NDVI或PV回归到树上。我们的研究结果表明，尽管PV的性能优于NDVI，但两种方法都高估了最近受干扰的林地内树木冠层的覆盖度，在这些林地中灌木对绿度指数的混杂影响高于未受干扰的林地。完整的SMA有效地量化了除未扰动补丁之外的链后补丁中的变化，但高估了已燃烧补丁中的树木覆盖率。在测试的三种方法中，只有完全SMA显示出有希望的能力来缓解年际气候变化对监测林地恢复过程的混杂影响。我们的结果可推广到其他半干旱景观，包括由矮矮树和马赛克草原植被混合而成的马赛克。

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