Wickham J, Riitters K H
National Exposure Research Laboratory, Office of Research Development, U.S. Environmental Protection, Agency, 109 T.W. Alexander Dr.; MD: 343-05, Research Triangle Park, NC 27711, USA.
Southern Research Station, United States Department of Agriculture, Forest Service, Research Triangle Park, NC 27709, USA.
Landsc Ecol. 2019 Sep 1;34:2169-2182. doi: 10.1007/s10980-019-00820-z.
Remote sensing has been a foundation of landscape ecology. The spatial resolution (pixel size) of remotely sensed land cover products has improved since the introduction of landscape ecology in the United States. Because patterns depend on spatial resolution, emerging improvements in the spatial resolution of land cover may lead to new insights about the scaling of landscape patterns.
We compared forest fragmentation measures derived from very high resolution (1 m) data with the same measures derived from the commonly used (30 m × -30 m; 900 m) Landsat-based data.
We applied area-density scaling to binary (forest; non-forest) maps for both sources to derive source-specific estimates of dominant (density ≥ 60%), interior (≥ 90%), and intact (100%) forest.
Switching from low- to high-resolution data produced statistical and geographic shifts in forest spatial patterns. Forest and non-forest features that were "invisible" at low resolution but identifiable at high resolution resulted in higher estimates of dominant and interior forest but lower estimates of intact forest from the high-resolution source. Overall, the high-resolution data detected more forest that was more contagiously distributed even at larger spatial scales.
We anticipate that improvements in the spatial resolution of remotely sensed land cover products will advance landscape ecology through reinterpretations of patterns and scaling, by fostering new landscape pattern measurements, and by testing new spatial pattern-ecological process hypotheses.
遥感一直是景观生态学的基础。自景观生态学在美国引入以来，遥感土地覆盖产品的空间分辨率（像素大小）有所提高。由于格局取决于空间分辨率，土地覆盖空间分辨率的不断提高可能会带来有关景观格局尺度的新见解。
我们将源自超高分辨率（1米）数据的森林破碎化测量结果与源自常用的（30米×30米；900米）基于陆地卫星的数据的相同测量结果进行了比较。
我们对两种数据源的二元（森林；非森林）地图应用面积密度缩放，以得出特定于源的优势（密度≥60%）、内部（≥90%）和完整（100%）森林的估计值。
从低分辨率数据切换到高分辨率数据会导致森林空间格局出现统计和地理上的变化。在低分辨率下“不可见”但在高分辨率下可识别的森林和非森林特征，导致高分辨率数据源对优势森林和内部森林的估计值较高，但对完整森林的估计值较低。总体而言，即使在较大空间尺度上，高分辨率数据也能检测到更多分布更具传染性的森林。
我们预计，遥感土地覆盖产品空间分辨率的提高将通过对格局和尺度的重新解释、促进新的景观格局测量以及检验新的空间格局 - 生态过程假设来推动景观生态学发展。
