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LandTrendr smoothed spectral profiles enhance woody encroachment monitoring
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2021-05-26 , DOI: 10.1016/j.rse.2021.112521
P.J. Gelabert , M. Rodrigues , J. de la Riva , A. Ameztegui , M.T. Sebastià , C. Vega-Garcia

Secondary succession (SS) is one of the main consequences of the abandonment of agricultural and forestry practices in rural areas, causing -among other processes- woody encroachment on former pastures and croplands. In this study we model and monitor the spatial evolution of SS over semi-natural grassland communities in the mountain range of the Pyrenees in Spain, during the last 36 years (1984-2019). Independent variables for ‘annual-based’ and ‘period-based’ modeling were drawn from a suite of Surface Reflectance Landsat images, LandTrendr (LT)-algorithm-adjusted images and LT outputs. Support vector machine (SVM) classifiers were trained and tested using all possible variable combinations of all the aforementioned datasets. The best modeling strategy involved yearly time series of LT-adjusted Tasseled Cap Brightness (TCB) and Wetness (TCW) axes as predictors, attaining a F1-score of 0.85, a Matthew Correlation Coefficient (MCC) of 0.67 and an AUC 0.83. Woodlands encroached above 480,000 ha of grasslands and crops during the study period. A model using LT outputs for the whole period also denoted good performance (F1-score = 0.85, MCC = 0.75) and estimated a similar area of woodland expansion (~509,000 ha), but this ‘period’ approach was unable to provide temporal information on the year or the encroachment dynamics. Our results suggest an overall proportion of 66% for the Pyrenees being affected by SS, with higher intensity in the west-central part, decreasing towards the eastern end.



中文翻译:

LandTrendr平滑的光谱轮廓增强了对木本侵害的监测

次生演替(SS)是农村地区放弃农业和林业实践的主要后果之一,除其他过程外,还引起了对先前牧场和农田的木本侵犯。在这项研究中,我们对过去36年(1984-2019年)西班牙比利牛斯山脉山脉中半天然草地群落上SS的空间演变进行了建模和监测。从一套表面反射Landsat图像,LandTrendr(LT)-算法调整后的图像和LT输出中提取了用于“基于年度”和“基于周期”建模的自变量。使用所有上述数据集的所有可能变量组合对支持向量机(SVM)分类器进行了训练和测试。最好的建模策略涉及以LT调整的流苏帽亮度(TCB)和湿度(TCW)轴的年度时间序列作为预测变量,F1得分为0.85,马修相关系数(MCC)为0.67,AUC为0.83。在研究期间,林地侵占了超过48万公顷的草地和农作物。在整个期间使用LT输出的模型也表现出良好的性能(F1分数= 0.85,MCC = 0.75),并估计了类似的林地扩张面积(〜509,000公顷),但是这种“期间”方法无法提供时间信息年度或入侵动态。我们的结果表明,受SS影响的比利牛斯山脉的总体比例为66%,其强度在中西部较高,向东端逐渐减小。马修相关系数(MCC)为0.67,AUC为0.83。在研究期间,林地侵占了超过48万公顷的草地和农作物。在整个期间使用LT输出的模型也表现出良好的性能(F1分数= 0.85,MCC = 0.75),并估计了类似的林地扩张面积(〜509,000公顷),但是这种“期间”方法无法提供时间信息年度或入侵动态。我们的结果表明,受SS影响的比利牛斯山脉的总体比例为66%,其强度在中西部较高,向东端逐渐减小。马修相关系数(MCC)为0.67,AUC为0.83。在研究期间,林地侵占了超过48万公顷的草地和农作物。在整个期间使用LT输出的模型也表现出良好的性能(F1分数= 0.85,MCC = 0.75),并估计了类似的林地扩张面积(〜509,000公顷),但是这种“期间”方法无法提供时间信息年度或入侵动态。我们的结果表明,受SS影响的比利牛斯山脉的总体比例为66%,其强度在中西部较高,向东端逐渐减小。75)并估计了类似的林地扩张面积(〜509,000公顷),但是这种“期间”方法无法提供有关年份或侵占动态的时间信息。我们的研究结果表明,受SS影响的比利牛斯山脉的总体比例为66%,其强度在中西部较高,向东端逐渐减小。75)并估计了类似的林地扩张面积(〜509,000公顷),但是这种“期间”方法无法提供有关年份或侵占动态的时间信息。我们的结果表明,受SS影响的比利牛斯山脉的总体比例为66%,其强度在中西部较高,向东端逐渐减小。

更新日期:2021-05-26
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