Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests
Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests
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DOI：
10.3390/rs12152438
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发表时间：
2020-07
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影响因子：
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通讯作者：
Katsuto Shimizu;T. Ota;N. Mizoue
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作者：
Katsuto Shimizu;T. Ota;N. Mizoue
森林干扰通常使用全球可用的森林变化地图或当地校准的干扰地图来估计。扰动图的选择取决于检测精度、处理时间和专家知识之间的权衡。然而，全球地图和地方地图之间的准确性差异尚未得到充分调查，因此，它们在估计森林干扰方面的最佳用途尚未得到澄清。本研究评估了每年的森林干扰检测可用的全球森林变化图和当地干扰地图的基础上，Landsat的时间分割算法在主要的收获干扰的地区。我们评估森林干扰检测精度的基础上，每年两个参考数据集。我们还使用了一个基于干扰的评估，调查的主题精度的基础上，每个干扰补丁。结果，我们发现，2001-2017年，全球森林变化图中生产者和用户对干扰的准确度分别为30.1-76.8%和50.5- 90.2%，分别对应于当地干扰图中的78.3-92.5%和88.8- 97.1%。这些值表明，局部扰动图实现了更稳定和更高的精度。以森林变化为基础的评估表明，两个地图都可能准确地检测到较大的干扰;然而，全球森林变化地图至少部分地检测到了更多的小规模干扰，其委托误差更高。总体而言，局部干扰图具有较高的森林干扰检测精度。然而，对于大于3公顷的森林干扰，全球森林变化图达到了相当的准确性。总之，全球森林变化图可用于检测较大的森林扰动，但应谨慎使用，因为小规模扰动的委托误差很大，估计面积和准确性每年都有变化。
Forest disturbances are generally estimated using globally available forest change maps or locally calibrated disturbance maps. The choice of disturbance map depends on the trade-offs among the detection accuracy, processing time, and expert knowledge. However, the accuracy differences between global and local maps have still not been fully investigated; therefore, their optimal use for estimating forest disturbances has not been clarified. This study assesses the annual forest disturbance detection of an available Global Forest Change map and a local disturbance map based on a Landsat temporal segmentation algorithm in areas dominated by harvest disturbances. We assess the forest disturbance detection accuracies based on two reference datasets in each year. We also use a polygon-based assessment to investigate the thematic accuracy based on each disturbance patch. As a result, we found that the producer’s and user’s accuracies of disturbances in the Global Forest Change map were 30.1–76.8% and 50.5–90.2%, respectively, for 2001–2017, which corresponded to 78.3–92.5% and 88.8–97.1%, respectively in the local disturbance map. These values indicate that the local disturbance map achieved more stable and higher accuracies. The polygon-based assessment showed that larger disturbances were likely to be accurately detected in both maps; however, more small-scale disturbances were at least partially detected by the Global Forest Change map with a higher commission error. Overall, the local disturbance map had higher forest disturbance detection accuracies. However, for forest disturbances larger than 3 ha, the Global Forest Change map achieved comparable accuracies. In conclusion, the Global Forest Change map can be used to detect larger forest disturbances, but it should be used cautiously because of the substantial commission error for small-scale disturbances and yearly variations in estimated areas and accuracies.