Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem
作者: Frederick D.L. HunterEdward T.A. MitchardPeter TyrrellSamantha Russell
作者单位: 1School of Geosciences, University of Edinburgh, Edinburgh EH9 9XP, UK;
2Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK;
3South Rift Association of Landowners, P.O. Box 15289, Nairobi 00509, Kenya;
刊名: Remote Sensing, 2020, Vol.12 (1)
来源数据库: Multidisciplinary Digital Publishing Institute
DOI: 10.3390/rs12010198
关键词: Grazing managementLandscape monitoringModel comparisonRemote sensingEcosystem monitoringSentinel-2Supervised classification
原始语种摘要: In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of...
全文获取路径: MDPI 

  • grazing 放牧
  • species 
  • imagery 成象
  • livestock 牲口
  • ecosystem 生态系
  • mapping 映象
  • wildlife 野生生物
  • accuracy 准确度
  • shrub 灌丛热带稀噬草原
  • degradation 减嚣夷酌